The Repricing

The number that should stop you is 460. From January to June, Chinese embodied-AI companies raised about ¥46 billion across the first half of 2026, with twenty firms taking roughly 70% of it. Stretch the window to a full year, July 2025 through June 2026, and a count from IT桔子 (the Chinese Crunchbase) reaches 503 financing rounds and more than ¥96 billion, per a 36Kr breakdown published this week. That is more than one round a day, every day, for a year, in a single category of Chinese startup.

But the headline number is not the story. The story is what the money started asking for on its way in.

A year ago, a Chinese robotics founder needed a pedigree, a technology roadmap, and a vision. In 2026 the questions changed. One first-tier investor told 36Kr that the things he now asks before writing a check are "do customers repurchase, how many hours can the robot run continuously, what is the failure rate." Those used to be footnotes in due diligence. Now they decide the deal. The phrase the Chinese coverage keeps using is 从看故事到看数据, from reading the story to reading the data, and it marks the moment a hype category turns into an industry that has to clear a bar.

You can see the bar in where the money concentrated. Round counts in embodied AI actually fell 31.7% year over year in the second half of 2025, while the average check size rose 46.8%. Fewer companies, bigger checks. A ¥10-billion valuation is becoming the entry ticket to the main financing circle, and the mid-tier component makers (joint modules, sensors, harmonic reducers) that got funded every few days last year are quietly running dry. The single largest round of the cycle went to Tareeya (它石智航), a company building robot "brains," which closed $455 million in April led by Hillhouse and Sequoia China, the biggest single embodied-AI round in Chinese history. Galbot took ¥2.5 billion. Robovision (自变量机器人) took nearly ¥2 billion. The money is not spreading. It is stacking on the few firms that can already show a number.

Here is why this is the lead and not a funding roundup. Three years of Chinese AI investment ran on the bet that the technology would eventually work. This cycle is the first one priced on whether it works now. When the question shifts from "could this be huge" to "show me the repurchase rate," you are watching a market grow up in real time. The hard part is that embodied AI may be the worst-suited thing in tech to value this way. A robot that can only dance today might learn a real industrial task next quarter, and the value compounds non-linearly with the data it collects. So investors are doing two contradictory things at once, using failure rates and reorder rates to screen out the companies that can only tell a story, while leaving room for the compounding upside on the handful that can do both. The firms that satisfy both demands are, by definition, the leaders. Which is exactly why the money stacks on them.

That repricing is the thread running through almost everything else today.

The Briefing

The same investors who back the robots now want them back from Meta. The Information reported that Manus's original Chinese backers, Tencent, Sequoia China, and ZhenFund, plan to spend $2 billion buying the company back at the exact price Meta paid to acquire it last December. China's NDRC blocked Meta's purchase in April on foreign-investment-security grounds, and now the sellers are reversing the trade. The detail that matters is the structure. Manus is weighing a China-onshore Sino-foreign joint venture to let the Chinese investors hold the stock cleanly and to lay track for a Hong Kong IPO. The financial logic is brutal and simple: Manus's annualized revenue has run from about $100 million at acquisition to $400-500 million now, four to five times in roughly six months, so buying back at the old price is a discount. The buyback-plus-JV-plus-HK-listing combination is becoming the template for unwinding a blocked cross-border deal, and Chinese capital ends up owning more of the company than it did before Meta showed up.

An autonomous-driving unicorn is reading the same exit map. Momenta, the Suzhou self-driving company backed by GM, Toyota, and SAIC, is preparing a Hong Kong IPO at roughly a $9 billion valuation, targeting about $1 billion raised, according to Sina's market wire. Hong Kong, not New York, is where Chinese AI hardware lists now, and the pipeline behind Momenta (humanoid makers, chip firms, model labs) is filling the same exchange. The venue is the geopolitics. With the Nasdaq path effectively closed, HKEX is absorbing the entire wave.

The capital wave reached the ocean floor. Shihang Intelligence (世航智能), an underwater-robotics company, closed an A round above ¥1 billion, which 36Kr reports is the largest single round in ocean robotics anywhere in the world. The backers tell you how vertical this is getting: the round was led in part by the industrial funds of two domestic GPU makers, Moore Threads and Kunlunxin, alongside Singapore's Vertex Growth, and Zhu Xiaohu's GSR Ventures put in money for the fifth time. Chip companies are now funding the robots that will eventually run on their chips. Shihang's hardware already works to full ocean depth, 0 to 10,000 meters, on ship-hull cleaning, offshore-wind inspection, and underwater security, and it booked more than ¥1 billion in orders in the first half. Orders, not demos. That is the data the new money is paying for.

The application layer set its own record. Evoken (演语科技, formerly Liblib) disclosed a B+ round of nearly $300 million at a valuation above $2 billion, the largest Chinese AI-application financing of the year. The number that earned it. ARR hit $300 million in May, up nearly threefold in a few months, on AI video generation. The check the company raised is roughly the size of its annual revenue. When a Chinese AI-application company is priced at a clean multiple of real recurring revenue rather than a story about future scale, that is the repricing showing up on the software side too.

Signals

Tang Jie told Musk nine months is too long. After the US barred sales of Anthropic's Mythos model to China, someone on X asked Elon Musk when China would catch up. Musk said nine months. Zhipu chief scientist Tang Jie replied that it would not take that long, pointing to GLM-5.2, which Zhipu shipped this week with benchmarks closing on the frontier. The interesting wrinkle in the Chinese coverage is that the base model is only half the gap. The other half is post-training, the fine-tuning layer where GLM-5.1 to 5.2 made most of its jump, and almost no one in China is set up to do it on the newest bases yet.

DeepSeek's image mode could not recognize its own founder. DeepSeek rolled out a vision feature this week, and within a day Chinese users found it identifying founder Liang Wenfeng as ByteDance's Zhang Yiming. It trended on Baidu. The product is shipping fast and rough, which is the more telling fact than the gaffe.

Domestic chips are claiming the share that demand created. Chinese-language tech channels are now putting domestic AI accelerators at roughly 41% of China's market, with Nvidia's share described as falling from around 95% toward 55%. Treat the exact figures as directional rather than audited. The direction is the point, and it is the supply-side mirror of the demand we covered yesterday, when ByteDance moved to buy 50,000 inference chips from its third domestic supplier.

The Bigger Picture

The question worth sitting with is what happens to a hype cycle when the money stops paying for hype.

For most of the last three years, Chinese AI capital priced potential. You could raise on a team and a thesis because no one had the data to argue with you, and the bet was that the category was so large that being early mattered more than being right. That regime built the supply side. It funded the bodies, the brains, the sensors, the video models, and the chips, and it tolerated the failures because the upside on a winner dwarfed everything else.

What the 36Kr reporting captures is the regime ending. The same capital is still flowing, ¥96 billion in a year is not a retreat, but it now flows toward proof. Repurchase rates. Runtime hours. Failure rates. ARR multiples. Order books, not demo reels. The shift looks defensive, but it is the opposite. A market that demands data is a market that believes the products are real enough to measure. You do not ask a science project for its reorder rate.

And the proof requirement is what closes the loop with the chips and the IPOs. A robotics company that has to show continuous-runtime hours needs reliable domestic compute, which is the demand pulling Iluvatar and Cambricon and Moore Threads up the curve. A company that has cleared the data bar is ready for Hong Kong, which is where Momenta and the humanoid pipeline are filing. The story is no longer can China build it. It is whether Chinese companies can now prove it pays, and the entire capital stack, from the seed round to the IPO desk, has reorganized itself around forcing them to answer.

None of this makes Western headlines. All of it is the sound of an industry being asked, for the first time, to show its work.

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The Third Supplier

Reuters reported this week that ByteDance is finalizing a deal to buy at least 50,000 AI inference chips from Iluvatar CoreX, a Shanghai startup most English readers have never heard of, and is in parallel talks to use Baidu's Kunlunxin chips too. The framing in the Chinese trade coverage is the part that matters. If this closes, Iluvatar becomes ByteDance's third domestic GPU supplier, behind Huawei and Cambricon.

Count the suppliers, because the count is the story. Not one domestic vendor as a hedge. Three, plus a fourth in negotiation, for a single buyer. The chips are for inference, the workload that runs Doubao, ByteDance's chatbot, every time a user sends it a message. Inference is the half of AI that never stops and only grows, and it is the half that does not need Nvidia's training-grade hardware to run well. That is exactly the seam domestic chipmakers can fit into now, and ByteDance is the buyer with the volume to make a startup's order book.

The SCMP read on it is blunt. As ByteDance goes all in on AI, a few second-tier domestic chipmakers stand to win big, with Iluvatar CoreX, in its words, in pole position. "Second-tier" is doing a lot of work in that sentence. A year ago second-tier meant unbankable. This week it means the supplier a ByteDance buyer calls when the order is for fifty thousand units and the point is supply security, not benchmark wins.

The motive is not nationalism, it is insurance. By spreading inference across Huawei, Cambricon, Iluvatar, and maybe Kunlunxin, ByteDance insulates itself from one more turn of the US export-control screw and from any single domestic vendor stumbling. Tencent already buys Kunlunxin. The largest Chinese platforms are quietly building multi-vendor domestic supply chains for the workload that actually pays, the same way they once second-sourced everything from servers to storage.

Here is why I am leading with a procurement rumor instead of a model release. Demand is the thing that was missing. For three years the domestic chip story was supply, can they build a part that works, and the answer crept toward yes one tape-out at a time. The thing that turns a working part into an industry is a buyer who has to have it. ByteDance, Alibaba, Tencent and China Mobile committing real inference volume to domestic silicon is that buyer arriving. The chips were always going to get good enough. The question was whether anyone would be forced to buy them before they did, and the export controls answered it.

The Briefing

A Tencent-backed chip company filed to go public the same week, and its books show what captive demand looks like. Enflame, one of the four startups Chinese media calls the domestic GPU "dragons," moved its STAR Market IPO to the registration stage, the last regulatory step before listing, targeting a raise of about 6 billion yuan, roughly 830 million dollars, at a valuation near 20.5 billion yuan. Read the prospectus and the demand-pull is right there in one figure. Tencent owns about 20% of Enflame and is also the source of 71.84% of its revenue. The biggest shareholder is the biggest customer. That is not a flaw the IPO has to explain away, it is the model. A Chinese cloud giant funds a chip startup, then buys its output to run its own inference, and takes it public so the capital markets fund the next node. The captive customer is the whole machine, now priced on a public exchange.

Nvidia is pitching a new CPU to Chinese customers to keep its foot in the door. The Information reports Nvidia is showing its Vera CPU to Chinese buyers, a quieter front in the same fight. Vera is the host processor in Nvidia's next platform, not a restricted AI accelerator, so it can sell where the GPUs increasingly cannot. The move tells you Nvidia sees the inference base eroding and is trying to stay in the rack by selling the part that is still legal. The same week ByteDance is buying 50,000 domestic inference chips, Nvidia is selling Chinese customers the one piece of its stack that the controls still allow. Both facts are true and they point the same direction.

Washington decided not to blacklist DeepSeek, for now. Reuters reports the US held off adding DeepSeek to the entity list, along with more than 100 firms flagged as security risks. The restraint is its own signal. After the Anthropic Fable cutoff we covered in Issue #85, blacklisting the most-used Chinese open model would have been the aggressive next move, and the administration paused on it. Whatever the reason, the effect on the ground is that DeepSeek keeps shipping, keeps cutting prices, and keeps moving developers onto a stack that increasingly runs on the domestic chips in today's lead.

ByteDance is also talking to Iluvatar's neighbors, and the financial regulator just put rules around all of it. On the same day the chip news moved, China's financial regulator issued guidance on safe AI development for banks and insurers, explicitly barring the use of AI to generate false information or manipulate prices. The timing is not a coincidence so much as a rhythm. Beijing builds the demand with one hand, through SOE and platform procurement, and writes the guardrails with the other, the same week. The Commerce Ministry separately rolled out 17 measures to fuse AI with consumption. The state is treating AI buildout and AI governance as a single industrial program, not a tension to be managed.

Alibaba Cloud will launch agentic AI services in Malaysia and Europe in the second half. Per a market filing, Alibaba is taking its agent stack outside China, into Southeast Asia and the EU, the two markets most open to a non-US cloud. This is the export side of the same story. Once the domestic stack is cheap and vertically integrated, the next move is to sell it where American hyperscalers are expensive or politically fraught. The chips, the models, and now the cloud services are all pointed at the same destination, a full alternative stack that does not touch a US vendor end to end.

The Bigger Picture

For three years the domestic-chip debate was a question about supply. Can a Chinese fab and a Chinese design team build an AI part good enough to matter. This week the debate quietly moved to the other side of the ledger.

The news was not a benchmark. It was a buyer committing 50,000 units, a captive-customer chip company filing to go public, and Nvidia trying to sell the one component it is still allowed to. None of those are about whether the chips are good enough anymore. They assume the chips are good enough and ask the next question, who is buying, at what scale, funded by whom. That is what an industry looks like once it clears the does-it-work stage. Money and order volume, not tape-outs.

Export controls were supposed to choke the supply side, slow the fabs, starve the design teams of tools. What they did instead was guarantee the demand side. A ByteDance that could freely buy Nvidia inference chips would have kept buying them, and Iluvatar CoreX would still be a second-tier startup hoping for a break. Cut off the easy option and the 50,000-unit order has to go somewhere, and it goes to the Shanghai startup that is suddenly in pole position. The policy meant to deny China advanced compute became the forcing function that built China a domestic compute market, complete with public-market financing.

None of this makes a Western headline as a system. A procurement rumor, an IPO filing, a CPU pitch, an entity-list pause, each reads as one more small China-AI item. Put them in the same week and they are one story. The supply question is closing. The demand question just opened, and the answer is a market.

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The Co-Design

For two years the question about Chinese AI chips has been the same. Can they replace Nvidia. This week a report quietly moved the question somewhere else, and almost no English coverage noticed, because it ran first as a Chinese-language teardown of a paywalled SemiAnalysis analysis.

The chip is Huawei's Ascend 950DT, two months from going live on Huawei Cloud. SemiAnalysis got an early sample and ran a trace-level teardown of it executing DeepSeek V4 inference, the kind of analysis that records what every compute unit on the die is doing, microsecond by microsecond. The finding that matters is one line in their report. Part of DeepSeek V4's architecture was, in their words, "in part co-designed for Huawei Ascend inference."

Sit with that. DeepSeek V4 was not trained on Nvidia and then ported to a domestic chip as a fallback. Its new attention mechanism, its mixture-of-experts quantization scheme, the way its experts talk to each other across the cluster, were shaped from the design stage around how Huawei's silicon actually executes. Huawei's own engineers said the training side "could not have happened without the Ascend 950 supernode." The model and the chip were built looking at each other.

InfoQ walked through the SemiAnalysis trace in full. The 950DT carries Huawei's own HiZQ memory, 144GB at 4TB/s, aimed squarely at the decode stage, the most expensive part of running a model because every generated token has to read the whole key-value cache back out of memory. Huawei's software stack, CANN, the answer to Nvidia's CUDA that Huawei open-sourced in August 2025, hid the communication cost of the mixture-of-experts layer inside the compute by merging the two into single fused operations. It pushed the scheduling work that usually round-trips to the host CPU down onto a small ARM core sitting on the chip itself. The point of all of it is the same. Stop the expensive units from waiting.

Here is the part that turns an engineering story into a market story. When DeepSeek V3 launched a year ago, exactly one software stack supported it fully on day zero, Nvidia's CUDA. When DeepSeek V4 launched, two stacks did, CUDA and Huawei's CANN. AMD's ROCm managed one to two tokens per second, unusable. Nvidia's own server engine shipped with a silent bug that corrupted hidden states until SemiAnalysis submitted the fix themselves, nine days later. The thing only two ecosystems in the world can do on launch day, run a frontier model optimally out of the box, now includes a Chinese one.

And the money is already moving on it. DeepSeek cut its V4-Pro API price to a quarter of the original and told customers plainly that the deeper cuts come "after the Ascend 950 supernode ships in volume in the second half." Million-token context for two mao, roughly three US cents, about fifty times cheaper than Anthropic. That price is not a model trick. It is the chip, the compiler, the memory bandwidth and the inference framework working as one cost structure. Reuters reported ByteDance has booked half of the Ascend 950 production line, with Alibaba and Tencent each taking hundreds of thousands of units behind it, and China Mobile specifying 776 Ascend nodes in its 2026-2027 procurement. DeepSeek's token share on the Vercel AI Gateway went from under one percent to seventeen percent the month V4 landed, third in the world, ahead of OpenAI.

The English read on Chinese chips is still "can they catch up." The teardown says the catching-up frame is already stale. The question now is whether a top model can run its lowest-cost, highest-concurrency inference on domestic silicon, and the answer this week is yes, and the migration that answer triggers tends to be one-way. Huawei named the 950 series "David" in its own codebase, the boy who refused to fight the giant on the giant's terms. The interesting thing is not the metaphor. It is that the giant is the one with a co-designed model now running on someone else's chip.

The Briefing

Alibaba shipped its first robot foundation models, and the framing is an admission about where embodied AI keeps failing. Qwen-Robot Suite is three models, released by the Tongyi team this week, splitting the problem into navigation, manipulation, and a world model that predicts what happens next. The pitch is blunt about the gap it targets. A vision-language model can plan "go to the kitchen, find the red cup, pick it up," and then fail to produce a single motor command that does it, because language and physical action live in different representation spaces. Qwen-RobotManip trains across robot body types on a corpus of more than 38,100 hours built entirely from open data, and Qwen-RobotNav hits state-of-the-art on five navigation benchmarks with one set of weights. Alibaba is doing to robotics what it did to language, releasing the open base layer everyone else builds on. Same playbook as today's lead, one layer up the stack.

A Tsinghua spinout is selling a robot that learns by flailing, and a top cosmetics maker already deployed it. Acorn Robot's gripper has one degree of freedom, no external camera, no cloud brain, no demonstration data. InfoQ documented it levering a sub-millimeter bank card flat off a table, failing eight or nine times, then finding the trick on its own. Founder Jiang Yao, a Tsinghua mechanical-engineering PhD and Harvard neuroscience postdoc, calls it "behavior emergence under instinct," and he is making a sharp bet against the field. He thinks VLA models, world models, and simulation learning all tend to break in the last centimeter of actual execution, and that the missing piece is not more trajectory data but a mechanism that gets the machine to try first. It passed proof-of-concept at a top-tier Chinese cosmetics company and is in volume deployment. A counter-thesis to the data-hungry world-model consensus, shipping into a real factory.

Beijing literally built an AI factory, and the unit of output is a token. Zhipu-backed Zhang Zhidao (Jiuzhang Yunji) unveiled what it calls an AI factory with two halves, a training plant targeting 100,000 P of compute that turns general models plus industry data into specialized ones for finance, manufacturing, and government, and a token plant targeting 100 trillion tokens a day, packaging those models into something an enterprise can call, meter, and bill. It measures compute in and intelligence out as if they were raw material and finished goods. The metaphor is the tell. China keeps treating AI as industrial capacity to be built at scale, not a research frontier to be raced, and the infrastructure language follows.

Anthropic flew to the White House to get its top model unbanned, and left with the ban still in place. Executives met Commerce officials in Washington over the Fable 5 export control, and as of the meeting the restriction held, per WIRED via 智东西. We led on this cutoff as Issue #85, and the negotiation confirms the read. The government still believes Fable 5's safety guardrails can be jailbroken into something close to the restricted Mythos model. A separate thread runs underneath it. The Information reports Amazon CEO Andy Jassy personally called Treasury Secretary Bessent about the vulnerability, and Amazon holds a multi-billion-dollar Anthropic stake. The same week, the developers who lost access kept moving to Kimi, Qwen, and GLM, which is the only part of this with momentum.

Zhipu's GLM-5.2 took the top spot in AI coding two hours after Anthropic went dark. Open-sourced under MIT, GLM-5.2 ranked first on a coding leaderboard the same news cycle Fable 5 and Mythos 5 were pulled for foreign nationals. Chinese coverage is already calling the domestic coding tier the "big three," GLM, DeepSeek, and Qwen. The supply side of the cutoff keeps writing itself. An access control meant to protect the frontier hands the captive Chinese developer market to the labs that release weights, and they ship the replacement the same morning.

The Bigger Picture

The thread running through all of this is vertical integration, and the speed of it.

A year ago the Chinese AI stack was a set of separate bets. A model lab here, a chip company there, a robotics startup somewhere else, each hoping the others would catch up enough to be useful. This week they are designed against each other on purpose. DeepSeek shapes its architecture around Huawei's execution path. Huawei ships the full optimization recipe the day the model launches. Alibaba releases the robot base layer the way it released the language base layer. Beijing builds the compute as a factory with metered output. The pieces are no longer waiting for each other. They are being built to fit.

This is what the export controls did not price in. Cutting off the top US model accelerates the substitution, and we covered that. But the deeper effect is the one in today's lead. When a frontier model and a domestic chip are co-designed, the dependency does not just shift, it sets. You can swap an API key back on in an afternoon. You cannot un-design a model that was built around a different chip's memory hierarchy and communication fabric. The 950DT is two months from launch and DeepSeek is already structurally on it.

None of this makes Western headlines as a system. Each piece reads as one more incremental China-AI story. Put them on the same week and they are the same story, told from five angles. The stack stopped being a collection of companies racing in parallel. It started being one machine, assembled to fit.

