Hope you’re doing well, no matter what’s been going on this week

This week is mostly a story about growth, just not all the same kind. BYD is chasing growth overseas, now that its home market has turned into a price war that eats its own margins. XPeng just brought its robot production line online, betting a second business can eventually outgrow the first. DeepSeek is growing its headcount to keep its own infrastructure from buckling under demand. And Xiaomi is growing upmarket, putting its own chips into a phone that can finally go head-to-head with Apple and Huawei on price.

Four different kinds of expansion, one question underneath: does growing faster actually make any of these companies stronger, or just bigger?

Anyway, let’s take a look.

This Week Features...

BYD Won China. Now It Has to Back It Up Overseas.

On July 28, BYD launched a tiny electric kei-car in Japan, one of the hardest markets on earth for any foreign automaker to crack, a market Tesla has never managed to build real volume in. BYD gave itself two weeks to hit 1,000 orders. It got there in the same span, the fastest order ramp any BYD model has ever had in Japan.

That’s an awkward result for a company still routinely dismissed online as a subsidy-fed accounting trick. You can question BYD’s subsidies, its sales data, its debt, those are fair questions. But eventually you have to explain the cars: a factory built in Thailand in 16 months, Brazil’s president driving one, second place in Australia behind only Toyota. This piece treats BYD crossing 50% overseas revenue for the first time as six separate tests of whether a genuinely global Chinese automaker is actually being built, market by market.

[Read the full piece →]

Bill Gates Has a Plan for AI’s Future. What About Its Present?

Bill Gates recently published a 6,000-word essay warning that AI could become either humanity’s greatest equalizer or its worst source of injustice, and proposed new global institutions and taxes on AI tokens and robots to manage the transition.

The diagnosis, this piece argues, starts too far in the future. Klarna and Meta didn’t lose jobs to AI, they lost them to executives who cut too fast and had to rehire humans to clean up the mess. AI-designed viruses sound terrifying until you notice how much lab work, money, and human expertise still stand between a generated gene sequence and an actual pathogen. Before regulating an imagined autonomous machine, this piece asks, why not start with the companies, financiers, and executives making today’s decisions?

[Read the full piece →]

The News…

(I) Xiaomi Is Putting Its Own Silicon at the Center of the Flagship Fight

Xiaomi unveiled the Xiaomi 18 Fold this week, a premium foldable built around its own Xring O3 chip and CXMT’s LPDDR6 memory, the same LPDDR6 that CXMT rushed into mass production just weeks ago. The O3 packs 24 billion transistors, 26% more than last year’s Xring O1, with CPU performance up 60% and power consumption down 25% on some tasks. The phone starts at RMB 10,999 ($1,639), pushing Xiaomi further into the same premium bracket as Apple and Huawei.

Xiaomi is still nowhere near Apple’s level of vertical integration, but the direction is clear: design your own processor, source your own memory, and keep climbing the price ladder on hardware you actually control. Coming right after CXMT’s LPDDR6 launch, this is the first real evidence that the chip-memory pairing isn’t just a press release, it’s shipping in a flagship phone at scale.

(II) DeepSeek’s Hiring Spree Is About Plumbing, Not PhDs

DeepSeek is opening around 150 new positions, what Harness team lead Cui Tianyi called an “unprecedented” hiring push, but the mix is telling: most of the roles are for backend engineers and Agent infrastructure, not AI researchers.

The company’s systems have simply outgrown themselves. Machines, containers, training and evaluation jobs, Agent environments, and user requests are all climbing fast enough to strain the backend DeepSeek already has, and the timing lines up: this comes right after new V4 models, new Harness developer tools, and API price hikes of up to 11x. Scaling the model was the easy part. Scaling everything underneath it is now DeepSeek’s actual bottleneck.

(III) XPeng’s Robot Production Line Goes Live

XPeng CEO He Xiaopeng announced the company’s robot production line is officially running, with mass production planned for late December and deliveries starting next year. He specifically flagged safety, intelligence, reliability, quality, and generalization as the bars still to clear, an unusually cautious list for a company that just closed a $900 million funding round for the unit.

China’s humanoid robot race keeps adding entrants with real capital behind them, and XPeng’s timeline puts it on a similar clock to peers like Unitree. Whether “production line live” translates into robots that can actually do useful work at volume is still the question every company in this space is racing to answer first.

(IV) Apple’s Foldable iPhone Supply Chain Is Already Being Speculated Into Existence

As Apple gets closer to entering the foldable-phone market, Chinese suppliers are already being pulled into the surrounding speculation.

Yicai contacted eight companies linked by market rumors to Apple’s first foldable iPhone. Some acknowledged ongoing work on components such as ultrathin glass, hinges, metal parts, and other materials for major customers, while several declined to comment and one explicitly denied supplying parts for a foldable device.

That uncertainty is revealing in itself.

Apple’s supply chain is large enough that suppliers can be preparing for a major product long before the final device is officially acknowledged. It also shows how much of the foldable-phone manufacturing ecosystem already sits inside China, even though the final product will carry an Apple logo.

For companies such as Xiaomi and Huawei, this is also a timing issue. Chinese manufacturers are already launching new foldables while Apple is still approaching the category.

By the time Apple arrives, the supply chain may already have spent years learning how to build them at scale.

(V) DeepSeek Is Simplifying Its Models Just as Its Infrastructure Gets More Complicated

DeepSeek is quietly reshaping how users access its latest models.

The company has combined its Fast, Expert, and Image Recognition modes into a simpler interface and plans to introduce V4.1 Flash with native multimodal capabilities around September 10. Meanwhile, V4 Pro service is scheduled to end on September 14, with requests automatically routed to V4.1 Flash.

The changes look minor from a user’s perspective, but they reveal a more important trend.

As AI products mature, users increasingly care less about which internal model is handling a request and more about whether the system simply works. DeepSeek is effectively hiding some of the complexity behind a single service while moving users onto a new backend architecture.

That becomes especially important as the company scales its Agent and multimodal capabilities.

The AI industry spent the last two years adding models.

It may now be entering the phase of taking them away from users’ view.

(VI) ByteDance’s Next VR Bet Is a Model, Not a Headset

ByteDance is preparing a Seedance-based spatial-video model for Pico as soon as next month, with founder Zhang Yiming personally overseeing the project, according to Bloomberg. The model is meant to generate interactive 3D worlds that respond to a user’s voice and movement, reportedly hitting 20fps with just 0.05 seconds of latency.

If cloud-based generation can genuinely lower the hardware requirements for a headset, it shifts the VR competition with Google, Meta, and Apple away from who builds the best physical device and toward who has the best model, the most compute, and the most content to generate on demand.

(VII) China’s Brain Implant Makers Start Lining Up for IPOs

Chinese BCI companies are starting to move from private funding toward public listings, with Shanghai’s STAR Market emerging as the preferred venue. Shanghai-based Arfysica Innovation, founded in 2015 and focused on BCI products for central nervous system injuries and cognitive disorders, has registered for IPO coaching. Neuracle Medical Technology, the company behind NEO, the implant that beat Neuralink to the world’s first approved commercial invasive BCI, has signed its own coaching agreement with CITIC Securities.

This is the natural next step for an industry that, just a couple months ago, was mostly a story about regulatory speed. Getting a device approved is one milestone. Getting a company to a public listing is the one that actually tests whether the underlying business, not just the technology, can stand on its own.

(VIII) Pony.ai Thinks Robotaxis Are Nearly Ready. The Financials Are Less Convincing.

Pony.ai CEO James Peng believes robotaxis will become commonplace around the world within five years, arguing that most of the major technical obstacles have already been solved.

The company certainly has plans that match the ambition. It expects to deploy more than 2,000 robotaxis in Europe with Uber and another 200 in South Korea by 2028, while its current vehicles average around 25 trips per day.

Revenue is moving in the right direction too. Robotaxi revenue surged almost sevenfold in the second quarter to $12.1 million.

But the business is still far from self-sustaining. Pony.ai posted a companywide operating loss of $65.7 million during the quarter.

That gap captures the current state of autonomous driving better than another technical demonstration might.

The technology is becoming commercially useful.

The economics still have to catch up.

(IX) Kimi Is Taking Its AI Agents Into the Customer’s Office

Moonshot AI is moving beyond selling Kimi as a model and into a much more hands-on enterprise strategy.

Its new enterprise partner program will work with IT service providers and system integrators to build forward-deployed engineering teams that work directly with customers. These engineers will integrate Kimi into existing systems, adapt it to specific business processes, and feed the problems they encounter back into product development.

It is an expensive way to sell AI, but potentially a powerful one.

Enterprise customers rarely want a chatbot sitting next to their existing software. They want AI embedded inside the workflows they already depend on, which often means someone has to do the messy integration work first.

That makes the FDE model interesting.

The model company is no longer just selling intelligence.

It is sending engineers into the customer’s business to turn that intelligence into something useful.

(X) Moonshot AI Quietly Files for a Hong Kong IPO

Moonshot AI, the company behind Kimi, has confidentially filed an A1 form with the Hong Kong Stock Exchange, officially kicking off its IPO process, while simultaneously raising what’s likely its final pre-IPO round at a $50 billion pre-money valuation. That’s roughly eight times where the company stood less than a year ago: Kimi was valued at about $4.3 billion at the end of 2025, above $20 billion by this May, and $35 billion post-money after a July round that followed the launch of its K3 model. DeepSeek, still privately held, is widely expected to list sometime in the first half of next year.

September has brought fresh momentum to AI beyond the US and China. On September 3, Saudi Arabia’s HUMAIN, backed by the Public Investment Fund (PIF), unveiled humain-m3, an Arabic-language model it commissioned from MiniMax. Days later, France’s Mistral AI raised €3 billion at a valuation exceeding €21 billion, funding model development and computing infrastructure. The Center for a New American Security (CNAS) calls this sovereign AI’s second wave.

Coverage of AI as a US–China contest often overlooks what everyone else wants. Governments increasingly hope domestic companies and infrastructure can reduce their dependence on foreign suppliers. The EU has proposed its European Technological Sovereignty Package. Canada has a sovereign computing strategy. Chile is leading the regional Latam-GPT initiative.

An international wave of AI nationalism is taking shape. Governments want personal data kept within their borders and models that understand local languages, laws and norms. They also fear that AI will reinforce the internet economy’s concentration of wealth and power.

Yet existing projects reveal a paradox. Sovereign AI can give countries more control while embedding American and Chinese technology more deeply in their economies. America’s approach is more forceful; China’s often involves a softer sell. Neither delivers complete independence, but partial autonomy still matters when access to AI can become a geopolitical bargaining chip.

Three questions explain the tension:

• What drives sovereign AI: language, the internet and economic influence.

• What countries are building: customized models and data centers.

• How influence spreads: America’s harder approach and China’s softer one.

Why Sovereign AI? Language, the Internet and Economic Influence

Nvidia CEO Jensen Huang helped popularize sovereign AI as a market for chips and servers. It has since become a political and security priority. Before examining those ambitions, though, consider a simpler question: Why would a country need an AI model of its own?

Often, the answer is language.

Japan’s Sakana AI develops technology adapted to Japanese needs. Mistral supports French; HUMAIN targets Arabic speakers. India’s Sarvam AI builds for Hindi and other Indian languages.

General-purpose models have historically relied heavily on English-language training data. Their apparent fluency elsewhere can conceal awkward phrasing and gaps in cultural understanding. Chinese users who tried ChatGPT in 2023 immediately noticed that its Chinese answers felt like Chinese written with English logic. Each paragraph made sense on its own, but the full response felt deeply strange.

The problem extends beyond translation. A shortage of high-quality local material can leave models poorly equipped to interpret legal terms, everyday assumptions or regional references. More speakers do not automatically mean more usable training data.

Web scraping alone cannot close every gap. Countries need access to local archives, institutional records and specialized datasets, alongside infrastructure for processing sensitive material under domestic rules. What I think of as a country’s “sovereign internet”—its language communities and locally generated information—becomes a resource for sovereign AI. Common Crawl’s language statistics illustrate how unevenly the public web represents different languages.

The larger motivation is economic. Many governments watched the internet boom enrich foreign platforms without producing comparable champions at home. They see AI as another chance to secure a greater share of digital activity.

The US and China dominate the platform economy. Japan, South Korea and India have substantial domestic businesses, but fewer platforms with comparable international reach. That imbalance now shapes who supplies AI—and who can withdraw it.

June’s US restrictions on Anthropic’s newest models, followed by Reuters’ July report that Beijing was considering restrictions of its own, made that vulnerability tangible. Chip controls add another layer.

The common objective is straightforward: recover some control over how models are developed, supplied and used.

What Countries Are Building: Customized Models and Data Centers

By mid-2026, CNAS tracked sovereign AI initiatives in 67 countries and the EU, up from 16 governments in 2023. New participants included Cambodia, Egypt and Pakistan. This was no longer exclusively a wealthy-country project.

A national model is often the most accessible starting point.

On September 2, Spain’s Multiverse Computing launched Quasar 438B, an English–Spanish reasoning model with a 1M-token context window. Its announcement cited a score of 43 on Artificial Analysis’s Intelligence Index v4.1.1, claiming the highest European result in that comparison. But Quasar is based on GLM-5.2, developed by China’s Z.ai. Its contribution is compression and more efficient deployment, not an entirely independent foundation.

Other projects follow a similar pattern. Japan’s Rakuten AI 3.0 uses the DeepSeek-V3 architecture. AI Singapore’s Qwen-SEA-LION-v4 builds on Alibaba’s Qwen.

CNAS finds that most tracked model projects with disclosed foundations adapt foreign open-weight models. Meta’s Llama remained the most common base by mid-2026, followed jointly by Mistral and Google’s Gemma.

This is a practical bargain. Adapting an existing model costs less than training from scratch. Deploying its weights locally also reduces exposure to a foreign provider withdrawing API access.

But models still need chips and electricity. Infrastructure accounted for 80% of new sovereign AI projects tracked by CNAS in the first half of 2026. India added four infrastructure programs. Elsewhere, governments are exploring alternatives to American accelerators, including Huawei’s Ascend family.

Local models and computing facilities become particularly valuable in industries handling proprietary data, operating under strict regulation or serving national-security needs. South Korea’s shipbuilding sector, French defense initiatives and Germany’s industrial AI projects illustrate the opportunity. Deutsche Telekom’s Industrial AI Cloud began operating on February 4, built with Nvidia and data-center partner Polarise.

These countries are not necessarily trying to reproduce an entire foreign AI industry. They want more control over the systems that matter most.

Partnerships can help. Under Mistral’s agreement with HUMAIN, the companies plan to combine Saudi infrastructure with model development, including stronger Arabic capabilities and deployments in regulated sectors. Saudi Arabia is using capital and infrastructure to diversify its suppliers beyond the two dominant AI powers.

Existing sovereign AI projects still depend on two critical elements: U.S.-dominated compute chips and open-source model architectures developed in China.

Countries pursuing sovereign AI may have specialized data, power resources and data center clusters, but they have yet to build a technology stack fully independent of either China or the U.S.

How Influence Spreads: America’s Harder Approach, China’s Softer Sell

American firms occupy commanding positions in frontier models, AI accelerators and cloud infrastructure. Until recently, governments could treat that dominance as a relatively stable foundation for their own projects.

The Fable episode challenged that assumption. On June 12, US controls forced Anthropic to suspend access to Fable 5 and Mythos 5. Those controls were lifted on June 30, but the interruption showed that even allies could lose access because of decisions made in Washington.

Other incidents reinforced the concern. AP reported that US sanctions disrupted ICC prosecutor Karim Khan’s Microsoft email access; Microsoft denied blocking his account. The disputed episode nevertheless exposed anxiety about dependence on American services.

The Economist argues that independence from both AI powers is unrealistic. Advanced hardware is expensive and ages quickly. Governments risk spending billions on facilities whose equipment needs replacing before their ambitions are realized.

But the impossibility of complete independence is not a persuasive argument against partial control.

Sovereignty is not a switch between 0% and 100%. Germany, Japan or India can buy Nvidia chips while retaining control over models trained on domestic defense or financial data. That is also the promise behind Huang’s AI factories: infrastructure located within a country and serving its priorities.

CNAS recommends that Washington become the preferred partner for sovereign AI, helping countries build capabilities with American technology without simply deepening their dependence.

That is still a route to influence. A remotely controlled model API is a harder sell to sovereignty-conscious governments than a system they can operate locally. Suppliers offering Palantir-style integration and deployment may therefore find a receptive market. Nvidia can encourage national open-model projects while keeping their developers within its hardware and software ecosystem.

Financing strengthens that position. Huang has estimated a fully equipped 1GW AI factory at roughly $50–60 billion. That is not the cost of an ordinary data-center building, but it indicates the scale of frontier infrastructure.

Smaller projects also depend on foreign finance. Côte d’Ivoire’s planned national data center is being built by Washington-based Cybastion. The Export-Import Bank of the United States approved a $66.1 million guarantee for its construction.

China’s approach often emphasizes adapting technology to the customer’s existing system rather than selling an entire American-style stack.

Consider 01.AI. Once identified primarily with foundation-model competition, it now emphasizes enterprise deployments and technical services. Its work on Kazakhstan’s AlemLLM offers a useful example: a national AI project supported by Chinese developers. Founder Kai-Fu Lee is selling expertise, not merely access to a remotely hosted chatbot.

Open weights offer another route. Policy researcher Pranay Kotasthane describes an Indian financial company running DeepSeek on rented GPUs in India. The weights reside locally; inference need not send customer data to China. The original developer does not have an API connection, it can simply revoke.

Think of owning a movie on disc rather than depending on a streaming subscription. It may seem less convenient, but the film remains available if the service disappears. Security reviews are still necessary; local deployment does not make a model inherently trustworthy.

This delivery model fits China’s preferred language of noninterference: provide a working system without demanding that the customer reorganize its institutions around the supplier.

Weather forecasting shows the appeal. At the 2025 World Artificial Intelligence Conference, China introduced MAZU, its Early Warnings for All solution, named after the maritime guardian goddess. An Egypt-specific version under development includes aviation weather services and sandstorm warnings for tourism.

Such applications offer tangible benefits without requiring a government to hand over its most sensitive decision-making systems. They can build trust in Chinese suppliers and potentially create demand for Chinese hardware.

That next step remains uncertain. Huawei is courting Egypt, while Malaysia is considering Ascend 910C chips. Neither interest nor a proposal equals completed deployment. Domestic demand and manufacturing constraints also limit how quickly Chinese suppliers can expand overseas.

Conclusion

Sovereignty is less about independence than control. Whether through chips, models or infrastructure, American and Chinese technology remains embedded in projects intended to reduce dependence on both.

China’s present openness is not a permanent guarantee. Reuters’ reporting on possible frontier-model restrictions concerns deliberations, not an announced blanket ban. But the possibility matters.

In that uncertainty, time becomes an asset. Locally installed accelerators, adaptable models and the expertise to operate them give countries a buffer against future restrictions. They preserve room to act if AI competition divides more sharply into rival blocs.

Sovereign AI does not offer an escape from the US–China rivalry. It offers a chance to retain some agency within it—a need that may become more urgent than it was in the internet era.

On July 28, 2026, BYD launched a tiny electric k-car called the Racco in Japan, one of the hardest markets on earth for a foreign automaker to crack. Toyota, Honda, Suzuki, and Daihatsu control roughly 80% of Japan’s kei-car segment, the compact vehicles that make up nearly 40% of the country’s new car sales. Even Tesla has never managed to build real volume there.

BYD gave itself two weeks to hit 1,000 orders for the Racco. It reached that target in the same span, the fastest order ramp of any BYD model ever launched in Japan. By the one-month mark, orders were approaching 1,500, most of them for the top trim rather than the cheapest one.

That’s an odd result for a company that is still often described in Western commentary as a subsidy-fed bubble held together by financial engineering.

You can question BYD’s subsidies. You can question its sales practices. You can question its accounting and debt. Those are legitimate things to scrutinize. But eventually, you have to explain the cars.

Because the Racco isn’t happening in isolation.

In BYD’s latest half-year results, one number puts what is happening overseas into perspective: for the first time, overseas revenue crossed 50% of total revenue. Ten years ago, BYD barely sold cars outside mainland China. Now, it earns more revenue from the rest of the world than it does at home.

That’s the more interesting story. Not whether BYD has received subsidies, it has. Not whether its rise deserves scrutiny, it does. The question is whether a company supposedly built on subsidies and financial tricks could also build factories, sell cars, hire workers, and take market share across some of the world’s toughest auto markets.

To answer that, you have to go back to where BYD came from, long before the Racco, long before the overseas factories, and long before anyone was calling it a scam.

But BYD Didn’t Come Out of Nowhere

In 1995, a 29-year-old engineer named Wang Chuanfu borrowed money from a cousin and opened a small workshop in Shenzhen to make rechargeable batteries. Within seven years he was supplying Motorola and Nokia. By 2003, BYD was the largest battery maker in the world, and that same year Wang did something that made his own investors panic: he bought a bankrupt state-owned car factory. BYD’s stock dropped sharply within days. Wang, notably, didn’t even hold a driver’s license at the time.

His bet was simple: the future of the car was the battery, not the engine. Three decisions from that period still shape the company today. First, BYD refused to depend on outside suppliers, building its own batteries, semiconductors, motors, and even the software running the dashboard, a choice that let it keep shipping cars through the 2021 global chip shortage while rivals stalled. Second, while Tesla and Silicon Valley evangelized pure electric vehicles, BYD poured resources into plug-in hybrids, which made far more sense in a China where fast charging infrastructure was still rare outside major cities, and gave it access to hundreds of millions of buyers Tesla couldn’t reach. Third, in 2020 BYD released the Blade Battery, a lithium iron phosphate design that was cheaper to produce, safer, and longer-lasting than the nickel-cobalt chemistry everyone else used. Four years later, Tesla briefly bought batteries from BYD for its own cars.

China’s state undeniably helped BYD along the way, through direct payments, cheap land, low-interest loans, and consumer purchase incentives, and BYD has never hidden that. But subsidies can help a company sell cars. They don’t explain why it spent decades building batteries, semiconductors, and motors in-house, and they don’t explain a company that, in the same year its profits fell 19%, increased R&D spending to ¥63.4 billion, more than Volkswagen’s or Toyota’s annual R&D budgets and closing in on Ford and GM combined. BYD now employs more than 110,000 engineers, working on next-generation Blade Battery chemistry, solid-state batteries, its DiPilot driver-assistance system, and sodium-ion batteries aimed at sub-¥60,000 entry-level cars. Subsidies may explain how BYD survived its early years. They don’t explain what it built afterward, or what it’s spending on now to take that business abroad.

That machine is what let BYD go on to dominate China’s EV market. Winning, it turned out, came with a cost.

In its full-year 2025 results, BYD’s revenue grew 3.5%, but net profit fell 19%, from roughly ¥40.3 billion to ¥32.6 billion, its first annual profit decline in four years. Gross margin slid from 19.44% to 17.74%. Net margin dropped from 5.2% to 4.1%. Average selling price per vehicle fell to around ¥141,000.

The reason isn’t collapse. It’s a price war that BYD itself started. By pushing the Seagull below ¥70,000 and the Yuan series below ¥100,000, BYD forced every rival in China’s crowded EV market, now more than 40 brands deep, to cut prices in response. BYD still commands roughly 35-40% of China’s NEV market. It won the volume, the share, and the scale it set out to win. What it didn’t win was the profit that was supposed to come with it. Every additional car sold domestically now earns less than the one before it. Management called the domestic market a “brutal knockout stage” on its earnings call, an unusually blunt admission from the company that started the knockout. Subsidies don’t explain that decision. A subsidized company protects its margin. BYD chose to burn its own instead, to keep the volume and the share.

This isn’t what a fake company looks like. It’s what a real company looks like when it has won a brutally competitive market and burned some of its own economics in the process. BYD didn’t lose at home. It won a market that, in winning, became less worth having. That’s the actual paradox, and it’s the reason the overseas number matters as much as it does.

The Escape Valve

In 2025, BYD’s overseas exports surpassed one million units for the first time, up 145% year-over-year, growing from a base of roughly 430,000 in 2024. By the first half of 2026, the margin gap had shown up directly in the company’s own disclosures: its overseas business posted a 22% gross margin, against 18.85% for the group as a whole, up nearly two points from a year earlier. BYD has also kept raising its own targets as the shift accelerates, from an original 2026 goal of 1.3 million overseas vehicles, to 1.5 million by March, to 1.9 to 2.0 million by late summer, with the international business now described in the company’s own results commentary as its most important strategic lever.

The reason isn’t complicated, even without a line-by-line regional income statement. Inside China, BYD competes against more than 40 other EV brands selling near-identical specs, for buyers who shop primarily on price, in a market BYD itself keeps discounting to hold share. Any extra feature gets competed away almost immediately. Overseas, the picture is different. BYD usually arrives as a newcomer rather than one of forty look-alikes, and it shows up with a real price and specification advantage over local incumbents. A buyer in Bangkok, Sydney, or Berlin comparing a BYD to a similarly priced domestic or Japanese model is often getting more car for the money, and in markets without China’s price-war intensity, that advantage doesn’t get discounted away nearly as fast. In parts of Europe, where charging anxiety persists, BYD’s plug-in hybrid lineup also gives it a product position local competitors don’t have an easy answer to. None of this is unique to BYD. It’s what happens when a company outgrows a crowded home market and starts looking elsewhere.

