The valuation gulf is the whole story
Line the eight up by price and a chasm opens in the middle. The two American leaders are valued at $852B (OpenAI) and $965B (Anthropic) — roughly 40× and 20× their own unaudited run-rates, both still deeply loss-making. The four Chinese labs sit one to two orders of magnitude lower: DeepSeek’s reported first external round implied ~$10B, Kimi ~$20B, MiniMax ~$4B. Mistral, Europe’s champion, is ~€11.7B. The gap is not mostly about model quality — DeepSeek’s R1 matched OpenAI’s o1 on reasoning, and GLM-5, M2 and K2 top the open-weight rankings. It is about two different theories of what you are buying.
Capital-maximalist vs efficiency-maximalist
The Western bet is capital-maximalist: raise unprecedented sums, buy unprecedented compute, and monetize a lead before it erodes. OpenAI has signed roughly $1.4T of multi-year compute commitments — about 9× its revenue — and its backers (Microsoft, Nvidia, Oracle) are also its suppliers. The Chinese bet is the mirror image: efficiency-maximalist. DeepSeek is self-funded by a quant fund, takes no outside VC, and matched the frontier under a hard export-control compute ceiling by out-engineering it (MLA, MoE, FP8). The whole cluster is a controlled experiment in whether the frontier is bought or engineered — and the Chinese labs have already proven you can get close for far less.
The question they all answer the same way
Here is the convergence that makes this a real cross-cut: every study, Western and Chinese, lands on the same uncomfortable verdict — the model layer is commoditizing. OpenAI’s own teardown concludes its moat sits at the distribution and app layer (~800M weekly users, 4M developers), not the model. Anthropic’s is enterprise and coding workflow lock-in. The Chinese labs make the point louder by giving their weights away for free — DeepSeek’s own line is that “closed-source moats are fleeting.” If the model is not the moat, then the $850–965B Western marks are bets on distribution and switching costs, and the capital-light open-weight labs are bets that neither will hold. They cannot both be right.
Open weights are China’s strategy, not a side-effect
The open-vs-closed split maps almost perfectly onto the geography. The three highest-valued Western labs are closed-weight; every Chinese lab here, plus Mistral, ships open weights under permissive licenses. That is deliberate. For a Chinese lab under a compute ceiling, open weights buy developer gravity, ecosystem reach and a credibility the capital can’t — GLM-4.5 hit 10M downloads in 30 days; K2 outscored GPT-5 on one hard benchmark. Open-weighting is how the efficiency bet distributes: you can’t outspend OpenAI, so you commoditize the thing it spent the most on. The risk, which the studies flag in unison, is that open weights also mean you may never capture the value you create.
Where they split — and the two wild cards
The labs agree the frontier is deflating fast (capability gets ~4× cheaper a year). They split on who survives it. The Western thesis: scale and distribution become the moat once the model isn’t one. The Chinese thesis: in a deflationary market, the low-cost producer wins, and the high-cost incumbents are over-built. Two names refuse the binary. xAI is its own bet — vertical integration (X’s data, SpaceX’s capital, Colossus’s compute) folded into SpaceX, ~$0.5B of revenue against a ~$6.4B operating loss, the most capital-intensive seat of all. Mistral is the European hedge — capital-light like the Chinese labs but Western-aligned, selling sovereignty and on-prem control rather than raw frontier scale. The cross-section’s sharpest read: the demand for intelligence is not in question. Whether anyone earns a durable profit selling it — at any spending level — is.