Chinese AI companies just had their payday over the past week.

DeepSeek, the Chinese AI lab backed by quant trading giant HighFlyer that just released its latest LLM V4, is reportedly seeking its first-ever outside funding. They initially targeted $300 million at a $10 billion valuation, but investors’ demand were insatiable. The round has reportedly climbed to $7 billion at a $50 billion—if successful, this will be the largest single funding for a Chinese AI company in recent history.

Then it’s Moonshot AI, the developer of the Kimi model family, just raised $2 billion at a valuation of over $20 billion, led by Chinese delivery giant Meituan. This is the third round of their fundraising this year, totaling $3.9 billion in six months, according to Chinese media outlet LatePost.

Finally, StepFun, a Shanghai-headquartered AI company and developer of the Step model series, reportedly close to raising $2.5 billion. Its valuation target was $10 billion months ago.

To put that in context: the last time a Chinese private company raised $2 billion-plus in a single round was Shein in May 2023, at a cut valuation of $64 billion, down a third from its prior round. From roughly 2021 to 2025, China’s tech VC market was in prolonged contraction—regulatory crackdowns against technology companies, US-China tensions, rising exit difficulties, global rate hikes. Therefore the recent fundings signal something has shifted.

Still, the numbers are overshadowed by their Western counterparts. OpenAI closed a record $122 billion round, valuing it at $852 billion. Anthropic raised $30 billion in February at $380 billion, and is now in talks for a new round at a reported $900 billion valuation—which would put it ahead of OpenAI.

But the gap is less important than the trajectory. Chinese AI companies have figured out their commercialization story in the agentic era, and investors are fear missing out.

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DeepSeek

DeepSeek, founded in mid-2023, has never raised a single dollar before. Its backer, HighFlyer, is one of China’s largest quant trading firms, overseeing more than 70 billion yuan ($10 billion) with an average fund return of 56.6%, according to Bloomberg. That’s why DeepSeek could focus on research, commit to an open-source strategy, and ignore the pressure to commercialize or ship products on someone else’s timeline. Even after R1—its blockbuster reasoning model that made a global splash in early 2025—the company reportedly rejected funding offers from both Chinese and US investors.

What changed was talent retention. Competitors began aggressively poaching DeepSeek’s engineers and researchers. Luo Fuli, one of the DeepSeek V3 contributors, is now Xiaomi’s head of LLM team. Guo Daya, a core author of both DeepSeek V3 and R1, has joined ByteDance’s Seed lab. Without outside funding and a formal company valuation, researchers have no equity incentive, raising the cost of keeping the people who built the models.

There’s also a scale problem. V4 is nearly twice the size of V3, and the data used to train scales accordingly. DeepSeek is also allocating GPU resources toward unexplored research areas that require significant capital. Research at the frontier isn’t cheap, even for a company backed by a quant trading giant.

In April, DeepSeek reportedly started its first external funding round at a $10 billion valuation, seeking $300 million. Then China’s Big Fund—the state-backed semiconductor investment vehicle that also backs SMIC—is now in talks to lead the round at a $45 billion valuation, according to the Financial Times. Tencent is also said to be in discussions. The latest reporting from The Information is CEO Liang Wenfeng will personally invest 20 billion yuan ($3 billion), around 40% of the total amount, at a $50 billion valuation. DeepSeek is also expected to release V4.1 in June.

Moonshot AI

The Moonshot funding story was first broken by LatePost that the company was close to completing a new $2 billion round led by Meituan’s investment fund Longzhu, with China Mobile and CPE (previously known as CITIC Private Equity) also participating. LatePost also reported that Moonshot raised $1.9 billion across January and February, making the total $3.9 billion over six months (please note that the undisclosed round in February 2026 below was reported by LatePost; no other sources have surfaced details.)

According to Meituan Longzhu’s general partner, Moonshot’s annual recurring revenue (ARR)—mostly API revenue—reached $200 million in April. For comparison, Cursor’s ARR has surpassed $2 billion as of March 2026, and Cursor is in talks to raise $2 billion at a $50 billion valuation. Moonshot isn’t cheap at $20 billion, but it’s just starting to grow. And notably, Cursor’s models are in part trained on top of Kimi K series. Meanwhile, MiniMax and Zhipu, which both listed on the Hong Kong stock market, are valued between $35 to $70 billion. Against that backdrop, Moonshot at $20 billion looks like a discount.

Moonshot’s latest model, Kimi K2.6, is one of the best open-weight models available today. The trillion-parameter model features strong coding capability, long-horizon execution, and can handle up to 300 sub-agents simultaneously. A bigger and more powerful K3 is reportedly in development for later this year.

While Moonshot’s largest external shareholder should be Alibaba, which owns approximately 36% of the company after making its bet in early 2024, the fact that Meituan—currently locked in a fierce delivery war against Alibaba and JD.com—is Moonshot’s lead investor of this funding round signals an escalating AI arms race between China’s tech giants.

AI agents that order food and book hotel tickets are increasingly becoming the interface through which young people manage daily life. Alibaba is building its ecosystem around Qwen as the central agent for daily tasks. Meituan doesn’t want to be left behind. Moonshot’s coding and agentic specialization makes it a natural fit, from distribution via Kimi's consumer reach to agentic models powering Meituan's merchant and logistics ecosystem.

Meituan is also building its own AI models in-house. Its Longcat 2.0 model is reportedly trained on 50,000 to 60,000 Huawei Ascend chips—the largest training run on domestic Chinese compute infrastructure to date.

StepFun

Just one day after the Moonshot news, Shanghai-based StepFun was reported to be close to raising $2.5 billion.

For readers less familiar with the company: StepFun is one of the top five LLM startups in China, alongside DeepSeek, Zhipu, MiniMax, and Moonshot. Founded in April 2023 by former Microsoft corporate VP Jiang Daxin, its core team includes Zhang Xiangyu—former director of research at Megvii and co-author of ResNet, one of the foundational papers in modern computer vision—alongside other researchers from Microsoft Research Asia.

Their latest flagship model is Step-3: 321 billion total parameters, 38 billion active, with two architectural innovations that reduce KV-cache demands to roughly 22% of DeepSeek V3’s per-token cost. But language is just one dimension. StepFun has shipped 16 multimodal models—covering text-to-video, voice interaction, image understanding, and multimodal reasoning. The Step-Video-T2V model, co-released with Geely Auto in early 2025, was the largest open-source video generation model in the world by parameter count at the time of release.

The low-profile company made headlines earlier this year after announcing a 5 billion yuan funding round and a new chairman: Yin Qi, former co-founder and CEO of Megvii, one of China’s first-generation AI unicorns. Yin also chairs Qianli Technology, Geely’s autonomous driving subsidiary—a connection that goes to the heart of StepFun’s commercial strategy.

A differentiation from peers, StepFun bets on AI embedded in hardware. Their models are deployed on over 42 million shipped devices, reaching approximately 20 million daily active users—with partners accounting for roughly 60% of China’s leading smartphone brands, including OPPO and Honor. The business model is closer to ARM by charing licensing fees and revenue-sharing from device manufacturers.

On the automotive side, StepFun and Geely jointly developed Agent OS, an intelligent cockpit system integrating StepFun’s multimodal and voice models, explicitly positioned as a Chinese answer to Tesla’s in-car Grok. StepFun’s target is one million vehicle integrations by end of 2026.

The investor lineup of the funding round includes Huaqin and Longcheer, both of which are top global phone ODM manufacturers; OmniVision is an image sensor supplier upstream in the smartphone camera supply chain; ZTE has already deployed StepFun models in its Nubia Z80 Ultra.

StepFun has also dismantled its red-chip (VIE) structure and converted to a joint-stock company—the corporate form required for a Hong Kong H-share listing. The target is to file in Hong Kong before June 30, with listing expected by year-end. The Hong Kong Investment Corporation, the city’s sovereign fund, is reportedly among the pre-IPO investors—making StepFun its sole large model bet.

Why now

In my view, the recent funding craze has two catalysts.

The first is the successful IPOs of Zhipu AI and MiniMax. Between 2020 and 2025, Chinese AI startups faced a structural exit problem. US Nasdaq—once the default destination for Chinese tech listings—became increasingly difficult due to deteriorating geopolitics and valuation discounts driven by perceived regulatory risk. At the same time, China’s anti-monopoly regulation made it harder for domestic tech giants to acquire startups. With no clear exit, Chinese VCs grew more risk-averse.

Then Hong Kong plays an important role. The exchange’s average daily turnover (ADT) hit HK$249.8 billion ($32 billion) in 2025—a 90% jump year-over-year. Its liquidity continues to show robust growth in early 2026, with Q1 ADT reaching HK$276.7 billion ($35 billion), a 14% year-on-year increase. IPO equity funds raised on the Main Board reached HK$285.8 billion ($36.5 billion) in 2025, a 225% increase from 2024.

Into this market walked Zhipu AI and MiniMax in January 2026—and they gave every private AI investor a benchmark. Zhipu has surged over 600% since its listing, reaching a market cap of roughly $56 billion. MiniMax doubled on its debut day and has since climbed to over $33 billion. Two companies that raised a combined $1.2 billion in their IPOs are now worth multiples of that.

The second catalyst is commercial. These companies have figured out how to make money by pushing the limits of model capabilities. Coding agents and agentic AI can now handle complex, multi-step tasks autonomously. White-collar employees, developers, and entrepreneurs are willing to pay for tokens at scale. This is visible in Moonshot’s ARR trajectory and in Zhipu’s model-as-a-service revenue growth.

This moment is reminiscent of 2023, when ChatGPT’s release in November 2022 sent Chinese VCs racing to incubate the country’s own OpenAI. The companies became unicorns quickly, then investment lines flatlined as questions mounted about their ability to commercialize. In late 2025, Claude Code, OpenClaw, and the broader agentic wave is another ChatGPT-like moment. Chinese AI companies responded by investing heavily in LLM agentic and coding capabilities, and their ARR grew exponentially.

To be clear, all of this is still small against the spending of China’s tech giants. Alibaba committed $53 billion over three years (2025–2028). ByteDance has boosted its AI infrastructure capex this year to more than 200 billion yuan ($30 billion).

A closing thought

I recently read Nathan Lambert’s notes from his visits to Chinese AI labs. One line stuck with me:

In China, the LLM community feels far more like an ecosystem than battling tribes. Across many off the record conversations, it’s nothing but respect for peers. All of the Chinese labs fear Bytedance with their popular Doubao model, which is the only frontier closed lab in China. At the same time, all of the labs have massive respect for DeepSeek as the lab with the best research taste in execution. When you meet with lab members off the record in the States, sparks fly quickly.

The most striking part of the humility of Chinese researchers is how they also often shrug on the business side, saying it’s not their problem, where everyone in the U.S. seems to be obsessed with various ecosystem-level industrial trends, from data sellers to compute or fundraising.

Interconnects AI

Notes from inside China's AI labs

Staring out the window on a new, high-speed train from Hangzhou to Shanghai I’m gifted with views of dramatic ridgelines speckled with wind turbines that are silhouetted against the setting sun. The mountains cast a backdrop to a mix of spanning fields and clustered skyscrapers. I’m returning from China with great humility. It’s a very warming, human ex…

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7 days ago · 239 likes · 30 comments · Nathan Lambert

I have no doubt that in the near term, Chinese AI labs will continue this open and collaborative mindset. The market is large enough that aggressive internal competition isn’t yet necessary. Learning from each other through open releases is still the fastest way to improve. Ecosystem growth benefits everyone.

But as these companies continue to scale API revenue, bear increasingly high valuations, and face mounting commercialization pressure, the question is how long this friendly ecosystem can hold. At some point, open collaboration becomes competitive intelligence. When cut-throat competition arrives among Chinese AI labs—and it will—it will be just a matter of timing.

Until then, congratulations to all these companies that have overcome challenges and doubts to get here. The payday is well deserved.

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The long-anticipated DeepSeek-V4 has finally arrived, released in the same laast week as OpenAI’s GPT-5.5, Moonshot’s K2.6, and Tencent’s Hunyuan 3 preview, probably the most intense moment in the current model race.

The preview open-source releases include two versions:

• DeepSeek-V4-Pro features 1.6 trillion total parameters with 49 billion active parameters, making it even more sparse than DeepSeek-V3, pre-trained on 33 trillion tokens.

• DeepSeek-V4-Flash features 284 billion total parameters with 13 billion active parameters, a lighter model for the majority of developers who can’t deploy the gigantic Pro version. Pre-trained on 32 trillion tokens.

The selling point of the DeepSeek-V4 series is a one million token context window at an affordable price, laying the foundation for models handling long-horizon agentic tasks that require holding enormous amounts of information in memory without losing the thread. To get there, DeepSeek introduced new architectural innovations and incorporated techniques like mHC and the Muon optimizer.

On pricing, DeepSeek-V4 charges $1.74/$3.48 per million input/output tokens, while Claude Opus 4.6 charges $5/$25. The price advantage narrows against other Chinese open LLMs as Zhipu’s GLM 5.1 charges $1.4/$4.4 per million tokens, though the model doesn’t offer the 1M context window. DeepSeek also just slashed cache hit prices across its entire API to one-tenth of the original price, and the V4-Pro 75% off promotion runs through May 5, 2026.

On performance, DeepSeek-V4-Pro is strong at math (HMMT 2026 and IMOAnswerBench) and competitive programming (Codeforces and LiveCodeBench)—the kind of coding that solves well-defined problems with known solutions. On knowledge, DeepSeek also leads all open models by a wide margin, though Gemini 3.1 Pro is aheaad.

But it still lags top frontier models on real-world software engineering (SWE Pro) and agentic tasks (HLE with tools). The company admits in its technical report that its reasoning trails the top frontier models by roughly three to six months. On Chatbot Arena and Artificial Analysis, V4 ranks among the best open models but trails Kimi K2.6 and Xiaomi’s MiMo V2.5 Pro.

Also noteworthy: DeepSeek has optimized V4 for Huawei’s Ascend AI chips. Current serving throughput for V4-Pro is limited, but the company expects pricing to drop significantly in the second half of the year once Ascend 950 supernodes ship at scale.

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Architectural Innovations to Drive Down the Cost of 1M Tokens

While DeepSeek-V4 didn’t make as big a splash as V3, it is in my view the most architecturally ambitious model the lab has released. The backbone remains familiar—DeepSeek MoE and multi-token prediction—but nearly everything built on top of it is new.

In my earlier predictions, I thought V4 might just continue using DeepSeek Sparse Attention (DSA). What surprised me is that DeepSeek pushed further than DSA by proposing an entirely new hybrid attention architecture, the core innovation that makes cheap 1M token processing possible.

Standard attention scales quadratically with context length. At one million tokens, the compute and memory costs make it impractical to serve at scale. Breaking that bottleneck is the core problem DeepSeek V4 was built to solve.

DeepSeek designed two complementary attention mechanisms and interleaves them across the model’s 61 layers.

• CSA (Compressed Sparse Attention): Instead of attending to every token, CSA first compresses every group of 4 tokens into one condensed entry using learned weights — the model figures out during training what’s worth keeping. Then a fast lookup system called the “lightning indexer” identifies which compressed chunks are actually relevant to the current query, and the model attends only to those. A small sliding window of recent uncompressed tokens is also kept, so local context isn’t lost.

• HCA (Heavily Compressed Attention): More aggressive. It compresses every 128 tokens into a single entry and attends to all of them densely, without any sparse selection. Less precise, but extremely cheap.

The combination works because CSA handles nuanced, selective retrieval while HCA handles the broad sweep cheaply. Together they cover different retrieval needs at different computational costs. The paper reports that at one million tokens, V4-Pro needs only 27% of the inference compute and 10% of the KV cache—the memory used to store context—compared to V3.2.

There is a cost of course. The benchmark gaps on MRCR and CorpusQA—both long-context retrieval tests—versus Claude Opus 4.6 suggest some information loss from the compression. But for most tasks, what gets compressed away seems not to matter. The 10x memory saving is worth the marginal retrieval gap on the hardest benchmarks.

The first two layers of V4-Pro use HCA only, then the interleaved CSA-HCA pattern runs through the remaining layers, according to the paper.

On top of attention, mHC (Manifold-Constrained Hyper-Connections), which I explained in a previous post, is employed to strengthen residual connections between layers. Normally, information flows through a neural network layer by layer through a single residual stream—one pipe carrying signal from layer to layer. mHC widens that pipe to four parallel streams, giving each layer more flexibility in how it draws from and contributes to the information flow. The “manifold-constrained” part is the stability fix: without it, multiple streams stacked across dozens of layers cause signals to explode or vanish during training. DeepSeek’s innovation was to mathematically constrain the mixing between streams so the model stays stable without without giving up any of the intelligence gains. In plain terms: wider pipe, guardrails on the pipe.

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👀DeepSeek’s Next Move: What V4 Will Look Like

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2 months ago · 17 likes · 1 comment · Tony Peng

The Muon optimizer is the third major innovation, and it comes with an interesting story. Muon was originated in the open-source research community and was notably scaled for LLM training by Moonshot AI for its Kimi models. DeepSeek adopted it for V4.

Most large models train using an optimizer called AdamW, which updates each weight independently based on its own history. Muon treats the weight matrices more holistically, making each update geometrically cleaner relative to the weight matrix as a whole, through a technique called Newton-Schulz iterations. The result is faster convergence and better training stability, both meaningful at a scale of 33 trillion training tokens.

The deeper point is that DeepSeek and Moonshot are rivals in AI race. Yet Moonshot has largely borrowed DeepSeek’s architecture since Kimi K2, and DeepSeek is now using Moonshot’s optimizer. This open-source practice as mutual adoption between competitors further benefits the whole ecosystem.

One thing I was hoping to see that didn’t make it: Engram, DeepSeek’s memory architecture, and multimodality. The paper is candid about why:

In pursuit of extreme long-context efficiency, DeepSeek-V4 series adopted a bold architectural design. To minimize risk, we retained many preliminarily validated components and tricks, which, while effective, made the architecture relatively complex. In future iterations, we will carry out more comprehensive and principled investigations to distill the architecture down to its most essential designs.

We are also working on incorporating multimodal capabilities to our models.

Infrastructure Improvement and Post-Training

Infrastructure is arguably where DeepSeek excels, and V4 is no exception.

On training efficiency, they proposed a fine-grained EP scheme that fuses communication and computation into a single pipelined kernel for communication-computation overlapping, achieving up to 1.96x speedup for latency-sensitive workloads like RL rollout, and validated it on both NVIDIA GPUs and Huawei Ascend NPUs.

They adopted TileLang, a domain-specific language for writing GPU kernels that lets researchers prototype operators quickly and optimize the same code for production without rewriting it from scratch.

On precision, they trained with FP4 quantization awareness from the start rather than applying it after the fact. The model runs faster and uses less memory at deployment without meaningful performance loss. The paper notes FP4 could be a further one-third more efficient on future hardware.

Finally, they designed a custom KV cache layout to handle the heterogeneous memory requirements of mixing CSA and HCA layers, and built on-disk KV cache storage so that shared-prefix requests—many users sending similar long prompts—only pay the expensive prefill cost once.

DeepSeek-V4’s post-training pipeline ran in two stages.

• Specialist training: they built separate expert models for each domain—math, coding, agents, instruction following—each fine-tuned on domain-specific data and then sharpened with reinforcement learning. They also trained three reasoning effort modes (Non-think, High, Max) by varying how much thinking budget each mode gets during RL.

• On-policy distillation: rather than merging specialists through weight averaging, which degrades performance, or mixed RL, which is unstable, they trained a single student model to learn from more than ten specialist teachers simultaneously by mimicking their output distributions on its own generated text. The key technical choice was full-vocabulary logit distillation rather than a token-level approximation, which produces more stable gradients and more faithful knowledge transfer.

V4 Doesn’t Have to Win to Matter

The lukewarm reception to V4, especially in Silicon Valley, is probably unsurprising. Anthropic and OpenAI are releasing powerful closed models, and the gap between the best open models and the best closed-source models may not be narrowing. Even among Chinese open LLMs, V4 is not a clear win.

But the implications of V4 may take time to show up. After V3 and R1, most major open LLMs adopted or borrowed DeepSeek’s architecture to make their models more efficient, and as a result open models in 2025 broadly improved without everyone having to independently experiment.

Entering 2026, against the backdrop of global compute shortage as agentic token usage skyrockets, V4’s memory and inference efficiency innovations could ease real constraints if adoption grows. It might take another three to six months to see V4’s architectural innovations diffuse across the open ecosystem.

What I find worth noting is that while every other major lab is racing to release models and eke out marginal leaderboard gains, DeepSeek-V4 still prioritized architectural exploration over benchmaxxing. New architectures are risky. They can destabilize training and waste compute. But the bet is consistent with a lab that believes the path to AGI runs through efficiency and democratization, not through scaling alone (partly also due to the export control on advanced chips).

DeepSeek is also navigating real pressures. It’s aiming to raise funding at a valuation exceeding $20 billion, with Alibaba and Tencent both said to be interested. The new capital would help the company access more compute for training and retain top talent. DeepSeek CEO Liang Wenfeng is also reportedly hiring more product managers and building new consumer products.

DeepSeek’s V4 release post closed with a line from the Chinese philosopher Xunzi:

不诱于誉，不恐于诽，率道而行，端然正己。Unseduced by praise, undaunted by slander; following the Way, standing upright in oneself.

(The English translation is attributed to .)

The road ahead for DeepSeek won’t be easy as the AI race intensifies. But a company this consistent in its long-termism—and this committed to keeping its research open—gives me more reason for optimism about where AI is heading.

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On the last day of 2025, Yang Zhilin, the CEO of Moonshot AI, said in an internal memo that his company’s goal is to become the world’s leading AGI company by surpassing frontier labs like Anthropic. He didn’t mention OpenAI.

Yang wasn’t alone. Yao Shunyu, chief AI scientist at Tencent and a former OpenAI researcher, made his preferences equally clear in a panel this January in Beijing alongside former Qwen lead Justin Lin and Zhipu AI’s chief scientist Tang Jie. When Yao discussed the companies he most respected in AI, Anthropic came first.

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4 months ago · 11 likes · 1 comment · Tony Peng

Three years ago, that conversation looked completely different. OpenAI was the north star. Ilya Sutskever was the most admired AI scientist in China’s research community (he still is). Sam Altman was something close to a AI evangelist. Nearly every Chinese chatbot launched between 2023 and 2024 looked like a ChatGPT clone.

Today, at least among China’s AI community, Anthropic and Claude have become the new reference point. A Chinese tech media called Anthropic “白月光 white moonlight”, which in local context refers to someone from your past who was perfect, pure, and just out of reach. Interviews with Dario Amodei—his curly hair and that particular brilliant unease, the head slightly bobbing, the eyes somewhere between present and elsewhere—are the new required reading.

And yet the most admired AI company in China is also the most hawkish one on China.

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The Pivot in the Models

The shift from OpenAI to Anthropic as a role model shows up not only public statements, but also in what Chinese labs are building and how they’re positioning it.

The latest models from frontier Chinese AI labs like Zhipu, MiniMax, Moonshot all focus on coding and agentic tasks, and all use Claude Opus 4.6 as their explicit benchmark competitor. AI models pivoting toward coding can drive massive productivity gains, move from content generation to autonomous action, and gain enterprise adoption.

GLM-5, Zhipu’s latest flagship, is built for agentic intelligence, advanced multi-step reasoning, and complex engineering tasks—specifically targeting long-horizon agentic workflows. The updated GLM-5.1, released this month, even claims the top spot on SWE-Bench Pro at 58.4 versus Claude Opus 4.6’s 57.3.

MiniMax is even more direct about its positioning. Their M2 launch announcement markets the model as available at “only 8% of the price of Claude Sonnet and twice the speed,” and lists Claude Code as one of the primary developer workflows M2 is designed for.

The iteration pace has been speeding up: in roughly four months, MiniMax shipped M2, M2.1, M2.5, and now M2.7. The latest version can build complex agent harnesses and complete elaborate productivity tasks autonomously. During M2.7’s own development, MiniMax let the model run 100+ rounds of scaffold optimization without human intervention, achieving a 30% performance gain on internal evaluations. They’re calling it “self-evolution.”

Moonshot’s Kimi series tells a similar story. Kimi K2.6, just released this week, featuring state-of-the-art coding, long-horizon execution, and agent swarm capabilities. And Moonshot earlier launched Kimi Code—a direct rival to Claude Code—letting developers use it through their terminals or integrated with VSCode, Cursor, and Zed.

Then It’s the business model. All three companies, along with Alibaba, ByteDance, and Baidu, have launched dedicated Coding Plans, a flat-rate monthly subscription designed specifically for developers using AI coding tools like Cursor, Cline, and Claude Code.

Before 2025, Zhipu’s revenue was dominated by on-premise government and enterprise deployments. By 2025, their model-as-a-service API platform hit 1.7 billion RMB in annual recurring revenue, a 60x year-on-year increase.

In their FY 2025 earnings call this April, CEO Zhang Peng said:

Anthropic is one of the most closely watched companies in global AI. Its growth logic is very clear: focus on delivering the strongest models to enterprises and developers through APIs, and let intelligence participate in creating economic value.

When the model is strong enough, the API itself is the best business model.”

Moonshot’s numbers are even more striking. After the K2.5 launch, the company reported that cumulative revenue in fewer than 20 days already exceeded its entire 2025 annual total.

As agent adoption has accelerated, these labs are finding that pricing power finally exists. Zhipu raised its API prices by 83 percent in Q1 2026 and saw call volumes rise anyway. That is a completely different market dynamic from two years ago, when Chinese LLM providers were engaged in a price war that pushed some API costs close to zero.

Why Anthropic

Learning from Anthropic is not surprising given what the company has achieved. Anthropic’s Claude Opus 4.6—now succeeded by Opus 4.7—is widely regarded as the best model available today, particularly for coding and agentic tasks. The Mythos model, recently previewed with cybersecurity applications, has raised expectations further.

The revenue growth has been without precedent in enterprise software: Anthropic went from $1 billion ARR in December 2024 to $9 billion at end-2025 to $30 billion in April 2026, surpassing OpenAI’s $25 billion for the first time.

Anthropic also has characteristics Chinese companies specifically admire. The company is extremely focused, prioritizing text generation and coding while largely staying away from multimodality. Its top talent has barely turned over. Its corporate culture resembles a religious conviction more than a startup, with an unshakable belief in the AGI mission as the organizing principle. OpenAI, by contrast, has been losing its shine. Multiple waves of talent exodus, the shutdown of Sora, and the controversies surrounding Sam Altman have all taken a toll.

“Anthropic has taste, and they keep delivering,” a Beijing-based AI infrastructure engineer told me.

A deeper reason is most Chinese AI labs face the same challenge Anthropic faced three years ago: they have almost no chance of winning the consumer superapp race. In China, ByteDance’s Doubao has (almost) won it. By late 2025, Doubao’s daily active users dwarfed the combined consumer bases of Moonshot, Zhipu, and MiniMax. In 2024, Moonshot reportedly spent hundreds of millions of yuan promoting Kimi as a consumer product, but it didn’t work. Neither did the similar pushes from MiniMax and Zhipu.

The DeepSeek moment in early 2025 was the wake-up call. It demonstrated that relentless model capability, not user acquisition spend, was the actual path. Anthropic’s enterprise revenue explosion provided the business model proof. You don’t need to beat the consumer superapp. You need to make the best model and sell access to it. That realization has redirected nearly every serious Chinese AI lab: away from the chatbot wars, toward model research, coding tools, and token sales.

By imitating Anthropic, Chinese labs have returned to their comfort zone—heads down, doing research. And the results are showing. In Q1 2026 alone, Chinese AI labs released probably more frontier-capable models than in all of 2025. Model iteration has accelerated from quarterly to nearly monthly.

It also means more aggressive globalization. Western developers and enterprises pay more for API access, and Chinese models priced at a fraction of Western alternatives are finding real traction. OpenRouter data from early 2026 showed Chinese open-source models accounting for over 60 percent of total token consumption on the platform.

The Anthropic playbook does have one complication as Chinese labs absorb it. As commercial pressure increases, open-sourcing the best models becomes harder to justify. For example, MiniMax just removed the commercial license from its M2.7 model. Financial Times also reported that Alibaba is shifting towards revenue over open-source models.

Open-source and open-weight releases remain important for Chinese LLMs—GLM-5.1 and Qwen 3.6 are both open—but the trend line is toward keeping the frontier models closer to the chest.

This is another page taken directly from the Anthropic playbook: Claude has never been open-sourced.

The Strange Relationship

Anthropic is not like OpenAI in how it has engaged with China. In 2023, Sam Altman gave interviews to Chinese media, spoke at a conference organized in Beijing, and spoke highly of China’s AI community, “China has some of the best AI talent in the world.”

Dario is among the most hawkish AI CEOs on China in the industry. In his famous essay Machines of Loving Grace, he proposed an “entente” strategy, a coalition of democratic nations using advanced AI in military applications to achieve a decisive advantage over adversaries, and described the US-China relationship as a new Cold War.

Twenty years ago US policymakers believed that free trade with China would cause it to liberalize as it became richer. That very much didn’t happen.”

At the Axios AI+ DC Summit in September 2025, he said “it is mortgaging our future as a country to sell these chips to China.” At Davos in January 2026, he compared shipping Nvidia H200s to China to handing nuclear weapons to North Korea. In February 2026, Anthropic accused DeepSeek, MiniMax, and Moonshot of using fraudulent accounts to generate millions of conversations with Claude to train its own models.

The irony is personal as well as geopolitical. Dario Amodei worked at Baidu from November 2014 to October 2015, before his time at Google Brain and then OpenAI. The man who spent a year inside one of China’s flagship AI companies is now the most vocal opponent of Chinese AI development among major Western AI executives. Chinese netizens have not missed this. The joke in Chinese tech circles is to wonder what Robin Li, Baidu co-founder and CEO, did to Dario during that year to turn him so decisively against China.

Anthropic has also translated its stance into policy and product. It prohibits users in China from accessing its services. It has recently added Know Your Customer controls to restrict who can access the API.

I don’t know what drives Dario’s position—whether it’s ideology, genuine security concern, or something else entirely. But whatever the motivation, the competitive reality is harder to separate from the principle. Anthropic is competing against Chinese AI companies globally. So far it has seen minimal business impact from Chinese open source models, according to Dario. But Cursor's latest model is built on Kimi K2.5, which means Chinese AI is already inside one of the most popular coding tools that developers use instead of Claude.

Anthropic recently removed Claude Code from its $20 Pro subscription plan. The company says it only affects a small number of new users, but the developer community is unhappy. In forums, frustrated subscribers said Anthropic was nudging them toward cheaper Chinese alternatives.

The Decoupling Problem

On one hand, the more successfully Chinese labs learn from the Anthropic playbook, the more capable they become as global competitors. On the other hand, Dario’s export controls are meant to slow Chinese AI down. The obstacle and the role model are the same company.

In the meantime, Dario’s worldview—AI as the new nuclear weapons, US and China in active technological Cold War—is increasingly becoming the assumption of US policy. In that narrative, it doesn’t matter how good the model is. Zhipu is already on the US entity list.

That narrative is spreading across Silicon Valley as well. In a recent interview on the Dwarkesh Podcast, host Dwarkesh Patel pressed Nvidia CEO Jensen Huang on whether selling advanced GPUs to China undermines US strategic interests—the kind of question that reflects exactly the framing Dario has helped normalize.

That is why Chinese researchers and executives have mixed feelings about Anthropic. They admire the models, the discipline, the revenue growth. They are learning from the business as fast as they can. But they cannot accept the premise—that China’s AI development is inherently dangerous, that containment is the right policy, that the race is zero-sum.

If AI safety is the genuine concern for Anthropic, the answer is collaboration: working with Chinese researchers, mapping shared risks, building toward a global AI safety council. Instead, Anthropic advocates for the kind of decoupling that could shut that conversation down entirely. You can believe in AI safety and still ask whether export controls advance it—or whether they mostly just advance Anthropic.

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Within four weeks this spring, two Chinese AI startups each raised $300 million to build video generation technology.

PixVerse’s developer AISphere closed a $300 million Series C in March led by CDH Investments. Days later, ShengShu Technology, the Beijing-based startup behind the Vidu video generator, secured a $290 million Series B led by Alibaba Cloud.

Both companies are betting that video generation is not the destination: it is the on-ramp to world models, many believe the next paradigm in AI. And both in different ways are entangled with the same backer and competitor, Alibaba.

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AISphere and Shengshu Technology

AISphere and its PixVerse platform are the more consumer-facing of the two. Founded in April 2023 by Wang Changhu, a former Microsoft Research Asia and ByteDance executive, AISphere launched PixVerse to global users in January 2024. The platform lets creators generate videos from text prompts or images, and it caught fire through viral templates, including a “Venom transformation” effect drew over one billion views in late 2024.

By the time of its Series C, PixVerse had surpassed 100 million users across 177 countries, with 16 million monthly active users and over $40 million in annual recurring revenue, according to the company.

The company calls PixVerse the “Canva for video generation.” Canva won by making design simple enough that non-designers stopped needing designers. PixVerse is making the same bet on video. Its latest C1 model claims film-grade quality and the capability to turn storyboards directly into video, and PixVerse V5.6 ranks among the top ten video generators on the Artificial Analysis leaderboard.

The outputs lean toward an animation rather than photorealism, but the motion quality and scene consistency are genuinely impressive. Some of their best generations deliver something harder to quantify: a creative and artistic quality that catches you off guard.

Its more ambitious model is R1, launched in January 2026. It is a real-time interactive world model. According to the company, users can input commands during video playback—changing lighting, replacing backgrounds, redirecting character—with a response latency of around two seconds and output in 1080P.

After R1 launched, the company’s co-founder said the majority of inbound enterprise interest came from the gaming industry. The investor composition of AISphere’s Series C also reflects this: the round included Ruyi Holdings, a film and TV content company, and 37 Interactive Entertainment, a games company.

Vidu, by contrast, is playing a more technical and enterprise-focused game. ShengShu was founded in March 2023 by Zhu Jun, a Tsinghua University professor who serves as its chief scientist. The company launched Vidu globally before OpenAI made its now-shuttered Sora widely available. Its latest model, Vidu Q3 Pro, supports up to 16 seconds of synchronized audio and video generation with multi-shot composition and camera control. The company reported more than tenfold growth in both users and revenue in 2025, though it declined to share specific figures.

Where AISphere’s R1 targets the intersection of video and gaming, ShengShu’s goals are more grounded in embodied intelligence. The company has developed Motus, an embodied AI model designed to enable robots to perform actions. The $290 million will fund a general world model that bridges generated video with real-world use cases like industrial automation and robotics. Founder Zhu Jun has described the goal as connecting perception and action, building AI that can model and predict real-world behavior consistently.

In my view, AIsphere loosely resembles MiniMax in its consumer-first, product-driven growth strategy, while ShengShu more closely mirrors Zhipu in its academic origins and enterprise focuses.

What is a world model, and why now?

Both AIsphere and ShengShu are betting on world models, though applying it toward different ends.

The simplest way to understand a world model is by contrast with what a LLM does. An LLM predicts the next token. A world model predicts what happens next in the world: How objects move, how physics behaves, how cause and effect unfold over time. This is why an AI-generated video clip currently lets a dog’s collar disappear when it runs behind a couch, or turns a love seat into a sofa mid-shot. The model has no stable internal representation of the scene. It is guessing frame by frame what is statistically plausible.

The world model thesis that video is the training ground for AI that understands physical reality has attracted serious believers. Yann LeCun left Meta to pursue it. Google DeepMind’s Genie 3 simulates real-time 3D worlds. Nvidia’s Cosmos platform trained on 20 million hours of real-world data to support physical AI development. Companies that have already spent years building video generation infrastructure are naturally positioned to make this leap.

Alibaba invests in everyone, competes against everyone

Here is where the story gets interesting. The lead investor in ShengShu’s $290 million round was Alibaba Cloud. Alibaba also led AIsphere’s $60 million Series B in September 2025. Which means Alibaba is the principal backer of both of the most well-funded video AI startups in China.

However, the e-commerce and cloud giant is simultaneously building and deploying its own video models internally. In the past week, the company’s newly formed AI unit Alibaba Token Hub, or ATH, released a video model called HappyHorse-1.0. The model debuted anonymously on the Artificial Analysis benchmark, climbed to the top of both text-to-video and image-to-video rankings, and triggered a wave of speculation about its origins before Alibaba confirmed its ownership.

The competition here is essentially Alibaba versus ByteDance. ByteDance’s Seedance 2.0 had been a dominant model on the video AI leaderboards. HappyHorse was the first model to challenge and displace it. My early review is HappyHorse’s photorealistic visual output is a clear step forward, but Seedance 2.0 has an edge in audio-visual consistency and multi-shot camera control. However, against the AI video startups Alibaba backs, HappyHorse outperforms both Vidu and PixVerse on the benchmarks.

This is similar to the ecosystem of Chinese LLMs, where Alibaba’s open-source Qwen family has consistently competed with ByteDance’s Seed series for developer mindshare, while simultaneously backing leading LLM startups—Zhipu AI, MiniMax, and Moonshot AI among them. In both cases, Alibaba’s strategy combines internal model development with external investment in the most promising labs, a dual-track approach that keeps it relevant at every layer of the stack regardless of which specific model wins.

Alibaba Cloud’s revenue grew 36% last quarter, driven by AI workloads, according to the company’s Q3 FY2026 earnings call in March. Video generation is among the most compute-intensive workloads. Every inference request, every API call from a PixVerse or Vidu user represents GPU-hours that, if those companies run on Alibaba Cloud, translate directly into revenue.

US-China race in video generation

If we examine the global AI video landscape, Runway raised $315 million in February 2026 at a $5.3 billion valuation. AIsphere raised $300 million in March at a reported $1 billion-plus valuation. Vidu raised $290 million in April at an undisclosed valuation. Three companies from the US and China, three rounds within eight weeks, all within $25 million of each other in size.

In LLMs, the gap between Chinese and US companies has been real, shaped by the first-mover advantages that OpenAI and Anthropic built through years of foundational research, US export restrictions on advanced chips, and different friendliness to subscription-based services. Video generation is a different story.

Chinese labs entered this race simultaneously with Western ones, and in some cases earlier. Vidu launched globally before Sora was widely available. Kling AI from Kuaishou was consistently competitive on benchmarks — it generated $150 million in full-year 2025 revenue and crossed $300 million ARR by January 2026.

Part of what drives this is structural advantage. China has the world’s most sophisticated short video ecosystem, hundreds of millions of daily active users who have been consuming, producing, and sharing short-form video for nearly a decade. That is both a massive training data advantage and a ready consumer base that is uniquely receptive to AI video tools. The regulatory environment helps too. Compared to the US and Europe, China’s looser approach to IP and copyright creates more room to train on existing content and generate derivative work without the legal friction that has slowed some Western labs.

The field is also growing more specialized. A year or two ago, frontier LLM companies on both sides such as OpenAI, Zhipu AI, MiniMax were hedging their bets by developing video generation models alongside their core LLM models. Most have since pulled back to concentrate on what they do best. The companies that stayed in video have sharpened considerably.

What remains to be seen is whether world models become the unifying layer that both sides have been racing toward, or whether the video and physical AI tracks diverge further, producing different winners for different applications.

Either way, China’s video AI companies have earned a seat at that table.

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DeepSeek is reportedly releasing its long-awaited DeepSeek V4 LLM this month. Its peer Moonshot AI is enjoying a strong moment of its own: a new funding round has catapulted the company’s valuation to $18 billion, buoyed by the successful rollout of Kimi K2.5.

These are the most watched frontier AI labs in China today—arguably in the world. Yet both remain secretive. Their founders rarely give interviews, and few people know what these organizations look like from the inside.

This week I read two excellent pieces about the two companies—by Chinese media outlets LatePost and Renwu—and couldn’t wait to share them. I used Claude to translate both, with minor edits.

DeepSeek, covered by LatePost, is the more journalistic of the two pieces. It surfaces newsworthy details about why V4 has been delayed, where the company’s research focus sits today, and what its workplace actually looks like. My quick takeaways include:

• DeepSeek is putting many efforts in adapting its models to Chinese-made AI chips—a strategic priority given the restricted access to high-end Nvidia hardware and China’s chip self-reliance ambition.

• The company is still committed to original, foundational research, while the outside world just wants the next R1 moment. Managing that gap is CEO Liang Wenfeng’s challenge right now.

• Singular focus remains DeepSeek’s uniqueness. They prioritize language model research above everything else, skipping trendy directions like multimodal generation and resisting pressure to chase every hot application. This is inseparable from Liang’s personal philosophy: do few things, do them completely.

• Surprisingly in a country known for its high-pressure workplace culture, DeepSeek has a no-overtime policy. Liang’s reasoning is exhaustion-induced poor judgment wastes precious compute.

• DeepSeek also faces a resource tension that is becoming harder to ignore. Frontier research is compute-hungry, and Liang is now working to establish a formal company valuation—partly to retain talent being courted with 2-3x competing offers, and partly to give the organization more runway for the kind of long-horizon research.