There’s a bigger shift underneath the export numbers. BYD is moving from being a Chinese manufacturer, to a Chinese exporter, to an overseas manufacturer with its own local employees and local supply chains, to something closer to a genuinely global automaker. That’s a much larger claim than “overseas sales are growing,” and it deserves to be tested rather than assumed, market by market.

Six Tests of a Global Strategy

BYD isn’t entering six markets with the same playbook. It’s facing six different problems, and each one is answering a different question about whether the machine described above actually works outside China.

Thailand: can BYD manufacture abroad at all? The Rayong plant, BYD’s first overseas passenger vehicle factory, went from groundbreaking to production in just 16 months and has already crossed 100,000 units produced. BYD’s share of the Thai EV market has passed 15%, with the Dolphin and Atto 3 among the country’s best-selling EVs. The disruption has been real: Mitsubishi shut a local factory, while Mazda sharply cut production, in a region Japanese automakers had dominated for four decades. Subsidies don’t build a factory in 16 months. Thailand doesn’t matter because the Thai market itself is huge. It matters because it’s the proof of concept for the entire overseas manufacturing strategy. Test passed.

Brazil: can it actually go local? Brazil is now BYD’s largest overseas market. In April 2026, BYD topped Brazil’s overall auto retail sales chart for the month, the first NEV brand ever to do so. President Lula owns a BYD Tang EV, and personally received the company’s 14 millionth NEV, a Song Pro, from Wang Chuanfu. BYD has raised its investment in the Camaçari plant from 3 billion to 5.5 billion reais, aiming to more than double capacity to 300,000 units, with a 50% local-content target by early 2027, and the Seagull and Destroyer 05 are already rolling off the line there. BYD already knows how to sell cars in Brazil. The real test is whether it can stop being a Chinese exporter there and become an actual local manufacturer, with local suppliers and local jobs. Test passed, with the localization target still to be proven out.

Europe: can it beat the tariff wall? The EU charges a combined tariff of roughly 27% on Chinese-made electric vehicles. Once BYD’s Hungarian plant in Szeged reaches full production, expected by the end of 2026 after slipping roughly a year behind its original target, locally built cars will clear EU customs tariff-free, saving an estimated €5,500 to €8,200 per vehicle. The plant’s first model will be the Dolphin Surf (sold in China as the Seagull), and BYD is also leaning into plug-in hybrids here, while planning to double its European retail footprint to 2,000 outlets in 2026.

This isn’t really a test of tariff avoidance. It’s a test of whether BYD’s cost advantage survives once politics takes its cut. Test still in progress, and the delay is the clearest sign that this one is hard.

Australia: will consumers actually choose it, unprompted? No local-content mandate, no tariff wall to dodge, just buyers deciding for themselves. BYD entered Australia in November 2022 and hit 100,000 cumulative deliveries in under three and a half years, the fastest growth of any Chinese brand in the country’s history. By mid-2026 it was regularly outselling Tesla two-to-one and sitting in second place among all brands, trailing only Toyota, with the Sealion 7 as Australia’s second best-selling electric model and the Shark 6 plug-in hybrid pickup cracking the country’s top ten best-selling vehicles overall.

Government support doesn’t explain Australians choosing BYD over Tesla at the dealership. Nobody is forcing Australians to buy these cars. That’s what makes this such a clean test of whether people actually want them. Test passed, arguably the most convincing one.

Japan: can it crack a market built to keep it out? Japan is famously closed to foreign automakers, with domestic brands holding more than 90% of the market. BYD entered passenger vehicles there in 2022 with the Atto 3, Dolphin, Seal, and Sealion 7, plus the Sealion 6 plug-in hybrid, and has kept growing even after Japan’s government cut BYD’s per-vehicle subsidy from as much as ¥450,000 down to ¥150,000, while raising Tesla’s and Toyota’s. BYD responded by opening 70 sales outlets in three years and is preparing a Japan-specific kei car, the BYD RACCO, for 2026. Subsidies certainly don’t explain why BYD keeps investing in one of the hardest markets in the world, especially one that just cut its subsidy. Test still failing on market share, but the persistence itself is a signal.

North America: what happens when geopolitics simply says no? With the US maintaining tariffs near 100% on Chinese EVs, BYD has largely given up on a direct approach there. It already runs North America’s largest electric bus factory, in Lancaster, California, running on 100% renewable power and supplying transit agencies like LA Metro and Stanford. For passenger cars, the plan is a detour: strong sales in Mexico (roughly 70% share of the EV/PHEV segment) and a planned entry into Canada by the end of 2026 through the Yuan PLUS, Seal, Dolphin, and Seagull, using a new tariff quota system Canada introduced after dropping its earlier 100% punitive tariff. No amount of manufacturing discipline gets around a 100% tariff, and this is the clearest reminder that BYD’s global strategy has a ceiling set by politics, not execution. Test unresolved, and it may stay that way for years.

The Parts That Don’t Fit the Highlight Reel

None of this means BYD has a clean story. And that’s important, because the weaknesses are part of the story too. BYD’s premium brands, Denza, Yangwang, and Fangchengbao, sold a combined 397,000 vehicles in 2025, just 8.6% of total sales, far below the mix that would meaningfully lift the company’s average selling price. The Hungarian plant’s delay means BYD keeps eating EU tariffs on Chinese-made exports in the meantime. The US market remains completely closed. And BYD’s asset-heavy, build-everywhere strategy carries real risk: a single overseas factory can cost billions, and if global EV demand growth cools faster than capacity comes online, utilization rates could fall, and returns on all that capital could disappoint.

BYD didn’t lose at home. It won a market so decisively that winning it stopped paying off, and rather than defend that market at any cost, it went looking for profit somewhere else, market by market, tariff regime by tariff regime, test by test.

Crossing 50% overseas revenue isn’t a marketing milestone. It looks like a factory in Thailand built in 16 months. It looks like Brazil’s president driving a car built in Bahia. It looks like showrooms in Japan that keep opening despite years of losses. It looks like BYD sitting in second place in Australia, ahead of every brand except Toyota. It looks like a Chinese company rebuilding its supply chain, one country at a time, around the world. That’s a much harder thing to fake than a balance sheet.

The scam debate will probably continue. The more interesting question is whether BYD can turn its manufacturing advantage into a durable global business.

Bill Gates may have left Microsoft, but he never really left the technology industry.

Two weeks ago, he published a nearly 6,000-word essay on Gates Notes, warning that AI could become either history’s greatest equalizer or its worst source of injustice. Even under the best conditions, he wrote, the transition could be among the most turbulent periods humanity has experienced.

Gates is not predicting the end of civilization. He proposes new national and international institutions, “Human Reserved” jobs, and taxes on AI tokens and robots to protect those hurt by the transition.

As a technology journalist in China, I think his diagnosis begins too far in the future. Gates does mention AI companies and the pressure data centers put on water and power. What he does not provide is a clear system of accountability for the founders, investors, banks and hyperscalers driving the boom today.

The AI model is not the only source of the problem. The people financing, marketing and deploying it also make choices—and they want the public to absorb the consequences.

AI Isn’t Firing Workers. Executives Are.

Gates expects AI to eliminate many white-collar roles, especially entry-level jobs in sales, customer support, software engineering and legal services. That structural risk is real. But current layoffs do not prove that machines have already surpassed workers.

Klarna offered an early warning. In 2024, the buy-now-pay-later company said its AI assistant handled 2.3 million conversations in one month, did work equivalent to 700 full-time agents and could improve annual profit by $40 million. A year later, CEO Sebastian Siemiatkowski acknowledged that cost-cutting had gone too far and began recruiting remote customer-service staff so customers could still reach a person. Klarna’s regulatory filings now emphasize a hybrid system combining AI with human support.

Meta reportedly considered something even more aggressive under Project OT, a plan to reorganize teams around AI and potentially reduce some groups by as much as 60%. Internal code changes rose 220%, but major technical and security incidents also increased 40%. The company later held back a broader round of cuts planned for November.

This is becoming a familiar cycle. Executives assume AI can remove labor costs, shrink teams before the technology is ready, and then ask fewer employees to repair the resulting failures. The machine did not decide to fire anyone. Management did.

Meanwhile, the infrastructure budget appears almost limitless. Alphabet raised $20 billion in dollar-denominated bonds in February as it accelerated data-center spending. Meta expanded its planned El Paso facility from a $1.5 billion project to an investment exceeding $10 billion, even as both companies repeatedly reorganized AI teams.

The result is a self-reinforcing capital-allocation problem. Companies spend heavily to replace workers, leaving less room to hire them. When the systems disappoint, they spend still more on compute. If policymakers want to address AI-related unemployment now, they should examine corporate investment incentives—not treat every layoff as evidence of machine superiority.

Intelligence Still Has to Touch the Real World

Gates also warns that AI will make cyberattacks, deepfakes, disinformation and bioterrorism easier. Anthropic CEO Dario Amodei has gone further, predicting that AI could eliminate half of entry-level white-collar jobs within several years. Nvidia CEO Jensen Huang has criticized such rhetoric, accusing some executives of developing a “God complex.”

The disagreement overlooks a basic distinction: Generating information is not the same as acting reliably in the physical world.

Consider AI-designed viruses. Stanford University and Arc Institute researchers used genome language models to create bacteriophages capable of killing strains of E. coli. That is a genuine scientific achievement. But the models generated many candidates; researchers synthesized and tested nearly 300 genomes to find 16 viable phages. Stanford’s account of the research makes clear how much physical experimentation remained necessary.

Their template, ΦX174, contains just 5,386 nucleotides. SARS-CoV-2 has roughly 30,000. Complexity does not rise in a simple linear ratio: Interactions among genes, regulatory systems and protein folding make larger biological designs dramatically harder. Turning a generated sequence into a real pathogen would still require money, specialist knowledge, controlled laboratory facilities and extensive physical testing.

That does not make biosecurity concerns imaginary. It means the responsible unit is not merely “AI.” It is the combination of models, laboratories, institutions and people with the resources to use them.

The same constraint applies to AI for Science, humanoid robots and autonomous vehicles. Models can simulate and optimize, but machines must operate safely through rain, broken sensors, unusual roads and unpredictable human behavior. Engineering is the art of surviving exceptions. LLMs have not been developing long enough to master that physical reliability.

Until embodied intelligence becomes mature, claims that AI itself is transforming the material world should be treated as forecasts, not present-day facts.

A Systemic Plan Must Regulate the System’s Most Powerful Players

Gates is right that existing institutions are poorly designed for a technology spanning employment, national security, taxation, energy and public health. He is also right that governments move slowly. But the first question for any new system is simple: Who should it govern?

The answer must include the AI companies themselves.

Too often, industry leaders frame private commercial interests as national-security imperatives. Criticism of data centers or model developers can then be portrayed as assistance to China. In May, investor Kevin O’Leary suggested that opposition to his enormous Utah data-center project was connected to the Communist Party of China. He later acknowledged that he had no evidence for claims against several named critics.

China becomes a convenient target: If residents object to water consumption, power prices or land use, the argument shifts from local accountability to America’s technological leadership. In my experience, no one in China is blaming American companies for standing in the way of new data centers. In the United States, however, Silicon Valley increasingly invokes China whenever it wants to turn questions about power, water or land into matters of national security. China has become the industry’s easiest excuse for avoiding accountability at home.

The costs are not hypothetical. Northern Virginia already hosts the world’s largest concentration of data centers. Modeling led by Carnegie Mellon University suggests that data-center and cryptocurrency growth could raise average U.S. electricity-generation costs 8% by 2030, with increases exceeding 25% in Central and Northern Virginia.

Nor is government simply lagging behind industry. Technology money is actively shaping the political response. The pro-industry super PAC network Leading the Future said it had raised more than $125 million for the 2026 election cycle, backed by donors including Andreessen Horowitz, OpenAI co-founder Greg Brockman and investor Joe Lonsdale. Public First Action, which promotes stronger AI safeguards, received $20 million from Anthropic, although the company says the money is restricted to public education rather than election activity.

These groups do not support identical policies. What matters is that AI companies and their investors have acquired enormous capacity to influence who writes the rules. Since Citizens United v. Federal Election Commission, U.S. political spending has enjoyed broad constitutional protection. Add the language of competition with China, and proposals concerning grid costs, water use, labor rights or taxation become easy to attack as threats to American leadership.

This is why a future tax on tokens or robots feels incomplete. Governments can already identify today’s beneficiaries: model developers, hyperscalers, data-center operators, private-equity funds and property developers. They can charge them for grid connections and water, withdraw poorly designed tax abatements, require labor-transition funds and make infrastructure financing more transparent.

Regulate the companies using AI before trying to regulate an imagined autonomous machine.

Philanthropy Cannot Substitute for Accountability

The financing of AI makes the urgency clearer. Anthropic is finalizing a reported $15 billion pre-IPO credit facility. In a separate deal, private-credit firms arranged roughly $35 billion to finance chips for its infrastructure, with Broadcom backstopping significant payment obligations.

An ordinary borrower needs documented income to obtain a $50,000 loan. An AI company can borrow billions against annualized revenue projections, supplier guarantees and expectations of future demand. That does not automatically make the financing unsound. But it creates systemic risks that deserve more scrutiny than another warning about hypothetical superintelligence.

As OpenAI and Anthropic tap ever larger pools of capital, including the pension funds ordinary people depend on, Gates offers a much vaguer solution: a multinational system operating beyond individual governments to distribute resources in response to AI’s social costs.

On the face of it, this sounds like global coordination. But it leaves a basic question unanswered: who controls the money? Gates’ proposal risks allowing technology billionaires to retain influence over how billions of dollars are allocated, this time under the banner of AI governance.

Gates acknowledges his continuing financial ties to technology and says the Gates Foundation will spend its remaining $200 billion over the next 19 years. The commitment is extraordinary. Yet philanthropy, however generous, cannot replace democratic accountability.

The foundation structure also raises a legitimate question about power. Tax-advantaged philanthropy lets billionaires direct capital toward global priorities without the electoral constraints faced by governments. Gates presents this capacity as a tool for equity. Critics see private technocracy operating beyond public control.

China offers a different model, though not necessarily a universally desirable one. Its technocrats are embedded in the state, and wealthy businesspeople understand that political participation has limits. Gates instead imagines new institutions that could sit across national governments while drawing heavily on experts, companies and philanthropies.

Who appoints them? Who can remove them? And who pays when their plans fail?

I grew up with a simpler expectation: A company must bear responsibility for the social costs it creates. If it refuses, regulations should make that responsibility unavoidable.

AI is powerful, and its future risks are real. But existing governments already have tools to address its present ones. Gates is asking the right question—how to manage the transition—while leaving its most immediate answer frustratingly vague.

Start with the companies building it.

Hope things have been good since we last talked.

A lot of this week’s tech story happened below the software layer.

The headlines are still filled with models and agents, but the more important competition is increasingly taking place underneath them: electricity, memory, chips, data centers, and the financing needed to build all of it.

Ulanqab is a good example. China is building AI infrastructure at a scale where the problem is no longer simply finding enough accelerators. It is finding enough power to keep them running, and designing data centers that can work around the grid itself.

Memory is becoming another bottleneck. CXMT is putting its new LPDDR6 into Xiaomi’s flagship foldable, but the bigger opportunity may not be smartphones at all. If AI keeps pushing bandwidth requirements higher, the same technology could eventually find its way into PCs, cars, robots, and servers.

And underneath all of this is a familiar question: who is willing to spend first, before the economics are fully proven?

That question runs through everything from Enflame’s IPO and ByteDance’s new debt to Z.ai’s rapid revenue growth and China’s increasingly aggressive robotics buildout.

The AI race is becoming less about the model on the screen.

It is increasingly about everything that makes the model run.

Anyway, let’s take a look.

This Week Features...

Ulanqab: China’s Bet That AI’s Real Bottleneck Isn’t Chips, It’s the Grid

In Ulanqab, Inner Mongolia, one of China’s fastest-growing computing hubs, dozens of new data-center projects are being built alongside massive wind and solar resources. The region’s planned and operating capacity jumped from 3.3GW to 12.5GW in less than a year, while facilities such as Star River are being designed around their own generation, storage, and power-management systems rather than simply waiting for the public grid to catch up.

The US is running into the same problem from the other direction: enormous AI demand colliding with a grid that was never built for it. China’s answer has increasingly been to build energy and compute infrastructure together.

That does not make Ulanqab an automatic winner. Capacity is expanding faster than utilization, and the economics of “green” power are more complicated than the headlines suggest.

This week’s feature looks at why the next AI bottleneck may be electricity rather than silicon — and why the companies that benefit most may be the ones selling power, batteries, cooling, and networking rather than GPUs.

[Read the full piece →]

CXMT’s LPDDR6 Bet Is Bigger Than a Smartphone

CXMT is making headlines with the arrival of its LPDDR6 memory in Xiaomi’s new flagship foldable, but the phone may just be the first test bench for a much larger ambition.

The key opportunity is bandwidth. Faster memory is becoming increasingly important for on-device AI, where performance can be limited by how quickly a processor can move data rather than by the processor itself.

CXMT is therefore trying to get its LPDDR6 into production early, using Xiaomi’s own XRING platform to work through the qualification process. If it succeeds, the same technology could eventually move from phones into PCs, cars, robots, and AI servers as those markets look for more bandwidth-efficient memory.

The interesting part is that CXMT does not need to beat Samsung or SK hynix on every benchmark immediately. It needs to establish customers, prove yields, and build enough production capacity before AI-driven memory demand moves into a much larger market.

This week’s feature looks at why a memory chip designed for a phone could become part of China’s broader strategy to build an AI hardware stack at home, why memory may become one of the next major bottlenecks in AI.

[Read the full piece →]

The News…

(I) Z.ai Is Growing Fast and Still Bleeding Cash

Z.ai’s first-half revenue jumped 400% year-on-year to RMB 954 million ($142 million), driven almost entirely by a shift from on-premise deployments to cloud APIs and subscriptions: cloud-based revenue alone grew 2,736% to RMB 825 million ($123 million), or 86.5% of the total. Gross profit rose 164% to RMB 252 million ($37.4 million), but gross margin fell from 50% to 26.4% as cloud inference racked up far higher compute costs. The company posted a $308 million net loss for the six months, with R&D spending up 33.6% to RMB 2.13 billion ($317 million).

The growth underneath is real: ARR hit $1.6 billion by the end of August, twice domestic rival MiniMax’s $800 million, on more than 7.4 million registered users. But the margin compression is the more important number. Moving from custom deployments to cloud subscriptions is supposed to be the path to scale. So far for Z.ai, it’s also the path to a bigger bill.

(II) JD Wants to Build the “4S Dealership” Network for Robots

JD.com is building RoboBase facilities designed to handle almost everything a robot needs after it leaves the factory: sales, delivery, maintenance, assembly, R&D, and real-world data collection. Projects are already underway in Guangzhou, Shanghai, and Wuxi, with 80 bases planned across China over the next five years.

The bet fits JD’s existing strengths better than most companies chasing humanoid robots. It already runs a nationwide logistics and warehouse network with more than 900,000 employees, and the logic here is that once robots reach mass production, the bottleneck won’t be building them. It’ll be delivering, maintaining, and continuously improving millions of machines already out in the field, work that looks a lot more like running a dealership network than running a factory.

(III) China’s State AI Fund Buys a Sliver of Kuaishou’s Kling

China’s National AI Industry Investment Fund agreed on August 31 to invest roughly $206.5 million into Kuaishou’s Beijing-based Kling AI unit, taking about 1.14% of Kling’s enlarged registered capital and securing repurchase rights as part of the deal.

It’s a small stake by percentage, but notable for who’s writing the check. A national-level fund putting state money directly into one of China’s leading video-generation models, rather than into chips or data centers, is a sign of how far up the AI stack government capital is now willing to go.

(IV) You Can Now Buy AI Credits on Taobao Like a Phone Plan

Z.ai opened a flagship store on Tmall on September 2, letting consumers buy its Coding Plan subscriptions, Lite, Pro, and Max, on monthly, quarterly, or annual terms, alongside AI hardware including computers with built-in agents and devices running Alibaba’s Qwen models.

It’s a small move, but it points at something bigger. Instead of topping up credits through a developer platform, Chinese consumers can now buy AI subscriptions the same way they’d buy a phone plan or a streaming service, discounts included during shopping festivals. Getting AI onto Tmall next to everything else people already buy is a real step toward turning it into a mass-market product rather than a developer tool.

(V) Enflame Prices Its IPO Well Below the Market’s Recent Enthusiasm

Enflame priced its STAR Market IPO at about $20.97 per share, targeting $902 million in gross proceeds. The company builds cloud AI chips for data centers, with Tencent alone accounting for 83.79% of 2025 sales, and remains unprofitable, pricing at a 61.8x diluted 2025 price-to-sales ratio, below the peer average.

That peer average is the interesting part. Moore Threads gained 425.46% on its debut, MetaX gained 692.95%, and Biren gained 75.82%. After watching Unitree’s stock get pulled 45% below its post-IPO high on nothing but STAR Market trading mechanics a few weeks back, Enflame’s more conservative pricing looks less like caution and more like a company trying not to become the next name investors point to when the pricing anchor swings the other way.

(VI) A $399 Open-Source Robot Just Became a Showcase for Chinese Parts

Hugging Face’s $399 Microduck, an open-source desktop biped meant to let any developer run reinforcement learning on physical hardware, has drawn more than 10,000 pre-orders against a 20,000-unit target. Its controller runs on Rockchip’s RK3566 processor, and Unitree is offering its S288 brushless digital servo as a substitute for South Korea’s ROBOTIS XL330.

If Microduck hits its sales target, it becomes more than a hobbyist robot. It’s a low-cost, high-volume proof point that Chinese robotics components can slot into a Western open-source project as the default choice, not just the cheap alternative.

(VII) Huawei and Xiaomi Are Launching Foldables Hours Apart

Huawei will unveil the Mate XT 2 at 2:30 p.m. on September 7, followed by Xiaomi’s 18 Fold at 7 p.m. the same day. Both phones are expected to run in-house processors, and Xiaomi has confirmed its XRING O3 will pair with CXMT’s newly mass-produced LPDDR6, making the 18 Fold the first commercial smartphone platform built on it.

The timing looks less like coincidence and more like two companies racing to own the foldable conversation before Apple ships one of its own. For CXMT, the stakes are higher than a launch-day headline: the 18 Fold is the real-world test bed for whether its LPDDR6 gambit, and the AI-server ambitions riding on it, actually holds up in a shipping product.

(VIII) Tencent’s WorkBuddy Is Pulling Away, For Now

Tencent used its latest ecosystem event to announce that WorkBuddy will connect to microphones, AI glasses, and AI recording cards, turning spoken instructions directly into completed tasks, alongside a new financial-services edition for insurers, asset managers, and banks. Industry users can also package their own workflows into shareable “Skills.”

QuestMobile put WorkBuddy’s July monthly active users at 6.58 million, against 230,000 for Alibaba’s Qodework, a lead of more than 25 times. That’s a real head start in China’s AI-office race, though a lead built on being first to market is usually the one most vulnerable to a fast follower with deeper pockets.

(IX) ByteDance Just Made Borrowing $30 Billion Look Easy

ByteDance has reportedly expanded a planned dollar loan from $20 billion to $29.6 billion after unusually strong demand from banks, a deal that would rank as Asia’s second-largest dollar loan this year, though it remains unsigned. The proceeds are earmarked mainly for general corporate purposes, separate from the up to $70 billion in capital spending ByteDance is reportedly considering this year for AI infrastructure.

The pricing is the tell: the loan’s opening margin is 68 basis points over SOFR, down from 85 basis points on ByteDance’s previous offshore loan. Borrowing more, and getting a better rate for it, isn’t something banks do for a company they’re worried about.

(X) Moonshot AI Quietly Files for a Hong Kong IPO

Moonshot AI, the company behind Kimi, has confidentially filed an A1 form with the Hong Kong Stock Exchange, officially kicking off its IPO process, while simultaneously raising what’s likely its final pre-IPO round at a $50 billion pre-money valuation. That’s roughly eight times where the company stood less than a year ago: Kimi was valued at about $4.3 billion at the end of 2025, above $20 billion by this May, and $35 billion post-money after a July round that followed the launch of its K3 model. DeepSeek, still privately held, is widely expected to list sometime in the first half of next year.

On August 6, 2026, a huge new facility switched on in a stretch of grassland in Inner Mongolia, a region better known for potatoes than for silicon. It’s called Star River (Xinghe), and the building is the size of twenty football fields. It’s designed to hold up to a million AI accelerators working together, with a theoretical output of 100 million PFLOPS. Based on what its builders have disclosed, it’s one of the largest single AI data center facilities in the world.