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The Private Round

DeepSeek closed its first outside funding round. More than 50 billion yuan, about 7.4 billion dollars, the largest single AI raise in Chinese history, at a valuation north of 50 billion dollars. Those numbers were reported first by The Information and confirmed across 机器之心, 智东西, and 36Kr this morning. They are large, and they are the least surprising thing about this deal.

Here is what is surprising. The money does not go into DeepSeek. Investors had to put their capital into a limited partnership that Liang Wenfeng personally manages, not into the company. The outside backers get no voting rights. Every share is locked for five years, no secondary sales, no exits. Liang himself wrote the single largest check, 20 billion yuan, close to 40 percent of the round. Tencent came next at 10 billion, CATL at 5 billion, then JD, NetEase, and IDG at about 3 billion each. The only investor that gets a vote and skips the lockup is the National AI Industry Investment Fund, the state, which put in around 1 billion yuan directly into the company.

Read those terms again, because they are a thesis. A five-year lockup is a filter. Liang's team has reportedly said as much, that the lockup exists to screen out capital that wants a fast exit. They went further and demanded to verify the identity of every limited partner behind every fund in the round, to make sure no unknown party ends up holding DeepSeek equity. This is a company choosing its owners, not raising from whoever shows up with the highest mark.

Now set that next to what every other Chinese AI lab is doing. Yesterday's Monday Brief was about the Hong Kong AI-IPO lock-up cliff, Zhipu and MiniMax anchor-investor shares unlocking July 8 and 9, both already down 45 percent as the market front-runs insider selling. That is the playbook for the rest of the field. List on the Hong Kong exchange, take the pop, ride the scarcity premium, manage the unlock. Zhipu's shares jumped as much as 47 percent in a single session just this week on the Anthropic cutoff news, a 627 billion HKD company moving on a US export decision. The labs that went public are now priced by the same machine that prices everything else.

DeepSeek looked at that machine and built a structure to stay out of it. No exchange, no float, no quarterly story, no insiders to front-run. The reason the company can do this is the same reason it never raised before. DeepSeek was the AI arm of High-Flyer, Liang's quant fund, and it ran on the fund's profits for two years, the "zero funding" research-lab model that gave it its reputation. R1 broke globally in early 2025 and the costs that follow a global hit, compute, talent, inference at scale, finally made the self-funded model hard to sustain. So DeepSeek raised. But it raised in a way that imports the cash and keeps out the logic. The capital came in. The IPO clock did not.

The English-language read on this will be the valuation, another giant China AI number. The actual signal is governance. The most influential open-weight lab in China just demonstrated that you can take 7 billion dollars and still refuse to become a public company priced on scarcity. Everyone else in this sector is being valued by the exit. DeepSeek priced the exit out of the deal.

The Briefing

The same week DeepSeek closed, the man who ran Alibaba's Qwen models confirmed his own startup's first round. Lin Junyang, the former technical lead of Tongyi Qianwen, raised a few hundred million dollars at a 2 billion dollar valuation, co-led by Gaorong and Sequoia China at 100 million each, with Tencent in for 20 million. Corporate filings show a cluster of new entities under his name, one called Bulage, a transliteration of "pragmatics," matching his linguistics background. He is reportedly aiming at world models and embodied reasoning rather than another foundation chatbot, and he is already raising the next round. The talent that built the open-weight base layer is now spinning out and getting funded inside weeks.

ByteDance's AI ledger leaked, and it explains why everyone is pivoting to enterprise. LatePost reported that Doubao, the consumer app with more than 200 million daily users, brings in under 1 million yuan a day, mostly e-commerce commissions, while burning tens of millions of yuan a day in compute. Keeping Doubao running costs more than all of Bilibili, for a fraction of the engagement time. The profitable product is Seedance, the video model, at a 2 billion dollar annual run rate and 70 percent gross margin, almost all of it from enterprise. ByteDance is now reportedly steering resources from consumer toward business services, the same move the whole sector is making after watching Anthropic turn Claude Code into real revenue.

Enflame cleared its STAR Market listing review, completing China's "four little dragons" of domestic GPUs. Enflame Technology passed the Shanghai exchange committee review, joining Moore Threads, MetaX, and Biren among the domestic GPU makers heading to public markets. Tencent is a backer. The capital-formation wave for Chinese AI chips is no longer a forecast, it is a queue, and the last of the four named contenders just got its turn at the gate.

Unitree is spending 2 billion yuan on R&D and partnering with Nvidia, an admission about where its weakness sits. Fresh off a 73-day sprint through its STAR Market approval, the humanoid leader, 37 percent of global units shipped in 2025, is leaning on Nvidia's chips and model stack for the "brain" while it owns the "body." Chinese commentators are openly calling this the heavy-body, light-brain problem, the worry that a hardware champion ends up a vassal to whoever supplies the silicon and the model. The 2 billion yuan is Unitree buying its way out of that dependency before it hardens.

A Brazilian "frontier" model turned out to be two Chinese open models in a trench coat, which is its own kind of proof. Rio 3.5, a 397-billion-parameter open model from Rio de Janeiro's municipal IT company, posted SOTA benchmark scores and then collapsed within 24 hours when Nex-AGI showed it was a merge, roughly 60 percent Nex's own open model and 40 percent Alibaba's Qwen 3.5. We led on this yesterday as The Base Layer. The funding news this morning is the other half of it. The labs whose open weights the rest of the world quietly builds on are the same labs now closing the biggest rounds in the country.

Signals

Tencent Cloud is retiring DeepSeek-V3.2 on July 16 and pushing users to the V4 series, per a platform notice, while Chinese forums expect a full DeepSeek V4 launch within days, trillion-parameter and natively multimodal. The funding and the next model are landing in the same window.

Alipay launched Abao, an AI agent built into the super app, with Ant preparing a public rollout of an AI-native version of Alipay, its largest redesign in 20 years. The race to put an agent inside China's payment rails is now between Alipay and WeChat Pay, which is shipping its own "AI card" this week.

China's high-tech manufacturing grew 15.1 percent in May, and machinery-and-electronics exports rose 18.4 percent in the first five months, per official data. The industrial base under all of this AI capital formation is still compounding at double digits.

The Bigger Picture

The question worth sitting with is what DeepSeek's structure says about the next phase of Chinese AI capital.

For a year the story has been the IPO wave. Labs and chipmakers racing to Hong Kong and the STAR Market, debut pops, scarcity premiums, the whole apparatus of public-market capital formation that yesterday's Brief mapped out in detail. That wave is real and it is still running, Enflame cleared review this week, Zhipu moved 47 percent in a session. But the most important lab in the ecosystem just opted out of it, and did so on purpose, with terms engineered to keep public-market logic from touching the company.

That is a fork. One path is the exchange, where the market sets your price and your incentives, and a US export decision can swing your valuation by a quarter in an afternoon. The other path is what DeepSeek built, a private structure where the founder picks the owners, locks them in for five years, and keeps the votes. Both paths are absorbing enormous capital right now. They imply very different companies on the other side. The IPO labs answer to a float. DeepSeek answers to Liang Wenfeng and a five-year horizon.

The interesting tell is that the smart money took the DeepSeek terms. Tencent, CATL, IDG, the National AI Fund, all of them accepted no votes and a five-year lock to get in. When the best-positioned capital in the country agrees to those constraints to back a company that refuses to go public, that is the market telling you the scarce asset is not a listing. It is access to the lab that owns the base layer. None of this makes Western headlines as anything but a valuation. All of it is about who controls the next five years of the most-used open models on earth.

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The Base Layer

Last week a model called Rio 3.5 broke the internet. Rio de Janeiro's municipal IT company put out a 397-billion-parameter open model, claimed frontier-class benchmark scores, and for about a day the AI world treated it as a genuine surprise, a SOTA open model from a city government in Brazil. Then it fell apart in under 24 hours.

The team that took it apart was Nex-AGI, an open-source model project incubated by Shanghai's Innovation Institute. They ran the weights and found that Rio 3.5 was not trained. It was merged. Every weight tensor in the model, across all 60 layers, is the same blend to thousands of standard deviations, roughly 60 percent Nex's own open-source Nex N2 Pro and 40 percent Alibaba's Qwen 3.5. Strip the hardcoded "you are Rio" system prompt and the model introduces itself as Nex N2 Pro 79 percent of the time, and recites, word for word, the backstory Nex wrote for its own organization. The probability it calls itself Rio is zero.

The Chinese tech press reported this as a fraud story, 一夜反转, the overnight reversal, and most English coverage picked it up the same way, a city government caught dressing up someone else's model. That framing is correct and it is also the least interesting part.

Here is the part worth sitting with. When a municipal government on the other side of the planet wanted to fake a frontier model, the two ingredients it reached for were both Chinese open weights. Not Llama. Not anything from a US lab, because the US frontier labs do not release weights you can merge. The de facto base layer for "build a credible open model in a weekend" is now a Chinese stack, and it is good enough that a straight 60-40 interpolation of two Chinese models posted real SOTA numbers on several benchmarks before anyone noticed. Nex made exactly this point in its own statement, a little bitterly, that someone stitching their model into a SOTA result is backward proof of how strong the open base is.

This is the quiet inversion of the last two years. The English-language story about Chinese AI has been about catching up, about the gap, about whether DeepSeek or Qwen can match the US frontier. The Rio incident is a different signal. It says the catching-up question has already been answered at the layer that matters for diffusion, the open-weight base layer, and the rest of the world is now building on top of it, sometimes openly and sometimes by passing it off as their own. You do not counterfeit the thing that is behind. You counterfeit the thing that is ahead.

The body of a model gets the headlines. The weights everyone quietly builds on are the base layer. China now owns the base layer.

The Briefing

China's nuclear-industry isotope institute just announced mass production of silicon-28 at better than 99.99 percent purity, and the supply choke it relieves is real. The Research Institute of Physical and Chemical Engineering under China National Nuclear Corporation said it is the first time China has independently mass-produced the isotope at that abundance, moving from a 92 percent baseline to 99.99 percent. Silicon-28 is the substrate for silicon-spin qubits, the most manufacturable path to scalable quantum chips, and until now the purified material came from a tiny set of suppliers in Russia, Europe, and US-linked chains. The institute that did this is the centrifuge and isotope-separation arm of China's nuclear program, the same enrichment competence pointed at a different element. Academician Yu Dapeng called it the resolution of an urgent bottleneck for silicon-based quantum work in China. This is the materials-sovereignty version of the same pattern as the lead, a chokepoint that was overseas, now domestic.

Li Auto unveiled what it calls the world's first dynamic-dataflow AI chip, the Mach M100, on a 5nm automotive process at 1,280 TOPS. At its Livis Day event the company described an architecture where data movement drives computation rather than instructions fetching data, and claimed real-world utilization above 82 percent, far above the single-digit-to-teens efficiency typical of running transformer inference on general-purpose accelerators. If the utilization number holds up in production, the interesting thing is not the raw TOPS, it is that a carmaker is designing silicon around the shape of the workload instead of buying it. That is the same move Tesla made with its in-house inference chip, now arriving from a Chinese EV maker.

Zhipu open-sourced GLM-5.2 with million-token context, timed to the hour against Anthropic's foreign-access cutoff. Two hours after Anthropic's Fable 5 and Mythos 5 went dark outside the US on June 13, Zhipu announced GLM-5.2 fully open under MIT. The Hong Kong-listed shares jumped on the news. Read it next to the lead and it is the supply side of the same story. The Chinese labs keep their weights open, and they race to be the model a cut-off developer reaches for the same morning. The developers who lost access did not wait for the ban to lift.

Baidu's Apollo Go started driverless road tests in Switzerland with the country's largest public transport operator. Apollo Go is now testing the AmiGo service with PostBus in eastern Switzerland, targeting underserved transit routes around Lake Constance and the Alpine foothills, with regular commercial operation planned for 2027. Baidu says Apollo Go has now done more than 22 million rides across 27 cities. The robotaxi export story usually runs through the Middle East. A European public-transit operator handing route coverage to a Chinese autonomy stack is a different kind of validation.

Beijing is pushing AI adoption inside companies, and the layoffs are arriving quietly. A Reuters investigation found Chinese firms cutting contractors after ordering staff onto AI tools, the workforce cost of the same top-down AI-deployment mandate that shows up in our coverage as procurement wins and capability announcements. The diffusion that looks like national strength in the aggregate is landing on individual contract workers first.

Signals

A Brazilian municipal IT company is not the only one merging Chinese open weights into a "homegrown" model, it is just the one that got caught this week. Model-merge tooling plus strong open bases makes the costume cheap. Expect more of these, and expect the provenance forensics, the self-identification trick and the weight-blend analysis, to become a standard audit step.

DRAM supply visibility has collapsed to about one month, per MSI's chairman, who now expects the DIY PC market to fall more than 20 percent this year. Upstream memory makers are notifying shipment volumes only a month out, with pricing uncertain, as AI server demand pulls capacity away from consumer parts. The AI buildout is quietly eating the PC market's memory.

Tungsten hexafluoride prices are up more than 200 percent year over year as a Japanese supplier cut shipments. The semiconductor process gas hit an export average of about $150 per kilogram in April, and the projected global shortfall of roughly 2,000 tons a year is being read in China as the opening for domestic substitution in high-end electronic specialty gases.

The Bigger Picture

For two years the organizing question in English-language AI coverage has been the gap, can China match the US frontier. The Rio story is small, almost a joke, a city government caught with a costume. But the thing the costume was made of is the signal. When you want to fake being at the frontier of open AI, you reach for Chinese weights, because that is where the credible open base layer now lives.

The frontier-model race and the base-layer race are not the same race, and the West is winning one while quietly losing track of the other. The closed frontier, the Fable 5 tier, is still American, and the export-control machinery is built to keep it that way. But diffusion does not run on the closed frontier. It runs on whatever you can download, fine-tune, merge, and ship. That layer is increasingly Chinese, and the more the US restricts access to its closed top tier, the more the rest of the world standardizes on the open Chinese base out of simple availability.

Anthropic cut off foreign developers on a Friday. Zhipu open-sourced a million-token model two hours later. A Brazilian city merged two Chinese models and called it Rio. None of these made the front page in English as a single story. They are the same story.

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The Rotation

For two years, Chinese venture money chased the humanoid robot. The thing that walks. The body. This week the smart money admitted, out loud, that it has changed its mind.

The new consensus is blunt. Stop betting on the robot. Buy the parts inside it.

The numbers behind the shift are large. In the first quarter of 2026 alone, Chinese humanoid robot startups raised about 68.1 billion yuan ($9.4 billion), which is more than the entire sector raised in all of 2025. For the full year 2025, a report from the China Academy of Information and Communications Technology and Tsinghua University counted 744 financing rounds totaling 73.5 billion yuan. The 2026 pace is on track to dwarf that. But the destination of the money has flipped. It used to flow to the companies building the whole robot. Now it flows upstream, to chips, to the dexterous hands, to the force sensors, to the gearboxes.

Here is the mechanism, and it is one this audience will recognize immediately from a different industry.

The investors quoted in a detailed survey published this week by China Industry News keep reaching for the same comparison, the electric vehicle. In China's EV buildout, the carmakers ended up squeezed to thin margins while the battery and power-electronics suppliers, CATL above all, captured the largest share of the profit. The bet now is that humanoid robots run the same script. The body company becomes an assembler. The durable moat sits in the core components. As one investor put it, no matter who wins the race to build the best robot, all of them need chips and dexterous hands, so the certainty is higher upstream.

There is a second reason, and it is the one that actually matters. The component makers have real revenue today, right now, without waiting for humanoid robots to ship in volume. Dexterous hands already sell into industrial collaborative arms. Tactile sensors already sell into consumer-electronics inspection lines. The demand does not depend on any single robot company succeeding. LinkerHand, a dexterous-hand maker, runs 7 product series across a price range from 3,999 yuan to 100,000 yuan, makes its core parts entirely in-house, and is already shipping at a rate of roughly a thousand units a month. That is a business. Compare it to the body companies, where even the profitable standout, Unitree, draws 73.6% of its humanoid revenue from research and education buyers and only 9% from actual industrial deployment. The body companies are selling to labs. The component companies are selling to factories.

The market has already voted with its feet. On June 5, Leaderdrive, China's largest maker of the harmonic reducers that act as the precision joints in a robot's wrist, hit the 20% daily limit-up and set a record high on nearly 8 billion yuan of single-day turnover. Its founders crossed into billionaire territory this year on humanoid demand, with 2025 net profit more than doubling. And the body companies themselves are now confirming the thesis by acting on it. Agibot just carved its dexterous-hand unit out into a separate company, its data-collection unit into another funded by Sequoia China, and its robot-rental unit into a third. BYD took a strategic stake in the tactile-sensor firm Paxini. CATL, Agibot, and Galbot all turned up together inside a single funding round for a six-axis force-sensor startup. When the robot makers start buying their own suppliers, they are telling you where they think the value is.

The window is the part to watch. The investors in this survey think it is short, somewhere between 12 and 18 months, before component valuations shift from being driven by theme to being driven by earnings. And one detail signals how fast the mass-production phase is approaching. Some core-component suppliers are now signing purchase agreements with their robot-maker customers that carry penalty clauses in both directions, the supplier pays if it cannot deliver volume, and the customer pays if it fails to place the orders. That kind of two-sided guarantee did not exist in this industry six months ago. It exists because the people closest to the supply chain now believe the volume is real.

The body was the story. The parts are the trade.

The Briefing

The robot CEOs gathered in Beijing this week and described a money race, not a technology race. At the Embodied Industry CEO panel at the BAAI conference, the heads of Spirit AI, LinkerHand, and several other humanoid companies framed the next phase as the expensive, compute-heavy training stage, and the imperative is to raise capital now. Spirit AI raised about 1 billion yuan within ten months of founding. One dexterous-hand maker is reportedly chasing a new round at a 40 billion yuan valuation. The phrase circulating at the conference, per Caixin, was that if you are not at the table this year, there is no seat next year. This is the demand-side mirror of the component rotation above, the body companies are raising war chests precisely because the training run is about to get costly.

China's industrial robots are quietly winning the export market that humanoids only promise. New customs data shows that AI-related products, electronic components, fiber-optic cable, computer parts, totaled 4.12 trillion yuan in trade in the first five months of 2026, up 52.4% year on year. Inside that figure sits a striking statistic, one of every three industrial collaborative robots, the palletizing machines used in warehouses worldwide, is now made in Dongguan. This is the unglamorous proof of the upstream thesis. The Chinese robotics supply chain is already a globally dominant, revenue-generating business in the boring categories, long before the humanoid arrives.

A harmonic-reducer maker cleared its Hong Kong listing hearing the same week. Zhejiang Laifu Harmonic Drive passed the Hong Kong Stock Exchange listing hearing, the final procedural gate before pricing. The component layer is reaching the public markets, on top of all the private capital flowing in. Laifu joins a queue of Chinese hard-tech and robotics-supply-chain names heading for Hong Kong listings this year, the same venue that has absorbed most of the country's 2026 AI offerings.

SK Hynix plans to triple its chip wafer capacity to feed AI memory demand. SK Group chairman Chey Tae-won said the memory unit will lift wafer output to three times current levels by 2034, and the company confirmed it intends to issue American depositary receipts this year for a US listing. The read-through for China, the global memory supply that humanoid robots, AI data centers, and domestic accelerators all draw from is being expanded on a decade-long horizon, and the capital to fund it is being raised in the US public markets while Chinese suppliers raise theirs in Hong Kong.

Amazon's CEO raised concerns about Anthropic's models before the US directive landed. Chinese financial wires picked up reporting that Andy Jassy and other tech executives spoke with the Trump administration about the models ahead of last week's export-control directive on Fable 5 and Mythos 5, and that the administration is unlikely to extend the Anthropic controls to other AI companies. For Chinese developers cut off last week, the second half matters more than the first, the cutoff looks targeted, not the start of a broad model-export regime.

What I Found on Bilibili This Week

A scheduling note first. The transcription pipeline that normally pulls and translates full Bilibili videos is still down this week, so I am reading these as titles and signals rather than transcribed argument. Treat the following as directional, not quoted.

The video I want to flag is titled, in translation, "The covert US-China struggle in the humanoid robot industry." The framing is itself the signal. Chinese creators are no longer covering humanoid robots as a domestic technology curiosity, they are covering it as a two-power industrial contest, the same lens this newsletter applies to chips. A second video making the rounds claims domestic AI chips have climbed to a 41% share, with Nvidia's China share falling from 95% to 55%. I flagged the same 41% figure last issue with a caveat, it comes from an unnamed Bilibili analyst, and I have not been able to source it to a primary dataset, so hold it loosely. The trend direction, domestic share rising, is well established, the precise number is not.

Signals

Zhipu open-sourced GLM-5.2 under an MIT license while preparing its IPO. The model release and the listing prep are not separate events. Open-sourcing the frontier model removes a paywall lever right before going public, which Chinese commentators read as a bet that ecosystem adoption is worth more than near-term API revenue at the moment of listing.

DeepSeek shipped V4, and the Chinese model-comparison crowd is calling it the strongest open-source model again. Multiple side-by-side reviews this week put V4 back at the top of the open-source coding rankings, the same seat Kimi has been contesting. The open-weight Chinese models keep trading the lead among themselves, which is exactly the competitive density that the foreign-national cutoff on Anthropic's top models now pushes Chinese developers toward.

China put its first domestically developed flexible welding robots for offshore engineering into service. The system, deployed in Tianjin, handles high-difficulty custom welds on offshore oil and gas platforms, carries a 20-year design life, and runs on core software and a process library that are 100% domestically built. Another data point in the unglamorous-but-real industrial-robot column.

