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> I feel like I am mourning something different: the loss of the desire to seek truth in tech, math etc.

This is what I've been feeling too. It feels increasingly like the professional world has become a tower of scams. It's not worth building technical skills or focusing on quality; your boss doesn't know or care what good work looks like, he/she just wants your work to look good enough to his/her boss, who knows even less about the actual work being performed. And everyone reports up to an MBA CEO who was brought in from a completely different industry.

I want to work in an industry where skill, diligence and expertise is rewarded and respected, but from what I'm seeing political ability is 95% of the game and caring too much about the work is a career liability.

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Not sure I agree - they dropped hundreds of papers, most of which were so badly written that they were incomprehensible. If it wasn’t for the lean certificates on 30% of them, the mathematical community would have told them to go and whistle.

I think the consensus here is that most of the papers are effectively correct. But it would take years of study just to understand most of the problem statement - so we have to wait for the mathematical community to digest the borderline-nonsense that OpenAI spewed out.

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Most likely that they look really correct and are not and require immense effort to understand, if even possible, to disprove their claims.

What we have is a DoS in research verification.

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Just tell the (AI) authors to go back and write clear explanations for why they matter and what the specific claim is.

I gotta say I think mathematicians brought this roblem on themselves, most math papers are absolutely incomprehensible to anyone who is not an expert in the field. Mathematicians spend so much effort trying to make their argument to other mathematicians that they've gradually abandoned making their field or findings comprehensible to anyone else and left that to science journalists. In turn, those journalists churn out very formulaic stories written to a template: 50% the human story of the mathematicians social/academic wanderings in pursuit of their goal, 50% an explainer with some nice graphics on the subfield they operate in and a mention of what sort of problem their insight solves, perhaps with a few colorful metaphors thrown in.

The last mathematical paper I can think of (but I'm not an expert, I'm sure there are some others) that was both mathematically rigorous and accessible enough to be readable was Mitchell Feigenbaum's Quantitative Universality for a Class of Nonlinear Transformations. This is accessible int he same way as a textbook - you have to work at it, but hte author is explaining his thought process and drawing the reader's attention toward things that matter or stand out as unusual.

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Including those with Lean verifications?
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+1 We need human verification.
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