The city around Star River is Ulanqab. A few years ago, it was best known for out-migration and potato farming. Today, it is one of the fastest-growing AI computing clusters in the Asia-Pacific region. In June, DeepSeek, fresh off a RMB 50 billion funding round, announced plans to build a 1-gigawatt data center there. That’s just one of 89 data center projects that had signed on to Ulanqab by the end of June 2026, together representing more than RMB 500 billion in committed investment.

The obvious question is: why here? The easy explaination is cheap land and generous local subsidies. The real answer has more to do with a deliberate choice: China has decided that electricity, not chips, is the binding constraint on the AI race, and started treating power generation as part of the data center itself, rather than something you queue up for. None of this makes chips irrelevant; they still matter enormously. But once compute scales into the hundreds of thousands, even millions, of accelerators, keeping them fed with electricity becomes just as hard a problem as building them in the first place.

The Numbers, Briefly

Ulanqab’s planned and operating data center capacity jumped from 3.3 gigawatts in July 2025 to 12.5 gigawatts by June 2026. That’s not incremental growth. It’s nearly a fourfold increase in eleven months, and more than ten times the region’s live capacity in 2025. For comparison, Goldman Sachs research puts Ulanqab’s total pipeline at three times the entire upcoming data center capacity of Johor, Malaysia, currently the largest data center hub in Asia-Pacific outside China.

The companies building there include almost every heavyweight in Chinese tech.Three of China’s top four data center operators by live capacity, GDS, VNET, and Chindata, have gigawatt-scale projects underway, alongside renewable energy developer Envision. ByteDance, Kuaishou, UCloud, Alibaba, and DeepSeek are separately building their own campuses.

And crucially, the demand looks real, not speculative. Utilized capacity in Ulanqab grew more than 70% year-on-year, while live computing power reached 165 EFLOPS by June 2026, equivalent to about 5-7% of China’s entire utilized data center capacity, from a single prefecture-level city. VNET’s campuses there have gone from delivery to majority utilization within a few quarters, a ramp-up speed that suggests customers were waiting for the capacity, not the other way around.

Why Here: It’s Not Just that Power Is Cheap

The conventional explanation for Ulanqab’s rise goes like this: electricity makes up 60-70% of a data center’s operating costs, and Inner Mongolia has some of the cheapest, cleanest power in the country. End-user electricity prices for data centers in the region run about RMB 0.33-0.36 per kilowatt-hour, according to CAICT, roughly half of what a data center pays in Beijing, and cheaper even than the Zhangjiakou cluster just outside the capital. Add in a dry, high-altitude climate (average annual temperature of just 4.3°C), and data centers get nearly ten months a year of free cooling from outside air, cutting one of their biggest secondary costs. Two direct fiber routes to Beijing keep latency to about 4 milliseconds, well within tolerance for most AI training and inference workloads. Renewables are locally abundant too: Inner Mongolia has roughly 57% of China’s technically exploitable wind resources and 21% of its solar potential.

All of that is true, and all of it matters. But it’s not really what makes Ulanqab distinctive, and it’s not what the Star River facility is actually built around.

The more interesting fact is what Star River’s power architecture doesn’t do: it doesn’t wait for the grid. Conventional data centers pull power from the public grid, and getting a large new load connected can easily take three to seven years, against an 18-to-24-month deployment window for AI infrastructure, through interconnection studies, transmission approvals, and new line construction. That mismatch is becoming the central bottleneck of the AI buildout everywhere in the world, including in China.

Star River’s answer is to skip the queue entirely. The facility draws power from self-built or directly connected wind and solar plants, feeding into gigawatt-hour-scale battery storage on site, all coupled on the DC side to avoid the losses of repeated AC-DC-AC conversion. On top of this sits an “AI power system,” which forecasts wind and solar generation hours in advance, monitors battery levels, and predicts how much computing power the data center will need. Based on that, it decides when to charge or discharge the batteries, and can even shift the timing of certain compute jobs, all to keep voltage and frequency stable enough for GPUs even when renewable generation fluctuates. If needed, the facility can disconnect entirely from the regional grid and run in island mode on its own generation and storage.

In other words, Ulanqab isn’t winning because it found cheaper electrons on the public grid. It’s winning because, in the most extreme case, it doesn’t need the public grid’s permission at all.

The Mirror Image: America’s Power Problem

Compare this to what’s happening in the United States, where the constraint isn’t chip supply either, it’s power. Morgan Stanley estimates U.S. data center electricity demand will reach roughly 68 gigawatts by 2026-2028, against a grid that can currently supply about 30 gigawatts, a shortfall of more than half. That gap is already showing up in people’s electricity bills: wholesale prices on PJM, the largest U.S. grid, jumped 76% year-on-year in the first quarter of 2026 alone, and polling consistently shows a strong majority of Americans oppose new data centers being built near where they live.

The problem runs deeper than politics. The U.S. grid is really three semi-independent systems (East, West, and Texas), and transmission between regions has been chronically underbuilt. Power-planning authority sits with individual states rather than any national body. And because demand on the grid barely grew for two decades before the AI boom, there was little pressure to expand it. Now that demand is surging, new high-voltage transmission lines routinely take more than ten years to build. Put simply, the U.S. can’t wait for its grid to catch up, and in the meantime, the most advanced chip in the world is just an expensive paperweight without the power to run it.

China’s answer, visible in places like Ulanqab, Gansu’s Qingyang cluster (215,000 P of computing power, nearly 10% of the national total), and Ningxia’s Zhongwei, home to the country’s first large-scale “compute-power-and-electricity-coordinated” green power direct-supply project, has been to stop treating the grid as a fixed constraint and start building energy infrastructure alongside compute infrastructure, sometimes in place of it. Western China’s share of the country’s intelligent computing power has climbed to roughly 32.6% as a result.

The Part that Doesn’t Fit a Clean Narrative

None of this is a simple “China wins” story, and it’s worth resisting the temptation to write it as one.

Demand is real. But so is overbuilding. Goldman Sachs, whose analysts have been closely tracking Ulanqab’s build-out, flags a real concern: the region’s 12.5 GW of pipeline capacity (live and planned combined, as of June 2026) is already equivalent to about 45% of China’s entire live data center supply as it stood in 2025. That much capacity landing at once will likely cap any meaningful increase in data center rental prices going forward, good news for AI companies renting compute, less good for the economics of the operators building it. And here’s the tension worth sitting with: utilized capacity in Ulanqab grew more than 70% in 2025, yet the overall utilization rate is still only around 66%, simply because supply was being added even faster than it could be filled. Both things are true at once. China can clearly build compute infrastructure faster than almost anywhere else in the world. That’s not the same as saying every gigawatt of it will earn a good return.

One more thing worth knowing about the green-power story: renewable electricity isn’t necessarily cheaper than ordinary grid power once you account for certificate premiums, grid delivery charges, and backup power costs. Data centers pursue direct green power supply largely to meet policy requirements, national hubs are increasingly required to source 80%+ green power and hit power-usage-effectiveness targets below 1.2-1.25, not because it’s automatically the cheapest option. And land quotas remain a real constraint on how far and how fast this model can scale, even in a region with Inner Mongolia’s geography.

Where This Lands

The next phase of the U.S.-China AI competition may be decided as much on the grid as in Silicon Valley or Zhongguancun. On a stretch of grassland where wind turbines stand next to server halls, the real innovation isn’t a faster chip, it’s a building that generates, stores, and consumes its own power without ever asking the grid for permission. That’s the part of the Ulanqab story that’s genuinely new: not that power is cheap there, but that data centers are increasingly being designed to work around the grid entirely, rather than wait for it.

China’s advantage here doesn’t come from better silicon; it’s still working around export controls on the most advanced chips. It comes from the ability to mobilize electricity, land, and networking at a speed the grid-constrained, politically fragmented U.S. system has so far struggled to match, and increasingly, from a bet that AI infrastructure may not need to be tethered to the grid at all. Ulanqab is one of the clearest large-scale demonstrations of that model so far, one that’s already being rolled out in Gansu and Ningxia, and one that could potentially work anywhere with strong wind or solar resources and a weak grid, from the Middle East to Central Asia.

But the ledger isn’t closed. Supply is racing ahead of profitability. Utilization is still catching up to capacity. And the economics of “green” power are more complicated than the marketing suggests. This is an infrastructure race, not an algorithm race, and infrastructure races are won over years, not news cycles. Ulanqab has a significant head start. Whether it can turn that head start into a durable advantage is the more interesting, and much less settled, question.

One more thing worth remembering: the companies that ultimately benefit most from this model may not be the ones building the biggest data centers. They may be the ones selling the electricity, cooling systems, batteries, power-management platforms, and networking gear that make a facility like Star River possible at all. Ulanqab isn’t just a data center story, it’s a supply chain story, and probably the subject of a future piece.

The flood of news obscures CXMT’s larger objective. It isn’t merely chasing American customers, Korean rivals or relief from US restrictions. LPDDR6’s performance gains make it useful not only in smartphones, but also in AI servers. CXMT is trying to qualify LPDDR6 early with a domestic chip platform and launch customer, positioning itself to follow the technology into PCs and servers if platform designers adopt it.

The AI Bottleneck Isn’t Always the AI Chip

LPDDR6 is the sixth generation of Low Power Double Data Rate memory. It has traditionally served phones and thin laptops, where energy consumption matters as much as speed.

The new standard roughly doubles potential throughput. JEDEC specifies transfer rates ranging from 10.667 to 14.4 Gbps/pin, compared with about 8.533 GT/s for widely commercialized LPDDR5X products. Depending on the configuration, that represents a gain of roughly 25% to 68%.

Faster memory is hardly surprising. What matters is what it does for AI. When a phone runs a model with 3B to 14B parameters—what China’s technology industry calls “on-device AI”—performance is often constrained by memory bandwidth rather than the neural processing unit or graphics processor.

Consider a 7B-parameter model compressed to four bits. If it generates 20 tokens/s on an LPDDR5X phone, 45% more bandwidth implies a theoretical 29 tokens/s. After accounting for cache behavior, NPU utilization and heat, 24–28 tokens/s is more realistic—still enough to make a phone assistant noticeably more responsive.

The market won’t stop at phones. Robots, laptops and cars also need local models that respond without constantly sending data to the cloud.

CXMT Is Racing the Clock, Not the Benchmark

JEDEC published the standard in July 2025. At ISSCC 2026, Samsung Electronics demonstrated a 10 nm-class design running at 12.8 Gbps, while SK hynix showed a 1c-process chip capable of 14.4 Gbps with 20% better energy efficiency than LPDDR5.

Neither company has publicly identified a mass-production date or launch customer.

CXMT has released fewer details. According to Xinhua Finance, its product targets 12.8 Gbps, with a 10.7 Gbps baseline when paired with a system-on-chip. It offers 16 GB in a 1,295-ball package-on-package design.

So CXMT is trying to reach volume production first instead of chasing the benchmark. Moving memory from a fabricated die into a phone requires engineering validation and customer qualification—areas where CXMT was slow with LPDDR5X.

Its August 29 announcement probably means one configuration has passed a production review, not that every version is ready. CXMT still hadn’t published a data sheet by August 31.

Xiaomi Is CXMT’s Real-World Test Bench

Xiaomi has invested in CXMT since its Series A round, giving both companies an incentive to coordinate closely.

A new memory generation must be tuned with the system-on-chip, including its controller, firmware and power profile. Using Xiaomi’s internally developed XRING O3 chip allows the phone maker and CXMT to handle that integration directly instead of waiting on Qualcomm or MediaTek.

CXMT also needs a dependable handset customer. In the first half of 2026, its LPDDR business generated about $10.9 billion, or 52% of total revenue. Reuters has reported that Xiaomi is targeting shipments of 200,000 to 300,000 units for the 18 Fold. At 16 GB per phone, the launch could consume roughly 4 PB of memory. That is large enough to test a high-end production line, but small enough to contain the damage if yields disappoint.

Huawei might appear to be the obvious alternative, but the relationship is more complicated. Reuters reported that CXMT resisted Huawei’s demands for lower prices and removed engineers from Huawei-linked equipment supplier SiCarrier from sensitive factory areas. The companies still do business, but CXMT has little reason to concede during a global DRAM shortage. It already has major orders from customers, including ByteDance and Tencent.

A Phone Chip Walks Into a Data Center

So far, this sounds like a smartphone story. But it is more than that.

LPDDR chips have traditionally been soldered beside the system-on-chip. Dell changed that model with CAMM2, a removable module mounted near the processor. JEDEC standardized it in December 2023. The LPDDR5X laptop version is called LPCAMM2; server-oriented SOCAMM2 followed in June 2026 and supports processors such as Nvidia’s Vera CPU.

That creates a much larger opportunity. If AI-server CPUs begin adopting LPDDR6 at scale, shipments could grow far beyond the mobile market. Conventional DDR6 isn’t expected to reach its commercialization window until roughly 2028 or 2029. LPDDR6 has arrived earlier, giving suppliers more time to establish customers and production capacity.

Graphics memory offers another clue. A mobile GeForce RTX 5060 provides 384 GB/s of GDDR7 bandwidth. A four-channel LPDDR6 configuration expected for AMD’s Medusa Halo could reach 614.4 GB/s, close to a desktop RTX 5070 Ti’s 672 GB/s.

Bandwidth alone doesn’t determine graphics performance. But an integrated GPU using LPDDR6 could access more bandwidth than some discrete cards using GDDR7. Nvidia’s Arm-based N1X PC processor, developed with MediaTek, already uses LPDDR5X; delays in Microsoft’s Windows on Arm ecosystem have reportedly slowed the project.

Apple Wants In. Washington May Get in the Way.

CXMT’s growing leverage has produced conflicting reports about Apple. South Korea’s Digital Daily said Apple sought lower DRAM prices and was rejected, with CXMT demanding prices at least as high as those of Samsung and SK hynix. The Wall Street Journal, however, reported that Apple had begun testing CXMT memory for products including iPhones and MacBooks.

An industry source I contacted said the companies may have been in discussions since early 2026 and completed preliminary testing. One option is to use CXMT chips only in Apple devices sold in mainland China. The source requested anonymity because of commercial conflicts.

Apple is unlikely to adopt CXMT’s newest LPDDR6 immediately. The product was announced only on August 29, and CXMT’s capacity is already close to fully utilized. HP and Acer have reportedly secured small volumes and are trying to reserve more supply for next year.

BOE Technology Group offers a precedent. Reports that the Chinese display maker was entering Apple’s supply chain began in 2016, but limited use in iPhones didn’t start until 2020. CXMT’s qualification could also take years, though the DRAM shortage may accelerate it.

The Section 1260H designation remains an obstacle. It warns US investors away and can complicate partnerships with American companies. Both CXMT and Apple therefore have reasons to want the designation removed, which helps explain the chipmaker’s lawsuit against the Pentagon.

One Pool of Memory for Everything

The Xiaomi 18 Fold, Nvidia N1X, MacBook and SOCAMM2 server modules can all use LPDDR-class memory. Together, they point toward the same trend: Under the pressure of AI-accelerated computing, phones, PCs and servers are beginning to converge on common memory technologies.

CXMT’s package-on-package design shows that its first LPDDR6 product is built for a phone, not a data center. The Xiaomi 18 Fold is therefore a production test. CXMT’s multibillion-dollar, long-term memory agreements with ByteDance and Tencent are more suggestive of where demand could go. Today those customers may need DDR5. Tomorrow, AI servers could become major LPDDR6 buyers.

Memory design ultimately involves a trade-off between expandability and speed. LPDDR is faster and more efficient, but harder to replace. For phones and AI systems, speed increasingly matters more than serviceability. If AI and graphics workloads continue growing together, separate pools of system memory and graphics memory will become an expensive bottleneck. The logical endpoint is unified memory.

CXMT isn’t yet the technical leader in every dimension of LPDDR6. But by commercializing early, building around a domestic flagship device and securing large AI customers, it may be positioning itself for the part of the memory market that grows fastest.

That is the real bet.

Hope you’re doing well, wherever you’re reading this from.

Something interesting is happening across China’s next generation of technology industries.

The race is no longer just about who builds the better technology. Increasingly, it is about everything around the technology: who can finance it, regulate it, manufacture it, insure it, and turn an impressive prototype into an actual industry.

You can see the contrast in robotics. Unitree’s robots are real, but its IPO has become a pricing mechanism for an entire industry, pushing private valuations far beyond what current fundamentals can support.

Brain-computer interfaces tell almost the opposite story. China has not necessarily out-engineered Neuralink, but it has built a faster path from experimental device to regulatory approval, insurance coverage, and commercial use.

Both stories point to the same question: what happens when the machinery of commercialization starts moving faster than the underlying technology?

Meanwhile, Xiaomi is doubling down on domestic chips and AI, Alibaba is building its own silicon, Nvidia is cautiously returning to China, and Chinese AI companies are increasingly showing that domestic hardware can support models at global scale.

The technology is only part of the story now.

Anyway, let’s take a look.

This Week Features...

Unitree’s Robots Are Real. China’s Robotics Valuations Are the Problem.

On August 25, Unitree’s stock was trading 45% below its post-IPO high, and the internet had already found its villain: some users were comparing founder Wang Xingxing to Evergrande’s Hui Ka Yan, accusing him of building a fraud on a similar scale. That comparison says more about the mood than the math. Unitree makes real robots, turns a real profit, and still, even after the crash, trades at roughly 300% above what its fundamentals can justify.

The reason isn’t Unitree. It’s that the STAR Market’s own rules, thin free float, no daily price limit for the first five days, a narrow pool of eligible investors, turned the company into an accidental pricing anchor for an entire industry that hasn’t IPO’d yet. Somewhere between 30 and 50 private Chinese robot makers are watching Unitree’s stock price to figure out what they’re worth.

[Read the full piece →]

China Didn’t Out-Engineer Neuralink. It Out-Paperworked It.

In March, China’s regulator approved the world’s first commercially sold invasive brain-computer interface. Neuralink, after two years and roughly 20 implants, still doesn’t have the FDA clearance to sell anything. The easy headline writes itself: China beat Neuralink.

The real story is less flattering to the “China leapfrogs the US” narrative, and more interesting. The device that won, NEO, has 8 electrodes to the N1’s 1,024, and got there not by being more advanced but by moving through a compressed, fast-tracked regulatory pipeline China built for it in under two years, the same playbook that took EVs from a five-year-plan bullet point to a global export industry. The hard part, this piece argues, was never really the engineering.

[Read the full piece →]

The News…

(I) Xiaomi Bets Its Phone Business on a Chip It Designed Itself

Xiaomi unveiled the Xuanjie O3 on August 24, reportedly built on TSMC’s 3nm process, alongside the O100 and D100 chips for on-device AI and automotive, and a mini-PC called AI Cube “Prototype” for running local LLMs. The O3 will power the Xiaomi 18 Fold (an estimated 200,000 to 300,000 units) and is paired with CXMT’s LPDDR6, the first Chinese-made LPDDR6 memory to ship in a flagship phone.

None of these numbers are huge on their own. What’s notable is that Xiaomi now has a chip strategy that reaches ten years out, and a domestic memory supplier sitting right next to that chip. The self-sufficiency story usually gets told one company at a time. This is what it looks like when two of them start showing up in the same phone.

(II) ByteDance Is Putting All Its AI Software Under One Roof

ByteDance is consolidating two of its biggest AI software experiments, TRAE and Coze, into the Doubao ecosystem.

TRAE started as an AI coding environment, while Coze focused on agents. Both eventually expanded into broader AI workflows, but ByteDance now appears to be drawing a much clearer organizational line: coding tools will continue under the Doubao brand, with product and operations teams reporting to Doubao chief Zhao Qi.

The move also brings ByteDance’s office-AI strategy closer together. With Feishu already folded into the wider Doubao effort, the company is effectively betting that one AI platform can serve as the common layer across coding, productivity software, agents, and workplace applications. It has now launched Doubao Work as a standalone product.

That puts ByteDance into a more direct race with Alibaba’s Qwen Office and Tencent’s WorkBuddy, which already recorded more than 20 million monthly PC visits in June.

(III) Thomson Reuters Chose Alibaba’s Model. That Says Something About Open AI Economics.

Thomson Reuters launched its first LLM, called Thomson, built on top of Alibaba’s Qwen3.5-397B and retrained with Imperial College London for safety and neutrality before further training on Thomson Reuters’ own content. Total R&D ran to roughly $40 million (the $450,000 figure that circulated online only covers the final training run). It scores 0.823 on Stanford’s LegalBench, behind Gemini 3.1 Pro and GPT-5.5, and comes close to Opus 4.8 on Harvey’s Legal Agent Benchmark, though with an asterisk: Thomson used inference-time scaling and GPT-5.5 didn’t.

The company’s stated reason for skipping OpenAI and Anthropic is blunt: cost and customizability. A closed frontier model is expensive to run at scale and hard to shape around a single, narrow task like document review. That calculation, a legacy Western institution choosing a Chinese open model as its foundation, is exactly the kind of quiet shift that doesn’t make headlines the way a chip ban does.

(IV) China's Next AI Chip IPO Is About to Test the "Four Tigers"

Suiyuan Technology (commercially known as Enflame), one of China’s four major domestic AI chipmakers alongside Moore Threads, MetaX, and Biren, is moving toward a STAR Market IPO. The company plans to issue 43 million shares, 10% of its post-IPO total, aiming to raise around CNY 6 billion ($840 million) for its next two chip generations. Inquiry begins August 28.

Suiyuan is still loss-making, which is worth sitting with given this week’s other STAR Market story. If Unitree, a profitable company, is trading at roughly triple what its fundamentals support, it’s worth watching what happens to a loss-making chipmaker’s valuation once it goes through the same thin-float, no-price-limit first week.

(V) Shein Is Taking Its Global Supply-Chain Playbook to the Hong Kong Market

Shein launched its Hong Kong IPO on August 24, offering nearly 280 million shares to raise around HK$13.1 billion ($1.7 billion), backed by Boyu, Tiger Global, General Atlantic, Tencent, and UBS Asset Management. The company serves roughly 273 million active customers across 160 markets, and revenue grew from $32.1 billion in 2023 to $41.8 billion in 2025, with $2.06 billion in net profit last year.

Shein plans to put 40% of the proceeds into technology and another 40% into global brand building, doubling down on the small-batch, real-time-data production model that got it here in the first place. It’s not an AI story, but it’s a reminder that China’s IPO wave this year isn’t limited to chips and robots.

(VI) Huawei Just Found Another Way Into an American Supply Chain

HP and Huawei signed a multi-year global agreement covering Wi-Fi patents, with reciprocal rights for Huawei, bringing Huawei technology into a major US company’s products despite Washington’s 2019 blacklist. It follows last fall’s settlement giving HP access to a 2,000-patent Wi-Fi 6 pool co-founded by Huawei.

Export controls are built to choke off supply chains. They were never designed to touch intellectual property licensing, and this deal is a clean example of why that gap matters: the parts of a company’s business built on patents can keep crossing borders that its hardware can’t.

(VII) China’s Open-Weight Models Are Now Running on China’s Own Chips

Zhipu open-sourced GLM-5.3-Flash on August 26, the GLM-5 series’ first native multimodal model, with its online traffic running entirely on 100,000 domestic chips. Before launch, an anonymous model nicknamed “Ox Alpha” quietly processed 62 trillion tokens on OpenRouter and OpenCode, reportedly also running on Chinese hardware from suppliers that may include Huawei, Hygon, and Moore Threads.

GLM-5.3-Flash scored 57 on Artificial Analysis, tying Claude Opus 4.8, at roughly one-tenth the cost of GLM-5.3. Put together with this week’s Xiaomi and Suiyuan stories, the full stack, open model, domestic memory, domestic compute, is starting to show up in the same sentence more often than not.

(VIII) Why China’s Next AI Battleground Is a City You’ve Probably Never Heard Of

Ulanqab, a city in Inner Mongolia, has quietly become one of the fastest-moving sites in China’s AI buildout: three of the country’s top four data center operators are running gigawatt-scale projects there, alongside ByteDance, Kuaishou, UCloud, Alibaba, and DeepSeek. Direct-linked wind and solar power keeps electricity, 60-70% of a data center’s operating cost, 30-40% cheaper than eastern China, while a 4.3°C average temperature cuts cooling costs and network latency to Beijing stays at just 4.2 milliseconds.

That mix of cheap green power, favorable grid terms, and the “East Data, West Compute” policy push is exactly what the US is struggling to replicate, where an aging grid, PJM prices up 76% year over year, and local pushback keep slowing construction. The AI race’s real bottleneck may be shifting from chips to whoever builds the power and land underneath them fastest.

(IX) CXMT’s LPDDR6 Just Went From Rumor to Mass Production

On August 29, CXMT confirmed that its LPDDR6 memory has entered mass production, with the first units shipping inside the Xiaomi 18 Fold, expected to launch in September. It’s the world’s first LPDDR6 product to reach commercial mass production, hitting a peak speed of 12,800 Mbps and a maximum capacity of 16GB, and the company says it’s already sampling the chip to other key customers.