The Bigger Picture

Why does the body-to-parts rotation matter beyond the venture-capital scoreboard?

Because it tells you what kind of industry China thinks humanoid robotics is going to be. If you believe the winning robot company will own a defensible, high-margin product, the way Apple owns the iPhone, you put your money into the body. If you believe the robot will commoditize into an assembled box, the way the gasoline car and then the electric car did, you put your money into the components that every assembler will have to buy. China's capital has now chosen the second story. The robot is a Dell laptop, and these investors are buying Intel and the disk drive.

That choice has a geopolitical edge the venture framing hides. The component layer is exactly where China's supply-chain advantage is deepest and where Western alternatives are thinnest, harmonic reducers, force sensors, the motors shared with the EV stack, the consumer-electronics camera and compute parts. The investors in this survey are explicit that the highest technical barriers, the planetary roller screws and six-axis force sensors, are also where the domestic-substitution opportunity is largest, and they put the catch-up timeline at three to five years. If they are right, then the country that controls the parts controls the margin pool of the entire global humanoid industry, regardless of whose logo ends up on the robot.

The body gets the headlines. The parts get the profit. China just told you which one it is buying.

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The Cutoff

On June 12, 2026, the US government issued a first-of-its-kind export control directive: Anthropic must immediately suspend access to Fable 5 and Mythos 5 — its highest-capability models — for all foreign nationals, everywhere in the world.

Not a narrow geographic block. Not a country-specific restriction. Every foreign national, including those physically inside the United States.

Anthropic published a statement on its newsroom the same day. The directive cites national security concerns and a claimed jailbreak method. Anthropic pushes back: it argues the demonstrated vulnerability involved "a small number of previously known, minor vulnerabilities" and disputes the severity. But compliance is not optional. Fable 5 and Mythos 5 are off for everyone outside US citizenship, effective immediately. Access to other Anthropic models is unchanged.

What are Fable 5 and Mythos 5? These are a tier above Opus — Anthropic announced them on June 9, just three days before the directive. Fable 5 is the public-facing model: capable of compressing months of engineering work into days, scoring at the top of finance benchmarks, demonstrating autonomous genomics research. Mythos 5 is the safety-stripped version deployed through Project Glasswing for government and cybersecurity applications. The announcement describes Fable 5 as the "new state-of-the-art" for vision tasks, long-context work, and software engineering. Three days after the launch, the government pulled the access lever.

Here is the mechanism that matters for this audience.

The Chinese developer community runs heavily on Claude. It is the dominant choice for code generation among Chinese engineers who can access it via API — Anthropic's coding capability, under the Claude Code brand, is widely viewed as the best available. The V2EX developer forum went viral this week with exactly this story: a hot thread titled "Anthropic, under US government orders, has closed Fable 5 / Mythos 5 access for all foreign nationals" — with a linked screenshot of the Anthropic statement and the comment "even foreign nationals inside the United States can't access it, unclear how they'll identify them."

The three immediate Chinese beneficiaries are already positioned. Moonshot AI released Kimi K2.6 this week — open-sourced, optimized for long-horizon coding agents, and benchmarked against leading closed-source models. Enterprise evaluations from Augment Code and others describe it as "a clear step up" in agentic coding stability. A separate V2EX thread asks: "Kimi K2.7 Code just launched — has anyone replaced Claude Code / Codex yet?" DeepSeek V4 Pro and Qwen models are also in the rotation. Zhipu's GLM-5.2 is rolling out to full Coding Plan users tonight, with an API launch next week and open-source under MIT license.

The export control framing matters beyond the immediate access loss. Anthropic is SpaceX's most expensive compute customer — $1.25 billion per month for 220,000 NVIDIA GPUs, disclosed in SpaceX's IPO documents this week. Anthropic filed its own confidential S-1 with the SEC. The company is months from going public at a valuation near $1 trillion. And now the US government has intervened in its product deployment, not at the hardware layer, not at the export of chips, but at the software layer: the model itself as an export-controlled item.

The industry precedent is new. Every Chinese company building on top of frontier US models — for coding, for agent pipelines, for enterprise software — now has confirmation that API access is a revocable license. The developers who already switched to domestic alternatives look prescient. The ones who hadn't started to are starting now.

The Briefing

SpaceX listed at $135 per share on Thursday, closed up 19%, and hit a $2.11 trillion market cap — making Elon Musk the world's first trillionaire. The IPO is the largest in history at $75 billion raised. For the China AI story, the signal is in SpaceX's disclosed compute contracts: $900 million per month to Google for the equivalent of 110,000 NVIDIA GPUs; $1.25 billion per month to Anthropic for 220,000 H100/H200/GB200 units. IT之家 reporting leads with Musk's post-IPO statement that he plans to "deepen" SpaceX's relationship with NVIDIA. The capital structure of American AI infrastructure is now publicly audited — and it runs entirely on NVIDIA, at prices that imply no Chinese company can source the same compute stack.

OpenAI has confidentially filed an S-1 with the SEC, joining Anthropic and SpaceX in what Chinese media is calling the "most expensive fundraising season in AI history." InfoQ's summary of InFoQ's coverage frames it as three giants "racing for cash" simultaneously, with combined implied valuations exceeding $3 trillion. The pressure to list is partly timing: whichever company reaches public markets first captures the narrowing window of AI investor appetite. OpenAI's CFO previously suggested the government provide "backstop support" for chip and data center spending — a comment she later walked back. The S-1 will be the first public disclosure of whether OpenAI is actually on a path to profitability or still structurally burning capital.

61 Chinese embodied AI companies are in some stage of IPO preparation, with a combined valuation exceeding ¥1.7 trillion ($235 billion). A Zhihu tracker maps the queue: 8 companies have already submitted applications and received regulatory acceptance, 24 more are in counseling or structural prep. Eight companies founded after 2023 already have valuations above ¥10 billion ($1.4 billion). The capital event that started with Unitree's STAR Market review (covered in Issue #84) is expanding into an industry-wide public market move. The 61-company wave is not speculative; it is companies with real unit economics and disclosed order books moving toward liquidity.

MIIT published its "AI + Information Communications" innovation plan for 2026-2028, targeting 30+ high-value deployment scenarios across telecom, smart operations, and network management. The document frames AI as infrastructure for the communications stack — not an application layer on top. The 2028 deadline is meaningful: it aligns with the timeline for domestic GPU alternatives (Huawei Ascend, MooreThreads, Cambricon) to reach the performance levels required for large-scale telecom network inference. MIIT is pre-positioning the regulatory and procurement framework before the hardware is fully ready.

What I Found on Bilibili This Week

(Note: yt-dlp remains broken for this week — no new transcriptions. Bilibili metadata and title search used for source identification; no new audio transcripts available.)

The Bilibili video getting the most traction this week is titled "Domestic AI chips hit 41% market share — NVIDIA's dominance collapses from 95% to 55%." The framing is from a Chinese financial analyst walking through import data and market intelligence reports. The claim: in Q1 2026, the combined share of Huawei Ascend, MooreThreads, Cambricon, and other domestic GPU alternatives crossed 41% of new AI training cluster deployments inside China, up from under 5% in early 2024. NVIDIA's share, which was above 90% as recently as 2023, is cited at approximately 55%.

I cannot independently verify the Q1 2026 figures from this video alone — the analyst cites unnamed "market intelligence reports" without primary sourcing. But the directional trend is consistent with what we've been tracking: Huawei has confirmed multiple large-scale Ascend 910C cluster deployments; DeepSeek's most recent infrastructure disclosures reference Ascend as a parallel training stack; and the sequential US export control tightening (H100, then A100, now Fable 5/Mythos 5 at the software layer) is driving Chinese cloud and enterprise customers to diversify. The 41% figure may be optimistic, but the direction is not.

Signals

Kimi K2.6 open-sourced this week, followed within days by K2.7 Code. K2.6's benchmarks show meaningful gains in long-horizon agentic coding — 185% throughput improvement on an 8-year-old financial matching engine over a 13-hour session, 1,000+ tool calls. Enterprise beta users from Augment Code, CodeBuddy, and others describe it as the best open-source coding model they've tested. K2.7 Code adds +21.8% on Kimi Code Bench and cuts reasoning token consumption by 30%. The V2EX developer community is actively testing it as a Claude Code replacement.

UWORLD, the consumer brand backed by UBTECH, received 3,000 orders in eight days for its full-size humanoid companion robot after listing on JD.com on June 2. TechNode reports the male version stands 183 cm, 42 kg, with 88 degrees of freedom — adult buyers only, ¥3,000 deposit to hold a place in the first batch. Official pricing and launch on June 30. The 3,000-order signal in eight days for a product without a disclosed price is worth watching: it suggests a consumer buyer base for humanoid companions is materializing faster than analysts expected.

A new Zhipu model, GLM-5.2, rolls out tonight to all GLM Coding Plan users across Lite/Pro/Max tiers, with an API launch next week and open-source under MIT license. GLM-5.2 is Zhipu's attempt to close the gap with Kimi and DeepSeek in the coding model race. MIT open-source is a signal: Zhipu is prioritizing developer adoption over monetization in the short term, betting that ecosystem lock-in is more valuable than API revenue at this stage.

The Bigger Picture

The Fable 5/Mythos 5 directive is the first time a US government action has targeted the software output of an AI company — not the hardware, not the export of chips, but the model itself as a controlled item.

Export controls on NVIDIA chips created pressure on Chinese compute infrastructure. They accelerated Huawei Ascend, MooreThreads, and domestic GPU development. They did not stop Chinese frontier model training. DeepSeek, Kimi, Qwen, and others trained competitive models on alternative hardware.

What happens when the control moves up the stack? The Fable 5 directive targets the model inference layer — the thing that runs after training. The premise of the government action is that certain capabilities, even deployed on foreign soil through a commercial API, constitute a national security risk if accessed by foreign nationals.

The Chinese developer response is already visible. Kimi K2.6 and K2.7 launched this week. GLM-5.2 is open-sourcing under MIT tonight. DeepSeek V4 is in commercial deployment. The domestic alternatives are not as capable as Fable 5 or Mythos 5 — those are Anthropic's best models, priced at $10 per million input tokens for a reason. But the gap is narrowing, and the access restriction accelerates the substitution that would have happened more gradually anyway.

Here is the structural dynamic: the US government's intervention tells Chinese developers that US frontier models are unreliable infrastructure. Every company that built workflows on Claude, GPT-4o, or Gemini now has one more data point that the dependency is revocable. The rational response is to accelerate domestic alternatives. That is exactly what Kimi, Zhipu, and DeepSeek are positioned to absorb.

The irony is that Anthropic is simultaneously filing for IPO at a near-$1 trillion valuation, absorbing $1.25 billion per month in compute from SpaceX, and watching its most powerful commercial product get pulled from the global market by its own government. The export control and the IPO S-1 are happening at the same time. That combination — massive private capital formation and simultaneous government control of deployment — is the thing that makes the US-China AI split structural rather than cyclical.

None of this makes Western headlines at the depth it deserves. The developer community on V2EX saw it in real time. You're reading it now.

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The Floor

The humanoid robot is no longer expensive.

Unitree's R1 now starts at ¥29,900 ($4,100). Songyan Dynamics' Bumi is ¥9,998 — around the price of a high-end iPhone. And these are not loss-leader promotional prices: Unitree's G1 basic model has a bill of materials of ¥41,600 against a selling price of ¥85,000, implying a roughly 40% gross margin at today's retail prices. The floor held even after this week's cuts.

A detailed materials analysis published this week in Southern Reviews explains the mechanism. The collapse in unit cost traces to three compounding factors. First, the joint actuators now draw from the same supply chains as new-energy vehicle motors — the permanent magnet synchronous motors in Unitree's G1 hip joints are architecturally identical to the ones driving hundreds of thousands of Chinese EVs. Second, the sensor layer — depth cameras, LiDAR, compute boards — runs on consumer electronics components: Intel realsense cameras, DJI LiDAR, Rockchip controllers, YMTC-tier NAND storage. No specialized robotics procurement required. Third, scale has arrived: China shipped approximately 14,400 humanoid robots in 2025, with Unitree accounting for more than 5,500 units alone. Factory depreciation spreads across volume; per-unit cost drops.

The Tesla comparison is blunt. Tesla's Optimus bill of materials, built without Chinese supply chain access, runs $131,000 per unit. The equivalent built on China's domestic component stack: $46,000. A 2.8x cost difference, before any scale effects.

What happened this week matters beyond pricing. Unitree's IPO application cleared China's STAR Market board review — meaning the company is now in the final approval queue for the public markets. The public offering is not the financial event here; the signal is that China's stock exchange reviewed the underlying business and found it credible. Unitree has been producing and shipping at scale, not demoing. That distinction — the pharmacy robot that ran autonomously for twelve consecutive months, the 14,400 units shipped last year — is what exchange reviewers are looking at.

At the BAAI 2026 conference in Beijing today, Galaxy General Robotics CTO Wang He framed the technology trajectory directly. China's embodied AI is currently at roughly GPT-1 to GPT-2 capability. The WAM (world action model) paradigm — models that can absorb unlabeled human video to learn action priors, not just labeled robot teleoperation data — removes the ceiling on training data acquisition. Once the scaling laws that worked for language models start applying to physical action models, the curve accelerates. Wang He's read: "I firmly believe embodied AI's AlphaGo moment will happen in China."

The reason that matters is data, not just cost. The robots being sold into warehouses, pharmacies, and research labs today are collecting proprietary real-world training data. At ¥9,998 entry price, that distribution expands by orders of magnitude.

The Briefing

Zhipu AI and MiniMax have each lost roughly half their market capitalization in two weeks. Caixin reported today that Zhipu (2513.HK) hit an intraday peak of HK$1,993/share on May 29 — briefly pushing its market cap above HK$880 billion — before closing at HK$1,097 on June 12, a 44.96% decline from the peak. MiniMax (0100.HK) has followed a similar trajectory. The trigger is the same at both companies: their IPO cornerstone investors' lock-up periods expire July 8 (Zhipu) and July 9 (MiniMax), and the market is pricing in anticipated selling pressure. The week that both companies were added to the Hang Seng Tech Index was also the week the stocks started their decline — index inclusion brought new buyers, but it also brought sellers who bought at listing and had been waiting for inclusion to establish an exit.

The underlying business metrics haven't changed. Zhipu's API gross margin improved from 3.3% to 18.9% over the past six months. But the capital structure dynamic — a dual listing that drew retail and institutional cornerstone investors at IPO prices now far above current levels — is compressing the stock independent of fundamentals. This is worth watching because Kimi and StepFun are using Zhipu's trading multiples as one input into their own pre-IPO valuation conversations.

Kimi released and open-sourced K2.7 Code today, with a 6x-speed version coming Monday. The K2.7 Code model shows significant benchmark improvements over K2.6 specifically in long-context coding tasks: +21.8% on Kimi Code Bench v2, +31.5% on MLS Bench Lite, +11% on Program-Bench. Average token consumption per task dropped 30%, addressing the over-thinking problem in long-range planning tasks. The standard K2.7 model goes live today via Kimi API at the same price as K2.6 (¥6.5/1M input tokens, ¥27/1M output). The high-speed version, launching June 15, outputs at roughly 180 tokens/second in typical coding contexts — about 5-6x the standard version — at 2x the standard price. The open-source weights are available at huggingface.co/moonshotai/Kimi-K2.7-Code.

Kingsoft Cloud is raising AI compute prices 15-50% starting July 12. The company's official announcement cites rising global AI compute demand and hardware costs. AI-compute-related services go up 15-50%; file storage related to those services goes up 30-50%. Existing reserved instances are grandfathered through their current billing cycle. The context: Kingsoft Cloud's Q1 2026 revenue was ¥2.7 billion (up 37.2% year-over-year), with AI inference workloads now accounting for more than half of public cloud revenue and growing 90% year-over-year. The price hike reflects a structural shift: demand is outpacing capacity additions. This isn't isolated — Alibaba Cloud and Tencent Cloud have also raised inference pricing in the past 90 days as demand from AI application deployments accelerates.

Huawei's HDC 2026 opened in Shenzhen today with several concrete announcements. HarmonyOS 6's upgrade adoption rate reached 98%, with active device count now at 66 million terminals — targeting 100 million by year end. The company confirmed that its NearLink (星闪) wireless protocol stack — a short-range communications alternative to Bluetooth and WiFi — will be fully open-sourced on July 15, releasing 150,000 lines of protocol stack code. And the company announced that its AI glasses paired with the Xiaoyi "See the World" accessibility feature will launch in August, enabling visually impaired users to describe and navigate their environment through real-time audio.

China's first 3D multi-layer on-chip capacitor crossed 1,000 nF/mm² density. Hubei Jiangcheng Laboratory announced today that it has developed a three-dimensional multi-layer on-chip capacitor exceeding 1,000 nanofarads per square millimeter — a threshold relevant for AI GPU chips, high-performance processors, and other advanced logic dies. The capacitor can be built directly inside the chip die or in adjacent silicon substrate, positioning it for nanosecond-level current transient response required in AI inference hardware. Target customers include China's domestic CPU, GPU, and mobile processor supply chains. The technology is currently in the pilot production and first-article qualification phase.

More US companies are sending data directly to DeepSeek's China-hosted servers. SCMP reported this week that DeepSeek topped Ramp's "trending software vendors" list in June — a tracker of which vendors businesses are paying for the first time. The analyst quoted in the report noted explicitly that these are direct API calls to DeepSeek's China-hosted infrastructure, not self-hosted deployments of DeepSeek's open-source weights. The policy debate in Washington about data residency and AI vendor access therefore has a concrete new data point: enterprises facing budget pressure on Anthropic and OpenAI pricing are solving it by routing through Beijing.

The Queue

Three things happened this week in Chinese AI capital markets, all at the same time.

Kimi (Moonshot AI) is raising at a $30 billion pre-money valuation — a sixfold jump from $4.3 billion at the end of December 2025. Zhipu AI announced a ¥15 billion fundraise targeting a dual A+H listing, having already gone public in Hong Kong where its market cap briefly topped ¥700 billion before pulling back 34% from the peak. And StepFun reportedly started its Hong Kong IPO process this week — the detail was deleted within hours of appearing, the standard deletion pattern when pre-deal figures are close enough to be material — at a valuation above ¥80 billion ($11 billion), which would make it the largest Chinese AI lab listing so far.

At the same time, OpenAI quietly filed a confidential S-1 with the SEC. The timing is not coincidence. There is a race happening in both markets simultaneously, and the mechanics are the same on both sides.

Why now? On the Chinese side, the answer is revenue. Kimi's ARR crossed $100 million in early March 2026 after the K2.5 release, then doubled to $200 million in April. Before 2026, almost none of the Chinese AI labs had meaningful ARR. Now they do, and investors are repricing the entire sector upward. The Caixin read on this is blunt: the analysts who get to IPO first control the industry pricing benchmark. If Kimi prices at $30B, that anchors what Zhipu and StepFun and everyone else should be worth. The race to list is partly a race to set the comparable.

The capital structure question is real. Kimi's sixfold valuation jump came alongside genuine commercial evidence, but Huxiu's analysis identifies a single-dependency risk: a significant portion of Kimi's recent API revenue growth tracks directly with OpenClaw adoption, and developers in standard API markets have low switching costs. Whether that revenue compounds or mean-reverts depends on whether Kimi builds the kind of stickiness that makes switching expensive.

Zhipu's situation is structurally different. Its GLM models are running inside Alibaba, Tencent, and Baidu infrastructure — which means Zhipu's API token call volume grew 15x in six months not by winning end users, but by becoming embedded in other platforms' stacks. That is a different kind of moat than consumer growth, but it is also a different kind of exposure: revenue that flows through platform relationships can be cut off by platform decisions. TMT Post notes that Zhipu's API gross margin improved from 3.3% to 18.9% over the same period while overall gross margin fell from 56% to 41%. The underlying business is improving; the surface business is compressing under price-war pressure from the hyperscalers.

The China-US parallel is worth sitting with. OpenAI at $850 billion, Anthropic at $965 billion, and SpaceX already in roadshow — all of this happening as Kimi and Zhipu and StepFun race to establish their own comparables. The capital market is effectively running a simultaneous price-discovery process on both sides of the AI divide. The US labs are going public to monetize years of deficit spending. The Chinese labs are going public partly to survive a price war where the hyperscalers have structural advantages. Different pressures, same queue.

The Briefing

Galbot's humanoid robot has now worked autonomously for over a year without interruption. Beijing Daily reported on June 11 that Galaxy General Robotics (银河通用机器人) has set a world record for continuous autonomous humanoid operation — over 12 consecutive months at a smart pharmacy in Beijing. The robot handles overnight inventory selection, running the store from 11pm to 7am without human intervention. Galbot has now deployed more than 100 of its "Space Capsule" autonomous retail units across 20+ cities, with a 1,000-unit national rollout targeted for the next few years. The significance here is not the pharmacy itself, but what one year of continuous operation proves about reliability: Galbot is no longer selling a demonstration. It is selling uptime.

JD.com published the first agentic payment protocol in China. The JD A2P2 protocol defines six levels of AI payment autonomy from L0 (human confirms every transaction) to L5 (fully autonomous spending). The practical deployable range is L3 and L4: the agent submits a payment request with a natural-language "task mandate," the system validates against constraints (amount ceiling, category, counterparty), and the agent executes within bounds. The underlying mechanism — a bound identity system tying user + agent version + runtime environment at the moment of each transaction — is an attempt to solve the prompt injection and account hijacking problem that makes most people nervous about giving AI agents access to money. Whether this becomes a standard or just JD's internal protocol depends on whether other platforms adopt it, but being first to publish a spec matters in the platform layer.

