We have to remove certain things from the models’ training:
- how long things take (e.g., “this is a day of work”)
- certain things are challenging (e.g., setting up App Store deployments)
- not finishing a task or over-chunking it (e.g., asked to do something e2e, stops halfway)
AI has seemingly increased drama across the board. Stocks up or down, problem solved or not, permanent underclass or machines of loving grace. Extreme hyperbole.
The only thing I’m sure of is this technology has changed my life and it’s only just begun.
Been using Opus/Sonnet 5.5 for a good while now. Noticed no nerf in subscription or bedrock.
I’m honestly not convinced nerfs are that real. I think we just fixate on the bad experiences and lose track of how good we have it.
“But what are you building?”
At work, it’s HPC/simulations predominantly, but also some full-stack work because… you just can.
At home: managing Proxmox and building my own health app since none of the others do what I want.
As for the personal app, $20 gets me a full time dev to work on the app plus a coach agent built into it. At this point, I don’t know why anyone would pay for other apps at least in this category.
People reading this seem to believe I think professional mathematicians couldn’t learn the technical aspects of AI/ML or quantitative finance.
No, most of them could. When someone says “any” and neglects the fact that these institutions have their pick of the litter, it’s a no.
I also want to claim that there are many mathematicians that would not put up with the working requirements of these jobs at all. It’s not a knock on them, people are different. You can’t just get a math degree and waltz onto Wall Street or into Anthropic.
> Any one of them could work as a quant or “AI Scientist” …
No, no they could not.
The system that was, is terrible.
I wish people dunking on this understood that until now, "being a mathematician" involved not just doing math and publishing it, but being part of the whole institution, acquiring your place in society, a community, livelihood and so much more. The goal was to advance math, but you got all these other important things alongside it.
AI decoupled that - now you can advance math without any of that. Replacing the existing thing blindly loses all the other benefits mathematics as an institution served our society and mathematicians are right to be upset about it.
Should we reject AI for math completely? Surely not, but if you're going to replace a system, you should make sure you replace all the functions it provided, whether or not they were intended by design. This is of course not the responsibility of AI companies, but it underscores the need for societal-scale coordination for dealing with these disruptions
This guy gets it.
I think of my goal as a mathematician as something like: to better understand the fundamental concepts “shape” and “number,” and to convey that understanding to others. I think this is a valuable goal for many reasons. The primary reason discussed on social media is the (admittedly slow, but I think very real) diffusion of mathematical ideas into technologies that better our lives. There are others, though: production of human capital (which plays some role in this diffusion), and diffusion of mathematical understanding into culture, for example. It is not obvious to me that even the bare technological need for mathematics can be filled without human experts; this seems to me to be a tricky question of institutional design.
For me the most compelling reason is simply that we feel a pressing need to understand. While deep understanding is largely limited to professional mathematicians, the existence of mathematicians and of the institutions they make up is a huge part of what makes mathematical understanding available to those who want it. I believe that the capacity to understand mathematics (and the universe as a whole) is part of a good life, and we should try to provide people with a good life.
We operationalize the goal of understanding in a number of ways, problem-solving and theorem-proving among them. My own research is oriented around certain open questions that I believe measure our lack of understanding (of numbers, shapes and so on), and I think resolving those is genuinely important. I would welcome a solution with any provenance. But I think proof is only a measure of progress—what actually matters (for technology, human capital, culture, a good life) is a bit harder to get at.
Crucially it is very hard to measure progress—plausibly theorem-proving is used in large part because it is easy to measure rather than because it is a perfect proxy for what we care about. I worry a bit that we may automate many easy-to-measure things without automating the correlates we care about for which they are proxies. This presents a clear problem for the design of institutions aimed at achieving progress.
In part because I think problem-solving is a proxy for something else, I don’t feel that my goal of understanding “shape” and “number” is particularly threatened by a machine that is good at problem solving. I expect that machine to be a substantial boon, instead. Ultimately I think we will operationalize mathematical progress in better ways. But how to do this is a difficult question and I think many on here are too quick to dismiss concerns about it.
You can be excited and also want room for junior (and senior) researchers to still do mathematics and science. Why create false dichotomy?








