@levie: Good post if you’re trying to understand AI diffusion. Progress driven by AI will ultimately be rate limited by its int…

Good post if you’re trying to understand AI diffusion. Progress driven by AI will ultimately be rate limited by its interaction with the real world. The reason coding, for instance, has been adopted so quickly is you can write a near infinite amount of code, test it, and run it -and it can add value- without anyone in the outside world ever having to do anything differently. A single person, from a computer, can just make something work end-to-end differently. This is not true for life sciences, where new drug development eventually needs to be tested for years. Doing a sale, which requires going back and forth with a prospect. Or even a contract, which has to be negotiated on the other side by your counterparty. “An AI will hand you a genuinely clever design for a jet turbine blade. It might be far more likely to work than anything your engineers came up with. It'll still probably fail, because that's the base rate at the edge of what anyone knows. The only way to find out is to build the blade and try to break it. That's the real limit on learning, and it doesn't care how smart you are. Coming up with ideas was never the hard part. The hard part is how fast reality answers them.” Incidentally, this is why you need an applied AI that actually takes intelligence and makes it useful within the workflows of existing industries. Model outputs -alone- are not enough in most cases. You need to actually change the underlying workflows, and you need systems to deal with the realities of the real world feedback loops that are in these industries. This is why there’s so much opportunity in the applied AI layer.