Two charts showing extremely important, widely held AI assumptions are outdated: Assumption 1: Chinese models are cheaper & more efficient. In fact, at almost every level of intelligence @ArtificialAnlys finds US models cheaper per task. Chinese models might be cheaper per token, but US models tend to be MORE efficient with tokens. Everyone seizes on papers from Chinese labs with architectural improvements for efficiency and assumes US labs are undisciplined. But ask yourself what OpenAI/Anthropic/Google have behind the scenes that would cut down on their #1 cost. And then add that they have more efficient chips. Should not be surprising that US labs can undercut! You can't undercut free, though, as anyone can download CN open weight models and use w/o paying the labs anything. BUT unless you have your own compute (few firms do), the days of firms running Chinese models for the cost of compute are numbered or over because Chinese AI labs are reportedly asking for 30% cut from cloud providers serving their models. This is likely main way these models are accessed, and the costs are going to be passed on. Assumption 2) US company spending on AI is spiraling out of control. @arakharazian finds that companies are getting better at picking the right cost/capabilities tradeoffs and getting better deals from competition between Anthropic and OpenAI. Use continues to rise, but at a cost that is leveling out. Controlling cost will put less pressure on firms to move to Chinese models, esp if they aren't cheaper anymore!