{"id":5748,"date":"2026-08-22T11:38:50","date_gmt":"2026-08-22T11:38:50","guid":{"rendered":"https:\/\/crosscountrymovingteams.com\/?p=5748"},"modified":"2026-08-22T11:38:50","modified_gmt":"2026-08-22T11:38:50","slug":"ais-three-body-problem-no-single-force-can-dictate-the-outcome","status":"publish","type":"post","link":"https:\/\/crosscountrymovingteams.com\/?p=5748","title":{"rendered":"AI\u2019s Three-Body Problem: no single force can dictate the outcome"},"content":{"rendered":"
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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 \u2013 and new obstacles faced \u2013 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\u2019s 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\u2019s Alex Karp told CNBC that \u201csomething has gone completely wrong\u201d with how the labs sell their product: enterprises, he argued, are \u201ctokenmaxxing\u201d \u2014 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.<\/p>\n

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Then there is the improvement in open models, especially from Chinese companies. Zhipu\u2019s GLM 5.2 and Moonshot\u2019s 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\u2019 Inkling and Nvidia\u2019s Nemotron 3 \u2014 highly capable, if not yet quite at the frontier \u2014 they offer a domestic alternative to the Chinese releases.\u00a0<\/p>\n

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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.<\/p>\n

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:<\/p>\n

Firstly, the discomfort with frontier pricing will ease \u2014 partly because competition will push prices down, and partly because the returns on AI spend will begin to show. Much of today\u2019s 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.<\/p>\n

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Secondly, the shift toward a multi-model world will continue, driven by competition and, ideally, by real differentiation in what each model does best.<\/p>\n

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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.\u00a0<\/p>\n

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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\u2019s worth from the product.\u00a0<\/p>\n

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Given these moves and countermoves, we remain in a three-body system, and its equilibrium is still unsettled. Much of today\u2019s noise \u2014 like the debate over open vs. closed, China panic, and the hand-wringing over returns \u2014 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.<\/p>\n

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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 <\/em>Fortune.<\/em><\/p>\n

Read more Retired racer Jimmie Johnson credits his multimillion-dollar success to shining his shoes and arriving 10 minutes early: \u2018Small things matter\u2019<\/a><\/p>\n

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OpenAI and Anthropic are one body, China’s open-weight models are another, and then there are the application companies built on top of both.<\/p>\n","protected":false},"author":1,"featured_media":5747,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[132],"tags":[],"class_list":["post-5748","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-disruption"],"yoast_head":"\nAI\u2019s Three-Body Problem: no single force can dictate the outcome - Cross Country Moving Team<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/crosscountrymovingteams.com\/?p=5748\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI\u2019s Three-Body Problem: no single force can dictate the outcome - 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