I see a pattern with AI companies. They always try to solve a really hard and not very useful problem. It's the same as with self driving car companies ten years ago: If you believe self driving tech is ripe for commercialization, the reasonable thing to do is something capital intensive and a special case where the technology most likely to succeed. For instance, heavy trucks automatically following others in formations for long drives. Saves gas, money, and potentially personnel.

There is a clear business case and buying large trucks is already a capex play. Then slowly work your way through more complex logistic problems from there. But no! The idea to sell was clearly the general problem including small cars that drive children to school through a suburban ice storm with lots of cyclists. Because that's clearly where the money is?

It's the same with AI. The consumer case is clearly there, people are easily impressed by it, and it is a given that consumers would pay to use it in products such as Illustrator, Logic Pro, modelling software etc. Maybe yet another try in radiology image processing, the death trap of startups for many decades now, but where there is obvious potential. But no! We want to design general purpose software -- in general purpose high level languages intended for human consumption! -- not even generating IR directly or running the model itself interactively.

If the technology really was good enough to do this type of work, why not find a specialized area with a few players limited by capex? Perhaps design a new competitive CPU? That's something we already have both specifications and tests for, and should be something a computer could do better than human. If an LLM could do a decent job there, it would easily be a billion dollar business. But no, let's write Python code and web apps!