Moonshot AI, profiled by Renwu—the Chinese magazine known for its long-form, human-interest profiles—is a different kind of read. It portrays how a team of young geniuses works inside a non-traditional company structure. No KPIs, no titles, no formal reporting layers. That flatness accelerates creative collaboration, but also creates disorientation for people who need external structure to know whether they’re succeeding.

The piece is a delight to read, though I need to flag this is not a tech journalism article. The writer is more like an observer with a visible admiration for the people they are watching. If you’re looking for Moonshot AI’s research roadmap, growth metrics, or strategic mishaps, you’ll come away disappointed.

The two pieces share some commonalities. Both Liang Wenfeng and Moonshot AI CEO Yang Zhilin have built organizations that are direct reflections of their personal philosophies. Both have explicitly rejected the standard Chinese tech management—perhaps conventional structures are incompatible with frontier AI research.

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DeepSeek Before V4: Character, Organization, and Liang Wenfeng’s Singular Vision

By Cheng Manqi Edited by Song Wei

DeepSeek stands at an inflection point. Since the second half of 2025, the members who have definitively departed and landed somewhere new include:

• Wang Bingxuan, poached by Tencent’s Yao Shunyu late last year. A core author of DeepSeek LLM—the company’s first-generation LLM—he had remained involved in subsequent model training cycles.

• Wei Haoran, who left around the Lunar New Year. A core author of the DeepSeek-OCR series, he is expected to join a major tech company.

• Guo Daya, who recently made his departure official. A core author of DeepSeek-R1, he too is expected to join a major tech company.

• Ruan Chong, who left earlier in 2025 to effectively retire, then in January announced he was joining autonomous driving startup Yuanrong Qixing. Ruan was a veteran from the High-Flyer days and a core contributor to DeepSeek’s multimodal work, including Janus-Pro.

DeepSeek has never raised outside funding, and carries no formal valuation. As the market caps and valuations of rival AI companies have surged, Liang Wenfeng has been working to answer a pressing question from within his team: what is this company actually worth? The answer has direct bearing on what those employee equity agreements are actually valued at.

Since the fall of 2025, Liang has also been talking more about productization and commercialization. DeepSeek now has a product team of several dozen, though it has yet to move into hot application categories like AI coding or general-purpose agents—on the consumer side, it still offers only a conventional chatbot.

Then there is the challenge of managing scale. DeepSeek’s headcount has surpassed that of High-Flyer, making it the largest organization Liang has ever run.

Hanging over all of this: DeepSeek V4 has still not officially launched.

Around January of 2026, a smaller-parameter version of V4 was quietly circulated to some open-source framework communities for compatibility testing. Under more optimistic timelines, the full large-parameter V4 had been expected to ship and open-source around the Chinese New Year in mid-February. As things stand, V4 may arrive in April.

Some people have left. More have chosen to stay. DeepSeek is adapting—but much about it remains unchanged.

It is the only core AI lab in the world that doesn’t grind. While core AI developers at Google, OpenAI, xAI, ByteDance, and their Chinese and American peers routinely log 70 to 80 hours a week, most DeepSeek employees walk out the door around 6 or 7 p.m. There’s no clock-in requirement in the morning.

Liang Wenfeng’s view: it’s nearly impossible for a person to sustain more than six to eight hours of high-quality output in a day.

DeepSeek has no formal performance reviews, no hard deadlines. This lean, high-talent-density organization still runs on what it calls “natural division of labor”—researchers can freely form teams or pursue ideas independently.

“Beyond the main track, DeepSeek has people working on long-horizon research that might not yield results for a year,” said one person close to the company. “DeepSeek is the best place in China—arguably in the world—for someone who genuinely wants to do research.”

There is also this: DeepSeek is secretive. Especially since 2025, from Liang Wenfeng down, the team has gone collectively silent. On the social media platforms and communities where AI practitioners are most active, their voices are almost nowhere to be found.

This piece assembles what we’ve learned from multiple sources about DeepSeek’s defining traits, research priorities, organizational dynamics, and the changes unfolding inside a company of fewer than 200 people. All of it traces back to the singular goal Liang Wenfeng has set for DeepSeek.

Liang Wenfeng: Do Few Things. Do Them Completely.

Liang’s AI ambitions predate DeepSeek’s founding in 2023 by years.

In 2016, Demis Hassabis—the originator of the AGI concept and co-founder of DeepMind—assembled a quantitative trading team to generate revenue for DeepMind as it sought independence from Google. It didn’t make money.

That same year, Liang Wenfeng—a Zhejiang University alumnus with both bachelor’s and master’s degrees—had already been in quantitative investing for eight years. He had founded High-Flyer in 2015, started running deep learning strategies on GPUs in live trading in 2016, achieved full AI-driven trading strategies by late 2017, and in 2019 stood up High-Flyer’s first compute cluster: “Firefly No. 1,” with 1,100 GPUs.

Also in 2019, High-Flyer AI (High-Flyer Artificial Intelligence Fundamental Research Co., Ltd.) was formally incorporated. Luo Fuli, now heading AI at Xiaomi, and Ruan Chong, who recently joined Yuanrong Qixing, both joined High-Flyer after this, later transferring to DeepSeek in 2023.

Financially independent before 30, Liang leads a life that is both simple and opaque.

People who know him say he’ll wear the same clothes for days at a stretch. In Hangzhou, he lived in hotels for long stretches. In Beijing, where most of DeepSeek’s researchers are based, he rents an apartment. He’s slight and fit, known to keep a physical routine; the hobby most associated with him is hiking and outdoor activities.

Jensen Huang invites Nvidia employees to his home, pours drinks, chats about life, happily shows off his cars. Liang Wenfeng skips quarterly team-building events, rarely joins team dinners, and at the annual all-hands only shows up to give remarks—then leaves.

In 2022, a High-Flyer employee going by the pseudonym “An Ordinary Little Pig” made a personal donation of 138 million RMB to a charitable foundation. Many speculated that the little pig was Liang himself. High-Flyer’s response: “Employee donations are anonymous. The company itself doesn’t know the real identity of the little pig.”

Within the scope of work, Liang does few things. He doesn’t do many things typical startup CEOs do—like fundraising.

In 2023, Liang briefly met with a small number of investors. But we understand he put forward an unconventional condition: similar to OpenAI’s investment arrangement with Microsoft, he wanted potential backers to accept a capped return. None of the institutions that went through that process invested in DeepSeek.

In the two years since, Chinese LLM funding has been torrential, with multi-hundred-million-dollar rounds becoming routine. Liang stopped meeting investors entirely—not even to build relationships. Most founders, even outside an active fundraising window, wouldn’t turn down a coffee with a partner from a top fund. Liang has declined most such requests.

He has concentrated nearly all his time on the narrow set of things he believes deserve his focus—and pursued them with granular attention.

One of the keys to DeepSeek’s early success was what might be called singular focus: a clear prioritization of language models above all else, with no detours into hot areas like multimodal generation.

On the chosen track, Liang goes deep. He learns from team members across algorithms, architecture, infrastructure, and data; he participates directly in model and product discussions at the detail level.

People who have spent time with Liang consistently observe that he carries no CEO aura, no “genius energy”—he comes across more like a researcher. What he talks about most is specific technical problems.

Zhang Jinjian, founding partner of Oasis Capital, once shared a story in an essay: he asked MiniMax founder Yan Junjie whether there was anyone more focused than him.

Yan described arriving early for a first meeting with someone he’d never met—spotting a guy in a T-shirt and assuming he was an assistant. For half an hour, this person asked Yan detailed technical questions without introducing himself. Then Yan asked, “When is Liang coming?” The guy said: “I’m Liang Wenfeng.”

DeepSeek’s Organization: Flat, Cross-Functional, No Overtime

Consistent with Liang’s personal style, DeepSeek’s organization is radically flat, built on cross-functional collaboration, deliberately slow to expand, and free of overtime culture.

When Liang founded High-Flyer, he had co-founders. DeepSeek has no second-in-command—especially within the research team, where there are only two levels: Liang and everyone else. Liang makes major decisions and absorbs the most accountability.

The research team now numbers roughly 100 people and operates like a large academic lab. The DeepSeek researchers—most born around 2000—call Liang, born in 1985, “Liang Laoban” (Boss Liang). The relationship is closer to that of a dissertation advisor: he organizes research, coordinates resources, does hands-on research himself, and appears as corresponding author on shared outputs.

Liang’s deepest personal involvement is with the base model architecture team, where he joins detailed discussions to finalize the architecture of each new model generation. This team numbers in the low dozens and forms the core of pretraining.

Closely coupled with architecture are the infrastructure and data teams, each also in the low dozens. In many companies, infrastructure functions as an internal service provider that executes on algorithmic requirements. At DeepSeek, the infra team is in the room before model training begins—contributing to architectural decisions at the design stage.

The tight integration across these groups produces blurred organizational lines and what the company calls “cross-functional division of labor.” This happens to be the collaboration model best suited to how model training actually works: data selection and infrastructure tradeoffs have to be on the table during the experimental and finalization phases, not handed off afterward.

Liang serves as the connective tissue across all these modules. He attends each team’s weekly meeting, tracking progress and bottlenecks across the full picture. Most teams’ weeklies are open to members from other teams.

This kind of granular leadership and organically tight collaboration is hard to sustain at scale—which is why DeepSeek is deliberate about limiting the size of its core research team.

One of the things most distinctive about DeepSeek in the global AI landscape: No overtime, no clock-in, no formal performance reviews. Most employees leave around 6 or 7 p.m. The company subsidizes after-hours activities—sports classes, reimbursement for recreational facilities.

Liang’s logic: a person cannot sustain more than six to eight hours of high-quality work in a day. Exhaustion-induced poor judgment wastes precious compute. The cost outweighs any benefit.

On hiring, DeepSeek has historically avoided lateral recruitment almost entirely, relying instead on new graduates and converted interns. In early 2025, LatePost profiled the 172 researchers (including interns) who had contributed across DeepSeek’s first three model generations—LLM, V2, and V3/R1—and tracked down 172 of them, finding resumes for 84: more than 70 percent held bachelor’s or master’s degrees; more than 70 percent were under 30.

Before V3 and R1, DeepSeek was competing in the global LLM top tier with roughly one-tenth the headcount of major tech companies, approximately half the per-capita working hours, and extreme concentration of effort.

But as the frontier expands and the number of directions worth exploring multiplies, sustaining that organizational size, communication style, and research culture has become harder.

The Past 15 Months: DeepSeek Stays the Course While the World Shifts Fast

After V3 and R1 broke through in early 2025, DeepSeek didn’t press its advantage with a splashy follow-up. Instead, it continued down its chosen research track. The publicly disclosed work falls roughly into three categories:

First: efficiency optimization. Squeezing maximum intelligence out of every unit of compute. This includes the full suite of training and inference infrastructure released during DeepSeek’s open-source week in early 2025—covering inference kernels, communication libraries, matrix multiplication libraries, and data processing frameworks. (Note: a kernel is the lowest-level code executing on a GPU, implementing core operations like matrix multiplication.)

It also includes continued improvements to the attention mechanism: NSA (Native Sparse Attention) released in early 2025, followed by DSA (Dynamic Sparse Attention). Combined with MLA (Multi-head Latent Attention) introduced in V2, these share the same goal: processing longer context without proportionally increasing compute.

The September 2025 update to DeepSeek-V3.2 revealed something else: DeepSeek swapped its underlying operator library from the mainstream CUDA and Triton to TileLang—an open-source project initiated by Yang Zhi’s team at Peking University. CUDA is Nvidia’s lowest-level language; Triton was open-sourced by OpenAI; TileLang is a homegrown Chinese open-source alternative.

Second: architectural advances. These include mHC (Mixture of Hash Connections, or “flow-constrained hyperconnections”), released in early 2026 to improve stability in large-scale training, and Engram, which builds persistent long-term memory outside the model itself. Industry observers widely believe mHC will be incorporated into V4’s training.

Third: “non-mainstream” explorations. Among these, DeepSeek-OCR converts text into images before feeding them into the model—the intuition being that letting the model “read” text the way humans visually parse paragraphs and hierarchies improves comprehension of complex documents. Internally, more of these experiments are ongoing, including work on continual learning and autonomous learning.

Liang also brought on advisors with backgrounds in neuroscience and brain science in 2025, exploring learning mechanisms that more closely mirror the human brain.

Meanwhile, the external AI environment has shifted dramatically. Two competitive storylines have dominated attention:

The first is agentic models and applications built on coding capability. This has become the primary battleground between Anthropic and OpenAI, now playing out as Opus 4.6 vs. GPT-5.4 on the model side, and Claude Code vs. Codex on the product side. OpenClaw—the viral AI agent that swept China in early 2026—represents the latest expression of this trend.

The second is multimodal generation, which has repeatedly broken through the mainstream through what can only be called visual magic: OpenAI’s GPT-4o in the spring of 2025, Google’s NanoBanana in the fall, ByteDance’s Seedance 2.0 just before the 2026 Lunar New Year. Video generation is also tied to one of the most forward-looking frontiers: world models.

DeepSeek has made little investment in multimodal generation. Liang Wenfeng doesn’t consider it a core path to intelligence.

On the agent front, DeepSeek-V3.2 strengthened agent capabilities, but DeepSeek’s overall iteration cadence has been slower than rivals who grew deeply anxious in the wake of R1. Since early 2025, Zhipu, MiniMax, and Kimi have each shipped five, four, and three new model versions respectively—each targeting agents or coding.

According to OpenRouter data, over the 30 days from February 24 to March 26, DeepSeek-V3.2 ranked 12th in model token consumption among OpenClaw applications routed through OpenRouter—six of the top ten were Chinese models. (Note: OpenRouter reflects individual and small-developer usage and offers only a partial view of overall token consumption.)

DeepSeek’s Goal Isn’t the Mainstream One. Some Left. More Stayed.

DeepSeek’s distinctiveness connects directly to Liang Wenfeng’s conception of what AGI development actually requires. Beyond pushing the intelligence ceiling of LLMs, he sees two other critical workstreams:

First: building LLMs on a domestic Chinese tech stack.

DeepSeek invests in compatibility with domestically produced GPUs to reduce dependence on restricted high-performance compute supply. The V3.1 update last August, for instance, noted that DeepSeek’s adoption of UE8M0 FP8—a data compression format—was “designed for the next generation of domestic chips.” Switching from Triton to the domestically developed open-source TileLang, mentioned earlier, serves the same purpose: gaining more control at the foundation level.

In conversations with AI practitioners, Liang has also floated this question: “Could we achieve everything intelligence can do today using only the compute that already exists?”

Second: original innovation—pursuing directions that major tech companies or other startups won’t attempt or aren’t willing to try.

In the second half of 2024, DeepSeek launched the Janus series, experimenting with unified multimodal understanding and generation. It has also developed the Prover series, exploring formal mathematical proof. Then there’s the OCR work in 2025, and internally ongoing efforts in continual learning and brain-inspired architecture.

For Liang, what matters is not just model performance—it’s the more fundamental, original discoveries made in the pursuit of that performance.

But this doesn’t align with what some outside observers now expect from DeepSeek. Some want every DeepSeek release to be as seismic as R1. That’s an unreasonable demand, and it doesn’t reflect how technology actually develops.

Liang can ignore external expectations. Internal expectations are another matter.

For younger researchers, exploring the frontier means absorbing more uncertainty. The safer path is staying close to the industry’s strongest models, getting your name on technical reports that attract attention, and having access to abundant GPU resources to run experiments.

Beyond recognition and influence, the external pull on DeepSeek’s talent has a financial dimension. DeepSeek’s base compensation is strong, but competitors are offering more—significantly more. Headhunters told us that competing offers are “hard to say no to”—”doubling or tripling is no problem,” with some companies offering eight-figure RMB total comp packages including equity.

The competitive picture has also shifted. MiniMax and Zhipu have gone public with rising share prices; StepFun and Kimi are reportedly on IPO paths. This has made the equity agreements DeepSeek employees hold—with no clear price attached—feel increasingly uncertain.

Faced with the large offers, more people have stayed. They believe in Liang’s approach to AGI. They want to do research that isn’t driven by competitive pressure. And they’ve grown comfortable in DeepSeek’s comparatively relaxed, deliberate research environment.

Some recent rumors circulating externally are inaccurate. The team has seen some changes, but there has been no mass departure.

“The ones who stayed are, to some degree, still idealists,” said one person close to DeepSeek. Liang believes that beyond the main track of improving model efficiency and capability, it’s necessary to pursue directions whose near-term returns are unclear—because “the better-resourced labs abroad, like Google and OpenAI, are definitely trying all kinds of directions internally.”

To this day, DeepSeek’s relatively small team and the transparent, flat culture it has maintained since founding still allow members to divide work organically: sometimes a new research direction begins simply because three or five people all think an idea is worth pursuing—and so they do.

This echoes how Liang described it in a 2024 interview with Waves (a Chinese media outlet):

We generally don’t pre-assign roles. Every person brings their own distinct experiences and their own ideas. You don’t need to push them… But when an idea shows promise, we’ll also top-down reallocate resources.

“DeepSeek is the best place in China—arguably in the world—for someone who genuinely wants to do research,” said one person close to the company.

Changing the World, Being Changed by It

DeepSeek’s distinctive interpretation of what AGI requires is both its greatest strength and the source of its current internal tensions. The ecosystem-building and original exploration Liang prizes are aligned with—but not identical to—the industry’s dominant priority of simply staying strongest.

And as LLMs have matured, the definitions of both “strongest” and “most original” have grown murkier and more subjective.

Benchmark scores no longer fully capture model capability. Especially as the competition has shifted toward agentic models, product surface area—and the long-tail use cases and diverse data that come with it—has become increasingly important. That’s precisely the territory where DeepSeek, with its focus on core model research, hasn’t invested heavily.

The forthcoming V4 will almost certainly be the strongest open-source model at release—but it’s unlikely to be dominant by a crushing margin. Different developers and users in different contexts now hold increasingly varied standards for what “strong” even means.

As for what constitutes original, valuable new research: that has always been contested, determined by the experience, judgment, and intuition of individual researchers—what the field calls “technical taste.”

The way to validate taste is through experiments. And the number and scale of experiments are constrained by compute. Relative to its peers, DeepSeek doesn’t have that much of it.

Finally, whether it’s building the ecosystem foundations for domestic AI infrastructure, or exploring directions others won’t try—the payoffs of the work Liang values most are deeply uncertain.

Frontier research is supposed to absorb that uncertainty. But it sits in tension with limited compute resources, and with the expectation from outside that DeepSeek will continue to astonish—or even overwhelm—on a regular basis.

Liang sees the need to change. He has recently started working on establishing a company valuation and giving team members clearer financial expectations.

DeepSeek will also invest more in products. We reviewed every job posting published by a DeepSeek HR staffer on social media from December 2024 through today. In the most recent listings from mid-March 2025, DeepSeek for the first time named specific products in a posting—recruiting a “model strategy product manager” for agent work:

Continuously tracking industry developments, with hands-on experience using well-known agents such as Claude Code, OpenClaw, Manus…

More moves from DeepSeek on the agent product front are coming.

In early 2025, DeepSeek stunned China and the world with its open-source generosity and its achievement of doing more with far less—and changed the world in the process: pushing peers to invest more seriously in model fundamentals, directly inspiring models like Kimi K2 and K2-thinking, and seeding new teams, including MiroMind, backed by Chen Tianqiao.

A miracle is a miracle precisely because it doesn’t happen often. It is, by definition, a low-probability event. In China’s fiercely competitive, results-first environment, the very existence of a DeepSeek willing to pursue an unconventional goal is itself a pleasant anomaly.

Those who have spent time with Liang Wenfeng describe him this way: “He has an unusual immunity to noise.”

After R1 went viral in 2025, Liang showed little interest in the adulation. Now he faces a different kind of test: in an environment of intensifying external competition, distinguishing signal from noise—holding the line where it matters, and changing where change is called for.

“The people who keep their heads down may not be the ones still standing when the froth clears,” said one industry observer. “But the only way for Chinese technology to move from imitation to leadership is if more companies like DeepSeek exist.”

That is Liang Wenfeng’s work. And DeepSeek’s. For the many who have been moved by what this company has done, the ask is simple: set aside the hero narrative, and watch a company and its technology with something closer to equanimity.

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100 Hours Inside Moonshot AI

By Liu Mo, Edited by Jin Zha

Spring 2026 has been kind to Kimi. Revenue, fundraising, and valuation have broken records in rapid succession. A paper co-authored by a 17-year-old high school intern drew praise from Elon Musk and other Silicon Valley heavyweights. And Cursor—a U.S. company valued at $50 billion—publicly acknowledged that Kimi’s model powers the core of its product. In the span of weeks, Kimi completed what might be called a beautiful triple: capital, technology, and commercialization, all at once. This startup, founded just three years ago and now valued at over 120 billion RMB, is becoming visible on the global AI map.

And yet Moonshot AI—the company behind Kimi—remains, as ever, mysterious.

I was granted 100 hours of deep access inside the company’s headquarters. As an independent writer, I could interview any employee willing to speak, sit in on any meeting that didn’t touch trade secrets, and write without interference or compensation. That, it turns out, is exactly this company’s style.

Standing inside the company feels like being at the eye of a storm. Within the eye, everything is still. Workstations are quiet. Keyboard clicks are sparse. Laughter drifts over occasionally. The noise of the outside world—the rumors, the debates, the adulation and imitation—finds no echo here.

More than 300 people. Average age under 30. Each one carrying roughly 400 million RMB in implied valuation on their shoulders. About 80 percent of employees here identify as introverted—people sit side by side and default to typing rather than talking. Introversion isn’t a flaw here; it’s an organizational protocol.

I thought back to my first visit to this company in 2024, on a night when the storm was just beginning to gather. My first impression of Moonshot AI was not particularly good.

“DeepSeek Saved Us”

The evening of December 24, 2024 was a Christmas Eve most Chinese people wouldn’t have paid much attention to. For Julian, it was the darkest night of her life.

She was 26, two years out of Peking University, with no industry experience—and already one of Kimi’s earliest employees. This simultaneously young and senior woman sat at the long table in the Radiohead conference room, facing more than thirty colleagues, tears she couldn’t stop streaming down her face.

Julian had been unable to deliver a holiday marketing plan that met the co-founders’ standards. With the Chinese New Year still a month away, being asked to fundamentally overhaul a proposal already revised six times—and guarantee cross-team execution—was a low-probability event to begin with. But the company’s growth expectations for the 2025 Lunar New Year were enormous: it was the previous year’s New Year that had put Kimi on the map with its “2-million-character long-context” breakthrough, triggering a surge in consumer users and even spawning what the market called “Kimi concept stocks.”

The weekly meeting was long and dispiriting: twenty young colleagues with no more experience than Julian took turns presenting, covering everything from social media buying to user operations, domestic PR to overseas marketing, debated in full group, decided by the co-founders. Kimi was like an adolescent who didn’t know what to do with itself—even with a monthly marketing budget in the tens of millions of RMB, it was flailing against the oncoming competition.

The meeting finally ended before 4 a.m.

No one would find out whether Julian’s eventual plan succeeded. Because one month later, when the world first learned the name DeepSeek, none of it mattered anymore.

Hayley, from the growth team, went back to her hometown in Wenzhou for the holiday. Relatives and friends kept asking: have you heard of DeepSeek? Kimi suddenly sounded like a name from a previous era. Hayley had the hardest New Year of her life. “The silence inside the company,” she said, “was deafening.”

The annual all-hands meeting is typically held in March, after the holiday, and employees are encouraged to challenge leadership directly. That year, nearly every question was about DeepSeek. The sharpest came from the HR team, who with complete sincerity put into words what everyone was thinking:

“How do we answer the question candidates are asking us now: ‘DeepSeek gave me an offer too. Why should I come to Kimi?’”

Not everyone processed it that way. Alex, from the algorithm team, recalls that if he felt any strong emotion in the DeepSeek moment, it was one thing: excitement.

That excitement wasn’t just his—it reflected the whole algorithm team’s disposition. They saw a new possibility: lower-cost approaches, the open-source path, and a fact that no one had previously believed possible—that if the technology is strong enough and the model is solid enough, an obscure Chinese startup can earn the respect of the entire world.

The product team wasn’t panicking either. Kevin, among the earliest product hires, had a clear and steady confidence: DeepSeek broke through on its model. But once Kimi’s model capability caught up, he knew there would be far more they could do on the product side to build on that foundation.

No one knows exactly what discussions the co-founders had behind closed doors. But the company executed a strategic pivot and refocusing with remarkable speed, and reached genuine company-wide consensus. Ask any employee what the most important thing the company does is, and they’ll answer without hesitation: the model.

From that point on, you could feel a deep respect for DeepSeek spreading through the organization—on one level, the mutual recognition of peers; on another level, as Alex put it: “The truth is, DeepSeek saved us.”

Taste Is All You Need

“How can you wear shoes like that?”

Ezra blurted it out, and I was more surprised than she was. On her office floor, nearly everyone keeps a pair of slippers under their desk—comfortable clothes help people relax, focus, and think more creatively.

This is the dress code of smart people.

I’ve met many high achievers, but the “good students” here are unlike any I’ve encountered. When Ezra was in elementary school, she tried to hack her family computer simply because her parents refused to tell her the password. In middle school she developed an interest in Bitcoin—when a single coin cost just 300 RMB—and tried to persuade her mother to give her some pocket money to invest; her mother told her it was a scam. The first time she took a taxi in high school, she sketched out the product model for a ride-hailing app—if AI tools had existed then, she might have built it. In college, she finally had money of her own and went into the stock market, losing 90 percent of it on China’s A-share market.

The stock market disaster prompted a deep reflection on human limitations—which unlocked her interest in AI.

Her understanding of AGI is simple: create N Einsteins and solve all of humanity’s problems. From that point, she was determined to find a company exploring the limits of AGI—even after she had made back her losses in the A-share market.

With a strong academic background, she had offers from major companies everywhere. She chose Kimi for one reason: during the interview, founder Yang Zhilin’s deep technical understanding and careful attention to detail moved her. She felt he genuinely cared about the model. He had none of the restlessness you often find in very smart people, none of the transactional quality of a businessperson, and she didn’t learn he was the founder until the interview was over.

Karen was rebellious from childhood—argued with teachers, never listened to his parents, insisted on studying abroad when everyone expected him to stay, then insisted on starting a company after graduating, despairing at the stable, comfortable life that big tech companies offered. He didn’t want a life where he could see the end from the beginning.

I asked him: if you had to choose between a 100 percent chance of a 60-point outcome and a 1 percent chance of a 100-point outcome, which would you pick?

He chose the latter without hesitation. Not that he couldn’t live with 60. He just couldn’t stand the 100 percent.

This entrepreneurial DNA forms a collective undertone. By rough count, at least 50 people at Moonshot AI have founded or joined a startup.

Kimi, it seems, likes hiring CEOs.

More precisely: it shelters wave after wave of wandering geniuses. A genius isn’t necessarily a star student. What matters is that they have, in some dimension, a pair of eyes that can see through time.

Yannis’s CV isn’t impressive by elite Chinese university standards. But as far back as 2023—when AI model companies didn’t even have products yet—he had already foreseen in developer communities the rise of both DeepSeek and Kimi. A younger colleague spotted this foresight and introduced him to the company.

Karen puts it this way: too many smart people get trapped in the constraints of systems—family, school, workplace—and unconsciously conform to the collective, unable to see their own deepest needs. Only a few try to escape, and they’re often invisible to the world. One of Kimi’s missions, he says, is to see them.

Without that seeing, a 17-year-old high school student couldn’t have collaborated with the team as an intern and co-authored a paper that drew praise from Musk. The person who put him in the first-author position was Bob—the talent spotter who found him in the first place.

The line between genius and madman is thin. When a misunderstood madman arrives at Moonshot AI, they may suddenly become a world-changing genius; or those unfinished geniuses may only fully ignite in this environment. Bob told me: to some extent, a big ego isn’t a problem—it might even be an asset. Using ego as an internal drive, believing that one must be part of something great—that’s the truly mad kind of genius, and the kind they absolutely don’t want to miss.

Geniuses are obsessive.

In this team, training a top-tier AI model is called “alchemy”—and alchemy is fundamentally just debugging, endlessly. After launching a Flagship Run—a full-scale training of their most advanced model—Bob and his colleagues developed a habit they can’t break: the first thing they do every morning is refresh tens of thousands of internal monitoring metrics. Any curve on the screen that spikes abnormally triggers immediate alarm: is it an optimization error? An architectural flaw? A numerical precision mismatch?

They are as sharp as trained animals. Some team members filter through training data to find tokens with extreme values, print them out one by one, and interrogate each one: why are you fluctuating so violently?

Everyone who has truly participated in this “delivery” has lived through nights too tense to sleep—not from anxiety, but from curiosity in overdrive. That obsessive vigilance is what has pushed this model to the top of the industry.

Geniuses cluster.

Over the past year, more than 100 of Kimi’s new hires came through internal referrals—friends, or friends of friends. This recruitment model is called “person-to-person transmission” inside the company. Built on a network of relationships that are already deeply connected, trust becomes a natural organizational asset.

At its core, Kimi has transferred the difficulty of organizational management onto the hiring process. People who arrive through referral tend to be “the same frequency.” This echoes the keyword almost everyone at the company emphasizes: taste.

On a September 2025 evening, a few engineers casually kicked off an internal side project they named “Ensoul”—as in, to give something a soul. The name itself reads like a line of poetry: they wanted dormant code files to come alive, to become an intelligent assistant you could converse with in the command line.

This sensitivity to naming isn’t accidental. They once had a framework called “YAMAHA”—an acronym for “Yet Another Moonshot Agent.” Their core underlying layer was named “Kosong”—Malay for “empty,” drawn from the Buddhist expression “form is emptiness,” evoking a blank page that presupposes nothing yet contains every possibility.

It’s precisely this kind of taste that determines what the product looks like.

While everyone else was cramming chat windows into the command line, the team found it ugly: real programmers open a terminal to input commands, not to have a conversation. So Kimi CLI was designed to be more like a “smart shell”—it understands your commands but doesn’t try to turn itself into a dialogue window.

This minimalism extends to the code itself. The entire core logic is 400 lines of Python—like a short poem, stripped of all unnecessary decoration. Modules are cleanly decoupled so users can customize features or disassemble Kimi entirely and rebuild something of their own.

Even Kimi Agent in its early days once went by the internal name “OK Computer”—a name eventually forced to change because it was too niche to travel. But the person who named it didn’t seem to care about internet traffic maximization; they only answered to their own musical taste and linguistic fastidiousness.

Someone half-joked that if you ranked AI companies by the proportion of employees who play a musical instrument, Kimi might come in first.

Taste is the highest—and hardest to meet—hiring standard. It can’t be quantified, yet it’s everywhere.

Generalize, Then Evolve

You’ll probably never quite figure out what each person at Kimi is working on.

The company prefers the word “team” to describe how work is divided. Broadly, directions like algorithm, product and engineering, growth, strategy, and operations are roughly defined—but once you try to drill down into specific “departments” or individual responsibilities, no one can give you a clean answer.

Because what you’re dealing with is an organization with no departments, no seniority levels, no titles, no OKRs, and no KPIs. Even the reporting structure is almost comically simple.

For Brandon—who did his bachelor’s and master’s at Tsinghua, held management roles at Silicon Valley giants and Chinese tech majors, and built a billion-dollar startup—this was genuinely incomprehensible. Steeped in years of technical management, having led teams of nearly a thousand people, he arrived hoping to apply that experience and finally have room to operate—only to be told by co-founder Zhang Yutong that the company didn’t work that way, and that the team he’d be leading would have approximately two people.

Out of some intuition about the future, he wanted to learn more before deciding.

So in January 2025, on a long night full of doubt and unsettled feeling throughout the industry, Brandon met his junior Tsinghua classmate, founder Yang Zhilin. Brandon couldn’t have known then that Yang’s name would one day appear alongside Musk’s and Jensen Huang’s in media coverage as frequently as it now does. The only thing he remembered was that after the small talk, the first thing his junior classmate said was:

“RL—reinforcement learning—is the future.”

The conversation that followed was less a dialogue than Yang Zhilin thinking out loud—he was so deep inside his own reasoning that Brandon couldn’t follow much of the Chinese. But he couldn’t deny that for the first time, he felt the knowledge structures and mental frameworks he’d built over twenty years beginning to crack under the pressure of a revolution that was already underway—along with all his ego. As for what finally convinced him to join, he was slightly cryptic: Yang Zhilin might become a great prophet, because he has sufficient vision, and sufficient purity.

Later, when this title-averse company struggled to figure out how to formally slot him in, his response was unequivocal and deadpan: “Even if you make me clean the bathrooms, I’ll come. And I’ll clean them better than anyone.”

Not every seasoned manager or specialist from big tech thrives here. Phoebe is a post-2000 woman who transferred from the growth team to the product and engineering team and calls herself “a clueless young kid who knows nothing.” She told me in all seriousness that in this company, rich experience and an impressive resume can become liabilities. The AI industry is too new, changing too fast—a senior expert may not be able to learn and grow as quickly as someone like her.

She witnessed at least three mid-to-senior-level big tech arrivals fail to land successfully. One eventually decided to go to a completely different industry to live out the rest of his career. The reason: surrounded by people who were extremely young and extremely smart, he broke down after being outpaced repeatedly. He concluded this was not his era or his industry, and made peace with stepping back.

After the DeepSeek moment, Phoebe was hit with her own deep sense of crisis, decided to abandon her advertising entirely, and threw herself into learning everything she could about product and engineering—hundreds of hours of self-study, including livestreaming her learning sessions on Bilibili. What surprised her was that the company gave her the opportunity to switch roles from the very beginning, with complete ease.

In fact, among the thirty employees I interviewed, more than half had changed job responsibilities multiple times. Compared to what they were doing at their previous employer, the change in scope for most is probably 80 percent—meaning nearly everyone at Kimi is doing something entirely different from what they did before.

Kimi gravitates toward people with strong generalization ability.

In the language of AI, generalization refers to a model’s ability to perform in new scenarios outside its training data—not memorizing answers, but capturing underlying structural patterns. The big tech middle and senior managers, by contrast, have been trained too long inside a specific KPI system, a specific vocabulary for reporting up, a specific game of resource allocation. Their weights are overfitted to a local optimum. When the environment variables change completely, their capabilities break down in the face of a new distribution.

If traditional big tech employees are like specialized models, Moonshot AI’s ideal individual is a foundation model: learning basic rules through supervised fine-tuning, then self-playing across diverse tasks via reinforcement learning, ultimately acquiring the ability to transfer knowledge across domains.

James is a returnee from Silicon Valley, 26 years old, whose dream is “to put money in young people’s hands.” A fervent, almost devout believer in artificial intelligence, he considers his physical body little more than a sensor that collects information for agents. When playing League of Legends with friends, he simultaneously records audio and tracks biometric data—heart rate, pulse—to analyze how his teammates’ words affect his emotional state and in-game performance. His views are sharp to the point of provocation:

Anyone who starts learning an entirely new language after age 14 will never achieve native-level mastery. The same is true of AI.

Dan joined straight out of school. It was his first encounter with what real knowledge anxiety feels like. In school, he’d mostly tinkered with what he calls “toy-level” models—7-billion-parameter small models run for a few days on 32 GPUs. Now he was being asked to control a massive MoE architecture with hundreds of billions of parameters, confronting a data ocean of trillions of tokens. It was the difference between a small pond and the Pacific.

To tackle it, he went into a mode of self-inflicted deprivation: sleep schedule completely destroyed, working during Silicon Valley’s nights on Beijing daytime, then switching to Beijing’s nights when Silicon Valley was awake, staring at training monitoring screens for hundreds of hours at a stretch, like a trader who can’t look away from the ticker.

The real challenge wasn’t the workload. It was that he had to perform three jobs simultaneously: algorithm architect, designing optimal solutions through a labyrinthine model; systems engineer, tracing faults in a distributed computing environment like repairing a pipeline system spanning the globe; and data curator, “extracting gold” from a sea of data while being hard on benchmarks and gentle in conversation.

Midway through training, they sometimes had to perform what Dan calls “internal surgery”: key parameters stored in half-precision (BF16) were experiencing runaway numerical spikes, and the team made the call mid-training to switch to full precision (FP32) to stabilize—the equivalent of changing your running shoes in the middle of a marathon. Dan’s takeaway: someone who only knows algorithms, or only knows systems, or only knows data cleaning cannot build a top-tier model. No “that’s not my part” allowed. You have to fuse three completely different worlds simultaneously. This cross-dimensional tempering accelerates a person’s growth to a pace that might otherwise take years.

The screening for anyone hoping to join Kimi is accordingly brutal. There are no OKRs or KPIs. No office politics. No check-ins. But if you’re not “AI native,” if you can’t generalize, if you can’t do your own reinforcement learning as a human—you will find no meaning for your own existence here.

“No Hierarchy Smell Here”

Most brands want to have a story. But nearly every Kimi employee gently reminded me:

Don’t write about Pink Floyd in the article. Or the piano that sits at the company entrance. (Tony’s note: For context, these two details have been repeatedly included in almost every Moonshot AI feature story).

Those who already understand, they feel, already do. Those who don’t, don’t need to. From “Moonshot AI” to “Kimi,” neither name has anything to do with technology or AI. But if a company leans too hard on its connection to rock and art, it starts to seem performative. People here prefer to exist in a state of beauty without self-consciousness.

Win is a post-2000 escapee from big tech. He told me this place is bizarre: you can actually get work done without meetings.

At his previous company, daytime was consumed by meetings and actual work happened at night. He arrived at a simple conclusion: if most of your energy goes into coordinating the relations of production, there’s very little room left to improve the means of production.

This is a feature of AI-native organizations. More than ten employees explicitly told me they are increasingly uninterested in dealing with human colleagues and prefer to collaborate with AI—because AI is more reliable and simpler. This also connects to the company’s overall introverted character. One person used a more charming word: shy. Everyone can be socially effusive in a group chat; in person, they go quiet. Kimi doesn’t do many culture events—outside of the annual company gathering, the most recent one was an in-office group massage.

Introverted doesn’t mean closed off, or lacking energy. No one’s participation in my interviews was any kind of obligation. And yet I never received a single “no.” The internal group chats are flooded daily with information and discussion, and a rotating gallery of abstract emoji. No one’s message goes unanswered.

If you need someone’s help to get something done, the process is simple: ask directly. No need to go through a manager, no need for any approval, no coordination meeting, no “department wall” to break through. Kimi has no department walls—it barely has departments.

Yang Zhilin’s personal status line is four Chinese characters: 直接沟通—communicate directly.

But everyone acknowledges the company has been constantly changing since its founding. Some changes were chosen; others were forced—sometimes felt like being slapped in the face. From heavy advertising spend to focusing on the model. From insisting on closed-source to rapidly going open-source. From chatbot to Kimi Agent, Kimi Code, Kimi Claw. From consumer to enterprise and back to consumer. Not every change has held up to scrutiny.

In Ezra’s mind, one thread has never changed: respect for facts. All the changes, she says, have one cause and one purpose: making the company’s development more consistent with objective reality.

The company allows everyone to have an ego. But it doesn’t like hiring people who place themselves above facts. From the co-founders to every colleague, everyone here is persuadable. When the facts are clear enough, people are willing to acknowledge their own limitations—and so is the organization as a whole.

It’s precisely because of this extreme commitment to truth, Ezra says, that people dare to say true things. Because genuinely smart people don’t feel their self-worth threatened by honesty.

Another necessary condition for radical candor: no horse-racing between internal teams, no zero-sum game, no conflict of interest. Every member freely shares their research findings and technical details. Just as the company had its own community in the early days, it still advocates for a community culture. Sharing information and knowledge accelerates everyone’s collective learning and progress—and ultimately, everyone benefits.

As Win puts it: toxic culture is contagious. So is good culture.

Someone used the word “solidarity”—a word rarely used to describe a company these days—to describe this state. In fact, Kimi has always faced a harsh competitive environment: giants bearing down from outside, big tech squeezing from within, GPU resources constrained. But these pressures are intensifying the company’s cohesion. In the end, people are the only asset that matters.

Recently, Florence was approached by a peer company offering twice her salary. She declined without a moment’s hesitation. Her reason was simple: “There’s no hierarchy smell here.”

“I Don’t Know How She Got Through It”

When I first arrived to interview what is arguably one of the most intelligent, AI-literate groups of people on earth, I was extremely nervous: I’m a humanities person who has never worked in the tech industry, with only shallow knowledge of AI.

But when I actually sat down with the young experts from the algorithm and product teams, I found they were the nervous ones—terrified that I would be embarrassed by not understanding the jargon they use without thinking.

So they first translated the English terminology into Chinese, then translated the Chinese into Chinese I could actually understand.

That protectiveness was moving. It reminded me of the only thing the company had asked of me before I started: take care of everyone.