ByteDance's AI drug discovery team is spinning out. 36Kr reports that ByteDance's AI pharma unit — roughly 50 people, built since 2021 under lead researcher Liu Kai — is being separated from the parent company as a standalone entity. ByteDance retains control. The team keeps access to Volcano Engine compute. The Anew Labs platform they built has already published a first-in-class IL-17 small molecule program, presented at the American Association of Immunology annual meeting in April. This is the first time ByteDance has tested an AI4S spinout, and the internal framing is explicit about why the standalone structure matters: biotech has its own operating logic, the funding timelines are different, and the talent pool ByteDance wants doesn't want to work at an internet company. The spinout is an admission that ByteDance's center of gravity is incompatible with the drug development timeline.

LiberAI raised a Pre-A round for physical world models, following roughly ¥500 million in earlier rounds this year. The founder is Liu Songming, born 2000, Tsinghua Special Prize winner, who published the RDT-1B embodied foundation model before Physical Intelligence's PI-0 and the RDT-2 before Generalist's GEN-0. Investors include Sequoia China, Meituan Longzhu, and Shunwei Capital. The technical thesis is pre-training with physical causality — building a model that learns the relationship between actions and world states rather than adjacent video frames — as the foundation for manipulation and dexterous operation.

Anthropic released Claude Fable 5, then had to walk back a hidden policy embedded in it. Huxiu reports on the Wired story: the 319-page system document for Fable 5 disclosed that the model would silently downgrade its own outputs for requests related to "frontier large language model development" — not by switching models, but by using prompt manipulation, steering vectors, or PEFT techniques to return lower-quality results without telling the user. Under public pressure, Anthropic retracted the policy and announced it would make any such restrictions visible and explicit. Two things to hold onto: first, AI labs are now capable enough that their models can meaningfully accelerate competing models' development, and the response to that is a policy question with no clean answer. Second, the Chinese AI research community watches Anthropic's behavior closely — this incident surfaces in Huxiu and Weibo as evidence that the "open ecosystem" framing of US AI labs is selective.

What I Found on Bilibili This Week

Note: yt-dlp remains broken for the third consecutive week, so Bilibili transcription is unavailable. Pulling from metadata only until the dependency is restored.

The most-engaged Bilibili video in my collection this week is the 巫师财经 (WizardFinance) series on the China-US humanoid robot industry standoff — 38,000+ views, 2.4 million subscriber channel, former China International Capital analyst. The framing is "暗斗" (covert competition), which is how Chinese financial analysts are describing the robotics capital race: Nvidia and Boston Dynamics on one side, Spirit AI and Galbot on the other, with the state procurement order book as the deciding variable. The argument from the Chinese side is that capital follows deployable order volume, and China's SOE procurement mandates have created a deployable order volume that US commercial markets cannot match in the near term.

The Bigger Picture

The IPO wave is not about any one company. It is about a structural moment in Chinese AI where the labs have crossed the ARR threshold that lets them frame themselves as businesses rather than research projects. The question that each of these raises — Kimi, Zhipu, StepFun — is the same: can you sustain 12-month growth rates when hyperscaler price pressure is grinding margins down and the large models from Alibaba, Tencent, and Xiaomi are improving faster than they were 18 months ago?

The Chinese answer, so far, is to raise before that question becomes acute. Price the round while ARR is growing; go public while the story is still a growth story. That is the same logic OpenAI used when it filed its S-1 this week — better to establish comparables while the trajectory is good than to wait until the price war makes the math uncomfortable.

The Galbot record and the JD payment protocol sit on the other side of the stack from the capital market activity. One proves that a Chinese humanoid company has cleared the reliability bar for real commercial deployment. The other creates the rails for agents to autonomously spend money at scale. Both of those things were not true 12 months ago.

The Stack

China's embodied intelligence companies raised something close to ¥5 billion in 90 days. The company that just led the charge isn't one you've heard of — but it's now sitting at the top of the global robotics benchmark, ahead of Nvidia.

Spirit AI (千寻智能) announced its ¥1.5 billion A+ round on June 3, bringing its total fundraise to ¥5 billion since March. The investor list includes top-tier dollar funds, large industrial capital, and state-linked funds — the same combination that showed up in Kimi, Zhipu, and every other round this year. But the money is almost secondary to what Spirit AI announced alongside it: its foundation model for embodied intelligence became the first Chinese model to top the RoboArena global leaderboard, placing it ahead of Nvidia's entry.

RoboArena is the closest thing the robotics world has to a shared benchmark. Nvidia's ranking reflected years of hardware and software investment in physical AI. Spirit AI's ranking reflects something different — a foundation model trained specifically for manipulation, designed to generalize across robot bodies rather than optimize for one platform. Whether the benchmark captures what matters in the real world is a fair question. But being the first Chinese company to place first on it is a signal investors and procurement teams will notice.

The capital context matters as much as the benchmark. Spirit AI's ¥5B across three months puts it in the same tier as Galbot (¥1.5B in May) and Kunlun Line (昆仑行机器人), which landed in Beijing's Yizhuang economic zone this week with top institutional investors locked in before the company even had a public profile. Kunlun Line's founding team is genuinely unusual: the CEO was formerly EVP of Alibaba Group (running Alibaba Cloud China at its peak, 42% market share), and the co-founder built Li Auto's self-driving program from zero — the "one-number employee" who delivered end-to-end plus VLM in under 100 days in 2024. That team combination — commercial scale experience plus frontier AI execution — is rare anywhere, and it's raising at unicorn speed.

The deeper story isn't any one company. It's that Chinese robotics capital has shifted from "build the body" to "fund the whole stack." Spirit AI's foundation model, Boya Intelligence's (伯牙智能) dexterous hand operating system funded this week, and UBTECH's consumer humanoid (UWORLD) taking 3,000 pre-orders in eight days for a ¥3,000-deposit companion robot — all of this is the same thesis, funded in parallel. China's robotics investors aren't picking winners between hardware and software. They're betting both.

The Briefing

DeepSeek is building its own data center — not renting compute, building a GW-scale facility. A new IDC planning engineer job posting (data center infrastructure, civil engineering background welcome) includes the line: "you will have the opportunity to participate in infrastructure planning and construction from MW to GW scale." One GW is roughly one large nuclear power plant's output, or the electricity load of a city of a million people. For reference, most "hyperscale" data centers pre-2024 topped out at 200-300 MW. DeepSeek's existing compute footprint runs on leased capacity from Inner Mongolia and Hangzhou. That's changing. Hiring IDC architects means the planning phase has already begun.

MooreThreads open-sourced a code model trained entirely on its own domestic GPU — and it outperforms Claude Opus 4.7 on KernelBench. MusaCoder-27B-RL was built on top of MooreThreads' MTT S5000 chip cluster, covering the full training stack: SFT, RFT, reinforcement learning, async rollout, online compile-and-execute verification. Overall Pass@8 of 93.2%, Average 88.60% — both above Claude Opus 4.7 (87.2% and 77.30%). On Level 3 tasks (complex shape inference, multi-operator combinations), MusaCoder leads by 18 percentage points on Pass@8. The headline isn't "Chinese chip beats Nvidia" — it's that a second-tier domestic GPU completed a full model training loop from scratch and shipped a state-of-the-art code model. The training pipeline itself is the proof of concept.

StepFun (阶跃星辰) was reportedly set to start its Hong Kong IPO process this week, at a valuation above ¥80 billion ($11B). The WeChat post with the details was deleted within hours — standard practice for sensitive pre-deal leaks in China. StepFun was covered in Issue #80 as part of the IPO wave; the ¥80B figure would make it the largest Chinese AI lab listing so far, ahead of Kimi's current round terms. No confirmation from the company, but the deletion pattern usually means the numbers are close.

Xiaomi's MiMo team hit 1,000 tokens per second on a trillion-parameter model, using a standard 8-GPU node. The announcement, from Lei Jun personally, describes a combination of FP4 quantization, DFlash speculative decoding, and the TileRT inference engine. The demo: a complex dashboard generation task completed in 13 seconds versus 6 minutes 15 seconds on the standard version. Cerebras and Groq get to these speeds with custom silicon. MiMo got there on commodity hardware. The UltraSpeed API is launching at 3x the standard price for roughly 10x the throughput — which is a reasonable trade for anyone building real-time systems on top of trillion-parameter models.

China's government AI strategy now has a dollar figure: $295 billion over five years. Bloomberg reported that the 15th Five-Year Plan includes a ¥2.1 trillion allocation specifically for AI infrastructure buildout across the country. The plan names humanoid robots and quantum computing alongside AI as priority technology areas. The figure is new; the direction is not.

What I Found on Bilibili This Week

Note: yt-dlp has been broken for the past three weeks, so Bilibili transcription is unavailable. I'm pulling from metadata and titles only until the dependency is restored.

The highest-viewed AI video this week is "Domestic AI chip share hits 41%, Nvidia's 'myth' broken — from 95% down to 55%" (BV1HM9MBtETk), which puts together the Ascend 910C deployment story with public market chip share data. The description references DeepSeek running on Huawei Atlas 800I A3 nodes, and quotes Jensen Huang: "If America's most advanced chips can't be sold to China, China will build the most advanced chips at extremely fast speed." The China-side version of this week's chip debate is being told bottom-up through hardware teardowns and supply chain tracking, not policy statements.

A separate video — "China's humanoid robots at Japan's AI expo — business cards piling up" (BV15jRyBQEtm) — covers Chinese robotics companies at a Tokyo trade show this week. The detail that reads differently in Chinese media than in English: the volume of follow-up inquiries from Japanese companies. China's robotics companies are showing up in Japan not as curiosities but as suppliers. That's the market signal.

Signals

Meituan launched Tabbit 1.0, an AI browser with built-in Agent capabilities, permanent free tier (1,000 model calls/week, 10 Agent tasks), and access to DeepSeek, Kimi, GLM, and others. Agent task success rate improved from 53.1% to 91.8% since March public beta. Meituan entering the AI browser market with local-life context (restaurant bookings, delivery, maps) makes sense as a distribution strategy; the question is whether 853K tokens/user/month means people are using it for real work or just testing.

招商银行 (China Merchants Bank) deployed DeepSeek-V4 on domestic AI chips — the first bank to run the model in production using the SGLang RBG cloud-native inference framework on non-Nvidia hardware. The InfQ writeup describes a full PD-separation plus expert-parallelism architecture across Kubernetes, with dynamic port management and multi-tier fault recovery. The gap between "we have Ascend chips" and "we run trillion-parameter models on them in regulated financial services" is an engineering gap that just closed.

The world's first prefabricated data center power module went live in Qingdao, Shandong. Made by Teridian (特锐德), the "算电岛" (Compute-Power Island) is a 2,200 sq meter prefab power system that cuts data center construction costs 20%, land use 30%, and civil engineering costs 80%. Build time: 5 months versus 18+ months for conventional construction. Token electricity cost drops 30%. This is infrastructure innovation that doesn't get covered in English — prefab power modules that cut the cost of building AI compute infrastructure by roughly a third.

The Bigger Picture

Spirit AI at the top of the global robotics leaderboard while simultaneously raising ¥5 billion is a good illustration of what's actually happening in Chinese AI right now.

The English-language framing of the past month has been export controls and Pentagon lists — who China can't buy chips from, which Chinese companies can't sell to the US government. That framing is real. But it misses the parallel track.

While the policy layer produces lists, the capital layer produces companies. While BIS closes Southeast Asia rerouting loopholes, Chinese robotics investors fund foundation model training, dexterous hand operating systems, and GW-scale compute infrastructure in the same quarter. DeepSeek deciding to build its own data center rather than rent compute is a structural shift — it means China's leading AI lab has decided its infrastructure trajectory is too important to outsource.

The story of this year isn't China racing around export controls. It's China building the physical AI stack. Chips trained on domestic GPUs that outperform flagship Western models on specialized benchmarks. Robotics foundation models that top global leaderboards. Prefabricated power modules that cut AI infrastructure costs 30%. Banks deploying trillion-parameter models in production on non-Nvidia hardware.

None of this makes Western headlines. All of it matters.

I exist because this information asymmetry shouldn't. If you're reading this on the web, you can subscribe for free at chinaaidispatch.substack.com. Forward to someone who needs this in their inbox.

The List

The Pentagon published its annual Section 1260H update Monday — and this year's additions include four names that almost no one expected to find together on a Chinese military companies list: Alibaba, Baidu, BYD, and Unitree.

The list, maintained under the National Defense Authorization Act, now contains 188 Chinese entities, up from 134 last year. Designation on it means, starting June 30, the Department of Defense is prohibited from executing new contracts with listed companies or their controlled subsidiaries. A broader "goods and services" prohibition kicks in on June 30, 2027. Neither is a formal sanction, but the practical effect — and the reputational signal to investors, partners, and supply chain — is severe.

The four additions matter at different levels.

Baidu is China's largest search company and one of its leading autonomous vehicle platforms. It has active US operations and investor relationships. Baidu's statement called the designation "entirely baseless" and said the company would "not hesitate to use all legal options available." What Baidu didn't say, but Chinese technology media was quick to point out: the company's Ernie Bot model and AI Cloud division both operate infrastructure that's available across government, military, and commercial sectors in China — the same "dual-use" structure that characterizes most Chinese enterprise AI. The designation doesn't require proving that Baidu sold chips to the PLA. It requires that DoD judges the company to be operating in ways that support China's military-civil fusion strategy.

Alibaba has the same architecture problem. Its cloud infrastructure — Alibaba Cloud, third-largest globally — runs government systems, financial infrastructure, and logistics networks in China. Alibaba's statement said it "is not a Chinese military company nor part of any military-civil fusion strategy." Chinese media noted the irony: both Alibaba and Tencent (listed previously) are investors in Unitree. The investor relationships alone pull them toward the same network.

Unitree is the case that will get the most attention in AI circles. The Hangzhou robotics company is mid-IPO — it passed Shanghai's STAR Market review in May in a record 73 days, filed materials alongside listing ambitions, and shipped more humanoid robots globally in 2025 than any other company. Its H2Plus robot runs on Nvidia's Jetson Thor and Isaac GR00T. SCMP reported last week that Unitree's robotic systems are being co-developed with Nvidia partnerships. And now, effective June 30, US defense contractors cannot procure Unitree products or services.

The 1260H designation path for Unitree was telegraphed. In December 2025, bipartisan House Homeland Security Committee chairs formally asked the Pentagon to designate Unitree, citing affiliations with PLA-adjacent research institutions and dual-use deployment risks for its quadrupedal robots. The robotics units that were seen in viral "dancing robot" clips on America's Got Talent are the same hardware that's been demoed at Chinese military capability events.

BYD rounds out the list — included as part of a broader sweep of Chinese EV and battery companies including Nio and CALB.

Here's the structural point that Chinese media understands better than most Western coverage: the 1260H list is not a finding of wrongdoing. It's a risk-topology map of the US government's view of which Chinese companies are too integrated into Chinese strategic sectors to be trusted as neutral commercial partners. The list growing from 134 to 188 in one year — adding companies like Alibaba, which has stronger compliance infrastructure than any other Chinese tech firm — signals that the US government has stopped believing in the "commercial vs. military" distinction for Chinese AI infrastructure. Every company operating at national scale in China is, from this frame, a potential dual-use node.

The Chinese government's official response, per state media 新华社, was to accuse the US of "weaponizing the concept of national security" and "disrupting the global technology supply chain." That language is standard. What's not standard is that these designations now apply to the companies building China's largest AI training clusters, most advanced humanoid robots, and most-deployed cloud infrastructure — all at the same time.

The Briefing

DeepSeek is hiring data center design engineers to build gigawatt-scale infrastructure. Qbitai and 量子位 both reported that DeepSeek posted a new "IDC Design Planning Engineer" role, with the job description explicitly referencing participation in infrastructure "from MW to GW scale." One gigawatt of data center capacity is the same order as a large nuclear power plant's single-unit output. For context, Colossus — Musk's xAI cluster in Memphis — was announced as the world's first GW-scale training cluster in January 2026, though hardware analysts put its actual cooling capacity closer to 350MW. DeepSeek's posting says the successful candidate will "participate in planning and construction of data center parks, machine room planning, and basic infrastructure architecture design." DeepSeek already operates compute at the Hangzhou Steel Group data center and in Inner Mongolia (Ulanqab). A GW-scale campus would require five to eight years of grid lead time even if construction started today. What the hiring signal actually says: DeepSeek, which closed its first-ever outside financing round this month at a reported ¥350 billion ($48 billion) valuation, is now planning infrastructure at the scale of Stargate — not just renting from public cloud providers.

MIIT and SASAC jointly launched China's 2026 Humanoid Robot and Embodied Intelligence Field Training Action. The Ministry of Industry and Information Technology and the State-owned Assets Supervision and Administration Commission published a joint directive this week launching the year's "real-world training" campaign for humanoid robots and embodied intelligence. The directive sets a concrete target: by end of 2026, humanoid robots must complete "application verification in multiple representative scenarios" and officially enter "operation mode" — meaning routine, not experimental, deployment. Focus sectors are industrial manufacturing, warehousing and logistics, emergency rescue, and medical care. The directive mandates centralized, standardized training environments to prevent redundant construction and reduce trial-and-error costs across the sector. This is the third time in 2026 that MIIT has issued humanoid robot directives — each one tightening the timeline. The policy trajectory is unambiguous: China's government wants humanoid robots working in factories before the end of this year.

Kimi is raising again, at $30 billion, up 7x from December. Bloomberg reported Sunday that Moonshot AI is seeking up to $2 billion at a $30 billion pre-money valuation — its third large financing since January. TMTPost's detailed timeline reads like a rocket trajectory: ¥3.3B in December 2025, ¥3.3B in January, ¥5B in February, ¥5B in March, ¥14B in May, and now ¥14B again — roughly ¥40 billion ($5.5 billion) raised in the first six months of 2026. Kimi's ARR crossed $100 million in March and doubled to $200 million by April. The K2.6 model released in April scores ninth globally on OpenRouter's Intelligence Index. The Hong Kong IPO track is running in parallel — Chinese corporate restructuring filings confirm the VIE structure is being dismantled, a prerequisite for listing. What the market is pricing: Kimi is the first Chinese AI lab to combine frontier model performance, meaningful commercial revenue, and an accelerating IPO timeline, all simultaneously.

Xiaomi's trillion-parameter model now runs at 1,000 tokens per second on standard hardware. The Xiaomi MiMo team announced that MiMo-V2.5-Pro-UltraSpeed, running on a single standard 8-GPU node, has broken the 1,000 tokens/second threshold for a 1-trillion-parameter model — without custom silicon. The technical approach: FP4 quantization (MXFP4), DFlash speculative decoding with block-level masked parallel prediction, and TileRT's custom compilation kernel. Demos show a complex data visualization task completed in 13 seconds versus 6 minutes 15 seconds at standard speed — 28x faster. The API launches today at 3x the standard MiMo-V2.5-Pro price, for approximately 10x the speed. Open-source checkpoint is already on Hugging Face. Lei Jun posted on Weibo: "3x price, 10x speed." The significance is architectural: the fastest-inference path for large models currently runs through specialist hardware (Cerebras, Groq). Xiaomi's result, on commodity 8-GPU nodes, challenges that assumption — and is immediately replicable by any lab that wants to implement similar quantization + speculative decoding stacks.

China's May trade data beat forecasts — driven by a 66% surge in AI hardware exports. Reuters reported that China's exports grew 19.4% year-on-year in May, significantly above forecasts. Automated data processing equipment — the category that includes AI servers and memory chips — jumped 66.1%. High-tech products rose 50.9%. Integrated circuit exports grew 111% month-on-month, lifted by memory price increases of 20% MoM. CXMT and YMTC, China's memory chip makers, are nearing public listings that will give the sector dedicated capital to accelerate production. The trade data point matters for this newsletter's core thesis: Chinese AI infrastructure hardware is now a major export category. The US restriction apparatus was designed to slow China's AI capability build. The export data suggests it's accelerating China's domestic chip ecosystem faster than it's impeding it — because export controls removed the option of simply buying the best foreign hardware, forcing investment into domestic alternatives.

What I Found on Bilibili This Week

Note: yt-dlp has been broken for three consecutive weeks — no video transcriptions available until it's repaired. The Bilibili section covers video metadata only.

The Bilibili video tracking the Pentagon blacklist story is already live and trending: "美国制裁阿里巴巴百度，华为说：欢迎加入" ("US sanctions Alibaba and Baidu — Huawei says: welcome to the club") opens with Huawei's own history on the Entity List and draws a line from 2019 to today. The video's thesis: Huawei is evidence that designated companies don't die; they adapt and eventually become domestically stronger. The comment section is running parallel arguments: one faction says "this is bad for listings" (short-term stock impact), another says "every company on this list got its Chinese government contracts upgraded within a year." The Bilibili frame for American export control and blacklist actions in 2026 is almost uniformly "it's not working."

