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  • Hacker News
  • This is probably the inescapable conclusion that most in the current panic-filled era of Math research discourse will come to, one way or another.
    by caaqil
  • Ironically, "graduating" is how vtubers announce retirement.
    by randallsquared
  • The notion that mathematics would be over just because it's slightly easier to find proofs is like the idea that astronomy would be over because of telescopes.
    by shiandow
  • The way I see it is this

    - if AI is so powerful that it is a superset of human capabilities in that it can not only solve questions but also come up with useful abstractions, then we just simply don't need mathematicians!

    - if AI is not that powerful but can automate in-distribution tasks, then that allows mathematicians to explore concepts much faster. Overall positive thing for mathematics

    We are obviously in the second camp (at least for now). Your job as a mathematician is to offload as much in-distribution task as possible to the LLM and just focus on the rest.

    by simianwords
  • The author uses airplanes as an analogy. I want to add a few historical facts here:

    - First, airplanes in the first 30 years were extremely rough and dangerous. When Dole hosted a contest for the first transpacific flight in 1927, 6 out of 8 teams failed. Ten people died as a result.

    - Second, people made a lot of silly predictions about this new technology. The New York Times extrapolated that people would soon start commuting to New York using personal airplanes.

    - Third, people made capital investments based on these wild guesses. For example, the architects of the Empire State Building installed a huge spire to prepare for the upcoming age of airborne traffic (intended to be used as an airship docking station!)

    The bottom line is that predicting the trajectory of a new technology is often impossible. As a society, we are currently in the experimental phase of LLM-based AI, testing all the possibilities one by one. Who knows which path leads where.

    by cloudbonsai
  • This piece reminded me, of all things, of Gandhi's essay "Hind Swaraj." He was pretty anti-technological-driven change.

    He was anti: - Machinery replacing handicraft (skill disappeared and poverty followed) - Labor-saving machines replacing human workers (workers became expendable) - Doctors replacing consequences (people lost self-control) - Railways replacing constraint and slowness (harm spread faster)

    But he loved the sewing machine.

    Gandhi’s test for whether to accept tech-driven change was whether it satisfied primary needs. Interpretation of those needs was certainly not universal. As he wrote: "...it is not the primary need of man to traverse distances with the rapidity of a motor car. The needle on the contrary happens to be an essential thing in life, a primary need.”

    That's the airplane in this post. Excitement or fear of the AI airplane is either judged as helping mathematicians (sewing machine) or doing something not quite right (the above machinery/doctor/railway)...

    by paulorlando
  • We are entering an age when intelligence is no longer a scarce resource. Bill Gates believes that in 20 years, we will reach an age of abundance [1]. But the next 20 years will bring tremendous social upheaval as we learn to adapt.

    https://www.nytimes.com/2026/08/26/technology/bill-gates-ai-...

    by flowerlad
  • Not sure why people are stopping at 'AI can do advanced math!' and calling that the field is gonna transform due to LLMs specifically. Pretty much any meta-formulation of math gives you some initial rules and a goal. From natural deduction and sequent calculus to dependent type theory and whatever flavour of category theory you choose, at a sufficiently abstract level, you'll end up with some form of search problem. ATP folks foresaw machines doing automatic proofing for decades, but it seems that noone had enough incentive in pursuing it at the cost of suffering the tediousness of the encoding process. Now AI labs took the opportunity to do something impressive and have shown that it's possible to close the ergonomics gap. But the deeper achievement is the decades of ATP and proof-assistant work that make machine-generated proofs mechanically certifiable in the first place.

    I am pretty sure in the next couple of years we'll see more evolved sledgehammers [0], maybe with LLM-like tech playing a role in encoding theorems, that will allow us to prove deeper than we can do manually, then help with the archeology and extraction of learnings from observing the 'trace' of certified proofs. So instead of explore -> understand -> solve, we'll get solve -> compress -> understand.

    [0] e.g. https://isabelle.in.tum.de/website-Isabelle2025/dist/Isabell...

    by gobdovan

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