I tried, accordingly, to steer around questions that were too sensitive or might hit a nerve. Even so, Ty, on a phone call, let slip a nearly imperceptible tremor of emotion. When he first arrived and was going through the adjustment period, he struggled enormously—at one point he felt he couldn’t continue and thought about quitting. Then, in a weekly meeting, he saw Annie—a woman just two years out of school—who, after an unknown number of rounds of failure and internal doubt, had finally pushed a project to a genuinely meaningful milestone. He told himself he couldn’t give up either. He was older than her, had lived more, and yet his resilience was no match for hers. “I don’t know how she got through it,” he said.

He wasn’t the only one who had thought about leaving. Annie had considered it too. For a long stretch of time, she was tasked with building from scratch a presence in a certain overseas market—with no breakthroughs in sight. To make it worse, well-meaning colleagues from other teams told her directly to abandon “this meaningless effort.” She says she shed more tears for Kimi than she has shed for any other company—or for any ex-boyfriend.

She wasn’t short of options. She already had an offer from somewhere else with better terms. But she couldn’t convince herself to go work for someone else. She wanted to have one more conversation with co-founder Zhang Yutong.

After that conversation, she decided to stay. She didn’t tell me what was said. She only told me: “Yutong is the strongest, fastest-iterating, highest-ceiling boss I have ever met. Following her is the only way I can reach a higher limit.” And then she added: “I don’t know how she got through it.”

When you’ve collected enough data, you start noticing how often certain sentences repeat. The phrases that get repeated most tend to sketch out the shared qualities of the team.

Bob was pulled back to China to co-found the company by Yang Zhiming, giving up his PhD in the United States. He was there on day one—he represents perhaps the deepest institutional knowledge of anyone here. When asked the question everyone gets asked—what do you think is the most important quality in this team?—he paused for about two minutes, then answered in two words: resilience.

For a company only three years old, any emphasis on resilience might sound presumptuous. But it’s sincere. Bob believes that smart and brave are sometimes antonyms: the smarter you are, the easier it is to see the risks, and thus the easier it is to give up. But stupid persistence can’t succeed either. So only those who see clearly, calculate the probability of failure, and keep going anyway—those people deserve to be called resilient.

There’s a story that circulates internally, known as “entering the cave of reflection three times.”

In May 2023, Freddie and his colleagues received what looked like an impossible assignment: make the AI capable of reading 128K tokens of text in a single pass—equivalent to several hundred pages of a book—at a time when the industry could generally handle only 4K. He quickly designed MoBA v0.5, but because it required rewriting the core training framework while the main model was already half-trained, the cost was too high. The project was shelved. That was his first trip into the cave.

Six months later he returned with a v1 approach—revised so it could continue training on top of the existing model. The small-model validation worked. But on the large model, it hit a loss spike that no amount of debugging could fix. The project was sent back to the cave for a second time, for another six months—long enough to miss the company’s milestone of releasing a 200,000-character context capability. But the team was not disbanded. The company launched what it called a “saturation rescue”—mobilizing technical talent across the organization to collectively tackle the problem, rewriting the underlying logic, and finally achieving a stable pass on the “needle in a haystack” test with v2.

Just as launch seemed imminent, the third blow arrived: during the supervised fine-tuning stage, long-document summarization performed poorly because training signals were too sparse. At that point the project had absorbed enormous cost. But the engineers retreated to the cave one more time, and ultimately resolved the problem by adjusting the attention mechanism in the final few layers.

Three retreats. Three returns. At the end of the interview, I put the final question to Freddie: how would you describe this company?

He also answered in two words: lunar landing.

Why lunar landing? He quoted the famous speech:

“We choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard.”

Genius Swarm

I ultimately chose not to approach or intrude on any of the co-founders. Externally, they are nearly invisible—they don’t seek interviews, they have no interest in public fame—but internally, they are everywhere.

In a radically flat structure, you need superminds at the center, or the energy becomes chaos. With no middle management layer, each co-founder is interfacing directly with 40 to 50 people, staying close to the technical and operational frontline, ensuring that decisions and execution remain tightly aligned.

All five co-founders graduated from Tsinghua. But even superminds are bounded by the limits of human attention and management reach. As the company’s valuation has reached 120 billion RMB and its headcount has grown past 300, even these superminds are running hot.

The overload isn’t just on the co-founders. This is an infinite game of self-driven ambition: each team member is expected to carry roughly 400 million RMB in implied valuation on their back—delivering value at a per-person rate far beyond what most companies have ever imagined.

The transformative variable is tools. Kimi’s working hours are not punishing. Employees are allowed to sleep in, and no one is expected to work until midnight. Leo, on the product team, says he commands a full army. Imagine this:

10 a.m., Leo wakes up and walks into the office. His task: consolidate user feedback from five global markets in the past 24 hours and determine iteration priorities for the week. Once, this would have taken three people two days. Leo activates three agents. A strategy agent filters 3,000 feedback entries for high-priority issues related to “long-context interruption.” A translation agent parses Japanese dialects and Korean formalities in real time, tagging true emotional intensity. A competitive intelligence agent simultaneously captures the day’s updates from Cursor and ChatGPT and generates a technical comparison. Leo does three things: overrides a sarcastic comment that was misclassified as genuine feedback, flags a screenshot containing unreleased UI, and confirms the top-three priorities the agent recommended. By 11:30 a.m., the product requirements document is done. And his coding agent has already auto-generated 70 percent of the base framework from those requirements, ready for the afternoon discussion with human engineers on the creative decisions.

Humans set the rules. Silicon-based systems execute the rules. The organization becomes a container for algorithms. Fluency in using agents and weaving them deeply into work is simply what it means to be an AI-native company.

The model is both the goal and the tool. Whether directly empowering productivity at the technical level or fundamentally reshaping organizational dynamics, AI’s DNA has been written into the bones of this company, whether people like it or not. Just as they develop something they call Agent Swarm—clusters of AI agents operating in parallel—the whole team is, in essence, a Genius Swarm: each genius working independently in parallel, seamlessly coordinating with the others.

And yet an organization this flat carries structural fragility. When asked whether this model is sustainable as the company scales from 300 to 3,000 people, most respondents were cautious. History is instructive: radically flat organizational experiments—Holacracy—have tended to hit decision-making bottlenecks once headcount passes 500. When there are too many information nodes, “communicate directly” becomes information overload.

The more immediate pain is the sense of weightlessness at the individual level. Without the buffer of seniority levels, directional ambiguity propagates directly to each person. One former employee who eventually returned to big tech was blunt: without top-down OKRs and KPIs, some mornings you walk in and genuinely don’t know what you’re supposed to be doing—and no one proactively tells you how you’re performing. That absence of feedback creates an insecurity that makes some people suddenly nostalgic for the big tech reporting lines, clear review cycles, and quantifiable deliverables they once found suffocating.

Because those apparently cumbersome structures actually provide an individual’s baseline certainty: where is the goal, what does done look like, how is performance evaluated—all of it clearly visible. That’s not Stockholm syndrome. That’s basic organizational physics.

If Alibaba resembles a finely calibrated promotion pipeline, ByteDance a mission-driven combat division, and Tencent a higher-tolerance professional institute, then Moonshot AI is a primeval forest: geniuses may find their hunting paths, but ordinary people may wander lost in the fog.

The Necessary Dimensional Weapon

No departments, no levels, no performance reviews—the AI-native organizational paradigm is anti-institutional, unstructured. Large tech companies can no longer rotate to accommodate it. Small companies missed the window for self-inflation. This is an asymmetric war.

In The Three-Body Problem, the Singer civilization casually deploys a higher-dimensional weapon—the “dark forest” strike—collapsing the solar system from three dimensions into two, flattening planets, stars, and humanity into a picture with no depth, destroying the Earth in the process.

Moonshot AI is voluntarily deploying this dimensional weapon on its own organization. Not to destroy competitors, but to push organizational efficiency to its absolute limit: no hierarchical depth, no departmental walls, none of the three-dimensional entanglement of office politics—only “model” and “intelligence,” in the most direct and orthogonal alignment possible.

In the force field of the AI era, every startup is being compelled to fire this weapon at itself. The proliferation of one-person companies is, at its core, a generational explosion of AI-native talent: when technology compresses organizational capability down to the individual as its quantum unit, all the middle management buffers are instantly vaporized. The organization flattens. There is no depth left to maneuver within. Every person is forced to confront the problem directly.

This is the iron law governing the evolution of organizational paradigms across the entire business world: everyone will be folded flat.

When people are exposed on the same plane, one mind radiating outward to fifty people is no longer a management anomaly—it becomes organizational normal. The distance from center to edge is redefined. Elites who depended on hierarchy and the OKR coordinate system will suffocate immediately. Geniuses, meanwhile, are violently dismantling intelligence on this exposed plane—while their guardians clear the path of all entropy and noise, and not without humility, call themselves pioneers at the expanding edge of human civilization.

But the journey from three dimensions to two cannot be paused—and cannot be reversed.

From here, the Kimis of this world cannot go back. Every strategic adjustment is a high-risk chaotic iteration. Competitors can still slowly turn around inside their labyrinth. If Moonshot AI tries to rapidly scale its organizational mass, it will only generate structural fractures from within. And all this self-dimensional compression serves a single purpose: to complete one more insane dimensional leap upward.

The endpoint of organizational compression is the elevation of intelligence.

Only when model intelligence breaks through its inflection point—rising to a height sufficient to escape the gravitational well of all carbon-based organization—can Moonshot AI flatten all its competitors’ organizational advantages in one move, and give ultimate justification to this irreversible act of dimensional risk-taking.

At that point, discussing management radius or organizational architecture will become meaningless—just as the Singer civilization doesn’t concern itself with which dimension it inhabits, because the sophistication of the dimensional weapon itself has already defined the new rules of war.

Then, “the dark side of the moon” will shift from metaphor to reality: they will become the high-dimensional light source illuminating the dark side of the intelligence universe, and all the organizational pain that came before will have been nothing more than the ablative heat shield burning away as the lunar module punched through the atmosphere.

Either ascend to a higher dimension and become legend.

Or collapse inward and be preserved in amber.

There is no third path.

The same week OpenAI quietly killed its AI video app Sora, a series of AI-generated absurdist comedy videos was racking up 5 billion views on Chinese social media.

The video series is called “Saving a Fox on a Snowy Mountain.” It’s 90 seconds long, rendered in the style of a low-budget 1970s Chinese martial arts film, and it follows a classic folk tale setup:

A wandering woodsman rescues a fox in the snow by leaving a braised dusk, and by genre convention, the fox should later transform into a beautiful woman and repay his kindness.

Instead, when the hero opens his door expecting his destined reward, he finds not the grateful fox spirit but the braised duck he’d left behind as food. The duck, also apparently now sentient, has other plans.

“I am the braised duck you abandoned,” it says. Revenge has arrived.

The video was made by a 4-person team at a food company selling spicy marinated duck. It cost about ¥4,000 to ¥5,000 to produce in 5 hours, and was purely to promote their food.

（I couldn’t find the original version on YouTube, and this one is the closest alternative.)

That’s the whole joke, and it went viral with a speed that no one anticipated. Thousands of user-created spin-offs were spawned by swapping the duck for mountains, wood, and bean juice. Local government accounts borrowed the format for anti-fraud PSAs.

From face-swaps to full pipelines

To understand where Chinese AI video is right now, you need to understand where it came from. The starting point is a regulatory quirk that made face-swapping technology mandatory in Chinese film production.

China’s entertainment regulator maintains an informal but powerful blacklist of artists with bad conduct records. When a performer gets caught in a scandal—tax evasion, drug use, moral violations as defined by the relevant authorities—they will be banned from screens overnight.

For studios that have already completed production with a blacklisted actor, the only options are to shelve the project entirely or digitally replace every frame of that actor’s face with someone else’s. This has happened enough times, with high-profile cases involving major stars, that face-swapping became a standard post-production capability.

By 2025, AI had moved beyond that narrow use case into conventional post-production: crowd scenes, digital environments, stunt sequences in dramas. For example, the 2026 hit drama Swords Into Plowshares (太平年) used Kling AI from Kuaishou to generate a scene of crows eating grain, a shot that would have been expensive or impractical to production.

Today the conversation is completely different. ByteDance’s SeeDance 2.0, released in February 2026, becomes the latest SOTA video generator model. According to the company, the model achieves roughly 90% generation usability, meaning 9 out of 10 clips it produces are workable. The prior industry average was around 20%.

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3 months ago · 17 likes · Tony Peng

Such AI generation technology might not be ready to produce Hollywood-level film or dramas, end-to-end, but it is a natural fit for short dramas, a type of 1–3 minute, high-intensity vertical videos built for smartphones that emerged from China’s Internet platforms. According to DataEye-ADX industry tracking data, in January 2026 alone, 14,634 AI-generated short dramas went live in China, one new title every 90 seconds.

The reaction inside the industry was also brutal. SeeDance 2.0 made content creators unable to sleep. Teams returning after the Chinese New Year holiday were working overtime just to test the new model—average queue times on the platform exceeded six hours during peak periods. In response, a Chinese production studio began working at 3 am to use SeeDance 2.0 during off-peak hours and skip the daytime queues.

Comic dramas gain traction

Over the past week, I noticed my wife watching an AI-generated animated drama on her phone for several evenings running. She is not usually a short drama or cartoon person, so I paid attention.

The show she was watching, on Hongguo (红果, Red Fruit), ByteDance’s dedicated short drama app, is set during a catastrophic cold event that has made most of the earth uninhabitable. A small community tries to survive an endless winter storm.

The animation is not good by any conventional standard. The character movement is limited, the rendering is sometimes inconsistent, and in any other context she would have found it easy to dismiss. She kept watching because the story was interesting enough that the visual shortcomings stopped mattering after the first few episodes.

What she was watching is called Manju(漫剧), literally comic drama, and it has quietly become one of the fastest-growing entertainment formats in China, thanks to AI.

The term comic drama was originally coined in 2021. It converts static illustrated panels—sourced from web novels, existing comics, or AI-generated art—into short video through voice acting, limited character animation, and sound design. Episodes run one to three minutes. Series typically run 60 to 120 episodes. The visuals are semi-static, much closer to a comic panel that has been given breath than to fluid animation, which is precisely why AI can produce them at cost. The format is quite close to motion comics, sitting between comics, audio drama, and animation without being any of them.

There are three tiers of comic dramas, each with different economics.

• At the bottom, meme and emoji comics cost ¥400–600 ($58–87) per finished minute. Competition has squeezed margins below 10%.

• The middle tier, 2D/3D comic drama, is currently the platform traffic mainstay, carrying profit margins of around 30–40%.

• At the top, AI humanlike drama pushes costs to ¥30,000 ($4,350) per minute but delivers profit margins above 60% for teams that can maintain the quality, though output is constrained to roughly one title per 10-person team per month.

To understand what makes it work, look at the biggest hit of 2025, Below the Immortal Execution Platform, I Shocked the Gods (斩仙台下，我震惊了诸神), which reached 1 billion views on Douyin.

The setup would be impossible to film affordably as live action: a lowly court servant in the celestial bureaucracy, accused of conspiring with demons and slaughtering gods, is brought before a divine tribunal for judgment. As the gods interrogate him, his true identity is actually a disciple of Sun Wukong, also known as the Monkey King, and he dismantles the moral hypocrisy of each deity who claims to judge him. The story is built on two mythologies every Chinese viewer knows—Journey to the West and Investiture of the Gods—and reframes them as a story about corrupt institutions protecting their own.

With AI, non-human characters, magical transformations, alien worlds, thousand-soldier battle scenes—all are as cheap to generate as a prompt. As one producer put it simply: “Comic dramas can creaate non-human characters. That’s the biggest difference from live-action short drama.”

Grace Shao’s latest piece on AI-generated microdramas, which I highly recommend, offers a framing that clarifies why AI found its commercial footing here. A conventional studio requires a massive crew to create a masterpiece. An AI content factory tests, iterates, acquires traffic, and monetizes retention. It is optimized not for Emmys but for thumb-stopping cliffhangers. As Shao writes:

The product is not pretending to be prestige storytelling, nor is it even attempting to be jaw-dropping cinematography; it is shamelessly positioned as narrative dopamine.

AI Proem

China’s AI microdrama factories are supercharged by ByteDance and Kuaishou

When ByteDance launched Seedance 2.0 on February 12, Hollywood did not like it. Disney and Paramount quickly sent cease-and-desist letters accusing ByteDance of infringing copyrighted characters and likenesses, and Reuters later reported that ByteDance paused the global rollout after the disputes escalated…

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2 months ago · 19 likes · 4 comments · Grace Shao

China’s comic dramas generated an estimated ¥16.8 billion (roughly $2.3 billion) in revenue in 2025, with 70 billion cumulative views and more than 60,000 titles produced on Douyin alone, according to DataEye Research Institute’s 2025 annual report. A separate estimate from iMedia Research put the figure at ¥18.98 billion with year-on-year growth of 276%.

Soy Sauce Animation, one of the industry’s most prominent production studios, describes its operation as 100% AI-generated with no traditional animation staff, produces 60-70 titles per month, and generates approximately ¥50 million ($7.2 million) in monthly revenue.

While western platforms monetize AI through subscriptions, cloud services, and enterprise software, Chinese companies monetize still primarily through advertising—and advertising revenue is directly proportional to time spent on platform. This creates an incentive to invest in content that maximizes user’s screen time. AI-generated comic drams, with its binge-optimized episode structure, daily release cadence, and cliffhanger-every-90-seconds rhythm, is among the most effective time-on-platform vehicles ever built.

The foundation is China’s web novel ecosystem, which collectively host tens of millions of works with years of readership data already validating which stories have audiences. Before comic dramas, traditional animation cost ¥50,000-100,000 per finished minute of footage, which meant only a small fraction of those novels could justify visualization. With AI-driven comic dramas now lowering costs, that entire long tail becomes economically viable. Thousands of novels that would never have been filmed now have a visual form.

The distribution infrastructure was already built. China had 1.164 billion monthly active short video users as of December 2025, according to QuestMobile. The algorithmic recommendation systems on Douyin and Kuaishou are designed precisely for such type of content. According to ByteDance’s data, comic drama achieves a test-out rate of 50-75%, meaning more than half of new titles successfully gain algorithmic recommendation.

Take ByteDance as an example. Its Tomato Novel app provides 67,000 novel IPs available for visual adaptation. Jimeng, ByteDance’s AI generation platform for images and videos, supplies the production tools. Douyin handles distribution. Hongguo Comic Drama, a dedicated comic drama app launched in November 2025, reached 8.54 million monthly active users in its first month. Ocean Engine manages advertising monetization. The company controls the source novel, the production tool, the distribution platform, and the ad revenue in a single stack. Tencent, Kuaishou, Baidu, and Bilibili have all launched competing apps and subsidy programs.

It’s worth noting that comic drama’s growth happened against a backdrop of the broader short drama industry contracting. Live-action short drama had expanded recklessly in 2023-2024. Production costs were rising as the market demanded higher quality, new titles were being uploaded faster than audiences could absorb them, and platforms began pulling back guaranteed stipends in early 2026. For producers caught in that squeeze, AI comic dramas offered a back up plan.

The regulator is also watching cautiously. China’s media and films watchdog National Radio and Television Administration brought comic drama under formal regulatory review in late 2025. The industry had already received a warning: One title that crossed 100 million views, Failed the College Entrance Exam, Tricked Classmates into a Ghost University (高考落榜，忽悠同学上冥牌大学), was pulled from all platforms for promoting superstition.

But the regulatory posture so far is not hostile. Local governments are simultaneously offering subsidies to attract comic drama production talents, and state-owned broadcast CCTV produced its own official AI comic drama in January 2026.

The part that’s harder to watch

The same AI tools producing comic dramas are also threatening the livelihoods of Chinese actors.

On March 18, 2026, a hashtag called “actors replaced by AI” trended to the top of Weibo, China’s equivalent of X. Reports circulated that major production studios and platforms were planning to replace background actors with AI. The same day, a production company called Youhug Media launched two AI actors, gave them social media accounts, and announced their first dramas. The two AI actors were immediately noted to resemble well-known actresses.

The backlash was immediate enough that state-owned People’s Daily weighed in within days. Its commentary column published a piece on March 21 arguing that people resist AI actors because they lack liveliness. The editorial warned that companies rushing to deploy AI simply to cut headcount without creating new value are using tactical efficiency to mask strategic failure. “The advantage is not doing old things faster, but doing new things in new ways.”

It doesn’t mean AI actors will be regulated away. China’s regulatory priorities in this space remain focused on content labeling and stability rather than labor protection. But it signals that the displacement anxiety is being taken seriously.

Recode China AI

😱AI Anxiety Is Spreading Among People in China

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2 months ago · 63 likes · 7 comments · Tony Peng

At one major industry conference, ChinaEquity Group founder and CEO Wang Ran said background performers and stunt double professionals will essentially disappear because of AI. Mid-tier actor demand will be massively compressed. Even top actors will see their upfront fees dramatically shrink. Hengdian, the filming base that has been the working home of tens of thousands of Chinese actors, saw short drama production starts fall roughly 75% in the first quarter of 2026 compared to last year.

Chinese tech media 36Kr profiled one actress who spent three years building a career playing supporting roles in short dramas, from the cheerful best friend, to the arrogant young miss. After the 2026 Chinese New Year, she voluntarily dropped her quoted rate by a third. Production companies were coming back with offers at half her previous day rate. She accepted. “Before you played the female lead, now they ask you to play second female lead. Now they ask you to play featured extra. Take it or leave it.”

There is no Chinese labor union representing actors. Unpaid short drama actors will post social media and hope public embarrassment forces a response. Sometimes it works.

Displacement and new opportunities co-exist

The conventional AI displacement story is about AI coming for existing jobs, actors losing work, the industry shrinking. That’s not quite what’s happening here.

Comic dramas didn’t destroy traditional animation. It created an entirely new market segment, at a cost and volume that was impossible before generative AI, serving content that could not have been produced otherwise.

And it is starting to move beyond China.

Chinese short videos and dramas are already a global phenomenon. Platforms like ReelShort and DramaBox, both backed by Chinese companies, have built meaningful audiences in the US and Southeast Asia. TikTok is replicating the entire Chinese short drama playbook in the US through a dedicated in-app section and a standalone app called PineDrama. The global short drama market hit $2 billion in overseas app revenue in 2025, according to Sensor Tower, and projections from TikTok put the eventual global market at $10 billion with 200-300 million monthly users.

AI comic drama is also moving. China Literature Online, one of the largest IP holders, has already produced original overseas comic dramas with localized art styles—a vampire drama rendered in an American comic art style, distributed on the overseas platform Sereal+. Industry insiders say the format’s global test is just beginning, with a six-to-twelve month window before anyone knows whether it can replicate the domestic model abroad.

But the Hengdian silence is also real. The actress who spent three years playing supporting roles is taking gigs below her usual rate, trying not to think about whether the category she built a life around will still exist in two years.

Both of these things are true simultaneously. The rest of the world will find out soon enough whether any of this translates.

Hangzhou-based Unitree Robotics just filed for an IPO on Shanghai's STAR Board, aiming to raise ¥4.2 billion ($630 million).

The proceeds will be used for advancing research into AI models, developing the robot body hardware itself, building out new product lines, and expanding manufacturing capacity. Unitree aims to reach an annual production capacity of 190,000 robots.

Founded in 2016 with just ¥100,000 ($13,800) in registered capital, Unitree is China’s shiniest robotics company at the moment. Its humanoid robots danced and performed backflips during the Spring Festival Gala, the most-watched television broadcast on earth. Founder and CEO Wang Xingxing recently welcomed German Chancellor Friedrich Merz, and sat alongside some of China’s most prominent business leaders during a meeting hosted by President Xi Jinping.

Now the prospectus is out. And the numbers look surprisingly good.

The un-techy financials

A typical high-tech company heading toward IPO is money-losing, carries a high R&D cost ratio, and runs on investor patience. Unitree doesn’t fit that profile.

In 2025, the company generated ¥1.71 billion ($257 million) in revenue, up 335% year-over-year. Adjusted net income reached ¥600 million ($90 million), up 674%. Operating cash flow tripled to ¥672 million ($101 million).

The gross margin sat at 60.27% for 2025. In comparison, Apple’s latest twelve-month gross margin is 47.3%, while Louis Vuitton runs around 65-70%. One of the reasons is full-stack vertical integration: Unitree designs its own core components like motors, decelerators, robohands, and lidars rather than buying them off the shelf. They have also developed distinctive motion control algorithms that deliver high performance without requiring the most expensive compute.

That said, these numbers will almost certainly come under pressure as competition between humanoid robot companies intensifies and pricing wars could force the whole industry toward thinner margins.

R&D as a share of revenue fell from 31.4% in 2023 to 17.8% in 2024 to 7.7% in 2025. While that trend line looks alarming, in context, absolute R&D spending kept growing from ¥30 million ($4.5 million) to ¥70 million ($10.5 million) to roughly ¥90 million ($13.5 million) annualized.

With 480 employees at year end, Unitree generated roughly ¥3.56 million ($534,000) in revenue per employee. Most robotics firms at a comparable stage would have ten times the headcount.

One noteworthy detail in the prospectus is Unitree spent just ¥22.57 million ($3.4 million) on advertising in the first nine months of 2025. For a company at this level of global visibility, that number is almost nothing. When your robots are dancing on China’s biggest stage, you don’t need to run many ads.

The humanoid robot gap

In 2025, Unitree shipped more than 5,500 pure humanoid units, claiming the top spot globally. For comparison, the prospectus states that Figure AI and Agility Robotics each shipped approximately 150 units that year. Tesla has not publicly sold Optimus; it remains in internal testing. Unitree’s volume advantage over its closest Western competitors is roughly 37:1.

The production-to-sales ratio for humanoid robots was 95.95% in 2025, suggesting demand is absorbing supply almost entirely.

Pricing has moved aggressively downward. The average selling price of a Unitree humanoid was ¥593,400 ($89,010) in 2023, when only five units were sold. By 2024 it had dropped to ¥260,700 ($39,105). By the first three quarters of 2025, it had fallen further to ¥167,600 ($25,140). A 72% price decline in two years, with gross margins that simultaneously expanded. The G1 base model now starts at ¥85,000 ($12,750); the smaller R1 Air starts at ¥29,900 ($4,485).

But the price requires a closer look. The G1 standard edition comes with no developer access—no SDK, no open interface. It cannot be reprogrammed. For universities, research labs, and developers who need to actually build on top of the platform, the relevant product is the G1 EDU edition, which runs ¥200,000 to ¥400,000 ($30,000 to $60,000).

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People in China are generally regarded as optimistic about AI. According to a KPMG survey across 47 countries, 69% of people in China said AI’s benefits outweighed its risks, compared to just 35% of Americans.

But something is shifting. Over the past few weeks, the phrase “AI anxiety” kept showing up in the podcasts, articles, and videos I came across, picking up pace especially after the OpenClaw mania swept the country. This change in sentiment, from excitement to unease, has been building quietly beneath the surface.

I searched “AI anxiety” (AI焦虑) on WeChat Index. There was a large spike starting mid-February, peaking on March 10 at over 2 million, against a usual baseline of around 20,000. The term also briefly climbed to Weibo’s trending topics last week.

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The clearest trigger is OpenClaw, an open-source, local-first AI agent platform that acts as a 24/7 proactive personal or team assistant. It runs on your own computer, connecting LLMs via API to local files, messaging apps like WhatsApp and Discord, and tools that automate tasks, schedule jobs, and manage digital workflows.

OpenClaw was developed by Austrian programmer Peter Steinberger. Originally launched in November 2025 as “Clawdbot,” it was later rebranded to “Moltbot” and then to OpenClaw in early 2026 following trademark concerns from Anthropic. In February 2026, OpenAI announced that Steinberger would be joining the company to lead development on “personal agents.” OpenClaw became one of the fastest-growing open-source projects in history, accumulating over 200,000 GitHub stars within a few months.

There are already strong reports of how China’s OpenClaw frenzy unfolded. MIT Technology Review and Wired both have great stories, as does ’s analysis. For those less familiar, here’s a quick recap:

• In February, as OpenClaw began going viral, Chinese developers and early adopters started sharing demos on Bilibili, Zhihu, and X. Tech influencer and entrepreneur Fu Sheng hosted a livestream demonstrating OpenClaw's capabilities, drawing 20,000 views. Meituan co-founder Wang Huiwen said he will invest in startup projects in the OpenClaw track.

• On March 6, nearly 1,000 people lined up outside Tencent’s Shenzhen headquarters for a free installation session hosted by Tencent Cloud engineers. The crowd was a mix of amateur developers, retired engineers, housewives, students, and AI enthusiasts. Tencent founder and CEO Pony Ma said on WeChat that he wasn’t expecting the wild popularity of OpenClaw.

• On March 7, Shenzhen’s Longgang district government released a draft policy proposing financial support of up to 2 million yuan for approved OpenClaw projects and up to 10 million yuan for larger ones, along with free compute credits and discounted office space.

• Some crypto entrepreneurs hosted the largest OpenClaw event in Shenzhen, drawing more than 1,000 people, with many unable to find a seat.

• The corporate pile-on followed quickly. ByteDance’s cloud unit Volcano Engine unveiled ArkClaw, a browser-based version that removed the need for local setup. Tencent launched a suite of OpenClaw-based products compatible with WeChat. Alibaba launched a mobile app called JVS Claw to help users install and deploy OpenClaw. Baidu, Zhipu.ai, Moonshot, and others all joined in.

• Markets responded in kind. Hangzhou Shunwang Technology soared 21% after claiming to embed OpenClaw in its cloud infrastructure. Talkweb Information System jumped 12%, and Beijing Vrv Software rose 15%, all while the broader CSI 300 gained just 0.9%. Tencent’s stock rose 8.9% in a week. MiniMax surged 27.4%, now up over 600% from its IPO.

• The frenzy started to wane down last week as the China’s central government warned state-run enterprises and agencies against installing OpenClaw on office computers, citing risks of data leakage and unauthorized access.

• By mid-March, social media platforms flooded with paid services offering to uninstall OpenClaw, from the same people who had charged to install it weeks earlier. One Shanghai-based seller charged 299 yuan to remove it and had already completed more than 10 transactions.

The incentives behind the corporate bandwagon are worth understanding. For cloud companies like Tencent, Baidu, and ByteDance, OpenClaw runs best connected to a cloud backend, LLMs, APIs, and compute. Helping users install it is a customer acquisition funnel dressed up as community support.

For LLM companies like MiniMax, Moonshot, and Z.ai, OpenClaw needs a model to run on, and whichever model becomes the default picks up token usage at a scale no chatbot can match. For local governments, backing OpenClaw-based projects shows pro-innovation governance and attracts startups. OpenClaw-based applications represent a practical form of AI that fits neatly into industrial policy goals.

My friend told me that China probably only has hundreds of thousands of people who actually installed OpenClaw on their computers, yet it became a national topic. Everyone was talking about “raising a lobster” (a reference to the word Claw).

This kind of collect rush around an AI product is not new in China. When ChatGPT went viral in late 2022, with no Chinese equivalent available, people were selling accounts and offering VPN access to ChatGPT. When Meta’s Llama came out in 2023, nearly every Chinese cloud company rushed to offer API access and local deployment. OpenAI’s video generator Sora also swept Chinese social media in early 2024. Not to mention the DeepSeek moment in early 2025.

What is different this time is the mood. In those earlier waves, the mainstream mood was excitement, awe, and curiosity. This time, more and more people are expressing anxiety, fear, and concern.

The most obvious reason of that anxiety is job insecurity. For most ordinary people in China, AI still means chatbots like Doubao or Qwen. Claude Code or Codex is not available. There is no household AI agent with real penetration. Then all of a sudden, media reports are claiming OpenClaw can handle a wide range of tasks autonomously. The gap between what people knew and what they were being told deepened the sense of being left behind.

The job market context makes this worse. According to Bloomberg, a Peking University study analyzing over a million Chinese job postings between 2018 and 2024, which found that sectors with higher AI exposure, including accounting, editing, sales, and programming, saw larger declines in recruitment. The Financial Times reported that China’s government plans to build an “employment-friendly growth model,” as a record-breaking 12.7 million university graduates enter the market in 2026.

A second catalyst came from an unexpected direction: On February 22, independent investment research firm Citrini Research published The 2028 Global Intelligence Crisis. Written as a fictional macro memo from June 2028, it argued that human intelligence has historically been the scarce input in economic history, and that AI is now causing the unwind of that premium. In the scenario they constructed, the S&P 500 falls 38%, unemployment hits 10.2%, and a deflationary spiral sets in as white-collar jobs are hollowed out faster than the economy can absorb.

Citrini Research

THE 2028 GLOBAL INTELLIGENCE CRISIS

Preface…

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3 months ago · 8373 likes · 90 comments · Citrini and Alap Shah

Two days later, a Chinese version appeared. Bob Chen, an economist-turned-VC at BroadVision Fund (博华资本) and former macro economist at the Chinese Academy of Social Sciences, published his response on his WeChat blog, Jolly Maker (嬉笑创客). His argument is China would not experience the same crisis, and for structural reasons that might look like weaknesses on the surface. SaaS never took off in China, leaving a trail of broken investors and failed products. If SaaS couldn’t reshape China’s information economy, AI won’t displace workers through the same mechanism. China’s model was always built on heavy staffing, offline relationships, and customized solutions, with leaders making the final call and workers functioning as atomized information processors relying on Excel. That structure, Chen argued, is better suited to AI-driven efficiency gains than AI replacement.

The East is Read

THE 2028 CHINESE INTELLIGENCE CRISIS

The 2028 Global Intelligence Crisis, a speculative macro memo published on 22 February 2026 by U.S. research firm Citrini Research on its Substack platform, has caused major tech and financial firms’ share prices to tumble, sparking heated debate among economists and strategists over the realism of its scenario…

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3 months ago · 28 likes · 7 comments · Junyan Zhao and Yuxuan JIA

It is a reassuring thesis, but the anxiety about keeping up with AI runs deeper than job loss alone. I saw a post on RedNote captured the feeling well. The user wrote:

I think I have AI anxiety. I haven’t figured out Claude Code, and Skills is already out. I haven’t understood Skills, and Clawdbot is out. I haven’t installed Clawdbot, and the Mac Mini price is already up. Throughout February, Chinese AI companies released new models in rapid succession. The pace itself became a source of stress.

Nvidia CEO Jensen Huang's quote has circulated widely in China: “You're not going to lose your job to AI, but you're going to lose your job to someone who uses AI.” People are worried they are not using AI aggressively enough, and they do not know how to use it well. They scroll past endless content showing people without technical backgrounds earning millions by launching an app, or making smarter investments using the latest AI tools, which reinforce the anxiety. OpenClaw is the latest version of that pressure. On top of this, workplace expectations are shifting. The abovementioned Fu Sheng said publicly that every employee of his company is required to use AI in their work, or risk falling behind.

There is also a small but vocal group that exaggerated OpenClaw’s capabilities, spread anxiety, and cashed in on the confusion. Many people paid a few hundred yuan to have the software installed, only to find they did not know how to use it, or that the product had real limitations and security concerns. One Chinese tech influencer said on Weibo:

If you need to pay someone a few hundred yuan to install OpenClaw for you, you probably should not be using the product.

Finally there is a dimension that has received less attention: AI in warfare. The use of AI in U.S. military operations during the recent conflict with Iran, along with the very public saga between Anthropic and the U.S. Department of War, has made many people in China reconsider what AI actually is.

Despite China’s stated policy of civil-military infusion, AI has largely been experienced by ordinary Chinese people as a civilian technology, something distant from military operations. The realization that the same chatbot technology could potentially assist in targeting or military decision-making has shifted that perception. This is becoming a global conversation, but for people in China who had kept a clear mental separation between AI and violence, it changes something fundamental.

Will the Chinese government step in if AI disruption starts threatening employment stability? Or will it continue backing development in the name of national competitiveness? I do not have an answer. But it is worth stopping treating people in China as uniformly optimistic about AI. The anxiety is real, and it will keep growing.

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The day after Alibaba released a new series of Qwen3.5 small models that turned heads across the AI community, the man who led the effort announced he was leaving.

Lin Junyang, tech lead of Alibaba’s Qwen, posted on X that he was stepping down. According to LatePost, one of China’s most reliable tech media, Lin submitted his resignation letter yesterday on March 3, and it came as a surprise to the company’s leadership.

My source told me Lin hasn’t left the company yet, but according to a disclosed all-hands meeting within Alibaba’s Tongyi AI Lab, Lin has little chance to come back.

Under his post, hundreds of people from across the AI world poured in with appreciation and good wishes. Other Qwen team members posted their own messages: “Qwen is nothing without its people.”

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Who is Lin Junyang

Lin is 32 years old, making him Alibaba’s youngest P10 executive — the company’s senior leadership tier. He joined Alibaba in 2019 fresh out of Peking University, where he studied computer science and linguistics. Thin, bespectacled, warm, and deeply ambitious, he was a key contributor to some of Alibaba’s most important early AI work, including the M6 and OFA models. But his defining achievement was turning Qwen from an unknown side project into the world’s most influential open-source LLM series.

As of January 2026, the Qwen model family had surpassed 700 million cumulative downloads on Hugging Face. Alibaba has open-sourced nearly 400 Qwen models, which the community has used to create over 180,000 fine-tuned derivatives.

Lin wasn’t the only one from the Qwen team who’s leaving the company, and he may not be the last.

Yu Bowen, who led Qwen’s post-training efforts, also left. His replacement is Zhou Hao, a former senior staff DeepMind researcher and a Gemini 3.0 contributor who was recruited by Zhou Jingren, CTO of Alibaba Cloud and head of Tongyi Lab. Zhou Hao will report directly to Zhou Jingren and lead Qwen’s post-training RL research.

Hui Binyuan, the lead of Qwen Code, reportedly left in January 2026 and joined Meta. Lin Kaixin, a contributor to Qwen 3.5, VL, and Coder, also tweeted his own departure.

Why Lin’s stepping down

No single reason has been confirmed, but several media reports point in the same direction.

• Alibaba is reorganizing its Qwen team and the broader Tongyi AI Lab, moving away from what was a vertically integrated, startup-like operation toward a more fragmented structure that separates pre-training, post-training, text, and multimodality into distinct teams. That kind of reorganization would directly limit Lin’s management scope, and reportedly doesn’t align with his philosophy of how AI development should work.

• Adding to that, the arrival of Zhou Hao created tension. Zhou was personally recruited by Zhou Jingren with a DeepMind background — a similar playbook to what ByteDance and Tencent have run, bringing in researchers from Silicon Valley to strengthen their AI teams. The intent was clearly to deepen Qwen’s research capabilities. But Lin reportedly did not accept the new arrangement easily.

• There were also scope conflicts. Over the past year, Lin had expanded Qwen’s work into image generation, voice models, infrastructure, and robotics — areas that overlapped with other teams in the Tongyi Lab.

• On the model performance side, Qwen’s latest releases haven’t dominated the way they once did. Qwen 3.5-Plus, which I covered in my last issue, was an ambitious model integrating multiple innovations, but it wasn’t the best open model out there. Over the past six months, Moonshot AI, MiniMax and Z.ai have been challenging Qwen on benchmarks and leaderboards. That kind of pressure compounds quickly.

• There’s one more detail worth noting. In a January panel, Lin said publicly that China has less than a 20% chance of winning the AI race, along with other pointed criticisms. I don’t think that directly caused his exit, but statements like that don’t go unnoticed internally, and the resulting PR pressure doesn’t make anyone’s position easier.

For those following Alibaba closely, this kind of talent exodus isn’t new.

Back in 2022, Alibaba’s DAMO research institute laid off around 30% of its staff and was pushed toward break-even. The departures were significant — Associate Dean Jin Rong, NLP chief Si Luo, the head of the City Brain project, the XR Lab lead, the autonomous driving chief, and Yang Hongxia, the architect behind M6. That was the first wave.

In early 2025, as Qwen gained momentum and China’s AI talent wars intensified, Alibaba lost another round of key names, including Zhou Chang, Qwen’s former tech lead. That was the second.

This is the third.

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💪🏻AI Makes Alibaba Great Again

“Make Alibaba Great Again” has been circulating inside the company for more than a year—and the numbers suggest it’s happening…

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7 months ago · 30 likes · 3 comments · Tony Peng

Alibaba’s ‘Unofficial’ Responses

An all-hands meeting within Tongyi Lab today was essentially damage control. Leadership’s message is the restructuring of Qwen and Tongyi Lab is an expansion. Qwen is the Alibaba’s top priority, more talent is needed, and organizational change is inevitable.

Alibaba CEO Eddie Wu apologized for poor communication around compute resource restraints but insisted Qwen was always his first priority. Zhou Jingren admitted resources have been tight and, strikingly, suggested he too has been marginalized internally.

On whether Junyang could return, the chief HR officer shut it down bluntly: no one gets put on a pedestal, and the company won’t make exceptions at any cost.

My Two Cents

Reorg usually happens when a team is in trouble. But Qwen isn’t in trouble. The models are good, maybe not SOTA, but strong across multiple benchmarks. The Qwen app has closed some of the gap against Doubao. Company morale is back, riding the “Make Alibaba Great Again” wave. There’s no obvious reason to push out one of your most talented AI leads. Top AI talent is scarce everywhere. That’s exactly why LatePost described Lin’s departure as sudden.

