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.