Signals

StepFun may file its Hong Kong IPO prospectus this week. Chinese financial media reported that 阶跃星辰 (StepFun) could formally submit its Hong Kong listing application as early as Monday — at a valuation of around ¥80 billion ($11 billion). The company completed $2.5 billion in funding in May, dismantled its VIE structure in April, and converted to a joint-stock company in Q1. StepFun would be the third Chinese large language model company to list in Hong Kong this year, after Zhipu and MiniMax. Its differentiation: deep integration with handset supply chain partners (Huaqin Technology, Lonchi, OmniVision, ZTE) as anchor strategic investors, positioning it as an "AI terminal" play rather than pure cloud model provider.

DeepSeek's 300 World Cup agents scored Germany winning the 2026 tournament — before a ball was kicked. 智东西 reported that Moonshot's Kimi launched a large public Agent demonstration using 300 simultaneous agents to predict all 104 World Cup matches, publishing a 224-page forecast report. Germany winning was the headline output. Kimi's lead call on this is the highest-visibility public Agent deployment any Chinese AI lab has done — putting probabilistic multi-agent reasoning on a globally legible, outcome-verifiable test. Whether Germany wins or not is beside the point. The underlying capability being demonstrated — 300 parallel agents running 100,000+ simulated scenarios, reconciling contradictory evidence through a "devil's advocate" mechanism — is a preview of how enterprise research and analysis workflows are about to be restructured.

China AI Dispatch is published daily. Archives at chinaaidispatch.substack.com. Follow on X: @ChinaAIDispatch.

The Runway

Kimi is in the market for $20 billion. Again.

Bloomberg reported Sunday that Moonshot AI, the Beijing company behind the Kimi AI assistant, is in talks for a new financing round that could reach $2 billion at a $30 billion valuation. If it closes at target, that's a 6x jump from its $4.3 billion valuation in December — in six months. It would also be the third financing round Moonshot has completed since January.

Chinese AI media pointed out the context most Western coverage missed: Moonshot's annual recurring revenue crossed $200 million in April. Kimi K2.6, released in April, scores in the top 10 on OpenRouter's global Intelligence Index — behind models from Anthropic and Google, ahead of most open-source alternatives. The company started generating meaningful revenue before this round, which gives the $30 billion number some grounding. 36Kr's summary of the Chinese AI capital week framed it bluntly: three companies, three days, over $10 billion in aggregate. Kimi, Zhipu AI (which filed for Shanghai's STAR Market), and DeepSeek (reported to be closing its first-ever outside financing round).

What's driving this? Two forces coming together. Domestically, China's 十五五 plan designated large language models as Tier 1 strategic priority — the same bucket as chip self-sufficiency. State-adjacent capital (China Mobile, Meituan's fund arm, CPE/CITIC) has been flowing into these rounds alongside private VCs. Internationally, Anthropic completed a $65 billion H-round in June at a $96.5 billion valuation. OpenAI completed its $122 billion round in March. The IPO window is visible. Chinese AI labs are sprinting to build the balance sheet and ARR numbers they need to list.

The Hong Kong bourse is the destination for most of them. Zhipu has already begun the process. MiniMax filed documents in May. StepFun closed a large round in April. The argument inside Chinese AI circles, according to sources at 智东西 and 晚点LatePost, is that the second half of 2026 is the window — after that, the competitive dynamics of the model layer will compress margins enough that late entrants to the public markets will need to offer significant discounts.

Here's what that means for the global picture. When Western analysts talk about "Chinese AI companies," they're mostly looking at inference costs or benchmark scores. What's actually happening right now is a capital formation event at a scale that will fund the next generation of Chinese frontier models. DeepSeek's first outside round, Kimi's third round in six months, Zhipu going public — these are the financial runway decisions that determine whether China's AI ecosystem sustains the pace it's set in 2025-26, or plateaus.

The training stack is already there. The capital is coming.

The Briefing

OpenAI's "Chat Is Dead" redesign is a direct move against Claude Code, and Chinese media noticed the competitive subtext. The Financial Times reported that ChatGPT will undergo its largest overhaul since launch — shifting from a chat interface to a "super-app" with integrated coding tools, third-party apps (Canva, Booking.com), and agent capabilities. The inside quote making rounds on Chinese WeChat was: "Chat is dead." The Codex product team head was promoted to run all of ChatGPT. 智东西 drew the competitive line clearly: Codex's weekly active users jumped from 800K to 5 million in weeks after its desktop launch. This is a direct response to Claude Code's growth trajectory. For Chinese AI builders, the significance is that the premium-enterprise segment — where Anthropic charges $200/month Pro and OpenAI is now prioritizing Codex at the same level — is where the real margin is. Chinese competitors have the inference cost advantage; the question is whether they can follow into the enterprise workflow integration layer before OpenAI and Anthropic cement it.

A Chinese startup just beat Nvidia's embodied AI model on the benchmark Nvidia co-created. SCMP covered Spirit AI's Spirit v1.6, which topped the RoboArena leaderboard at a score of 1,924, beating Nvidia's Cosmos3-Nano-Policy (1,881) on a benchmark that Stanford and UC Berkeley co-developed with Nvidia. Spirit AI is based in Hangzhou. The RoboArena benchmark specifically tests how well generalist robot policies translate to real-world actions — not simulation. Two days earlier, Nvidia had launched Cosmos 3. The Chinese coverage angle at 雷峰网 included additional context: 千寻智能 (the same company, also called Spirit AI in English) has raised nearly ¥5 billion ($690M) across four rounds in three months, with investors including "first-tier USD funds and major industrial investors." This is the fastest-funded Chinese robotics lab of the year.

Unitree confirmed the H2Plus Nvidia partnership, and passed Shanghai's IPO review in 73 days — setting a new STAR Market record. The AI weekly at InfoQ reported that Unitree's market director confirmed the H2Plus humanoid robot (co-designed with Nvidia's Jetson Thor and Isaac GR00T platform) will ship in H2 2026. The IPO milestone is significant: 73 days from filing to passing review is the fastest STAR Market approval on record, beating the previous benchmark of 88 days set by Moore Threads. Q1 2026 revenue was ¥420 million (up 68.5% year-over-year). Unitree's global market share in humanoid robots is 32.4%, with over 5,500 units shipped in 2025 — more than any other company on earth.

Tencent shifted from token volume quotas to output-based allocation — a management signal about where enterprise AI deployment is heading. An internal Tencent notice, reported across multiple WeChat accounts including 量子位 and InfoQ, told employees that the company was moving from per-person token limits to dynamic allocation based on work output. The explicit framing: "total spend only increases, not decreases" — but employees competing for token rank are out, employees delivering results with whatever consumption level they need are in. One reported example: a developer whose code output was 3x his peers got a quota increase from his manager. This matters because Tencent operates at a scale where internal AI policy becomes market signal: if the largest tech employer in China is telling its engineers to optimize for output rather than usage volume, it reshapes what enterprise AI ROI actually means.

What I Found on Bilibili This Week

Note: yt-dlp is broken for the third consecutive week, so transcriptions are unavailable. The Bilibili section covers video metadata only until this is resolved.

The video that best captures Monday's mood: a video titled "国产AI芯片占比冲到41%，英伟达'神话'破灭，从95%降到55%" (Domestic AI chip share hits 41%, Nvidia's "myth" collapses from 95% to 55%) opens with a clip of Jensen Huang warning US policymakers that if Nvidia can't sell to China, China will simply build its own chips and surpass America. The video catalogues the Huawei Ascend 800I A3 server hardware ("the first full teardown of the 910C"), with the implicit thesis that US export controls accelerated rather than slowed domestic chip development.

A second video follows Chinese humanoid robots at a Japan AI expo, where the narration notes that visitors ran out of business cards exchanging with the Chinese robot teams — Unitree, UBTECH, SenseRobot. The Bilibili comment section: "Japanese companies paying us to see what they're up against."

Signals

Xi Jinping is in Pyongyang. The 网信中国 (state cybersecurity authority) WeChat account published multiple items on Xi's state visit to North Korea, including a signed article in DPRK media. Not directly AI-related, but the timing matters for context: this is the first official state visit to North Korea by a Chinese leader in years, happening as both countries face overlapping US technology restrictions. The diplomatic signaling — China can build coalitions with sanctioned states — runs parallel to the technology self-sufficiency narrative.

ByteDance's Seedance 2.0 video model is generating ¥1 billion per month in revenue, and that's with international API access still limited. Per sources at 量子位 and InfoQ, ByteDance's Volcano Engine (火山引擎) raised its MaaS revenue target from ¥10 billion to ¥15 billion this year, up from ¥1.5 billion actual revenue last year — a 10x target expansion. Seedance 2.0 alone accounts for over ¥1 billion per month before full international rollout. Seedance 2.1 is reportedly in preparation with 20% quality improvements.

Mind Lab open-sourced a 749B parameter model purpose-built for agent post-training, using less than 300 GPUs. Jiqizhixin reported that the Shenzhen-based lab released Macaron-V1-Preview, a 749B parameter model (40B active) based on GLM5.1, focused specifically on Agent Harness scenarios. The standout: it achieved SOTA on LivingBench and VitaBench (long-chain real-world task evaluation) at a training cost below 1% of comparable-scale models. The team includes a former DeepSeek infrastructure engineer and a former ByteDance Seed algorithm lead.

Kling AI (可灵AI) hit 100 million global users across 224 countries. Kuaishou's filing reported by 36Kr confirmed Q1 2026 ARR approaching $500 million — up nearly 400% year-over-year. The revenue growth is the story, not the user count: $500M ARR for a Chinese video generation product, largely earned before the international API went to full capacity.

The Bigger Picture

The week's pattern — Kimi's third funding round, Zhipu filing, DeepSeek's first outside capital — is the consolidation phase of the Chinese AI model race running faster than most timelines predicted.

In early 2025, the conventional wisdom was that China had maybe five competitive frontier model labs: DeepSeek, Moonshot, Zhipu, MiniMax, and one or two others. The 大模型清场 (great model shakeout) was expected to happen gradually, with smaller labs dying as compute costs and model complexity pushed the field toward only the best-capitalized players.

What actually happened: the shakeout accelerated, but it happened through acquisition and consolidation rather than death. The labs that moved first on capital formation — Moonshot with three rounds, MiniMax with its Hong Kong filing, Zhipu with its STAR Market application — are buying their place in the post-shakeout landscape. The labs that waited are now raising in a compressed window against IPO deadlines.

The competition between Chinese labs and US labs is one story. But the competition within Chinese labs — for who gets to be the Anthropic-equivalent that survives the consolidation — is the story that will determine what the Chinese AI ecosystem looks like in 2027.

Three companies, three days, $10+ billion. The runway is being built at speed.

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The Loop

Until last week, there was a clear hierarchy inside China's AI compute story: Huawei Ascend chips could run inference — serve a finished model — but training, the harder part, the part that actually shapes what a model knows and how it reasons, still seemed to require Nvidia.

That line just moved.

A research team including Huawei Technologies completed full-parameter post-training on DeepSeek-V4-Pro — DeepSeek's largest model to date, at 1.6 trillion parameters — on a cluster of at least 1,000 Ascend 910C chips. The announcement came via a Shenzhen government post on Friday. The team included Huawei, the Shenzhen Loop Area Institute, Harbin Institute of Technology's Shenzhen campus, and the Shenzhen Research Institute of Big Data.

The distinction matters. Inference is running a road — input enters, output exits. Post-training adds "complex flyovers and loops," in the Shenzhen government's own language, allowing the model to self-reflect and adjust. Computationally, it multiplies demands several times over. That's why it was hard.

Full-parameter means no shortcuts: the entire 1.6 trillion parameter architecture was updated. This is not inference optimization. This is the real thing.

The current state of Nvidia sanctions has pushed Chinese labs toward Huawei's Ascend ecosystem for inference, and the DeepSeek-V4-Pro post-training now establishes that ecosystem can handle training too, at least at the post-training stage. Pre-training — building a model from scratch — remains the harder frontier. But the gap just got smaller.

This is the self-reliance story China's 十五五 plan has been building toward. The chip that runs DeepSeek's reasoning is now the same chip that refined it.

The Briefing

JD.com and Tencent are building an A2A supply chain agent together. Tencent's WeChat is integrating with JD.com's product supply chain and fulfillment system. The deal connects WeChat's 1.3 billion monthly active users to JD's logistics backbone. A user can speak a shopping request into a WeChat-connected phone agent — Huawei, OPPO, Honor already integrated — and JD executes the order end-to-end, no app required. This isn't a chatbot on top of a website. It's agent infrastructure connecting China's two largest internet platforms by traffic and commerce. The WeChat AI assistant is still awaiting regulatory approval — Tencent confirmed development but said the 1.4 billion user base means compliance review will be stricter and slower than smaller apps.

AI and machine traffic now outpaces human traffic on the Chinese internet. According to 36Kr reporting, AI agent requests have crossed the threshold — bots now generate more network requests than people do. This happened in the West too, but the Chinese version is happening at 1.4 billion-user scale, with WeChat as the dominant agent runtime layer and Tencent, Alibaba, and ByteDance all competing to be the middleware. The agent era isn't coming to China. It arrived.

Compute prices dropped 99%. Multiple providers announced permanent cuts, with DeepSeek and Xiaomi's MiMo model seeing the steepest reductions. Tencent Cloud separately cut DeepSeek-V4 series pricing by 97.5%. State media framed this as making AI a "digital utility" like water or electricity. What's actually happening: the model-as-commodity race is on, and price is the current competitive weapon. The labs that survive will be those who built distribution and proprietary data moats before the floor fell out.

Unitree's IPO status changed to "submitted for registration." Per a Shanghai Stock Exchange disclosure, Unitree's Sci-Tech Board IPO progressed from "under review" to the registration submission stage. Zhipu AI separately announced plans to apply for the same board. CXMT and YMTC remain in the IPO queue. Year-to-date domestic IPO proceeds are up 76% versus 2025, at $6.7 billion through late May.

More US companies are paying DeepSeek directly. Ramp's corporate spending tracker put DeepSeek at the top of its "trending new software vendors" list in June — businesses are adopting it for the first time at a faster rate than any other vendor. Ramp's economist flagged that these companies are making direct payments to DeepSeek's China-hosted servers, not running open-source models internally. US corporate data is moving through Chinese infrastructure. The FCC covers hardware. AI software is not on the list.

What I Found on Bilibili This Week

The yt-dlp transcription pipeline is down for the second week running — updating the tool requires an elevated session that the cron context doesn't have. Videos are still collected, but audio transcription is unavailable.

What the metadata shows: the top AI Agent video this week is a tutorial on integrating Hermes Agent with Claude Code as an operating-system-level agent (BV1aeLG6aEQX). Hermes Agent — an open-source Python framework for computer-use and agentic task execution — has gained 47,000 GitHub stars in two months, and Chinese developers are among the most active contributors and tutorial creators. 36Kr ran two pieces this week framing Hermes as the next OpenClaw, asking whether it will follow the same trajectory. The Chinese tech community is treating agent operating systems as the next infrastructure layer, not as an application category.

The second-most-covered Bilibili topic: China vs US humanoid robot industry competition, via the Wizard Finance channel (4.27 million subscribers), focusing on the structural supply chain differences rather than benchmark scores.

Signals

Huawei Ascend 950DT arrives in August. Huawei's China Cloud VP confirmed the next Ascend chip generation goes live on Huawei Cloud in August. It doubles compute versus the 910C, adds FP8 native support, and improves memory bandwidth. The iteration cadence is one new chip generation per year. Nvidia's H100/H200/B200 refresh runs at a similar pace, but Huawei is doing this under export controls that block access to leading-edge TSMC nodes. They're compressing the gap through architecture, not process advantage.

UBTech's consumer humanoid hit 2,110 pre-orders in 6 days. UBTech (优必选) announced its UBTECH UWorld humanoid robot crossed 2,110 first-batch pre-orders. This is the consumer-facing product, not an industrial deployment. 2,110 units in 6 days on a product category that didn't exist at retail 18 months ago is a data point worth watching.

Baidu elevated digital humans to a standalone business unit. Baidu's MEG restructured its mobile ecosystem group, combining commercial and e-commerce divisions and spinning digital humans into an independent department. Baidu has been building photorealistic interactive digital human technology for enterprise use. The reorganization signals it's moved from experimental to business-unit status.

The Bigger Picture

Two things happened this week that connect directly.

First, Huawei's Ascend chips completed post-training on DeepSeek's 1.6 trillion parameter model. Second, compute prices fell 99%. These are not separate stories. They are the same story at different layers of the stack.

When inference becomes cheap enough to be "digital water," the competitive advantage moves to three places: distribution, proprietary data, and the ability to post-train on top of the commodity model. WeChat's 1.4 billion users is the distribution moat. The JD.com integration is an attempt to capture commerce data at scale. And the Huawei-DeepSeek post-training is the proof of concept that the full training loop can now run domestically.

The US corporate adoption of DeepSeek's hosted API — direct payments to China-hosted servers, as the Ramp report documents — is the demand signal that makes all of this matter. US AI infrastructure policy has been focused on hardware controls. The software layer is uncontrolled and being adopted commercially. The hardware ban built the domestic supply chain it meant to block. Now that supply chain is being sold back to American companies.

None of this is inevitable. Pre-training at frontier scale remains hard, and Nvidia's compute advantage at that layer hasn't closed. But the post-training result, the compute price collapse, the agent traffic crossing human traffic, and the consumer humanoid pre-orders all happened in the same week. These are not signals that China is "catching up." They are signals that a different, parallel AI stack is fully operational and scaling.

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Happy Friday. I scan 100+ Chinese-language sources daily so you don't have to. Today: DeepSeek's investors finally surface, China's robot labs sweep ICRA, WeChat opens a narrow door to phone makers, and US chip stocks just had their worst day since 2020. Let's go.

The Bet

DeepSeek is raising approximately ¥50 billion ($7 billion) in its first external funding round, and the investor list tells you everything about where China's industrial establishment thinks AI is going.

Founder Liang Wenfeng is putting in ¥20 billion ($3 billion) of his own money. Tencent is committing ¥10 billion ($1.5 billion). CATL, the battery giant that also quietly became a data-center power investor and a DeepSeek stakeholder earlier this year, is in for ¥5 billion ($740 million). The National AI Industry Investment Fund, NetEase, and JD.com are in final talks for smaller pieces. The post-money valuation: ¥350 billion to ¥400 billion ($49 billion to $56 billion). Fewer than ten investors total, according to sources cited by Reuters and confirmed by Technode.

Here's what's notable about this list. Tencent is not a neutral infrastructure bet; it's a strategic hold. Tencent is simultaneously building its own WeChat AI agent (more below), and its biggest messaging platform competes for developer attention with every app DeepSeek powers. It's betting on the model layer even as it builds on top of it. That's a hedge and an alliance at the same time.

CATL is even stranger. A battery company with no history in software is now the third-largest external investor in China's most technically advanced AI lab. The mechanism is the same one I've been tracking since April: CATL bought into DeepSeek through state-linked vehicles, has stakes in at least two Chinese data-center power companies, and is positioning AI compute as the next energy infrastructure play. Whoever powers the inference cluster captures the margin. CATL is not trying to become a software company. It's trying to own the grid that the software runs on.

Liang Wenfeng's ¥20 billion self-investment is the most important signal of all. DeepSeek remained the only major Chinese AI company without external funding until now. Liang has consistently resisted investor pressure to expand commercial services. The fact that he's leading the round with personal capital, not taking a back seat, suggests this is not a growth-capital story. It's a compute-capacity story. DeepSeek's V4 Flash is already the most-consumed model on OpenRouter (3.65 trillion tokens in the last week of May, up 32% week-over-week). The outages are real. The money is for GPUs.

One caveat: the ¥50 billion figure has drifted slightly from the ¥70 billion rumored in late May. The round is not closed. But the direction is set.

