putting intelligence inside software itself, so developers can build programs that reason about intent and make probabilistic decisions rather than simply generate text for a human to interpret
when software doesn't do what it's supposed to do, it has a bug. And as AI becomes more widely deployed, labs need the same kind of telemetry, debugging, incident reporting, and operational discipline that previous generations of software eventually developed.
Software engineering is verifiable, you can run it through the compiler and see if it works. AI has obviously gotten really good at making software. If you believe the world is just software, that means AI has gotten good, it's done. You can see the labs are talking about how they've achieved AGI or they will very soon and it is very much connected to the ability of AI to write software. I'm just not discounting that that's important. I'm just saying once you get outside of software, verifiability gets way harder.
Lots of companies are just databases. Software engineering is verifiable, you can run it through the compiler and see if it works. AI has obviously gotten really good at making software. If you believe the world is just software, that means AI has gotten good, it's done. You can see the labs are talking about how they've achieved AGI or they will very soon and it is very much connected to the ability of AI to write software. I'm just not discounting that that's important. I'm just saying once you get outside of software, verifiability gets way harder.
1mo ago
Underscored — save the words that stop you in your tracks.
