These seems like a glass-is-half-empty view.

5% are succeeding. People are trying AI for just about everything right now. 5% is pretty damn good, when AI clearly has a lot of room to get better.

The good models are quite expensive and slow. The fast & cheap models aren't that great - unless very specifically fine-tuned.

Will it get better enough so that that growth rate in success pilots grows from 5% - 25% in 5 years or 20? Who knows, but it almost certainly will grow.

It's hard to tell how much better the top foundation models will get over the next 5-10 years, but one thing that's certain is that the cost will go down substantially for the same quality over that time frame.

Not to mention all the new use cases people will keep trying over that timeline.

If in 10-years time, AI is succeeding in 2x as many use cases - that might not justify current valuations, but it will be a much better future - and necessary if we're planning on having ~25% of the population being retired / not working by then.

Without AI replacing a lot of jobs, we're gonna have a tough time retiring all the people we promised retirements to.

> 5% is pretty damn good, when AI clearly has a lot of room to get better.

That depends if the AI successes depended much on the leading edge of LLM developments, or if actually most of the value was just "low hanging fruit".

If the latter, that would imply the utility curve is levelling out, because new developments are not proving instrumental enough.

I'm thinking of an S curve: slow improvements through the 2010s, then a burst of activity as the tech became good enough to do something "real", followed by more gradual wins in efficiency and accuracy.