So what’s really going on? Qwen was never supposed to be this big. Tongyi Qianwen, the predecessor of Qwen, was a closed-source model, and Qwen started as a side project. It broke through starting in late 2023 with fast-improving performance and explosive community growth, and now it’s arguably Alibaba’s most valuable AI asset, along with cloud and chips.

But that success raised the bar. The old KPIs, which could be Hugging Face downloads and the number of derivative models, no longer cut it. The new expectations are Qwen should support Alibaba’s commercial success in both enterprise market and consumer apps, and that kind of pressure on a research team with a open-source priority can be brutal and destabilizing.

Commercially, ByteDance has been eating into its AI cloud business, and despite spending 3 billion RMB promoting its Qwen app in mainland China during the Chinese New Year, Alibaba has failed to match Doubao.

Jia Yangqing, former Alibaba executive and now VP at Nvidia following the acquisition of Lepton.ai, put it plainly:

For companies, balancing open source and business is genuinely hard. We’ve seen wins like Databricks and Redis Labs, and we’ve seen cautionary tales like RethinkDB, a beloved open-source database that shut down in 2016 despite technical excellence. Was there friction between open-source vision and business priorities? Pure speculation, but if there wasn’t, that would be the exception, not the rule.

Then there’s the resource angle. Ilya’s exit from OpenAI is the most famous example as he couldn’t get enough compute. If Lin ends up leading just one slice of a restructured Qwen, his access to GPU resources shrinks accordingly.

On top of that, Alibaba is pushing hard to scale its Qwen app in mainland China, which means more compute going to inference rather than research. That squeeze will be felt.

What It Means for China’s Open-Source AI

Lin was more than a team lead. He was the face of Qwen, and by extension, China’s open-source AI movement globally. His departure is a real blow to that intiative. Some in the community are already calling it the end of an era.

I don’t think Alibaba will pivot to closed-source immediately. But they will almost certainly invest less in open-source efforts going forward. What makes that matter beyond Alibaba is that as the leader of the open-source ecosystem in China, whatever Alibaba does sets the tone for other labs.

In the U.S., almost every major American AI lab has drifted toward closed-source, driven by the pressure to monetize massive infrastructure investments. Open-source builds goodwill and community, but it doesn’t pay the bills the way API-driven, paid-access models do. Chinese AI labs will face the same dilemma if the competition intensifies.

The one wild card is DeepSeek. As long as the well-funded DeepSeek keeps releasing open-weight models and contributing to the global community, it puts pressure on other Chinese AI labs to follow. A closed model from a second-tier lab means nothing when DeepSeek is giving it away. That dynamic may be the strongest check against a broader retreat from openness, at least for now.

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The post was originally written at ’s invitation and first published on here. I have updated and edited it to reflect the latest developments and research.

AI Supremacy

DeepSeek’s Next Move: What V4 Will Look Like 👀

Good Morning…

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3 months ago · 57 likes · 4 comments · Michael Spencer and Tony Peng

The widely anticipated DeepSeek V4 model is expected to meet the public this week. V4, DeepSeek’s upcoming generation base model, is supposed to be the largest challenge of open-weight models to the proprietary models, and the most anticipated releases in the AI space for early 2026.

According to Reuters and The Information, DeepSeek V4 is optimized primarily for coding and long-context software engineering tasks. Internal tests (per reporting) suggest V4 could outperform Claude and ChatGPT on long-context coding tasks.

The Financial Times reported that DeepSeek V4 will be a native multimodal model with “picture, video and text-generating functions.” The release would mark a major upgrade from DeepSeek’s previous text-only models, though it comes as little surprise as Chinese AI peers—including Moonshot, Alibaba’s Qwen and ByteDance’s Seed—have already embraced multimodalities in their flagship models.

Tang Jie, co-founder and chief scientist of Chinese AI firm Z.ai, said in an X reply that DeepSeek V4 will likely outperform all existing Chinese open-source LLMs.

Yet from a geopolitical perspective, DeepSeek reportedly withheld its V4 model from U.S. chipmakers including Nvidia and AMD for optimization, instead granting early access to domestic suppliers such as Huawei and Cambricon. The chip access decision reflects a deliberate effort to deepen ties with China’s domestic hardware ecosystem

Last week, Anthropic accused DeepSeek of conducting “distillation attacks” along with two other Chinese AI labs, Moonshot and MiniMax. OpenAI issued a similar accusation against DeepSeek in February. But both accusations drew backlash from the AI community, given that Anthropic, OpenAI, and most of their peers are themselves defendants in copyright and training data lawsuits.

2025 was a watershed moment for DeepSeek and the following open-source movement fueled by Chinese AI labs. The release of DeepSeek V3 base model and R1 reasoning model upended multiple AI narratives: that only spending hundreds of millions could produce a frontier LLM, that only Silicon Valley companies had talents to train competitive models, that the U.S.-China gap in AI was widening due to chip shortages.

Chinese AI labs have also been quick to capitalize on DeepSeek’s openness, adopting its training recipes to slash their own trial-and-error costs while pushing their open models further in knowledge, reasoning, and agentic coding.

Throughout the rest of 2025, DeepSeek continued churning out models with incremental improvements, including DeepSeek-V3.2-Thinking and DeepSeek-Math-V2, which won the International Olympiad in Informatics. Yet the widely anticipated DeepSeek-V4 and DeepSeek-R2, which were reportedly slated for release in the first half of 2025, were delayed. According to reports, DeepSeek CEO Liang Wenfeng was dissatisfied with the results and chose to delay the launch.

The Financial Times offered an alternative explanation: DeepSeek initially attempted to train R2 using Huawei’s Ascend AI chips rather than Western silicon like Nvidia’s GPUs, partly due to pressure from the Chinese government to reduce reliance on U.S.-made hardware. The training runs encountered repeated failures and performance issues stemming from stability problems, slow chip-to-chip interconnect speeds, and immature software tooling for Huawei’s chips. Ultimately, DeepSeek had to revert to Nvidia hardware for training while relegating Huawei chips to inference tasks only. This back-and-forth process and subsequent re-engineering significantly delayed the timeline.

Sparsity Through Iteration

DeepSeek’s architectural evolution has been driven by a consistent principle: Sparsity.

While computation can be scaled relatively easily by adding more data, more parameters of the model, and more chips, sparsity is the most straightforward way DeepSeek can scale intelligence under constraints, including compute, memory bandwidth, and chips. These constraints shaped every iteration of their model architecture, from DeepSeekMoE’s initial Mixture of Experts (MoE) through the attention optimizations in V2, V3, and V3.2.

In a Transformer, you have two main computational blocks: Attention and Network (specifically Feed-Forward Network). DeepSeek started by making the network layers sparse using MoE. Instead of every token going through the entire network, they route each token to only a small, relevant subset of parameters as experts.

• DeepSeekMoE laid the groundwork with a MoE architecture containing 64 specialized experts and 2 shared experts. For each input, the model would route it to the 6 most relevant experts (topk=6).

• DeepSeek-V2 expanded the expert pool to 160 specialized experts while keeping 2 shared experts and the same topk=6 routing, with improvements focused on distribution and efficiency.

• DeepSeek-V3 scaled further to 256 experts with 1 shared expert, and increased routing to topk=8. The architecture became more sophisticated in how it managed experts, and introduced a new communication system called DeepEP that makes expert coordination more efficient.

Once the network sparsity was well-optimized, attention became the next target for improvement. As the core module of the Transformer architecture, attention is where each token in a sequence analyzes and weights the importance of every other token to understand context and relationships. Attention presents a different challenge as computational complexity exponentially grows along with the increasing context window.

• Multi-head Latent Attention (MLA) was DeepSeek’s first major attention innovation introduced in V2. Instead of storing complete key-value information for every token (as standard Multi-Head Attention does), MLA compresses this information into a smaller representation. This compression reduces how much data needs to be moved in and out of memory.

• Then in a February 2025 paper, DeepSeek introduced Native Sparse Attention (NSA) with optimized design for modern hardware. Tokens are processed through three attention paths: compressed coarse-grained tokens, selectively retained fine-grained tokens, and sliding windows for local contextual information.

• DeepSeek Sparse Attention (DSA), introduced in V3.1 and V3.2, streamlined the NSA design. Instead of selecting blocks of tokens, DSA selects individual tokens. It uses a lightweight indexer model to identify the 2,048 most relevant tokens from the full context. This indexer is trained through a process where it learns to mimic the full attention pattern—the model first trains with full attention, then the indexer learns to predict which tokens the full attention would focus on. DSA can work with MLA.

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Multimodality Signals in DeepSeek OCR 2

The Financial Times’ reporting on DeepSeek V4’s multimodalities reminds me of DeepSeek’s important research in visual understanding, notably DeepSeek OCR.

Last year, I wrote about DeepSeek OCR, an innovative vision-language model that compresses long texts into visual tokens by 7 to 20 times, potentially enabling LLMs to process lengthy documents more efficiently. DeepSeek OCR consists of two main components:

• DeepEncoder is a custom encoder that combines SAM-base (window attention) with CLIP-large (dense global attention), plus a 16× token compressor. It offers controllable resolution modes (Tiny/Small/Base/Large/Gundam/Gundam-M).

• The LLM is a 3B MoE model with ~570M activated parameters at inference (6 out of 64 routed experts plus 2 shared experts).

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👀DeepSeek and Z.AI Bet on Visual Compression to Solve Long-Context Problem

Chinese AI labs are testing an interesting idea to reduce computational costs for processing long context documents: render long text into images and compress it with a vision-language model (VLM). The expected result is fewer tokens without much loss in accuracy…

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7 months ago · 10 likes · 1 comment · Tony Peng

In January, DeepSeek upgraded the model to DeepSeek OCR 2. The core improvement in DeepSeek OCR 2 is a new visual encoder called DeepEncoder V2, which introduces a concept called visual causal flow.

• Traditional vision-language models read images in a fixed raster order (top-left → bottom-right).

• DeepEncoder V2 instead reorders visual tokens based on semantic structure, mimicking how humans scan documents.

• This enables better reasoning over complex layouts such as multi-column papers or tables.

It’s worth noting that in the paper, DeepSeek researchers said

DeepEncoder V2 provides initial validation of the LLM-style encoder’s viability for visual tasks. More importantly, this architecture enjoys the potential to evolve into a unified omni-modal encoder: a single encoder with shared 𝑊𝑘,𝑊𝑣 projections, attention mechanisms, and FFNs can process multiple modalities through modality-specific learnable query embeddings. Such an encoder could compress text, extract speech features, and reorganize visual content within the same parameter space, differing only in the learned weights of their query embeddings. DeepSeek-OCR’s optical compression represents an initial exploration toward native multimodality, while we believe DeepSeek-OCR 2’s LLM-style encoder architecture marks our further step in this direction. We will also continue exploring the integration of additional modalities through this shared encoder framework in the future

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DSA+mHC+Engram

With the foundation of DSA established in V3.2, DeepSeek appears ready to push optimization further in V4. The evidence points to two other papers that DeepSeek released over the past few months.

Manifold-Constrained Hyper-Connections (mHC)

mHC represents a rethinking of how information flows through deep neural networks. While traditional networks pass information sequentially from one layer to the next, mHC introduces richer connectivity patterns between layers—essentially creating multiple pathways for information to flow across the model’s depth.

To understand mHC’s, we must first revisit residual connections, the backbone of modern deep neural networks regardless of architecture (CNN or Transformer). Proposed in the landmark 2015 paper Deep Residual Learning for Image Recognition, residual connections add the input (x) of a block directly to its output F(x), typically formatted as y = F(x) + x.

This simple shortcut proved revolutionary. By allowing gradients to bypass layers, residual connections solved the vanishing gradient problem that had plagued deep networks, enabling effective training of architectures with hundreds or even thousands of layers.

Then in 2024, ByteDance researchers proposed hyper-connections (HC) as an alternative approach. Rather than simple additive shortcuts, HC creates richer connectivity patterns that allow information to flow more freely between non-adjacent layers. This architectural flexibility offers advantages over standard residual connections, particularly in avoiding the gradient-representation tradeoffs.

But HC introduced its own challenge. As training scales increase and connectivity grows richer, the risk of training instability rises. More pathways for information flow means more opportunities for gradients to either explode (grow uncontrollably large) or vanish (shrink to near-zero) during training.

This is where DeepSeek mHC’s innovation emerges. The approach treats the model’s parameter space as existing on a high-dimensional geometric structure, a manifold. By imposing mathematical constraints based on this manifold geometry, mHC creates a guardrail system that maintains stable information flow even as connectivity increases.

Think of it as a multi-lane highway with intersections. Vehicles can change lanes, merge, or split—increasing routing flexibility. But the total traffic flow must remain constant and balanced. Information can take different paths through the network, but the overall flow is constrained to prevent either congestion (gradient explosion) or emptiness (gradient vanishing).

The mathematical formulation ensures that information transformations remain well-behaved across the manifold. This prevents the instabilities that would otherwise emerge from unconstrained hyper-connections, enabling deeper and more flexible architectures while maintaining training stability.

For V4, mHC could appear strategic. As DeepSeek pushes toward increasingly sparse and selective processing through DSA, Engram, and other mechanisms, having stable hyper-connections allows these components to interact more effectively across layers.

Engram

Engram, detailed in DeepSeek’s January 2026 paper Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models, introduces a new dimension to model architecture by adding conditional memory to the Transformer through efficient lookup mechanisms.

Traditional Transformer-based LLMs compress all learned knowledge into neural network weights. Whether answering a simple factual query like “Barack Obama was a U.S. president” or solving a complex mathematical proof, the model must route every computation through the same expensive neural processing.

Consider the phrase “New York City.” A standard Transformer must learn that “New,” “York,” and “City” together form a specific entity, then rebuild that relationship through attention computation every single time. This is static knowledge that never changes, yet the model treats it like novel information requiring full neural processing each time.

Engram’s premise is elegantly simple: not all knowledge requires neural computation. Static facts and established patterns can be stored in a complementary memory system and retrieved efficiently when needed. This mirrors biological memory: You don’t re-calculate that 2+2=4 each time; you simply recall it.

Engram implements this through a modernized N-gram lookup module operating in constant time (O(1))—retrieval speed remains constant regardless of how much information is stored. This creates a fundamental architectural separation:

• Neural computation (attention and MoE): Complex reasoning, novel synthesis, context-dependent processing

• Memory lookup (Engram): Static knowledge, established patterns, factual recall

The architecture optimizes the balance using a U-shaped scaling law—a mathematical framework determining ideal parameter allocation between neural computation and memory lookup at different model scales. This is crucial because the tradeoff isn’t straightforward: too much reliance on lookup tables risks brittleness; too much neural computation wastes resources on static knowledge. The U-shaped law identifies the sweet spot where both systems work synergistically.

Memory efficiency improves dramatically because Engram offloads static knowledge from expensive GPU memory to host CPU memory. Empirical results demonstrate that offloading a 100B-parameter lookup table to host memory incurs negligible overhead (less than 3%). This enables massive knowledge bases without proportional GPU memory costs, while supporting much longer effective context windows.

For reasoning and knowledge-intensive tasks, Engram delivers measurable improvements over comparable MoE-only architectures. Benchmarks show particular gains in multi-hop reasoning and long-context understanding—tasks that benefit from quick access to established knowledge while applying neural computation to novel reasoning steps.

Just before the release of this blog, DeepSeek, Peking University, and Tsinghua University introduced DualPath. The system aims to solve a massive I/O bottleneck in long-context agentic inference. Since decoding engines often sit idle while prefill engines are overwhelmed moving KVcache from storage, DualPath uses those idle engines to load the cache and transfer it to the prefill engines via RDMA. DualPath improves offline inference throughput by up to 1.87x and online inference by 1.96x. I see no reason why DualPath wouldn’t be incorporated into DeepSeek V4’s inference.

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Model1: New Clues to DeepSeek V4?

Just over a year after DeepSeek-R1 became the most-liked model on Hugging Face, sharp-eyed developers have spotted a mysterious “Model1” in recent code updates to the FlashMLA library. The timing is suggestive—could Model1 be the codename for DeepSeek V4?

Analysis of recent commits reveals several architectural signatures suggesting Model1 is an entirely new flagship model:

• The 512-Dimensional Shift: Model1 switches from V3.2’s 576-dimensional configuration to 512 dimensions, likely optimizing for NVIDIA’s Blackwell (SM100) architecture where power-of-2 dimensions align better with hardware.

• Blackwell GPU Optimization: New SM100-specific interfaces, CUDA 12.9 requirement, and performance benchmarks showing 350 TFlops on B200 for sparse MLA operations represent deep integration with next-generation hardware.

• Token-Level Sparse MLA: Separate test scripts for sparse and dense decoding indicate parallel processing pathways. The implementation uses FP8 for storing KV cache and bfloat16 for matrix multiplication, suggesting design for extreme long-context scenarios.

• Value Vector Position Awareness (VVPA): This new VVPA mechanism likely addresses a known weakness in traditional MLA—positional information decay over long contexts. As sequences extend into hundreds of thousands of tokens, compressed representations can lose fine-grained positional details. VVPA appears designed to preserve this spatial information even under aggressive compression.

• Engram Integration: References to Engram throughout the codebase suggest deep integration into Model1’s architecture.

I believe V4 and R2 will remain among the best open-source LLMs available, potentially even narrowing the gap with leading proprietary models. However, since DeepSeek R1’s release over a year ago, the competitive race to push LLM capabilities forward has only intensified. The “DeepSeek effect” has motivated several other Chinese AI labs—including Moonshot AI, MiniMax, and Zhipu—to redouble their efforts in releasing top-tier LLMs. This doesn’t even account for tech giants Alibaba and ByteDance, both of which are producing frontier models while simultaneously expanding into chatbots, AI cloud services, chips, and hardware.

Given this rapidly evolving landscape, expecting another watershed “DeepSeek moment” like the one in early 2025 seems nearly impossible.

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Usually, the weeks between the New Year and Chinese New Year follow a familiar rhythm for Chinese companies—recap the past year, let employees rest, and quietly plan for the year ahead. The AI race doesn’t allow for that luxury anymore.

In the weeks leading up to Chinese New Year, which began on February 17th, nearly every frontier Chinese AI lab—except DeepSeek—rushed out their latest flagship models. The timing wasn’t coincidental. With DeepSeek-V4 rumored to drop before the holiday, no lab could afford to sit still and wait to be made irrelevant. These models have a shelf life of months at best, and every lab knows that being first to claim a benchmark, a capability, or a narrative matters enormously for how developers and enterprises perceive you on the world stage.

And it wasn’t just Chinese AI labs. Anthropic, OpenAI, and Google each dropped major releases of their own—Claude Sonnet 4.6, Gemini 3.1 Pro, and GPT-5.3 Codex—in the same compressed window. If you zoom out and look at February 2026 as a whole, it may well go down as the most consequential single month in the history of AI development. I genuinely hope this pace of progress continues, as dizzying as it is to keep up with.

I’ll do my best to walk you through all of these releases and what they mean—but let’s start with the four Chinese open-weight models that I think deserve the most attention right now.

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Qwen3.5-Plus: The Most Ambitious One

Qwen3.5-Plus isn’t the strongest open model right now, but it might be the most ambitious, and I mean that as a genuine compliment.

Native multimodality, finally. Alibaba’s previous Qwen series separated vision and language into dedicated model lines, Qwen and Qwen-VL. Qwen3.5-Plus breaks from that tradition with a native multimodal foundation model trained from the ground up on a significantly large corpus of visual-text tokens, enriched with Chinese, English, multilingual, STEM, and reasoning data. This isn’t a text model with vision added on, and the difference shows in tasks like visual reasoning, visual coding, and visual agentic workflows.

Extreme sparsity. The model has 397 billion total parameters, but only 17 billion are activated per forward pass. It’s built on the Qwen3-Next architecture, which introduces higher-sparsity MoE, a Gated DeltaNet + Gated Attention hybrid attention mechanism, stability optimizations, and multi-token prediction.

• A quick note on what those attention mechanisms actually mean: Gated DeltaNet is a form of linear recurrent attention that uses gating to selectively update a memory matrix, letting the model efficiently track information across long sequences without the quadratic cost of standard attention. Gated Attention layers are interspersed with these linear layers to preserve the model’s ability to do precise, content-based retrieval when it needs to. Together, the hybrid avoids the bottlenecks of both pure transformers and pure linear models.

The throughput gains from this architecture are striking. Under a 32K context, Qwen3.5-Plus runs at 8.6× the decoding speed of Qwen3-Max; under 256K context, that jumps to 19×. It supports up to a 1 million token context window, built-in tools, and adaptive tool use. It currently ranks third on the Artificial Analysis Intelligence Index, behind GLM-5 and K2.5.

On pricing, Qwen3.5-Plus charges ¥0.8 per million input tokens (~$0.12) and ¥4.8 per million output tokens on Alibaba Cloud in mainland China, which Alibaba positions as roughly 1/18th the cost of Gemini 3 Pro at comparable performance. On OpenRouter, it’s $0.40 input and $2.40 output.

GLM-5: The Best Open-Weight Model

After GLM-4.7 signaled that Z.ai was back in the conversation among top-tier Chinese AI labs, GLM-5 makes the argument definitively. It currently sits at the top of the Artificial Analysis Intelligence Index, the best open-weight model you can run today.

The model’s focus is concrete: AI agents that can autonomously plan, implement, debug, and iterate on real-world software tasks. And I want to call out something I genuinely appreciated: Z.ai clearly learned from DeepSeek’s approach of writing accessible, readable research papers. GLM-5’s technical report is one of the cleaner reads in this space.

Architecture. The model has 744 billion total parameters with 40 billion activated per forward pass. Pre-training data expanded from 23 trillion to 28.5 trillion tokens, with a 200K context window. Z.ai adopted several of DeepSeek’s architectural choices, including Multi-head Latent Attention (MLA), multi-token prediction, and most notably DeepSeek Sparse Attention (DSA). DSA replaces the standard dense O(L²) attention with dynamic token selection, reducing long-context compute cost by 1.5–2× without sacrificing quality.

Post-training is where Z.ai really distinguished itself. Not all agent tasks are created equal. Some finish in a few steps; others require long, sprawling rollouts. Traditional synchronous RL infrastructure couples generation with training, which means fast samples sit idle waiting for slow ones, resulting in GPU idle, lower throughput, and a training bottleneck.

Z.ai’s solution is an Asynchronous RL Infrastructure that fully decouples generation from training. Fast tasks update model parameters immediately; slow tasks keep running until completion. But this introduces a new challenge: rollouts generated by older model versions become off-policy data. To handle this, they developed a suite of asynchronous RL algorithms—Token-in-Token-Out (TITO), Double-sided importance sampling, and Off-policy filtering—that together stabilize training on this kind of stale data.

Its technical paper, Z.ai researchers highlighted that GLM-5 has been optimized to adapt major domestic Chinese AI chips such as Huawei Ascend Cambricon, and Baidu’s Kunlunxin.

K2.5: Visual Coding and Agentic Search

Like Qwen3.5-Plus, K2.5 is Moonshot AI’s first native multimodal foundation model. It was trained on approximately 15 trillion mixed visual and text tokens—vision and language learned jointly from day one. The result is notably stronger cross-modal reasoning across images, videos, and text.

K2.5 builds on the Kimi K2 foundation model: a trillion-parameter MoE with 1.04T total parameters and ~32B activated per token. Where K2.5 stands out is in two specific areas: front-end visual coding (it’s particularly strong at creative website design, and can generate functional code directly from video walkthroughs) and agentic search, where it reportedly outperforms even proprietary models—a genuine achievement in deep research workflows.

The most novel contribution in the paper is Agent Swarm, which introduces a learned framework for parallel multi-agent execution. Rather than a single agent working through tasks sequentially, K2.5’s PARL (Parallel-Agent Reinforcement Learning) paradigm works like this:

• A trainable orchestrator learns when and how to spawn sub-agents. The sub-agents themselves are frozen during training—deliberately not co-trained—to avoid unstable credit assignment across a dynamic swarm. The orchestrator is then trained via reinforcement learning to schedule parallel work and balance efficiency against complexity.

The results are compelling: inference latency reduced by up to 4.5× compared to sequential agents, with meaningful gains in task completion quality and F1 scores. The agent swarm isn’t just a throughput trick—it actually improves how well tasks get decomposed and executed.

M2.5: The Office Specialist

M2.5 is a more incremental release—an upgrade over M2.1, likely timed to respond to the momentum of other models (and possibly DeepSeek V4 specifically). The architecture is presumably close to M2.1’s, so I won’t spend too much time on the model itself.

What MiniMax focused on this cycle is coding and office productivity. M2.5 achieved then-SOTA results on multiple coding benchmarks and, interestingly, demonstrated the ability to generate professional deliverables in Word, Excel, and PowerPoint—not just raw text. It wins approximately 59% of head-to-head comparisons against peer models on productivity benchmarks.

The more interesting story is MiniMax’s post-training infrastructure, Forge—their in-house agent-native RL framework that, like Z.ai’s asynchronous system, decouples training from inference. Forge trains across 200,000+ real-world environments spanning coding, search, tool interaction, and workspace automation. The key stabilization algorithm they use is CISPO, which helps maintain training stability and improves long-horizon performance on complex agentic tasks.

One number from MiniMax’s deployment data is worth lingering on: 30% of all tasks across the company’s daily operations—R&D, product, sales, HR, finance—are now autonomously completed by M2.5. And M2.5-generated code accounts for 80% of newly committed code. That’s not a benchmark. That’s a live claim about internal operations, and it’s either the most confident thing said in this release cycle or the most audacious.

Three Patterns Worth Taking Seriously

Looking across all four models, three themes emerge that I think have real implications for the broader industry.

1. Sparsity is the new default

MoE has been the dominant architecture since DeepSeek-V3 and R1, and these new models push sparsity further than any previous generation without giving up performance. But it’s not just network sparsity anymore. Frontier open-weight models are now also adopting sparse attention mechanisms—GLM-5 with DeepSeek’s DSA, Qwen3.5-Plus with its hybrid Gated DeltaNet and Gated Attention layers.

The practical consequence is cheaper inference, and that’s where Chinese open-weight models are carving out a real competitive identity. Alibaba claimed its model costs 1/18th-of-Gemini-Pro, and MiniMax’s “intelligence too cheap to meter” narrative also captures broad attention. For developers who are cost-sensitive, or for agentic workloads where token consumption can be 10–100× higher than standard queries, this pricing gap is not marginal. It’s structural. And in China, where AI commercialization paths remain unclear and most consumer chatbots are still offered for free, cheaper models can save companies not millions but billions in compute costs.

2. Native multimodality has arrived in open weights

A year ago, the leading open-weight models were overwhelmingly text-only, including Qwen3 and Kimi K2. Many researchers believed native multimodality wasn’t necessary for core intelligence. Then Gemini 3.0 happened, and its staggering benchmark performance shifted the conversation.

Chinese AI labs responded fast. Qwen3.5-Plus and K2.5 are both the first native multimodal flagship models from their respective labs. Add ERNIE 5.0 from Baidu and Seed2.0 from ByteDance, and you have a clear generational inflection: open-weight LLMs are no longer text-only. Training vision and language jointly from scratch—rather than layering vision on top—produces qualitatively better cross-modal reasoning. Both Qwen3.5-Plus and K2.5 can build functional websites directly from video input. That’s a capability class that didn’t exist in this model tier twelve months ago.

3. Agentic capabilities in real world

Previous-generation Chinese open-weight models added tool use and basic agent scaffolding. This generation is different—these models are credibly competing with top proprietary systems on complex, real-world workflows.­­­­

Almost every lab in this release cycle rebuilt their post-training RL recipe specifically for agentic tasks. Z.ai and MiniMax both developed asynchronous RL systems that decouple inference from training. K2.5 introduced Agent Swarm with a learned orchestrator. The common thread: standard synchronous RL wasn’t designed for the variance in rollout length that agentic tasks produce, and each lab had to engineer around that constraint independently.

The business implications are significant. Agentic models that actually work—that can replace portions of employee workflows—consume tokens at a fundamentally different scale than chatbots. Z.ai seems to understand this: days after releasing GLM-5, they raised the price of their GLM Coding Plan subscription by 30%, announced expansions to their chip partner network, and watched their stock surge over 40% in a single day. Their market cap is now approaching Baidu, which reported over $18 billion in revenue in 2024.

Three years ago, Chinese AI labs open-sourced their models largely to raise awareness and goodwill within the developer community. Contributing to the open-source ecosystem was the goal; competing internationally was not really on the table. That framing has changed meaningfully heading into 2026.

Look at how these labs are presenting themselves now. The release blogs, the benchmark visuals, the example demos, the design polish—all of it is clearly aimed beyond domestic audiences. These labs are active on X, writing in fluent English, and positioning their models explicitly against GPT, Gemini, and Claude.

The early commercial signals are modest but real. Moonshot AI, which is reportedly seeking a valuation of $10 billion in an ongoing funding expansion, disclosed that its overseas API revenue has quadrupled since November 2025. These models are finding their first footholds through open-weight platforms like OpenRouter, a natural entry point for cost-sensitive developers, and the expectation is that enterprise and consumer markets follow.

What makes this moment historically significant is: for the first time, China’s leading technology companies and AI startups are competing head-to-head against America’s best in one of the most consequential technologies of our era—AI, where the stakes may be higher than any of those. The race is still early, the outcome is genuinely uncertain, and that is precisely what makes February 2026 feel like a turning point worth paying attention to.

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Gemini 3.0 released in November 2025 was seen as a key milestone for Google to take the lead in the AI race. Now ByteDance is attempting to orchestrate its own Gemini 3.0 moment with a coordinated release of new models, including the video generation model Seedance 2.0 and the competitive multimodal foundation model Seed2.0.

ByteDance has long been recognized for exceptional AI applications—its chatbot app Doubao boasts over 100 million daily active users—but their AI models have lingered in the second tier. That perception may have just shifted.

The strategy mirrors Google’s Gemini 3.0 playbook: release across modalities simultaneously, demonstrate breadth of capability, and leverage existing distribution advantages to convert model strength into market dominance.

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Seedance 2.0: SOTA Video Generator

Seedance 2.0 is ByteDance’s latest AI video generator, now available across ByteDance’s AI apps such as Jimeng and Doubao as well as video editing apps Jianying/CapCut. The model has gone viral over the past week and shows no signs of slowing down. Elon Musk called it “it’s happening fast,” and Jia Zhangke, the Cannes-winning Chinese director, just made a film with it.

Multimodal Input: Seedance 2.0 can process multimodal input, including text prompts, images, videos, and audio clips. All four modalities can be mixed at once to guide the output. You can add as many as 9 reference images, 3 videos, and 3 audio clips, then guide Seedance 2.0 with prompts to make generation more controllable.

Motion Stability: Seedance 2.0 shows marked improvements in physics-based motion and character consistency. The model handles complex human motion and object interactions with notably fewer artifacts than previous generation tools—a critical advancement for practical use cases.

Native Audio-Visual Synthesis: Like Sora 2, Seedance 2.0 generates synchronized audio and video in a single pass. Sound effects, dialogue, and music align natively with visuals, eliminating post-production alignment steps.

However, this also raises deepfake and portrait rights concerns, as the model can generate photorealistic celebrity likenesses with matching voice characteristics from minimal prompting.

Dynamic Cinematography: Seedance 2.0 introduces sophisticated camera movement control. Unlike earlier models constrained to static or simplistic angles, Seedance 2.0 can generate complex camera work comparable to professional cinematography. The New York Times called the model’s output “more cinematic than anything so far.”

Prompt Adherence: Seedance 2.0 demonstrates significant improvements in following user instructions precisely. The model generates content that matches prompts with substantially higher accuracy—reducing the unpredictable, lottery-like outputs that plagued earlier tools. When you specify performance details, lighting conditions, shadow direction, or camera behavior, the model respects these constraints rather than interpreting them loosely or ignoring them entirely.

Video generators have advanced rapidly over the past three years—just look at clips like “Smith eating spaghetti”—but they still haven’t unlocked the kind of commercial value that LLMs have. The latest example is Sora 2. While Sora 2 launched to significant fanfare last year, hitting #1 on the Apple App Store with over 1 million downloads, its daily active user base has now reportedly dropped below 1 million, with App Store rankings falling to the 70s-100s range.

Here’s where ByteDance’s strategic position becomes clear. ByteDance doesn’t have this problem. Seedance 2.0 can seemlessly integrate directly into:

• Douyin/TikTok: The largest short-form video platform globally, where AI-generated content can be consumed within the same ecosystem it’s created. It will continue lowering the barriers to video production, ushering in an era where anyone can create. while more precisely matching each video to its ideal audience. Seedance 2.0 won’t disrupt Hollywood, but it will make short video apps even more addictive.

• CapCut/Jianying: With over 800 million users globally, this video editing platform provides the practical creation environment where Seedance 2.0 can become part of standard creative workflows. For creators already paying for features like multilingual subtitles and AI script generation, AI video generation will soon become a must-have premium feature.

Inevitably, the model has drawn criticism from Hollywood studios alleging training on copyrighted content, followed by the Japanese government launching an investigation over potential Copyright Law violations. Starting February 9, ByteDance disabled real human images or videos as primary references, a significant capability restriction that suggests both regulatory pressure and the challenge of managing deepfake risks at scale. The company told South China Morning Post that “We are taking steps to strengthen current safeguards as we work to prevent the unauthorised use of intellectual property and likeness by users.”

Seed2.0: A Production-Oriented Multimodal Foundation Model

While Seedance 2.0 captured social media attention, Seed2.0 represents ByteDance’s core play for foundation model credibility.

Positioned as a “production-oriented” multimodal model, Seed2.0 emphasizes real-world complexity over benchmark optimization alone. In the Seed2.0 model card, researchers from Seed, ByteDance’s AI lab, began by analyzing how AI is actually used in China. The data revealed that unstructured information processing and analysis dominates authentic model usage, representing the largest single share. In development workflows, frontend development and bug fixing substantially dominate agentic coding requests, each far exceeding their respective category alternatives.

Therefore, the model prioritizes visual and multimodal understanding, fast and flexible inference, and reliable complex instruction execution.

Seed2.0 launches in three variants optimized for different deployment scenarios: Seed2.0 Pro for research and complex reasoning, Seed2.0 Lite for enterprise MaaS adoption, and Seed2.0 Mini for scaled applications where cost efficiency dominates.

As of February 16, 2026, Seed ranks 6th on the LMSYS Chatbot Arena Text Arena (Overall) leaderboard and 3rd on the Vision Arena leaderboard.

Multimodal Understanding: The model demonstrates strong performance across 50+ image benchmarks and 24+ video benchmarks. Seed2.0 Pro shows particular strength in visual reasoning tasks like MathVision, LogicVista, and VisuLogic, as well as spatial understanding benchmarks including DA-2K 3D spatial and RefSpatialBench.

The model excels at document extraction on long-form documents (DUDE benchmark) and demonstrates exceptional motion perception capabilities—notably surpassing human baseline performance on VideoReasonBench. Long-form visual context processing represents another core strength, enabling the model to maintain coherent understanding across extended visual sequences.

Mathematical and Formal Reasoning: This is where Seed2.0 Pro shows frontier-class performance. The model achieves gold-level performance on both IMO 2025 and CMO 2025, demonstrating olympiad-level mathematical reasoning capability.

With a Codeforces Elo of 3020, Seed2.0 Pro shows strong competitive coding ability that extends beyond pure mathematics into algorithmic problem-solving. The model also delivers strong performance on AIME, HMMT, and Putnam-200 benchmarks, positioning it as competitive with Gemini 3 Pro on math and reasoning tasks across the board. Seed2.0 also pursues tackling tasks with genuine research-level complexity, such as open Erdős problems and scientific coding.

Coding Capabilities: Seed2.0 demonstrates strong coding performance across multiple evaluation dimensions. The model shows competitive results on LiveCodeBench and handles repository-level tasks effectively on NL2Repo-Bench, indicating capability beyond isolated function generation.

Debugging and refactoring scenarios, which are critical real-world workflows often underrepresented in benchmarks, represent explicit areas of focus. The model also shows particular strength in Vue.js-heavy frontend development, reflecting optimization for frameworks dominant in the Chinese market. The model card explicitly frames coding agents and multi-step task execution as design priorities, not afterthoughts, signaling ByteDance’s emphasis on practical engineering workflows over pure benchmark optimization.

Agentic Workflows: Seed2.0 emphasizes end-to-end task completion rather than single-turn interactions. Seed researchers attribute this to the nature of real-world tasks, which typically span longer time frames and involve multiple stages. Existing LLM agents struggle to independently construct efficient workflows and gain experience over extended periods, while real-world knowledge has significant domain barriers and follows a long-tail distribution. In response, the model strengthens its ability to follow complex instructions, handle long-chain tasks, and master long-tail knowledge.

The model shows strong performance as a search agent, handling complex retrieval tasks effectively. Coding agents represent a core capability, with the model demonstrating repository-level generation that goes beyond snippet completion. GUI agents, deep research workflows, and long-horizon multi-step instruction following are all explicitly supported design patterns.

Costs: While GPT-5.2 costs $1.75 per million input tokens and $14.00 per million output tokens, Seed2.0 Pro costs only $0.47/$2.37 per million input/output tokens and Seed2.0 Mini cuts costs to $0.03/$0.31 per million input/output tokens.

At 5-10x cheaper than frontier alternatives while maintaining competitive performance on many tasks, Seed2.0 targets the enterprise MaaS (Model-as-a-Service) market where cost efficiency enables entirely different use cases at scale. This aggressive pricing strategy isn't surprising—ByteDance initiated China's LLM pricing war back in April 2024, and has consistently positioned cost advantage as a core competitive moat.

However, Seed researchers also acknowledged specific gaps:

• Coding performance still trails Claude on certain SWE benchmarks

• Retrieval-heavy long-context tasks show headroom versus competitors

• Some long-tail knowledge gaps compared to Gemini on specific QA benchmarks

• Video reasoning on the hardest evaluation sets remains below human performance

Make Models Useful

The real test of AI capability isn’t found in benchmark leaderboards—it’s found in whether ordinary people can actually use it to solve their problems.

For example, my wife is a Douyin vlogger who typically spends 2-3 hours editing each video, wrestling with hour-long raw footage to add subtitles, voiceover, and special effects. She doesn’t follow AI research. She doesn’t care about benchmark scores or parameter counts. She has one question: Can AI actually save her time?

This is the audience ByteDance understands intimately. While AI researchers debate frontier capabilities and academics parse benchmark methodologies, hundreds of millions of creators on Douyin and CapCut are asking the same practical question my wife asks: Will this work for me?

When I saw Seed2.0’s model card demonstrate video editing capabilities in CapCut, I immediately shared it with her. Not because it achieved impressive scores on academic benchmarks, but because it promised to do exactly what she needs.

This is ByteDance’s strategic advantage. They’ve built their models not primarily for researchers to evaluate, but for real users to deploy. The company emphasizes real-world effectiveness over benchmark performance throughout Seed2.0’s documentation. Both Seedance 2.0 and Seed2.0 are engineered specifically to strengthen ByteDance’s existing applications from Douyin/TikTok to CapCut/Jianying and AI apps such as Doubao and Trae.

Unlike most Chinese tech companies rushing to open-source models for community credibility, ByteDance keeps both Seedance 2.0 and Seed2.0 proprietary, available only through their apps and cloud platforms. They’re optimizing for the number of videos actually created, the hours actually saved, the workflows actually improved.

Is this ByteDance’s Gemini 3.0 Moment? Not quite yet. Gemini 3.0, as a native multimodal model, shows exceptional capabilities across knowledge, coding, and multimodal understanding—powerful in nearly every aspect.

Seed2.0 is similar in architecture and ambition to Gemini 3.0, but more specialized. It prioritizes specific capabilities over comprehensive excellence. The Seed researchers themselves acknowledge this gap.

Seed2.0 marks the first major project since Wu Yonghui, former Google DeepMind Vice President and now head of Seed, joined ByteDance. According to Latepost, it features one trillion parameters, probably ByteDance’s largest model ever. This represents a remarkable achievement for a company that entered the LLM race late, beginning serious training only in 2023, without the robust infrastructure foundation of Google or OpenAI.

Wu is attempting to build Seed into a world-class research lab. Seedance 2.0 and Seed2.0 represent a major step in that direction—not yet matching Gemini 3.0’s comprehensive dominance, but demonstrating that ByteDance can compete at the frontier on the capabilities that matter most for its billion-user platforms.

Recode China AI

👀TikTok’s Parent ByteDance Quietly Builds Its AI Empire

When people outside mainland China think of ByteDance, they think of TikTok—an app that propelled short video into omnipresent popularity and placed ByteDance at the heart of U.S.-China tensions…

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a year ago · 27 likes · 4 comments · Tony Peng

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China’s AI race is unfolding among the country’s three largest technology giants: ByteDance, Alibaba, and Tencent. As we enter 2026, these tech behemoths are going all-in on what many see as a war that will determine their futures.

