As long anticipated, the economics of general purpose LLM AI technology is shifting, and the frontier model race is no longer the story. Distribution, deployment cost and integration depth now decide who wins in enterprise AI, not who has the newest benchmark screenshot.

Three signals converged this week: enterprise buyers scrutinising cost-to-serve rather than parameter counts, infrastructure vendors pushing compute closer to the edge to cut latency and cost, and a market that has stopped rewarding capability announcements with a share price bump. The capability gap between frontier labs has narrowed to the point of irrelevance for most buyers. What hasn't narrowed is the gap between vendors who can integrate cleanly into an existing stack and those who can't.

If you are still selecting an AI vendor primarily on model benchmarks, you are optimising for the wrong variable. Ask instead what it costs to run at your actual volume, and how many of your existing systems it breaks.

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