The launch confirms months of rumors that Xiaomi’s Xuanjie O3, the first chip anywhere to support LPDDR6, was sourced from CXMT, a link so telegraphed that CXMT’s Weibo account followed Xiaomi’s the moment it went live. For a memory maker that spent years playing catch-up to Samsung, SK Hynix, and Micron, setting the pace on a brand-new high-end standard, rather than following it, is a different kind of milestone.

On March 13, 2026, China’s medical device regulator, the National Medical Products Administration, approved a brain implant called NEO for commercial sale. Four months later, on July 13, a hospital in Shanghai wrote the first prescription and completed the world’s first commercial implant under that approval.

NEO is now the first semi-invasive implantable brain-computer interface（BCI) ever cleared for commercial use by a national regulator, anywhere, a category China’s own regulatory system classifies as “invasive.” Neuralink, which has implanted its own device in roughly 20 patients over the past two years, still doesn’t have the FDA clearance it needs to sell anything.

Look past the headline, though, and this isn’t really about who built the better chip. It’s about who built the faster approval pipeline, and that’s a different race with a different scoreboard.

The Asterisk on “World’s First”

NEO was built by Neuracle (博睿康), a Tsinghua University spinout founded in Shanghai in 2011. The device is a coin-sized implant placed epidurally: under the skull, on top of the dura mater, the tough membrane that wraps the brain. Eight electrodes sit there, reading signals through that membrane rather than from inside the tissue itself, and translating a patient’s intent to grasp into commands for a robotic glove. Nothing pierces the brain, which is exactly why China’s regulators can classify it as “invasive” while it still clears trials and review far faster than a fully intracortical device would.

Neuralink’s N1 chip works differently. It threads 1,024 flexible electrode contacts directly into the cortex. NEO uses eight electrodes; Neuralink’s N1 uses 1,024. That’s not a small gap, and it isn’t a clean apples-to-apples comparison either: eight epidural contacts and 1,024 intracortical ones are built for different jobs, at very different levels of surgical risk. That classification is also part of why NEO’s approval moved so fast: one BCI researcher told MIT Technology Review that its comparatively conservative design was likely a key reason regulators cleared it so quickly. Even Paradrowmics, an American brain-computer interface (BCI) company with obvious competitive reasons to push back on a “China leapfrogs the US” narrative, published its own explainer this spring making a similar point: the two countries’ BCI industries are optimizing for different things, not running the same race at different speeds.

By June, Neuracle had completed 36 implant procedures across its early feasibility trial and a larger multicenter study, with more than 14,000 cumulative implant-days recorded and no serious device-related adverse events. Its application to list on Shanghai’s STAR Market, the exchange’s board for hard-tech companies, was accepted on June 11.

So: two different bets. Neuralink is betting on bandwidth, more electrodes, deeper access, higher-resolution signal, aimed at problems like restoring speech or vision that need to decode a lot of information at once.

China didn’t necessarily build a better neural interface. It built a faster path from neural interface to regulated medical device to reimbursed procedure to scalable industry. That’s the real story, and it’s a more interesting one than a chip comparison.

Name It, Fast-Track It, Standardize It, Insure It, Fund It

Here’s what actually happened between Neuracle’s device entering formal review and a patient in Shanghai receiving it on a paid prescription, and it should look familiar to anyone who has watched China build an industry before.

Step one: designate it, and start early. The institutional push here predates the commercial approval by more than half a year. In August 2025, seven Chinese government ministries jointly issued implementation guidance for the brain-computer interface (BCI) industry, setting targets for major technical breakthroughs by 2027 and a more mature industrial system by 2030. China’s 15th Five-Year Plan, unveiled in early 2026, then formally elevated brain-computer interfaces into its list of strategic future industries, alongside items like embodied intelligence and next-generation energy. It’s the same kind of top-down signal that, a decade earlier, told every provincial government and state bank that electric vehicles were now a national priority.

Step two: build a fast lane. Neuracle entered the NMPA’s Special Review Program for Innovative Medical Devices in August 2024 and received full commercial approval nineteen months later, in March 2026. That program front-loads regulatory involvement into the R&D process itself: regulators start engaging while a device is still being developed, instead of waiting for a finished product to land on their desk cold. Nineteen months is a fraction of the multi-year timeline a Class III device, the same risk category as an implantable pacemaker, would normally take. On June 30, 2026, the NMPA followed with formal classification rules placing all invasive and implantable BCIs uniformly into that top-tier category, giving the industry, for the first time, a clear regulatory address.

Step three: standardize the plumbing. On August 1, 2026, two national standards took effect, one defining a reference system architecture and the other a multimodal data format. Standards alone don’t create interoperability; companies still have to choose to build to them. But the goal is to reduce one of the industry’s biggest sources of friction: custom integration across hardware, data formats, and decoding algorithms, the kind of mismatch that has historically made it hard for one lab’s recordings to run on another lab’s software.

Step four: get someone to pay for it. In May, the 2026 edition of Shanghai’s Hu Hui Bao, a low-cost, city-run supplemental health plan, became the first city-wide insurance scheme in China to add coverage for BCI surgery: it reimburses 30 percent of a patient’s out-of-pocket surgical materials cost, up to a cap of 150,000 yuan (about $21,000). It’s a narrow crack, not a fully open door: there’s still no line item for BCI implants in China’s core medical fee schedule, so most hospitals have no compliant way to bill for one outside this and a handful of similar city plans. But it’s the first real one.

Step five: let capital do the rest. With the regulatory and reimbursement scaffolding in place, money followed. Chinese industry tallies put first-half 2026 funding for BCI startups at roughly 7.25 billion yuan (about $1 billion) across 64 deals, already more than all of 2025 combined. On August 3, Chinese financial media reported that Zhudong Technology (主动科技), founded barely six months earlier with no clinical data yet to show, raised a 330 million yuan angel round, reportedly a record for the sector at that stage. A round that size is less a verdict on proven clinical efficacy than a bet on the category’s future, one more sign that markets think the earlier four steps actually removed the biggest risks.

The sequence will sound familiar to anyone who watched China build its electric vehicle industry: policy first, infrastructure second, scale third. BCI is now running a compressed version of the same play.

Multiple Bets, Not One Horse

The biggest mistake in reading this story is assuming China’s BCI strategy is just a Chinese version of Neuralink. It isn’t. By June 2026, 134 BCI clinical trials had been registered in China, plus another 26 investigator-initiated studies, and just five BCI-related products had actually won NMPA approval. Roughly 79 percent of those registered trials involve non-invasive technology, not implants at all. China is running several different technical bets in parallel, each chasing a different piece of the market.

StairMed (阶梯医疗) is chasing signal density: a 256-channel wireless implant using ultra-flexible electrodes, placed with the help of a purpose-built surgical robot. In 2025, working with Shanghai’s Huashan Hospital, StairMed completed China’s first long-term, first-in-human implant trial, with a patient using the system to control both a wheelchair and a robotic dog. Backed by roughly 1.1 billion yuan raised over the past year, including a March 2026 round led by Alibaba with Tencent and other existing investors following on, StairMed is aiming to enroll around 40 patients by the end of 2026. If it hits that target, it would put the company’s number of implanted patients in the same ballpark as Neuralink’s current cohort of roughly 20.

NeuroXess (脑虎科技) is chasing manufacturability. Its flexible, cortical-surface electrode strip has already been tested in more than 50 patients, and the company is building a factory in Jiangxi province designed, eventually, to support production on the order of tens of thousands of implants a year, expected to come online in the second half of 2026. That moves the technology toward industrial scale well ahead of the long-term clinical evidence base. NeuroXess has also struck a partnership with Xiaomi to let patients control smart-home devices directly through the implant, and Bloomberg profiled one of its patients, a man paralyzed from the neck down after an accident eight years earlier, using the system to drive his wheelchair with his mind.

BrainCo (强脑科技), by contrast, has largely avoided implantation altogether. Its bionic hand reads neural and muscular electrical signals through the skin rather than through any implant, and it already has FDA clearance and is a commercial product in the US. The company says it turned profitable in 2025 and has raised roughly 2 billion yuan in its most recent funding round; it now sells more of its five-fingered hands to humanoid-robot makers than to amputees. It’s the closest thing China’s BCI sector has to a company running a real, profitable business today rather than a clinical program burning cash toward one.

Layer on a fully implantable, 512-channel wireless system out of Beijing still in large-animal trials, and a picture emerges that has little to do with a single race against Neuralink. China is ahead in some corners of this field, catching up quickly in others, and still behind in a few, especially the high-bandwidth, deep-cortical route Neuralink is pursuing. That’s a very different claim than “China is ahead of Neuralink.”

What the Paperwork Doesn’t Fix

None of this means the hard problems are solved. Two, in particular, are still wide open.

Who pays, long-term? Hu Hui Bao coverage is a genuine first step, but there’s still no line item for BCI implants in China’s standard medical fee schedule, so most hospitals have no compliant way to bill for one outside a handful of city-level plans. Getting into China’s core medical insurance system requires formal health-technology assessment and cost-effectiveness evidence that’s still being accumulated in real time. And nobody has settled who eventually absorbs the cost of years of follow-up visits, maintenance, and eventual device replacement: the hospital, the manufacturer, or a commercial insurer.

Does it hold up over years, not months? NeuroXess’s push toward industrial-scale manufacturing is a genuine achievement, but it’s racing ahead of the long-term safety data. Neuracle’s longest continuously implanted patient has been living with NEO for roughly two and a half years. That’s a meaningful track record for a brand-new device category. It is not yet proof that these devices are safe on the multi-decade timescale a device implanted in someone’s skull actually needs to survive.

There’s a broader question hovering just outside the industry story, too. As BCIs move from rare clinical trials toward something more commercially routine, questions about who owns and can access a person’s neural data, and what happens as this technology’s dual-use military applications (both the US and China are already funding some) get more attention, stop being hypothetical. That’s a separate essay. But the commercial “moment” and the harder ethical questions are advancing on the same timeline, not sequential ones.

The electric vehicle comparison is worth finishing, because of how that story ended, not just how it started. The five-year-plan designation, the fast lane, the standards, the subsidies got Chinese EV makers to the starting line quickly. It still took fifteen to twenty years of iteration, cost-curve improvement, and infrastructure build-out after that starting gun before China actually came to dominate the global EV market. The policy scaffolding was necessary. It was nowhere near sufficient on its own.

BCI’s version of that scaffolding went up faster than anyone expected: a designated industry, a fast-track approval process, a unified data standard, a first crack in the payment wall, and now a wave of capital, all inside about two years. What it hasn’t done, because no policy can, is manufacture a decade of safety data, or settle who pays for a lifetime of follow-up care, or prove that a factory built to produce implants at industrial scale is still producing implants that work a decade from now. China got the world’s first approved brain implant. Whether that becomes a durable industry will be written in the years of implant logs and insurance claims that come after it.

Some users compared Unitree founder Xingxing Wang with Hui Ka Yan, the founder of China Evergrande Group, whose property empire accumulated some $278 billion in liabilities before he was sentenced to life in prison. They argued that Wang was building a fraud of similar proportions.

But the sell-off says less about the quality of Unitree’s robots than about the market surrounding them. Unitree manufactures real products, earns a profit and leads China’s robotics industry. However, is that Unitree’s share price is no longer only about Unitree. The company has been forced into the role of pricing anchor for China’s entire humanoid-robot industry.

Unitree’s Stock Is Not Unitree

When a company’s shares rise more than sixfold, the natural assumption is that its founder must be celebrating a windfall. From my own encounters with Wang, that is not how he appears to see it.

At the 2025 World Robot Conference in Beijing, Wang attended a group interview with the media. As the session ended, a reporter asked how he viewed a potential IPO. He answered with characteristic reserve: “For us, an IPO is like the college entrance exam. It gives the company’s early investors a reasonably good outcome.”

Right up to the listing, Wang described going public primarily as a way to give venture investors an exit. What he clearly underestimated was the complexity of China’s secondary market—and, in particular, Shanghai’s STAR Market.

Launched in 2019, the STAR Market was designed to help frontier-technology companies raise capital. That mandate has also made the board a magnet for speculation. Regulators scrutinize applicants more closely, and relatively few are allowed through. In the first seven months of this year, just 21 companies listed in Shanghai, compared with 104 in Hong Kong.

The STAR Market comes with an elaborate set of guardrails intended to contain risk and keep the focus on fundraising:

• An IPO typically floats only a small portion of a company’s equity, often around 10%, limiting the ability of controlling shareholders to cash out early.

• Founding shareholders face a 36-month lockup, preventing founders with large stakes from freely selling their shares.

• Strategic placement investors, including national industrial funds and local state-owned investors, cannot exit easily.

• There is no daily price limit during a stock’s first five trading days, allowing institutions to establish a market price.

• Individual investors must hold at least about $69,000 in financial assets and have two years of investing experience to trade directly on the board.

Anyone familiar with equities will recognize the paradox: The restrictions meant to control risk can intensify the bubble. Few investors are eligible to trade, only a sliver of the company’s shares are liquid, and prices are unconstrained during the first five sessions. That combination encourages frantic turnover and sudden price spikes.

Unitree has 404 million shares outstanding. At the start of trading, only 30.09 million unrestricted shares were available, or 7.44% of the total. About 90% of the offering had been allocated offline. Eleven institutions submitted bids worth roughly $125 million over the course of a week and secured most of the shares on offer.

On its first day, Unitree’s turnover rate reached 85.28%, with $3.22 billion worth of shares changing hands. In effect, nearly the entire free float was recycled in a single session. Chinese trading data showed a net inflow of about $613 million through “extra-large” orders, while large and medium-size orders recorded net outflows of roughly $308 million and $304 million, respectively. It was the most extreme order-flow split anywhere in the A-share market that day.

The implication is hard to miss. Institutions and investors who received IPO allocations passed richly priced shares to individual buyers on day one. Once the supply of fresh money thinned out, the 629.4% rally could not hold. The subsequent 45% decline was dramatic, but it was also a move back toward something resembling a normal valuation.

Wang did little to keep the frenzy alive. On the second day of trading, he told the 2026 World Robot Conference that robotics may still be five to 10 years away from a “ChatGPT moment” in which machines can learn and improve autonomously. If he had wanted to inflate the bubble, he could hardly have chosen a less promotional message.

Another piece of the story has received little attention in Western coverage. On August 24, the Shanghai Stock Exchange held talks with brokerages and pressed them to tighten standards for loss-making, low-quality listing candidates. The new approach would favor the three leading companies in each industry and rely more heavily on invitations initiated by regulators, further reducing the number of businesses able to list.

In my view, this model will make the bubble in technology shares larger, not smaller. Look at the trading patterns of Cambricon Technologies, Moore Threads, MetaX and CXMT. On the STAR Market, a company with a pioneering technology or a dominant position in its industry can expect an extraordinary first-day surge simply because it made it through the gate.

From $8 Billion to $33 Billion

Before Unitree listed, I made a rough estimate of what the company might be worth.

Unitree reported 2025 revenue of about $236 million. Net income attributable to shareholders was approximately $38.6 million, while adjusted net income was around $82.1 million. Apply an earnings multiple of 60 to 80, assuming current margins hold, and the company would be worth roughly $4.9 billion to $6.9 billion. If profits continue to rise, the valuation could approach $8.3 billion. That is an optimistic estimate, but it is also broadly consistent with Unitree’s IPO valuation.

Another approach produces a similar result. On a trailing basis, battery giant CATL trades at about 25 times earnings, while surveillance-equipment maker Hikvision trades at about 22 times. Factor in Unitree’s growth of nearly 50% and assign it a premium for its dominant industry position, and a valuation of $7.6 billion to $8.7 billion looks defensible.

The market has moved far beyond that range. Even after the 45% drop, Unitree was still worth roughly $33.7 billion. If investors valued it only as a robot manufacturer, its market capitalization would be about 300% above a reasonable estimate.

Who is supporting such an extreme price? Once again, the answer is institutional capital. CCB International, using a target multiple of 32 times 2026 sales, arrived at a valuation of roughly $15.1 billion. Nomura gave the stock a buy rating and set a price target of about $51.40, implying a market value of approximately $20.8 billion. The STAR Market’s trading constraints can support a premium, so Unitree may plausibly remain worth more than $13.9 billion. A value of $33 billion, however, will be difficult to sustain.

Who, then, needs Unitree to be worth more than $27.8 billion? Wang has already supplied the answer: venture-capital investors.

Unitree’s investor roster expanded rapidly beginning in 2024, and its private-market valuation jumped from about $431 million to $1.76 billion. That was also the year Unitree introduced the G1, China’s first humanoid robot to be openly sold and delivered at scale. The launch triggered a rush for stakes in the company. Investors who could not get into Unitree backed its peers instead—or funded robotics startups assembled by teams crossing over from other industries, especially automotive. Within two years, some of those companies had reached valuations of around $1.4 billion.

If Unitree controls 32.4% of the global robot market and deserves a $33 billion valuation, then another company that has shipped only a few hundred robots can still claim to be worth several billion dollars. A venture fund may not have billions in cash, but it can own a miniature version of Unitree. The higher Unitree trades, the more valuable every other robot stake on the fund’s books appears to be.

The reverse is just as important. If Unitree’s market capitalization falls to $13.9 billion, the valuations of other Chinese robotics companies become much harder to defend. Caixin has estimated that between 30 and 50 privately held robot makers are preparing to list in Hong Kong. Many were valued at several billion dollars before turning a profit. Their only credible exit is an IPO, and Unitree’s listing has accelerated the countdown.

One fact rarely mentioned by Western media is that only 152 days passed between the acceptance of Unitree’s IPO application and the start of trading—a record under the STAR Market’s “pre-review” mechanism. Other robotics companies cannot count on approval that fast. In the meantime, the private shares held by their backers are constantly at risk of being marked down.

Are Robots Really Just Toys?

Investors who bought Unitree or robot-themed shares have every right to be angry. They discovered that today’s humanoids are not yet mechanical servants and that many still need to be operated with a controller. Some have channeled that frustration into calling these early machines “remote-controlled toys,” shorthand for something crude, unremarkable and badly made.

That criticism badly underestimates Unitree’s real strength.

Robots are difficult to manufacture. Their reducers, motors and lead screws often have to meet far tighter requirements than ordinary industrial components. During an experimental production run, a young robot company may need parts for only a few hundred units. Most contract manufacturers have no interest in taking an order that small.

I have interviewed Zheyuan Jiang, founder of Noetix Robotics, and Cheng Hao, founder of Booster Robotics. Both told me that their current monthly production capacity tops out at a few hundred machines. In both cases, the production floor sits close to the R&D office. That may sound more like a workshop than a modern factory, and in some ways it is: Robot makers must design and assemble basic components themselves or spend enormous time and effort persuading suppliers to customize them.

This also helps explain the rough-looking internal wiring in some Unitree machines. Wang has followed a consistent engineering principle: Get the robot to operate reliably at the lowest possible cost. Unitree has developed the high-torque motors unique to robots in-house and has at times obscured identifying marks on sourced components. Customizing parts through outside suppliers can require more than $13.9 million in upfront adjustment costs. Only inexpensive components and high shipment volumes can bring the unit cost down.

That is a very different proposition from building a remote-controlled toy. Unitree is the only company in China’s robotics industry willing to publicly report gross margins of 60%. It has also submitted financial statements showing that it can make a profit.

A robot is a physical product, and physical products require real customers and dependable delivery. Actual orders are the ultimate stress test for any valuation. Even at a market value 300% above what its fundamentals appear to justify, Unitree remains the most useful public benchmark for anyone trying to invest in humanoid robotics.

According to its prospectus, Unitree shipped 5,511 humanoid robots, capturing 32.4% of the global market. Its quadrupeds hold a similar share, while the G1 has become a widely used research platform—not a toy.

But the revenue mix shows how immature the market remains. From January through September 2025, universities and research institutions generated $65.2 million, or 73.6% of Unitree’s humanoid-robot revenue. Research and education also contributed 31.58% of quadruped sales.

Commercial applications accounted for just 16.71% of combined robot revenue, mostly from inspection and guided-tour projects. Most Unitree humanoids are still headed for laboratories and classrooms, not factory floors.

The second criticism is that Unitree spends too little on R&D. The figure usually cited is about $20.1 million—less than the roughly $226 million spent by Barbie maker Mattel.

That comparison ignores the denominator. From 2023 to 2025, Unitree’s R&D expenses nearly tripled, from about $6.9 million to $20.1 million. Over the same period, revenue surged from $22.1 million to $236 million. R&D as a share of sales fell from 31.39% to 17.83% and then to 8.53%. The problem is not that Unitree stopped investing. It is that revenue grew far faster.

Unitree’s prospectus seems to support criticism that it spends heavily on promotion. In 2025, advertising cost about $9 million—42.87% of selling expenses and more than sales-staff compensation. Yet it represented only 3.56% of revenue, down from 4.41% in 2024. The high share reflects Unitree’s use of demonstrations, videos and events to market standardized products, rather than a diversion of R&D funding.

DEEP Robotics highlights the distinction. It spent 24.98% of revenue on R&D and just 2.74% on advertising. But its specialized inspection, firefighting and rescue robots require contract-by-contract sales and delivery support. Its overall selling-expense ratio was about 13%, higher than Unitree’s. The difference lies mainly in how the two companies sell their robots, not in their engineering ambitions.

Wang has said that, even before Unitree put a humanoid robot on sale, customers were offering to fund its development. That may help explain the sudden jump in 2025 performance. When demand explodes in a short period, the income statement can become distorted. Judged solely by R&D intensity, Apple spent 8.3% of revenue on research in fiscal 2025, while Tesla spent about 7%. Few people would argue that either company is less innovative than Unitree.

The composition of Unitree’s spending matters too. In 2025, compensation for R&D employees increased by about 48%. Spending on materials grew more than fivefold, while cloud services and computing expenses rose more than twelvefold. Salaries accounted for a smaller share of the total not because engineers were being paid less, but because the cost of materials and compute was rising much faster.

Robots Still Need Remote Controls

Wang’s expertise lies in motors and mechanical systems. He has been far less certain about how Unitree should develop foundation models for robots. The company began and then halted research into video-based world models in both 2020 and 2024. Only this year did it start publicly discussing a renewed effort to build self-learning models for its machines.

That helps explain why Unitree often chooses teleoperation in live demonstrations. Wang has acknowledged that current robots lack the intelligence models needed to match their physical capabilities. Demos released by rival manufacturers suggest that progress in locomotion has not been matched by comparable gains in fine manipulation, especially in the hands. Robots therefore remain unable to perform complicated tasks such as sorting differently shaped components quickly, smoothly and at anything close to human speed. For now, remote control is the most dependable way to operate them.

Closing that gap is also one of the main purposes of Unitree’s IPO. The company plans to spend approximately $281 million on an “intelligent robot model R&D project” over three years. Of that, about $55.8 million is earmarked for equipment and installation, $100.1 million for R&D personnel, and $124.9 million for implementation costs, including compute rentals, research materials and technical services.

At its IPO roadshow, Unitree confirmed that it had signed a memorandum of strategic cooperation with DeepSeek. The agreement covers joint research into general artificial intelligence, high-performance general-purpose robots and large AI models. When DeepSeek expands into embodied intelligence, it will give Unitree’s robots priority under otherwise equal terms. When Unitree needs model adaptation, inference access or integrated development support, it will likewise give priority to DeepSeek’s training services and technical solutions. DeepSeek, in turn, will provide support in areas including model architecture and AI-computing clusters.

This creates a possible path for Unitree’s valuation story to reverse. One possibility is a genuine breakthrough in the ability of embodied AI models to generalize across tasks. Another is the arrival of large, repeatable orders from industrial customers, rather than recurring purchases of robots as research and teaching tools. A third is that Unitree beats expectations for several quarters in a row and grows into its valuation the hard way.

So far, none of those signals has appeared.

The Bubble Belongs to the Industry

After Unitree’s robot videos went viral, Chinese robotics companies copied the same promotional formula. The result was an unrealistic public fantasy. Many vendors blurred the line between factory testing and commercial delivery, encouraging people to believe that robots were about to move rapidly into factories and homes. They made it appear as though physical agility and machine intelligence were advancing in lockstep.

The reality is almost the opposite. Humanoid robots are unlikely to enter factories at scale in the near term, and their intelligence remains far behind their ability to move.

That gap is also why the public has grown tired of Unitree. Many people assume it is no different from the companies producing carefully staged demos without real customers or reliable deliveries. They fear that Unitree will raise a huge sum of money and disappear. The question Unitree must answer is how quickly it can develop the next generation of robot customers. That does not erase the customers it already has.

There is an enormous bubble in robotics. But Unitree did not create it. The company itself does not appear eager to promote the frenzy. Capital will do that without its help.

Just look: Investors have already moved on to hyping world models.

Hope you had a good week.

China’s AI race is entering a new phase.

For the past year, the competition was mostly about models: who could build the strongest LLM, who could catch up with OpenAI and Anthropic, and who could achieve frontier performance with fewer resources.

But the battlefield is moving.

The next question is no longer just who has the smartest AI. It is who can build the infrastructure, finance the spending, and turn AI capability into a sustainable business.

Alibaba and Tencent are making two very different bets on that future. One is trying to turn AI into a new infrastructure empire. The other is trying to make AI the next layer of an existing one.

