daxfohl

Yup, I imagine within a year or two OpenAI and Anthropic will have in-house versions of pretty much everything anyone would ever use, with in-house agents hardening and improving it at a torrid pace that the human/code-review/github cycle could never keep up with. Their models will default to using these in-house versions, and be trained in all the idiosyncrasies of those tools and libraries. Their coding agents will phone home with feedback about usability that in-house agents will act on immediately, operations agents will do the same re bugs and runtime performance. I don't really see a how or why a human-run project on open source would even bother anymore.

People keep talking about this like there's a finite amount of math to be done, and then the party's over. But that's never how math has worked, is it? Every problem you solve, ten new ones open up. Like a fractal, the more you zoom in, the more detail emerges. No matter how much better AI is at solving problems, it's not going to generate the "final, complete compendium of mathematics" that that seems to hover over this post.

Math is meaningful because ... some people like to do it. The same as any other human pursuit. It doesn't need a reason beyond that. And AI won't change that. There will continue to be things to explore, things to find out, things that are maybe just at the edge of AI's reach and needs a human to decide whether it's worth continuing to explore or not. (Remember, AI isn't free).

So, IDK, I think for people who enjoy exploring math, there will always be interesting areas to explore. AI just gives us a better flashlight.

BTW I do agree that there's going to be an incident soon, whether intentional, accidental, or paperclip-factory, that leads governments around the world to shut all this down for some time, perhaps even shutting off access to GPUs entirely. It seems unavoidable. But that's just a temporary respite and skirts the core philosophical premise of the post.

I find myself less worried about it than at first. I think what we'll see are that some things are low-hanging fruit and can be solved just by tireless search. Maybe half the millennium and other such high-visibility problems will fall this way.

Others, I think, will be beyond both human and AI. And so what then? Mathematicians just throw in the towel and say it's not worth trying? Of course not. We will continue that pursuit, and as we do, new ideas will arise and new problems will need to be solved. It's math. There is no end.

It's easy to look at the current landscape and see AI ticking off solutions to problems and imagine that soon there will be nothing left. Machines replaced the need for much manual labor, but they also established a basis for an economy that provides the opportunity for more labor. This is the situation with math now. It will take some getting used to. There will be little-to-none pencil-to-paper working out of problems anymore, but there will always be work to do, things to solve, curiosities to unravel. And it will still be professional mathematicians who are the ones most capable of directing that effort. Because, if nothing else, they're the ones whose curiosity is piqued by the problems. Which, let's face it, has been 99% of the motivation for graduate-level math in the first place.

There's the the old question: is math invented or discovered? I think it's both: the problems are invented, and the solutions are discovered. In the age of AI, the discovery part will be greatly affected, but the invention part will remain firmly in the human domain.

That's exciting, and kind of makes sense in retrospect. Sometimes a "fresh pair of eyes" on a problem can be all you need. Someone who comes in with a different background and can understand the problem in different terms and work on it from a different angle. It doesn't even have to be them doing the work, just a "that kind of reminds me of ... did you think about trying something like that?" that can get a team unstuck after thinking about it in the same way and never making progress.

My manager said "load-bearing" during a presentation the other day and I almost lost it.

Pretty sure this was LLMs musing about LLMs. A little too slick with the analogies and a lot of the "you don't get x, you get y" mic drops.

Also, find the right manager and the right role. With a manager that expects a highly regimented routine, you probably aren't going to do so well. With a manager that lets you explore and be more creative, you might do a lot better.

I've found one of my own strengths is in finding ways to use existing features, maybe with slight modifications, together to do the things that customers want, allowing the team to avoid several large projects and the resulting maintenance burden entirely. My first manager understood the value of that and we worked really well together for a few years. After a reorg, my subsequent manager considered it lazy and PIP after six months. I don't fault them, and different management styles work for different people. But make sure you find someone you're compatible with.

To be devil's advocate, two things may offer a glimmer of hope:

First, math, generally, is useless. I mean, yes there are of course practical uses of basic thru undergrad-level math, and some beyond that. But for many mathematicians, the sum result of their entire career may lead to exactly zero results that have any real-world value. The entire field they work in may have meaning only to the handful of other individuals on the planet that also work in that field. But to those handful of people, the meaning defines their lives. From a socio-economic perspective, those departments should have been defunded a century ago. Yet they continue. Why? Because it scratches an itch. Not just for those individuals in the field, but also for us as a species. To stop exploring, to eliminate the search for pots of gold that may be buried in some odd corner of sphere packing, or coloring theorems, or Garside categories, and to put a boundary on the limits of our understanding, just because they aren't immediately applicable, is an idea that most humans would not be willing to sacrifice, even if it reduced their tax burden a couple cents. If it was going to happen, it'd have happened already.

The second is, even with AI, it's not free. As the software industry is discovering, far from it. So, given that, who is going to decide what theorems to research and how much it's worth? Congress? Of course not. AI itself? In theory that sounds plausible, but that falls victim to thing 1 above: most math is useless, so AI itself has no value metric it can assign to things, and besides which, without the human element, once the initial curiosity has subsided, there'd be no reason to continue any funding for AI to do it. So no, the only possible owners of this is going to be mathematicians themselves, the ones who care about the field and deeply understand the kwah of their vision.

Combining these, there's a future where, humanistically, "nothing changes". The method changes, the efficiency changes, the scope changes, but the work itself: publishing proofs, remains the domain of professional mathematicians. AI will enable them to be dramatically more daring and broad in their investigations and scope, and will likely write the entirety of the proof. However it will remain the work of the mathematicians to determine, what areas are worth spending limited AI resources on to investigate further, how far to go down rabbit holes, how to prioritize potential connections, and what the ultimate meaning of the findings is. So rather than being an end of mathematics, it could be a dawn of something far greater than anything we've ever seen before.

"We broke our monolith into microservices and wish we hadn't"

Google should bring back Google Reader. But make it only for bots. And then drop it again once it gets popular.

For me, besides creatine which has been genuinely transformative at age 50, I've gained a lot more from the stuff I've dropped (dairy milk, ~2/3 of my caffeine (mostly by drinking reduced-caff coffee or tea and eliminating soda), sweets of course) than stuff I've added. But of the latter, I'd say fiber and fruits have been the biggest additions, partly in themselves and partly that they make it easier to avoid the bad stuff. I tried experimenting with a few other supplements, but most of them were meh at best.

So, "take things to the next level" with some pears and oatmeal and chia seeds! Now I just need a sponsor.

> [CEOs] expressed more extreme concern about the labor market impacts of A.I. in private conversation, but suddenly became optimists once I turned on the mic.

At some point once the rate of investment capital starts to decline, they'll make a hard pivot from the investor-wooing method of "blaming AI for layoffs", to the more politically expedient method of blaming minorities and immigrants. That'll be the signal for the transition from power grabbing to power ossification, and the point at which change becomes a lot harder.

So far there's no moat though. A lot of that kind of stuff is available open source too if you look for it (and was available before claude desktop). And for anything that doesn't exist, with coding agents now you can write one up in an afternoon.

It's kind of paradoxical in a way. By making writing software cheap, they've made it much harder to create a moat for themselves that involves only software. It'll be interesting to see how they respond.