This is one outlet's own report from Fortune — the article as it was filed.
AIPROPX ReportFortune · 1h ago
AI’s Three-Body Problem: no single force can dictate the outcome
Two celestial bodies orbiting each other trace stable, predictable paths. Add a third, and the system turns chaotic: each body is large enough to bend the others’ paths, and a small nudge by one can swing the trajectory of all three. That is how I have come to see the AI economy in 2026 — as three bodies pulling on one another: the closed-source frontier labs, led by OpenAI and Anthropic; open-weight models, mostly out of China; and the application companies built on top of both. Each is powerful enough to reshape the others’ orbit, but none can dictate where the system finally settles.
A few events over the last few weeks and months have increased the instability in the system. The most dramatic shift is the momentum built – and new obstacles faced – by the frontier labs. Led by Anthropic, they have seen unprecedented demand and, with it, extraordinary revenue growth. On the one hand, that has diffused AI’s benefits more broadly through the economy; on the other, it has sharpened the focus on demonstrable ROI and the search for cheaper alternatives. Spending on AI now runs, by my estimate, to somewhere between 0.5 and 1 percent of all white-collar salaries in the United States. At this scale, it deserves to be scrutinized. In July, Palantir’s Alex Karp told CNBC that “something has gone completely wrong” with how the labs sell their product: enterprises, he argued, are “tokenmaxxing” — spending furiously on tokens with no matching gain in productivity. And in the same period competition at the frontier has intensified, with Meta (Muse Spark 1.1) and xAI (Grok 4.5) both fielding increasingly capable models alongside Anthropic, OpenAI and Google.
Then there is the improvement in open models, especially from Chinese companies. Zhipu’s GLM 5.2 and Moonshot’s Kimi K3 now perform at or near the frontier on several important benchmarks. Priced at a fraction of comparable closed models while sitting so close in capability, they have created strong momentum for the open-weight ecosystem. Meanwhile US open-weight models are adding credibility of their own. Led by Thinking Machines’ Inkling and Nvidia’s Nemotron 3 — highly capable, if not yet quite at the frontier — they offer a domestic alternative to the Chinese releases.
And finally, there is the reaction to these developments from AI application companies. Many of the leading ones have ramped up efforts to build on top of open-weight models, targeting lower costs and greater control.
Three-body systems are notoriously difficult to predict. Nevertheless, I think it is possible to discern some broad trajectories over the course of the second half of 2026:
Firstly, the discomfort with frontier pricing will ease — partly because competition will push prices down, and partly because the returns on AI spend will begin to show. Much of today’s anxiety is a timing mismatch: adoption is running ahead of utility. For most prior technologies, like cars and cell phones, mass adoption followed decline in prices. In the case of AI, adoption happened much faster. The payoff will come, as faster growth for some companies and cost savings for others, and eventually as higher productivity across the economy.
Secondly, the shift toward a multi-model world will continue, driven by competition and, ideally, by real differentiation in what each model does best.
Thirdly, U.S. open-weight models will become genuine alternatives to the Chinese ones and win real adoption as a result. They will also have a clearer business model, making it easier for customers to take longer-term bets. Eventually I expect the distinction between open and closed to diminish, as the frontier labs themselves support model personalization for specific needs of the customers.
In other ways, too, the players will converge frontier labs going deeper into the product stack to widen their moats and sustain high margins; and application companies going deeper into the model stack to build moats of their own. That convergence is rational: software companies typically enjoy 70%+ gross margins while customers feel they get their money’s worth from the product.
Given these moves and countermoves, we remain in a three-body system, and its equilibrium is still unsettled. Much of today’s noise — like the debate over open vs. closed, China panic, and the hand-wringing over returns — looks temporary. The genuinely interesting question is not whether AI pays off, but who captures the value when it does: the labs at the frontier, the open models nipping at their heels, or the applications that own the customer.
The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune .
AIPROPX is an independent multi-source news index — we track, compare, and connect coverage from across the web into one place you won't find anywhere else.