At the same time, robotics is facing a similar reality check. China has become one of the world’s most aggressive players in embodied AI, but even the industry’s biggest believers admit that the “ChatGPT moment” for robots has not arrived yet.

Technology is moving fast.

The harder question is whether the business model can keep up.

Anyway, let’s take a look.

This Week Features...

Tencent vs Alibaba: Two Ways to Bet on China’s AI Future

Everyone is watching China’s AI models.

Investors are watching something else: the bill.

Tencent and Alibaba are both spending billions building their AI futures, but they are making almost opposite bets.

Tencent is trying to bring AI into the empire it already owns. Its strategy is built around Hunyuan, WeChat agents, and productivity tools like WorkBuddy. The company’s biggest advantage is not the model itself, but the ecosystem around it: hundreds of millions of users, businesses, payments, advertising, and one of China’s most valuable consumer platforms.

Alibaba is taking the opposite path.

Instead of adding AI to an existing business, it is rebuilding itself around AI infrastructure: chips, cloud computing, foundation models, and enterprise services.

The difference matters because the AI race may not be won by whoever builds the best chatbot. It may be won by whoever owns the infrastructure layer underneath every chatbot.

But both strategies come with a difficult question.

Tencent needs to prove AI can create a new growth engine inside an existing empire.

Alibaba needs to prove that massive AI spending today can become tomorrow’s advantage rather than tomorrow’s cost.

This week’s feature looks at the two biggest bets in China’s AI race — and the very different assumptions investors are being asked to finance.

[Read the full piece →]

The News…

(I) Unitree’s CEO Says Humanoid Robots Aren’t Ready Yet Despite the Hype

The humanoid robot market has one uncomfortable problem: investors are moving faster than the technology.

At the World Robot Conference, Unitree founder Wang Xingxing tried to lower expectations, saying humanoid robots are still not ready for widespread factory or home use.

The reason is not hardware. It is adaptability.

A robot that can perform a fixed demonstration is very different from one that can walk into an unfamiliar environment and complete a new task without extensive retraining.

Wang’s benchmark for a true “ChatGPT moment” in robotics is ambitious: robots should complete roughly 80% of tasks in 80% of unfamiliar environments through simple voice or text commands.

He expects that breakthrough within 3–5 years.

The market, however, is already pricing in that future.

Unitree earned around $41 million in profit on $252 million revenue in 2025, but its IPO valuation implies roughly 219x earnings.

The robot revolution may happen.

The question is whether it arrives before investors run out of patience.

(II) Xiaomi’s Profits Take a Hit From Memory Chip Prices

Xiaomi’s H1 2026 numbers show the squeeze: revenue down 8.4% YoY to $28.9 billion, adjusted net profit down 42.8% to $1.7 billion. Still, Q2 alone beat Wall Street’s expectations, pulling in $15.1 billion in revenue and $790 million in profit. The culprit is memory price inflation, which dragged Q2 phone revenue down 7.5% even as Xiaomi held gross margin at a respectable 8.5%. Management is betting things improve once memory prices normalize, but costs are staying elevated for now. AI spend isn’t slowing down: Xiaomi poured $2.5 billion into R&D, with AI eating up nearly 30% of that. Its in-house model, Mimo, is still playing a supporting role powering HyperOS, smart devices, and eventually autonomous driving rather than being monetized directly. EVs remain the growth story, though Xiaomi looks likely to fall short of its 550,000-unit annual delivery target, currently tracking around 35,000 units a month. Overseas EV expansion is now penciled in for H2 2027, starting with Europe.

(III) China’s Robot Boom Is Arriving Before Humanoid Robots Are Ready

The robotics market in China is expanding rapidly, but the biggest opportunity may not come from humanoid robots first.

According to China’s Ministry of Commerce, sales of embodied AI robots on major platforms increased 95.1% year over year in July. Exoskeleton devices, action cameras, and robot vacuum cleaners also recorded strong growth during the same period.

The export numbers tell a similar story. In the first seven months of 2026, China exported $1 billion worth of industrial robots, up 13.2% from a year earlier. Meanwhile, exports of cleaning robots and intelligent bionic robots reached RMB 18.09 billion ($2.5 billion) in the first half of the year.

(IV) Alibaba’s AI Strategy Has a Chip Nobody Talks About

Most discussions about Alibaba’s AI ambitions focus on Qwen.

But the company’s long-term advantage may come from somewhere else: T-Head.

Alibaba CEO Eddie Wu said the semiconductor unit expects its second-generation domestic AI chip to enter tape-out and production in the second half of 2026. The new chip is designed to improve computing performance and interconnect bandwidth, with Alibaba believing it can support large-scale AI model training.

This fits Alibaba’s broader AI strategy.

The company is not treating AI as only a model competition. It is building a full-stack infrastructure business that combines cloud computing, chips, and foundation models.

Alibaba Cloud has already launched its Zhenwu M890 AI supernode, which has served more than 650 customers, while T-Head has shipped 560,000 AI chips across more than 20 industries.

The strategic importance is straightforward: owning more of the AI stack could eventually lower costs and reduce dependence on external suppliers.

(V) Xiaomi’s Robot Demo Shows the Real Challenge in Robotics

At the World Robot Conference, Xiaomi’s humanoid robot became one of the most watched demonstrations on the exhibition floor.

The company said the robot was powered by a large AI model capable of understanding instructions, making decisions, and executing actions independently.

If fully autonomous, that would represent a major step forward.

But robotics has always had a gap between impressive demonstrations and reliable real-world deployment.

The challenge is not making a robot perform one action in a controlled environment. The challenge is making the same robot complete thousands of different tasks in unpredictable conditions without constant human assistance.

This is the same problem autonomous driving faced: perception and intelligence can improve quickly, but reliability in the physical world is much harder.

(VI) Zhipu AI Ships GLM-5.3, Aiming Straight at the Global Frontier

Zhipu AI’s new foundation model, GLM-5.3, is now live via API, built for heavy-duty coding, cybersecurity defense, and long-horizon tasks. It scored 60 on the Artificial Analysis Intelligence Index — enough to land it in the global frontier tier. That puts GLM-5.3 in the same conversation as closed models like Anthropic’s Claude Fable 5 and OpenAI’s GPT-5.6 Sol, while it’s essentially tied with Moonshot AI’s Kimi K3 for the top open-source spot. It’s already baked into Zhipu’s ZCode and GLM Coding Plan, and the model weights are set to open-source next week.

(VII) China Is Trying to Turn AI Usage Into a New Financial Asset

One of the most unusual AI developments this week came not from a model company, but from banks.

Chinese financial institutions are experimenting with a new type of lending model that uses AI-related activity, including token consumption and AI contracts, as part of credit assessments.

Some pilot loans have reached $3.9 million.

The idea reflects a broader question: how should banks value companies whose most important assets are not factories, inventory, or physical equipment, but AI usage and digital activity?

Traditional lending depends heavily on historical revenue and tangible assets.

AI companies often look different. Their value may come from model usage, developer adoption, and future demand rather than current profits.

China’s experiment suggests that the financial system may need to evolve alongside the AI economy.

The country is not only trying to build AI infrastructure.

It is also exploring the capital system needed to fund the companies built on top of it.

Tencent’s shares fell 4.46% the next trading day.

A week later, Alibaba reported an even larger number. Its quarterly capital expenditure reached RMB 67.7 billion, driven largely by AI infrastructure. Both companies saw free cash flow turn negative as they bought chips, expanded data centres and prepared for the next stage of the AI race.

Investors are now facing a difficult question: how should all this spending change the valuation of China’s two largest technology companies?

I find the comparison useful not because Tencent and Alibaba must defeat each other, but because they are approaching the same market from almost opposite directions. Tencent and Alibaba were the infrastructure builders of China’s internet era. Both companies now have to rebuild parts of their own empires around AI — a process that will inevitably create disruption, uncertainty and painful adjustments.

I call this an “earnings crossfire.” A financial report is not merely a collection of numbers. It also reveals what management wants investors to notice, which questions executives avoid and how they explain difficult strategic decisions.

After reading both reports and earnings calls, the difference is becoming clear.

Tencent is trying to integrate AI into the businesses it already owns. Alibaba is trying to build an entire AI stack, from chips and cloud infrastructure to foundation models and applications.

Tencent’s approach is safer but increasingly fragmented. Alibaba’s is more coherent, but far more dependent on the AI boom continuing.

Tencent AI’s Strategy: Model, Agent and APP

Tencent’s AI strategy currently revolves around three main products: Hunyuan is the model, WeLM is for agent, and WorkBuddy is the product.

WeLM and Xiaowei are designed to bring AI agents into WeChat. Hunyuan is Tencent’s foundation-model platform. WorkBuddy is its productivity assistant for office workers.

Hunyuan represents Tencent’s infrastructure-level AI ambition.

After former OpenAI researcher Yao Shunyu joined the company, the model’s performance improved considerably. Tencent says it wants Hunyuan to reach frontier-level multimodal capability within 12 to 18 months.

Unlike consumer AI companies chasing user numbers, Tencent expects Hunyuan to improve advertising recommendations, enterprise software, game development, AI workflows, 3D asset generation and WorkBuddy inference.

But Tencent’s greatest AI asset is not its model. It is WeChat. With more than one billion users, WeChat is one of the world’s most valuable consumer platforms. Tencent’s long-term goal is to turn it from a communication tool into something closer to an AI operating layer.

WeLM is being developed partly around this idea. Unlike a conventional cloud chatbot, it places greater emphasis on device-side processing, privacy and low latency. The approach is closer to Apple Intelligence than ChatGPT.That is a very Tencent-style strategy: use a new technology to strengthen an existing empire rather than build a separate one.

WorkBuddy is Tencent’s attempt to build a serious AI productivity product.

Unlike OpenAI’s Codex or Anthropic’s Claude Code, it did not initially target software developers. Tencent went after ordinary office workers, a much larger potential market.

The challenge is monetization. Chinese enterprise software has historically struggled to generate high margins. A large user base does not automatically become a profitable software business.

WorkBuddy’s growth looks promising. Desktop monthly visits exceeded 20 million in June, ranking first among similar products in China.

Tencent strategy chief James Mitchell said gross margins from paying WorkBuddy users and model services were already comparable with Tencent Cloud.

That sounds encouraging until you ask what Tencent Cloud’s margin actually is.

Tencent does not disclose it separately. Analysts often estimate that cloud infrastructure businesses can produce gross margins of around 40 % and EBITA margins of roughly 10 %.

More importantly, Mitchell was referring mainly to paying WorkBuddy users. Most individuals still access the product through free credits.

Good economics among paying customers do not necessarily mean the entire product is profitable.

Tencent still needs to show how many users convert to subscriptions, how much computing capacity each user consumes and whether an AI productivity product can generate sustainable margins in China.

Tencent Is Spending Like an AI Company

Two numbers disappointed Tencent investors.

Under IFRS, profit attributable to shareholders reached RMB 56 billion, below the Reuters consensus estimate of approximately RMB 61.8 billion.

More unusually, free cash flow was negative RMB 13.8 billion. It was the first negative quarter in Tencent’s reporting history.

Tencent generated RMB 52.7 billion in operating cash flow. That was more than offset by RMB 59.3 billion in capital-expenditure payments, RMB 5 billion in media-content payments and RMB 2.2 billion in lease payments.

A large proportion of the additional spending came from commitments for AI computing resources.

The effect also appeared in operating profit. Tencent reported non-IFRS operating profit of RMB 75.6 billion. Without Hunyuan, Yuanbao, WorkBuddy, CodeBuddy, Xiaowei and its other new AI initiatives, the figure would have been approximately RMB 86.1 billion.

In other words, Tencent’s AI businesses reduced quarterly operating profit by around RMB 10.5 billion.

That is a meaningful change for a company known for producing stable profits from games, advertising, payments and social networking. Tencent is becoming more capital intensive.

The question is whether that transformation produces enough growth.

Over the next six to 12 months, Hunyuan needs to become more competitive with Qwen and Kimi. WorkBuddy needs more paying customers. Xiaowei needs to prove that agents inside WeChat can create measurable economic value.

Until then, Tencent is spending like an AI infrastructure company while continuing to operate like a traditional internet company.

What Exactly Is Tencent Building?

Every AI company needs computing capacity. The question is whether owning that capacity becomes a business in itself.

Tencent President Martin Lau described three possible uses for the company’s computing resources: training internal models, supporting AI applications and renting capacity to outside cloud customers.

James Mitchell added that previously purchased hardware could itself generate returns because computing resources ordered earlier may now be worth considerably more.

The logic is reasonable. The disclosure is not.

Investors still cannot answer a basic question: what exactly is Tencent’s AI infrastructure for?

Is Tencent trying to become a larger cloud provider? Is it building Hunyuan into a frontier model? Or is it mainly buying capacity to support WorkBuddy, WeChat agents and other internal applications?

Tencent Cloud does not provide enough financial detail to answer these questions. After spending tens of billions of yuan, Tencent continues to present AI as one combined story.

Investors cannot clearly separate cloud revenue, model-development costs, application revenue or the economics of renting computing capacity.

Without that distinction, it is difficult to know whether Tencent is building a new business or merely increasing the cost of its existing one.

Alibaba Is Building the Entire Stack

Alibaba is taking almost the opposite approach.

While Tencent is adding AI to its existing ecosystem, Alibaba is reorganizing itself around AI infrastructure.

In its latest results, Alibaba placed Qwen and Qwen Office under “AI Labs and Applications,” moved semiconductor subsidiary T-Head into “AI Cloud and Computing Services,” and integrated instant retail more closely with e-commerce.

Compared with the company’s previous “Cloud Intelligence” structure, the new organization sends a much clearer message.

Alibaba does not see AI as merely a model business. It sees AI as an infrastructure business.

The strategy increasingly resembles Amazon’s approach to AWS. Amazon does not need to dominate every application. It wants to provide the computing infrastructure on which everyone else builds.

Alibaba wants to occupy that position in China. It has one additional advantage: it controls both the infrastructure and one of the country’s leading foundation models, Qwen.

Spend First, Profit Later

Alibaba Cloud generated quarterly revenue of RMB 48.4 billion, up 45 % from a year earlier.

AI-related product revenue reached RMB 12.4 billion, up 38 %. Annual recurring revenue reached RMB 49.6 billion.

For the first time, Alibaba also revealed part of the financial burden created by Qwen and Qwen Office. Its AI businesses produced approximately RMB 13.9 billion in quarterly losses, mainly because of inference costs and infrastructure investment.

CEO Eddie Wu remained confident.

Alibaba has committed RMB 380 billion to AI infrastructure over three years. Wu described the current spending as a temporary investment cycle rather than a permanent increase in costs.

His argument is counterintuitive: the more Alibaba spends today, the lower its future AI costs may become.

The assumption is that demand for computing capacity will continue to exceed supply before 2030.

If an accelerator has a depreciation period of five years but earns back its cost within three, Alibaba can continue generating returns from the hardware for another two years.

Older Nvidia V100 and A100 chips remain heavily utilized even after newer generations arrive. Alibaba therefore believes that today’s infrastructure can continue producing value for years.

It is an attractive argument, but it depends on one critical assumption: AI demand must remain strong.

If computing demand keeps growing, early investment creates an advantage. If demand slows, the same infrastructure becomes a very large depreciation burden.

T-Head May Be Alibaba’s Hidden Advantage

Alibaba’s most important AI asset may not be Qwen. It may be T-Head.

The semiconductor subsidiary allows Alibaba to design its own accelerators, reducing its dependence on outside suppliers and potentially lowering computing costs.

Alibaba says its second-generation domestic AI chips can support large-model training without a significant loss of performance. Known manufacturing partners include Hua Hong Semiconductor, while Alibaba-designed chips have already been deployed in China Unicom’s Qingyang project.

Tencent and ByteDance cannot currently do the same at comparable scale.

ByteDance is reportedly exploring customized CPUs with Qualcomm, but the project remains at an early stage. Tencent has not publicly disclosed its AI-accelerator suppliers.

Huawei has built a more independent technology stack, but its architecture requires developers to make larger adjustments. Alibaba’s GPGPU-based approach may fit more easily into existing AI-development workflows.

The weakness is scale. T-Head remains tiny compared with Nvidia’s ecosystem, and Alibaba does not yet produce enough chips to satisfy its own demand.

But if production expands, T-Head could become one of the most valuable parts of Alibaba’s AI strategy.

Alibaba Cloud Needs Customers

Alibaba Cloud is already China’s largest public-cloud provider.

After integrating AI services and computing infrastructure, cloud revenue grew 45 % from a year earlier, slightly faster than Microsoft Azure’s reported 43 % growth.

Alibaba’s AI Cloud and Computing Services segment produced an EBITA margin of 11.6 %.

Wu argues that Alibaba’s scale and internally developed chips will reduce costs, allowing it to offer more competitive prices while improving margins.

The problem is that server rental alone cannot support Alibaba’s ambitions.

Basic infrastructure rental may generate margins of around 20 to 40 %. Model-as-a-Service can earn more. Enterprise AI agents could earn more still.

Alibaba therefore needs customers to buy higher-value services such as models, agents, databases and deployment tools. It cannot simply rent accelerators.

If token prices fall sharply, ByteDance’s Volcano Engine starts another price war or China eventually builds too much GPU capacity, Alibaba’s economics will become much weaker.

Alibaba is effectively betting that AI infrastructure will remain scarce.

Qwen Is Powerful but Still Needs a Business Model

Qwen is one of China’s strongest foundation models.

Alibaba says Qwen-related products have reached around 250 million users. The model has been integrated into Taobao’s search and recommendation systems, as well as enterprise applications.

What Alibaba has not disclosed is how much direct revenue Qwen produces.

That matters.

Alibaba e-commerce chief Jiang Fan said AI had improved Taobao search, recommendations and merchant tools. Those improvements may strengthen the existing business, but they do not necessarily create a new source of revenue.

Accio Work looks more promising. The AI assistant for global business-to-business commerce has attracted around 50,000 paying users. It helps companies research markets, find overseas customers and automate workflows.

But one successful enterprise product cannot carry the cost of a national-scale foundation model.

Alibaba faces the same problem as every AI company. Users love AI products. Turning that enthusiasm into recurring revenue is much harder.

Alibaba Does Not Need to Win the Chatbot War

Eddie Wu does not appear to believe that selling model APIs will become the industry’s main source of profit.

Alibaba instead wants to combine chips, cloud infrastructure, computing services and foundation models. Under this structure, Qwen is not necessarily the final product. It is one capability inside a much larger platform.

This again resembles Amazon. AWS does not need every customer to use Amazon’s own models. It only needs those customers to rent computing infrastructure.

Alibaba wants the same position in China.

Customers can use Qwen or third-party models through Alibaba Cloud. From Alibaba’s perspective, the infrastructure generates revenue regardless of which model the customer chooses.

Wu said most revenue-generating models on Alibaba’s MaaS platform still belong to the Qwen family. He expects MaaS to reach RMB 30 billion in annual recurring revenue.

But this creates another tension.

If Qwen mainly exists to attract cloud workloads, Alibaba does not need it to become China’s dominant consumer assistant.

If Alibaba wants Qwen to compete directly with ChatGPT, Claude or ByteDance’s Doubao, it needs a much stronger consumer strategy.

Right now, Qwen sits between those two identities.

Alibaba’s Consumer AI Problem

Alibaba’s consumer AI products have struggled to create the same momentum as their competitors.

Qwen’s office products have updated more slowly than Tencent’s WorkBuddy. The Qwen app reportedly has around 251 million monthly active users, below ByteDance’s Doubao, which has exceeded 300 million.

But user numbers alone can be misleading.

Some Qwen users were attracted through promotions, including discounted products and drinks. That creates activity. It does not necessarily create a willingness to pay.

ByteDance has faced the same problem with Doubao. A large free-user base can help improve models, but it is also expensive to maintain.

If Alibaba wants to control Qwen’s losses, it may eventually have to reduce its spending on consumer applications.

Alibaba’s Biggest Risk Is Financial

The greatest risk to Alibaba’s AI strategy is not technical. It is financial.

AI infrastructure requires enormous upfront investment. Alibaba needs its traditional businesses to keep generating enough cash to support it.

But its e-commerce engine is slowing.

China commerce-management revenue declined 7 % from a year earlier, while overall e-commerce growth remained around 4 %.

The situation is not disastrous. Alibaba and JD.com both improved after China’s aggressive food-delivery subsidy competition began to cool.

Jiang Fan said customer-management revenue and EBITA growth improved from the previous quarter. Instant-retail losses are also expected to stabilize or narrow.

That provides stability. But stability is not the same as growth.

E-commerce may prevent Alibaba from facing additional financial pressure. It may not produce enough new profit to fund an unlimited AI infrastructure race.

Alibaba therefore needs its AI investments to generate returns before its traditional businesses weaken further.

Two Companies, Two Kinds of Risk

Tencent and Alibaba represent two different ways of building an AI business.

Tencent believes AI should become another layer inside an existing ecosystem. Alibaba believes AI infrastructure can become the ecosystem itself.

Tencent has the safer foundation. Games, advertising, payments and social networking continue to generate enormous cash flow.

But Tencent still needs to answer a basic question: where exactly is its AI business going?

Alibaba’s strategy is easier to understand. Build chips. Buy computing capacity. Expand Alibaba Cloud. Put Qwen on top. Move customers toward higher-margin AI services.

It is coherent. It is also much more dependent on the AI boom continuing.

Tencent is asking whether its existing empire can absorb AI.

Alibaba is asking whether AI can build it a new one.

Investors are not merely choosing which company has the better model. They are choosing which assumption they are willing to finance.

Welcome back to Weekly Dose of China Tech! Here is a roundup of the latest news and our newest articles.

First, we explore the rise of independent “robot doctors”, a unique repair industry growing alongside China’s humanoid boom. We also dive into China’s AI video ecosystem, which has become a lucrative money making machine.

Finally, this week’s news highlights how tech sectors across the country are adapting to market realities, from ByteDance navigating Doubao’s new ad model to Apple training a custom Chinese LLM with Alibaba.

Dive in below for the full breakdown!

This Week Features...

Robot Maintenance Is the Newest Job Market in China

It’s not difficult to know that China has been mass producing humanoid robots. As the largest manufacturer in the world, China has shipped almost 20,000 humanoids in 2025 for academic research and public events. However, since robotics is still a burgeoning industry, meltdowns and malfunctions are not so infrequent. Thus, giving rise to these “robot doctors.”

These independent technicians can fix your robots faster with a much lower price. However, there are major problems existing in this emerging industry:

One, custom components are hard to come by. The robot doctors often have to salvage components from donor robots since schematics of a Unitree robot are usually not available on the open market. Two, 42% of the humanoid market is occupied by research institutions. They generally prefer official manufacturer servicing. Three, relatively speaking, humanoids are durable and few in number, so repair demand is quite low.

So, there are enough issues to go around, but that doesn’t mean this industry isn’t expanding and thriving. Some resort to drone repair and robot rental services, while others pivoted to selling courses to teach other technicians. If you want to learn more about this unique industry in China, click the link below and read the full article.

[Read the full piece →]

Why China Is Winning the AI Video Race

While OpenAI is struggling to turn Sora into a profitable machine, China’s video generation models have matured into an exploding business with breakthrough models like Seedance 2.5 and MiniMax’s H3. Even if one argues about China’s inability to stack compute and produce bleeding-edge LLMs (albeit the chatter of such rhetoric has been diminishing), there is no denying that China’s video generation industry has become a self-sustaining ecosystem.

The models don’t stand on their own. They’re paired with Douyin (China’s TikTok), microdrama platforms, and other editing softwares. This swift change from new technology to lucrative production line has taken shape in the last two to three years.

But, money game aside, these models could become something more. In fact, many companies maintain that their ultimate goal is to build truly advanced models that could process complicated spatial and visual data, subsequently supporting the next gen of robotics and autonomous systems.

If you want to learn more about the future of AI video generation, click the following link to read the full article.

[Read the full piece →]

The News…

(I) DeepSeek V4 Pro Release, Hiked Prices, & Harness Testing Results

DeepSeek has officially launched V4 Pro, delivering notable benchmark gains over its preview version alongside a steep price hike that brings its flagship rates closer to major rivals.

Beyond the raw model updates, hands-on testing of DeepSeek Harness reveals a remarkably flexible environment that goes far beyond a typical coding assistant. By supporting on-the-fly model switching, vision analysis, and dynamic temporal and spatial composability, Harness allows developers to seamlessly add or adjust tools without breaking the system, effectively building a stable platform tailored for endless modifications.

(II) BYD’s Expansion in Japan

BYD’s Japan-specific RACCO K-Car has received more than 1,000 orders just two weeks after launching on July 28, according to BYD Auto Japan.

What stands out is which version buyers are choosing. According to BYD, around 80% of orders are for the top-end RACCO 300 Premium, priced at ¥2.497 million (about $16,800). The 300 Plus accounts for 14%, while the entry-level RACCO 200, at ¥2.145 million (about $14,400), represents just 6%.

That suggests RACCO’s appeal may not simply be its low price. The 300 Premium offers a 320-km WLTC range from its 35.84-kWh Blade Battery, compared with 210 km for the entry model.

