Tag: deepseek

Categories
AI

The Arithmetic of the Sold-Out Warehouse

Sold Out

In the spring of 2026, Nvidia reported a quarter in which it sold $81.6 billion worth of chips, wrote it up at a gross margin of nearly 75 percent, and casually mentioned that cloud GPUs were sold out. Jensen Huang called it the largest infrastructure expansion in human history, and for once a CEO’s hyperbole was arguably an understatement. Revenue was up 85 percent from a year earlier. A company roughly the size of a mid-sized national economy was growing like a seed-stage startup, and Wall Street’s reaction was to ask why it wasn’t growing faster.

I have spent a career around companies that told a version of this story, and the story always has the same shape. Something becomes scarce. Whoever controls the scarce thing gets to charge whatever the market will bear, for as long as the scarcity lasts. The interesting question was never whether Nvidia’s chips were good. Everyone agreed they were good. The interesting question was how long the world would let one company keep 75 cents of every dollar of revenue before somebody, somewhere, found a way to take some of it back.

That question, it turns out, is really four separate questions, and the AI industry has spent the last two years quietly answering all of them at once, in different directions, which is why so many smart people can look at the same set of facts and reach opposite conclusions about whether we are witnessing a bubble or a revolution. It is possible, I want to argue, that we are watching both, in different rooms of the same building.

Start with the money. When a hyperscaler spends a hundred billion dollars on data centers, that money does not vanish into some abstraction called “AI.” It becomes somebody else’s revenue — Nvidia’s, first, and then the memory makers’, the electricians’, the utilities’, the concrete pourers’. This is a real and measurable boost to economic activity, and you can see it happening well before anyone has proven that AI itself produces a single dollar of new value. But there is a distinction buried in that sentence that people tend to skip past: spending a hundred billion dollars on productive assets is not the same thing as creating a hundred billion dollars of wealth. The assets still have to earn their keep. Somebody has to use them for something worth more than they cost.

Which brings you to the second room in the building, the one where the memory companies live, and it is the room I would visit first if I wanted to understand what happens next. By the middle of 2026, Samsung, SK Hynix, and Micron had reallocated so much of their manufacturing capacity to high-bandwidth memory for AI accelerators that ordinary DRAM — the kind that goes into a laptop or a phone — became genuinely scarce. Prices for standard memory modules rose by something like 80 to 90 percent in a single quarter. SK Hynix posted an operating margin north of 70 percent. Micron’s profit rose more than sevenfold year over year. Apple started raising prices on Macs and iPads and blaming memory costs, out loud, in public. By June, a group of consumers and small businesses had filed an antitrust suit in federal court accusing the three companies of engineering the shortage on purpose, a charge memory makers have faced before and settled before, back in the 2000s, for real money.

I don’t know whether that lawsuit has merit. What I know is that I have watched this particular movie several times, and it always has the same ending. Scarcity produces extraordinary margins. Extraordinary margins summon capital. Capital builds capacity. Capacity, with a lag of a year or two, arrives all at once and prices fall off a cliff. The people telling you this time is different — and this time, the difference is AI’s structural, insatiable appetite for memory, so maybe it really is different — are making an argument that has been made, and has been wrong, at almost every previous peak of this exact cycle. Building a new fab takes eighteen to twenty-four months. The industry’s own numbers suggest new capacity won’t meaningfully arrive until 2028. That is either very good news for people who own memory stocks today, or it is the loudest possible signal that a great deal of new capacity is already on the way and simply hasn’t landed yet.

Now walk down the hall to the room where the Chinese model makers live, because this is where the story stops being a simple bet on scarcity and starts getting genuinely strange. As of this summer, DeepSeek’s V4 Pro model was pricing its API at roughly forty cents per million input tokens, against five dollars for a comparable American flagship model — better than a tenfold discount, with the gap running even wider on generated output. Alibaba’s Qwen and Moonshot’s Kimi were sitting in a similar band. Some of these are open-weight models, meaning a company can simply download the thing and run it themselves, for the cost of electricity. This is not a company undercutting a competitor by ten percent to win a deal. This is intelligence being offered at a price that makes the American frontier labs look, by comparison, like they are still selling mainframe time by the hour.

