Just like Deepseek, China's Kimi K3 is forcing Western AI labs to question their compute advantage

Somaia argues that the entire Western consensus, from export controls to the hyperscalers' hundreds-of-billions investment race to the "Compute Moat" investment thesis, rests on a single assumption: that computing power determines capability.

But scarcity has forced innovation. Moonshot AI's in-house Mooncake stack for AI training was built precisely because the startup didn't have enough GPUs, Somaia says. "A small lab with taste can compress the compute needed to make a frontier model, even if it can't afford to serve one."

Dylan Patel, founder of hardware analysis firm SemiAnalysis, agrees. "What they did with an extremely talented small team, strong research in RL, arch, data helps make up for lot of the compute deficit," he writes. But Patel also points out that Chinese companies can easily rent GPUs outside of China, which makes a portion of the export restrictions pointless.

A Google Deepmind researcher calls Kimi K3 "insanely good"

Western AI labs often accuse Chinese companies of a form of data theft through distillation, where a smaller AI model learns from the output of a larger one and essentially free-rides, threatening Western AI labs' business models. Until now, distillation has been the go-to explanation for how Chinese labs stay competitive despite having less compute.

For Kimi K3, that explanation apparently doesn't hold up. "These results seem impossible to explain through distillation alone," writes Michiel Bakker, an AI researcher at MIT and Google Deepmind, calling the model "insanely good." Google's own flagship model, Gemini 3.5 Pro, meanwhile, has been delayed for months according to Bloomberg because it isn't hitting performance targets, especially in coding, its main use case. The company's AI strategy is drawing criticism again, and Google is also facing regulatory headwinds in AI search, particularly from Germany.

Dean W. Ball, Head of Strategic Futures at OpenAI and a former government advisor, calls Kimi a "very good model" that in agent-based coding sessions matches "the best public models from Q1 2026." But he also notes that it seemed "very token hungry," making it "not obvious to me that this model is actually that cheap to run."

He's not wrong. According to Artificial Analysis, Kimi K3 costs an average of $0.94 per task. That's close to GPT 5.6 Sol at $1.04 but roughly half the cost of Opus 4.8 at $1.80. It's still cheaper than the top Western models, but the gap has narrowed compared to the previous version, and it's much pricier than earlier open-weight Chinese models.

OpenAI strategist warns against "full AI communism"

Still, Ball says he's surprised the Chinese government allows such powerful models to be released as open-source. He attributes 75 percent of it to strategic blindness, saying the CCP is "very Yann LeCun-y" in how it assesses AI risks and doesn't see any existential threats.

The rest comes down to a lack of computing capacity for client-side inference, which makes the open-weight strategy an unintended byproduct of U.S. export controls. The companies also know that hardly anyone would pay for Chinese models below the frontier, Ball claims.

Open-weight models are "inherently decelerationist," Ball argues, because they slow down further AI investment. One possible outcome of a world dominated by them would be "full AI communism," with AI as a public good provided by the state as digital infrastructure. That's what China is proposing, according to Ball, who calls this scenario a "dystopian hellscape."

That an OpenAI strategist is criticizing open-weight models this sharply is, of course, not without self-interest. His company relies on a closed business model and faces growing price pressure from providers like Moonshot AI and Deepseek.

Regulatory fog instead of outright bans

Ball predicts the Trump administration will create regulatory risk around using Chinese open-weight models. There's no need to ban open source, he argues, calling it "one of the dumber motifs of AI policy discussion." Authorities would only need to create enough uncertainty through "soft law," like having the Federal Reserve issue warnings about potential backdoors in Chinese AI models. The rationale wouldn't even need to be well-founded.

The goal is a middle ground with enough risk to deter regulated companies from using Chinese models, without spooking the hyperscalers so badly that startups migrate to less reputable providers. Ball expects the government to roll out some version of this strategy.

More efficient AI can still mean more demand for compute

Kimi's progress doesn't necessarily mean less computing power is needed, but if it did, U.S. tech companies' massive infrastructure buildouts would look unnecessary, likely triggering a stock market crash. The opposite is more likely, though. The Jevons paradox suggests that more efficient models lead to more AI being deployed, which could actually drive even more demand for computing power.

According to SemiAnalysis, Kimi K3 has 2.8 trillion parameters and is so large it doesn't fit on a single Nvidia DGX B200, even with FP4 quantization. It needs more powerful systems like the GB300 NVL72 or B300, each with 288 GB of memory per GPU.

Again, the parallels to Deepseek are hard to miss. Back then, skeptics predicted a compute surplus and briefly rattled the markets. Instead, demand for computing power climbed as reasoning models gained traction, ironically driven in part by Deepseek's own models. Or as Google Deepmind CEO Demis Hassabis puts it, "Nobody in the world knows what happens next."