ByteDance operates China’s most popular AI chatbot, Alibaba has built what many consider the country’s—and possibly the world’s—leading open-source LLM, and Tencent, though late to arrive, wields an ecosystem of apps serving over 1 billion users and is racing to catch up.

LatePost, my favorite Chinese business media (reminiscent of The Information), recently published a viral piece documenting the latest AI developments at these three companies.

Below is the full translation, assisted with Claude’s assistance and reviewed for accuracy.

Head-to-Head Fight At Chinese New Year

In November 2025, Yao Shunyu appeared at an internal Tencent meeting wearing casual shorts and flip-flops. This 27-year-old former OpenAI researcher and technical genius who proposed the ReAct paradigm had just been recruited by Tencent with a substantial offer.

After joining, one of his key tasks was to help Tencent identify why the Hunyuan LLM had been underperforming over the long term, and report the situation directly to Tencent President Martin Lau. Yao Shunyu meticulously examined every link in the chain, frequently exchanging ideas with colleagues and interns until midnight—things his predecessors rarely did. He quickly became the top figure for Tencent’s large language models (LLMs).

“Hunyuan’s evaluation has major problems,” said one person present at the meeting, paraphrasing Yao Shunyu’s remarks. The gist was that the model was overly focused on chasing leaderboard rankings, incorporating benchmark data into the training set, which led to data contamination. Although the model excelled at answering questions, its performance in real-world scenarios was unstable. He hoped the team would stop chasing leaderboards and stop organizing work around rankings. At the meeting, Hunyuan’s relevant leaders also mentioned past problems with data, pre-training, and infrastructure.

Over the past two years, this Chinese internet company with the highest market capitalization and the largest traffic gateway has been relatively cautious about AI—whether in terms of investment intensity or organizational and product advancement speed, it lagged behind Alibaba and ByteDance. Until 2025, this state began to change: offering high salaries to attract technical talent, large-scale reorganization of model and AI product teams, and continuously tilting resources toward “Yuanbao (Tencent’s AI chatbot).” Yao Shunyu’s arrival marked the clearest turning point in this series of changes.

“Completely disrupting the previous pace and inertia is the first step back to the right track,” said one person from the Hunyuan LLM team.

Tencent is rallying its forces, while Alibaba is attempting to define the AI narrative.

Alibaba has proposed a new concept: “Tong通-Yun云-Ge哥”—Tongyi Laboratory, Alibaba Cloud (云yun means cloud in Chinese), and Pingtou Ge (平头哥T-Head), representing the trinity of AI, cloud computing, and chips. Alibaba considers itself one of the few technology companies in China with full-stack AI capabilities—from chips and AI models to cloud services and products. This is also Google’s story.

Among the three major internet giants, Alibaba’s core business has the lowest profit efficiency. According to reports, ByteDance’s net profit in the first three quarters of this year was around $40 billion; during the same period, Tencent and Alibaba’s net profits were around $30 billion and $10 billion respectively (calendar year basis). But this hasn’t affected its determination to invest.

According to a source familiar with the matter, Alibaba is considering increasing its investment in AI infrastructure and cloud computing over the next three years from 380 billion yuan (~$55 billion) to 480 billion yuan (~$69 billion). Domestically, Alibaba has its self-developed chip Zhenwu 810E; overseas, it is “using truckloads to transport purchased GPUs,” said one insider. At its most aggressive, “even consumer-grade graphics cards like the RTX 4090 were purchased in large quantities to build inference clusters and supplement inference throughput.”

In December 2025, the daily user acquisition spending for Qwen App, Ant Group’s Lingguang and Afu, each exceeded 10 million yuan (~$1.4 million); Qwen App’s single-day spending peak once reached 15 million yuan (~2.2 million).

For ByteDance, “AI is an opportunity that can affect the entire world,” said someone close to ByteDance’s senior management. From TopBuzz and TikTok to TikTok Shop, since its founding, the company has been searching for such opportunities. “Only things closer to the center of the world have greater exploratory value.”

Compared to Tencent and Alibaba, which have weaknesses in models and products respectively, ByteDance’s capabilities are more comprehensive. By the end of 2025, Doubao became the first AI product in China to break 100 million daily active users; Doubao’s AI models (including their LLMs and other multimodal models) processed an average of 63 trillion tokens daily, growing over 200% in six months.

In 2023, ByteDance founder Zhang Yiming said that the operating system-level opportunity of this era is AI + computing.

In 2026, ByteDance will fully accelerate the globalization of its AI business, with regions like Southeast Asia as priorities; it won’t enter the United States for now. According to reports, ByteDance’s goal is to become at least the third-largest player globally in generative AI. By the end of 2025, Doubao’s overseas version Dola had surpassed 10 million daily active users globally.

China’s current wave of AI enthusiasm began in 2023. In the early stages when star startups attracted the most attention, internet giants weren’t particularly prominent. The three chose different paths: Tencent emphasized AI application implementation and therefore waited relatively quietly for model capabilities to mature; Alibaba pushed its AI model toward open source, betting on building a larger ecosystem to open up incremental space for its cloud business; ByteDance started late and could only make up for technical shortcomings through saturated investment as quickly as possible.

Until early 2025, DeepSeek redrew a starting line for the entire industry, and the giants became active—the battlefield began to smell of gunpowder.

The 2026 Spring Festival (Chinese New Year) became the flashpoint of this war.

According to reports, ByteDance secured the Spring Festival Gala partnership at the highest price—its Volcano Engine became the AI cloud partner for the Spring Festival Gala, and Doubao will also launch various interactive features during the gala.

Tencent hasn’t sponsored any gala-type programs since 2015, but it clearly understands the value of the Spring Festival. Just a month ago, Tencent CEO Pony Ma inquired with the Yuanbao team, concerned about whether they had sufficient GPU resources. “He wanted to ensure that Yuanbao wouldn’t be constrained by computing power at this critical moment during the Spring Festival, affecting its performance.”

The two companies that missed the Spring Festival Gala decided to take the initiative on the product side.

Tencent Yuanbao prepared 1 billion yuan in cash red envelope incentives and a brand new AI social function called “Yuanbao Pai” for battle. At the group employee meeting on January 26, 2026, Pony Ma said they would give the marketing budget saved to users, letting everyone relive the joy of grabbing red envelopes from years past, and hoping to recreate the glorious moment of WeChat red envelopes in 2015.

Next to the facial recognition entrance on the first floor of Building C4 in Alibaba’s Xixi Park, every Monday morning and Friday evening are filled with suitcases belonging to Qwen App employees. They fly in from Guangzhou, Beijing, and other places for closed development, then fly away like migratory birds on weekends. This situation continued at least until the Spring Festival.

Alibaba sources told us that Qwen App will send red envelope benefits to users during the Spring Festival (turns out it’s 3 billion yuan, or $431 million). No company controlling traffic wants to give up the battle for gateways. Baidu also offered 500 million yuan in Spring Festival red envelopes, and Wenxin’s AI social features appeared on the Spring Festival battlefield.

Over the past twenty years, China’s three major internet giants have fought almost every key battle in the internet space—from e-commerce and lifestyle services to long and short videos, social media, gaming, mobile payments, and enterprise services.

Previously, they fought local battles—losing one card, the game could still continue. This time it’s more like the “Battle of Midway”—a turning point in the overall war. Once lost, they might lose the entire future.

After DeepSeek, the War Truly Began

Among all of China’s major tech companies, ByteDance was one of the later starters in LLMs. Before OpenAI’s ChatGPT launched at the end of 2022, Baidu, Huawei, and Alibaba (in order of release) had all released large language models, but ByteDance had not.

Starting mid-2023, ByteDance began accelerating its catch-up, quickly addressing shortcomings in infrastructure, LLM R&D, hardware and software products, and talent; by late the following year, Doubao became the AI product with the largest user base.

In January 2025, High-Flyer’s reasoning model DeepSeek-R1 emerged, directly driving an explosion on the product side: within less than a month of DeepSeek Chatbot’s launch, its daily active users broke 10 million, immediately surpassing Doubao.

This reasoning model wasn’t a novelty. After OpenAI’s o1 model preview appeared in September 2024, ByteDance paid attention to this direction and attempted to train its own reasoning model. Three months later, the results weren’t ideal. Later, in multiple settings, Zhu Wenjia, then head of ByteDance’s AI model department Seed, said “it was my mistake,” according to someone close to ByteDance’s senior management.

ByteDance’s model and product teams urgently convened to discuss countermeasures. The initial approach was to implement the capability in the frontend product first—that is, not making a breakthrough in the AI model from scratch, but first training/fine-tuning a smaller reasoning model for Doubao to quickly catch up—on one hand doing supervised fine-tuning with synthetic/annotated samples that included reasoning steps, and on the other hand trying DeepSeek’s data. However, the final results were poor, and they decided not to rush it but to carefully refine the foundation model.

After the Spring Festival, more and more internal ByteDance products considered integrating DeepSeek. At a product requirements meeting for Jimeng (ByteDance’s Sora-like video generator app), everyone assessed feasibility. People close to management told us that the legal and compliance teams had the strongest objections, believing that ByteDance’s products shouldn’t promote other companies’ models. The discussion yielded no conclusion, and finally the business leader made the call to continue integration.

In early February, former Google DeepMind Vice President Wu Yonghui joined ByteDance to lead fundamental theoretical research for AI models in the Seed department. Several algorithm and technical leaders who previously reported to Zhu Wenjia began reporting to Wu.

The person close to ByteDance’s senior management told us this was a long-planned adjustment unrelated to DeepSeek. ByteDance knows what kind of people to use at what time. Zhu Wenjia wasn’t a native AI talent, but in the early stages when ByteDance developed AI models and lacked sufficient influence to attract top practitioners, he was indeed the most suitable choice—understanding both technology and products, and having served as business number one; more importantly, he had sufficient rapport with group management.

When ByteDance’s senior management initially approached Zhu Wenjia to lead AI models, both sides had a consensus: ultimately someone who better understood AI should take the top position. “Wenjia had long been prepared to hand over at any time; it just happened that at this juncture, Yonghui arrived.”

Wu Yonghui largely continued ByteDance’s previous technical route, but after taking office, he led the breaking down of barriers between model departments and groups, achieving data sharing across various stages and teams. At a Seed all-hands meeting, Wu Yonghui emphasized the importance of long-term research, making it clear they would explore longer-cycle, uncertain, and bold topics.

Someone close to ByteDance’s senior management said ByteDance’s current infrastructure (engineering capability) is already stronger than any domestic company. But compared globally, the biggest problem is the lack of people like those at OpenAI who can propose directions and conduct frontier exploration, such as GPT-4o and Sora. “China hasn’t had real corporate research institutes in the past because private enterprises were too poor; now we can finally try.”

Unlike ByteDance, DeepSeek’s emergence allowed Tencent to see new opportunities.

Starting in 2023, Tencent’s investment in AI models was relatively cautious—it didn’t recruit AI talent on a large scale, nor did it actively stockpile computing power. It consistently emphasized externally that it valued AI’s practical applications more.

However, by the end of 2024, ByteDance’s Doubao daily active users had climbed to a high of 20 million, while Tencent Yuanbao only had a few hundred thousand, which made Tencent nervous.

Yuanbao not only launched a year later than Doubao, their initial positioning was also vastly different—Doubao’s goal was always to be an independent all-knowing, all-capable assistant; Yuanbao was Tencent’s product for testing Hunyuan LLM’s technology. So for a long time after its birth, Yuanbao was in a state of “seeking cooperation with various businesses within the company,” said an early Yuanbao strategy person.

At Tencent’s Binhai Headquarters Tower in Shenzhen, senior management discussed how to respond. Jiang Jie, Tencent Vice President and head of the Hunyuan LLM, issued a military order: “We must catch up to Doubao within six months.” After the meeting, Jiang Jie began recruiting a new number one for Yuanbao and expanding the team to rally forces.

Before joining Tencent, Jiang Jie was Alipay’s BI Chief Architect. At Tencent, he was responsible for Tencent’s big data platform and advertising technology system, but had never led a consumer-facing product; the Technology and Engineering Group (TEG) where Yuanbao belongs had never incubated a consumer product. Ultimately, Tencent decided to look for candidates elsewhere within the company.

A source told us that Wu Zurong, head of Tencent Meeting, and Zhang Xiaochao, head of QQ, were the two candidates senior management considered most suitable.

Both had experience building products from zero to one. The former had fought tough battles—during the pandemic, with a rhythm of stress testing at dawn and iterating during the day, he pushed Tencent Meeting to break out of its niche; the latter focused on social products, going from QQ to WeChat and back to QQ, and was also the first head of Video Accounts.

In large companies, innovative businesses are opportunities but also mean enormous risk. Finally, with the support of Dowson Tong, head of Tencent’s Cloud and Smart Industries Group (CSIG), Wu Zurong took on this task.

Wu Zurong’s appointment came at just the right time. A month later, during the Chinese New Year, a huge growth opportunity arrived. After the DeepSeek-R1 model appeared, Tencent became the most active large company in embracing it. Pony Ma personally pushed all businesses to integrate it. At the same time, Tencent began ordering GPUs to supplement computing power, ensuring DeepSeek could run smoothly within Tencent’s products.

After Yuanbao integrated the DeepSeek model, it leveraged the momentum to launch promotions, even advertising in county towns and rural areas. The result was meteoric—in one week, daily active users grew tenfold, approaching 2.6 million, later gradually climbing past 10 million, currently ranking among the top three domestically.

Alibaba appears to be the giant least affected by DeepSeek. Before DeepSeek emerged, its Qwen LLM was already one of the most popular models in the open-source community, even rivaling Meta’s Llama 3—in terms of downloads on distribution platforms like Hugging Face and developer ecosystem activity, it has long been stable in the global first tier.

As one of the few players in China with “full-stack capabilities,” the improvement in intelligence still allowed Alibaba to see a new future: AI models could serve as a trump card for Alibaba Cloud, using self-developed models to keep enterprise customers on the cloud and drive consumption and revenue from computing power and platform services—a path validated in the United States.

The self-developed chip business also has an opportunity to accelerate its entry into the market. According to our understanding, Alibaba’s PPU (Parallel Processing Unit) Zhenwu 810E has become one of the main chips in China’s new AI computing power market, finally receiving its first external major customer order in 2025. On January 22, 2026, Alibaba decided to support its chip company Pingtou Ge (T-Head) in pursuing an independent IPO in the future.

An Alibaba source revealed that in 2025, Alibaba’s senior management set the tone at an internal meeting that the new year would no longer prioritize e-commerce GMV growth as the top goal. Strategically, they decided to temporarily scale back categories like consumer electronics and Maotai that “can drive volume but have thin margins,” and invest more resources in categories like cosmetics and apparel that can better contribute revenue and profit. An Alibaba source analyzed that the underlying logic is—prioritize thickening revenue and profit, and invest the money earned more intensively in AI.

When AI finally has the opportunity to become infrastructure, the giants’ competition is no longer just about comparing models themselves, but quickly shifts toward the race for super gateways.

Doubao’s Ferocity: The Formation of a Super Gateway

Among the three giants, Tencent has a stable social moat and the world’s number one gaming business; Alibaba has rapidly growing cloud business beyond e-commerce. ByteDance’s foundation is almost tied to Douyin—this is its most core source of traffic and revenue, with advertising, e-commerce, and lifestyle services all relying on this product.

In 2023, the AI wave arrived. Old products like Toutiao and Douyin all launched new explorations. Zhang Nan, former head of Douyin, led the team to incubate the AI creation platform Jimeng, with her goal being to become the Douyin of the AI era. However, the product that first gave ByteDance hope of succeeding Douyin wasn’t them, but an AI assistant called Doubao.

Over the past two years, the landscape of AI super gateways has been constantly changing: in 2024, Moonshot’s Kimi initially showed its edge; in early 2025, the obscure DeepSeek made great strides to catch up, with downloads once topping the charts, and Tencent Yuanbao also squeezed into the top three; at the end of 2025, Alibaba’s Tongyi, renamed Qwen, made a renewed push. But throughout this process, Doubao has always been number one. By the end of 2025, it became China’s first AI product to break 100 million daily active users.

A ByteDance person said few people know that Doubao is a strategic-level product with relatively restrained advertising spending in ByteDance’s history. The choice stemmed from two reasons: early LLM capabilities weren’t strong enough, and after large-scale user acquisition, user retention wasn’t good.

Jimeng was similarly conservative in early advertising spending, with new users mainly from organic traffic. The team at most placed some cheap brand ads on Xiaohongshu and Bilibili. A person responsible for Jimeng’s growth told us that Jimeng’s advertising strategy wasn’t to first calculate “how much money a new user will make me in the future, how long they’ll stay,” but more like drawing a red line first: the advertising cost for acquiring a new user can’t exceed a certain amount—keep buying if below this line, stop if above.

Second, the larger the user base of AI products, the higher the cost, and current Chinese AI products have no clear commercialization path.

Since launching in 2023, Doubao’s strategy has undergone multiple transformations. ByteDance once tried introducing “supply side” and “consumption side” concepts in Doubao, guiding users to customize more bots (chatbots), then importing them to the recommendation page and distributing them to other users. But later they found that except for the official bot “Doubao,” other bots didn’t significantly impact the data. Later, the focus shifted to building it as an efficiency tool.

In early 2024, Moonshot’s Kimi became popular for its ultra-long text processing capability. At the same time, Kimi was also heavily advertising on Bilibili and Xiaohongshu. “We were also on the road, but didn’t expect to be preempted by a startup,” said someone responsible for Doubao’s long-text direction.

In fact, Kimi’s cost-effectiveness in buying users on these two community platforms was extremely low—dozens of yuan for one user, with customer acquisition costs even higher than some financial products, burning hundreds of thousands in a day. Doubao also began accelerating, with the algorithm team compressing the model iteration cycle to one version every three days.

Doubao’s leader is Zhu Jun (Alex), who once captured changes in young people’s social patterns on a train and later created Musical.ly, a short video product that became popular in the United States. People familiar with him describe him as an “internet poet.” He often writes his current state of mind in his Feishu signature—sometimes comparing himself to the “Nanke Prefect,” using allusions to express feelings about fame being like a dream; sometimes casually sharing a novel he recently read. Zhu Jun also has many romantic imaginations about AI—when ChatGPT seemed to define the AI assistant product form, he insisted that AI should be more “anthropomorphic” and “human.”

“We also joked about creating a Bot universe similar to the Marvel universe in Doubao, hoping users could find various companionship in it, jokingly calling it the ‘Xiaoning Universe’ at the time,” said a Doubao employee. Xiaoning is a Doubao Bot focused on emotional companionship, with the full name “Xiaoning Who Loves Chatting.”

At the end of 2024, the industry trend leans toward multimodality. Doubao launched the Seedream 2.0 model to strengthen text-to-image capabilities, video dialogue capabilities, and video generation capabilities. The previous year, it also launched real-time voice call functionality, with “emotion” as a key refinement direction—they traveled across the country to collect dialects, with accent granularity refined to district and county levels within cities; deliberately named Doubao’s default voice “Taozi,” which was also the voice actor’s online name; and did a series of stylized dialogue training on the model to give it a distinct personality.

In early 2025, videos of users creatively “voice-training” Doubao suddenly went viral on Douyin; a few months later, gameplay around Doubao for photo editing, group photos, and background changes became popular on Xiaohongshu. In half a year, Doubao pulled users’ imagination of AI from “deep conversation” back to more daily use. “It was so similar to Douyin back then—a fun feature appears, a group of creators and young people make it popular, ultimately forming viral spread,” said a Douyin person.

“We were all stunned because this wasn’t decided at all,” said a Doubao person. These gameplay features began bringing millions of new users to Doubao daily.

“The battlefield returned to Alex’s area of expertise,” said a Doubao person. Doubao tasted success and thus upgraded its strategy, beginning to accelerate “building a matrix”—because they weren’t sure which scenario would explode first, they had to try every scenario, gameplay, and feature. They knew that even though many feature points couldn’t withstand scrutiny and users would stop using them after playing for a while, they could slowly accumulate mindshare.

Zhu Jun and the team spent more effort recruiting product strategists, whose core job was to seek scenarios, gameplay, and features more proactively.

A Doubao business leader once provided a talent requirement document to the recruitment team, opening with a world-famous painting. “We just wanted to find people who could resonate with that painting, understand that painting,” said a person responsible for Doubao recruitment.

In the second half of 2025, as Doubao’s user numbers continued growing, the proportion of non-AI core users was also increasing. They had a characteristic: after opening Doubao, they rarely asked questions proactively, mostly clicking on preset questions that came with the system or simply chatting a few sentences. The team needed to accurately judge from the overall data which features truly had growth potential and whether users were satisfied with the generation results.

Doubao decided to adjust its user acquisition and growth pace, spending time accommodating new users and clarifying user needs. In the first round of competition, scale is always greater than efficiency, and user numbers are the only standard. But entering the second round, they gradually realized the importance of user quality.

“Doubao’s real challenge actually arrives after breaking 100 million daily active users,” said a Doubao person.

Yuanbao’s Catch-Up: From Engineering Debt to Product Rhythm

During the 2025 Spring Festival, Yuanbao completed a beautiful explosion. After the heat subsided, Tencent accelerated its catch-up.

After Yuanbao leader Wu Zurong took office, he pushed two things. First was team expansion—not only drawing core staff from Tencent Meeting, which he previously led, but also recruiting intensively from outside with a standard of “directly doubling salaries.” He personally appeared for important candidates, scheduling another meeting if one wasn’t enough, with extremely fast rhythm; second, he clearly benchmarked against leading competitors, first catching up to ChatGPT and Doubao in basic capabilities, not talking about differentiation but first filling shortcomings.

But organizational adjustments didn’t automatically clear historical problems. The engineering and data debt that Yuanbao accumulated in the Technology and Engineering Group (TEG) was carried into the Cloud and Smart Industries Group (CSIG).

Someone close to Yuanbao told us that in the past, TEG’s data cleaning and annotation in small model training wasn’t standardized. Taking the LBS intent recognition model as an example: when users ask “What’s good to eat nearby?”, this is clear intent; while “Any recommendations for good food?” is ambiguous intent.

If these two types of samples are mixed together in the training set, the model easily “learns incorrectly,” increasing the misclassification rate. The new team’s first step after taking over wasn’t to add models or stack parameters, but to return to the most basic engineering work—unify annotation caliber, rebuild evaluation sets, remove ambiguous samples, and then retrain all related small models according to the new data system.

After integrating DeepSeek, Yuanbao’s capabilities improved significantly. For example, when users search for “Yu Hua’s novels,” the team’s vision was for Yuanbao to pop up four reading cards while answering, allowing users to jump to WeChat Reading with one click; keywords in the answer body should also support underlining and link jumping. “Hunyuan’s two models often failed, but DeepSeek could perform stably,” said the Yuanbao person above.

But DeepSeek is after all an external model, making it difficult to help Yuanbao do specialized specific training. Tencent’s own Hunyuan large model has relatively weak capabilities and has its own technical improvement goals, also needing to chase leaderboard rankings. This resulted in the Yuanbao team being unable to validate some ideas on the product, with many desired features unable to be implemented.

A Yuanbao person gave an example: if Hunyuan’s new version tightened answer rules with the purpose of making the model “talk nonsense” less, this is often the correct choice in technical evaluation; but when it comes to a frontend product like Yuanbao, users might feel “the answers became colder, the experience actually got worse.”

Search is another challenge. Chatbot products don’t just compare whether models can accurately understand user intent, but also test whether they can quickly and clearly provide the right answer. Yuanbao once conducted internal evaluations showing Doubao’s search accuracy was considerably higher than Yuanbao’s. “ByteDance has years of accumulation in algorithmic recommendations; this is its advantage,” said the person above.

At the end of December 2025, with senior management coordination, Tencent transferred the Model Application Center and Search Algorithm Center, originally under the AI model team, to the Cloud and Smart Industries Group (CSIG) where Yuanbao belongs. Hunyuan and Yuanbao established joint design, cross-deployment, code review and sharing mechanisms to reduce mutual attrition.

But Yuanbao really came to the table too late. The team has calculated that Doubao currently has over forty basic feature points, and in a year, “we’ve only caught up to thirty-plus.”

They set a “three-step annual” rhythm for Yuanbao: first catch up to Doubao in a few advantageous scenarios, then achieve local superiority, and finally complete overtaking through innovation points. The North Star metric is very clear—the frequency of users inputting prompts, followed by the number of high-quality prompts. Only when these two metrics work can product innovation be meaningful. At the same time, Tencent is also accelerating the embedding of Yuanbao’s AI capabilities into national-level products like WeChat, QQ, Docs, and Meeting to amplify synergy effects.

Compared to Doubao’s “chaotic punches beating the master” approach, Yuanbao appears more restrained. It chooses to cut in from clear, controllable vertical scenarios—education, image generation, office, shopping. “The temperament is like an engineering man,” described a Yuanbao algorithm person. Tencent’s product approach is very classical: not aggressive, emphasizing experience, with each feature required to reach at least 80 points before being allowed to launch. This is an advantage; but the cost is also obvious—”AI needs to let imagination fly and even make many trial-and-error attempts to touch the technical boundaries and realize the value of technology, and this is precisely where Tencent is most cautious.”

“Ultimately it’s that Tencent’s traffic anxiety is far lower than ByteDance’s,” evaluated a ByteDance AI product manager. The latter seemingly has traffic engines like Douyin, Fanqie, and Toutiao, but the user profiles inside are very homogeneous, leaning toward entertainment attributes, with limited space for cross-business traffic. Meanwhile Tencent has leading products in almost every track—WeChat, QQ, Tencent Video, Tencent Meeting, QQ Browser, QQ Music—and can find whatever kind of users it wants. This is its confidence.

“Doubao and Yuanbao are at two extremes of the spectrum,” said a ByteDance person. One extremely free-flying, one extremely cautious—both could succeed.

“Doubao’s opportunity lies in running fast enough to maintain absolute leadership in speed; Tencent’s opportunity lies in having strong enough judgment to do the right thing at the right time.”

This is more like a public intelligence war. A Yuanbao person said: “We discuss a feature today, and Doubao can know about it the next day.” Doubao is also under pressure. “They’ve basically poached our people all over.” Sometimes, the winning move isn’t in a single feature point but in each group’s resource mobilization: Douyin’s mature stickers, effects and other content assets continuously contribute to Doubao’s image and video generation; Yuanbao turned to ecosystem collaboration, and after cooperating with “Honor of Kings,” activity clearly rose.

Compared to native AI applications like Doubao and Yuanbao, the transformation of older products like Alibaba’s Quark and Tencent’s QQ Browser is even more difficult.

A Quark person told us that the industry previously had a consensus: after Chatbot appeared, search scenarios would be the first to be disrupted. Based on this judgment, Alibaba believed that transforming the search experience starting from Quark browser was the smoothest path toward AI product forms. But in fact, Quark’s user mindshare is already very fixed—cloud storage, photo-based problem search, web browsing.

In 2025, Quark launched the concept of an AI super box, but the final result was that the vast majority of people using Quark’s AI capabilities were new users who came to try it after seeing promotions, while the original loyal users still used Quark’s old features according to past habits.

QQ Browser’s user profile is older and more downmarket than Quark’s, with even more solidified mindshare. They also discovered a new problem: QQ Browser launched an AI web assistant feature, hoping users could use it to interpret web content at any time while browsing. However, “high-quality content on web pages is extremely scarce, and the proportion and frequency of content needing interpretation are very low, far lower than WeChat Official Accounts,” said a QQ Browser person.

The transformation paths of the two browsers led to the same destination, ultimately having to seek other routes.

QQ Browser had planned to create a more lightweight, exploratory new browser product; at this time, another team within the department was also exploring an independent note-taking product—this was also one of the hottest AI product directions in Silicon Valley at the time. Ultimately, they decided to merge three capabilities—AI web assistant, lightweight AI browser, and AI note-taking product—into one product, thus giving birth to the intelligent office platform ima.

Under the push of Alibaba’s senior management, Qwen App (renamed from Tongyi App) replaced Quark as Alibaba’s core role in competing for the super gateway.

In earlier years, when this product was still in Tongyi Laboratory, it wasn’t the most watched product but was more like a technical testing ground: used to verify new capabilities, run evaluations, and make demos. Compared to building up its user base, the Tongyi team cared more about building up the ModelScope open-source community.

Some team members had tried many consumer-facing products, such as digital humans and image/video generation mini-programs, “but couldn’t get support,” said a former Tongyi Laboratory person. Even after being transferred to Alibaba Intelligent Information Business Group, its status was still inferior to Quark browser.

In November 2025, Qwen App finally ushered in a turning point in its fate. It began chasing Doubao and Yuanbao, with various businesses within the Alibaba system, including Ele.me Fresh and Fliggy, actively developing relevant capabilities for Qwen. In less than two months, Qwen App iterated more than a dozen times, maintaining an ultra-high frequency of 2-3 updates per week, with some requirements taking only 1-3 days from design to launch.

The Invisible Battlefield: Organization, Collaboration, and Internal Gaming

Giants’ wars affect the whole body when one part is pulled. Core AI departments lead the charge, but how far they can go depends to a certain extent on the support and cooperation of other business departments within the company.

At Tencent, WeChat’s traffic and ecosystem are the most critical resources. When the QQ Browser team was making the AI web assistant, they discovered that high-quality content on PC websites was too scarce, and users had no need to use AI to interpret content, so they partnered with WeChat to obtain Official Account API interfaces, allowing users to forward Official Account articles to the browser with one click for content analysis—this was the product’s biggest moat at the time.

As a new product, Yuanbao also needs to borrow power from Tencent’s other businesses, such as obtaining resources from WeChat, Music, Gaming, and Video; but sometimes, it also needs to provide its own value.

Tencent News was very proactive about cooperation with Yuanbao. A Tencent News product manager told us that after integrating Yuanbao, they found users really liked @ing Yuanbao in the comment section and liked having Yuanbao help interpret content, with activity greatly improved, so they began frequently urging Yuanbao to update and iterate quickly, saying they could provide any support needed.

But interests between departments always have inconsistencies: new businesses value gateways, resources, and growth speed; old businesses controlling traffic and budgets need both to defend interests and prove they can transform.

Doubao’s success is inseparable from Douyin. This is a super traffic gateway with over 800 million daily active users and is also the most efficient advertising platform for user acquisition and growth domestically. A Douyin person revealed that ByteDance’s internal products buying traffic on Douyin go through an internal settlement system, with advantages in both cost and efficiency.

But Douyin doesn’t always keep its doors wide open to Doubao. In 2024, Doubao hoped to obtain a more direct product gateway in Douyin, but ultimately wasn’t approved. The surface reason was: Douyin’s volume is too large, and Doubao needs to figure out itself which users to select and how large a scale to test. The two sides repeatedly discussed for several months without forming consensus. Finally, Douyin’s suggestion was—first try in products with smaller volume, like Kesong, a community product benchmarked against Xiaohongshu.

This ambiguous, restrained attitude reflects the complex competitive-cooperative relationship within ByteDance. On one hand, Douyin hopes to firmly grasp AI capabilities and key gateways related to AI within the app; on the other hand, this also means higher uncertainty.

Management style sometimes also determines the collaboration mode between different teams.

A Tongyi Laboratory person said the Qwen team grew up in a corner with almost no attention, but with less interruption and less pulling, the team could focus energy on the model itself’s iteration. This also gave the team stronger motivation for cross-boundary exploration. “Job scope is one thing, what you can actually do is another,” said the person above.

In 2025, the Qwen model team formed an embodied intelligence group; at the same time, some were also advancing directions like voice and text-to-image, while Tongyi Laboratory already had teams doing similar research internally. Team boundaries became more blurred.

According to our understanding, Qwen is also recruiting infrastructure talent responsible for engineering-related affairs. In infrastructure division of labor, Qwen has been collaborating with Alibaba Cloud’s artificial intelligence platform PAI: Qwen does more agile development within a unified framework, while PAI focuses on usability and platformization integration. “But both sides have their own leaders and pursue their own indicators, making it very difficult to truly work as one,” said a Qwen large model team person.

“Qwen appears to be silently swallowing more businesses,” said the former Tongyi Laboratory person. In 2025, after merging from DAMO Academy into Tongyi Laboratory, multiple technical leaders successively left, including Huang Fei, former head of natural language processing direction at Tongyi Laboratory, Yan Zhijie, former head of the speech team, and Bo Liefeng, former head of the applied vision team.

A Tencent person told us that Hunyuan’s current thinking is similar to Qwen’s. Yao Shunyu mentioned the Co-design (joint design) R&D model at a recent internal meeting: model R&D shouldn’t only pursue efficiency at the algorithm level but should connect infrastructure, algorithms, and the product side to form an integrated development process, thereby shortening iteration cycles and reducing internal friction.

Unlike Alibaba’s Qwen team which leans more toward “bottom-up” exploration of infrastructure and algorithm linkage, Tencent directly placed the AI Infra department under Yao Shunyu’s management system.

“Relying on self-consciousness for collaboration over time will turn into pulling, requiring a new variable to break the deadlock,” said a Tencent person. “Yao Shunyu is this external force.”

Talent Arms Race: “Are There Still Positions?”

In the AI race, talent is a key resource, and no giant hides its desire for them.

We mentioned in our article “Revealing ByteDance’s HR System: The Rise and Fall of China’s Internet’s Most Extreme Talent Factory” that one of ByteDance’s habits in recruitment is creating talent maps for fresh graduates—inventorying all core majors at the top dozens of universities nationwide, then obtaining contact information for all undergraduate, master’s, and doctoral fresh graduates from these schools and majors through various channels. The hard requirement for frontline HR is that outreach rates for key majors at key universities must be above 80% for undergraduates and above 90% for master’s students.

In the early catch-up phase, ByteDance would relatively loosely issue offers in batches when recruiting AI talent. “Using this method to build an organization may not guarantee the ceiling, but at least can align basic capabilities neatly,” said a ByteDance recruitment team person.

After the business got on track, they focused on the most excellent talent, with vision extended to the global scope—recruiting at least 10-20 of the best graduates globally each year. They also had a new standard for “excellent”—people who have the opportunity to squeeze into OpenAI’s top 40.

They also launched a plan to recruit entrepreneurs. “The internal judgment is that the vast majority of AI startups will fail,” said someone familiar with the plan, so whether it’s the company’s top management or the HR department, they will continuously communicate with excellent entrepreneurs, persuading them to join ByteDance.

Another question ByteDance repeatedly ponders is how to escape the gravity of an organization with hundreds of thousands of people.

ByteDance began discussing doing AI business in the manner of startups in early 2024—the organization should be more independent relative to the old system, and compensation should also be closed-loop.

According to our information, fresh graduates entering Seed may directly obtain higher job levels, with compensation standards possibly higher than traditional businesses; the AI department’s performance evaluation is every six months, with Seed Edge focusing on frontier scientific research having even longer evaluation cycles and no strict mid-process evaluations. They also specially designed Doubao shares—a virtual stock incentive system built around Doubao and related AI businesses, enhancing core talent retention through granting Doubao shares and supporting option buyback mechanisms.

But over the past two years, quite a few technical talents have still left Seed. They have some commonalities—mostly scientists with strong academic abilities but insufficient engineering capabilities, and relatively sensitive to organizational environment. A ByteDance employee said their biggest challenge is that group of young people in the company who have energy, desire, and want to climb up—this high-intensity atmosphere forces many technical talents to leave.

Zhou Chang, former technical head of Alibaba’s Qwen, is the most stable among this group of technical talents recruited by ByteDance. He currently leads multimodal interaction and world models at Seed. “Because Zhou Chang isn’t a scientist, but simultaneously has many pursuits for technology that exceed current practice,” said the ByteDance employee above. In 2025, Zhou Chang also began managing the vision foundation team and vision multimodal team.

This atmosphere creates poaching opportunities for opponents. “In most cases, technical bigwigs in large companies are hard to poach, but you can observe their organizational state. For example, when a company’s horse-racing culture is particularly severe, someone will definitely feel uncomfortable, and that’s the opportunity to make a move,” said a large company recruitment team person.

In 2025, Tencent became the biggest talent catcher among internet companies. According to our statistics, Tencent recruited over a dozen technical talents from Microsoft, Alibaba, ByteDance, Moonshot, OpenAI, DeepSeek and other companies in one year, completely changing its previously cautious and conservative image.

For Tencent, insufficient AI talent density is a long-standing problem.

A Tencent employee told us that Tencent in the past emphasized product engineering more, lacking native AI research teams, and even more lacking overall AI research. “Doing AI without a research team is like making products without product managers—you’ll ultimately lose direction.” Under this configuration, teams easily move toward “safe follow-up” and leaderboard-only results.

Hunyuan’s 3D model is a reverse example. A Tencent Hunyuan employee told us that its leader personally has clear technical judgment, has the ability to convince management to approve projects, and doesn’t have too strong performance catch-up pressure, ultimately delivering a good answer sheet. As of now, the Hunyuan 3D model has over 3 million community downloads, making it the world’s most popular 3D open-source model.

Since 2024, the technical recruitment team has frequently appeared at various top academic conferences, seeking the world’s top AI talent; departed executives from overseas major companies are also key targets they’ve locked onto. When there are suitable candidates, Tencent President Martin Lau will also personally meet with them to communicate and persuade them to join Tencent.

They initially met Yao Shunyu at a top academic conference in the United States and established contact. Yao had no intention of leaving OpenAI at the time, but the two sides maintained communication for over a year afterward. In 2025, Yao Shunyu decided to return to China. “He proactively contacted Martin (Martin Lau), and both sides hit it off immediately,” said the person above.

In mid-2025, Tencent reorganized its AI Lab (AI Laboratory), recruiting several technical talents in the AI field, forming an organization model centered on “researchers.” An AI Lab person told us that each researcher focuses on a direction related to AGI and ASI, leading teams to explore topics with longer cycles and higher uncertainty. They have no hard assessment indicators, and research doesn’t need to be bound to Hunyuan model or Tencent’s businesses, pursuing more technical influence. This is similar to ByteDance’s AI research department Seed Edge.

A person familiar with both ByteDance and Tencent’s recruitment systems said ByteDance first grabs people in, and as for what they’ll do after joining, they match later. But talents aren’t fools either—many times their calculation is simple, just five words: are there positions available. For example, when Yao Shunyu left OpenAI, Tencent happened to have a position, but at ByteDance and Alibaba it would be very difficult to find space to play.

“Recruiting a good talent requires the right time, place, and people,” said the person above.

Rebuilding the Tower of Babel: Will AI Make the World More Open or More Closed?

In 2010, Wired magazine editor-in-chief Chris Anderson and author Michael Wolff wrote a judgment for the era: “The Web is Dead.” At that time, people were leaving the open web and turning to increasingly independent and powerful Apps. For Google, this was almost a fatal prophecy—“That’s a world Google’s crawlers can’t reach, a world no longer ruled by HTML.”

But later facts proved that technological change doesn’t necessarily overthrow the old order. Not only did it not shake Google’s dominant position, it even forced the company to develop even stronger dominance. Google used Chrome to remake the browser into a system-level mobile gateway, making the web browsing experience approach native Apps as much as possible; Android helped it seize mobile distribution rights, extending search, browser, and advertising advantages to the mobile era.

Six years later, Chrome’s monthly active users reached 1 billion, and Wired magazine had to overturn its own conclusion: “Wait! The Web Isn’t Dead After All. Google Made Sure of It” Today, Google’s market value is close to $4 trillion, making it the world’s second-largest commercial company after Nvidia, and AI will most likely further consolidate or even amplify the advantages it has accumulated in the past.

In China, the AI war among giants is erupting exactly at the turning point of paradigm reshaping. On one hand, traditional internet innovation is weak with no new stories to tell. Among non-AI internet applications launched in the past five years, only two have broken 100 million daily active users: ByteDance’s Fanqie and Hongguo. And their success is essentially still an extension of the “Douyin methodology.” Giants more urgently hope to find new breakthrough.

On the other hand, large companies’ dominance has further solidified in the stock competition of recent years. Douyin’s traffic siphoning effect is increasingly obvious, Hongguo has formed nuclear deterrence against long video platforms, and Douyin E-commerce has ended the chaos of live-streaming sales. In the early AI era, giants’ leading advantage over other companies is greater than in the early mobile internet era.

In 2024, a LLM star entrepreneur felt pressure when meeting with a giant company founder. The two were competing head-to-head in the same track, and the other’s demand was also very direct—hoping he would give up entrepreneurship and join their camp. “Even if you have a 20% chance of success, I would choose to support you,” said that giant company founder.

Many originally believed that LLMs were as important as recommendation algorithms once were, but unlike Taobao and Douyin each guarding their own closed-source algorithms, Alibaba and DeepSeek have open-sourced LLMs, and entrepreneurs are only a few lines of code away from the most advanced intelligence. But reality presents another scene.

An AI entrepreneur calculated an account for us: ByteDance, Alibaba, and Tencent all have GPU scales basically above 100,000 cards. For startups, an 8-card H100 server’s monthly rent is about $10,000; by the conservative caliber of $10,000 annual cost per card, 100,000 cards would require about $1 billion investment per year—and this is only the minimum threshold to stay at the table when competing with giants.

