I could have sworn there was research that stated the more you use these tools the quicker your skills degrade, which honestly feels accurate to me and why I've started reading more technical books again.
How's that working out for you in the context of working with AI tools? Do you feel like it's helping you make better use of them? Or keeping your mind sharp?
I've been considering getting some books on core topics I haven't (re)visited in a long time to see if not having to write as much code anymore instead gives me time to (re)learn more and accelerate.
Not until large-N research is done without sponsorship, support, or veiled threats from AI companies.
At which point, if the evidence turns out to be negative, it will be considered invalid because no model less recent than November 2027 is worth using for anything. If the evidence turns out to be slightly positive, it will be hailed as the next educational paradigm shift and AI training will be part of unemployment settlements.
I would even say it's likely the opposite. My output as a programmer is now much higher than before, but I am losing my programming skills with each use of claude code.
My software development skillset has improved. I’m learning and stress testing new patterns that would have taken far longer pre-AI. I’m also working in new domains and tech stacks that would have taken me much longer to get up to speed on.
People who use AI mindfully and actively can possibly improve.
The olden days of buidling skills and competencies are largely dying or dead when the skills and competencies are changing faster than skills and competency training ever intended to.
I mean, maybe things have changed (I finished college about 20 years ago), but I don't remember producing large volumes of stuff as being a particularly important part of a CS degree.
Between a challenging job market, increasing new frontiers of learning (AI, MLops, parallel hardware) and an average mind like mine, a tool that increases throughput is likely to be adopted by masses, whether you like it or not and quality is not a concern for most, passing and getting an A is (most of my professors actively encourage to use LLMs for reports/code generation/presentations)
I would urge you to leverage some critical thinking, re-read what I stated, and identify where I said that the models are of no use to me. If the ability to think for yourself without AI assistance hasn't fully atrophied on your end you may be able to see that you are the moron in this thread.
Interesting post. The First Proof experiment really showed us the near future of AI/math interactions, some impressive success, but also lots of extremely hard to verify text, misformulated lean "proofs" etc. but progress on AI does math has indeed been impressive
This is a very interesting contribution to the AI/math space. I hope it can be seen by nonmathematicians interested in this. The mathematicians involved are quite well known (Martin Hairer is a Fields medalist). See https://www.reddit.com/r/math/comments/1qx77l7/a_new_ai_math... for some discussions.
It's a peer review platform build on atproto tech (aiui the vision), not to be social media, though I would not be surprised if it has elements of that
Peer review goes beyond the formal process, in the court of IRL. Social media is one place people talk about new research, share their evaluations and insights, and good work gets used and cited more.
Arxiv has been invaluable in starting to change the process, but we need more.
Has a bit of a leg up in that if it's only academics commenting, it would probably be way more usable than typical social media, maybe even outright good.
This also can be observed with more advanced math proofs. ChatGPT 5.2 pro is the best public model at math at the moment, but if pushed out of its comfort zone will make simple (and hard to spot) errors like stating an inequality but then applying it in a later step with the inequality reversed (not justified).
My favorite early chatgpt math problem was "prove there exists infinitely many even primes" . Easy! Take a finite set of even primes, multiply them and add one to get a number with a new even prime factor.
Yes this is the standard proof of infinitely many primes but note that my prompt asked for infinitely many even primes. The point is that GPT would take the correct proof and insert "even" at sensible places to get something that looks like a proof but is totally wrong.
Of course it's much better now, but with more pressure to prove something hard the models still just insert nonsense steps.
I think a more realistic answer is that professional mathematicians have tried to get LLMs to solve their problems and the LLMs have not been able to make any progress.
I think it's a bit early to tell whether GPT 5.2 has helped research mathematicians substantially given its recency. The models move so fast that even if all previous models were completely useless I wouldn't be sure this one would be. Let's wait a year and see? (it takes time to write papers)
It's helped, but it's not correct that mathematicians are scoring major results by just feeding their problems to gpt 5.2 pro, so the OP claim that mathematicians are just playing off AI output as their own is silly. Here, im talking about serious mathematical work, not people posting (unattributed AI slop to the arXiv).
I assume OP was mostly joking, but we need to take care about letting AI companies hype up their impressive progress at the expense of mathematics. This needs to be discussed responsibly.
I think "pretty soon" is a serious overstatement. This does not take into account the difficulty in formalizing definitions and theorem statements. This cannot be done autonomously (or, it can, but there will be serious errors) since there is no way to formalize the "text to lean" process.
What's more, there's almost surely going to turn out to be a large amount of human generated mathematics that's "basically" correct, in the sense that there exists a formal proof that morally fits the arc of the human proof, but there's informal/vague reasoning used (e.g. diagram arguments, etc) that are hard to really formalize, but an expert can use consistently without making a mistake. This will take a long time to formalize, and I expect will require a large amount of human and AI effort.
It's all up for debate, but personally I feel you're being too pessimistic there. The advances being made are faster than I had expected. The area is one where success will build upon and accelerate success, so I expect the rate of advance to increase and continue increasing.
This particular field seems ideal for AI, since verification enables identification of failure at all levels. If the definitions are wrong the theorems won't work and applications elsewhere won't work.
Do we?
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