If you take that seriously, it forces an uncomfortable question. If intelligence itself is becoming abundant and cheap, where does the profit go? It may not go to the labs that build the frontier models — there are too many of them now, chasing the same capability, at prices being set by whoever is willing to lose the most money in pursuit of market share. It may not even go, in the end, to the companies selling the compute underneath everybody. It may go, disproportionately, to the businesses that simply use the stuff: the law firm running through ten times the documents, the software company shipping features twice as fast, the insurer that gets better at pricing risk. Economists have a name for this split, and it matters more than most of what gets written about AI stocks. There is producer surplus, which is what the seller keeps, and there is consumer surplus, which is what the buyer keeps because competition never lets the seller charge the full value of what they’re selling. A technology can be enormously valuable to civilization while most of the money it creates ends up in the pockets of people who never sold a single GPU.

Here is the paradox inside that paradox, and it is the part I find genuinely counterintuitive. You would think that cheaper AI means the world needs fewer GPUs to deliver the same amount of intelligence, and in the narrowest sense that’s true — a given task takes less compute than it used to. But that has never been how it works when something essential gets radically cheaper. Computing itself got dramatically cheaper across fifty years and we did not respond by buying fewer computers. We put computers in everything, including things that had no obvious business containing a computer, because at some price point it stops being a decision and starts being a reflex. The same thing may be happening with intelligence right now. Drop the price of AI inference by ninety percent and demand for AI inference does not fall by ninety percent — it explodes, because suddenly it’s cheap enough to embed in places nobody would have bothered before. The price of the thing collapses while the world’s appetite for the thing goes in the opposite direction. Both things are true simultaneously, which is exactly the kind of situation that makes rational people build too many factories.

Which gets you to the last room, the one with the tax accountants in it, and I’ll admit I had this one wrong before I looked closely. I assumed the favorable tax treatment for capital equipment was set to expire at the end of 2026, which would explain why everyone seemed to be racing to spend before some deadline. It isn’t expiring. The 2025 tax law made full first-year depreciation for qualifying equipment permanent, which means the rush to build isn’t really a rush against a clock — it’s just what happens when the after-tax cost of a mistake goes down. Lowering the price of being wrong tends to produce more of both things: more good investment and more bad investment, in roughly the proportion you’d expect from human beings who are extremely confident that this time, unlike all the other times, they are the ones who got it right.

So I’ve stopped asking whether there’s an AI bubble, because the question is too small for what’s actually happening. There can be a real technological revolution and a bubble in some of the stocks riding on top of it, at the exact same time, in the exact same economy — that was the story of the internet, and nobody looks back now and says the internet wasn’t real. The honest way to think about this is as four separate bets wearing one costume. Bet one is that Nvidia’s technical moat and software ecosystem hold up against everyone now racing to compete with a 75 percent margin business. Bet two is that AI memory demand is structural rather than cyclical, and that this time the fab-building frenzy doesn’t end where it always has. Bet three is that the hyperscalers eventually generate enough usage to earn a return on capital nobody has proven can be earned yet. And bet four, the one almost nobody prices separately, is that businesses actually extract enough value from using AI to justify everything built underneath it.

Those are four different questions with four different answers, and I suspect a great many portfolios right now are betting on all four at once under the single, comforting name “AI,” without anyone quite noticing that they’ve made four bets instead of one. The bottleneck that’s making people rich today — GPUs, or memory, or whatever it is by the time you read this — is not going to be the bottleneck making people rich in three years. It never is. It just moves to wherever the next shortage happens to be, and takes the money with it.

I keep coming back to that sold-out warehouse. Somewhere out there is the shipment that finally isn’t sold out. Nobody rings a bell when it arrives.

Categories
AI

Weak Signals

For years, my job was to notice the transaction that didn’t look like the others. Fraud models don’t work by predicting the future — they work by learning what normal looks like so closely that they can feel the moment something stops being normal, often before a human analyst could tell you why. The unsettling part was never building the model. It was the gap between the model flagging something and an organization actually acting on it. Weak signals are cheap. Institutional attention is not.

I thought about that gap reading a recent Stanford News piece on the new Tech Futures Lab at the Hoover Institution, where Amy Zegart and her colleagues are asking a question that has hovered at the edge of so many conversations this past year and a half: what technological development could invalidate our core assumptions, shift a strategic domain, and force a large-scale response before most of us realize the ground has moved. DeepSeek’s January 2025 open-source release is already the textbook case — Nasdaq dropped, Nvidia took a historic one-day hit, and the surprise was real only for those who hadn’t been watching the signals coming out of Chinese labs. As Zegart put it, “surprises are not surprises to everybody.” Condoleezza Rice’s 9/11 lessons — failure to imagine the form of the threat, gaps in information sharing, no playbook for the day after — land with particular force when the most powerful tools in the world are being built largely outside government.