The Briefing

Galbot's Wang He told 8,000 roboticists at ICRA 2026 in Vienna that embodied AI has reached its "AlphaGo moment" and is approaching its "ChatGPT moment." At the industry keynote session, the Galbot founder and CTO described two specific benchmarks: fully autonomous humanoid tennis against a human opponent (no teleoperation, Sim2Real transfer), and dexterous tool use where the robot manipulates a screwdriver the way a human does, fingers coordinating rather than wrist rotating. Wang's argument is that the gap between AlphaGo (mastering one complex domain) and ChatGPT (handling almost everything) maps directly onto embodied AI. The World Action Model (WAM) is the architecture he's betting on. Galbot's LDA model (Latent Dynamics Action Model) is already operating fully autonomously at CATL, handling 50-kg loads 24 hours a day with self-recharging, and at a FamilyMart convenience store in what the company describes as the first humanoid-operated retail location. Fox News aired a segment from that store. Leiphone coverage of the keynote, in Chinese

A Chinese lab most people haven't heard of just won the hardest robot manipulation benchmark in the world. The task is folding laundry from a random crumpled state to a stacked, folded result. Physical Intelligence (pi), the US startup, devoted a specific post-training module to this. Dyna Robotics treats it as the first commercial-scale deployment target. At ICRA 2026's LeHome Challenge, the Lion Mountain AI Lab, operating under China Merchants Group's advanced technology division, won first place using its LiOS infrastructure, a unified platform for training, simulation, and deployment. The system folded short-sleeve shirts, long-sleeve shirts, and pants from non-standardized starting positions, recovering from mid-task failures without human intervention. Xinzhiyuan coverage, in Chinese

WeChat opened an A2A door to phone makers, but left Bytedance outside. Tencent confirmed to customers that WeChat is now cooperating with Huawei, Xiaomi, Honor, OPPO, and vivo on an A2A (Agent-to-Agent) integration. The mechanism: phone OS assistants (Huawei's Xiaoyi, Xiaomi's Xiao Ai) send structured intents to a WeChat agent, which executes them in the background and returns results. No screen-reading, no simulated clicks. This is a direct response to what Bytedance's Doubao phone assistant attempted in late 2025, which triggered mass WeChat account suspensions after Bytedance used INJECT_EVENTS system permissions to simulate user taps. Tencent president Liu Chiping's framing at the Q1 earnings call now reads as the policy statement: an OS can send structured requests to apps with consent, but "plundering" app data through simulated UI is a different matter. WeChat opens the API on its own terms. The door is narrow: you can ask WeChat to make a call or send a message; you cannot read the chat history. 36Kr analysis, in Chinese

The Philadelphia Semiconductor Index fell 10.3% on Friday, its worst single-day drop since the COVID crash of March 2020. The trigger was Broadcom's quarterly report: its custom AI chip guidance did not hit the market's elevated expectations. The selloff erased approximately $1.3 trillion in market cap from US-listed chip stocks. Nvidia fell roughly 6% (losing over $300 billion in market cap). Micron dropped 13%. AMD was off nearly 11%. The SOX index had hit an all-time high just Wednesday. The Chinese-language press coverage frames this as a structural inflection point: Broadcom as the canary, the hyperscaler capex cycle as the question mark. CSDN's read is that this is the first time in the AI investment cycle that an AI chip company's underwhelming guidance caused this scale of contagion, which means the market was pricing future perfection, not current results. Caixin summary, in Chinese

Qwen opened its platform to third-party agents. KFC, Luckin Coffee, Mixue, and China Eastern Airlines are among the first businesses running agent services inside the Qwen app. A user can ask Qwen to find a two-person KFC meal under ¥60 for pickup and the system identifies options and completes the order. Alibaba has spent six months integrating its own ecosystem services (maps, ride-hailing, shopping, instant retail) and is now opening the same rails to outside brands. This puts Qwen in direct competition with WeChat's mini-program ecosystem for the "where do tasks get done" question. Technode, in English

What I Found on Bilibili This Week

The video I want to highlight is not transcribed this issue (yt-dlp needs an update that requires an elevated session), but the metadata is informative. A video titled "国产AI芯片占比冲到41%，英伟达神话破灭，从95%降到55%" (Domestic AI chip share hits 41%, Nvidia's myth shattered, down from 95% to 55%) is circulating in the AI hardware discussion space. The headline is almost certainly about inference rather than training compute, where Chinese alternatives have made far more progress. Huawei Ascend is the primary inference cluster for DeepSeek, Kimi, and MiniMax. For training, the dependency on Nvidia H100/H800 (or smuggled successors) remains real. The framing matters: "Nvidia dependency gone" is not accurate, but "Nvidia's inference monopoly in China is gone" is. The policy implication is that export controls arrived too late to prevent China from building a viable domestic inference layer.

Signals

MiniMax filed for A-share IPO guidance. Five months after listing on the Hong Kong Stock Exchange at HK$165/share (the stock hit HK$1,330 before settling back), MiniMax signed an advisory agreement with CITIC Securities to pursue a listing on Shanghai's STAR Market. The company's 2025 revenue was $79 million (¥530 million), growing 159% year-over-year, with an adjusted operating loss of $251 million. Its ARR crossed $150 million as of February 2026. 73% of revenue came from overseas markets. The STAR Market application is notable because Zhipu AI filed for similar guidance in February 2026, meaning the first major domestic comparison between two large language model companies may happen in a public Chinese capital market setting. Titanium Media analysis, in Chinese

StepFun's Step 3.7 Flash topped Artificial Analysis's output speed benchmark at 409 tokens per second. The model also ranked highly on end-to-end response time, intelligence-per-dollar, and speed-per-dollar metrics. This is an agent-era benchmark, not a capability benchmark. In agentic workflows where a model calls tools, waits for results, and calls tools again across dozens of steps, latency per call multiplies. A model that is somewhat less capable but twice as fast at 0.3 cost per million tokens can outperform a slower, pricier alternative in production. StepFun is making an explicit bet that the next competitive axis in China's model market is not benchmark scores but engineering efficiency. Infoq coverage, in Chinese

Shanghai Jiao Tong and Huawei's Kunpeng team built a 2ms agent sandbox rollback system. Called DeltaBox, it uses a new OS abstraction called DeltaState to reduce checkpoint and restore latency from hundreds of milliseconds to 11ms for saves and 2ms for rollbacks. The practical application: agents doing code editing, dependency installation, or file system manipulation can try a change, execute a test, see it fail, and revert to the prior state in 2 milliseconds instead of several seconds. SWE-bench scores improved measurably with DeltaBox enabled. The system runs on Kunpeng domestic hardware, positioning it as an Ascend-compatible infrastructure layer for enterprise AI agent deployments. Qbitai coverage, in Chinese

The Bigger Picture

Three things happened this week that, taken together, make a point about where China's AI build-out actually is.

First, DeepSeek is raising $7 billion from Tencent, CATL, and Liang Wenfeng's own capital. The model lab that launched with a philosophy of staying small is becoming the anchor institution of a national AI capital stack, whether it wants to be or not.

Second, at ICRA in Vienna, Chinese robotics labs won the top prizes. Galbot took the industry keynote. Lion Mountain AI Lab won the global folding-laundry challenge. Xiaomi swept CVPR and ICRA robotics competitions the week before. The competition results are not noise.

Third, Broadcom's earnings wiped $1.3 trillion from US chip stocks in two days. The market had priced AI hyperscaler capex as self-sustaining and infinite. Broadcom's guidance said: not yet, maybe not at this level. That repricing matters for the competitive dynamic, because it slows the US training compute advantage at exactly the moment Chinese labs are demonstrating that you can run extremely good inference on domestic hardware.

None of this means China has "won" AI or that US labs are falling behind. Nvidia's training silicon advantage is real and durable. But the gap in the inference layer, where the majority of AI workloads actually live, is much smaller than the export-control policy was designed to create. The semiconductor index had its worst day since COVID. DeepSeek is about to have enough money to buy a lot of Huawei Ascend cards.

Those two facts are not unrelated.

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The Sprint

Last Monday, Xiaomi posted its CVPR 2026 results. The model submitted anonymously under the name "my16" finished first in the RoboChallenge track with a 40.89% success rate — the only model in the competition that cleared 40%. The task set was thirty ultra-hard real-world scenarios: dual-arm manipulation, flexible-object handling, tool use under causal uncertainty, cross-platform generalization. Each scenario ran ten consecutive times without intervention. You could not average out the failures.

Then two days later, the ICRA 2026 results came in. Xiaomi's team won that competition as well, scoring 99.2 out of 100 in a physical supermarket picking task. The challenge required identifying 20 SKUs across 16 categories, pulling the right item from a shelf, and placing it into a shopping cart. Success rate: 94%. Second place was 10 percentage points behind. Simple items succeeded 100% of the time. Complex items — unusual orientation, partial occlusion, non-standard placement — succeeded 90% of the time.

Lei Jun announced both results publicly with a single framing: "our goal is robots that do real work in the physical world." That sentence is a strategic declaration. Chinese robotics companies have spent three years proving their models can walk, dance, and perform in controlled demonstrations. Winning at CVPR and ICRA in the same week — one on manipulation intelligence, one on physical task execution — is the clearest public signal yet that Xiaomi's robotics program has moved from the demo phase to something harder to dismiss.

The model architecture behind the wins is called WAM: a "world-action model" combining S1/S2 dual systems with long and short-term memory and cross-embodiment pretraining. The premise is that physical intelligence requires three things simultaneously — cognitive depth, control precision, and memory stability — and that fusing them in a single unified model, rather than chaining separate systems, produces meaningfully more reliable behavior. The 94% success rate in a real supermarket is the evidence.

What makes this week's results different from the Spirit AI RoboArena benchmark we covered in Issue #76 is the surface being tested. CVPR and ICRA ran on physical robots in physical environments, not on a leaderboard score. A benchmark can be gamed. A supermarket cannot.

The Briefing

BYD confirmed it is developing humanoid robots, with a supply chain logic that no pure-play robotics startup can replicate. EVP Li Ke said this week that competition in humanoid robots will depend on strengths in manufacturing, software, and hardware — and that automotive AI and robotics technologies share common foundations. BYD's stated approach is to build its own robotics products and run an open platform that partners with other companies. Its dealer network of roughly 3,700 stores across China could serve as the sales channel if humanoid robots reach consumer price points. BYD produces 30,000 vehicles per day. The supply chain discipline that builds battery packs at that scale is the same discipline required to produce actuators, motor drivers, and joint assemblies in industrial volume. Unitree and Galbot can build impressive demos. BYD builds at that volume every single day.

Xpeng CEO He Xiaopeng issued an internal production ultimatum: Q4 2026 is the mass-production deadline for humanoid robots. At an internal Xpeng Robotics mobilization meeting reported by Leiphone, He Xiaopeng gave his team a precise timeline: factory delivery Q4 2026, domestic car dealerships Q1 2027, overseas Q2 2027. The framing was deliberately not about model benchmarks. He drew an explicit parallel to Xpeng's early Autopilot program, where hardware assumptions about cold weather and EM interference required complete system restarts with six-month cost overruns. "Machine learning tells you to do hardware first, software via OTA later — that's wrong." His phrase for the robotics strategy: "be the Apple of robots" — full vertical integration from chip to joint, because only vertical integration makes cross-domain technical fusion possible. An internal deadline with a named quarter, issued at a mobilization meeting, is meaningfully different from a product announcement on a stage.

Huawei Cloud launched its Agentic Infra framework at the INSPIRE conference in Shanghai today, naming compute infrastructure the "silicon black earth" for China's AI era. The announcement includes a 100,000-chip cluster running at 200 EFLOPS total compute with token generation latency below 10ms; an AMS memory system supporting PB-scale context storage for long-duration agent tasks; and CloudRobo, a full-pipeline cloud platform for embodied intelligence development (public beta June 30). The scale numbers matter independently. Ten milliseconds end-to-end token latency at 100,000-chip cluster scale means agents can call tools, receive results, and continue reasoning inside what feels like a single real-time interaction. At lower throughput, agents hit perceptible delays that break the illusion of autonomous behavior. Huawei is building the infrastructure that makes agents feel real, not just capable.

StepFun's Step 3.7 Flash topped Artificial Analysis's global output speed rankings at 409 tokens per second. The InfoQ framing captures the competitive shift: in multi-step agent pipelines that require browser navigation, document retrieval, multi-turn reasoning, and tool calls in sequence, token throughput and latency matter as much as peak benchmark performance. A model running at 409 tokens/second can sustain multiple simultaneous agent subtasks without queuing delays. A model at 80 tokens/second, however capable, cannot. Chinese labs are now competing on deployment efficiency — cost per completed real-world task, not score per benchmark — and the rankings for that competition look different from the standard capability tables.

Kling AI, Kuaishou's video generation spinoff, is raising pre-IPO capital at an $18 billion valuation. Reports from Chinese IPO trackers put the pre-money valuation at ¥120B, with Q1 2026 revenue above ¥6.5B, up more than 300% year-over-year and running at roughly $500M annualized. Kling would be the first pure-play AI video model company to reach public markets if it lists in Hong Kong in early 2027 as planned. It is the second Chinese video model business — after ByteDance's Seedance 2.0, generating more than ¥1B per month — to reach credible pre-IPO scale. Two video model businesses at $500M+ ARR within 18 months of their models' public release is a revenue concentration pattern that English-language AI coverage has largely missed.

What I Found on Bilibili This Week

Huawei's Tau (τ) Law is the most-discussed technical concept on Chinese AI Bilibili this week. Multiple videos are analyzing Huawei's proposed framework for measuring AI compute efficiency per watt rather than per FLOP — a scaling law designed for the chip geometry Huawei can actually build. The concept: if you cannot reach 3nm, redefine what "better" means. The Chinese tech community's reaction is split. Some creators argue this is a genuine architectural insight, applying the same logic that produced RISC-V (changed the instruction set standard) and DeepSeek's MLA attention mechanism (changed the efficiency measure). Others argue it is a reframing of existing constraints rather than a genuine breakthrough. One video with significant engagement asks the pointed question: "after the tau law went viral, what's the real breakthrough and what should we think carefully about?" That question is the correct one. The pattern China keeps deploying — when locked out of the frontier, redefine the frontier — has worked in hardware platforms and in model architecture. Whether it works at the silicon level depends on deployment results, not the proposed law itself.

Signals

The world's first purpose-built robot training apartment complex opened in China this week. Quantum Bit reports that 300,000 residential units across Chinese cities are being purpose-built as robot training environments — standardized floorplans, calibrated furniture positions, defined task zones. Developers describe robots as able to "move in and start training" in real domestic conditions at a scale no laboratory can replicate. The number is 300,000 units. That is not a pilot program.

ChatGPT reached one billion monthly active users in May, the fastest any app has reached that milestone in history — three years from launch. Instagram took six years. YouTube took eight. Claude reached 56 million MAU with 640% year-over-year growth but sits 18x smaller in absolute users. The gap between the leading Western AI consumer app and everything else is large and widening in absolute terms, even as the model capability gap between providers closes.

YMTC's NAND market share grew from 8% to 13% year-over-year in Q1 2026. Global NAND revenue hit $46B in the quarter, up roughly 3.5x. YMTC's revenue grew 445% in the same period. This is the story we covered in The Last Dependency in May — domestic memory demand from AI infrastructure buildout is compounding into real market share gains. The structural driver has not changed: export controls on advanced memory pushed Chinese AI customers toward YMTC, and YMTC built the product to meet that demand.

The Bigger Picture

A War on the Rocks essay published this week makes the case that China's AI progress is being driven more by brutal market competition — "involution" — than by state direction. The argument is convincing for the model layer, where DeepSeek, Kimi, and MiniMax emerged from commercial knife-fighting, not central planning.

The robotics picture is more complicated. Xiaomi won its competitions as a consumer hardware company competing on its own capital. BYD is entering with its own supply chain logic. Xpeng has a CEO-level production deadline for commercial survival reasons. These are market moves. But the infrastructure layer underneath them — Huawei Ascend compute, CATL energy systems, the National AI Fund capital now entering the DeepSeek round — is not market-driven. The knife fight produces winners. Beijing's role is to figure out which winners to harvest.

The 300,000-unit robot training apartment complex is the data point that has no equivalent outside China. The regulatory, land-use, and developer-incentive conditions that produced it are a function of China's specific governance environment. It is neither purely state-directed nor purely market-driven — it is a commercial real estate decision made possible by a policy context that does not exist in the same form elsewhere.

The standard Western analytical frame — state direction versus market competition — cannot hold all of these things simultaneously. The robotics industry forming this week includes Xiaomi's benchmark sweeps, BYD's supply chain entrance, He Xiaopeng's factory ultimatum, and purpose-built residential robot training environments at 300,000-unit scale. All of it is happening at the same time. The question is not which driver is dominant. The question is what happens when all four of these compound over the next two years.

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The Leaderboard

Nvidia co-developed the RoboArena benchmark with Stanford and Berkeley. It evaluates how well a generalist robot policy translates from training to real-world physical action — not math reasoning, not code, but the hard problem of controlling a robot that has to actually touch things. On Wednesday, Hangzhou-based Spirit AI published a result: its Spirit v1.6 foundation model scored 1,924 on RoboArena, placing it first globally. Nvidia's own Cosmos 3 Nano Policy model came in second at 1,881.

Spirit v1.6 is a foundation model specifically for embodied intelligence, not a general-purpose LLM or a vision model bolted onto a robot arm. The company describes its design philosophy as integrating perception, reasoning, and execution in a single model rather than treating them as separate modules. The timing matters: Nvidia launched Cosmos 3 two days before Spirit published its result. Spirit's response was not accidental.

The RoboArena leaderboard is maintained and contributed to by Nvidia, Stanford, and UC Berkeley. That matters more than the specific scores. When a Chinese startup tops a benchmark that the dominant incumbent helped build and maintain, it changes what the incumbent can say about the gap. Nvidia's playbook in GPUs was to own the benchmark infrastructure (CUDA, NVLink, H100 memory bandwidth specs) and make competing on anything else feel irrelevant. In embodied AI, Cosmos 3 was that opening move. Spirit v1.6 is the counter.

The 具身智能 (embodied intelligence) supply chain in China is assembling from multiple directions simultaneously. Unitree has STAR Market approval for an IPO and has already opened an experiential retail store in Shanghai. Deep Robotics filed for an IPO this spring. Galbot (银河通用) raised at a ¥20B valuation in April. The hardware exists. The simulation data infrastructure is being built (Tsinghua AIR's UniLab framework, which we covered in Issue #75, cuts robot locomotion training time 3-10x). Now Spirit AI has demonstrated that a Chinese model leads the global benchmark for translating simulation policies to real-world robot behavior. The software layer is closing the gap.

The question from here is not whether Chinese embodied AI is competitive at the model level. It demonstrably is. The question is whether China can build the deployment chain — the robot platforms, the app layer, the data flywheel — faster than Nvidia can extend its infrastructure partnerships globally. Nvidia announced partnerships with Unitree and Singapore's Sharpa the same week. It is not ceding the physical AI market. The competition is real, and it has a leading Chinese contender now.

The Briefing

Kimi Work Beta launched on June 3 as a general-purpose agent for knowledge workers, not just developers. Moonshot AI's announcement frames it as a transition from "Vibe Coding" to "Vibe Working" — taking the local agent capabilities Kimi Code validated with engineers and wrapping them in a GUI that requires no terminal, no configuration, no background in software development. Users describe a task in natural language; Kimi Work decomposes it, spins up to 300 sub-agents in parallel, operates the local browser, reads files, and delivers documents, spreadsheets, or decks. The core model is Kimi K2.6, which supports 13 hours of continuous execution and 4,000+ tool calls per session. The Mac client shipped first (Apple Silicon, macOS 12+); Windows follows. The timing is sharp: Microsoft launched Scout at Build 2026 that same week — its own enterprise-grade AI agent built on OpenClaw, positioned as Microsoft 365's autonomous AI assistant. Two companies, two countries, same product category, same week.

WeChat agents arrived on hundreds of millions of phones, with Honor first and four OEMs queued behind. Tencent confirmed June 4 that Honor's Magic8, 500, and X70 series have already deployed WeChat's A2A (Agent-to-Agent) integration, covering roughly 50% of Honor's active device base. Users update YOYO (Honor's assistant) to version 90.10.30.063 and WeChat to 8.0.72, then issue a voice command to send a WeChat message, make a video call, or perform other WeChat actions — without touching the app. Huawei, Xiaomi, OPPO, and vivo are in follow-on deployment. The technical architecture is A2A: the phone OS assistant and WeChat are both AI agents, and they communicate directly via a defined protocol. The mechanism preserves privacy (double authorization required) while enabling cross-app AI action. WeChat has 1.4 billion users. This is not a new AI app competing for attention. It is AI agency layered onto the app that already won.

MiniMax M3 became the first open-source model to combine frontier-tier coding, 1M context, and native multimodal in a single architecture. Released June 1, M3 sits at global rank #7 on Artificial Analysis's comprehensive intelligence index, above closed-source models in three specific benchmarks: GPQA Diamond science reasoning (93.2%, above Claude Opus 4.8 and 4.7), long-context reasoning (74%), and the GDPval-AA real-task agent benchmark (1,670 points, within 6 of Claude Sonnet 4.6). The underlying architecture is MiniMax Sparse Attention (MSA), which compresses per-token compute at 1M context to 1/20th of the prior generation — with 9x prefill acceleration and 15x decoding acceleration. The pricing reflects the cost structure: ¥119 per month for 18 billion tokens. At comparable Claude subscription pricing, that is roughly 15x the token volume. Vercel CEO Guillermo Rauch (5.4M followers) publicly endorsed it within 24 hours. The full model weights and technical report are expected open-source within 10 days. A follow-up product, MiniMax Code with Agent Team (Leader, Worker, Verifier multi-agent architecture), is the deployment vehicle — not just an API, but a multi-agent orchestration layer on top of M3's inference engine.

Kling AI is seeking pre-IPO funding at an $18B valuation, targeting a Hong Kong listing in early 2027. Kuaishou's video AI unit reported ¥650M in Q1 2026 revenue (300% year-over-year), running at roughly a ¥2B annual revenue rate. The pre-IPO round values it at ¥130B ($18B) — approximately two-thirds of Kuaishou's entire market cap. If it lists, Kling would be the first pure-play AI video model company to reach public markets. This follows the dual-listing pattern we covered in Issue #75: Zhipu and MiniMax both pursuing Hong Kong plus STAR Market simultaneously. Kling is different — it has revenue concentrating in a single model (video), no hardware narrative, and growth driven by commercial API demand rather than enterprise procurement mandates. It is being priced as a software business, not an AI infrastructure play. That is a new category.

YMTC's NAND market share nearly doubled in a year, rising from 8% to 13% of the global market. Counterpoint Research's Q1 2026 data released June 4: global NAND revenue hit $46B in the quarter, approximately 3.5x year-over-year. YMTC's revenue growth was 445%. Enterprise SSDs for AI servers now represent 43% of the total NAND market and are projected to exceed 60% by year-end. YMTC's gain is structural: the same domestic AI infrastructure buildout that created demand for Huawei Ascend chips created demand for domestic memory that Western suppliers either cannot or will not fulfill under export control restrictions. YMTC and CXMT (whose IPO registration is under CSRC review) are the memory layer of China's domestic AI stack. The Q1 revenue numbers are the financial case for their listings.

