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Comment by HNisCIS | Hacker News Reader

I take the opposite view but to the same conclusion. I've heard the joke "the more advanced you get the worse you get at math" from a lot of mathematicians. I'd bet most mathematicians can't do a long division problem without having to think pretty hard about it, because they've long since moved past that skill.

All that said, people still learn and do manual math at all levels in order to advance the field even though calculators and python notebooks exist.

I think people get too wrapped up in LLMs being the entirety of the future when the LLMs themselves are entirely a product of the past. An LLM is less a thinking machine than a lossy JPEG for language and to an extent, knowledge represented in language. As such, if you want to expand the field and move into the future you aren't going to rely solely on an algorithm that reverts to the mean.

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I think a major difference between math and software development is that software development tends to get easier after one gets through the educational barrier to entry, whereas math (and many other topics) become more complex/puzzly. Something that could highly accurately revert to the mean would be insanely useful when the overwhelming majority of what you're doing is mundane and has been done a million times before - the analog of something like basic arithmetic or calculus.

Take for instance most CRUD apps. They're all doing basically the same stuff just with variations on interface/schema. And that's a huge chunk of all professional software development. We're already at the point where you can describe said schema/interface and get a pretty good implementation of it, and things will likely continue to only get better. I think the job-apocalypse is unlikely, but I also think it's unlikely that 'manual coding' will be anywhere near as significant a part of the economy in the future as it is today.

I agree with the second part, 90% of apps are just a vague rehashing of 100 other apps, very little novel software ever gets written, especially at the component level. The point being that if you want to create truly novel systems you best know how to program, even if an LLM is writing the less novel parts of that system.

If we're smart about this, maybe it means we get to do novel work a little more frequently. That said, I fear that a lot of people don't look at every single LLM output and think "Eh it's workable but it doesn't spark joy" and anyone who doesn't think that is likely going to be seeing their QOL decreasing. At that point your time might be better spent learning how to make Molotovs.

I'm in the same boat! It's a reasonable stance but the specific examples aren't air tight. I'm a Mathematician and I can't do long division, I could probably work it out? Like you say, long division was part of a journey that made me very mathematically capable, even if it's no longer an active contributor. My capacity for pencil and paper calculus is undoubtably many many fold worse than my peak around undergraduate studies!

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