‘Selling coffee beans to Starbucks’ – how the AI boom could leave AI’s biggest companies behind

How much do foundation models matter?

It might seem like a silly question, but it’s come up a lot in my conversations with AI startups, which are increasingly comfortable with businesses that used to be dismissed as “GPT wrappers,” or companies that build interfaces on top of existing AI models like ChatGPT. These days, startup teams are focused on customizing AI models for specific tasks and interface work, and see the foundation model as a commodity that can be swapped in and out as necessary. That approach was on display especially at last week’s Boxworks conference, which seemed devoted entirely to the user-facing software built on top of AI models.

Part of what is driving this is that the scaling benefits of pre-training — that initial process of teaching AI models using massive datasets, which is the sole domain of foundation models — has slowed down. That doesn’t mean AI has stopped making progress, but the early benefits of hyperscaled foundational models have hit diminishing returns, and attention has turned to post-training and reinforcement learning as sources of future progress. If you want to make a better AI coding tool, you’re better off working on fine-tuning and interface design rather than spending another few billion dollars worth in server time on pre-training. As the success of Anthropic’s Claude Code shows, foundation model companies are quite good at these other fields too — but it’s not as durable an advantage as it used to be.

In short, the competitive landscape of AI is changing in ways that undermine the advantages of the biggest AI labs. Instead of a race for an all-powerful AGI that could match or exceed human abilities across all cognitive tasks, the immediate future looks like a flurry of discrete businesses: software development, enterprise data management, image generation and so on. Aside from a first-mover advantage, it’s not clear that building a foundation model gives you any advantage in those businesses. Worse, the abundance of open-source alternatives means that foundation models may not have any price leverage if they lose the competition at the application layer. This would turn companies like OpenAI and Anthropic into back-end suppliers in a low-margin commodity business – as one founder put it to me, “like selling coffee beans to Starbucks.”

It’s hard to overstate what a dramatic shift this would be for the business of AI. Throughout the contemporary boom, the success of AI has been inextricable from the success of the companies building foundation models — specifically, OpenAI, Anthropic, and Google. Being bullish on AI meant believing that AI’s transformative impact would make these into generationally important companies. We could argue about which company would come out on top, but it was clear that some foundation model company was going to end up with the keys to the kingdom.