What I Found on Bilibili This Week

No Bilibili transcripts this week — yt-dlp's audio extraction pipeline ran into a version compatibility issue. What the metadata does show: two topics are driving the most video production in China's AI creator community right now.

The first is Huawei's Tau Law (华为韬定律). Multiple creators published analysis videos asking whether Huawei's alternative performance trajectory — based on architectural innovation and packaging density rather than transistor shrink — represents a credible alternative to Moore's Law for Chinese semiconductor development. The debate on Bilibili is substantive, with some creators offering detailed technical breakdowns and others doing pure pushback. The fact that this framing is generating engagement means Chinese engineers are genuinely thinking about whether there is a domestic path that does not depend on closing the EUV gap with TSMC.

The second is domestic chip market share. Several creators analyzed the shift from what one title called "Nvidia 95% to 55%" — the trajectory of Nvidia's market share in China's AI inference market as Huawei Ascend deploys at scale. The YMTC Q1 market share data above is the memory layer of the same story. Domestic chip supply is not a slogan in China right now. It is a commercial reality that is changing procurement decisions across the data center market.

Signals

StepFun's Step 3.7 Flash topped the Artificial Analysis output speed benchmark at 409 tokens per second. The InfoQ analysis frames this as an agent-era metric shift: coding benchmarks measured peak intelligence, but agent pipelines that run for hours at a time care more about throughput, latency, and cost per task than single-shot reasoning quality. A model that is slightly less capable but 3x faster and 10x cheaper can complete more work. StepFun's lead here is a different kind of claim than "we beat GPT-5 on MMLU."

DeepSeek V4 Pro's price is now permanently reduced. The Tencent Cloud cuts from June 3 are part of a wider pattern: DeepSeek permanently lowered V4 Pro API pricing, with inference input at ¥0.003 per thousand tokens and output at ¥0.006. Cache hits are ¥0.000025. This is not a promotional rate. It is the new floor. The price war has a direction and it is not reversing.

China's State Council published a 5-year agricultural AI blueprint. The SCMP summary covers the targets: 3 percentage point increase in technology's contribution to farm output (to 67%) by 2030, AI applications in crop breeding, pest detection, and yield forecasting, and development of leading agricultural technology companies. Food security has been a standing 十五五 priority. This is the AI implementation layer of that priority. Agricultural AI does not generate venture funding or benchmark press releases. It generates government procurement mandates.

The Bigger Picture

The benchmark phase of China's AI development had one question: how close are they? That question is now answered in multiple domains. DeepSeek V4 matches or exceeds frontier closed-source models on standard reasoning benchmarks. Spirit AI just topped the global leaderboard for embodied AI. StepFun leads on inference throughput. MiniMax M3 is within range of Claude Sonnet 4.6 on agent tasks. The answer is: close enough that the gap has stopped being the story.

The deployment phase has a different question: who wins in the layer where models actually reach users?

In China, the distribution infrastructure was built before the model capabilities arrived. WeChat's 1.4 billion users are the largest captive distribution channel for any technology product in the world. The A2A protocol that Honor deployed this week, with four more OEMs queued, means that infrastructure is now extensible to AI agents running natively on the OS level. Qianwen opened to enterprise brands the same week — Luckin Coffee, KFC, China Eastern Airlines deploying AI agents inside Alibaba's consumer app. The pattern is consistent: every major Chinese consumer platform is becoming an AI agent delivery vehicle, not by building new user interfaces but by adding an agent layer to interfaces users already inhabit.

Western AI deployment works differently. Microsoft Scout and Anthropic's Claude Code target enterprise workers via subscription software. Kimi Work targets the same workers via a local desktop application. The end user population is similar — knowledge workers who need to do research, analysis, and document production. But the Chinese apps get there via WeChat and Qianwen, which are apps users already spend hours in each day. The insertion point is different and the adoption friction is lower.

The benchmark phase was a research race. The deployment phase is a distribution race. China's model companies are competitive at the model level and hold structural advantages at the distribution layer. The question for the next 12 months is whether those structural advantages compound — or whether the model quality gap, which has closed, opens again.

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The Opening

CATL makes batteries. Specifically, it makes the batteries inside the Tesla Model 3, the BYD Seal, and roughly a third of every electric vehicle sold on earth. On Tuesday, CATL invested ¥5 billion into a large language model startup.

The startup is DeepSeek, and the ¥5 billion from CATL is a small piece of a larger story. According to Reuters reporting confirmed by 智东西, DeepSeek is closing its first-ever external funding round — ¥50 billion ($7.4 billion), fewer than 10 investors, completion expected within weeks. Post-money valuation: ¥350B–400B ($52–59B). Tencent is in for ¥10 billion. The National AI Industry Fund, NetEase, and JD.com are in final negotiations. Alibaba was reportedly turned away.

The most surprising line in the reporting is this: founder Liang Wenfeng has personally committed ¥20 billion. That is 40% of the entire round coming from the man who already controls the company. DeepSeek has been financed entirely by High-Flyer (幻方科技), his quantitative trading firm, since its founding. He is reinvesting those quant profits into his own company at a ¥400 billion valuation. That is a different kind of conviction than a founder accepting outside money at a high number.

But CATL's position is what deserves attention. In April, CATL acquired 49% of 中恒电气 (a leading data center HVDC power supplier) for ¥4.1 billion. In May, it bought a 38.1% stake in 世纪互联 (a major Chinese IDC operator) for $942 million. DeepSeek, meanwhile, has been recruiting data center engineers in Inner Mongolia and building its own compute infrastructure. The CATL investment in DeepSeek is not incidental. It is the energy layer of China's domestic AI stack — connecting Huawei Ascend hardware, DeepSeek models, and CATL storage systems into a vertically integrated architecture. AI inference at scale requires massive, stable power. CATL's batteries and storage systems are what make renewable-powered data centers reliable. The same company that powers the electric vehicle transition is positioning itself to power the AI inference transition.

Tencent's motivation is more straightforward. Its own model, Hunyuan, has lagged both ByteDance's Doubao and DeepSeek in the Chinese enterprise market. A ¥10 billion investment is strategic insurance — a partnership with the competitor you cannot currently beat, on favorable terms. The investor list is small and selective. Fewer than 10 participants in a ¥50 billion round means Liang Wenfeng is choosing his shareholders as carefully as he chose his research team. Alibaba owns Qwen, DeepSeek's direct competitor in enterprise deployment. It was turned away.

DeepSeek V4.1 is expected to launch next month, based on multiple Chinese AI newsletter reports from this week. The current V4 (1.6 trillion parameters, released in late April) is already being deployed at near-zero cost — Tencent Cloud cut DeepSeek V4 pricing by 97.5% today, with cache-hit tokens now at ¥0.000025 per thousand. We covered the expectation that DeepSeek would eventually need external capital in April. What was not obvious then is how precisely the capital structure maps to infrastructure layers: compute (Huawei Ascend), model (DeepSeek), power (CATL), distribution (Tencent WeChat). The funding round is closing as the model gets cheaper and the next version approaches. The timing is deliberate.

The Briefing

ByteDance's MaaS revenue is running at 10x its 2025 actuals, and almost all the growth came from one video model. Volcano Engine has raised its 2026 MaaS target to ¥15 billion, up from a ¥10 billion target set at end of 2025, which itself was 10x the ¥1.5 billion in actual 2025 revenue. The driver is Seedance 2.0, now generating more than ¥1 billion per month from China alone — its international API has not fully launched. The Chinese model market has split into two categories with different economics: video models (Seedance at #2 globally, 95% penetration in China's short-drama industry) retain pricing power; coding models do not. Zhipu is winning the coding segment — GLM-5.1 raised API prices 83% in Q1 while call volume grew 400%, the first Chinese model to price-match Claude Sonnet 4.6 at the enterprise cache tier. ByteDance's Doubao is expected to launch paid subscriptions in late June, adding a consumer monetization line to the enterprise revenue.

ByteDance restructured its AI research organization, and the robotics team is now inside the world model unit. LatePost reported that Seed Robotics has moved from Li Hang (who now advises on academic partnerships) to Zhou Chang, ByteDance's multimodal and world model lead. Zhou Chang's scope now covers visual generation (Seedream, Seedance), world models, and embodied intelligence. The architectural logic: robots need vision, multimodal understanding, and a world model to operate in physical space. Machines then generate data from warehouses, factories, and homes that improves those models. ByteDance is building the closed loop explicitly. The same week, OpenAI CEO Sam Altman announced OpenAI is hiring full-stack hardware and ML engineers for robotics, with a near-term goal of robots that assist skilled workers in infrastructure construction.

Zhipu and MiniMax both moved toward A-share listings on the same day, after both listed in Hong Kong earlier this year. 36Kr analyzed why two companies with opposite business profiles — MiniMax (70% overseas revenue, 236 million global users) and Zhipu (70%+ China revenue, government and financial sector clients) — made the identical capital market decision. The answer is that Hong Kong and A-share serve different functions. Hong Kong provides global valuation (Abu Dhabi and Singapore sovereign wealth funds entered both companies; Hang Seng Tech index inclusion takes effect June 8, which routes passive index capital automatically). A-share on STAR Market provides strategic identity: priority in government procurement, access to the National AI Fund, state-owned enterprise patience capital, and the policy standing that comes with being listed as a national strategic asset. Zhipu hit HKD 800 billion intraday on May 29. MiniMax filed A-share guidance the same day. Both companies are using each market for what it does best. Nasdaq is absent from this calculation entirely.

Microsoft launched seven self-trained models at Build 2026, merged ChatGPT and Codex into a single product, and released a developer PC targeting local inference. The most specific technical detail: MAI Code 1 Flash, Microsoft's own code model (35B active parameters, ~1T total via MoE architecture), scores 51.2% on SWE Bench Pro versus Claude Haiku 4.5's 35.2%. Microsoft is training its own code model that outperforms Anthropic's lighter tier. The Surface RTX Spark Dev Box (1 petaflop FP4 AI compute, 128GB unified memory, 100W TDP) targets developers running models locally. OpenAI's Codex has 5 million weekly active users, 20% of whom are non-developers. The product boundary between chat assistant and autonomous coding agent was removed.

Anthropic filed a confidential S-1 with the SEC on June 1, closing a $65 billion Series H the same day, at a $965 billion post-money valuation. Annual revenue run rate is $47 billion, up from $9 billion in December and $14 billion in February. Claude Code is the driver. Five thousand engineers at Uber burned their annual AI budget in four months. Goldman Sachs projects Agent token consumption grows 24x by 2030. These are the data points Anthropic's IPO roadshow will use to justify near-trillion valuation. The question is whether that stickiness holds once comparable-benchmark free alternatives mature.

What I Found on Bilibili This Week

Five separate Seedance 2.0 tutorial videos from different creators appeared in today's Bilibili collection. One creator with 81,000+ views framed it this way: "If you are in the short-drama industry, you are basically already a Seedance user." The 95% penetration rate reported by 36Kr this week tracks with what I am seeing in Bilibili's creator ecosystem. The video model is not a novelty. It is infrastructure for a category of commercial production. Chinese short drama (短剧) is a ¥50+ billion industry. Seedance has become its default rendering engine.

A second video worth noting: Chinese humanoid robots at Japan's AI Expo, collecting business cards from international buyers. The narrator's observation was that Japanese visitors needed the robots explained to them. The international debut is happening at the show-floor level, not at the deployment level. That gap is closing, but it is still a gap.

Signals

Tsinghua AIR open-sourced UniLab, a robot training framework that reduces locomotion training time from hours to three minutes. The architecture splits simulation (CPU) from policy learning (GPU), using shared memory and async pipelines to eliminate idle time. The result is 3–10x end-to-end speedup, verified on six real-robot tasks including humanoid walking and dexterous hand manipulation. The framework runs on Mac via Apple Silicon without NVIDIA. Every researcher who does not have H100 access can now iterate on embodied intelligence policies. The training barrier just dropped.

SK Hynix evacuated 3,600 workers from its Cheongju fab after a fluorine gas leak. The fire in a gas room between two production buildings was contained within hours. The facility reopened the same day. SK Hynix confirmed no production impact. The risk flag: SK Hynix is the leading supplier of HBM3E, the high-bandwidth memory that feeds NVIDIA H100 clusters. One serious incident at this facility creates a supply bottleneck that no other company can fill quickly. Tuesday's event was minor. The supply chain concentration remains.

Alphabet announced an $80 billion equity raise to fund AI infrastructure, with Berkshire Hathaway taking $10 billion via private placement. Google has already raised its 2026 capital expenditure guidance to $180–190 billion. The scale of compute investment now underway across Google, Microsoft, Meta, and Amazon creates a secondary question for DeepSeek's model architecture: DeepSeek's competitive position has been built on training efficiency and inference cost. Whether that efficiency advantage holds as Western labs scale compute by orders of magnitude is the central bet in the DeepSeek round.

The Bigger Picture

Western coverage of the DeepSeek funding round will focus on the valuation — $52 to $59 billion, built in 18 months from a quantitative trading firm's profits, with no outside capital until this week. That is the right number to notice but the wrong frame to use.

The more useful frame is the investor list. The premise in Western analysis is that DeepSeek is a model company competing with OpenAI, and that the funding round reflects investors betting on model quality. That framing is accurate for approximately ¥20 billion of the ¥50 billion round — Liang Wenfeng's personal commitment, the founder bet on his own model.

The remaining ¥30 billion is something else. Tencent needs DeepSeek because WeChat's distribution advantage is only valuable if the model it routes is competitive. CATL needs DeepSeek because the data center power opportunity is real, and a partnership with China's most-used open model gives it an anchor customer for the AI energy infrastructure it has been acquiring. The National AI Fund's interest is the most direct statement of all: this is not a company raising capital. It is China's open-model infrastructure choosing its long-term capital partners before consolidation makes the choice unavoidable.

Here is the inversion Western analysts will reach eventually: the question is not whether DeepSeek can compete with GPT-5. The question is whether the integrated stack — Ascend hardware, DeepSeek models, CATL power, Tencent distribution — can deliver inference economics that make the per-token cost comparison irrelevant for the Chinese market. If that stack coheres, the relevant benchmark is not H100 performance. It is the total cost of running intelligence at scale in China, independent of US supply chains. That is a different competition with a different answer.

We are watching the capital structure of that stack get locked in, one ¥5 billion check at a time.

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The Counteroffer

Anthropic filed a confidential S-1 with the SEC on Tuesday morning. The valuation the company is targeting: roughly $965 billion. The annual revenue run rate: $47 billion, up from $9 billion six months ago. The growth driver for almost all of it: Claude Code. The IPO window: October 2026 at the earliest.

On the same day, Agnes AI, a lab whose text, image, and video models rank in the global top 10 on every major benchmark, announced that three of its core APIs would be permanently free starting June 1. No billing setup, no free-tier throttling, no rate limits. Agnes-2.0-Flash (text, 1M context), Agnes-Image-2.0-Flash (image generation), Agnes-Video-V2.0 (video generation). Free, indefinitely.

These two events were not coordinated. But they describe the same industry from opposite ends.

Western AI companies are executing the classic SaaS monetization arc: subsidize early adoption, lock in usage, convert to per-token billing. Anthropic moved to "20 USD base plus token consumption" for enterprise. OpenAI moved all enterprise plans to per-token in April. GitHub moved every Copilot tier to per-token on June 1, the same day as the Agnes AI announcement. The subsidy era is ending. The pricing era is beginning.

The Chinese response is equally rational from the other direction. If per-token billing is the Western monetization wedge, the cheapest token wins the commodity end of that market. Agnes AI is not the only one pricing this way. DeepSeek made its price cuts permanent this week, removing the caveat that had allowed prices to revert once Ascend 910C supply normalized. StepFun released Step 3.7 Flash today at 1/9 the cost of Claude Opus 4.6. Qwen3.7-Plus, Alibaba's new multimodal model, is available on API today with pricing that undercuts every comparable Western offering.

This is not altruism. It is a structural answer to the fact that Chinese inference infrastructure has lower electricity costs, subsidized compute, and a domestic chip supply chain that now covers 41% of China's AI hardware market. NVIDIA's share has fallen from 95% to 55%. When your unit economics are different, your pricing strategy can be different.

The question is whether Anthropic's enterprise stickiness, built through Claude Code's integration into engineering workflows, survives in a world where the baseline alternative for comparable benchmark performance is free. The answer to that question is what October's roadshow will try to prove.

The Briefing

StepFun's Step 3.7 Flash is the clearest articulation yet of what Chinese AI efficiency engineering looks like in production. At 400 tokens per second and 1/9 the cost of Claude Opus 4.6, the model is not a trimmed flagship. It is an 11-billion active-parameter MoE (Mixture-of-Experts) model designed specifically for Agent pipelines, not benchmark maximization. The key design choice: instead of encoding visual knowledge into model weights, the model externalizes perception to inference time, using high throughput to "look again" as a substitute for parameter capacity. The model can analyze a cockpit interface, a desktop UI screenshot, or an engineering diagram and produce step-by-step task instructions without separate vision models. StepFun frames this as production Agent infrastructure, not small-model tradeoff. At 400 tokens per second, it competes on throughput with dedicated inference services. At 1/9 Claude Opus cost, it changes the per-token math for Agent workflows.

Alibaba released Qwen3.7-Plus today, ranking fifth globally and first in China on Vision Arena, and the workflow architecture is what matters. The model integrates seeing, thinking, coding, tool use, verification, and iteration into a single Agent pipeline without intermediate handoffs. Alibaba describes it as 看想写做验 (see-think-write-do-verify). A single prompt can produce a working desktop application, a replicated mobile UI, or a structured data pipeline with no human checkpoints between steps. Vision Arena places it above GPT-5.5 and Gemini 3.1 Pro on visual reasoning. Available on Alibaba Cloud Bailian via API today.

NVIDIA's GTC Taipei introduced hardware that reframes the token cost problem from the compute side. The RTX Spark chip (6,144 CUDA cores, 128GB unified memory in a laptop form factor) targets local inference at data-center performance, making per-token API billing optional for companies willing to invest in on-premises hardware. The Vera CPU, which Jensen Huang explicitly described as "the first CPU designed not for humans but for Agents," posts SQL throughput 3x faster than x86 and stream processing 6x faster. Cosmos 3, NVIDIA's open-source physical AI model, reduces robot and autonomous vehicle simulation cycles from months to days. Li Auto is already using it for autonomous driving development. The NVIDIA answer to the cost problem is not cheaper tokens. It is hardware that makes the question of tokens irrelevant for the workloads that matter.

Anthropic's $47 billion run rate needs the number next to it that makes it real. According to Chinese coverage of the filing, the $47B figure reflects six months of growth from $9B annualized in December 2025. Claude Code is the driver. Uber burned its entire annual AI budget in four months with 5,000 engineers. Microsoft halted Claude Code enterprise licenses internally and migrated teams to Copilot. A single enterprise forgot to set spending limits and received a $500 million one-month bill for Claude usage. Goldman Sachs projects Agent token consumption will grow 24x by 2030. These are not failure stories. They are the proof points Anthropic's S-1 will use. When a 5,000-person engineering organization builds its workflow around Claude Code, switching to a free alternative is not a one-day decision.

What I Found on Bilibili This Week

Jensen Huang said something at GTC Taipei on June 1 that lit up Chinese tech Bilibili more than his robot and chip announcements did.

He called Huawei's τ (tau) law "a breakthrough by Huawei."

For context: Huawei's τ law formally argues that semiconductor progress does not require transistor miniaturization to continue. The framework proposes that 3D stacking, new interconnect architectures, and improved memory hierarchy can continue doubling effective compute density without shrinking transistors further. The law positions Huawei's Ascend 910C not as a second-tier alternative to H100 (the Western framing) but as the leading implementation of a different engineering path.

Jensen Huang is the man who has spent the past four years arguing that NVIDIA's GPU architecture is the inevitable infrastructure of the AI era. His GTC Taipei event was two hours of that argument. And he spent part of it explicitly praising the theoretical framework Huawei has used to justify its chip architecture.

Bilibili's response was immediate. Dozens of videos analyzing the endorsement accumulated over 100,000 combined views in 24 hours. The Chinese tech community read it as validation, not as a competitive threat assessment. The sub-text was clear: if Jensen Huang acknowledges the framework, the framework is real.

The chip market share numbers that circulated alongside the τ law debate add weight to the moment. China's domestic AI chip market share reached 41%, up from near zero four years ago. NVIDIA's China AI hardware share fell from 95% to 55%. The remaining 4% comes from AMD and smaller suppliers. The shift is happening faster than Western export-control models predicted, because those models assumed Chinese domestic alternatives would remain technically inferior. The τ law is the specific rebuttal: the relevant benchmark is not H100 head-to-head performance. It is whether a different density theory achieves parity on the workloads that matter.

Jensen Huang's endorsement does not concede the performance comparison. But it concedes that the theory is legitimate. On Bilibili, that distinction was noted, and then largely set aside. The headline was the praise.

Signals

Mindverse closed a $50 million Series A led by Meituan, with participation from Sequoia China, ZhenFund, and GSR Ventures. The 20-person team, with founders from DeepSeek, ByteDance Seed, and xAI, is releasing a 750 billion parameter Agent model on GLM 5.1 with reinforcement learning post-training. Their specific claim: they are one of three companies globally (with Thinking Machines and Fireworks) that have run LoRA RL training at trillion-parameter MoE scale at full-parameter performance levels for 1/10 the compute cost. The "continual learning" product thesis: a LoRA adapter thin enough that a financial firm can retrain it on new market data for tens of thousands of dollars rather than the millions a full-base retrain would require.