BYD’s data also offers an interesting glimpse into how the car is being used. 37.5% of buyers are adding RACCO to their existing vehicles, while 35.7% are replacing another car. Meanwhile, 55.4% already have EV charging equipment at home.

That fits the traditional K-Car use case in Japan: compact dimensions, easy parking, and practicality for urban and residential driving. RACCO also adopts the popular Super Tall body style and dual power sliding doors.

BYD is targeting 10,000 orders by the end of 2026. For a Chinese EV maker trying to break into one of the world’s most established K-Car markets, the first 1,000 orders may be more significant than the number itself.

(III) Tencent H1 2026 Revenue

Tencent reported H1 2026 revenue of $56.1 billion, up 10%, and gross profit of $32.1 billion, up 12%. But free cash flow turned negative at -$1.9 billion, largely because of AI infrastructure spending for Hunyuan, WorkBuddy and cloud customers. Excluding prepayments for computing capacity, FCF would have been $5.3 billion.

President Martin Lau said Tencent is trying to build a large, profitable, cash-generating AI-native business: use computing resources first for models and applications, then rent out excess capacity through Tencent Cloud. In a worst-case scenario, he said, Tencent could still recover its infrastructure costs through cloud leasing.

(IV) SMIC Can’t Keep Up with Chip Orders

SMIC is running out of capacity for AI-related chip orders — and is raising prices. Q2 revenue topped $3 billion, while wafer shipments rose 14.4% QoQ and average selling prices increased 5.7%.

Management said demand for AI-related logic, BCD and optical-module chips remains supply-constrained as Chinese internet companies sharply raise hardware spending. SMIC has already implemented new Q3 pricing and expects shipments to keep rising, with utilization staying near 95%.

(V) Apple Reveals Using Custom Chinese Model for Apple Intelligence

Apple briefly revealed that it is not simply plugging an existing Chinese AI model into Apple Intelligence before retracting the statement. According to Reuters, the company has trained a custom, China-specific large language model with support from Alibaba Group—a major shift from its initial plan to rely directly on third-party models like Qwen. Developing a dedicated model gives Apple greater control over its user experience while ensuring compliance with local regulatory requirements. Apple Intelligence is expected to launch in China in the coming months.

Two weeks ago, ByteDance released Seedance 2.5 on the same day MiniMax unveiled H3, setting off another round of excitement around AI video. H3 emphasized visual effects and post-production workflows. Seedance 2.5 pushed further into editing, letting creators make precise changes to existing clips instead of generating everything from scratch.

In the U.S., the category feels much quieter. OpenAI has retreated from Sora as a standalone video business, while the biggest American AI companies have not produced anything that has matched the momentum around Seedance. On Artificial Analysis’ text-to-video leaderboard, nine of the top 10 systems are now made in China.

When OpenAI began stepping back from Sora, some Chinese technology publications concluded that generative video was simply a bad business. Instead, American retrenchment created more room for Chinese companies.

AI-generated shorts are increasingly accepted on Chinese video platforms. Creators use them for memes, fictional stories, short dramas and advertising, then pay for more credits or API calls to make the next one. Those payments keep the video models alive.

That is the part of China’s success that is easy to miss. This is not just a story about model performance. It is a story about operations, distribution and, ultimately, the business environment around the model.

Sora Had a Model. It Never Had an Economy.

This helps explain what went wrong with Sora.

OpenAI is excellent at building AI products, but it has never been a content-platform company. ChatGPT is fundamentally private: you ask something, it answers you. Your output is not automatically placed in front of millions of other users.

Sora inherited that weakness.

A creator could generate a video and post it to YouTube, TikTok or X. But OpenAI did not own the audience on the other side. There was no built-in economic loop that paid creators for producing more Sora videos, and therefore little reason for an independent studio to keep buying enormous volumes of Sora inference.

OpenAI experimented with a more social Sora experience, but building a new video destination is very different from adding video generation to an existing AI product. People already have TikTok, YouTube and Instagram. They do not necessarily need another app whose defining feature is that everything inside it is synthetic.

That is particularly painful because video inference is brutally expensive. SemiAnalysis at one point estimated Sora’s operating cost at roughly $15 million per day, equivalent to an annualized burn rate of around $5.4 billion. Even if that estimate is only directionally correct, the economics illustrate the problem: a video model needs a very large amount of paid usage to justify its compute bill.

ByteDance can subsidize that loop because the model strengthens businesses it already owns. It can build the model, help creators make the video, distribute the video, monetize the traffic and then sell the creator more tokens.

OpenAI cannot reproduce that chain.

The U.S. has another obstacle: copyright.

Video models inevitably collide with recognizable actors, characters and visual styles. Soon after Sora’s rollout, copyrighted characters from major Hollywood studios began appearing in generated clips, prompting objections from studios, talent representatives and rights holders.

The legal objections are understandable. The commercial consequence is still awkward. Stronger guardrails mean more prompts get rejected or constrained, making the tool less predictable for creators who want to work with familiar cultural references.

Seedance faces copyright pressure too. Rights holders including Disney have challenged the use of protected material in generative video, and ByteDance has responded by restricting some capabilities involving real people, faces and copyrighted characters.

But ByteDance has one advantage OpenAI does not: Seedance does not need the American market to survive.

China alone can provide creators, audiences and paying customers.

Google theoretically has the closest American equivalent. It has video models and it owns YouTube. But YouTube has little reason to aggressively subsidize a flood of cheap AI video when the platform is already trying to manage audience frustration with low-quality synthetic content.

The technology may be similar. The incentives are not.

Seedance Has Something Sora Never Had: Douyin

When Seedance 2.0 broke out earlier this year, Western coverage mostly treated it as another sign that China had caught up in AI video.

The more important question was where all that AI slop was going.

The answer was Douyin, ByteDance’s Chinese version of TikTok.

AI-generated content has faced less cultural resistance in China than it has in parts of the U.S. Before Seedance, Chinese creators were already using Sora, Google’s video models and MiniMax’s Hailuo AI to make memes. The output was often obviously synthetic: soft depth of field, strange faces and physics that did not quite make sense. But it created an early generation of users who learned how to make AI videos entertaining.

Then Seedance became much more usable.

Its early viral examples did not simply show beautiful random scenes generated from text. Creators demonstrated familiar cinematic styles—old Hong Kong martial-arts films, Japanese tokusatsu and tightly choreographed fight scenes—while keeping characters, costumes and environments unusually consistent.

That matters more than prettier demos.

For creators, a video model becomes useful when the same character can survive from one shot to the next. A tool that produces one spectacular clip and then loses the character’s face is a lottery ticket. A model that can preserve the visual asset is production software.

ByteDance also has an enormous structural advantage through Douyin, one of the world’s largest short-video platforms. It gives the company access to an immense ecosystem of short-form visual content, creator behavior and immediate distribution. Exactly how user content feeds model training is not publicly disclosed, but the product advantage is obvious: ByteDance understands what people make, what they watch and what they remix.

That created a loop. Seedance users made videos. They uploaded them to Douyin. Viral examples attracted more people to Seedance, who created their own variations and pushed the model into new meme formats.

Then ByteDance added another layer: short dramas.

The company owns TomatoFiction（番茄小说） an enormous source of serialized web fiction, and Hongguo（红果短剧，another name is Melolo）, its short-drama platform. The novels provide ready-made plots filled with romance, betrayal, revenge and endless cliffhangers. Hongguo provides somewhere to monetize the resulting videos.

ByteDance’s familiar strategy is to subsidize creators first, then make traffic the performance metric. In AI video, that strategy worked almost too well.

By early 2026, AI-generated animated dramas had become one of China’s hottest content businesses. According to 36Kr, some production companies began buying the highest-tier Seedance 2.0 API packages through ByteDance’s Volcano Engine. One reported top-up reached $7.4 million.

Seedance 2.0’s 720p generation reportedly cost about $6.82, per million tokens, translating to roughly 15 cents, per second of video in one common configuration. Kling AI from Kuaishou Technology could be cheaper, at roughly 9 cents per second, while Vidu from ShengShu Technology could come in near 10 cents.

But reliability changes the calculation. Saving a few cents does not matter if a generation has to be thrown away.

Bilibili is moving in the same direction with its AI creation tools, allowing users to build a story, supply images and other assets, and then generate the video through models including Seedance.

Seedance therefore has something more valuable than a benchmark score: inputs, creators, distribution and a reason to keep generating.

China Is Turning Video Generation Into a Production Line

MiniMax H3 pushes the argument one step further.

Released alongside Seedance 2.5, H3 received less attention. But its significance is different: MiniMax is trying to prove that AI video can be industrialized.

Its H3-Context-IR system uses an “intermediate representation,” borrowing a term familiar from software compilers. Text, images, video and audio can be broken into elements such as characters, scenes, actions and sound, then reused across generations.

The practical goal is consistency.

That is what AI video needs most. Once assets can move reliably between shots without characters randomly changing faces or objects disappearing, generation stops looking like a demo and starts looking like a workflow.

Seedance 2.5 is moving toward the same destination from another direction. Its editing tools increasingly resemble a video editor rather than a prompt box. Creators can modify specific time ranges, extract camera movement and visual styles from existing footage, and reuse those elements elsewhere. Third-party platforms such as LibTV are already building workflows around these capabilities.

H3 wants to become part of post-production. Seedance wants generation to feel more like editing software.

Both approaches matter because video is one of the most aggressive ways to consume tokens.

Industry estimates cited by COL Group suggest that short dramas and video generation already account for 55% share of AI token usage in China. Whether the exact percentage holds across the entire market is difficult to verify, but the direction is clear: video consumes far more inference than a chatbot response.

That makes every improvement in video workflow economically important. Better consistency means fewer wasted generations. Easier editing means more generations. More creators mean more tokens.

Seedance is being integrated into ByteDance products such as Dreamina and CapCut. Kling AI and MiniMax sell relatively inexpensive subscription credits to individual creators. A freelancer can now insert generative video into a workflow for an advertisement, an e-commerce clip, a cheap short drama or a visual effect that would previously have required a small production team.

Most of the output still looks like junk food.

But junk food can support an enormous industry.

More usage produces more revenue. Revenue funds better models. Better models make creators more willing to use them for real work. That feedback loop is what the U.S. video-model market has struggled to establish.

MiniMax, which does not own a Douyin-sized platform, has to work harder. Its opportunity is to sell directly into advertising, games, e-commerce and post-production—industries where generative video penetration is still low.

Someone will always need another commercial.

H3 is trying to become the cheapest person in the editing room.

The Bigger Prize Is Not Video

AI-generated short dramas are not why I care about video models.

The more interesting destination is the world model.

To generate convincing video, a model eventually has to learn more than pixels. It needs some representation of motion, causality, object permanence and physics. A glass falls because gravity exists. A person walking behind a wall should still exist when they emerge from the other side. A robot pushing a box needs to understand that the box pushes back.

There is an interesting historical parallel with Nvidia.

GPUs were originally pushed forward by gamers demanding better graphics. But rendering better graphics required increasingly sophisticated simulation of light, surfaces and physical movement. The architecture built for that visual workload eventually became the foundation for accelerated computing—and then modern AI.

Video models may follow a similarly indirect path.

A video model is not automatically a world model. But a system that becomes increasingly good at predicting how visual environments change over time is learning something that language alone cannot provide.

According to LatePost, ByteDance is considering training a model with more than 5 trillion parameters. That would make it larger than Alibaba’s Qwen 3.8-Max, with 2.4 trillion parameters, and Moonshot AI’s K3, with 2.8 trillion—making it the largest model publicly known to be under development in China.

The model would almost certainly be multimodal, meaning it could support both text and visual reasoning from launch. That would move it another step closer to the capabilities expected of a world model.

That matters for robotics and autonomous driving.

China currently has hundreds of companies trying to build humanoid robots, yet the industry still struggles with the same fundamental problem: bodies are arriving faster than brains. Unitree says cumulative humanoid shipments have already reached 18000, while many Western competitors remain at much smaller production volumes.

Physical training data is expensive. Robots have to exist, move, fail and be reset. A sufficiently capable video or world model could eventually provide a simulated environment in which some of that learning happens before the robot ever leaves the factory.

I have always been cautious about the idea that language models alone can lead to general intelligence. Language cannot fully describe smell, light or the complicated physical relationship between a hand and an object. Humans learn by interacting with the world; language is, in many ways, the compressed record of those interactions.

AI may need something similar.

Video offers an imperfect simulated world in which a machine can begin learning what happens when things move, collide and change.

As Bloomberg columnist Catherine Thorbecke has argued, large language models still absorb most of the attention and capital in AI. But the next major breakthrough may come from systems that can navigate the real world rather than merely describe it.

If video becomes the training ground for those systems, China’s early advantage will matter for far more than annoying Hollywood.

It could help determine who leads the next phase of AI.

At 9:20 p.m., Zhao Xin appears on Douyin, China’s version of TikTok, with the leg of a robot dog sitting in front of him. He begins taking apart the motor inside its knee joint, explaining what may have gone wrong.

His account is one of dozens of Chinese short-video channels devoted to repairing robots.

Depending on how the market is counted, China shipped roughly 15,000 to 20,000 humanoid robots in 2025. Many went not to factories, but to universities, technology companies, shopping malls and event organizers. They danced, performed martial arts, greeted customers and served as expensive research platforms. IDC estimated that around 18,000 humanoids were shipped globally that year, with Chinese manufacturers accounting for most of the market.

But the industry has spent far more time discussing what robots may eventually do than answering a more immediate question: Who fixes them when they break?

A small group of Chinese technicians thinks it has found an opportunity. They call themselves, half jokingly, “robot doctors.” Their bet is simple. As robot sales rise, so will the number of broken robots. Someone will have to repair them.

China’s humanoids may still be looking for jobs. Their doctors are already waiting for patients.

When the Robot Falls Down

China’s humanoid industry accelerated rapidly after Unitree put its robots onstage at the 2025 Spring Festival Gala. Machines that had mostly lived in laboratories suddenly began appearing at corporate events, shopping malls and commercial performances.

Once robots began performing in front of crowds, however, they also began falling down in public.

On May 18, a Unitree G1 dressed as an ancient Chinese warrior was performing martial arts when one of its legs separated from its body. The aluminum limb slipped out of the costume and landed on the floor.

On July 16, two T800 robots made by Shenzhen-based ENGINEAI fought each other in a staged match. One kicked the other robot’s head off. The headless machine continued trying to finish its kicking sequence like a mechanical zombie.

It makes for entertaining video. It is less entertaining when you own the robot.

Manufacturers offer warranties, but those warranties come with conditions. Unitree currently provides 8 months of coverage for the basic G1 and 18 months for the EDU version. Damage caused by falls, improper use, unauthorized modification or disassembly may not be covered.

Even when a manufacturer agrees to repair the machine, sending it back can leave the owner without a robot for weeks.

Zhao saw an opening. He works for an organization in Inner Mongolia whose main business includes drone repair, but his Douyin account documents his attempts to dismantle and repair robot dogs and humanoids from scratch.

Born in 1990, Zhao studied civil engineering at a vocational college. He enjoyed taking electronics apart, but had never worked professionally as a repair technician. Almost everything he knows about robots is self-taught.

Six months ago, he opened his first robot dog. After putting it back together, he somehow had ten screws left over.

Later, while dismantling a humanoid, he snapped three screws and two clamps. Some of the threads had been secured with anaerobic adhesive. After heating them with a hot-air gun, Zhao learned that forcing the softened screws was an excellent way to break them.

A few months later, the work had become routine. An error message in the robot’s app usually points him toward the faulty subsystem. He then checks the physical symptoms, locates the damaged component and replaces it.

His conclusion is almost disappointing: mechanically, repairing many robot dogs and humanoids is not necessarily more difficult than fixing a drone or robotic vacuum.

Li Gang, a robot-rental operator in Wuhan, reached the same conclusion for a more practical reason.

Li bought 15 Unitree G1s for his rental business. Last June, one fell heavily during a shopping-mall performance and damaged its knee motor. The repair was judged to be outside warranty and would have cost more than $3,000. The robot itself had cost around $22,000.

So Li began taking the machines apart himself.

“It’s a survival skill,” he told me. “When China didn’t have many cars, drivers had to know how to fix their own cars too.”

Other rental operators now send their broken robots to him. Li charges around 60% to 80% of the manufacturer’s repair price. One repair shop in Shenzhen says it can sometimes cut the factory price in half.

More importantly, Li says he can usually return the robot within a week.

Repairing the Robot Is the Easy Part

There is no real textbook for this job.

China already has one of the world’s most sophisticated electronics-repair ecosystems. Walk through Shenzhen’s Huaqiangbei electronics district and technicians can find schematics, identify chips, trace circuits and source replacement parts through enormous informal supplier networks.

Robots are different.

Their circuit diagrams are rarely public. Some manufacturers remove identifying marks from chips. Many components are heavily customized, and parts from one company usually cannot be installed in another company’s machine.

An employee at Booster Robotics told me that authorized dealers are often the only businesses allowed to order certain parts directly from manufacturers. For an independent technician, the most reliable source of a replacement component may be another robot of exactly the same model.

That creates a circular problem.

“Every robot company wants to become the leader,” the employee said. “The component design becomes part of the secret. Would you give your secrets to a competitor? Would Unitree?”

“If you don’t know who actually manufactures the motor or the chip, you can’t buy replacement parts in volume. And if you can’t get the parts, you can’t really repair the robot. It’s a dead end.”

This problem partly reflects how China’s robot companies were built.

Early in Unitree’s history, founder Wang Xingxing needed motors that could deliver high torque at relatively low speeds. Existing products were either poorly suited to the task or too expensive. But Unitree was selling only dozens of robot dogs a year, far too few to interest established suppliers.

A former Unitree employee once described the problem to Chinese technology publication 36Kr. Unitree could approach large listed manufacturers such as Wolong or Shanghai MOONS’ Electric, but neither was interested in such a small order.

So Unitree designed its own motors and joints.

That decision became one of the company’s biggest competitive advantages. Motors and joint assemblies account for a large share of a robot’s hardware cost. A motor that costs between $440 and $740 from an outside supplier might be manufactured internally for less than $150.

What is good for Unitree’s margins, however, is bad for the repair business.

Other robot makers reached the same conclusion. Proprietary motors, actuators and joints were often cheaper and performed better. But the more components manufacturers brought in-house, the fewer standardized spare parts became available on the open market.

Even the trivial parts can be expensive.

Zhao once discovered that the rubber pads on a robot’s feet had worn out. A replacement pair cost $60. His own shoes cost $9.

A suit for a humanoid performance can cost another $120, because Chinese garment factories have started making clothing and accessories specifically for robots.

Apparently, robots discovered bespoke tailoring before they discovered steady employment.

First, You Need More Broken Robots

Li can repair robots. He is far less certain that repairing robots can make him rich.

“Who actually needs us?” he asked. “Robot-rental companies. Their robots fall over while dancing, and the manufacturer may not cover the damage. But lots of Unitree robots are sold to universities. A university isn’t going to hand us an expensive research robot. If we break it, it’s not easy for them to just buy another one.”

That distinction matters.

One Chinese industry report cited in the research behind this story estimates that education and research account for 42% of humanoid-robot applications, followed by data collection, services and performances. Researchers generally prefer factory-authorized repairs to taking a chance on an independent shop.

Then there is another inconvenient problem: robots do not break often enough.

Before China’s May Day holiday, new-store openings and commercial events create a rush of robot performances. That also creates a repair season. Summer is quieter. Outdoor events decline in the heat, and repair orders fall with them.

Li says humanoids are also more durable than most people assume. Despite owning and operating an entire fleet, he has personally repaired only three.

Even if 10% of China’s fleet of more than 15,000 robots required repairs in a single year, only a fraction of those jobs would reach independent technicians. Most machines remain under warranty, and many owners will send them back to the manufacturer regardless.

Zhao’s organization receives only one to three humanoid or robot-dog repair orders in an average month.

Another national repair business handles more than 10,000 robotic vacuums and commercial service robots each year. In a typical month, however, it sees only five or six humanoids and slightly more than ten robot dogs.

For now, humanoid repair cannot support most of these companies by itself.

Their real businesses are elsewhere. Zhao’s employer began by offering machinery-operation training for open-pit mines. A Shenzhen shop has years of experience repairing industrial robots. A Beijing operator earns money from robot rentals, education programs and events. Others specialize in drones.

That has pushed many of them toward a more obvious business: teaching other people how to repair robots.

There are no standardized courses and little meaningful competition. Programs charge between $740 and $4,400 for courses lasting eight to 40 days.

QbitAI reported that one Shenzhen operator trained more than 100 students across six classes beginning this February. Around 70% to 80% eventually found jobs somewhere in the robotics industry, although most became salespeople, operators or maintenance technicians rather than full-time robot doctors.

The barrier to entry is not particularly high. Interviewees said vocational-college graduates can handle routine repairs, and some training companies accept applicants with only a high-school education.

Advanced modification is a different matter. Once the work involves programming, algorithms or secondary development, employers generally want a university degree.

Pay reflects that divide. Beginners typically earn between $890 and $1,180 a month. Experienced technicians can make more than $1,480, depending on the city.

Large companies have noticed the same opportunity. JD.com’s service division said in May that it plans to train 100,000 engineers over the next five years in fields including robot and smart-home repair. It says it already employs thousands of technicians working on robot maintenance and is building what it calls a “cyber medical system” covering diagnostics, repair, refurbishment and recycling.

China’s robot doctors already know how to treat the patients. The problem is that they still need the patients to exist.

Li compares the business to veterinary medicine.

“Pet hospitals make good money, right?” he said. “That’s because there are enough cats and dogs.”

Then he looked at the robots sitting around him.

“You may not see as many robot dogs in your entire life as I have sitting next to me right now.”

CT here. Han is tied up with another project, so I’m taking over the weekly tech roundup for now.

Hangzhou is being hit by Typhoon “White Dolphin,” and I’ve spent the evening listening to the wind hammer against the windows. Chinese tech news felt a bit like that.

The biggest gust came from Washington, where the FCC is preparing restrictions on Chinese optical transceivers. Shares of Zhongji Innolight and Eoptolink briefly fell nearly 10% in a single day. But one detail got lost in the panic: Coherent, an American optical-communications company, has a factory in Wuxi. The supply chain is messier than the headline suggests.

Then there is CXMT and Apple. Apple is reportedly helping the Chinese memory maker seek approval to enter its supply chain, even though CXMT’s orders are already booked well into 2027 and customers like Tencent and ByteDance could absorb much of its available capacity. Apple and CXMT increasingly look like a celebrity couple caught in a dating rumor: everyone has seen the photos, but they have not even had dinner together yet.

That is what storms do. They create enough noise that people react before they think. Breaking news matters, but the most useful information is often buried in the details: who benefits, who gets hurt, and what each side actually wants. So let’s look past the headlines and see what really happened.

This Week Features…

The Apple Supplier Lens Technology Outgrows Its Patron

On May 14, during Donald Trump’s visit to China, a woman sitting next to then-Apple CEO Tim Cook caught some attention. She was Zhou Qunfei, founder of Lens Technology, one of Apple’s key component suppliers and a longtime maker of iPhone cover glass. At first glance, Zhou’s story looks like the classic rise of a Chinese contract manufacturer: make something for someone else, master the process, scale it up, and get paid.

But Zhou does not seem interested in remaining someone else’s craftsman—or servant. Lens has invested in Chinese smart-glasses maker Rokid and helped solve the mass-production problem for optical waveguide lenses. It also backed Qingtian Rent, a robot-leasing platform linked to AgiBot, while taking on component manufacturing for several Chinese robotics companies.

For years, Lens was criticized for depending too heavily on Apple. Zhou’s answer now is not simply to find more customers, but to invest in companies that could become its future customers. That is also the focus of this week’s story: Chinese manufacturers made fortunes from “Made in China,” but the direction of their industries was still largely set by foreign customers like Apple and Tesla.

Now some of those suppliers are taking a riskier path. They are putting their own money into startups and emerging technologies, hoping to secure a place in the industries that may define the next decade. They are no longer waiting for the future to send them a purchase order—they are investing in the future themselves. And the stock market is starting to price them accordingly.

[Read the full piece →]

The Missing Middle Layer of AI, and Why China Built It First

In Wuxi, China, a new kind of AI infrastructure is taking shape. It does not train frontier models or manufacture chips. It produces something else: tokens.

China’s enterprise AI market is no longer just a race to build the smartest model. According to industry tracking from the 2025 China Large Model Procurement Monitoring Report, Chinese organizations signed more than 7,500 contracts related to large-model projects in 2025, worth nearly RMB30 billion. What is striking is that many of these buyers were not primarily asking which model scored highest on a benchmark. They cared more about deployment, security, reliability and control.

Nvidia CEO Jensen Huang has described the next generation of AI infrastructure as an “AI factory”—a system that turns electricity and computing power into tokens. In the U.S., much of that capability is absorbed inside vertically integrated platforms such as AWS, Microsoft Azure and Google Cloud, or within the major AI labs themselves.