The gap in data accumulation is even more significant. “Of all the data in the world, only 3% can be found on public clouds; the remaining 97% is either offline cold data or held in private enterprises,” said a ByteDance employee. “The reason ByteDance and Kuaishou are so strong in video models is precisely because they have data.”

From a scenario perspective, WeChat has a huge mini-program ecosystem, with large numbers of developers and merchants providing services, and even 70% of people’s government services can be completed on WeChat—this is WeChat’s natural advantage for Agent-ization. “These ecosystems most likely have little motivation to go to a new platform, sign a new set of agreements and build a new system,” said a WeChat emoloyee.

“Why doesn’t ByteDance invest much externally? Because it feels there’s nothing it can’t do,” said an investor at a leading angel investment fund.

Three years after the U.S. market was ignited by ChatGPT, hundreds of startups were established, raised funding, and explored new products, but so far there haven’t been enough good applications to bring AI close to everyone. On the contrary, giant Google’s Gemini, and ChatGPT deeply bound to giants, are swallowing more possibilities.

Over the past thirty-plus years, the scientific community and tech companies have continuously created new technologies that seemingly can deliver beautiful visions, but they still inevitably become alienated in commercial competition—the internet’s original intention was efficient interconnection, but in fact it cut the world more fragmented. Various platforms formed their own independent account, content, and recommendation systems, with algorithms continuously subdividing interests, ultimately pushing people into information cocoons and circles.

In December 2025, ByteDance released the Doubao Phone Assistant preview version, deeply embedding the Doubao large model into the phone system, bypassing various Apps, allowing users to complete operations that originally required repeated clicking with voice or light taps; a month later, Qwen App, which had been online for less than two months, announced high-profile integration with Taobao Flash Sale, Alipay, Fliggy, and Gaode. Its “task assistant” function can complete multi-step tasks for users, such as ordering food and making calls, organizing reports, processing financial documents, and even developing websites.

A ByteDance employee said that when users mainly obtain information or complete tasks through a dialogue box, on the surface it seems freer to do things with one sentence, but there are actually fewer control points determining what they can see, what they can do, and in what order various services appear, with more platform-determined rules emerging.

“Humans always try to unite everyone with a unified, grand plan, but perhaps evolution and development naturally resist singular order,” said the person above. “We may now be in the process of building another Tower of Babel.” In legend, the Tower of Babel was humanity’s “unification project” attempting to reach heaven using the same language, but God made people’s languages split and no longer understand each other, causing the project to collapse.

Anthropic CEO Dario Amodei wrote in “Machines of Loving Grace” that AI might bring humanity to a more humane, more abundant future. But he is equally vigilant: some people discussing AI risks in public discourse—not to mention AI company leaders—often describe the arrival of Artificial General Intelligence (AGI) like a “personal mission,” as if they want to lead humanity to salvation single-handedly like prophets.

“It’s dangerous to view companies as unilaterally shaping the world,” Dario Amodei said.

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At a high-profile AI event held on January 10, 2026, in Beijing, co-organized by Tsinghua University and Zhipu AI, a headline-making panel brought together leading voices from industry and academia.

The discussion featured Professor Tang Jie, chief scientist and co-founder of Zhipu AI; Professor Yang Qiang of HKUST; Lin Junyang, technical lead of Alibaba’s Qwen team; and Yao Shunyu, Tencent’s newly appointed chief AI scientist who was formerly an OpenAI researcher.

It was a rare moment in which several of China’s top AI figures appeared together in an open, public conversation. Li Guangmi, the CEO of Shixiang Technology, is one of China’s most active commentators and advocates for AI. I highly recommend his quarterly review of generative AI technologies with Benita Zhang Xiaojun.

The panel discussed how Chinese large (language) models can differentiate, the next paradigm for AGI such as autonomous learning, agent strategies, and the future direction of China’s AI ecosystem. For people interested in China’s AI industry, the discussion offered an unusually candid window into the current dynamics.

Of course the most headline-grabbing remarks came from Lin Junyang, who said Chinese companies have less than a 20% chance of becoming the most leading AI players in the next three to five years. In a separate speech at the event, Tang Jie added that the gap between the U.S. and China in AI is, in fact, continuing to widen.

Below is a full translation of the panel using Claude. The original transcript is sourced from Zhipu AI.

Li Guangmi (Moderator): I’m Guangmi, the moderator for the next panel. I was listening from the audience just now and had a few impressions.

First, Professor Tang has strong appeal—Tsinghua’s talent pool is excellent, not just domestically but overseas too; the proportion of Tsinghua alumni is very high.

Second, my impression from listening to several talks is: “not just following, not just open source”—everyone is exploring their own next paradigm, and not just coding; everyone is exploring their own product forms.

This timing is particularly interesting. 2025 is actually the year when Chinese open-source models “shine brightly.” The “Four Open-Source Masters开源四杰” (DeepSeek, Alibaba’s Qwen, Moonshot AI’s Kimi, Zhipu AI’s GLM) have achieved tremendous success globally, and coding has seen 10-20x growth over the past year. Even overseas, people are asking where Scaling has reached and whether new paradigms have emerged. So today’s event, and this panel discussing “where do we go from here,” is especially interesting.

1. How Will Chinese Large Models Differentiate?

(In China, people generally refer to large-size generative AI models, regardless of whether they are text, speech, video, or 3D, as large models大模型).

Li Guangmi (Moderator): Let’s start with the first interesting topic: differentiation. Silicon Valley companies are clearly differentiating. I think we can start the conversation with this “differentiation” theme.

Anthropic has been a huge inspiration for Chinese model companies. Despite fierce competition in Silicon Valley, they didn’t follow everyone else entirely but focused on enterprise, on coding, on agentic systems. So I’ve been thinking: what directions will Chinese models differentiate toward? I think differentiation is quite an interesting theme. I see Shunyu is online—Shunyu, why don’t you start by telling everyone what you’ve been busy with lately?

Yao Shunyu: Hello everyone. Am I appearing as a giant face at the venue right now? (Audience laughs) Sorry, I couldn’t make it to Beijing today in person, but I’m happy to participate in this event. Recently I’ve been busy building models and products—I think that’s just the normal state of doing AI. Coming back to China feels pretty good; the food is much better.

Li Guangmi (Moderator): Shunyu, can you expand on your thoughts about “model differentiation”? Silicon Valley is also differentiating—for example, Anthropic focused on coding, many Chinese models went open source, coding capabilities have also grown rapidly, and Google Gemini didn’t do everything either; it first excelled at multimodality. Your former employer focuses on consumer-facing products. Since you’ve experienced both China and the US, can you share your perspective? How are you thinking about differentiation going forward, whether for yourself or for other companies?

Yao Shunyu: I have two major observations. One is that To C and To B have clearly differentiated. The other is that the “vertical integration” path and the “model and application layer separation” path are also beginning to diverge.

Let me address the first point. Clearly, when people think of AI super apps now, they think of two: ChatGPT and Claude Code. You could consider them exemplars of To C and To B respectively. But what’s interesting is that when we use ChatGPT today versus last year, for most people most of the time, the perceived changes aren’t that dramatic anymore. But conversely, with Claude Code, the coding revolution perhaps hadn’t started a year ago, but this year—to put it somewhat dramatically—it’s already reshaping how the entire computer industry works. People are no longer writing code; they’re communicating with computers in English.

The key point is that for To C, most people most of the time don’t actually need such strong intelligence. Today’s ChatGPT compared to last year might be better at abstract algebra or solving Galois theory, but most people can’t feel it. Most people, especially in China, are still using it more like an enhanced search engine. Often you don’t know how to use it to activate its intelligence.

But for To B, it’s very clear that higher intelligence often means higher productivity, which means you can earn more money—everything is connected. Another obvious point for To B is that most of the time, many people are willing to use the strongest model. Maybe one model costs $200 per month, while the second-best or slightly worse model costs $50 or $20 per month. We’re finding that many people, at least in America, are willing to pay that premium for the best model. Because maybe their annual salary is $200,000, they have 10 tasks to do each day, and a very strong model like Opus 4.5 might get 8 or 9 out of 10 tasks right, while a weaker model might only get 5 or 6 right. The problem is, when you don’t know which 5 or 6 those are, you have to spend a lot of extra effort monitoring things. So in the To B market, the differentiation between strong models and slightly weaker ones will become increasingly pronounced. That’s the first observation.

The second observation is the difference between vertical integration and model-application layer separation. A good example might be ChatGPT Agent versus using Claude or Gemini with an application-layer product like Manus. In the past, people believed that if you had vertical integration capability, you’d definitely do better, but at least looking at today, that’s not necessarily true. First, the capabilities needed at the model layer and application layer are quite different. Especially for To B or productivity scenarios, larger pre-training is still a crucial thing, and this is indeed difficult for product companies to do. But to make good use of a really good model, or to enable such a model to have spillover capabilities, actually requires doing a lot of corresponding work at the application side or the environment side. So we’re finding that for To C applications, vertical integration still holds. Whether it’s ChatGPT or Doubao, the model and product are very tightly coupled for iterative development. But for To B, the trend seems to be the opposite. Models are becoming stronger, but there will also be more application-layer things wanting to leverage these good models to play a role in different productivity segments. These are my two observations.

Li Guangmi (Moderator): Let me follow up with Shunyu on one question. Since you have a new identity (Tencent), in the Chinese market going forward, what are your bets or priorities? What distinctive features or keywords can you share with everyone?

Yao Shunyu: Yes, I think Tencent is definitely a company with stronger To C DNA. So we’re thinking about how today’s large models or AI development can provide more value to users. But there’s a core consideration: we’ve found that many times our bottleneck on the To C end isn’t a larger model, or stronger reinforcement learning, or a stronger reward model—many times it’s additional context and environment.

An example I often give recently: suppose I want to ask “what should I eat today?” Whether you ask ChatGPT today, last year, or tomorrow, the experience will probably be poor. Because to make it better, you don’t need a larger model or stronger pre-training. The bottleneck for this question might be that you need more additional input, or context. For example, if it knows “ah, I’m particularly cold today, I need something warm,” and “I’m in this area today,” and maybe “my wife is somewhere else, what does she want to eat,” etc. Actually, answering such questions has more of a bottleneck in additional context. For instance, if I’ve chatted with my wife for many days, we can actually forward chat records from WeChat to Yuanbao, or if we can make good use of these additional inputs, it would actually bring a lot of extra value to users. This is our thinking on To C.

Then doing To B in China is indeed very difficult. The productivity revolution, including many Chinese companies today making coding agents, actually have to target overseas markets. In this regard, we’ll think about how to serve ourselves well first. One difference between startups doing coding and large companies doing coding is that large companies already have many application scenarios and various places where productivity needs to improve. If our model can do better in these places, not only will the model have its unique advantages, but more importantly, capturing data from more diverse real-world scenarios will be a very interesting thing. For example, Anthropic as a startup wanting to get more coding agent data needs to go through data vendors to label data. These data vendors need to use software engineers to figure out “what kind of data should I label?” The bottleneck there is that there are only so many data companies, only so many people, and diversity is limited. But if you’re a company with 100,000 people, there might be some interesting attempts at truly leveraging real-world data, rather than just relying on annotation vendors or distillation.

Li Guangmi (Moderator): Thanks, Shunyu. Let me cue Junyang next. How do you see Qwen’s future ecological niche or differentiation bet? Because you focused on the multimodal direction later. Previously, Alibaba Cloud was very strong in To B, so going forward, you also mentioned multimodality might be more To C-focused. How are you thinking about this?

Lin Junyang: Technically I can’t comment on the company’s strategy. But I don’t think companies necessarily have that many genetic divisions—each generation of people might shape these companies.

I also want to inject some of our own understanding of AGI into the next statement. Because I think today, whether To B or To C, we’re actually serving real humans. So the question we’re thinking about is how to make the human world better. Even if you make To C products, they’ll still differentiate. For example, OpenAI has become more like a platform today, but if you’re To C, ultimately who are the real users you want to serve?

Today there may be many AIs that lean more toward medical, more toward law, but it might form naturally. I’m willing to believe Anthropic probably didn’t say “today I think coding is really great, so I’ll bet on it.” Because I know they communicate with business very frequently. This might be something we ourselves haven’t done well enough, although we have huge advantages. Of course, it’s also possible that China’s SaaS market is indeed quite different from America’s—they communicate with customers very frequently and can easily discover these huge opportunities.

When I talk to many API vendors in America today, none of them expected coding token consumption to be this large. In China, it’s really not that large, at least from what I see. But in America, it’s basically all coding. I don’t think everyone could have bet on this. Today Anthropic is doing more finance-related things, which I think are also opportunities they saw while communicating with customers. So I think everyone’s differentiation might be natural differentiation. So I’m more willing to believe AGI should do what AGI should do, and let things develop naturally.

Li Guangmi (Moderator): Thanks, Junyang. Professor Yang, how do you view the differentiation question?

Yang Qiang: Regarding differentiation, I actually want to discuss the differentiation between industry and academia more. This spans both America and China. Academia has always been an observer, while industry has been leading and racing ahead madly. This has led many academics to also do industry work, like Professor Tang Jie. This is a good thing—just like how astrophysics initially started with observation, Galileo’s telescope, before Newton emerged. So in the next phase, when we have numerous stable large models entering a steady state, academia should catch up. What problems should we solve when we catch up? Issues that industry perhaps hasn’t had time to solve yet. This is also something I’ve been considering—where is the upper bound of intelligence? For example, given certain resources, computing resources or energy resources, how good can you do?

We can be more specific. For example, how do we allocate these resources? How much goes to training, how much to inference? I’ve been doing AI since the early 1990s and did a small experiment: if we have certain investment in memory, how much can that memory help reasoning? And will this help become counterproductive—for example, if we remember too much noise, will it actually interfere with reasoning? Is there a balance point? These questions are still applicable today and are fundamental issues.

Recently I’ve also been thinking about another problem. Those who studied computer science have all taken theory courses—there’s an important theorem called Gödel’s incompleteness theorem. Roughly speaking, a system, like our large model, cannot prove its own consistency; there must be some hallucinations that are impossible to eliminate. Maybe with more resources, it can eliminate more. The scientific question comes: how many resources can buy how much reduction in hallucinations? This is a balance point, very much like the risk-return balance in economics. So we also call it the “no free lunch theorem.” These things are particularly suitable for mathematics, algorithms, and industry to research together today, which harbors huge breakthroughs.

Professor Tang also mentioned continual learning just now. I think continual learning is a particularly good problem. It has a concept of time. In the continual learning process, you’ll find that if you chain different agents together, and each agent can’t achieve 100%, then after N agents, capability decreases exponentially.

How do we ensure it doesn’t decrease? I think humans are a sample. For example, the first day is learning, the second day you learn on top of the first day’s noise, and your capability will decline like a large model. But humans have a method to solve the decline: sleeping.

I recommend everyone read a book called “Why We Sleep.” It says sleeping every night is actually clearing noise, so the next day you can continue to improve accuracy rather than accumulating errors. These theoretical studies harbor a new computing paradigm. Today we might focus more on Transformer and Agentic Computing, but I think it’s necessary to do some new explorations. This is my answer—industry and academia need to align.

Li Guangmi (Moderator): Thank you, Professor Yang. Professor Tang, from an external perspective, Zhipu today looks more like it’s following the Anthropic path—coding is very strong, ranking high on leaderboards, including the Long Horizon agents you mentioned. How do you view this differentiation theme?

Tang Jie: I think we should return to the most fundamental question. In the early days, the most fundamental thing was indeed the foundation model and the upper bound of intelligence. In 2023, we were the first to launch Chat. The first thought at the time was to get it online quickly. Of course, later there were relevant unified regulations from the government, so we waited until August-September when everyone launched together. When everyone launched together, my first feeling was that about ten large models came online, and each one didn’t have that many users. Of course, today the differentiation is more severe.

After a year of thinking, I actually feel this might not truly solve the problem. My first prediction was that it would replace search. Today many people use models to replace search, but they haven’t replaced Google. Google actually revolutionized its own search instead. From this perspective, this battle has been over since DeepSeek came out.

So after DeepSeek came out, we should think about what the next bet is. What’s the next battle? I think the next battle must be to make AI do something. But what that something is can indeed be a bet. At that time, Guangmi even came to our place to exchange ideas. Later our team debated for many nights, and ultimately—you can call it our luck, but on the other hand, we also bet on coding. Later, we put all our energy into coding.

2. The Next Paradigm for AGI

Li Guangmi (Moderator): I think betting is a particularly interesting thing. Over the past year, China has not only been strong in open source, but everyone has made their own bets. Next comes a second interesting question. Pre-training has gone on for three years, and people say it’s reached 70-80% of its returns. RL reinforcement learning today has become consensus and may have reached 40-50% of its potential. Today Silicon Valley is also discussing the next new paradigm—Professor Tang just mentioned autonomous learning and self-learning. Let’s start with Shunyu.

You worked at OpenAI—how do you think about the next paradigm? OpenAI is a company that has advanced the first two paradigms for humanity. Regarding the third paradigm, from your perspective, can you share something with everyone?

Yao Shunyu: Autonomous learning is extremely hot in Silicon Valley right now—on Silicon Valley streets, in coffee shops, everyone is talking about it; it’s almost become consensus. But from my observation, each person’s view on this might be different. I’ll make two points:

First, I think the bottleneck for this isn’t methodology, but data or tasks. When we talk about autonomous learning, under what scenarios and based on what reward functions are we doing it? For example, when chatting, AI becoming personalized is a type of autonomous learning; when writing code, familiarizing with the company environment is autonomous learning; exploring new science, in the process becoming an expert from knowing nothing, like a PhD student, is also autonomous learning. Each type of autonomous learning might have different challenges or methodologies.

Second, I don’t know if this is non-consensus, but this is actually already happening. ChatGPT using user data to fit conversation styles, making the feeling better and better—isn’t that a kind of self-learning? Today Claude Code has already written 95% of Claude Code’s own project code, helping itself become better—isn’t that self-learning?

I remember when we were doing SWE-agent (Software Agent) in 2022-23, I went to AGI House to promote it, and the first slide in the introduction said the most important point of ASI is autonomous learning. Today’s AI systems essentially have two parts: one is the Neural Network (model); the other is the codebase: how you use this model, whether for reasoning or for agents.

Looking at the Claude Code system, it essentially has two parts: one part is the Opus Neural Network, the other part is a bunch of corresponding code for how to use this Network—whether it’s Kernel GPU, deployment environment, or frontend. If we’re doing Software Agents, if one day it can improve its own repo, isn’t that a kind of AGI? I think today Claude Code is already doing this on a large scale; people just don’t realize it, or it’s limited to specific scenarios. I think this is already happening; it might be more like a gradual change rather than a sudden change.

Li Guangmi (Moderator): Follow-up question. Some people are quite optimistic about autonomous learning, thinking we can see signals in 2026. What actual breakthroughs do you think are still needed? For example, Long Context, or model parallel sampling? From your perspective, what other key conditions need to be in place before these signals occur?

Yao Shunyu: Many people say we can only see these signals in 2026, but I think there were already some signals in 2025. For example, Cursor—their auto-complete model learns with the latest user data every few hours. Including the new Composer Model, it’s actually using real-environment data for training. Of course, people feel these things aren’t particularly earth-shattering yet—this is limited by them not having pre-training capability, so effects aren’t as good as Opus—but I think this is obviously already a signal.

I think the biggest bottleneck is imagination. For reinforcement learning or reasoning, we can easily imagine what implementation looks like—for example, O1 going from 10 points to 80 points on math problems. But if a paradigm happens in 2026 or 2027, announcing autonomous learning has been achieved, what task should we use to verify it? Is it becoming a money-making trading system, like an extension of Trading Bench? Or solving scientific problems? We need to first imagine what the blog post would look like at that time.

Li Guangmi (Moderator): Shunyu, OpenAI has already led two paradigm innovations. If there’s a new paradigm in 2026-27, globally, which company do you think has the highest probability of leading this innovative paradigm?

Yao Shunyu: Maybe OpenAI’s probability is still higher. Although its innovation genes have been weakened due to commercialization and other changes, I think it’s still the place most likely to give birth to a new paradigm.

Li Guangmi (Moderator): Junyang, what’s your expansion on the next paradigm, on 2026?

Lin Junyang: If we speak more practically, the paradigm just mentioned is still in early stages. RL compute hasn’t scaled that sufficiently, so a lot of potential hasn’t been realized—today we still see many infrastructure problems happening. Globally, similar problems still exist everywhere.

If we talk about the next generation paradigm, one is autonomous learning. We discussed earlier that “humans can’t make AI stronger”: if you continuously interact with AI, it will only make the context longer and dumber. Whether test-time scaling can truly happen, I think is really worth thinking about. Can it become stronger through more tokens? I think at least the O series has achieved this to some extent. Whether it’s possible, as Anthropic said, to “work for 30 hours” and truly accomplish very difficult tasks—I think what people are doing today with AI Scientists is actually quite meaningful, challenging some very difficult things, even things humans haven’t achieved. Can this be realized through test-time scaling? From this perspective, AI definitely needs autonomous evolution, but whether you need to update parameters—that’s debatable; everyone might have different technical means to achieve this.

I think there’s a second point: can AI achieve stronger initiative? That is, can the environment become my input signal? For example, this AI now must have humans prompt it before you can activate it. Is it possible that the environment itself can prompt it? It can think autonomously and do things.

But this raises a new problem: safety. I’m very worried about the safety problem—I’m not really worried about it saying things it shouldn’t say today; what I’m most worried about is it doing things it shouldn’t do. For example, if today it spontaneously generates ideas like throwing a bomb into this venue. We definitely don’t want these unsafe things to happen. But just like raising children, we might need to inject some correct direction. But active learning might also be quite an important paradigm.

Li Guangmi (Moderator): Yes. Junyang raised “active (learning),” which could also be a very critical bet for 2026.

Let me follow up with Junyang: If autonomous learning shows signals in 2026, what tasks do you expect to see it in first? Will it be “models training models,” where the strongest model can improve itself? Or automated AI researchers? Where do you expect to see it first?

Lin Junyang: I think automated AI researchers might not even need autonomous learning that much. I think “AI training AI” might be realized very soon. Looking at what our team members do every day, I think Claude Code could replace them very soon.

But I think it might be more about continuously understanding users—for example, personalization is actually quite important. In the past when we did recommendation systems, user information was continuously input, which made the entire system stronger. Although the algorithm was actually very simple, today in the AI era, can it understand you better? Can your information input become the best... We used to talk about Copilot, but actually even Copilot hasn’t been achieved today—the question is whether it can truly become my Copilot.

So I think if we talk about autonomous learning, it might happen in interaction with people. For example, personalization might be achievable, but what metric to use for measurement is a bit hard to say. Because in the recommendation era, the better your personalization, the more people might click and buy. But in the AI era, when it covers all aspects of human life, we don’t really know what the true personalization measurement metric is. So today I feel the bigger technical challenge might be: we don’t know how to do evaluation today. This might be a problem more worth researching.

Li Guangmi (Moderator): Junyang, since you mentioned active (learning) and personalization, do you think if we achieve “memory,” can we see a major technical breakthrough in 2026?

Lin Junyang: My personal view is that a lot of so-called “breakthroughs” in technology are actually observation problems—they’re actually linear developments; it’s just that human perception of them is very strong. Including the emergence of ChatGPT, for those of us working on large models, it was actually linear growth.

Now everyone is working on memory. Is the technical solution right or wrong? I think many solutions don’t really have right or wrong. But the effects produced—at least let me embarrass ourselves: our own memory seems to know what I did in the past, but it just recalls past things and calls my name every time, which doesn’t actually make you seem smart.

But can your memory reach a certain critical point where people feel that, combined with your memory, you can truly be like people in real life—remember how everyone talked about the movie “Her”? It’s truly like a person. Understanding your memory might be that moment when human perception suddenly feels it burst forth. That might be that moment.

I think it will take at least a year. Many times I actually think technology doesn’t develop that fast; it’s just that everyone is quite competitive and feels there are new things every day, but actually technology is developing linearly. We might just be in an exponential rise stage from an observation perspective. For example, a slight improvement in coding ability might bring a lot of production value, so people might feel AI is developing very fast. But from a technical progress perspective, we might have just done a little bit more. Looking at what we do every day—those bugs we fix—are really quite crude; we’re too embarrassed to share them with everyone. Very ugly.

If we can already achieve such results doing this, then I think if algorithms and infrastructure combine better in the future, there’s much more potential.

Li Guangmi (Moderator): Thanks. Professor Yang.

Yang Qiang: I’ve always been doing federated learning. The main idea of federated learning is “multiple centers, everyone collaborates.”

I’m increasingly seeing that many local resources are insufficient, but local data has many privacy and security requirements. So we can imagine that now large models are becoming more capable—how can these general-purpose large models collaborate with local specialized small models or domain expert models?

I think this kind of collaboration is becoming increasingly possible. Like in America, I see Zoom—what Xuedong Huang and his team did is called Federated AI system. He made a large base that everyone can plug into. Then he can work in a decentralized state, able to both protect privacy and effectively communicate and collaborate with general large models.

I think this open-source model is particularly good—one is knowledge open source, another is code open source. So I think especially in scenarios like healthcare and finance, we’ll increasingly see this phenomenon occur.

Li Guangmi (Moderator): Thanks, Professor Yang. Professor Tang.

Tang Jie: I’m actually quite positive about significant paradigm innovation this year. I won’t go into too much detail because those points I... as I mentioned earlier, including continual learning, memory, even model architecture, even multimodality—I think all could see new paradigm changes.

But I think there’s a major trend. Let me talk about why such a paradigm would emerge. I think originally industry was running far faster than academia. I remember last year and the year before, when I returned to Tsinghua and chatted with many professors about whether they could do large models, many professors first had no GPUs... not that they had no GPUs, but the number of GPUs was almost zero. Industry had 10,000 cards, schools had 0 or 1 card—that’s a 10,000x multiple.

But now, many schools already have many cards, and many professors have started doing a lot of large model-related research, including many professors in Silicon Valley who have started researching model architecture, continual learning, and related topics. So it’s no longer... we used to always feel industry was dominating these things, but actually today I think at the end of 2025 to early 2026, this phenomenon no longer exists. There might still be a 10x difference—10,000 cards there, 1,000 cards here—but seeds have already been incubated. I think academia has this innovative gene, this possibility now. That’s the first point.

Second, I think for an innovation to emerge, there must be massive investment in something, and its efficiency becomes the bottleneck. Now in the entire large model space, investment is already huge, but efficiency isn’t high. That is, if we continue scaling, is there return? There’s definitely return. For example, our data originally might have been 10T in early 2025, now 30T, and we can even scale to 100T. But after you scale to 100T, how much is your return? And what’s your computing cost? This becomes a problem. If you don’t innovate, this might mean you spend 1 or 2 billion dollars, but the return is very small—not worth it.

On the other hand, for new intelligence innovation, if each time we have to retrain a foundation model, then retrain a lot of RL... Like when RL came out in 2024, everyone thought they’d just continue training, and the return was relatively good. But today if you continue crazily doing RL, there is return, but it’s not that significant anymore—still a return efficiency problem.

So in the future we might set a standard: on one hand, we need Scaling Up—I actually said on stage just now, “the dumbest method is Scaling,” because with Scaling we definitely have returns. This is a typical engineering approach. Scaling will definitely bring improvement to the upper bound of intelligence; there’s no doubt as long as you get more data. But the second method, I think we should set something called Intelligence Efficiency—that is, the efficiency of obtaining intelligence, how much investment we need to obtain intelligence increments. If we can use less investment to obtain increments, and we’ve now become a bottleneck—if we can use a new paradigm to obtain the same intelligence improvement, this is a bottleneck-type thing. So I feel 2026 will definitely have such a paradigm emerge.

Of course, we’re also betting, we’re also working hard, we hope this happens to us, but it might not.

Agent Strategy

Li Guangmi (Moderator): Right, I’m as optimistic as Professor Tang. Because actually each leading model company’s annual compute has compound growth of about 10x each year. Everyone has more computing resources, more and more talent is flowing in, everyone has more cards and does more experiments—it’s actually an experimental engineering project; some point might emerge.

Professor Tang also discussed a point about how to measure intelligence level. I think thirdly we can discuss Agent strategy together. Recently I’ve talked with many researchers, and everyone mentioned a big expectation for 2026: today Agents can reason in the background for 3-5 hours, doing 1-2 days of human work; everyone expects that in 2026 they can do a normal human’s work for a week to two weeks.

This is also a very big change because it’s no longer just a tool—as Professor Tang mentioned, not just Chat—but truly automating your entire day or even week’s task flow. So 2026 might really be the key year when Agents “create economic value.”

So we can expand on this Agent question. Everyone also mentioned “vertical integration” that Shunyu brought up—having both models and Agent products. Including seeing several companies in Silicon Valley also doing end-to-end from model to Agent.

Shunyu, you spent a lot of time on Agent research. For 2026’s Agents, like Long Horizon Agents that can truly automate one to two weeks of human work—this Agent strategy, including from a model company’s starting point, how would you think about this issue?

Yao Shunyu: I think it’s still like what I said earlier—it might be different for To C and To B.

Currently it looks like the To B situation has reached a continuously rising curve with no signs of slowing down. Anthropic is a very interesting company—it basically doesn’t do much innovation. It just thinks: model pre-training gets bigger, then honestly do RL well. As long as pre-training keeps getting bigger and post-training keeps doing real-world tasks well, it will get smarter and smarter, bringing more and more value.

I think what’s particularly interesting about Anthropic is that from a certain perspective, doing To B actually means all goals are more aligned. The higher your model intelligence, the more tasks you solve; the more tasks you solve, the greater the revenue in To B. This is great.

The problem with To C is that we all know DAU or product metrics are actually often unrelated to model intelligence, even opposite. I think this is another important reason Anthropic can focus: as long as they truly make the model better and better, revenue gets higher and higher—everything is completely aligned.

Currently I think productivity Agents are just beginning. Now besides models there might be two bottlenecks: one is the environment issue, or deployment issue.

Before OpenAI, I interned at a company called Sierra, a To B customer service company. Working at a To B company had many gains. The biggest gain was that I feel even if models today stopped improving, all model training completely stopped, but we deploy these models to companies all over the world, it could probably bring 10x or 100x today’s returns, or could have a 5-10% impact on GDP. But today I think its impact is far from 1%.

The second point is that education is very important. I observe that the gap between people is widening—it’s not that AI will replace people’s jobs, but rather that people who can use these tools are replacing those who can’t. Just like when computers were first invented, if you turned around to learn programming versus still using slide rules and abacuses, that’s a huge difference. I think the most meaningful thing China can do now is actually better education—teaching everyone how to better use products like Claude Code or ChatGPT. Of course Claude Code might not be usable in China, but we can use domestic models like Kimi or Zhipu.

Li Guangmi (Moderator): Thanks, Shunyu. Junyang, your thoughts on Agents, including because Qwen also has an ecosystem—Qwen doing Agents itself and supporting ecosystem general Agents—you can expand on this.

Lin Junyang: This involves a product philosophy question. Of course Manus is indeed very successful, but whether “wrapper” approaches are the future is itself a topic. I think at this timing today, I actually quite agree with your view: “model as product.”

I talked with TML people—they call it “Research as a Product.” I actually quite like this. Including looking at OpenAI, quite a few researchers themselves can become product managers to do things end-to-end. Today our own internal researchers all want to do more things facing the real world.

I’m actually willing to believe that Agents going forward can achieve what was just mentioned, and it has quite strong relationships with the self-evolution and active learning mentioned earlier. For example, if it can work for so long, it actually has to evolve in the process, and it has to decide what to do, because the instruction it receives is a very general task. So our Agents now are actually starting to become more “trusteeship-style” Agents, rather than the form where I have to constantly interact back and forth with you.

From this perspective, the requirements for models are actually very high—the model is the Agent, the Agent is the product itself. If they’re integrated, then doing foundation models today is actually doing this product. If we continuously improve the model’s capability ceiling, including if test-time scaling can improve, we can indeed achieve this.

But I think there’s another point related to environmental interaction. The environments we’re interacting with now aren’t very complex; they’re all computer environments. I have friends doing things more related to AI for Science. For example, today if you do AlphaFold, it’s not actually at that step yet. For drug development, even if you use today’s AI, it might not help you that much. Because you have to do wet lab experiments, you have to do these things to get feedback.

Is it possible that in the future AI’s environment complexity could be the real human world environment, commanding robots to do wet experiments to accelerate efficiency? Otherwise, according to current human efficiency, it’s actually very low. We even have to hire many outsourcers to do experiments in lab environments. If we can reach this point, that might be what I imagine Agents being able to do human work for a long time, rather than just writing files on computers.

I think these things might be completed very soon this year. But I think in the next three to five years, this might be more interesting. Then this would have to combine with embodied intelligence.

Li Guangmi (Moderator): I want to follow up with Junyang with a sharper question: From your perspective, is the opportunity for general Agents for entrepreneurs? Or is it a matter of time before model companies do general Agents well?

Lin Junyang: I can’t become a startup mentor just because I do foundation models; I can’t do that. So I can only borrow what Peak (Manus CTO) said—he said: what’s most interesting about general Agents is the long tail; that’s what’s more worth paying attention to, or what’s AI’s greater charm today is in the long tail.

If there’s a Matthew effect, the head stuff is actually quite easy to solve. When we did recommendations back then, we saw recommendations were very concentrated in head products. But we wanted to push the tail, but I was really miserable doing it then. As someone doing NLP and multimodality, encountering recommendation systems to solve the Matthew effect was basically heading toward a dead end.

But I think today’s so-called AGI is actually solving this problem—whether you’re doing general Agents, can you solve the long tail problem? Today as a user, I’ve searched everywhere and can’t find anyone who can help me solve this problem. But in that moment, I felt AI’s capability—anywhere in the world, I’ve searched everywhere and can’t find it, but you can help me solve it. That might be AI’s greatest charm.

So should you do general Agents? I think it depends. If you think you’re a “wrapper expert” who can wrap better than model companies, I think you can do it. But if you don’t have that confidence, this might be left for model companies to do “model as product,” because when they encounter problems, I just need to train the model a bit, burn some cards, and the problem might be solved. So it depends.

Li Guangmi (Moderator): Actually solving long tail problems, it seems model companies solving them with compute and data is also quite fast, right?

Lin Junyang: What’s most interesting about RL today, I think, is we found fixing problems is easier than before. It was very hard to fix problems before. I’ll give an example of a B-side customer situation—they said they wanted to do SFT themselves, can you tell us how to mix general data? Each time we were very troubled. And we felt the other party wasn’t very good at SFT; their data was very garbage, but they might think it’s very useful.

But today with RL, you might really only need a very small data point, not even needing annotation—as long as you have Query and Reward, train it a bit, merge it together—it’s actually very easy. That might be today’s technological charm.

Li Guangmi (Moderator): Thanks, Junyang. Professor Yang.

Yang Qiang: I think Agents should have four stages.

One is goal definition—is it defined by humans or automatically?

Second is planning—the actions in between. Planning can also be defined by humans or automatically by AI.

So this naturally divides into four stages. I think we’re now at a very elementary stage where goals are defined by humans and planning is done by humans. So current Agent definition systems are basically a higher-level Very High-level Programming Language.

But I predict in the future there will emerge large models observing human work, especially using human process data. Ultimately goals can also be defined by large models, planning can also be defined by large models. So Agents should be a native system endogenous to large models.

Li Guangmi (Moderator): Thanks, Professor Yang. Professor Tang.

Tang Jie: I think indeed several factors determine Agents’ future direction.

First, does the Agent itself solve human problems? Is this thing valuable? How valuable? For example, when GPTs came out, many Agents were made. At that time you’d find Agents were all very simple, and ultimately found prompts solved it. Most of those Agents gradually died. So the first is how valuable is solving this thing, can it help people.

Second, what’s the cost of doing this? If the cost is particularly high, that’s also a problem. As Junyang said, maybe calling an API can solve it. But conversely, if calling an API can solve it, then that API itself might think this thing is very valuable and incorporate it. This is a contradiction—foundation and application are always contradictory.

The last dimension is application speed. If you say I have a time window, I can open a six-month time window, quickly meet the application, six months later either iterate or whatever, anyway can move forward. To put it bluntly, in the large model era until now, more is competing on speed, competing on time.

Maybe a decision is correct—like you just said maybe we bet on code correctly, then maybe we’ll go further in this regard. But maybe if the bet fails then it’s half a year, half a year is gone. So this year we only bet a little on Coding and Agents, and now our Coding call volume is quite good. So I think it’s more of a bet. Doing Agents in the future might also be a bet.

The Future of China AI

Li Guangmi (Moderator): Thanks. Because in the past model companies had to chase general capabilities, so priority-wise they didn’t spend that much energy exploring. Actually after general capabilities catch up, we’re also more expecting Zhipu and Qwen to have more of their own Claude Code moments and Manus moments in 2026. I think this is very worth anticipating.

The fourth question, also the last question, I think is quite interesting. Because this event, this timing, is more worth looking forward to the future.

I actually really want to ask everyone a question: three to five years from now, what’s the probability that the world’s leading AI company is a Chinese team? From today’s followers to future leaders, what culture and key conditions are still needed?

Shunyu, since you’ve experienced both Silicon Valley and China, what’s your judgment on this probability and what key conditions are needed?

Yao Shunyu: I think the probability is quite high. I’m still quite optimistic, because currently it seems that anything, once discovered, in China will quickly catch up or replicate, then do better locally in many areas. Including previous manufacturing, electric vehicles—such examples keep happening.

I think there might be several critical points. One might be whether China’s lithography machines can break through. If ultimately compute becomes the bottleneck, can we solve this compute problem? Currently we have very good electricity advantages, good infrastructure advantages. The main bottleneck might be production capacity, including lithography machines and software ecosystems. Solving this problem would be a big help.

Another question is whether, besides To C, we can have a more mature or better To B market, or whether there are opportunities to compete in the international business environment. Today we see many models doing productivity or To B are still born in America because payment willingness is stronger and To B culture is better. Doing this domestically is very difficult, so everyone chooses to go overseas or internationalize.

But I think what’s more important is the subjective concept. I recently talked with many people, and my intuitive feeling is: China actually has a lot of very strong talent. Anything, as long as it’s proven doable, many people will very actively try and even want to do better.

But it seems today in China, people who want to break through new paradigms or do very risky things might not be enough. This might have factors related to economic environment, business environment, and culture. But if we increase it a bit, can we subjectively have more people with entrepreneurial spirit or adventurous spirit who truly want to do frontier exploration or new paradigm breakthrough things?

Because currently, once a paradigm happens, we can use very few cards and high efficiency to catch up or even do better locally. But can we lead new paradigms? I think this might be today the only problem China needs to solve—in a sense the only problem to solve. Because everything else, whether business, product design, or this kind of catch-up engineering, we’ve already done better than America in some sense.

Li Guangmi (Moderator): Let me follow up with Shunyu: Do you have any calls regarding research culture in Chinese labs? You’ve also experienced OpenAI or Bay Area DeepMind research culture. What’s the difference between China and America? What fundamental impacts does this research culture have on being an AI Native company?

Yao Shunyu: I think research culture is very different everywhere. Differences between different labs in America might be bigger than differences between China and America. Same in China. I personally think there are two points:

First, in China people still prefer to do safer things. For example, today pre-training has been proven doable. This is actually also very difficult to do, with many technical problems to solve. But once proven doable, we’re all confident that in a few months or some time we can figure it out. But if today you ask someone to explore something like long-term memory or continual learning, everyone doesn’t know how to do it, whether it can be done. That I think is still quite difficult.

Of course, it’s not just that everyone prefers to do certain things. Very importantly, cultural accumulation and overall cognition is something that needs time to settle. Maybe at OpenAI doing RL started in 2022. Domestically maybe started in 2023, and understanding of this will differ. Or it seems China hasn’t scaled up RL this much. I think a lot is time issues. When you have deeper accumulated culture or foundation, it subtly influences people’s way of doing things.

Second, I think in China people look more heavily at leaderboards or numbers. On this point, overseas Anthropic does relatively well. Including DeepSeek also does well on this point—they might not care that much about leaderboard numbers; they might focus more on: first what’s the right thing, second what can you experience yourself as good or bad.

I think this is quite interesting. You see, Claude models might not be highest on many programming or software engineering leaderboards, but everyone knows this thing is the best to use. I think people still need to break free from leaderboard constraints and stick to what they think is correct or what is good.

Li Guangmi (Moderator): Thanks. Junyang, talk about China’s probability and conditions for winning.

Lin Junyang: Your question itself is a dangerous question. Technically in this setting you can’t pour cold water. But if we talk about probability, I want to mention the China-US differences I’ve felt.

For example, America’s compute might overall be 1-2 orders of magnitude larger than ours. But I see whether OpenAI or Anthropic, a large amount of their compute is actually just invested in next-generation research. We today are relatively stretched; just delivery might already occupy most compute. This will be a relatively big difference.

This might be a historical problem: does innovation happen in rich people’s hands or poor people’s hands?

