This is fascinating. I used to tell my students to read papers and implement the theory as code. Sometimes, this was even the very first implementation of the math people had written up years ago.

Today, I can give an agent a few papers or even let it to do the research itself and come up with a first implementation of the research to see if the idea is worth pursuing or not. It does a better job than most students would.

The learning experience for the student from doing the actual work is missing here – and it can't be replaced – that much is clear. But from a pure research planning perspective, this unlocks a whole new paradigm for exploring new ideas.

We're accelerating fast, in every direction at once.

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My big concern is the reliability of these solutions. Super for prototyping, but when you need to buckle down on something that people’s livelihood matter, not so sure.

Case in point, I had a bug that took me months to track down the root cause. AI was very helpful in helping me to implement improvements that were not the root cause, but it was the hard knocks of experienced observation that I tracked down the root cause. And guess what, AI helped me.

The big concern I have, is that hard-lived experience is now short-circuited: maybe great for innovation, but maybe not so great for reliability.