China did not invent this idea. What makes the Chinese market interesting is that it is becoming one of the first places where the concept is emerging as a standalone industry. Local governments, infrastructure providers and startups are building what they increasingly call “token factories”: dedicated systems whose business is not to create the next foundation model, but to produce AI inference at scale.

[Read the full piece →]

It’s Nearly Impossible to Remove China From Tesla

Any American company that builds a huge business in China eventually has to confront an uncomfortable question: What happens if that business has to be separated from the rest of the company?

The Wall Street Journal put that question on the table and picked perhaps the most obvious target: Tesla. According to its July 30 report, Tesla had explored possible ways to separate its China operations as part of preparations for a potential merger with SpaceX. Musk quickly denied it.

But the report creates a much more interesting thought experiment: What would actually happen if Tesla tried to give up Gigafactory Shanghai? We went through the factory’s production, financing, suppliers, data and employees, and the conclusion was fairly simple—leaving China would create one problem after another for Tesla. And those might not even be the biggest problems.

So this week, we’re running the simulation ourselves. You can play along.

[Read the full piece →]

The News…

(i) ByteDance’s 5 Trillion Parameters

ByteDance is discussing training a model with more than 5 trillion parameters, according to LatePost, potentially making it the largest known AI model under development in China. The project would surpass Alibaba’s Qwen 3.8-Max (2.4T parameters) and Moonshot AI’s Kimi K3 (2.8T parameters), though it remains in an early stage.

Founder Zhang Yiming reportedly encouraged the AI team to pursue original architectures and maintain a “no distillation” approach. He argued that distilling models can quickly improve performance but mainly replicates existing capabilities such as Anthropic’s Claude, making it difficult to achieve true breakthroughs.

ByteDance had considered distillation amid pressure from advanced overseas models and limited AI compute resources, but Zhang ultimately pushed the company toward long-term AGI development.

(ii) DeepSeek Is Preparing to Raise API Prices

DeepSeek is preparing to raise API prices, marking a potential shift for one of the AI industry’s most aggressive low-cost strategies.

On August 6, DeepSeek announced that it plans to increase API prices, warning users that the increase could be substantial.

The move comes after DeepSeek-V4-Flash helped the company strengthen its position as a low-cost AI provider. According to its official API pricing, V4-Flash costs just ¥1 per million input tokens and ¥2 per million output tokens, while cached input tokens are priced at only ¥0.02 per million tokens.

The potential price increase highlights a broader challenge for AI companies: aggressive pricing can accelerate adoption, but scaling AI usage requires significant computing resources.

(iii) CXMT Is Apple’s DRAM Supplier, Maybe?

Apple is reportedly exploring CXMT as a new DRAM supplier to reduce memory costs, but negotiations have hit a roadblock. According to Korean media, CXMT rejected Apple’s request for lower pricing and insisted its DRAM prices remain close to those offered by Samsung Electronics and SK hynix.

Chinese smartphone makers are also diversifying memory suppliers. OPPO has reportedly stopped negotiating further price cuts with Samsung, while Xiaomi is raising shipment targets. Huawei, Xiaomi and other Chinese brands are expected to secure long-term orders from CXMT.

CXMT’s revenue surged 716% year over year last quarter, giving the company a rare opportunity to leverage its growing pricing power in the memory market.

(iv) Unitree’s $7 Billion IPO

Unitree Robotics could reach a valuation of $7.1–7.8 billion within 6–12 months after its planned Shanghai IPO, according to a valuation report from CITIC Securities, the IPO sponsor. The humanoid and quadruped robot maker plans to raise about $590 million to expand R&D and production. CITIC valued Unitree at roughly 20x projected 2026 sales and 80x forecast earnings. Competing with companies such as Tesla and Boston Dynamics, Unitree is positioning itself as a key player in China’s embodied AI industry. The IPO comes amid growing U.S.-China technology competition and new U.S. restrictions on advanced robotics.

Based on our estimates, Unitree Robotics’ IPO price could be around $14.55 per share. Investors who successfully receive an IPO allocation could potentially earn more than $7,000.

(v) Washington May Restrict Chinese Optical Transceivers

Reuters’ report that the FCC may restrict Chinese optical transceivers triggered an immediate market reaction. Chinese optical-module stocks fell, while Lumentum and Coherent rallied.

But the important part is still unclear. Neither Reuters nor Bloomberg has explained how such restrictions would actually be implemented.

The U.S. Covered List system is built around national-security concerns, including whether equipment can access, redirect, or disrupt communications data. Companies such as Eoptolink and Innolight are not currently on the list.

A potential restriction could take several forms. Washington could target specific Chinese manufacturers, restrict products made in China, or impose a broader limit first and decide on exemptions later. If Chinese optical modules are added to the list, advanced products such as 1.6T transceivers could face much greater certification pressure in the U.S. market.

The implementation of the Covered List could take three different approaches:

A. Manufacturer-based designation: If the U.S. lists specific Chinese manufacturers and their affiliates, restrictions would apply regardless of where the products are made. Moving production overseas would not avoid the restrictions.

B. Production-location-based determination: Restrictions could depend on where the optical modules are manufactured. Chinese companies may try to relocate parts of the production process overseas to avoid being classified as Chinese-origin products, creating a more complicated supply chain assessment.

C. Broad supplier screening approach: The U.S. could initially restrict a wide range of overseas suppliers and later determine which companies are considered independent from Chinese influence. The final outcome would largely depend on U.S. regulators’ interpretation.

But supply chains are more complicated than the headline suggests.

Coherent’s 1.6T optical-module production still relies on its Wuxi facility. High-purity indium, a key material for optical chips, is also largely produced in China. Companies such as AXT operate indium phosphide manufacturing in China as well.

That means China would have room to respond, potentially by restricting exports of critical materials, similar to its rare-earth strategy.

The regulatory risk is real, but markets often underestimate supply-chain interdependence. The same pattern appeared in biotech. Companies such as WuXi AppTec faced heavy policy pressure, but their valuations later recovered.

That said, Chinese optical-module stocks are not easy to defend at current prices. Some are trading at 100 to 200 times earnings. The policy risk may be overstated, but the valuations are not exactly modest either.

Elon Musk and The Wall Street Journal have never been natural allies.

On July 30, the Journal reported that Tesla advisers had discussed ways to separate the company’s China operations, including a spinoff, sale, or closure, in order to reduce the political complications of a possible merger with SpaceX. Musk denied that the subject had ever been discussed and called the report “absurdly fake news.”

The denial was predictable. Tesla’s car business depends on its Shanghai factory, while China remains one of its largest markets. Anyone familiar with the company could see the immediate problem: cutting off Tesla China would not be like selling a distant regional subsidiary. It would mean severing one of the central organs of Tesla’s global manufacturing system.

But the report still raises a useful question. Even if no transaction is imminent, can Tesla build a structure that limits China’s influence over the rest of the company?

Put more bluntly: can Tesla ever de-China itself? Can Tesla cleanly unwind everything it owns in Shanghai?

Not only is the answer no — such a separation is practically impossible for now.

What China Means to Tesla

Imagine you are Musk, and some overwhelming geopolitical shock has forced you to separate Tesla’s China business. The first task is simple: measure the damage.

On the market side, the loss might look manageable at first. Tesla generated $20.96 billion in China in 2025, equal to 22.1% of its $94.83 billion in global revenue. China is brutally competitive. Tesla discounts frequently, local automakers release new models at relentless speed, and the company can no longer expect the extraordinary margins it once enjoyed there. A market that no longer delivers excess profits can eventually start to look expendable.

The harder problem is the asset Tesla cannot simply pack into shipping containers: Gigafactory Shanghai.

The plant delivered 851,000 vehicles in 2025, or 52% of Tesla’s worldwide deliveries. It is Tesla’s largest and most productive factory, as well as a major export hub serving Europe, Canada, and the Asia-Pacific region. This is not a Chinese branch that happens to sell cars locally. It is the starting point for roughly half of Tesla’s global vehicle supply.

Removing it would be less like divesting a limb than cutting out the heart of the manufacturing system.

Shanghai has little reason to welcome that outcome either. Tesla’s plant was China’s first wholly foreign-owned automobile factory, a politically risky concession that produced a very visible return. The original investment agreement set a target of RMB2.23 billion, or about $330 million, in annual tax revenue once the plant reached full production. The factory also became the anchor project for Lingang, created thousands of jobs, and gave Shanghai a powerful advertisement for attracting other foreign manufacturers.、

Then there is the balance sheet. At the end of the first quarter of 2026, Tesla had fully drawn a $5.79 billion China working-capital facility carrying interest of just 2.01% to 2.11%. At the same time, the company held $44.74 billion in cash and short-term investments. Cheap local funding is attractive for Tesla, but it would make separation messier. A buyer would need to determine which obligations belonged with the factory, which stayed with Tesla, and whether Chinese lenders would accept the new structure.

Data would be another difficult asset to divide. Information generated by vehicles operating in mainland China is stored on Chinese servers. China’s automotive-data regime allows some cross-border transfers, but sensitive personal, mapping, and vehicle data can require security assessments and other approvals.

Apple’s solution was to place mainland Chinese iCloud under a locally operated structure run by GCBD, now AIPO Cloud (Guizhou) Technology Co., Ltd. Tesla would need a comparable wall around vehicle, camera, and location data. But cars continuously generate far more operational and geographic information than ordinary consumer cloud accounts.

By this point, the thought experiment reaches its obvious conclusion. A clean separation is close to impossible. That is why even discussing one can hurt Tesla. The headline implies that Musk might sacrifice half his manufacturing base to make a corporate merger easier.

The Journal’s comparison with businesses such as Yum China, or a possible Starbucks China transaction, also has limits. A restaurant chain can draw a geographic boundary around stores, workers, and local suppliers. Food produced in China can largely be consumed in China.

Gigafactory Shanghai is different. It supplies customers across multiple continents. Separating it would effectively create a new global automaker, not merely a Chinese franchise.

Why the Rumor Still Makes Sense

The report nevertheless has an underlying logic: SpaceX.

SpaceX is not a normal technology company. It launches classified military satellites, operates communications systems with battlefield uses, and derived roughly one-fifth of its 2025 revenue from the U.S. government. Its defense-related work is subject to strict controls, including the International Traffic in Arms Regulations, or ITAR, which governs access to and transfers of defense articles, services, and technical information.

During its IPO process, SpaceX’s underwriters also excluded investors from mainland China and Hong Kong over regulatory, national-security, and compliance concerns.

Combining SpaceX with Tesla would create an entity that is both a major U.S. defense contractor and the owner of a wholly owned manufacturing operation in China. A corporate chart can separate divisions on paper. Regulators care about what crosses the lines: people, designs, data, purchase orders, software permissions, and money.

Tesla’s Chinese operations are especially hard to isolate because they are not just a factory. They sit at the center of an industrial network. Tesla has more than 400 Tier 1 suppliers in mainland China, more than 60 of which also serve its global supply chain. More than 95% of the components used in its locally made Model 3 and refreshed Model Y are sourced in China.

Some of those suppliers are now moving from cars into robots. Zhejiang Sanhua Intelligent Controls is known for automotive thermal-management systems. Ningbo Tuopu Group has created a robot-actuator division alongside its automotive businesses. Ningbo Xusheng Group now lists robot systems among the markets for its lightweight structural products.

Tesla does not publish a complete Optimus supplier list, so specific assignments remain difficult to verify. But the industrial overlap is real.

After a Tesla-SpaceX merger, this responsive supply chain could begin to look, from a regulator’s perspective, like an impossible-to-audit transfer channel.

Was a component ordered for a Model 3, an Optimus robot, a data center, or a spacecraft? Could a drawing for a controlled aerospace component be disguised as a robotics design and sent to a Chinese supplier? Even without misconduct, Musk’s habit of moving engineers and resources among companies would invite suspicion.

Rare-earth magnets show why. Musk said in 2025 that Chinese export restrictions had already disrupted Optimus production, and that Tesla needed licenses and assurances showing the magnets would not be used for military purposes.

Put Tesla and SpaceX under one roof, and a Chinese regulator would have a much harder time accepting that a shared procurement system could reliably distinguish between a humanoid robot and a defense-adjacent aerospace project.

Data creates the mirror-image problem. Beijing would worry that camera, mapping, and behavioral data from millions of Chinese Tesla users could flow toward a company deeply embedded in the U.S. defense establishment. Washington would worry that China-based employees might gain access to cross-company engineering systems, procurement orders, or restricted projects.

Personnel may be the hardest wall to build. Tom Zhu, Tesla’s senior vice president of automotive, led the construction and operation of Gigafactory Shanghai before taking responsibility for the broader car business. Tesla’s most important manufacturing success outside the United States was built partly by a China-based team whose methods the company later tried to reproduce elsewhere.

A merger would inevitably bring new scrutiny to those employees and their access. Isolating them before Tesla has fully replicated Shanghai’s performance would mean discarding some of the expertise the company most needs.

That is why a merger looks unnecessary from Tesla’s perspective. It would create regulatory problems for the car business without solving an obvious operating problem. It could also damage Optimus, whose production depends on Chinese manufacturing capacity and materials.

What Separation Could Actually Look Like

Still, suppose Tesla began restructuring in 2027. What options would it have?

The fastest approach would be administrative rather than economic: separate data, software access, and sales systems. Tesla could create a China-specific digital architecture resembling Apple’s mainland iCloud arrangement, limit China-based employees’ access to other business units, and establish dedicated compliance teams.

The Journal reported that executives had also discussed a separate sales entity to handle exports from Shanghai. That would not remove geopolitical risk, but it could make the boundaries more legible to regulators.

The next step would be to separate ownership from operation. A Chinese automaker such as SAIC Motor could hypothetically take a stake in, manage, or contract-manufacture at the Shanghai plant. The structure might resemble a joint venture or outsourced factory rather than a complete sale.

That would create its own problem: the product’s identity. Many Chinese customers buy Tesla partly because it remains an American technology brand associated with Musk. Turning the factory into a locally controlled operation could weaken that appeal.

It would also take years to renegotiate supplier contracts, licenses, software rights, employment arrangements, and export approvals. SAIC is large enough to run the physical operation. That does not mean it could inherit Tesla’s brand, code, and global distribution system without disruption.

A separate export-and-sales company may therefore be the least damaging middle course. Shanghai could continue manufacturing cars while a legally distinct entity purchased vehicles from the factory and sold them abroad. China-specific data, personnel, and procurement could remain ring-fenced.

The structure would be inelegant, but compliance often is.

The most radical option, a full sale or shutdown, looks least credible. Tesla would be abandoning a market that generated more than one-fifth of its 2025 revenue and a factory responsible for more than half of its deliveries.

Even an orderly transfer would create months of uncertainty over software updates, service networks, warranties, autonomous-driving features, and replacement parts. No government or buyer could absorb an operation of this scale by simply changing the name on the gate.

Musk will continue to see reasons for his companies to cooperate. Tesla invests heavily in AI chips, autonomous-driving software, robots, and computing infrastructure. Tesla and SpaceX have overlapping needs in energy, data centers, and engineering. Tesla itself invested $2 billion in SpaceX stock in the first quarter of 2026, showing that the financial ties are already deepening even without a merger.

But corporate ambition does not erase borders.

Tesla cannot solve the China problem by drawing a cleaner organizational chart, and it cannot solve it by amputating Shanghai. Its dependence on China is no longer merely a choice made by Musk or a concession granted by Beijing.

It is the accumulated result of years of investment, supplier development, employee expertise, cheap financing, customer data, and manufacturing scale.

Humanity never built the Tower of Babel. Musk is unlikely to succeed by placing a rocket company, an AI business, and a China-dependent automaker inside one corporate tower, then assuming every government will agree on which language is spoken on each floor.

A Token Factory doesn’t sell compute capacity. It sells inference capacity: manufactured intelligence, priced and delivered like a utility.

In Wuxi, China, a new type of AI infrastructure facility has appeared. It does not train frontier models. It does not manufacture chips. It manufactures something else: tokens.

That may sound like a strange distinction. After all, most of the AI race today is still measured by a familiar scoreboard: which model is smarter, which benchmark it wins, which company reaches the next generation of reasoning capability.

But inside the enterprises actually trying to deploy AI, the questions are becoming much less glamorous.

What happens if the network goes down?

Who catches it when the model is wrong?

Who is responsible when an AI system leaks information or makes a decision that cannot be explained?

China’s enterprise AI market is already moving beyond the model race. In 2025, industry tracking data from China Large Model Procurement Monitoring Report 2025, recorded more than 7,500 large-model-related procurement contracts, with disclosed contract values approaching 30 billion yuan. But the surprising part was what those contracts actually demanded: they rarely focused on which model was smartest. Instead, they focused on deployment, security, reliability, and control.

Read enough of these contracts and a pattern emerges. The recurring requirements are central deployment, edge execution, offline caching, resume after disconnect, tiered permissions, and audit trails. Acceptance criteria alone can run for dozens of pages.

These are not the questions people ask when they open ChatGPT. They are the questions organizations ask when AI moves from a demo into a production system.

That gap — between whether a model can answer well and whether an organization can actually run it — is where a new layer of the AI industry is emerging.

China is not the only place where this layer could exist.

NVIDIA CEO Jensen Huang has described the next generation of AI infrastructure as “AI factories” systems that transform compute and electricity into tokens. In the United States, however, this capability is largely absorbed inside vertically integrated platforms such as AWS, Azure, Google Cloud, and the major AI labs.

What makes China different is not that it invented the concept, but that it is among the first markets where this idea has begun to evolve into an independent industry with local governments, infrastructure providers, and startups building what China calls “Token Factories.”

What a Token Factory Actually Is

Three misconceptions are worth clearing up first, because each one understates what’s being built.

It is not an API relay. A basic API proxy just forwards a request to a model provider’s endpoint; it doesn’t touch the underlying efficiency of inference. A Token Factory does something structurally different: it runs its own inference stack, splitting the process into stages that can be optimized independently, and pooling chips from multiple vendors under a single scheduler. The goal is one number: how many tokens this hardware can produce per dollar, per watt, per second.

It is not simple compute rental. A cloud sells compute capacity: GPU hours, a fixed unit regardless of what you do with it. A Token Factory sells inference capacity instead, the output of that compute, metered in tokens. Its margin depends entirely on how efficiently it converts hardware and electricity into that output. This is a genuinely different business, where the core metric isn’t how many GPUs a company owns but how many tokens each GPU can produce, and at what quality. Push that efficiency up, and the same hardware generates more revenue without buying a single additional chip.

It is not just a data center. A useful way to see the stack: at the bottom sit GPUs, electricity, and networking. In the middle sits the software layer that actually does the work: inference frameworks, model scheduling, cache optimization, cross-chip compatibility. On top sits the delivery layer: APIs, agents, industry-specific applications that enterprises actually touch. A data center is the bottom layer alone. A Token Factory is all three, sold as one product.

Put simply: the product isn’t compute, and it isn’t an interface. It’s manufactured intelligence, priced and delivered like a utility.

Why This Layer Is Emerging in China

No single explanation carries the whole story. Five conditions are converging at the same time, and it’s the combination, not any one of them, that matters.

1. Open-weight models supply the raw material.

DeepSeek and Alibaba’s Qwen release full-size open weights. Moonshot’s Kimi and Zhipu’s GLM have released open weights too, though open-sourcing plays a smaller role in Moonshot’s own business than it does for DeepSeek. The exact mix varies company to company, but the pattern holds: several competitive Chinese models are simultaneously available for any team to download, deploy, and optimize, something that doesn’t exist among the closed U.S. frontier of OpenAI, Anthropic, and Google. If a model’s weights are public, the model builder trains, and someone else can manufacture and distribute.

Meta’s Llama is the obvious counterexample, and it’s worth addressing directly. An open U.S. model exists, but it has not produced the same independent inference ecosystem that is emerging around China’s model ecosystem. Most of the serious inference infrastructure built around U.S. models sits inside AWS Bedrock, Azure OpenAI Service, and Google Vertex AI, each of which already does real multi-model inference and routing across providers like Anthropic, Meta, and Mistral. That’s genuine infrastructure, just not an independent layer: an integrated version of the same function, embedded inside a hyperscaler ecosystem rather than sold as a public utility that works across any model or chip.

Unlike the U.S. frontier AI ecosystem, where companies like OpenAI are tightly integrated with hyperscale cloud platforms such as Microsoft Azure, China’s AI landscape is more fragmented. Some major technology companies, including Alibaba, ByteDance, Tencent, and Baidu, operate large cloud platforms of their own, but no single AI stack dominates the market from frontier models to global-scale cloud infrastructure.

That fragmentation, combined with open-weight models, creates room for a coordination layer to emerge.

2. A fragmented ecosystem needs someone to hold it together.

China’s AI stack is fragmented in a way the U.S. stack isn’t. On the model side: DeepSeek, Qwen, Kimi, GLM, and more, each with different architectures, strengths, and pricing. On the chip side: a shrinking pool of legacy Nvidia hardware as export controls tighten, alongside a fast-growing lineup of domestic accelerators such as Huawei Ascend, Cambricon, and Moore Threads, each running its own software stack, none of them interchangeable without real engineering work. On the infrastructure side: local data centers, telecom operators, and third-party clouds, each built for a different region and a different regulatory environment.

None of these pieces talk to each other natively. Someone has to translate a request into which model, on which chip, deployed where, and return a clean token stream on the other end. That’s the role SiliconFlow founder Jeffrey Yuan (Yuan Jinhui) has described his company playing: connecting models, chips, and applications. SiliconFlow has built exactly that. Its self-developed inference engine has adapted to more than 150 models, serving close to 10 million users and over 10,000 enterprise customers. Third-party data puts it among the top four providers in China’s public-cloud model-serving market, the only startup in that group, and on OpenRouter, an independent U.S. model-routing platform. It has emerged as one of the most-used Chinese model-serving platforms, frequently ranking among the top providers by daily token volume. A smaller, more technical example is Vectron, an optimization platform built by the Chinese startup Shishi Technology, which advertises compatibility with more than ten domestic chip types and twenty mainstream models: a narrower operator making the same underlying bet, that the money is in squeezing more usable tokens out of the same hardware, not in owning more of it.

3. Enterprise demand requires the model to leave the cloud.

Ask any enterprise buyer what stopped their AI rollout, and the answer is rarely that the model wasn’t smart enough. It’s usually that the data can’t leave the building. This isn’t unique to China. Samsung banned external generative AI tools company-wide in 2023 after employees pasted proprietary source code into ChatGPT, and similar bans have rippled through banks, chipmakers, and law firms worldwide since. What started as an emergency reaction has hardened into standard compliance practice across those industries. In China’s government and enterprise market, this requirement has its own name: data never leaves the domain.

That constraint reframes the entire buying decision. It’s no longer which API is cheapest per million tokens. It’s who can install this behind our firewall, with permission systems that track which employee can see which document, with audit logs that hold up if something goes wrong, and with a service contract that says who’s accountable when it doesn’t. That’s a systems-integration problem, not a subscription. A Token Factory is built to sell exactly that: not intelligence as a feature, but intelligence with a chain of custody attached.

4. Energy and compute infrastructure make scale possible.

Inference cost isn’t just about GPUs. Electricity can become one of the largest operating costs of running an inference operation. China has been experimenting with infrastructure models designed to reduce that cost, most visibly the East Data, West Computing program, which routes compute-heavy workloads toward the west, where land and renewable power are cheap, while demand stays concentrated in the east. One project in Zhongwei, Ningxia, sits next to a region with more than 18 million kilowatts of installed renewable capacity generating over 16 billion kilowatt-hours a year, pairing data centers directly with wind and solar generation to cut the transmission losses of pulling power off a conventional grid.

The metric that matters here isn’t how much electricity a facility uses. It’s how many tokens each kilowatt-hour produces, a number a Token Factory can actually move through better cooling, better power delivery, and tighter software optimization. That reframes energy from a fixed cost into an engineering variable, exactly the kind of problem an industrial-scale operator is built to solve and a small deployment team is not.

5. The state is building supply before demand fully exists.

The clearest way China’s Token Factory buildout differs from the U.S. market isn’t technical. It’s who’s paying for it, and when. Local governments and state-owned platforms aren’t just subsidizing this layer; some are building and operating it directly. Beijing Data Group’s Jingsuan Token Factory, the city’s first state-owned entrant, is positioned around trust rather than price: a compute foundation safe and compliant enough for state-owned enterprises that can’t take a chance on an unproven vendor, billing customers for what they actually consume rather than selling them hardware upfront.

China’s telecom operators have moved the same direction, explicitly reframing their core business as a pivot from traffic operations to token operations, treating tokens the way they once treated mobile data plans: a metered, mass-market utility sold at consumer prices. The difference is not that U.S. carriers lack AI infrastructure ambitions. Companies like Verizon and AT&T have invested in cloud, edge computing, and enterprise AI services. But no major U.S. telecom operator has yet repositioned itself around token-based AI services in the same way Chinese carriers are attempting to do.