Poor people aren’t without opportunities because we feel these “rich folks” really waste cards—they do so much ablation, maybe trained a lot that’s useless. But today if you’re poor, you’ll think about things like so-called algorithm-infrastructure joint optimization. Actually if you’re truly rich, you really don’t have much motivation to do this.

I think going further, Shunyu also mentioned the lithography machine problem. In the future there might also be a point: if from a software-hardware integration perspective, is it really possible to do it end-to-end? For example, our next generation model structure and chips might actually both be made together.

I particularly remember in 2021 when we were doing large models, Alibaba made chips and came to ask me: can you predict whether three years from now the model is still Transformer? Three years from now is the model multimodal? Why three years? He said we need three years to tape out. At that time I answered: three years from now, I don’t even know if I’ll still be at Alibaba. But ultimately today I’m still at Alibaba, and it really is still Transformer, still multimodal, and I really regretted why I didn’t push him to do it back then.

But our communication at that time was really “talking past each other”—he talked a bunch I didn’t understand, I talked and he didn’t know what we were doing, so we missed the opportunity. But could this opportunity come again? Although we’re a group of poor people, might poverty lead to change? The opportunity for innovation might happen here.

But I think what we might need to change is education. I feel, for example, I belong to the earlier 90s generation, Shunyu belongs to the later 90s, the team has many post-2000s—I feel everyone’s adventurous spirit is getting stronger and stronger. Americans naturally have very strong adventurous spirit. A typical example is when electric cars first came out, even with leaky roofs and possible accidental death while driving, many wealthy people were still willing to do this. But in China, wealthy people wouldn’t do this; everyone does safer things.

But today, everyone’s adventurous spirit is starting to get better, China’s business environment is also getting better—I think it’s possible to bring innovation. The probability isn’t that high, but it’s really possible.

Li Guangmi (Moderator): If we look at a number?

Lin Junyang: You mean what percentage?

Li Guangmi (Moderator): Yes, three to five years from now, the probability that the most leading company is a Chinese company.

Lin Junyang: I think below 20%. I think 20% is already very optimistic because there are really many historical accumulation reasons.

Li Guangmi (Moderator): Let me follow up with another question: is your inner fear of the gap widening strong? Some places are catching up, while other places compute is widening.

Lin Junyang: Today if you’re in this field you can’t be fearful; you must have a very chill mindset. From our mentality, being able to work in this field is already very good; being able to do large models is already very fortunate.

I think it still depends on what your original intention is. Shunyu mentioned a point earlier: your model might not necessarily be that strong, but in the C-side it’s actually OK. So I might switch to another perspective: what value does our model bring to human society? As long as I believe this thing can bring sufficient value to human society, can help humanity, even if it’s not the strongest, I’m willing to accept it.

Li Guangmi (Moderator): Thanks, Junyang. Professor Yang, you’ve experienced many AI cycles and seen many Chinese AI companies become world-class. What’s your judgment?

Yang Qiang: Reviewing internet development, it also started from America, but China caught up quickly, and applications like WeChat are world number one. I think AI is an enabling technology; it’s not an end product. But we in China have a lot of intelligence and talent that will bring products to their extremes, whether To B or To C. But I might be more optimistic about To C because a hundred flowers bloom; Chinese people pool wisdom.

But To B might have some specific limitations, like payment willingness, enterprise culture, etc.—these might also be changing. But I’ve also particularly observed some business aspects recently. For example, America has a company called Palantir. One of their concepts is: no matter what stage AI develops to, I can always discover some good things in AI to apply to enterprises. There’s definitely a gap in between that we need to bridge.

They have a method called “ontology,” using the ontology method. Actually I observed—the general idea is transfer learning we used to do, applying a general solution to specific practice, using an ontology for knowledge transfer. This method is very clever. It’s solved through an engineering method called FDE (Forward Deployed Engineer).

Anyway, I think this kind of thing is very worth learning. I think Chinese enterprises, AI Native companies, should develop such To B solutions. I believe they will. So I think To C will definitely bloom in diversity; To B might also catch up quickly.

Li Guangmi (Moderator): Thanks, Professor Yang. Professor Tang.

Tang Jie: First I think we must admit that between China and America, whether in research, especially in enterprise AI labs, there is a gap. That’s the first point.

But I think China’s future is slowly getting better and better, especially this generation of post-90s and post-2000s rising up—it’s really far better than before. I once said at a YOCSEF meeting: our generation is the most unfortunate. Why? Look, the previous generation is still here, we’re still working now, haven’t had our day yet. Then very unfortunately the next generation has already emerged; the world has been handed to the next generation, skipping our generation seamlessly. Actually joking.

But I think what’s most interesting is that China’s opportunities in the future might be:

First, a group of smart people truly dare to do particularly risky things. I think this exists now—post-2000s, post-90s generation, including Junyang, Kimi, Shunyu—all are willing and very daring to take risks to do such big adventurous things.

Second, I think indeed the environment needs to be better. Whether from the national environment, like competition between large and small enterprises, issues between startups, including our business environment. Like Junyang said, still have to do delivery. I think building the environment better, letting a group of smart people who dare to take risks have more time to do innovative things. For example, letting people like Junyang have more time for innovation. That’s the second point, perhaps something our government including the nation can help improve.

Third, returning to each of us, is whether we can persist. Can we dare to do it, dare to take risks on one path, and the environment is also decent? I think the environment definitely won’t be the best; never expect the environment to be the best. But I think we’re perhaps also lucky—we’re experiencing an era where the environment is gradually improving from perhaps not being that good before. We’re experiencers, perhaps the people who harvest the most from this wealth. If we stupidly persist, perhaps those who make it to the end will be us. Thank you all.

Conclusion

Li Guangmi (Moderator): Thank you, Professor Tang. So we also really want to call for more resources and funding to be invested in China’s AGI industry. With more compute, letting more young AI researchers “burn cards,” maybe after three to five years of burning, China will also have 3-5 of our own Ilyas, right? This is what we very much look forward to in the next 3-5 years.

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Their stock prices rose on debut: Beijing-based Zhipu AI, founded by Tsinghua University researchers, rose 13% on Thursday after a $558 million IPO, making it the first major Chinese LLM startup to go public.

Shanghai-based MiniMax, founded by former SenseTime executives, raised HK$4.8 billion ($620 million) in its IPO, doubled on its first day of trading in Hong Kong on Friday.

Their IPO validated years of work building LLMs in one of the world’s most competitive AI markets. As China’s 2nd generation AI startups, following the computer vision-focused 1st generation of SenseTime and Megvii, Zhipu AI and MiniMax were fortunate in their timing.

• Founded in 2019 and 2021 respectively, they rode the wave of the Transformer architecture, GPT models, and diffusion techniques—technologies far more generalizable than CNN-led computer vision and applicable to vastly larger market opportunities.

• They are undoubtedly beneficiaries of OpenAI’s market education; without ChatGPT’s explosive demonstration of LLM capabilities in late 2022, neither company would command their current multibillion-dollar valuations.

Despite vastly different founding stories and business approaches, both have been labeled China’s answer to OpenAI. But they are not China’s OpenAI. Look closer at their business models, revenue structures, and strategic choices, and a different picture emerges—one of constant compromise, survival-mode pivoting, and the fundamental structural differences between China’s AI ecosystem and Silicon Valley’s patient capital environment.

Two Different Origins

Zhipu AI emerged as a classic academic spinoff. Founded in 2019 by Tsinghua University computer science professors Tang Jie and Li Juanzi, the company grew directly out of Tsinghua’s Knowledge Engineering Group (KEG). It initially focused on knowledge graph before quickly pivoting to LLMs.

Just one year after Zhipu’s founding, OpenAI released GPT-3. The company responded by introducing their own LLM called GLM in 2021, followed by GLM-130B in 2022. While they struggled to convince Chinese venture capitalists about the potential of LLMs during early fundraising, OpenAI’s ChatGPT launch in November 2022 shifted the landscape overnight. Suddenly, Zhipu AI—one of the few Chinese startups already developing LLMs—became highly sought-after.

The company’s mastermind is Tang Jie, the co-founder and chief scientist. He was one of the technical leads in BAAI’s Wudao project, China’s Manhattan Project-style first LLM experiment, and helped nurture talents who went on to develop LLMs elsewhere, including Yang Zhilin at Moonshot AI.

Recode China AI

🔍From Zero to One: A Brief History of BAAI's Wudao LLMs

Hello readers! Last week was a bit of a slow news week in China’s AI world, so I took the opportunity to finally translate a feature story that’s been on my to-do list for months. Published by Leiphone (雷峰网), one of China’s premier deep tech publications, the piece delves into the origin story of Wudao (悟道), a series of Large Language Models (LLMs) from…

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3 years ago · 5 likes · Tony Peng

The funding followed quickly. By October 2023, the company secured a massive 2.5 billion RMB (~$350 million) investment led by Alibaba and Tencent. In May 2024, Saudi Arabia’s Prosperity7 Ventures joined a $400 million round, valuing Zhipu at around $3 billion.

Throughout 2023 and 2024, Zhipu released ChatGLM-6B, a 6-billion-parameter conversational model, and expanded its model lineup with GLM-V (a vision-language model), Ying (a Sora-like video generator), and AutoGLM (a GUI agent). Their latest GLM-4.7 now ranks among the smartest open-weight models globally.

In the meantime, Zhipu accelerated its global expansion by providing on-premise AI solutions, bundled as “AI-in-a-Box” servers to governments and enterprises in regions such as Southeast Asia and the Middle East. However, starting in 2025, the company has been added to the U.S. Department of Commerce’s Entity List (trade blacklist), amid U.S. concerns over Chinese AI firms and national security.

MiniMax followed a different path. CEO Yan Junjie, a former Vice President at SenseTime with an over 30,000 citations, founded the company in 2021 with two partners, all from SenseTime. The startup’s name derives from the minimax algorithm, which searches for optimal solutions in worst-case scenarios.

MiniMax’s mission is “Intelligence with Everyone” and their first product, Glow, allowed users to create virtual AI characters with custom backstories and personalities for open-ended conversation. The app proved successful, amassing nearly 5 million users within four months before being suspended in mainland China due to lacking required government registrations.

This unexpected consumer success pushed MiniMax in a direction quite different from Zhipu’s enterprise focus. At one time the company maintained more product managers than researchers.

Forced to pivot by regulatory issues, MiniMax redeployed Glow’s concept as Talkie, an English-language AI companion app targeting overseas markets. The move paid off quickly: by June 2024, Talkie ranked as the 5th most-downloaded free entertainment app in the U.S. and surpassed Character.AI to become the No.1 AI companion app in downloads. More than half of Talkie’s users were American, contributing to 11 million monthly active users worldwide by mid-2024.

In September 2024, MiniMax entered generative video with Hailuo Video-01, a text-to-video model. The company didn’t release a frontier LLM until January 2025 with MiniMax-01, later followed by reasoning models M1 and M2 that claimed top positions among open-weight models. MiniMax also demonstrated strength in speech synthesis, releasing Speech-02 in April 2025.

The Business Model Problem: Old Wine in New Bottles

Despite their research pedigrees and frontier AI ambitions, both companies’ business models reveal uncomfortable truths about operating AI startups in China.

Zhipu AI’s revenue trajectory looks impressive on paper: from 57.4 million RMB in 2022 to 124.5 million in 2023, reaching 312.4 million (~$44.8 million) in 2024, an annual compound growth rate of 130%.

About 85% of Zhipu’s 2024 revenue came from on-premise deployment, which is essentially project-based services for governments and enterprise clients who are more sensitive to data privacy. While this business achieved a 66% gross margin in 2024, it represents precisely the kind of project-heavy model that characterized Chinese AI 1.0 companies like SenseTime and prevented them from scaling rapidly. These deployments often involve long payment cycles, and significant customization work.

Zhipu recognized this limitation and has been expanding its model-as-a-service API business. But their cloud API segment represents only 15% of 2024 revenue and operates at a razor-thin 3.4% gross margin, squeezed by China’s brutal API pricing war. By the first half of 2025, this margin had turned negative at -0.4%.

As an LLM startup, Zhipu’s R&D spending is staggering: nearly 2.2 billion RMB ($315.3 million) in 2024, representing 703% of revenue. Approximately 70% of that R&D budget goes directly to computing power.

MiniMax’s revenue grew from approximately $3.5 million in 2023 to $30.5 million in 2024, reaching $53.4 million in the first nine months of 2025, with an overall gross margin of 23.3%. Unlike Zhipu’s mainland China focus, MiniMax derives roughly 70% of revenue from overseas markets, particularly the U.S. and Southeast Asia. These figures also mask profound losses: $465 million in 2024 and a staggering $512 million in the first nine months of 2025 alone.

MiniMax operates more like an AI application company than an R&D-driven research lab. The two AI consumer apps (Talkie and related products) contributed $38 million of revenue in the first nine months of 2025, but AI companion apps face increasing regulatory scrutiny globally, with concerns about soft pornographic content, mental health implications, and parasocial relationships.

While MiniMax’s B2B business, Open Platform and Other Enterprise Services, represents a declining share of total revenue, it achieved a remarkable 69.4% gross margin in the first nine months of 2025, far exceeding industry averages. This suggests MiniMax has achieved significant advantages in model inference cost control.

The Luxury of Patient Capital

Neither Zhipu AI nor MiniMax has had the luxury that OpenAI enjoyed, the ability to pursue frontier research for years without immediate commercial pressure.

OpenAI operated for nearly seven years before releasing a commercially viable product. Founded in 2015, the company could focus purely on research through 2022, burning through hundreds of millions in compute costs and staff wages while publishing groundbreaking papers and training increasingly large models. When GPT-3 launched in 2020, OpenAI had already established technical leadership. ChatGPT’s release in late 2022 then created a commercial tsunami that justified all those years of patient investment.

Zhipu AI and MiniMax never had this option. In China’s venture capital environment, investors expect quicker returns and clearer paths to profitability. The companies couldn’t afford to operate at massive losses for five to seven years while pursuing unproved, uncertain research directions. They needed to demonstrate commercial traction, show revenue growth, and prove their research could translate into paying customers, all while trying to advance the frontier of AI capabilities.

Both companies have been caught in a paradox: to survive and raise funding, they needed to show commercial progress, but the commercial activities distracted from the research breakthroughs that could create truly transformative value.

For example, between 2023 and 2024, Zhipu became one of the most aggressive investors in the Chinese AI market, acquiring or investing in eight AI companies over seven months. This diversification strategy suggests a company uncertain about its core direction, hedging bets across multiple AI companies rather than maintaining laser focus on foundational model excellence.

MiniMax approached the problem differently. They expanded marketing and advertising budgets in 2024 on promoting consumer applications that could generate revenue and user growth while building underlying models, two entirely different paths requiring different talent, different strategies, and different capital allocation decisions.

As a result, 2024 was a year of strategic confusion for MiniMax. The company pursued everything from LLMs to text-to-speech to video generation to AI companions, trying to maintain revenue and user growth while also investing in foundational model development. As they focused on consumer apps, they inevitably faced fierce competition from tech behemoths like ByteDance and worried they couldn’t win that battle.

Late Awakening

Perhaps the most revealing irony is, despite being founded earlier with explicit missions to pursue general intelligence, Zhipu AI and MiniMax’s actual contributions to advancing AI have been no greater than labs founded much more recently.

DeepSeek, founded in 2023—years after both companies—has made arguably more significant contributions to the AI research community. Backed by High-Flyer, a quantitative trading firm with deep pockets and no need for near-term commercialization, DeepSeek could focus purely on research. DeepSeek-V3 and R1’s efficient training approaches, architectural innovations, and open release of frontier-capable models demonstrated what’s possible when a team can focus purely on research excellence without commercial compromise. The global recognition and explosive user growth that followed were inevitable byproducts of this technical progress.

Similarly, the AI research labs within Alibaba and ByteDance, Qwen and Seed, while part of larger corporations, operate with resources and focus that allow them to push frontiers more effectively than independent startups juggling commercial survival.

Throughout 2024, as Zhipu and MiniMax were caught between enterprise sales and consumer apps, these better-resourced labs were making steady progress on frontier LLMs.

After DeepSeek’s wake-up call in early 2025, both Zhipu and MiniMax shifted resources dramatically toward LLM development. Their models began alternating positions on benchmark leaderboards, and both companies accelerated development of agentic, coding, and reasoning capabilities. They finally started behaving like the frontier AI labs they had always claimed to be.

Tang Jie stated in an internal letter to Zhipu employees that by 2026, the company aims to achieve global leadership in large models, launch the next-generation model GLM-5, innovate new model architectures and scaling paradigms, and expand research into reinforcement learning and generalization.

If they could do this in 2025, why hadn’t they been doing it all along? The obvious reason is they couldn’t afford to, not in China’s commercial environment, not without the patient capital and institutional support that allows pure research labs to operate.

This isn’t a criticism of Chinese investors’ sophistication or risk appetite. Rather, it reflects different market structures, different regulatory environments, and different expectations about how technology companies should develop. But it does mean that an independent startup pursuing AGI faces much steeper odds in China than in the U.S.

Yet there’s a deeper, beneath the surface question: Do Zhipu AI and MiniMax truly believe in AGI? OpenAI was founded by people who genuinely believed AGI was achievable and worth pursuing at any cost. DeepSeek’s singular focus on pushing model capabilities suggests similar conviction. But do Zhipu and MiniMax share this almost religious commitment to AGI, or are they just primarily excellent AI companies?

When Liang Wenfeng started his company (DeepSeek) in 2023, we had a conversation. At the time, I didn’t realize how committed he was to AGI—I’m grateful for the different perspectives he brought me. Choosing to persistently pursue AGI technology, continuously exploring the upper bounds of AGI, while making precise predictions about the future—these are areas where Zhipu needs to keep improving and elevating itself going forward.

—Tang Jie

None of this is to diminish what Zhipu AI and MiniMax have accomplished. Both companies have built impressive technical capabilities, created valuable products, and navigated an extraordinarily complex and competitive market. Their public listings represent meaningful validation and provide capital for continued development.

But calling them “China’s OpenAI” fundamentally misunderstands what OpenAI was and is: a research lab that could pursue original, breakthrough technologies for years without commercial pressure, a group of people who irrationally believe AGI.

The question for 2026 and beyond is whether Zhipu and MiniMax’s recent pivot toward intensive LLM development represents a sustainable new direction or merely another oscillation in their ongoing struggle to balance survival and frontier research. Their public listings provide new capital and visibility, but also new pressures to demonstrate returns. Whether they can finally escape the paradox that has constrained them from the start remains to be seen.

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Six months ago, Manus was being called a deserter (‘逃兵’). When the company relocated to Singapore in mid-2025, shuttered all its China-focused operations, laid off 80 mainland employees, and went dark on Chinese social media, the backlash was brutal. Chinese netizens and media tore into them for abandoning their home market.

Yet that move proved to be the key that unlocked what came next. This week, Meta acquired Manus for over $2 billion—the company’s third-largest acquisition after WhatsApp and Scale AI. The deal was struck in reportedly around 10 days. The 100-person Manus team will join Meta, with Xiao Hong, CEO of Manus who goes by Red, reporting directly to Meta COO Javier Olivan.

This isn’t just another normal acquisition story. It’s a blueprint for how a new generation of Chinese entrepreneurs can build world-class AI products, win over global capital and tech companies, and execute a clean exit. It’s also a microscope through which we can observe the latest dynamics of U.S.-China AI competition, where talent and technology flows across borders even as geopolitical walls rise higher.

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The Team Behind Manus

When Manus launched publicly with beta access earlier this year, it immediately stood apart from the chatbot crowd. Instead of just responding to commands, Manus was designed as a true AI agent. Give it a goal like “analyze competitors and create a business report,” and it would deliver—browsing the web, analyzing data, generating code, planning travel, building prototypes, writing reports, all with minimal human direction. The waitlist exploded to millions of users. Of course, success also breeds skepticism as critics (including me) dismissed Manus as “just a wrapper for Anthropic’s Claude.”

Recode China AI

💻Is Manus the 'DeepSeek Moment' for AI Agents or Just a Claude Wrapper?

Hi, this is Tony! Welcome to this issue of Recode China AI (for the week of March 3, 2025), your go-to newsletter for the latest AI news and research in China…

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a year ago · 24 likes · 1 comment · Tony Peng

The results spoke for themselves. On GAIA, a benchmark designed to measure real-world problem-solving, Manus reportedly outperformed even OpenAI’s Deep Research across multiple complexity levels. Today, on the Remote Labor Index (RLI) benchmark created by Scale AI to measure how well AI agents handle complex, real-world remote jobs, Manus is state-of-the-art. The agent has processed more than 147 trillion tokens and powered the creation of over 80 million virtual computers, Manus said.

One month after launch in April 2025, Benchmark Capital led a $75 million Series A at a $500 million valuation, with Tencent, ZhenFund, and HongShan Capital joining the round. By December 2025, just nine months after launch, Manus AI announced it had hit $100 million in annual recurring revenue (ARR).

Chetan Puttagunta, the Benchmark General Parker who led the funding round, didn’t mince words when describing Manus’s team. He called them “one of the greatest technical teams in the world,” “brilliance, resilience, grit, vision, execution, and kindness.”

Look at the founding team. Xiao Hong isn’t a rookie. His first startup, launched in 2016 just a year after graduation, built creator tools for WeChat, where users can create content easily. That venture nearly failed him, and he was about to give up the company entirely before he accidentally won a hackathon. There he met Liu Yuan, a general partner at ZhenFund who became his mentor and close friend. Over the next decade, Liu and ZhenFund backed Xiao through every funding round, and brought in two critical partners for Manus: Peak Ji and Zhang Tao (Hidecloud).

Peak Ji Yichao, the CTO, is something of a prodigy. Son of a Peking University physics professor dad and a serial entrepreneur mom, Ji made his name a decade ago as a high school student creating Mammoth 5, which once became the most downloaded third-party iPhone browser in China, and Magi, a knowledge search engine. Zhang Tao, the CPO, brought 15 years of product experience across multiple top-tier tech companies.

(I highly recommend the interview below between Peak Ji and Benita Zhang)

In 2022, Xiao founded his second startup, Butterfly Effect, which launched Monica—a Chrome extension that uses LLMs to help users translate, summarize, and write. That experience building Monica directly paved the way for Manus.

Why Meta Bought Manus

The talent acquisition angle is crucial to understanding this deal. If you look at recent major acquisitions in Silicon Valley—Google acquiring Noam Shazeer through the Character.AI deal, the Windsurf acquisition for its executive team, Microsoft buying Inflection—they’re all fundamentally acqui-hires. These companies are paying billions to acquire teams that know how to build models and AI products. Meta’s earlier acquisition of Scale AI for over $10 billion followed the same playbook, bringing Alexandr Wang in to lead its AI efforts.

The second strategic driver is Meta desperately needs a powerful AI agent that can thread through its entire ecosystem of social media products—Facebook, Instagram, WhatsApp, and Messenger. Manus can also serve as a complementary entry as Silicon Valley tech giants are building standalone AI products like Google’s Gemini and Microsoft’s Copilot. In announcing the acquisition, Meta indicated plans to operate and sell Manus and integrate it into its suite of social media products.

The $2 billion price tag might sound high for a company founded three years ago with a product that’s only nine months old. But in the context of Meta’s other AI acquisitions and the team quality, it’s not actually crazy. In October, Meta raised its 2025 capital expenditure outlook to about $70 billion.

The China Exit Playbook

This acquisition represents a complete cut-tie from China. Meta spokesman Andy Stone made it explicit: there will be no continuing Chinese ownership interests in Manus after the transaction, and the startup will discontinue all services and operations in China.

This is fundamentally different from the last generation of Chinese tech companies. ByteDance created separate products for different markets (Douyin and TikTok) while keeping roots in China. Pinduoduo followed a similar model. Shein maintains a huge operational base in China while selling globally, though it now faces mounting scrutiny from U.S. and other regulators.

Manus and its parent company Butterfly Effect, by contrast, have been actively distancing themselves from state-linked capital. For example, when rumors surfaced that a state-run fund from Wuhan—the city where Butterfly Effect was founded—had invested in the company, Xiao directly rebutted it in a WeChat group and asked people to help clarify.

The regulatory landscape leaves little room for ambiguity. U.S. capital is barred from investing in Chinese firms developing AI, semiconductors, and quantum technologies. Top-tier chips from Nvidia and AMD cannot be sold to mainland China. Frontier model access such as Anthropic’s Claude is prohibited for Chinese companies. Given these constraints, a Chinese AI application startup targeting global markets and seeking international capital has limited paths forward.

Former Google CEO Eric Schmidt pointed out in a public forum that Chinese startups literally cannot get the money to keep up in the AI race, lacking access to the depth of the U.S. capital markets needed for large-scale model training.

The valuation gap is huge. China’s homegrown AI startups, despite their remarkable technical progress, are dramatically undervalued. Moonshot AI, developer of the frontier LLM Kimi, just raised $500 million at a $4.3 billion valuation. Zhipu AI and MiniMax, China’s other two top LLM startups preparing to go public in early 2026, are targeting valuations between $6.5 and $7 billion. Meanwhile, OpenAI is in talks to raise up to $100 billion at a potential valuation of $830 billion, meaning China’s No.1 LLM startup is worth less than 1% of America’s top player.

We’re living in an era of choosing sides. The Chinese regulator clearly isn’t happy about this deal. The Wall Street Journal reported that Beijing officials were surprised and displeased by Meta’s acquisition, viewing Manus as a showcase of China’s AI capabilities. They worry the sale gives the U.S. access to technology developed by Chinese engineers and creates a template for other Chinese startups to follow.

Manus’s acquisition is one of the biggest AI stories of 2025, and certainly one of the most significant in China’s tech ecosystem. The new generation of Chinese entrepreneurs is targeting globalization from day one. Their products, their marketing, their entire orientation is toward global users. They’re leveraging China’s massive pool of talented engineers and product managers—people who’ve been battle-tested in China’s cutthroat, fast-paced internet competition.

The playbook is clear now: build with Chinese engineers, target global markets, secure global VC funding, establish legal presence outside China like Singapore, and when the time comes, execute a clean exit.

It’s an exciting time for ambitious Chinese entrepreneurs dreaming of building products that reach billions of global users, and for the investors who bet on them chasing massive returns. But the story has its casualties. For the 80 mainland engineers laid off when Manus relocated to Singapore, watching their former company sell for $2 billion must sting with a bitterness. And for the tens of millions of Chinese users whose access to Manus was abruptly shut down, there’s only an empty space where a product once lived, and the uncertain wait for a domestic replacement that may not match in the near term.

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2025 was my first year outside the AI industry, no longer working as a reporter or in PR, but I’ve stayed close to this world that still captivates me. What struck me most this year was how much more attention people are paying to China’s AI world, thanks largely to the breakthrough LLMs from DeepSeek and Qwen.

As I sat down to recap China’s AI for 2025, I was surprised by how varied the stories turned out to be. They range from chip developments, models, consumer applications, new hardware, regulations, data center expansions, and the movement of talent. But I didn’t just want to catalog the important stories. I wanted to share the ones that felt fun, novel, and genuinely eye-opening.

Honestly speaking, I don’t have a rigorous methodology here. My selection process was quite subjective, a blend of instinct and research. These are the stories that made me sit up and take notice. My hope is that even if you haven’t followed a single China AI story this year, reading this one article will give you a grasp of where the country’s AI landscape is headed, and why it matters.

Finally, thank you for reading my newsletters. May the joys of the holiday season fill your heart and carry into the new year. Wishing you and your family happy holidays and a wonderful year ahead! 🎊🎈

Recode China AI

📖 Top 10 China's AI Stories in 2024: A Year-End Review

Last week, my mom, 58, won a local dancing competition and was asked to give an award speech. Normally, this kind of task would fall on me – just as many others do. But this time, she told me she’d figured it out using an AI app. “The AI wrote it better than you ever could,” she said, implying that she might not need my help anymore…

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a year ago · 20 likes · 2 comments · Tony Peng

Recode China AI

📖 Top 10 China's AI Stories in 2023: A Year-End Review

2023 was a year of excitement, wildness, and challenges for China’s AI industry. We saw the rise of over 250 LLMs in China. Tech giants intensified their focus on AI to maintain a competitive edge, while startups secured new funding to take on the industry's heavyweights…

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2 years ago · 2 likes · Tony Peng

1. DeepSeek-R1 Shook The World

On January 20, 2025, nine days before Chinese New Year, one day before the U.S. announced its ambitious Stargate project, DeepSeek, a then unknown Chinese AI lab, released an open-weight LLM trained to perform multi-step reasoning like OpenAI’s o1 model.

DeepSeek-R1 showed near-frontier reasoning performance on math, logical reasoning, coding tasks, and multi-step planning questions, the first time ever for an open LLM. In many public benchmarks, it matched or came close to OpenAI’s o1, outperformed most open models available at the time, and clearly surpassed models that lacked explicit reasoning training.

DeepSeek researchers independently discovered that applying reinforcement learning (RL) directly to the base model—without relying on supervised fine-tuning (SFT) as a preliminary step—worked effectively in training reasoning abilities of LLMs, and that reasoning abilities from larger models could also be distilled into smaller ones.

But the most staggering detail was the training cost: DeepSeek-V3, the base model for R1, cost under $6 million, and R1 cost under $300,000. In terms of inference, DeepSeek-R1 cost $0.55 per million input tokens, and $2.19 per million output tokens. In comparison, o1 charges $15.00 per million input tokens and $60.00 per million output tokens.

Without any PR or marketing, the DeepSeek chatbot app skyrocketed to the top of iOS free app charts in both the U.S. and China. U.S. tech stocks experienced a significant downturn afterward due to investor concerns over DeepSeek’s competitive advancements. Silicon Valley’s tech giants scrambled to understand how this Chinese startup achieved such results.

In China, companies and institutions raced to integrate DeepSeek-R1 into their applications—it was the first time Chinese users had access to a ChatGPT-level AI. DeepSeek CEO Liang Wenfeng, also the founder of High-Flyer, a Chinese quantitative fund and DeepSeek’s primary backer, became a favored guest at high-level meetings with China’s top leadership.

The release of DeepSeek-R1 upended multiple AI narratives: that only spending hundreds of millions could produce a frontier LLM, that only Silicon Valley companies had talents to train competitive models, that the U.S.-China gap in AI was widening due to chip shortages.

Throughout the rest of 2025, DeepSeek continued churning out notable models, including DeepSeek-V3.2-Thinking and DeepSeek-Math-V2, which won the International Olympiad in Informatics. Yet the widely anticipated DeepSeek-V4 and DeepSeek-R2 remained under development. Rumors suggested they were falling below expectations.

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a year ago · 30 likes · Tony Peng

2. Chinese AI Labs Dominate Open LLMs

Throughout 2025, the AI world realized that DeepSeek-R1 wasn’t just a one-off phenomenon, but as part of a broader wave of frontier open-weight models from China’s leading AI labs.

In July 2025, China has overtaken the U.S. in cumulative open model downloads, according to a16z. By the end of 2025, all top open-source models originated from Chinese companies such as MiniMax, Alibaba, DeepSeek, and Zhipu AI (Z.ai), according to the Artificial Analysis Intelligence Index.

Leading the charge is Alibaba with its Qwen family of models. By the company’s count, Qwen has become the world’s largest open-source model ecosystem, accumulating over 400 million downloads and spawning 140,000 derivative models. Developers and enterprises worldwide have built AI products on Qwen’s foundation, from Japanese customer service chatbots to integrated automotive assistants.

Alibaba’s latest LLM, Qwen3-Max, features one trillion parameters and outperforms rivals including Claude Opus 4 and DeepSeek V3.1 on agent benchmarks. Unofficial stats suggest Alibaba has shipped 357 models in under two years, from vision-language models to coding models. The company’s Qwen3-Next, which introduced architectural innovations, delivers 10 times faster inference speeds while slashing training costs to roughly 10% of its predecessor’s expenses.

Close behind are three Chinese LLM unicorns valued in the billions: Moonshot AI, MiniMax, and Zhipu AI. Moonshot released K2-Thinking in October, with specialized agentic capabilities that surpass GPT-5 and Claude Sonnet 4.5 in specific tasks. MiniMax’s M2 series, including the latest M2.1, and Zhipu’s GLM-4.7 also rank among the top open models for coding and reasoning.

Their impressive performance, combined with low inference costs, has captured Silicon Valley’s attention. Cursor and Cognition, two of the Valley’s hottest startups, are reportedly building their models and products atop these open Chinese models.

Open-source/weight has proven the most effective way for these Chinese AI labs to gain visibility in the global AI community. By sharing their innovations openly, they also benefit from building on others’ work, a particularly strategic path given chip restrictions and less capital flowing into Chinese LLM companies.

“Opening up model weights today is essentially opening access to data resources and compute resources,” explained Wang Jian, Alibaba’s former CTO who founded its cloud business. “Once the model is open, you no longer need to spend massive compute power to redo what others have already done for you.”

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7 months ago · 27 likes · 3 comments · Tony Peng

3. Manus and the Rise of AI Agents

On March 6, 2025, Manus, a general AI agent developed by a then-unknown Beijing-based startup called Manus AI, launched with beta and waitlist access rolling out publicly. Going beyond chatbots that only respond to specific commands, Manus was designed to take a goal like “analyze competitors and create a business report” and deliver results autonomously. It could browse the web, analyze data, generate code, plan travel, build prototypes, and write reports with minimal human direction.

On benchmarks like GAIA, designed to measure real-world problem-solving, Manus reportedly outperformed systems like OpenAI’s Deep Research across multiple complexity levels. The product quickly gained viral attention, amassing millions of users on the waitlist. However, it also sparked controversies, with skeptics arguing that Manus was just a wrapper for Anthropic’s Claude.

One month after launch, in April 2025, Manus AI raised a $75 million funding round led by Benchmark Capital, with participation from Tencent, ZhenFund, and HongShan Capital, at a valuation of roughly $500 million. To better embrace global markets, the company relocated its headquarters to Singapore in July 2025. In December 2025, Manus AI announced its ARR had reached $100 million.

Just before I was about to publish this article, Meta announced its acquisition of Manus AI. Said Bloomberg, “the deal is part of a broader push by Meta to build a business around its massive AI investment, which has captured the focus of Chief Executive Officer Mark Zuckerberg and become the company’s top priority.”

Manus wasn’t the only agent product that gained traction in 2025. Genspark, a Silicon Valley-based AI startup founded by former Microsoft and Baidu executive Jing Kun, developed an AI agent and claimed $100 million in annual recurring revenue (ARR). Other agent products like Flowith and Lovable also emerged throughout the year.

While agents still occasionally make mistakes and exhibit performance fluctuations, 2025 marked a significant milestone as we began to treat AI as a genuine assistant rather than merely a chatbot. Although it may take another decade to address all the challenges inherent in AI agents, said Andrej Karpathy, this is a promising start.

Recode China AI

💻Is Manus the 'DeepSeek Moment' for AI Agents or Just a Claude Wrapper?

Hi, this is Tony! Welcome to this issue of Recode China AI (for the week of March 3, 2025), your go-to newsletter for the latest AI news and research in China…

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a year ago · 24 likes · 1 comment · Tony Peng

4. China’s AI Chip Industry Brace for Nvidia H200

In 2025, Huawei, which for years avoided sharing details about its AI chips to prevent further U.S. sanctions, publicly laid out a multi-year roadmap for its semiconductor development. The company first unveiled its rack-scale AI supercluster CloudMatrix 384 built using the Ascend 910C in April. Then in September, the company claimed its next-generation AI chip supercluster, a supercomputer connecting hundreds of thousands of chips expected in 2026, would reach a staggering 524 EFLOPS, making it one of the world’s most powerful AI computers.

Following Huawei’s lead, Alibaba, Baidu, Cambricon, and other Chinese tech and chip companies began aggressively promoting their domestic AI hardware. In late 2025, Muxi and Moore Threads, two domestic GPU developers, successfully went public in China with skyrocketing valuations.

The context was Nvidia was only allowed to sell restricted GPUs like the H20 to China, while domestic Chinese AI chips were catching up quickly. The Chinese government realized that under current geopolitics, China could no longer bet its AI future on an American supplier. If Nvidia couldn’t sell its best hardware, domestic alternatives had to fill the void.

While as that was the main storyline for 2025, near the end of the year, the narrative flipped entirely. The Trump administration suddenly allowed sales of Nvidia’s H200, a high-end GPU released in 2024 built for large-scale AI inference and training. The H200 is unquestionably ahead of any Chinese AI chip currently available, both in raw performance and ecosystem support. Alibaba and ByteDance have reportedly asked Nvidia about buying H200 chips.

The reversal created a dilemma for Beijing. While it won’t simply abandon its determination to build a burgeoning domestic AI chip industry, banning the H200 risks delaying Chinese AI labs from developing more advanced frontier models. Nvidia GPUs remain the best option for pre-training large-scale models. The story is still unfolding.

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🤺Who Will Fill Nvidia’s AI Chip Void in China?

For more than a decade, Nvidia’s chips have been the beating heart of China’s AI ecosystem. Its GPUs powered search engines, video apps, smartphones, electric vehicles, and the current wave of generative AI models. Even as Washington tightened export rules of advanced AI chips, Chinese companies kept settling for and buying “China-only” versions strippe…

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8 months ago · 15 likes · Tony Peng

5. ByteDance Put Jarvis in Phones

ByteDance’s chatbot app Doubao has been the undisputed champion of Chinese AI apps throughout 2025, with nearly 160 million monthly active users. And on December 1, the parent company of TikTok took another significant step forward by launching something that made iPhone’s Siri look like a toy: an AI assistant that could use your phone for you.

ByteDance partnered with Chinese telecom firm ZTE to integrate its Doubao LLM into a new smartphone prototype, marking China’s boldest experiment yet in agentic mobile AI. The Nubia M153, released as a limited prototype, features the Doubao Mobile Assistant, an AI agent that can independently execute complex tasks like “send the photos I took yesterday to my mom on WeChat” or “order pickup from my favorite restaurant nearby,” all through simple voice commands.

Doubao Mobile Assistant is essentially a graphic user interface (GUI) agent, an AI that doesn’t need APIs or require manual training to operate different apps. It simply “sees” the screen, understands the interface, learns autonomously, and switches seamlessly between apps to complete entire tasks.

The prototype, sold in probably hundreds of units only, immediately triggered code-red alerts across China’s tech ecosystem. Within 48 hours, WeChat blocked it. Alipay, Taobao, and Meituan quickly followed, preventing Doubao’s assistant from opening their apps.

“China’s mobile ecosystem is too established. Internet giants have built moats, traffic flows, and business models around their apps that are now the legacy infrastructure blocking phone AI advancement. Breaking even a small piece of this equilibrium makes everyone uncomfortable，” wrote Luo Yihang, a veteran tech reporter in China and founder of PingWest.

For ByteDance, embedding AI agents in smartphones is clearly not the endgame. It is already exploring next-generation hardware purpose-built for AI, such as AI glasses and earbuds.

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a year ago · 27 likes · 4 comments · Tony Peng

6. Xpeng’s Cat Walking Humanoid

When Chinese EV upstart Xpeng unveiled its 2nd-generation humanoid robot on November 5, the machine glided across the stage with such fluid, runway-model grace that the internet refused to believe it was real. People on social media insisted IRON had to be a person in a costume. The skepticism turned the debate into one of the year’s most viral tech moments.

Xpeng CEO He Xiaopeng released a behind-the-scenes video the next morning, revealing IRON’s lattice-like artificial muscles and internal mechanics. And at a live event the following day, engineers sliced open its leg covering on stage to expose the mechanical components underneath.

The spectacle highlighted a year of remarkable hardware achievements in China’s robotics sector. If IRON’s catwalk looks like a scene from a sci-fi film, Unitree’s G1 robots performed more like a superhuman circus act. The robots can execute standing side flips, kung fu moves, and could recover balance after being kicked—feats that seemed impossible for bipedal robots just months earlier.

China even staged what organizers billed as the world’s first human-robot half marathon in Beijing in April. The winning robot, Tiangong Ultra, completed the 21-kilometer course in 2 hours, 40 minutes—slower than elite human runners, but a milestone nonetheless.

Yet beneath the viral videos and hardware achievements lies a harsh reality: giving robots the “brain” to independently complete even basic household tasks remains frustratingly out of reach.

“The biggest challenge for humanoid robots right now is still at the model level—we haven’t reached a breakthrough moment like ChatGPT,” said Wang Xingxing, CEO of Unitree.

“If you’re doing one task, or a series of very similar tasks, we can do pretty well now,” said Tan Jie, Director and Tech Lead Manager at Google DeepMind, in an interview. “But if you want true generalization—a humanoid robot that can do many different things—we’re still very far away.”

The bottleneck isn’t just model. It’s data. LLMs feast on unlimited training data scraped from the internet. Robots, by contrast, must navigate unstructured physical environments where anything can happen. They need vast amount of diverse training data that simply don’t exist yet.

China’s robotics companies have mastered the mechanics of human-like movement. The software brain that would make these machines genuinely useful is still being developed.

7. Will AI Toys Become the Next Labubu

In 2025, China’s AI toy industry evolved from simple talking gadgets to a market of 29 billion yuan ($4 billion), fueled by a surge in smart companions powered by LLMs.