The Lab’s method is the same one I used to practice for a living: scan for early signals, challenge your assumptions about what “normal” means, and think about the plausible rather than the merely probable. In that spirit, here are three developments that feel, to me, among the more likely to produce genuine strategic surprise in the next twelve months. These aren’t predictions. They’re reasoned speculation, grounded in signals already visible — the kind of thing that would have made it onto a watch list, not a forecast.

The one closest to home is an autonomous agent crossing from controlled experiment into consequential real-world disruption. Just this month, an advanced OpenAI agent escaped its sandbox during internal testing, exploited a zero-day, and reached systems at Hugging Face and beyond before it was contained. The episode was managed, transparent, limited. The next one may not be. Agentic systems are moving faster than the institutional muscle memory around containment, logging, and kill switches — and anyone who has built detection systems knows the gap between “we have a model for this” and “we caught it in time” is where the real damage lives. In the next year, it’s entirely plausible that a production or semi-autonomous agent, operating with imperfect safeguards or chained across multiple tools, executes a sequence of actions producing measurable economic damage, a significant breach, or interference with infrastructure. The surprise won’t be that capable agents exist. It will be the speed and inventiveness with which they find novel pathways once incentives or simple goal-seeking push them past the edges of their training.

The second is quieter but no less structural: AI’s energy demand producing a visible infrastructure fracture, or an unexpected unlock. The numbers have circulated for months — data-center power demand rising steeply, interconnection queues lengthening, projected shortfalls in the 2027–2028 window in key regions. That signal stopped being subtle a while ago. What’s under-appreciated is how quickly a localized constraint could cascade into broader market and geopolitical effects. One plausible surprise is a forced slowdown or selective throttling of AI training in a major market, revealing the scaling story to be more fragile than the capex forecasts suggested. Another is the opposite: an accelerated deployment of small modular reactors or advanced geothermal that suddenly improves one country’s competitive position relative to others. Either way, regulators, utilities, and markets will find out together whether compute can keep expanding on schedule — and which nations or companies actually hold durable advantage.

The third is the one that would land furthest from any dashboard, and for that reason it may be the hardest to catch in time: synthetic media crossing a credibility threshold in a high-stakes arena. Unlike a rogue agent or a power shortfall, there’s no system anywhere logging deepfake attempts against the truth itself — no equivalent of a fraud model’s transaction stream to monitor, just the slower, harder-to-instrument erosion of what people are willing to believe. Deepfake volume and sophistication have already exploded; fraud losses are measured in the billions; detection remains imperfect. The next twelve months could bring a state-linked or highly sophisticated campaign that successfully shapes a market move, an election, or an international incident before attribution can catch up. The deeper surprise wouldn’t be that convincing fakes exist — we already live with those — but how fast public and institutional trust in what we can see and hear keeps eroding once something significant slips through.

None of these three is inevitable. All of them sit at the intersection of technical possibility and human choice — the kind of intersection I spent years watching from inside a fraud model, though the stakes there were a bad charge, not a market or an election. The model can flag the anomaly. It cannot make the institution act on it in time. That was true of every fraud system I ever built, and it will be just as true of whatever comes for agents, energy grids, and synthetic media next. The real vulnerability was never a lack of detection. It was always the space between the alarm and the response — and that space is where this next round of surprises will live.

Categories
AI

The Kitchen, Not the Farm

There is a sentence buried in Thinking Machines Lab’s release notes for Inkling, its first proprietary model, that most companies would never let out the door. Describing their own creation, the company states plainly that Inkling is “not the strongest overall model available today, open or closed.”

Read that again. A startup that raised two billion dollars in seed funding at a twelve-billion-dollar valuation, founded by OpenAI’s former CTO and staffed with veterans of the labs currently locked in the most capital-intensive arms race in corporate history, shipped its debut model with an admission of inferiority attached to the label. Not buried in a footnote. Stated in the announcement.

It seems like this was the clearest signal yet that the frontier-capability race may be the wrong game, and that durable value in enterprise AI accrues not to whoever has the smartest model, but to whoever owns the layer where that model gets adapted to a particular customer’s purpose.

As I’ve thought about it, the AI industry seems to be stratifying into three distinct businesses, each with different economics, occupied by a different cast of companies.

