light_triad

The big problem with education is that all the work is done for grades instead of aiming to deliver value to others. It's important for students to practice but if you raise the bar high enough then AI is just one tool that helps you but doesn't have all the answers. If AI can do all the homework we have to ask ourselves how much value the homework had in the first place.

> Now there's no such thing as a startup, there are only "AI startups", where you build a company that somebody else can write a prompt for in 30 seconds.

The AI hype and AI doom are 2 sides of the same coin. Yes you can make a cool demo quickly with a prompt, but building a real company with moats is still possible, much easier even. The moats have just changed: proprietary data, distribution, access rights, eval sets, integrations, compliance, brand, just not the code.

Some sectors are less productive, but the reality is more mundane: only about 30% of the global workforce are knowledge workers. Adopting AI in real workflows takes time, and that time saved might not be used productively. Also solving Millennium problems is an amazing feat of engineering and mathematics, but doesn't change people's lives that much.

You're right using Microsoft products is like mixing Momento with Waiting for Gadot :)

I recently switched to New Outlook and it's full of weird subtle bugs. It takes them years to fix things and make them barely useable. Given their track record I'm not optimistic for Copilot given both the vision and the execution are questionable at best.

William Thurston's argument (from the quote) was that the point of math isn't to produce more math but to better understand the nature of reality, just like the point of code is not to generate more code but to increase user value.

AI is a tool that reshapes knowledge work but the ultimate goal remains about value.

The terminology can be confusing because in corporate speak being uncompromising can mean being opinionated. Not "we made no tradeoffs" but "we didn't split the difference."

The article means it as tradeoff rather than concession, so the argument is "to avoid design by committee you have to compromise on purpose."

This is the taste question applied to Mathematics in a similar way it's been applied to code. In an era of AI abundance, the question becomes what the goals are and what gets verified and digested (adopted by users). Goodhart’s law: the goal of producing code is not just about maximizing the number of tokens used, but the productivity gains and economic surplus.

This could serve as the template for any field in the age of AI: "We are not trying to meet some abstract production quota. The measure of our success is whether what we do enables people to understand and think more clearly and effectively about math (or products, or science, or hardware...)"

A good example of "founder bias" where big companies are read as not innovating, when in fact their goal is to squeeze as much juice from their user base and strengthen their monopolistic position and pricing power. From the outside it looks like blindness and atrophy but from the inside it's the main bread and butter.

The sighted engineer is also cave adapted, just to a different cave.