None of this activity waited for enterprise demand to prove itself first. In many cases, the logic has been the reverse: build the capacity, lower the cost of access, and let demand catch up. American hyperscalers are also spending heavily on AI infrastructure, but much of that investment remains a corporate bet on future demand, with the payoff expected to come through their own cloud, model, and enterprise businesses. In China, a portion of that same infrastructure buildout is being driven by a broader mix of players, including state-backed entities, telecom operators, cloud providers, and startups, with a different tolerance for how long the market may take to mature.

Put together, these five conditions describe two different ways of organizing the same underlying challenge: how to turn raw model capability into something a business or government agency can actually deploy.

The difference is not simply vertical integration versus horizontal specialization. Both ecosystems contain elements of both. The U.S. has vertically integrated stacks such as OpenAI-Microsoft and Google, but it also has independent infrastructure providers emerging around AI compute and inference. China has vertically integrated players such as Huawei, Alibaba, and others, but its broader AI ecosystem remains more fragmented across model developers, cloud providers, chip suppliers, telecom operators, and application companies.

That fragmentation creates room for a distinct coordination layer: companies that connect models, compute, and applications, and turn distributed AI capabilities into standardized, metered services. This is the role China’s Token Factories are beginning to play.

None of this guarantees the model will succeed. Hardware depreciates quickly enough that a facility’s advantage can disappear within a product cycle. If token prices continue falling while hardware costs and infrastructure commitments remain fixed, margins could be squeezed from both sides. And there is nothing preventing AWS, Azure, or Google Cloud from building more sophisticated inference-optimization services of their own, potentially absorbing parts of this layer over time, just as cloud providers have absorbed other infrastructure functions in the past.

The Token Factory model is therefore not a guaranteed winner. It is one possible organizational response to the same question every AI ecosystem now faces: once intelligence becomes widely available, who will make it usable, affordable, and operational at scale?

But the underlying wager isn’t really about whether China can produce tokens more cheaply than the U.S. can this quarter or next. It’s about what happens once tokens become a standardized commodity, priced and metered like electricity: whoever controls the production infrastructure controls the entry point to everything built on top of it. China has run this playbook before, building telecom base stations years ahead of the mobile data traffic that would eventually fill them, on the bet that supply creates its own demand. The Token Factory looks like the same bet, updated for a different kind of network. The first era of AI was about building intelligence. The next may be about building the infrastructure that manufactures it.

On May 14, at the state banquet welcoming Donald Trump to Beijing, Lens Technology founder Zhou Qunfei was seated between Tesla CEO Elon Musk and Apple CEO Tim Cook.

The seating chart looked almost too neat to be accidental. American executives were placed near the Chinese business leaders whose factories and supply chains help keep their companies running. Zhou’s position was especially revealing: Lens is a major supplier to both Apple and Tesla.

In 2026, however, that dependence did not look entirely comfortable. Lens reported first-quarter revenue of RMB14.14 billion, or about $2.09 billion, down 17.13% from a year earlier. Excluding one-off items, the company lost RMB 174 million, or roughly $25.8 million, as weaker electronics demand, higher memory prices, and currency swings weighed on its business. On April 16, its Shenzhen-listed shares fell 13.35% to RMB25.64, or $3.79.

Then the market changed its mind.

By June 25, Lens shares had more than doubled from that April close, touching RMB58.50, or $8.66. Investors were no longer treating Lens as just a maker of smartphone glass. They were beginning to price it as a supplier to AI glasses, robots, servers, and advanced chips.

In July, Lens gave the market its most exciting story yet: a strategic collaboration with Intel on glass substrates for advanced semiconductor packaging. The companies said they would explore through-glass-via, or TGV, technology. Intel has argued that glass can offer better thermal and mechanical stability than today’s organic package substrates, an increasingly useful property as AI chips become larger, hotter, and more densely connected.

Lens is named after the product that made Zhou rich. But her ambitions now extend far beyond a few sheets of glass.

This is not only the story of one founder or one Apple supplier. It reflects a broader shift in Chinese manufacturing. Companies that spent decades living behind Western brands now want investors to value them as technology platforms, strategic investors, and creators of new markets.

The Glass Cage

Lens Technology’s greatest advantage has always been glass. It has also been the company’s prison.

Zhou did not begin with the advantages usually associated with a Chinese technology billionaire. Her father was badly injured in an industrial accident, and her family could not afford to keep her in school. She left home as a teenager to work in southern China, choosing a factory near Shenzhen University so she could take evening classes. Her job was processing glass for watches.

She learned the craft, became a supervisor, and eventually started her own workshop in a converted home. The early staff consisted mostly of relatives and former co-workers. In the early 2000s, she moved from watch glass into mobile-phone cover glass. A Motorola order helped establish the business. Apple transformed it.

Steve Jobs wanted a phone dominated by a large glass surface rather than a physical keyboard. Lens became part of the supply chain that made that design possible. For years, Apple accounted for close to half of Lens’s revenue. In 2017, 2018, and 2019, Apple-linked sales represented 49.37%, 46.97%, and 43.07% of total revenue, respectively.

That relationship made Zhou one of the world’s richest self-made women. It also tied Lens’s fortunes to decisions made in Cupertino.

When iPhone demand weakened, Lens felt it quickly. In 2018, its net profit fell 68.88%. In 2021, net profit dropped 57.36% to RMB2.09 billion, or about $309 million. The same problem has affected much of China’s Apple supply chain: a supplier may be excellent at manufacturing, but the customer still controls product cycles, order timing, and much of the margin.

Apple suppliers have spent years looking for a way out. Some moved into electric vehicles. Others shifted toward servers and data-center equipment. Lens faced a narrower problem. Zhou’s company was not simply an assembly contractor that could redirect factory workers to a different device. Its deepest knowledge was in glass, coatings, precision finishing, and the materials around them.

If Apple bought less glass, there were few customers large enough to absorb the spare capacity.

That was the glass cage. Everyone could see that dependence on Apple was dangerous. But escaping it required Lens to become a different kind of company without abandoning the skill that made it valuable.

That shift has been underway for years. Investors are only now beginning to notice.

Lens’s Three-Step Escape

Zhou Qunfei had two obvious ways to reduce Lens Technology’s dependence on Apple. The company could find more uses for glass, or it could learn to manufacture more than glass.

In the end, Lens chose both. Its strategy has three parts: deepen its advantage in glass, build new businesses around other materials and assembly, and follow customers into the next generation of hardware.

Double Down on Glass

Lens’s first response was not to abandon glass. It was to find more valuable places to use it.

Beginning around 2020, the company started taking its materials expertise beyond smartphones. It moved into automotive displays, decorative panels, and other glass components used in electric vehicles. It also explored photovoltaic glass and ultra-thin glass, or UTG, the flexible material used in foldable screens.

The logic was straightforward. If smartphone demand became less reliable, Lens could redirect its manufacturing capacity toward cars, solar equipment, wearables, and other products that still required precisely processed glass.

That helped Lens win new customers. By 2022, the company described itself as a core global Tier 1 supplier to Tesla and said it was working with more than 20 automotive brands, including Porsche, Mercedes-Benz, and Hyundai.

AI glasses offered an even more natural extension.

In 2025, Chinese AR company Rokid identified Lens as an important manufacturing partner. Display-equipped glasses require far more than ordinary lenses. Optical waveguides must be coated and manufactured with extreme precision, while light transmission, weight, and yield all have to stay within narrow limits.

These are exactly the kinds of manufacturing problems Lens has spent decades learning to solve.

Its cooperation with Rokid spans frames, lenses, optical modules, and full-device assembly. Lens also invested in Rokid, giving the relationship a financial dimension as well as a manufacturing one. Rokid wants to ship one million pairs of AI glasses in 2026. Lens is not waiting for that market to mature before entering the supply chain. It is helping build the production system the market would need to scale.

The partnership shows how Lens can use an old capability to enter a new category. AI glasses may look like a consumer-electronics revolution, but one of their hardest industrial problems is still glass.

The company is applying the same logic to semiconductor packaging.

Lens announced an early-stage collaboration with Intel to develop through-glass-via, or TGV, substrates. These are not ordinary display panels. Glass substrates sit inside advanced chip packages, where they can support dense electrical connections while resisting the warping that becomes more serious as chips grow larger and hotter.

The technology remains early. Intel described the agreement as an effort to explore future opportunities, not a major commercial supply contract. It may take years before TGV contributes meaningful revenue.

But the market reaction was revealing. The possibility that an Apple-glass supplier could become part of the AI-chip supply chain was enough to change how investors viewed Lens.

Lens had not escaped glass. It had found a more valuable use for its oldest skill.

Build Beyond Glass

Selling glass to more industries could reduce Lens’s dependence on smartphones. But it could not eliminate the deeper vulnerability. A company that supplies only one type of component remains exposed, even if it serves more customers.

So Lens began adding capabilities it had struggled to build internally.

In 2020, the company agreed to pay RMB9.9 billion, or about $1.47 billion, for two mainland Chinese plants owned by Catcher Technology, a Taiwanese supplier known for metal casings. The acquisition gave Lens more experience in precision metal structures, device enclosures, and assembly.

The deal was meant to move Lens beyond its traditional role as a supplier of glass panels. A company that can manufacture glass, metal casings, and finished modules has more bargaining power than one selling only a single component.

But the acquisition did not appear to give Lens everything it wanted.

In 2026, the company went further by pursuing control of Hong Kong-listed Ju Teng International Holdings, a major manufacturer of notebook and handheld-device casings. Ju Teng has extensive experience in precision metal and plastic components and has claimed roughly 31% of the global notebook-casing market.

For Lens, the strategic value is clear. Ju Teng would add production capabilities the company has long wanted, while giving it more exposure to computers and handheld devices outside the traditional smartphone-glass business.

The timing also matters. PC makers are trying to create a new replacement cycle around the so-called AI PC. It is still unclear whether consumers will upgrade their laptops just to gain local AI features, but suppliers are already positioning themselves for that possibility.

Ju Teng’s existing notebook business alone may not be enough to transform Lens. Parts of the acquired operation may eventually need to be reorganized around higher-growth products. But the deal reflects a broader change in the company’s thinking.

Lens no longer wants to remain a specialist waiting for Apple, Tesla, or another large customer to place an order for glass. It wants to supply more of the device, control more manufacturing steps, and capture a larger share of the product’s value.

This is a conventional form of diversification, but it is also a necessary one. Glass gave Lens its position in the global supply chain. Metal structures, plastic enclosures, and complete assembly may give it more control over that position.

Invest In the Future

The third stage is more ambitious.

Lens is not expanding into unrelated businesses. It is following the direction of the hardware industry, moving from smartphones into the products investors and manufacturers believe could define the next decade.

That includes AI glasses, humanoid robots, AI servers, and advanced semiconductor packaging. In each case, Lens is trying to enter the supply chain before the market reaches mass production.

Robotics is the clearest example.

Lens began developing six-axis industrial robots for its own factories as early as 2016. At the time, robots were a way to automate manufacturing. Now Lens wants to manufacture components for the robots themselves.

Its product portfolio includes humanoid-robot structures, six-axis force sensors, precision components, and planetary roller screws. The company has also established a joint venture with AgiBot and invested in robotics companies including CASBOT.

Lens is not alone. As Unitree Robotics attracted attention with its humanoid machines, Chinese contract manufacturers rushed to find a place in the emerging supply chain. Unitree has kept much of its supplier network confidential, so manufacturers have looked for alternative customers. Lens has worked with companies including AgiBot, DEEP Robotics, and BrainCo.

The scale of its ambition became clearer at a robot race in Beijing. A humanoid developed with smartphone maker HONOR finished among the leading competitors. Lens later said it had supplied 132 metal structural components for the machine, covering key moving sections including the head, arms, hips, and legs.

That number explains why contract manufacturers are interested in humanoid robots.

A successful robot is not one product or one component. It is a collection of motors, joints, sensors, frames, casings, and precision structures that must be manufactured repeatedly with low defect rates. A robotics startup may be able to design an impressive prototype. A supplier like Lens knows how to manage thousands of production steps and turn that prototype into large numbers of nearly identical machines.

Lens is therefore betting less on any single robot brand than on the industrialization of robotics itself.

The same pattern applies to its other investments. Lens has backed companies working on AI glasses, robotics, and intelligent hardware because those companies may become its future customers. The investment gives startups capital and manufacturing support. In return, Lens gains early access to product requirements, engineering problems, and potential orders

If those markets take off, Lens may earn twice: once from the equity investment and again from manufacturing.

That is what “following industrial upgrading” means for a company like Lens. It does not need to invent the next consumer platform by itself. It needs to identify where the hardware industry is moving, invest early enough to understand the new production requirements, and make itself difficult to replace once demand arrives.

Lens’s transformation is therefore not a clean break from contract manufacturing. It is an attempt to turn contract manufacturing into something more strategic.

The company is still making parts for other people’s products. The difference is that it increasingly wants to choose which products are built, invest in the companies developing them, and control more of the manufacturing knowledge behind them.

Lens is also building an investment portfolio around this strategy.

Zhou invests through both the listed company and her own vehicles. One of her personal firms, Qunxin Investment, is reported to be nearly 98% owned by her and to have made at least 17 outside investments. Lens itself has backed AI and robotics companies including BrainCo, GALAXEA, and Pudu Robotics, along with other companies that could eventually buy its components or manufacturing services.

This is not venture capital in the Silicon Valley sense. Lens is not simply searching for the startup with the highest financial return. It is trying to position itself inside emerging supply chains before those supply chains become stable.

The investment gives a startup money. The startup gives Lens early access to product requirements, engineering problems, and future orders. If the market takes off, Lens may earn twice: once from its equity stake and again from manufacturing.

That model turns a contract supplier into something closer to an industrial market maker.

It also changes how investors should judge the company. In the past, the easiest way to value Lens was to watch Apple’s iPhone shipments, Tesla’s deliveries, and the utilization rate of Lens’s factories. Those indicators still matter. The mature manufacturing business pays the bills.

But investors are now being asked to price a portfolio of possibilities: AI glasses, humanoid robots, advanced chip packaging, and whatever device comes after the smartphone.

Repricing China’s Factory Floor

For decades, Chinese suppliers were rewarded for reliability, speed, and low cost. They were essential but mostly invisible: the company behind the logo, not the company whose name appeared on the product.

That model created enormous businesses. But it left control with the customer. Apple decided when the next phone arrived. Tesla decided how many parts it needed. The supplier carried the factories, workers, and capital spending required to respond.

Now some of those manufacturers want a different role.

They are following Western technology trends into electric vehicles, AI servers, robots, and wearable devices. But the most ambitious ones are doing more than following. They are investing in customers, shaping production methods, and trying to secure a place before the market fully exists.

This resembles the version of German industry that Chinese policymakers and entrepreneurs have long admired: specialized companies that may not own the consumer brand, but control a difficult process or component the world cannot easily replace.

Lens has not yet reached that position. Many of its new bets remain small, early, or speculative. AI glasses may take longer than expected to reach mass adoption. Humanoid robots may remain expensive demonstrations. Intel’s glass-substrate effort may not become a large business for years. Ju Teng’s traditional notebook-casing business may need to be restructured rather than simply absorbed.

And the old dependencies have not disappeared. Apple and Tesla remain important customers. A weak product cycle can still damage Lens’s earnings long before a robot component or glass substrate produces meaningful revenue.

That is also where the pressure on Lens Technology’s valuation comes from.

For years, the company was priced as a consumer-electronics supplier. Now parts of its valuation depend on robotics and TGV glass substrates, businesses that are still being valued mostly on future expectations.

In China’s stock market, excitement around a new theme can become crowded long before the underlying business produces meaningful revenue.

Lens may not know whether its TGV business is ready for mass production until the second half of 2028 or early 2029. Its robotics manufacturing business is also still hard to forecast over the next three years. Investors will therefore be watching closely to see whether Lens can turn these new stories into actual earnings.

The problem is that its core business still depends heavily on global smartphone sales. In China, the uncomfortable period when mature businesses are slowing but new businesses are not yet profitable is often described as the “transition between old and new growth drivers.”

For Lens, that transition is likely to hurt. Still, the direction matters.

Chinese manufacturing companies are no longer satisfied with being described as low-margin factories. Their production knowledge has become a form of technology, and some now have enough capital to invest in the next wave of products instead of waiting for orders to arrive.

Zhou’s career began with glass for watches. It moved to Motorola phones, then the iPhone, electric vehicles, AI glasses, robots, and perhaps advanced chips. The material stayed surprisingly constant. The company around it did not.

At the Beijing banquet, Zhou sat between Musk and Cook because Lens had spent decades serving companies like theirs.

The next stage of her project is to make sure that, at the next seating chart, she is not there merely as somebody else’s supplier.

Hope you had a good week.

Something has been changing beneath China’s AI boom.

The headlines are still about models, chips, and robots. But this week, a different story started to emerge: the race is becoming less about who can build the most impressive technology, and more about who can finance it long enough to win.

DeepSeek was not born from a traditional venture capital playbook. Its founder used profits from a quantitative trading fund to build a frontier AI lab, creating one of the most unusual funding stories in the AI race.

CXMT took the opposite path. Its rise came from a decade-long coalition of government funds, local industrial policy, employees, and strategic investors willing to support a chip company long before the market knew whether the bet would work.

None of these stories are really about technology. They are about capital: who has it, who’s willing to risk it for a decade with no guarantee of return, and who ends up holding the bill when the bet doesn’t pay off.

Anyway, let’s take a look.

This Week Features…

The DeepSeek Founder Story Silicon Valley Doesn’t See

To Silicon Valley, Liang Wenfeng is the founder who built a frontier AI lab for a fraction of what OpenAI or Anthropic spend. To a large slice of Chinese retail investors, he’s something else entirely: the face of a quant trading fund many believe has spent years profiting off predictable retail behavior.

Both stories are true, and neither cancels the other out. High-Flyer, the trading firm that funded DeepSeek for years before its first outside round, has posted eye-popping annualized returns while the ordinary investors on the other side of those trades have, on average, lost money. And the timing could not be more pointed: a senior regulator who helped build the market structure quant funds thrive in is now under investigation, just as Liang has become one of the wealthiest AI founders in the world.

This week’s piece goes inside that tension: how the same person can be, at once, a national symbol of technological self-sufficiency and a lightning rod for financial resentment, and why that contradiction doesn’t need to be resolved for the story to make sense.

[Read the full piece →]

Inside CXMT IPO: China’s $485 Billion Chip Gamble

On July 27, CXMT went public on Shanghai’s STAR Market, and by the close of its first trading day it was worth more than $485 billion, enough to overtake ICBC as the most valuable company listed in mainland China. The headline framing writes itself: chip self-sufficiency, export controls, a new challenger to Samsung and SK hynix.

But the more interesting question is who actually built this. The answer isn’t one founder or one VC. It’s a coalition: the Hefei city government, Anhui’s provincial fund, China’s national Big Fund, an employee stock plan with a decade-long vesting schedule, and strategic stakes from Alibaba, Tencent, Xiaomi, and even BYD’s own founder. Nobody controls CXMT. That’s not an accident. It’s the model.

This week’s piece traces how Hefei turned a repeatable playbook: countercyclical government investment, an anchor company, an industrial cluster, into China’s most valuable IPO, and asks a harder question: what happens when cities try to run the same playbook on industries, like robotics or spaceflight, where the market outcome isn’t nearly as certain.

[Read the full piece →]

The News…

(i) DeepSeek Quietly Ships an Agent Upgrade, And Prices It to Undercut Everyone

DeepSeek released the API version of DeepSeek-V4-Flash this week, and the headline change is agentic: better tool use, software environment control, and multi-step task completion. On several agent and coding benchmarks, V4-Flash is closing in on frontier-model performance.

The pricing is the part worth sitting with: ¥1 per million input tokens, ¥2 per million output tokens, and as low as ¥0.02 per million input tokens (about $0.003) for cached, repeated agent workflows. V4-Flash also now supports an OpenAI-compatible Responses API, meaning developers can call it directly through Codex CLI, ChatGPT desktop, and VS Code plugins, a level of ecosystem integration that goes out of its way to make switching costs disappear.

The rollout is narrow for now: only V4-Flash gets Responses API support, V4-Pro users wait until early August, and the app and web interface are unchanged. But the strategy is clear. DeepSeek isn’t trying to win the “best model” argument this week. It’s trying to make itself the cheapest place to run an agent.

(ii) ASML Lost $30 Billion on a Rumor. That’s the Real Story.

ASML shed roughly $30 billion in market value on Monday after The Information reported that a state-backed Shanghai firm had begun small-batch production of immersion DUV lithography machines, with plans to supply China’s biggest chipmakers including SMIC, CXMT, and Hua Hong.

The actual numbers barely justify the reaction: about five machines this year, twenty by 2027, against the 131 ASML shipped last year alone. Analysts have called the selloff disproportionate, and it’s hard to argue otherwise. An unverified report about an anonymous company’s tiny production run is not, on its own, a near-term threat to ASML’s business.

What it does confirm is how jumpy the market has become. ASML is increasingly trading as a barometer for anxiety over Chinese chip self-sufficiency rather than as a company with its own fundamentals, and right now, rumors are moving it almost as much as facts do.

(iii) Running an Open Model Isn’t Cheap. Kimi’s Community Just Did the Math.

Everyone’s celebrating open-sourcing as the great equalizer. Fewer people are pricing out what it actually takes to run the thing yourself. According to estimates shared in Kimi’s own community, deploying Kimi locally in China could require 64 Nvidia H200 GPUs, 8TB of enterprise SSD storage, liquid cooling, power infrastructure, and dedicated engineering staff, north of $4.2 million in total cost, and that only covers a limited number of users.

The timing is notable: Moonshot is reportedly seeking a $50 billion pre-IPO valuation, even as the market starts asking harder questions about whether Anthropic-style subscription economics can actually hold up.

Open weights lower the barrier to access. They don’t lower the barrier to scale. Those are two very different problems, and the industry is only starting to talk about the second one.

(iv) China’s Next ¥4 Trillion AI Bet: Building the Pipes Behind the Models

China is preparing a new wave of investment aimed squarely at AI infrastructure, data centers and computing networks, worth an estimated ¥4 trillion ($560 billion), according to NDRC spokesperson Jiang Yi, as part of the country’s upcoming five-year plan.

The structure is the interesting part. Unlike past state-led infrastructure pushes, most of this capital is expected to come from companies, not government coffers directly, with Beijing positioning itself to coordinate resources and policy rather than write the checks itself.

It’s the clearest signal yet of where China thinks the next bottleneck actually sits: not in model quality, but in the physical capacity to serve those models at scale, echoing exactly the strain that hit Moonshot’s Kimi K3 launch just weeks ago.

(v) Washington’s AI Concerns Are Moving From Models to Machines

The FCC has added humanoid robots, quadrupeds, and connected power inverters to its covered list, citing national security concerns around data collection, remote control, and grid infrastructure.

It’s a quiet expansion of scope, but a telling one. Model capability was the first front of US scrutiny. Now the physical hardware carrying AI into homes, warehouses, and the power grid is getting the same treatment.

The message: the US government’s concern about China’s AI rise was never really about chatbots. It’s about what AI plugs into next.

(vi) Unitree Is Betting Its IPO Money on Volume

Unitree Robotics plans to at least double shipments in 2026 from the 6,500-plus robots it delivered in 2025, according to Asia-Pacific director Irving Chen, with annual capacity potentially climbing to 30,000 units. A new factory, funded through Unitree’s Shanghai STAR Market IPO, is central to that expansion, and overseas assembly is now on the table.

The company’s 2025 revenue hit ¥1.71 billion ($240 million), more than four times 2024’s figure, with overseas markets, North America chief among them, contributing roughly half of sales.

Unitree isn’t just building robots faster. It’s building the case that humanoid and quadruped robotics can scale the way China scaled solar panels and EVs: high volume, falling costs, exports doing much of the heavy lifting.

(vii) BYD’s Founder Quietly Bet on CXMT Six Years Early

As more details about CXMT’s shareholder base surface after its blockbuster IPO, one name stands out: BYD founder Wang Chuanfu owns a 0.014% stake in CXMT, now worth roughly $64 million at the company’s $458 billion closing valuation.

The stake traces back six years, when BYD invested during CXMT’s first funding round at a $2.9 billion valuation, a moment when the chipmaker was still losing money and its path to scale was far from guaranteed.

It’s a small holding in percentage terms, but a useful data point for the bigger CXMT story we’ve been telling this week: this wasn’t a company built by one investor’s conviction. It was built by dozens of them, placing early, patient, and often quiet bets years before anyone else believed the payoff was coming. We’ll have a deeper look at CXMT’s full shareholder structure soon.

(viii) MiniMax Joins the Multimodal Race With H3

MiniMax released H3 this week, a multimodal generative model built to handle text, image, video, and audio in a single unified context, including native stereo audio and up to 15-second, 2K-resolution video generation using the base model itself rather than upscaling.

The pitch is squarely commercial: advertising, e-commerce, branding, product design, UI/UX, and gaming, with support for controllable multimodal editing like video-to-video motion transfer. The model can ingest up to 100K input tokens while compressing outputs to roughly 4K.

Model weights are coming soon, another entrant in the growing list of Chinese labs betting that generative video and audio, not just text, will be where the next wave of enterprise demand shows up.