Cheap Models Are Eating the AI Market From the Bottom Up
As frontier labs race to make powerful models cheaper, the real disruption isn't happening at the top of the stack.
Everyone obsesses over which model is the smartest. That’s the wrong argument. The more interesting thing happening right now is that capable models are getting genuinely cheap, and that changes who builds with AI and what they build.
The floor is falling out
When a model that competes with last quarter’s flagship costs a fraction of the price, the economics of agents shift entirely. Suddenly you can run dozens of parallel calls without a spreadsheet anxiety attack. You can build workflows that retry, verify, and loop without watching your billing dashboard like it owes you money. Cost was always the invisible ceiling on what agents could actually do in production. That ceiling is getting higher, fast.
Small models, big ambitions
The interesting bet is that “cheaper” doesn’t mean “worse” anymore. Smaller, faster models optimised for specific tasks are outperforming larger generalist ones on the workloads that actually matter to most teams. Agentic pipelines don’t need a philosophy degree. They need reliable tool calling, low latency, and predictable output formats. A leaner model that does that well beats a powerful one that thinks too hard and bills too much.
What this actually breaks
The labs that built their moats on being the only ones who could afford to run the best models are in a tricky spot. If capable inference gets cheap enough, the differentiator stops being the model and starts being the workflow, the memory, the integration layer. That’s where product companies win and foundation model providers start looking like commodity suppliers. We’ve seen this pattern before in cloud infrastructure. It doesn’t end well for the people who thought the underlying resource was the product.
The price floor dropping isn’t a footnote. It’s the story.