We would do well to focus as much on the diffusion of AI technology as the development of it.
Every time a new model comes out, there is a debate about how much smarter it is than previous models. Developers talk about all sorts of different tests and metrics to evaluate the capabilities of each model. And while we can argue about whether advances in capability are slowing down or not, it’s clear that we have mode remarkable progress.
It seems clear at this point that we are engaged in a race of sorts with China around AI. And, like the space race, I think it can be an incredibly powerful motivator - and I think it’s important to future of democracy and capitalism that we win this race. Which is why I’m worred we’re running the wrong one.
Much of our discussion about the AI race centers around either investment in building infrastructure or developing the most capable models - measured in either model capabilities or data center capacity. And this is an important part of the race. There is a good case to be made that we are largely winning in thise area (though I would love to see this competition spur greater investment in energy infrastructure in the US).
But it also misses a critical component, which is diffusion of AI technologies - how broadly we are taking advantage of these capabilities . It is far less clear that we are winning this part of the race. Often laws and regulations in the US prevent us from leveraging many AI (or even just basic ML) technologies in critical areas like healthcare, financial services, or education. Having the best models is only useful if you can take advantage of them where it matters most.
So, as we debate how we make sure the most powerful AIs are made in America and how to regulate AI so that it can be developed and deployed safely - we also need to make sure we are thinking about how we enable reasonable deployment of these technologies broadly so that we can benefit from these amazing inventions.