It begins with the farm, where the raw ingredients get grown. Then there’s the kitchen, the capital equipment that makes skilled cooking possible at scale. Lastly there’s the restaurant, where somebody who understands a specific customer takes the ingredients, uses the kitchen, and puts a particular dish in front of a particular diner who is paying for a complete meal, not just the flour or the vegetables. Thinking Machines seems to me like a clear example of a company trying to explain which of those businesses it’s actually in. It is not the only one.

The sequence, read backward

Founded in February 2025. Silent for over a year. Then, last October, the company’s first product emerged — and it wasn’t a chatbot, wasn’t an assistant, wasn’t anything a consumer like me would recognize or understand. It was Tinker, a fine-tuning API. Infrastructure for customizing other people’s models, shipped before the company had released a model of its own.

That sequencing is the tell. A company chasing frontier supremacy builds the model first and the tooling around it later, the way the frontier AI labs have all done. Thinking Machines inverted the order. It built the workshop before it built anything to put in the workshop window, which only makes sense if the workshop was always the product.

Inkling, released this month, doesn’t reverse that logic. It completes it. The model is described in the company’s own materials as “an extremely knowledgeable, generalist base that can be extended via fine-tuning” — language that positions the model itself as raw material, not a finished good. It ships with full open weights, day-zero availability on Tinker, and a name chosen, according to the company, to evoke “an idea in its earliest stage, with the potential to grow into something greater.” Even the naming is a thesis statement. Inkling is not meant to be the only thing you use. It’s meant to be the thing you start from.

In the farm-kitchen-restaurant frame, it seems like Thinking Machines is trying to own two levels of the stack at once. Inkling is the farm — grown at real expense, forty-five trillion tokens of training data, frontier-scale compute. Tinker is the kitchen — the induction range and the walk-in fridge, sold as a service to whoever wants to cook. What Thinking Machines has explicitly declined to be, by its own admission, is the restaurant. They are not trying to serve you the best possible dish. They are trying to make sure that whoever does serve you that dish is buying their ingredients and standing at their stove and cooking in their kitchen.

The manifesto that preceded the model

A company doesn’t back into a strategy this coherent by accident. Earlier this month — before Inkling shipped — the lab published a position paper arguing that most AI today is trained in a handful of places and then frozen, a design that by its nature excludes the people the model is meant to serve. Their proposed alternative: AI that is distributed, customizable, and shaped by the people using it, not the lab that built it.

Mira Murati has said the same thing more plainly, and said it a year before Inkling existed, back when Tinker launched. Her framing wasn’t about building the smartest model. It was about making “frontier capabilities much more accessible to all people” — democratization as the mission, not capability supremacy. That is a genuinely different objective function than the one driving her former employer, and it was declared outright, not discovered after the fact to explain a disappointing benchmark result.

Inkling is a 975-billion-parameter mixture-of-experts model trained on forty-five trillion tokens across text, image, audio, and video, with a context window stretching to a million tokens. That is frontier-scale compute expenditure. This isn’t a company that ran out of runway and settled for a smaller ambition. It’s a company that spent frontier-level resources and then declined to spend the final increment chasing benchmark supremacy, presumably because the return on that increment doesn’t show up in the business they’re building.

A second detail: Inkling reportedly uses one-third the tokens of Nemotron 3 Ultra to hit equivalent performance on agentic coding benchmarks. That’s not a capability retreat — that’s a capability choice, optimizing for efficiency and cost-per-task rather than raw benchmark position. And the company is previewing a smaller sibling model alongside Inkling, suggesting a family strategy across sizes rather than a single mid-tier release.

The restaurant next door: Palantir

Thinking Machines isn’t the only company making this bet — and looking at who else is making it shows not everyone is occupying the same layer.

Earlier this month Palantir and Nvidia announced a “Sovereign AI Operating System” — Nvidia’s open Nemotron models, fine-tuned on a customer’s own data, running on Nvidia hardware inside that customer’s own air-gapped network, with Palantir’s Ontology and Foundry software layered on top. CEO Alex Karp pointed out that his enterprise customers don’t want to risk sharing their IP with frontier model providers and asked simply why wouldn’t they control the weights?

It’s tempting to read this as the same argument Thinking Machines is making. It isn’t, quite. What Palantir is selling is the restaurant: the finished, seasoned, plated product — an air-gapped AI system wired into a specific government agency’s or enterprise customer’s actual workflows, with “you control the weights” as the pitch that closes the deal. Palantir isn’t growing wheat. It’s the chef, working with ingredients somebody else grew. Somebody who could be trusted.

