this post was submitted on 07 Oct 2026
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I think people really need to understand the "it just took a bunch of public work and connected the pieces" isn't a gotcha for LLMs: it's one of the sales pitches. The cross-discipline general knowledge combined with the ability to churn huge datasets to find connections across already-known work, and extrapolate to or derive novel findings, is exactly one mode of superhuman success that AI companies have been trying to achieve.
The bigger issue is that they may be camping human efforts then kill-stealing the last hit on the boss by burning millions of dollars to get a proof a few days/weeks/months earlier than when it would have happened without AI. Basically, waiting for problems to be all-but-formally solved, then beating the researchers to the punch; like the recent Millenium controversy
Exactly.
Like in the quoted picture, there's not a huge amount of people who have deep knowledge of Number Theory and the number of them who are also similarly versed in the math behind Quantum Physics could probably be counted on one hand and those people only have so much time.
Meanwhile, the only limitation in creating these AI systems is in how much RAM/compute we can manufacture.
You need large volumes of high quality training data too, which are btw much harder to come by now
There are massive libraries or repositories of scientific/mathematical published research throughout higher education institutions and across various technological industries. The required training data exists.