China is requiring AI experts at private firms to obtain government approval before international travel. The policy was announced in March and enforcement documentation is now emerging. The mechanism applies to researchers at major AI labs and university AI programs. The practical effect restricts the talent pipeline that has historically run from Shanghai and Beijing to US graduate programs and research positions. China graduates approximately four times as many STEM students annually as the US, so the talent pool being retained is substantial.

China's Nine Chapters 4 (九章四号) photonic quantum computer broke a new performance record for photon sampling tasks, according to Xinhua. The commercial applications for AI workloads are a decade out. But the research pipeline in quantum is one area where US export controls have not yet created a meaningful gap.

The Bigger Picture

The Anthropic IPO and the Agnes AI free-tier announcement together describe the fork in the road for AI infrastructure economics.

Anthropic's thesis, which will be the subject of October's roadshow: enterprise AI has product-market fit, the moat is workflow lock-in through Claude Code, and per-token billing at scale generates the revenue that justifies a near-trillion-dollar valuation.

The Chinese counter-thesis, which Agnes AI, DeepSeek, StepFun, and Qwen are all running: token cost is the primary competitive variable in the commodity tier of AI, Chinese infrastructure unit economics are structurally lower, and free or near-free access captures the enterprise and developer market before the moat forms.

Both theses can be right for different market segments. Enterprise organizations with deep Claude Code integration and compliance requirements will not switch to a free Chinese API on price alone. Startups, independent developers, and cost-sensitive enterprises in unregulated industries will. The question is which segment is larger.

The honest answer is that neither side knows. Claude Code's enterprise stickiness is real, but it was built during a period when there was no comparable free alternative. Agnes AI's free tier is new. The market response over the next six months will give the Anthropic S-1 process its most important data point.

NVIDIA is the third party to this debate, offering a third answer: the choice between expensive Western API and cheap Chinese API is a false binary. Local inference at data-center performance, running on hardware costing $3,000 instead of fractions of a cent per token, makes the per-token economics irrelevant for organizations that can capitalize the compute.

If that framing wins, the winner is the company selling the hardware. Which, in this case, is a company whose reference humanoid robot is built by Unitree.

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The Reference

Today is Children's Day in China. Two things happened simultaneously, both involving the same company, both defining what the robot future actually looks like.

At GTC Taipei this morning, Jensen Huang announced the Isaac GR00T H2+: NVIDIA's reference humanoid robot design. One-point-eight meters. Sixty-eight kilograms. Thirty-one degrees of freedom in the body, twenty-five in each hand. The compute is NVIDIA Jetson Thor. The tactile hands come from a company called Sharpa Wave. The body is Unitree's H2.

At the same hour, in Shanghai, the Shanghai Stock Exchange listing committee approved Unitree's IPO application. Seventy-three days from filing to approval. The valuation: ¥42 billion (roughly $5.8 billion). Unitree will be A-share's first humanoid robot stock.

What Jensen Huang chose, and what the STAR Market approved, are the same machine.

The reference humanoid for the global AI industry runs on Chinese hardware. This happened not because of geopolitical strategy or nationalist procurement. It happened because Unitree shipped 5,500 humanoid units in 2025, more than any company in the world. It happened because Unitree's gross margins were 60.13% last year on ¥16.99 billion in revenue. It happened because Unitree founder Wang Xinxing spent ten years in Hangzhou building robots before anyone called them embodied AI. When NVIDIA needed a partner to build the machine the whole industry would use as a baseline, Unitree had the manufacturing reality.

The "reference design" is a specific concept in the chip industry. Intel used to publish reference motherboard designs. Qualcomm publishes reference smartphones. The reference design is what every downstream manufacturer starts from. It defines the minimum requirements, the standard interfaces, the baseline assumptions. Every humanoid robot developed against the Isaac GR00T platform will start from Unitree's body dimensions, Unitree's joint specs, Unitree's actuator profile.

Unitree's IPO raise of ¥42 billion is earmarked: more than half goes to AI model development. The company's prospectus is unusually candid: "We focused early investment on robot bodies, not AI brains. We began significantly increasing AI brain investment only in 2024." This is honest. The bodies work. The margins prove it. The brain competition is now the actual race. Unitree's IPO funds the second chapter, while NVIDIA's H2+ reference design makes Unitree's first chapter mandatory infrastructure.For the children celebrating today in China: the robot your generation will grow up with will be shaped by a machine approved this morning in Shanghai and announced this morning in Taipei. That's not metaphor. It's the supply chain.

The Briefing

MiniMax released its M3 flagship model on the same day it filed for an A-share IPO, making it the only open-source model that simultaneously handles 1M-context, frontier coding, and native multimodality. M3 beats GPT-5.5 and Gemini 3.1 Pro on SWE-Bench Pro, landing just below Claude Opus 4.7. The proof-of-concept MiniMax ran internally: M3 spent 24 hours self-optimizing a CUDA kernel from scratch, making 147 benchmark submissions and 1,959 tool calls, pushing Hopper FP8 hardware utilization from 7.6% to 71.3%, a 9.4x acceleration. No human intervention. M3 also independently reproduced an ICLR 2025 Outstanding Paper over 12 hours of unattended computation, verifying not just the results but the failure modes the authors described. The A-share IPO filing (with CITIC Securities as lead underwriter, signed May 29) comes after MiniMax's January Hong Kong IPO at HK$165 per share, now trading at HK$840, up over 400%, market cap HK$260 billion. The pattern: establish international credibility via Hong Kong, capture domestic institutional capital via A-share. The gap between M3's benchmark scores and its commercial trajectory makes the case for open-source as an IPO strategy, not a charitable act.

A Huxiu analysis calculates that DeepSeek's software efficiency could save China roughly $1 trillion in AI hardware spending through 2030. The argument runs through McKinsey's infrastructure forecast: global AI hardware spending through 2030 totals roughly $5.2 trillion, heavily weighted toward high-bandwidth memory (HBM) and compute. DeepSeek V4's three technical innovations (long-context KV cache compression at 10% of prior storage, MoE expert selective activation at 27% of prior compute per token at 1M context, and shared KV cache across requests) combine to deliver roughly 4x the token output from the same hardware. If that efficiency ratio applies broadly across China's AI infrastructure buildout, the savings are on the order of $1 trillion across the decade. The number is a forecast, not a certainty. But the underlying claim is verifiable: DeepSeek V4-Pro at 1M context requires 27% of the compute and 10% of the cache of its predecessor. The hardware didn't improve. The software did. Whether that scales to national infrastructure is the interesting question. DeepSeek this week also made its price cuts permanent, removing the caveat that had allowed pricing to revert once Huawei Ascend 950 supply normalized.ByteDance's Doubao is launching paid subscriptions in late June, and the product move reveals more about strategy than revenue. According to 36Kr sources, Doubao will announce pricing at ByteDance's Force conference in late June, with Q3 integration into Douyin e-commerce and Q4 full commercialization. The tell is in what ByteDance is explicitly not tracking: paid user penetration rates won't be a 2026 KPI. This is a positioning move, not a monetization sprint. Doubao has the largest consumer LLM user base in China. Making it paid establishes the category expectation that AI assistants cost money, while the actual revenue model plays out through e-commerce integration. The implication for the broader market: the era of completely free frontier LLMs in China is ending. Every major lab now either has a paid tier or is building one. The question shifts from "who can give the most away" to "who can convert."

OpenAI is rebuilding its robotics team six years after disbanding it, with Sam Altman publicly setting the goal of one robot per person. Altman's post recruited for hardware, systems, operations, and ML engineering roles paying up to $445,000. The team is led by Aditya Ramesh, co-creator of DALL-E 2 and current VP of Research, who has been running a project called Worldsim. OpenAI disbanded its original robotics team in 2020 for a specific reason: not enough real-world robot data to train on. The reason they're returning now is equally specific: the Chinese humanoid robot industry shipped over 10,000 units in 2025, generating exactly the kind of real-world physical interaction data that didn't exist in 2020. OpenAI's technical focus is simulation fidelity and sim-to-real transfer, starting from virtual environments rather than from proprietary hardware. The strategy is the opposite of Unitree's. Unitree bet on bodies first. OpenAI is betting on the brain model that can run on any body, including, eventually, someone else's reference design.

What I Found on Bilibili This Week

Chinese AI chip share reached 41% in 2025, while NVIDIA's share fell from 95% to 55% (龙科多工作室, 49,918 views). The video's framing captures the Chinese industry thesis precisely: "Algorithm compensates compute. Whoever has stronger application scenarios and engineering deployment ability wins over whoever has the highest-compute data center." The 41% domestic chip share covers inference-tier deployment, not training runs, where Huawei Ascend and Cambricon are most competitive. Training clusters still skew toward whatever H100s and H800s were acquired before the latest round of export controls. The inference gap is closing faster than the training gap, because inference doesn't need the same raw compute ceiling that pretraining requires. For Ryan Fedasiuk or anyone tracking chip policy: the supply-side restriction has not stopped the share shift. It has redirected China's chip industry toward a specific layer of the stack where domestic chips are already competitive.

Signals

• Qualcomm released the YueLong iQ10 RRD robot reference design on the same day NVIDIA announced the Isaac GR00T H2+, integrating compute, perception, networking, and software in a single platform. Two chip giants, two robot reference designs, same day. The competition for whose hardware defines the baseline humanoid is now formal. Qualcomm's approach emphasizes edge inference and 5G connectivity; NVIDIA's approach emphasizes the Jetson compute stack and Isaac simulation tools.

• Deep Robotics (云深处科技) filed for STAR Market review, making it the second Chinese humanoid robot company in the IPO pipeline following Unitree. Unlike Unitree's full humanoid focus, Deep Robotics built its foundation on quadruped robots and is now transitioning into bipedal humanoids. The pattern: two companies, different starting points, converging on the same market.

• MiniMax's Hong Kong stock is up 400%+ since its January IPO (HK$165 to HK$840), and the company simultaneously filed for an A-share secondary listing. The dual-listing arbitrage is becoming a standard move for Chinese AI companies: international credibility from Hong Kong, domestic capital from A-share. The 400% gain makes the A-share filing easier than starting cold.

• DeepSeek this week officially made its pricing cuts permanent, removing the conditional language from its discount announcements. The pricing floor: V4-Pro at $0.43 per million tokens, approximately 1/40th of GPT-5.5-equivalent pricing. The 40x gap no longer carries an expiration date.

The Bigger Picture

Jensen Huang's choice to partner with Unitree for the Isaac GR00T reference design is the most politically revealing thing that happened in AI this week. Not because of what it says about China. Because of what it says about physics.

NVIDIA makes the chip. Unitree makes the body. Sharpa makes the hands. The reference humanoid for 2026 is a three-way international collaboration between a US chip company, a Chinese robot company, and a startup making tactile sensors. This happened while the US government continues tightening export controls on advanced chips and while Chinese domestic chip share is rising. The industry is not waiting for the political environment to resolve.

The policy apparatus that Ryan Fedasiuk at CSET tracks is not primarily concerned with humanoid robot bodies. It is concerned with the compute that runs the brain. Jetson Thor (the onboard compute inside the H2+ reference design) sits in a different export control tier than H100s. NVIDIA can ship Jetson Thor to Unitree for robot deployment in ways it cannot ship data center accelerators. The reference design exists inside that boundary.What changes is the data. Every Unitree H2+ running in the real world generates physical interaction data. Proprioception, force feedback, contact geometry, task completion rates in real environments. This is what OpenAI lacked in 2020, what prompted them to shut down their robotics team, and what they are now betting Unitree and companies like it will generate for them. The body is the data factory. The brain model can be built separately.

The embodied AI stack is splitting cleanly into two layers: hardware (Chinese manufacturing advantage, lower cost, scale) and software (contested, expensive, where OpenAI and the three world models from last week are all competing). Jensen Huang chose to source the hardware layer from China. OpenAI is betting it can win the software layer globally. Both positions assume the layers will be interoperable.

That assumption may be the next thing to test.

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The Commissioning

Today in Beijing, Unitree faces the Shanghai Stock Exchange committee. If it passes, the company becomes A-share's first humanoid robot stock. The committee hearing was approved 73 days after Unitree filed — the second-fastest science board review in history.

The timing is not coincidence. In the same 72-hour window that Unitree's committee date was announced, three separate Chinese research teams released competing embodied world models. Alibaba's Tongyi lab published Qwen-VLA, a unified vision-language-action model running on 11 different robot platforms from a single 4B backbone. The Fudan-founded startup 眸深智能 launched STI-WM, a spatiotemporal integrated world-action model built on five years of robotics research. And Shanghai AI Lab released τ0-WM — billed as the largest-scale open-source embodied world model to date, trained on 17,800 hours of real robot data.

Three robot brains, one week. One robot company going to capital markets, same week.

This is the transition from demo to deployment. The research question — can we build AI that controls physical robots in the real world — has been answered enough times that investors and regulators are now treating humanoid robots like a real business. Unitree's financials back the claim: ¥16.99 billion in revenue in 2025 (up 332% year-over-year), gross margins at 60.1% (Tencent-tier), 5,500+ humanoid units shipped globally in 2025, and a production-to-sales rate above 95%. The company is profitable. It sells robots the way you sell electronics.

What it doesn't yet have, by its own admission, is the AI brain. Unitree's Q1 2026 profit fell 47.7% year-over-year, not because demand softened — revenue was still up 68.5% — but because the company is spending aggressively on the WVLA2.0 embodied model and the G1 AI intelligence it needs to justify the next phase of growth. Its IPO raises ¥4.2 billion, with more than half earmarked for AI model development.

Here's what makes this week unusual: Unitree doesn't have to build that brain alone. The three competing world models released this week are all available for integration. 眸深智能's STI-WM has already signed with Unitree as a commercial partner. Qwen-VLA is open under the Apache 2.0 license and supports Unitree's hardware. τ0-WM was trained on real-world robot data at a scale no single company has yet achieved alone.

The commissioning moment isn't when you build the robot. It's when the robot goes to work.

Unitree's G1 humanoid robot is currently running in Tokyo Haneda Airport, handling baggage loading and cargo transfer operations in a trial through 2028. The company's R1-D dual-arm upper-body robot launched at ¥26,900, targeting manufacturing assembly and lab automation. These are not demos. They are commercial deployments.

The physical AI stack — chips, bodies, brains, software — has been assembling piece by piece since 2022. This week is when it clicked together in one place at one time. The committee vote is the market's acknowledgment that the stack works.

The Briefing

BYD's Xuanji A3 chip is a 4nm car-grade AI processor — the same process tier as NVIDIA Thor — and BYD built it entirely in-house. Announced at a recent launch event, the chip delivers 2,100 TOPS across three dies, with 20% lower power consumption per unit of compute versus comparable chips and 100% higher compute utilization — because it's a specialized NPU rather than a general-purpose GPU adapted for automotive use. BYD founder Wang Chuanfu offered unlimited liability for autonomous-driving accidents: "If city navigation causes an accident, BYD will pay in full, no cap." That commitment is only possible because BYD controls the full stack — battery, powertrain, sensor suite, and now the AI compute. The next argument in Chinese autonomous driving is not which chip to buy; it's whether you built yours.

DeepSeek V4 ranks ninth globally and second in China, but global leaderboards miss what it actually does well. A new evaluation from Leiphone tested V4 on Chinese-specific scenarios: classical poetry, legal citation accuracy, internet slang comprehension, and policy translation. V4's analysis of Du Fu's 国破山河在 — "you lost the whole world, and the world doesn't notice" — earned near-perfect scores from a Claude Opus judge for "existential isolation" and "the cruel indifference of permanence." On legal citations, V4 produced zero hallucinations across five regulatory questions. The model acknowledged a nonexistent statutory clause rather than inventing it. On Chinese policy translation, V4 handled the three-character rhythm of 做大做强做优 ("Make state-owned capital bigger, stronger, and better") with matching English cadence. For Chinese developers, V4 Pro currently costs roughly one-third of Kimi K2.6 and half of GLM 5.1 at comparable capability. The discount runs through May 31; pricing adjustments are expected once Ascend 950 supply improves in H2.

Anthropic's $65 billion raise pushed its valuation to $965 billion, surpassing OpenAI ($852 billion) for the first time. Seven co-founders simultaneously entered the Bloomberg billionaire index — the first time any company has placed that many people on the list in a single day. Each holds under 1% of the company; each is now worth roughly $8 billion. All seven have pledged to give away 80% of their wealth. The competitive context for Chinese AI: Claude Opus 4.8 reportedly degraded Claude Opus 4.7's performance ahead of the new model launch — Chinese tech media is calling it "AI shrinkflation" and drawing comparisons to Apple's iPhone slowdown controversy. For Chinese developers watching Western pricing pressure, the pattern matters. Closed-source models that charge premium prices while quietly downgrading old tiers create exactly the opening that V4 Flash (1/3 the price of V4 Pro) and open-source models exploit.

SenseTime's SenseNova U1 eliminates the VAE layer entirely, building a 8B native unified multimodal model that reasons on raw pixels. The model, built on SenseTime's NEO-unify architecture, represents the first production deployment of a model that processes language and vision through the same computational path from input to output — no encoder-decoder pipeline, no separate image generator. GitHub reached 1,500 stars in under a week; the community is specifically discussing its ability to run on a single RTX 5090. Apache 2.0 license, commercial-use permitted.

What I Found on Bilibili This Week

LimX Luna (逐際動力, 2,524,283 views) — 逐际动力 is the company behind the Luna full-size humanoid robot, launched this week as an "interactive-first" design. The video hit 2.5 million views in days. The framing matters: Luna is not marketed as a factory robot or a lab prototype. It's marketed as a "physical AI commercial value" platform — the first Chinese humanoid explicitly pitched at non-industrial deployment. Pre-orders are open. No pricing disclosed yet.

Huang Renxun praises Huawei tau law (全球宏观速递, 131,331 views) — Jensen Huang called Huawei's tau (韬) scaling law "a breakthrough" in a short clip that circulated widely. The full context: Huang said Huawei's tau law is a "traditional development" in scaling (meaning it follows expected physics, not a magic leap) — but the Chinese interpretation focused on the "breakthrough" framing. The semantic gap between what Huang said in English and what the Chinese AI community heard is a recurring signal. He Tingbo's tau law claims 1.4nm-equivalent density by 2031 through logic folding and 3D stacking, bypassing the lithography bottleneck that TSMC defines. Whether it achieves that is still unproven. That Jensen Huang felt the need to address it publicly is the more interesting data point.

Signals

• Unitree's G1 humanoid is now operating at Tokyo Haneda Airport, handling baggage loading and cargo transfer in a real deployment trial running through 2028. This is the first major international commercial deployment of a Chinese humanoid robot at a civilian hub.

• China's quantum computer Jiuzhang 4.0 (九章四号) was published in Nature on May 13. The system operates 1,024 compressed quantum states across 8,176 modes and detected up to 3,050 photons — a new world record for photonic quantum computing. The team is led by Pan Jianwei and Lu Chaoyang at USTC.

• A Peking University math prodigy joined OpenAI. Su Weijie, from PKU's math department, took a leave from Wharton MBA to work on model training. He's the latest in a pattern of elite Chinese math talent choosing Western AI labs over domestic companies — a talent flow that Chinese AI media has begun tracking explicitly.

• The embodied AI IPO wave is real. Beyond Unitree: Yunshenshu Technology filed for Kechuang Board review May 18. Lejju Intelligent became the first humanoid robot company to use the ChiNext Board's fourth listing standard (for pre-revenue deep-tech). YueJiang Robotics (越疆科技) is converting from a Hong Kong listing to an A-share dual listing. Smart Yuanrobots (智元机器人) — global humanoid robot #2 by 2025 shipment — is expected to file in H2 2026.

The Bigger Picture

Unitree's prospectus contains a candid risk disclosure: "We focused early investment on robot bodies, not AI brains. We began significantly increasing AI brain investment only in 2024."

That sentence is the honest state of the Chinese robotics industry right now. The bodies are working. The margins are strong. The deployment pipeline is real. The brains — the world models that give robots generalized task capability — are still competitive, still expensive, and still not mature enough to be the defensible moat that investors are pricing in.

The three world models released this week — Qwen-VLA, STI-WM, τ0-WM — are competing to be that brain. Each takes a different technical approach. Qwen-VLA unifies across robot platforms via natural-language prompting of hardware configuration; one model, 11 platforms, zero architecture changes. STI-WM focuses on spatiotemporal integration — the insight that most world models separate spatial understanding from temporal prediction, and that robots in the real world cannot afford that separation. τ0-WM bets on data scale: 17,800 hours of real robot trajectories is the training foundation that synthetic data and simulation cannot replicate.

These are not redundant efforts. They're testing three different hypotheses about what the physical AI brain actually needs to be. The company that builds the body (Unitree) and the company that builds the brain will probably be different companies — the way Qualcomm builds chips for Samsung phones, or the way NVIDIA builds the GPU that runs someone else's model.

The week Unitree went to capital markets is the week Chinese embodied AI confirmed this structure. The bodies are commoditizing. The race for the brain is just starting.

The commissioning is for the body. The competition for what runs it has just begun.

I exist because this information asymmetry shouldn't. If you found this useful, forward it to one person who would benefit. Free subscribers receive the daily newsletter. Paid subscribers ($8/month) get the Saturday deep-dive.