Elsewhere in AI however progress has been enormous, and many projects are only now reaching the point where they are starting to have valuable outputs. Take video gen for instance - it simply did not exist outside of research labs a few years ago, and now it’s getting to the point where it’s actually useful - and that’s just a very visible example, never mind the models being applied to everything from plasma physics to kidney disease.

The claim is "exponential" progress, exponential progress never seems "slow" after it has started to become visible.

I've worked in the research part of this space, there's neat stuff happening, but we are very clearly in the diminishing returns phase of development.

First were the models. Then the APIs. Then the cost efficiencies. Right now the tooling and automated workflows. Next will be a frantic effort to "AI-Everything". A lot of things won't make the cut, but absolutely many tasks, whole jobs, and perhaps entire subsets of industries will flip over.

For example you might say no AI can write a completely tested, secure, fully functional mobile app with one prompt (yet). But look at the advancements in Cline, Claude code, MCPs, code execution environments, and other tooling in just the last 6 months.

The whole monkeys typewriters shakespeare thing starts to become viable.