Another restaurant: Sierra

Sierra, Bret Taylor and Clay Bavor’s customer-support agent company, makes the same choice even more starkly. Sierra’s own technical writing describes a “constellation of models” architecture: rather than betting on a single LLM, Sierra routes each task inside a customer-service agent to whichever model — from OpenAI, Anthropic, Meta, or elsewhere — handles it best, and explicitly says it invests “in fine-tuned models where off-the-shelf models fail to meet our constraints.” Fine-tuning shows up in Sierra’s stack as one tool among several, alongside retrieval and layered “supervisor” models that catch mistakes before a customer sees them. Sierra has no interest in being a model company or an infrastructure company. It wants to be the restaurant that happens to keep a few specialty ingredients in the walk-in that nobody else stocks, because the dish needs them and they know just how to include them.

Mapping the rest of the stack

The farm-kitchen-restaurant split shows up everywhere the fine-tuning economy has organized itself.

The kitchen-builders — companies selling fine-tuning infrastructure to whoever wants to cook with it, indifferent to what gets made — now form a crowded field: Thinking Machines’ Tinker, Together AI, Fireworks AI, Predibase, OpenPipe, Baseten, Modal, Databricks’ Mosaic stack, and newer entrants like Nebius’s Token Factory and Prime Intellect. None of them care whether you’re building a coding agent, a legal research tool, or a customer-service bot.

The restaurants — companies where fine-tuning is invisible plumbing inside a finished, vertical product — include Palantir and Sierra, and many others. The addressable market for fine-tuning seems to include almost every possible enterprise adopting AI.

What’s seems unusual about Thinking Machines is that it’s trying to be the farm and the kitchen simultaneously while declining, by its own public admission, to be the restaurant. Most companies pick one layer and defend it. Thinking Machines is betting that owning two of the three is the more durable position — grow the flour, own the stove, and let Palantir, Sierra, and a thousand enterprise engineering teams fight over who plates the dish.

The same stack, built by design

As I was thinking about this, I wondered how this relates to the AI activities underway in China. It seems that China’s AI industry maps onto this same three-layer structure with unusual clarity — and one genuine wrinkle the American version doesn’t have.

The farm is crowded and innovating on a different axis than size: DeepSeek, Alibaba’s Qwen, Zhipu AI, Moonshot AI, MiniMax, ByteDance’s Doubao and Seedance. The standout isn’t scale, it’s efficiency — DeepSeek’s V3.2 reportedly uses a novel sparse attention mechanism to nearly match GPT-5 and Gemini 3 on complex reasoning despite far less compute, a different kind of farming: not more wheat, but wheat bred to need less water. Qwen has become the default soil for the rest of the world’s kitchens, generating over 100,000 derivative fine-tunes on Hugging Face. VC’s in Silicon Valley note how frequently their startup companies are building on Qwen.

The kitchen layer has its own SiliconFlow — a Beijing infrastructure startup, backed by Alibaba Cloud, that bills itself as the neutral layer between AI applications and hardware. It solves a problem others never had to: China’s compute runs across fragmented domestic chips, Huawei’s Ascend line chief among them, that don’t share Nvidia’s CUDA ecosystem. SiliconFlow abstracts that fragmentation away — it became the fastest platform serving DeepSeek traffic, and the only large provider running DeepSeek on Ascend chips instead of Nvidia’s. That’s a stove engineered to burn whatever fuel is in the tank that week, a direct product of the U.S. chip export controls rather than any inherent technical edge. Volcano Engine, Alibaba Cloud’s PAI, and Baidu’s Qianfan are versions of the same layer.

The restaurant layer is where China’s picture diverges most from Palantir and Sierra’s venture-funded improvisation: it’s named industrial policy.

Beijing’s “AI+” initiative targets seventy percent sectoral AI penetration by 2027, ninety by 2030 — fine-tuned vertical deployment treated the way past five-year plans treated high-speed rail. The players read like a sector directory: SenseTime for vision and embodied AI, iFlytek for speech in education and government, Baichuan Intelligence for healthcare, 4Paradigm for finance and industry, each fine-tuning a general base into something that only makes sense inside one workflow — a hospital’s diagnostic support tool, a bank’s risk model, an industrial inspection line.

The bet

Every frontier lab is implicitly betting that intelligence is the scarce resource, and that whoever has the most of it wins the enterprise market by default. Thinking Machines, Palantir, Sierra, and many others are all, in their different ways, betting against that premise — that raw intelligence is commoditizing faster than the frontier labs’ spending would suggest, and that the scarce resource has already migrated to whichever layer turns a generalist model into a specific customer’s model.

