AI Models Are Getting Cheaper, but the Bill Might Actually Get Bigger

Kimi K3 costs a fraction of Claude Opus to use. It has 2.8 trillion parameters, putting it in the same tier as Claude Opus and GPT-5.5. Over the past few weeks, Coinbase, DoorDash, Siemens, and Airbnb have been quietly switching parts of their AI workflows to cheaper Chinese open-source models, cutting their AI bills in half or more. The signal most people are reading into this: AI is getting cheaper, and demand for computing power will follow. But that logic is missing a step. Kimi K3 is cheap in the sense that its license and API fees are cheap. The money that used to flow to companies like Anthropic and OpenAI is flowing elsewhere. But running a model with 2.8 trillion parameters still takes roughly the same computing resources as running any other model at that scale. Openness didn't shrink that number. Gavin Baker, a well-known US tech investor, laid out a counterintuitive case a few weeks ago: if cheaper models take share from expensive ones, the same budget buys more compute, ROI improves, total usage goes up, and hardware demand could actually grow. Kimi K3 going open-source pushes this further: companies can fine-tune it and run it on their own servers. Each company hosting its own 2.8-trillion-parameter inference stack is far less efficient than thousands of companies sharing a cloud service. Total hardware demand gets larger, not smaller. Next time you see a headline about AI getting cheaper, it's worth asking: cheaper for whom? Coinbase cut its AI bill in half. Nvidia's chip orders this quarter are still climbing.