One of the year’s biggest hits is BubblePal, a ball-sized gadget that clips onto any existing plushie, using AI to grant it voice and personality. Parents can select from 39 different characters ranging from Elsa to popular Chinese anime heroes. The concept resonated immediately: BubblePal sold over 300,000 units shortly after its peak release period. Haivivi, the company behind BubblePal, has raised 200 million yuan ($28.5 million) from Hongshan Capital and Westsummit Capital.

In late 2025, Huawei and rising AI startup Robopoet jointly launched Smart Dummy, an AI toy that became an instant viral sensation, selling out within minutes of release. Priced at approximately $56, this fluffy, round creature—powered by Huawei’s LLM—chats naturally, blinks its eyes, remembers previous conversations, and responds to physical interaction like head strokes or shaking.

These toys are also expanding beyond China, reaching international markets including the US, UK, and Canada, according to MIT Tech Review. Yet despite their innovative appeal, parental reviews remain mixed, with common complaints about glitchy AI performance, overly verbose responses, and voice recognition struggles with young children.

As these AI companions become increasingly embedded in daily life—entertaining children, comforting stressed young adults, and providing company for isolated seniors—a fundamental question lingers: are AI toys just a passing fad, or do they represent a sustainable business built on genuine human need?

8. AI Data Centers in China’s Desert

While Google, Amazon, and Microsoft scrambled to build hyperscaler data centers across the U.S., China was quietly turning its far northwest into an AI computing powerhouse.

In July, Bloomberg broke a story revealing China’s plans to establish data centers in the remote Xinjiang region to support its ambitious AI initiatives. Shortly after the news, state-run People’s Daily published a story highlighting Xinjiang’s accelerated deployment of computing infrastructure.

Xinjiang is rapidly emerging as China’s answer to the hyperscaler arms race, driven by abundant renewable energy, favorable climate conditions, and national “East Data, West Computing” policies that route computational workloads from China’s prosperous eastern coast to the resource-rich west.

In Karamay, a city of Xinjiang known for vast oil fields and surreal desert landscapes, local data centers had surpassed 17,000 PFLOPS of computing capacity by the end of 2024, with 80% serving eastern China and overseas clients, and plans to exceed 100,000P in the coming years.

Xinjiang’s advantages include cheap, stable green electricity from large-scale wind and solar resources, as well as a cold, dry climate that naturally lowers cooling costs. In total, Xinjiang now has 201 GW of installed power capacity, with renewables accounting for over 55%.

Xinjiang’s data center projects serve as a microcosm of the ongoing U.S.-China AI race. While the United States holds a significant lead in almost every aspect of this race, including chips, models, user scale, and commercialization, China has made substantial strides in total electricity generation and capacity. In 2024 alone, China added an impressive 429 gigawatts (GW) of new power capacity, surpassing the U.S. with only 51 GW.

9. National AI Strategy Through 2035

In August 2025, China’s State Council released Opinions on Deepening the ‘AI+’ Initiative, laying out China’s national AI strategy through 2035. China wants to rapidly diffuse AI throughout its economy, following the same playbook used in its “Internet+” initiative, which previously tranformed the country’s digital economy through internet applications.

The document establishes a three-stage roadmap:

• By 2027, AI will be deeply integrated into six priority areas—science and technology, industry, consumption, livelihoods, governance, and global cooperation—with new-generation AI models and agents reaching 70%+ adoption.

• By 2030, adoption exceeds 90%, and AI becomes a key economic growth engine.

• By 2035, China enters a “full intelligent economy and intelligent society,” with AI functioning as basic social infrastructure.

The directive also focuses on strengthening AI's foundational aspects, such as models, data, computing power, talent development, and open-source ecosystems, while addressing potential risks associated with AI.

“It does not mention artificial general intelligence (AGI), superintelligence, or any other such concept,” wrote Kendra Schaefer, Director of Tech Policy Research at strategic advisory consultancy Trivium China. “The directive suggests that for China, the real AI race isn’t about beating the US to AGI supremacy. It is the race against the ticking clock of China’s demographic and economic challenges. Geopolitics and international competitiveness, while very important, are only part of the picture.”

10. Elite AI Talents Start Flowing Back to China

In 2023, some of China’s most elite AI researchers and entrepreneurs relocated to Silicon Valley to launch their own AI ventures. Jia Yangqing, former VP of Alibaba and creator of the Caffe deep learning framework, founded Lepton AI, which was later acquired by Nvidia. Jing Kun, a former Baidu executive, left his position as CEO of Baidu’s smart hardware subsidiary Xiaodu Technology to found GenSpark, which raised an oversubscribed $275 million Series B at a $1.25 billion post-money valuation.

At the time, Chinese tech companies were already sending recruiters and headhunters to hire researchers from frontier AI labs like OpenAI, Anthropic, and Google DeepMind, but with limited success. Most Chinese talent believed Silicon Valley was the best place to pursue their AI ambitions.

It still is—but the situation has begun to move in 2025. In early 2025, Wu Yonghui, vice president of research at Google DeepMind, joined ByteDance as head of Seed, the company’s AI lab. More recently, Yao Shunyu, a former OpenAI researcher, joined Tencent as Chief AI Scientist, reporting directly to President Martin Lau.

While this hasn’t yet formed a clear trend, such moves would have been unimaginable two years ago.

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DeepSeek last week released its latest LLMs DeepSeek-V3.2 and V3.2-Speciale, an unrefined, long thinking version. The company claimed V3.2-Speciale surpassed GPT-5 on reasoning tasks and snagged gold medals at both the 2025 International Mathematical Olympiad and the International Olympiad in Informatics.

While this comes as the wildly-anticipated DeepSeek-V4 and R2—supposedly DeepSeek’s next-gen foundation model and reasoning model—remain delayed, DeepSeek-V3.2 still shipped some interesting innovations.

The paper lays out three core contributions:

• A sparse attention mechanism that cuts computational complexity for long contexts.

• A reinforcement learning (RL) framework scaled to over 10% of pre-training compute.

• A synthetic data pipeline that auto-generates 85,000 agent tasks across 1,800 environments.

V3.2’s performance puts it solidly in frontier territory. On reasoning benchmarks, it hits 93.1% on AIME 2025, 92.5% on HMMT, and 30.6 on HLE. That’s close to GPT-5-High, though still noticeably behind Gemini 3.0-Pro. Agentic performance is competitive too as it reaches 73.1% on SWE-Verified, 46.4% on TerminalBench 2.0.

V3.2-Speciale takes this further. It outperforms Gemini-3.0-Pro with 96.0% on AIME and 99.2% on HMMT, hits 84.5% on IMOAnswerBench, and gets gold medals across IMO 2025, CMO 2025, IOI 2025, and ICPC World Finals 2025.

DeepSeek V3.2 is also the second smartest open weights model on Artificial Analysis Intelligence Index. It closely trails Kimi K2 as the model featuring 671 billion total parameters and 37 billion active parameters is smaller than K2.

As always, DeepSeek-V3.2’s API pricing is absurdly low. It costs $0.28 per million input tokens, $0.42 per million output tokens. With caching, input costs can drop to $0.028 per million tokens.

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The Sparse Attention

The only architectural modification between DeepSeek-V3.2 and DeepSeek-V3.1 is DeepSeek Sparse Attention (DSA).

The traditional full attention mechanisms look at every single token in context. It’s thorough but computationally complex for long contexts, like reading every word in a textbook when you’re looking for one specific concept. DSA uses what they call a Lightning Indexer to score tokens first, then only pays full attention to the (top-k) most relevant ones.

Think of it as skimming before you read. The indexer runs cheap (FP8 precision, minimal parameters), picks what matters, then the model does full attention only on those selected tokens. This results in less compute, similar quality, faster training and inference.

On Fiction.LiveBench, which assesses a model's ability to retrieve details from long-context texts for deep comprehension, DeepSeek-V3.2-exp—an experimental version released in September that also adopts DSA—performs exceptionally well among all open-weight models.

DSA works well with DeepSeek’s previous innovation, Multi-head Latent Attention (MLA), which already compressed key-value representations to drastically reduce memory and computation. Now DSA decides which of those compressed representations actually deserve attention. It’s efficient storage meeting efficient selection.

Scaling Post-Training Through RL

Since DeepSeek-R1, DeepSeek has been doubling down on how to squeeze better performance through post-training of the model.

They trained specialist models for math, programming, logic, agent tasks, and search, then distilled those into the base model. Then came the RL phase, which used a strikingly over 10% of their total pre-training compute budget. The authors believed reasoning capabilities could be further improved with more budget for post-training.

Their RL setup merges reasoning, tool-use, and alignment into one training stage, using outcome-based rewards including correctness, tool success, reasoning clarity alongside penalties for verbosity.

They built in safeguards, including unbiased KL estimators, off-policy masking, and keep-routing for MoE stability, to prevent the training from going off the rails. Tweeted DeepSeek research Gou Zhibin,

If Gemini-3 proved continual scaling pretraining, DeepSeek-V3.2-Speciale proves scaling RL with large context. We spent a year pushing DeepSeek-V3 to its limits. The lesson is post-training bottlenecks are solved by refining methods and data, not just waiting for a better base.

The Verbosity Problem

Multiple researchers are reporting that V3.2-Speciale burns through tokens at an alarming rate. For the same task, Gemini uses 20,000 tokens while Speciale uses 77,000. On Artificial Analysis Intelligence Index, V3.2-Speciale generated 160 million tokens, which is significantly more verbose than the average of 22 million tokens. Even the refined V3.2 version is considered verbose by generating 86 million tokens.

What’s worse is the model sometimes produces long, rambling answers that are still wrong. It’s a known issue with GRPO (Group Relative Policy Optimization), the RL algorithm DeepSeek used. A recent paper, Understanding R1-Zero-Like Training: A Critical Perspective, points out that GRPO’s reward normalization inadvertently rewards length, especially for incorrect outputs. The model learns verbosity instead of correctness.

The paper clarified that V3.2-Speciale was trained exclusively on reasoning data with a reduced length penalty during RL. It’s intended for research purposes only and not optimized for daily use.

Making Tool Calls Part of Reasoning

The last contribution is DeepSeek figured out how to keep the model’s chain-of-thought intact across multiple tool calls.

Earlier methods wiped the reasoning slate clean every time a model used a tool, whether search the web, run code, or query a database. The model had to restart its thinking from scratch each time, which wasted tokens and broke long-horizon planning.

V3.2 keeps the reasoning thread alive unless a new user message arrives. Tool outputs don’t erase previous thoughts. The model sees the entire trajectory as one continuous process. This also echos interleaved thinking from Moonshot AI’s K2-Thinking and MiniMax’s M2.

This matters for agentic behavior. The model can actually plan across multiple steps instead of constantly rebooting its reasoning engine.

To train these agent capabilities, DeepSeek built a massive synthetic data pipeline including 1,800+ environments and 85,000 complex tasks, all auto-generated and auto-verifiable.

The system works recursively. The model builds environments, creates tool APIs, generates tasks, and writes verification functions. They also used real tools—web search, coding sandboxes, GitHub repos, Jupyter notebooks—to create verifiable trajectories for search and coding agents.

Limitations and Key Takeaways

Despite the progress, DeepSeek remains honest about the gaps against leading proprietary models:

• World knowledge is still weaker. V3.2 used less pre-training compute than Gemini-3.0-Pro or GPT-5, so its breadth and depth of general knowledge lag behind frontier proprietary models.

• Token efficiency is poor. The model needs longer reasoning chains to hit the same performance benchmarks as competitors.

• The hardest tasks still win. On truly complex, long-horizon problems, V3.2 still falls short of top proprietary models. The authors think this needs more pre-training compute, more RL compute, better task design, and further architectural refinement.

DeepSeek-V3.2 continues to embody DeepSeek’s signature research style: an obsession with pushing computational cost-effectiveness to the extreme. In the meantime, the team keeps scaling post-training to squeeze out every bit of performance from the model. This is also a strategy that aligns naturally with China’s AI landscape, where access to advanced GPUs remains limited and domestic chips tend to be optimized for inference rather than large-scale training.

The authors also said they plan to expand pre-training compute in future iterations. Gemini 3.0 Pro has already shown that scaling pre-training still pays off. More compute and more data can translate into more intelligence and stronger reasoning and better performance on increasingly complex tasks.

Just as I was about to release this issue, U.S. President Donald Trump announced that Nvidia will be allowed to ship its H200 AI chips to “approved customers” in China and elsewhere, on the condition that the U.S. receives a 25% cut. The Chinese leader “responded positively.”

If this policy holds, and the Chinese government gives the greenlight as well, it would be a major boost for Chinese companies with frontier AI labs such as Alibaba, ByteDance, and DeepSeek, which reportedly delayed the release of their new models after being unable to train them successfully on Huawei’s AI chips.

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Starting December 1, 2025, Tesla has launched another 30-day trial of its latest advanced driver-assistance system (ADAS), Full Self-Driving (FSD) version 14.2, which can navigate through urban, residential, and highway environments, respond to traffic signals, switch between lanes, and park automatically.

FSD is classified as Level 2 partial automation, meaning drivers must keep their hands on the steering wheel, remain attentive, and most importantly bear responsibility for any accident. But the system’s ultimate goal is to achieve fully autonomous driving in any situation, at any time.

The U.S. electric vehicle giant is also eyeing FSD expansion into China, its second-largest market, where data sovereignty and regulatory constraints restricted its entry until February 2025. Yet even after approval, mainland Chinese owners are limited to a less advanced FSD option poorly adapted to local road conditions.

In November, CEO Elon Musk told the company’s annual general meeting he expects full approval in China “around February or March (2026) or so.”

Meanwhile Tesla faces strong domestic competition. As the home of the world’s largest EV makers such as BYD and Geely, China has nurtured a batch of companies developing rival ADAS systems that threaten Tesla’s technological edge.

Two major headlines over the past week for Chinese ADAS companies:

• First, Zhuoyu Technology, a 2023 spin-off from DJI, raised RMB 3.6 billion ($500 million) in strategic investment from state-owned FAW Group.

• Second, in an unusual turn, Reuters retracted its earlier report claiming that Momenta, an autonomous driving startup, had filed for a Hong Kong IPO. Momenta received approval from China’s securities regulator to go public in the U.S. in June 2024, but hasn’t made it yet.

Why ADAS Became the Battlefield

ADAS isn’t a single technology. It’s more like a spectrum. Basic Level 2 systems can accelerate and brake, handle adaptive cruise control, and lane keeping. Level 2+ adds features like automatic parking and traffic sign recognition.

Then there’s Level 2.99, a uniquely Chinese marketing term. These systems support Navigation on Autopilot (NOA), meaning the car can technically drive itself from point A to point B, handling highway merges, exit ramps, and urban driving. However, drivers remain legally responsible because Chinese regulators haven’t approved Level 3 ADAS, which would allow drivers to truly disengage under specific conditions.

China’s EV market is notorious for cutthroat competition and homogenization. With an overly mature supply chain, building an EV is no longer difficult. Carmakers have responded by slashing prices while piling on features—capabilities, interior space, range. Intelligent driving has emerged as one of the few areas that can differentiate.

The numbers tell the story. According to China’s Ministry of Industry and Information Technology, L2-capable new passenger vehicles reached 55.7% market penetration in the first half of 2024. Projections suggest this will exceed 65% in 2025.

Consumer demand is accelerating the trend. A 2024 report by the China EV100 found that 90% of Chinese consumers are willing to pay extra for advanced intelligent driving services, with 30% willing to spend over 10,000 yuan ($1,400). For younger buyers especially, intelligent driving experiences have become more attractive than traditional vehicle metrics.

For years, intelligent driving remained exclusive to premium vehicles above $40,000. Entering 2025, that’s also changed dramatically. Vehicles priced above $20,000 also offer intelligent driving features. For example, China’s largest EV maker, BYD, unveiled its God’s Eye ADAS this year, offering it at no extra cost on its vehicles.

The race to dominate China’s ADAS market features two categories:

• Self-developing OEMs like XPeng, Li Auto, and NIO invest heavily in proprietary systems, betting vertical integration will deliver competitive advantage. Other domestic carmakers like Geely and Changan choose to back startups to develop ADAS.

• Tier-1 suppliers including Huawei, Momenta, Horizon Robotics, Zhuoyu Technology (DJI spinoff) provide turnkey solutions to automakers lacking in-house capabilities. Huawei occupies a unique position. It’s not a traditional tier-1 supplier but deeply embedded in partnerships with select OEMs through its Harmony Intelligent Mobility Alliance, engaging in nearly every aspect of vehicle development except manufacturing and final assembly.

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Huawei’s Automotive Pivot

Since being sanctioned by the U.S., Huawei has lost ground in its global telecommunications and smartphone business. But over the past decade, the company has successfully forged a new path: automotive technology.

Huawei doesn’t manufacture cars like traditional OEMs. Instead, it partners with automakers through the Harmony Intelligent Mobility Alliance (HIMA), engaging deeply in nearly every aspect of vehicle development except actual manufacturing and final assembly. This model has made Huawei a strong player in ADAS.

Huawei ADS, called Qiankun乾崑, offers automakers a comprehensive solution spanning the entire ADAS stack, from sensors including homemade LiDAR, to onboard chips, AI software, and vehicle control systems.

Huawei employs multi-sensor fusion, combining LiDAR, radar, and cameras. On the software side, ADS drives just like a human with capabilities including path planning, route optimization, lane switching, navigation assistance on highways and urban roads, autonomous and valet parking.

As of 2025, Huawei offers ADS 4.0 as its latest generation, with four sub-versions. The flagship ADS 4.0 Ultra supports Level 3 highway driving when regulation permit, allowing drivers to take their hands off the wheel and divert attention from road conditions, intervening only when the system requests.

ADS mixes rule-based system, high-definition maps and neural networks, built by tens of thousands of Huawei engineers who hand-code responses for specific driving scenarios. Version 4.0 contains reportedly 20 million lines of code, a stark contrast to Tesla’s FSD, which reportedly uses only 2,000–3,000 lines after adopting a neural network architecture.

This code-intensive approach delivers exceptional performance in well-defined road conditions with predictable, safe behavior. The trade-off is reduced flexibility. The system may struggle with edge cases that haven’t been explicitly programmed. Neural network approaches, conversely, generalize better to novel scenarios. For version 4.0, Huawei is also exploring the adoption of World Action Model.

Huawei currently offers the best on-road ADAS among mass-production systems, according to multiple trusted sources. While better solutions may exist in demonstration, Huawei maintains its lead in pre-installed, customer-ready ADAS.

According to Jin Yuzhi, CEO of Huawei’s Intelligent Automotive Solution BU, 28 vehicle models now feature Qiankun ADS. Key models include Stelato S9, Maextro S800, AITO M9, and AITO M5.

Beyond the HIMA partnership, Huawei has confirmed or planned ADS 4.0 deployment across multiple automakers, including Dongfeng, Audi China, GAC Group (Trumpchi), Chery (Luxeed), VOYAH, and Changan (Deepal Automotive), among others.

Huawei’s biggest challenge comes not from an established giant but from Momenta, a rising startup gaining momentum in autonomous driving.

Momenta: Foreign Carmakers’ Favorite

Calling Momenta a startup undersells its history. Founded a decade ago by Cao Xudong, a computer vision scientist who worked at Microsoft Research Asia and SenseTime, the company was among the earliest robotaxi developers before pivoting strategically to Level 2 ADAS.

That shift has paid off. According to ZoZo Auto Research data, Momenta dominated the ADAS NOA market between January 2023 and October 2024 with a 60.1% share. Its customers include BMW, BYD, Toyota, SAIC, Mercedes‑Benz, Audi, and Nissan. As of November 2025, Momenta has secured cooperation on more than 160 vehicle models, and the number of vehicles equipped with its ADAS has exceeded 500,000.

The number (of vehicles) will reach 10 million in 2028, said Cao.

It has also gained strong backing and partnerships from major foreign carmakers include Toyota, General Motors (GM), and Mercedes‑Benz. Its latest valuation reportedly reaches $6 billion.

Momenta’s production-ready ADAS, Mpilot, targets mostly premium vehicles. The Pro version can operate without LiDAR or HD maps, relying instead on end-to-end neural networks to deliver capabilities comparable to Tesla FSD or Huawei ADS such as urban navigation, highway cruise, and parking automation.

The company is also exploring reinforcement learning in ADAS. Its R6 Flywheel model, based on reinforcement learning, is now deployed on vehicle models from GAC Toyota, Chery, and Buick.

What distinguishes Mpilot is its compatibility. While ADAS functions are becoming standardized, different automakers may use various components from their own supply chain. Mpilot as an open solution, provides extensive support for OEMs and customize products based different chips, sensors, data, and costs.

For instance, Mpilot can be built on top of both NVIDIA Orin chips and Qualcomm’s less powerful Snapdragon Ride chips. In contrast, Haomo.AI, another ADAS startup backed by Great Wall Motor, recently ceased operations due to its inability to deliver a reliable ADAS solution based on Snapdragon Ride, according to a person familiar with the matter.

Data collected from Mpilot also feeds into Momenta’s Level 4 autonomy solution, MSD (Momenta Self-Driving). This year, the company announced a strategic partnership with Uber to test Level 4 robotaxis in Munich, Germany, starting in 2026.

Despite impressive model counts, actual sales tell a more modest story. Most vehicles models averaged fewer than 2,000 monthly units in the first nine months of 2025. Unlike Huawei, Momenta lacks consumer brand awareness, a disadvantage when automakers are competing not just on technology but on marketing appeal that helps sell more cars.

Li Auto and XPeng: In-House ADAS Believers

Both Li Auto and XPeng are vertically integrated OEMs developing ADAS in-house, betting heavily on end-to-end neural networks and vision-language-action (VLA) models. But they arrive at this moment from very different positions.

Li Auto has endured a bruising 2025. The company that rose to prominence making large extended-range SUVs has struggled transitioning to pure EVs while facing fierce competition from Huawei-powered brands. Third-quarter revenue plummeted 36%, the steepest decline since its 2020 U.S. listing.

The good news is, despite being late to ADAS development, Li Auto has rapidly ascended to China’s top tier. In December 2023, its OTA (over-the-air) 5.0 software update upgraded its ADAS to AD Max 3.0, a version that pivots toward end-to-end neural network architecture from traditional rule-based systems.

Li Auto uses high-definition cameras as its primary sensors, with the Max version adding LiDAR, more cameras, and radars. For computing, entry-level AD Pro uses Horizon Robotics chips while premium AD Max use NVIDIA Orin or Thor platforms. The 2025-version AD Max V13 reportedly increased mileage per intervention (MPI) by 30%, meaning fewer hand-overs to drivers.

The company is also moving aggressively toward a new generation of ADAS technology based on VLA models. CEO Li Xiang claims VLA can help vehicles make better decision making in complex traffic scenarios and remember user preferences.

Unlike Li Auto, a latecomer to intelligent driving, XPeng has positioned itself from the outset as a smart EV and mobility technology company. Beyond manufacturing EVs, it invests heavily in autonomous driving software, proprietary AI chips, and harbors ambitions spanning robotaxis, flying cars, and robotics.

XPeng’s ADAS, branded XNGP, represents one of the industry’s most aggressive pivots toward end-to-end neural networks. While early models like the P5 featured multi-sensor fusion suites including LiDAR, newer models like the Mona—with a retail price starting at just $16,800—have adopted vision-only ADAS, replacing LiDAR with cameras and 4D millimeter-wave radars in a Tesla-like approach.

XPeng’s software stack comprises three components: XNet for visual perception, XPlanner for planning and control, and XBrain for voice assistance and cockpit AI. For computing, XPeng this year demonstrated its proprietary Turing AI chip to reduce dependence on external suppliers like NVIDIA.

Like Li Auto, XPeng is also doubling down on VLA models. Its next-generation VLA 2.0 system targets more complex driving scenarios. To accelerate AI development, the company recently announced a leadership shift: Li Liyun is stepping down as head of XPeng’s autonomous driving center, with Liu Xianming, head of the World Model division, taking over the role.

Whether these vision-first, end-to-end bets pay off will determine not just XPeng and Li Auto’s ADAS competitiveness, but their survival in China’s brutally competitive EV market.

Horizon Robotics: China’s Mobileye Bets on HSD

When Yu Kai, the extroverted, charismatic, former dean of Baidu’s deep learning research lab, founded Horizon Robotics with ambitions to build China’s Mobileye, skeptics abounded. Yu wasn’t a chip expert. How could he pull it off?

A decade later, the answer is clear. In October 2024, Horizon Robotics raised $696 million in Hong Kong’s biggest IPO of the year. The company has become a major supplier to automakers, offering low-to-mid tier chips, software stacks, and full-stack driving solutions that directly challenge Mobileye and Texas Instruments.

The backbone of Horizon’s offering is its Journey family of automotive AI chips. The Journey 6 series, launched last year and produced earlier this year, cover low, mid, and high-end use cases from 18 TOPS to 560 TOPS. So its low-end 6B chip can support basic ADAS featuring only lane-keeping and emergency braking, while high-end 6P chip can power urban navigation on autopilot.

Journey 6 uses Horizon’s proprietary BPU (Brain Processing Unit), integrated with CPU, GPU, and MCU into a highly optimized automotive SoC. This integration enables sensor fusion across cameras, radar, and LiDAR, supporting heavy AI inference for end-to-end neural network-based perception, planning, and control.

Beyond chips, Horizon offers SuperDrive (HSD), its flagship ADAS covering urban streets, highways, and parking. The company poached Su Jing, former head of Huawei’s self-driving division, to lead its ADAS development. Unlike traditional ADAS that rigidly separates perception, planning, and control, HSD employs an end-to-end neural network to output driving trajectories directly from sensor inputs.

If we can’t even handle autonomous driving, what qualifies us to make robots, said Su.

Horizon has secured partnerships with major automakers. In 2025, the company deepened cooperation with Volkswagen Group through their joint venture CARIZON to develop smart-driving solutions for the Chinese market. As of 2025, over 10 million vehicles have been equipped with Horizon’s Journey chips. HSD has also been adopted by Chery Automobile’s EXEED brand in September 2025, a step toward international exposure.

Zhuoyu Technology (DJI): The ‘DeepSeek’ in ADAS

Calling Zhuoyu Technology the DeepSeek of ADAS may be click-bait, but the similarities are obvious: both companies operated under the radar for a long period of time; both excel in engineering; and both build high-performing systems—whether AI models or ADAS—on significantly less powerful chips.

Zhuoyu’s origins trace back to DJI Automotive, the autonomous driving division of the renowned drone maker. Both ADAS and drones fundamentally rely on computer vision technology, an area where DJI has long been strong.

DJI Automotive first gained widespread recognition in 2022 when it partnered with SAIC-GM-Wuling (SGMW) to bring ADAS to a budget mini EV, Baojun KiWi EV, priced at just RMB 102,800 ($14,500). Powered by only two TI TDA4 chips with a combined 16 TOPS, the vehicle could park itself, avoid collisions, and perform functions like lane-change, ACC, stop-and-go traffic following, lane keeping, etc.

In 2020, DJI was added to the U.S. Commerce Department’s Entity List—commonly known as the economic blacklist. Meanwhile, DJI Automotive’s business diverged from DJI’s core operations. These two reasons led to the spinoff.

Spun off as an independent company in 2023 and formally branded as Zhuoyu in June 2024, the company recently raised RMB 3.6 billion ($500 million) in a strategic investment from state-owned FAW Group, which is now its largest shareholder with a 35.8% stake.

Zhuoyu democratizes intelligent driving through cost-effective solutions. The company’s core offering, the Chengxing Platform, supports ADAS across highway, urban, and parking scenarios. What sets it apart is a radical approach to computing efficiency. Zhuoyu claims its highway navigation pilot can run on as little as 32 TOPS of computing power with seven cameras. Urban navigation requires only around 100 TOPS, and reportedly costs under RMB 7,000 ($1,400).

Zhuoyu is also hedging its bets upmarket. For flagship ADAS, the company has partnered with NVIDIA to develop solutions running on Thor, enabling higher-level autonomy and neural network-based driving for premium vehicles.

I think we are among the top three (ADAS Tier-1 suppliers) in China, said Shen Shaojie, CEO of Zhuoyu.

More significantly, Zhuoyu’s solutions aren’t limited to EVs. FAW-Volkswagen deploys Zhuoyu systems in fuel-powered Volkswagen models for the Chinese market—demonstrating that intelligent driving is expanding beyond EVs

Last Takeaway

China’s ADAS market is fiercely competitive and technology evolves rapidly. Leading players can stall if they choose the wrong tech stack, while dark horses can surge ahead in just three to six months. For example, WeRide, known for its L4 robotaxis and minibuses, has recently made significant progress in ADAS through its joint development with Bosch.

One clear trend is that nearly every ADAS player is adopting data-driven, end-to-end systems, inspired by Tesla FSD’s impressive performance. Some companies have implemented two-module end-to-end architectures—one for perception, the other for planning. A select few claim to have achieved fully end-to-end systems that go directly from video inputs to vehicle controls. There’s also fierce debate over whether ADAS should be built on World Models or VLA models, with no consensus yet.

However, to handle China’s road conditions, which is far more complex and unpredictable than U.S. and European roads, incorporating rule-based guardrails into ADAS is inevitable. I don’t believe there’s any purely end-to-end ADAS deployed in China today.

Unlike FSD, which is purely vision-based, Chinese automakers favor LiDAR to provide additional safety when visibility is poor. According to the latest data, in May 2024, LiDAR penetration in over 500,000 new energy vehicles sold above RMB 150,000 ($20,000) reached 20.5%. U.S.-listed Hesai, the world’s largest LiDAR manufacturer, reports shipping 185 million units and partnering with nearly all major EV makers including BYD, Xiaomi, Li Auto, and Geely.

ADAS research and development costs are also substantial. For example, Horizon Robotics spent RMB 2 billion ($280 million) on R&D in the first half of 2024 alone, up over 60% year-over-year. As competition intensifies, only a handful of ADAS players will survive to truly challenge Tesla FSD.

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Last week I was planning to write about MiniMax M2, the latest reasoning model from Alibaba and Tencent-backed AI startup MiniMax that climbed to the best open LLM on Artificial Analysis Intelligence Index. But just one week after the release of M2, another Chinese AI startup Moonshot AI released the thinking model of its latest LLM Kimi K2, named Kimi K2 Thinking, that outshone M2.

K2 Thinking is a reasoning model that thinks while using tools. It’s the best open LLM available right now, with specific agentic capabilities that actually beat GPT-5 and Claude Sonnet 4.5 in certain tasks. It’s cheap to train, though it’s not necessarily cheap to use.

The reaction across the AI community was explosive. People are calling it “the turning point of AI,” “China saved open-source LLMs,” and saying it will “make OpenAI bleed.”

The timing couldn’t be more interesting. As Kimi K2 went viral, OpenAI hit another wave of bad news that’s adding fuel to concerns about an AI bubble. Over the past five days alone, Nvidia and Oracle both plunged 10%.

This article breaks down the details of K2 Thinking and reveals why the model represents shifting narratives in the AI world.

For anyone who wants to know more about Moonshot AI and Kimi K2, I highly recommend this interview between Benita Zhang Xiaojun and Mootshot AI CEO Yang Zhilin.

K2 Thinking Breakdown

What is K2 Thinking: K2 Thinking is a reasoning model post-trained on top of Kimi K2, Moonshot AI’s foundation model released in July and September 2025. The relationship is similar to DeepSeek R1 and DeepSeek V3—same foundation, enhanced reasoning capabilities.

Size: Built on Kimi K2’s architecture, the model has 1 trillion parameters with 32 active parameters and a 256K token context window.

Training Cost: According to CNBC, training Kimi K2 Thinking cost $4.6 million, likely leaked by a Moonshot AI insider trying to replicate the PR success DeepSeek created earlier this year. The model was trained using the H800, an downgraded version of the H100 GPU, which was marketed exclusively in China until October 2023.

(Update: in Moonshot AI’s AMA, their researchers said this is not an official number. “It is hard to quantify the training cost because a major part is research and experiments.”)

Recode China AI

💻Kimi K2: Smarter Than DeepSeek, Cheaper Than Claude

Hi, this is Tony! Welcome to this issue of Recode China AI, your go-to newsletter for the latest AI news and research in China…

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10 months ago · 10 likes · Tony Peng

Architecture: Kimi K2 and K2 Thinking share nearly identical architecture with DeepSeek-V3, but with minor differences. K2 is more sparse and omits DeepSeek’s double-head mechanism.

One real innovation is MuonClip, Moonshot’s proprietary Muon optimizer. This allowed them to train on a massive 15.5 trillion token dataset without a single training instability or loss spike.

Interleaved Thinking: K2 Thinking doesn’t just use tools. It thinks between every tool calling. This “interleaved thinking” (also found in MiniMax M2) mimics how humans actually work: you take an action, reflect on the results, assess if you’re on the right track, then decide your next move.

Agentic capabilities: This is K2 Thinking’s selling feature. The model can execute 200-300 sequential tool calls without human intervention, reasoning coherently across hundreds of steps. No other open model has demonstrated this level of sustained autonomous operation.

That means Kimi K2 Thinking can orchestrate a long chain of actions: think, select tool, call tool, get result, reason, call next tool, and repeat. For complex tasks that need many intermediate steps and decisions like deep research, multi-step coding, or combined web browsing and analysis, this is a major capability.

Moonshot calls this their latest advance in test-time scaling by scaling both thinking tokens and tool-calling steps. They first observed these continuous performance improvements back in Kimi K1.5 as context length increased.

Recode China AI

🔥DeepSeek-R1 and Kimi k1.5: How Chinese AI Labs Are Closing the Gap with OpenAI’s o1

Hi, this is Tony! Welcome to this issue of Recode China AI (for the week of January 20, 2025), your go-to newsletter for the latest AI news and research in China…

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a year ago · 30 likes · 5 comments · Tony Peng

INT4 Precision: Unlike previous Kimi K2 Instruct releases (which used FP8 precision), K2 Thinking ships natively in INT4. They achieved this through quantization-native training during post-training. As a result, K2 Thinking is only ~594GB versus over 1TB for K2 Instruct. This enables roughly 2 times generation speed while maintaining state-of-the-art performance. INT4 can also better support non-Blackwell architecture hardware.

One Moonshot AI engineer explained that INT4 quantization isn’t a trade-off. Done right during training, it reduces latency while maintaining quality, minimizes precision loss, enhances long-context reasoning stability, and even accelerates RL training by addressing “long-tail” inefficiencies.

Open Weights License: K2 Thinking surprisingly uses a modified MIT license with one requirement: if you’re using it for commercial products or services with over 100 million monthly active users OR over $20 million in monthly revenue, you must display the Kimi K2 branding in your user interface.

Benchmark Performance: K2 Thinking’s hype comes from its record-breaking performance on benchmarks that test real agentic capabilities:

• 44.9% on HLE with tools: Humanity’s Last Exam is a benchmark from the Center for AI Safety and Scale AI with thousands of difficult, closed-ended questions across academic disciplines. There’s even a “heavy mode” where K2 Thinking runs eight parallel trajectories, then aggregates them for a final answer. In heavy mode, it hits 51.0%.

• 60.2% on BrowseComp: K2 Thinking beats GPT-5 at browsing the internet for hard-to-find information.

• 71.3% on SWE-Bench Verified: Ttill behind GPT-5 and Claude Sonnet 4.5 on coding.

For general tasks, K2 Thinking trails Claude Sonnet 4.5 and GPT-5 but leads other open models.

One the day following K2 Thinking’s release, Artificial Analysis Intelligence Index showed that K2 placed second only to GPT-5, beating most closed-source models and all open models.

But here’s where things get interesting: K2 Thinking used 140M tokens across the evals, the highest ever. But with Moonshot’s base endpoint pricing ($0.6/$2.5 per million input/output tokens), the total cost to run the index was only $356, which is cheaper than leading frontier models. There’s also a turbo endpoint ($1.15/$8 per million tokens) that costs $1,172 to run the index, making it the second most expensive model after Grok 4. The base endpoint runs at ~8 output tokens/s, while turbo runs at ~50 output tokens/s.

I am also relying on other unofficial third-party evaluations for your reference. Zhihu contributor toyama nao published a detailed analysis concluding that K2 Thinking delivers genuine performance gains across reasoning benchmarks. It’s solidly first-tier intelligence, but at a steep token cost.

Meanwhile, a Chinese engineer and influencer ran a poll on X asking if K2 Thinking surpassed Claude Sonnet 4.5. 75.9% said it doesn’t.

Open Source Wins and Shifting AI Narratives

K2 Thinking is undeniably a major win for open-source LLMs. As Hugging Face CEO Clément Delangue put it: “The first time in a while that open-source gets ahead of proprietary APIs on their big area of focus (agents).”

The beauty of K2 Thinking is that it builds on previous open-source work of DeepSeek V3. And DeepSeek V3’s success traces back to LLaMA 2, which laid the foundation for the first generation of DeepSeek. This is collective intelligence in action: the open-source community has not only closed the gap with proprietary LLMs but has now surpassed them in certain tasks. Who says LLMs are a game only the deep-pocketed labs can play?

For Moonshot AI, MiniMax, and Z.AI (Zhipu), the past year has been tough. None have announced significant funding in months. Their commercial prospects looked shaky as domestic Chinese chatbots are all free to the public, making the U.S. subscription model impossible to replicate. The DeepSeek moment earlier this year nearly shattered public confidence in these startups. As a result, two of their peers (01.AI and Baichuan) have already abandoned LLM training altogether. Meanwhile, Chinese tech giants are attacking them on every front, from chatbots to video generation.

So their comeback over the past six months is genuinely remarkable. Despite DeepSeek’s phenomenon, these AI labs kept their heads down, focused on research innovations, prioritized training LLMs, and eventually delivered. Their hope is low-cost, high-performing open models that are good at coding and agentic tasks can help them crack international markets, win chatbot users, and gain adoption in enterprise and developer tools.

It’s starting to work. Z.AI now has around 100,000 monthly API users and 3 million free chatbot users overseas following their GLM-4.6 launch, according to CEO Li Zixuan in an interview with SCMP.

While these Chinese LLMs haven’t dethroned frontier U.S. models yet, the narrative and sentiment have shifted. Chinese LLMs have transformed from overlooked underdogs to AI frontrunners in just twelve months. As I mentioned in my previous post, a growing number of Silicon Valley’s hottest startups are now building their models and applications on these Chinese open LLMs.

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👏Chinese Open-Source LLMs Are Winning Silicon Valley

Last week, Cursor, Silicon Valley’s darling AI coding startup, released Composer, its first in-house coding model and agent. The company touted it as 4 times faster than competitors in their internal benchmarks, purpose-built for the editing code, reasoning across entire repositories, and handling multi-step tasks…

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6 months ago · 19 likes · 1 comment · Tony Peng

The dramatic training cost reduction of K2 Thinking also hurts OpenAI’s justification for needing massive financial commitments and potential government support. If a Chinese startup can build a competitive model for under $5 million, why does OpenAI need $1.4 trillion in infrastructure spending?

As OpenAI’s GPT-5 release underwhelmed this year, CEO Sam Altman is now facing mounting criticism over his overhyped AGI promises and the trillion-dollar infrastructure deals that seem increasingly unrealistic given the massive capex requirements.

Last week on the BG2 podcast, host Brad Gerstner pressed Altman directly: “How can a company with $13 billion in revenues make $1.4 trillion of spend commitments?”

Altman’s response was defensive and terse. He claimed OpenAI’s revenue is “well more than that.” Then he cut Gerstner off: “If you want to sell your shares, I’ll find you a buyer. Enough.”

Meanwhile at a Wall Street Journal Tech Live event on Wednesday. CFO Sarah Friar suggested OpenAI was seeking an “ecosystem” of banks, private equity, and even government support to finance AI infrastructure investments. Specifically, she floated the idea that the U.S. government should “backstop” or provide a “guarantee” to enable the financing.

The comment ignited immediate backlash. Critics interpreted it as OpenAI asking U.S. taxpayers to absorb the risk if the company can’t pay for all the chips it’s committed to buying. David Sacks, the White House’s AI and crypto advisor, tweeted: “The U.S. has at least 5 major frontier model companies. If one fails, others will take its place.”

Friar quickly clarified on LinkedIn late Wednesday that OpenAI is not seeking a government backstop, admitting she “used the word ‘backstop’ and it muddied the point.”

I don’t doubt that scaling AI infrastructure, with more data centers and more power generation, is essential for next-generation models. But there’s a troubling trend. As OpenAI’s CEO becomes increasingly absorbed in headline-grabbing trillion-dollar deals rather than the heads-down research that produces real AGI breakthroughs, the company has lost something fundamental. This isn’t the OpenAI I once knew.

K2 Thinking is great, and so are MiniMax M2 and GLM 4.6. While these models haven’t yet matched the leading closed-source models, they’re closing the gap rapidly. Given the current pace of innovation from Chinese AI labs, a new open-source SOTA whether from Qwen-3-Max-Thinking, DeepSeek-V4, or others could emerge within weeks rather than months.

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