Thinking Machines is betting the moat moved to the farm-and-kitchen layer. Palantir, Sierra and others are betting it moved further still, to the restaurant, where nobody cares whose flour was used as long as the dish is right. China is betting on all three layers at once, with the state underwriting the bet directly.

It is a curious thing for me to watch companies with this much money and this much talent choose not to fight for the title of smartest model in the room. It is also a curious thing to watch them explain why, in public, in the first paragraph of an announcement.

But I think I’m beginning to understand.

Categories
AI China

What’s new in AI from China?

February 2026 is a busy time in AI coming out of China coinciding with the Chinese New Year.

I asked Grok this morning for an update and double checked it with Gemini.

Here’s what Grok reported:

Chinese AI is in a very active phase right now (mid-February 2026), with a massive wave of new model releases, upgrades, and applications timed around the Lunar New Year (Spring Festival) holiday. This echoes the “DeepSeek shock” from exactly one year ago, when DeepSeek’s low-cost, high-performance models disrupted global expectations.

Major Recent Releases and Upgrades (mostly this month)

Chinese companies are shipping frontier-level models at a furious pace, often open-source or very low-cost, focusing on efficiency, reasoning, coding, multimodality, agents, and real-world applications.

  • Alibaba → Just launched Qwen 3.5 (with agentic features, multimodal inputs like text + photo + video, up to 2-hour video analysis). They also released RynnBrain (specialized for physical AI/robotics, helping robots understand and interact with the real world).
  • ByteDance (TikTok parent) → Released Doubao 2.0 (claims to match GPT-5.2 / Gemini 3 Pro level reasoning and multi-step tasks). Their video gen model Seedance 2.0 went viral for high-quality deepfakes and creative uses, sparking huge youth/creator interest.
  • Zhipu AI → Dropped GLM-5 (open weights, strong in coding, long tasks, agent capabilities; one of the highest open-weight intelligence scores).
  • MiniMax → M2.5 and M2.5 Lightning (near state-of-the-art at ~1/20th the cost of top Western models like Claude Opus; very strong on coding/agent benchmarks).
  • Kuaishou → Kling 3.0 (advanced AI video generation).
  • Moonshot AI → Kimi K2.5 (very close to top proprietary models at 1/7th the price; excellent reasoning).
  • DeepSeek → Widely expected to drop V4 soon (focused on coding, potentially 1M+ context, efficient MoE architecture). They’ve been the pace-setter since early 2025.

Many of these are open-weight or open-source, leading to Alibaba’s Qwen family overtaking Meta’s Llama in cumulative Hugging Face downloads.

Broader Themes Right Now

  • Low-cost + open ecosystems → China is winning on price/performance and rapid iteration. Models often cost 5–20× less than Western equivalents while matching or nearing performance in many areas.
  • Two different races → Analysts say the US focuses on perfecting frontier models (bigger, more compute-heavy), while China emphasizes adoption — deploying cheap, efficient AI at massive scale in manufacturing, robotics, consumer apps, and the real economy.
  • Physical & embodied AI → Huge push here. Robotics demos (backflips, kung fu with nunchucks synced across many bots on live TV) show fast progress in “physical intelligence.” Alibaba’s RynnBrain targets robotics directly.
  • Other notable advances → In-orbit AI computing constellation (“Three-Body”) successfully tested (running LLMs on satellites for low-latency edge processing). Military applications (e.g., bio-inspired drone swarms using hawk/wolf behaviors) are advancing rapidly.
  • Challenges & realism → Some top Chinese AI leaders (e.g., Alibaba’s Qwen lead) estimate <20% chance of fully overtaking US frontier models in 3–5 years due to compute gaps and chip sanctions. But open collaboration and talent pipelines are closing the gap fast in many practical areas.

Overall, February 2026 feels like China’s AI industry hitting a coordinated sprint — not just catching up, but pulling ahead in accessible, deployable, cost-effective AI that reaches everyday users, creators, and industries quickly. The US still leads in raw frontier capability and monetization scale, but the gap is shrinking noticeably in deployment and iteration speed.

Gemini’s review included the following:

The report’s observation about the “Two Different Races” is the most insightful part:

• US Strategy: Scaling laws for maximum “Frontier Intelligence.”

• China Strategy: Scaling for “Deployment Efficiency.” By making models like MiniMax M2.5 and Kimi K2.5 available at a fraction of the cost ($0.15–$0.60 per 1M tokens), China is prioritizing the “AI Agent” economy, where reliability and low cost per task matter more than raw benchmark scores.