As AI Gets Cheaper, the Real Business Opportunity Is Shifting. Here’s Where the Value Is Moving.

Résumé détaillé

As models become cheaper and more interchangeable, more value may move to the companies that own the customer relationship, workflow and distribution.

For the past few years, the AI industry has operated on a fairly simple assumption: Better models would create more valuable companies. The enormous investment going into chips, data centers and foundational models has largely been built around that idea.

In October, Reuters reported that China’s STAR50 technology index had fallen 40% in less than four months, wiping out around $480 billion in market value. AI-related stocks had grown to represent 42% of Chinese market capitalization and 57% of trading turnover across mainland China and Hong Kong.

Some of this is simply what happens when valuations run ahead of financial performance. But I think there is a more interesting change happening underneath the market correction. AI is continuing to become more capable while the cost of accessing that capability is falling and competition between model providers is increasing.

For businesses using AI, that is very good news. For every company investing billions to build the underlying technology, the economics may be more complicated.

The AI industry could create enormous economic value without the companies building foundational models capturing most of it.

The pace of model development is still extraordinary, but the gap between the strongest models is getting smaller.

The 2026 Stanford AI Index found that Anthropic, xAI, Google and OpenAI were all within 25 points of each other on the Arena leaderboard in March. Chinese models are also closing the gap, with Stanford describing the performance difference between leading U.S. and Chinese models as having “effectively closed.”

That matters because when several providers can complete the same task well, the buying decision starts to change. Businesses begin looking beyond which model is technically the best. Cost matters. Reliability matters. Speed matters. The ability to use a model inside an existing workflow matters.

I have seen this change in how we approach AI internally.

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