The idea that if only all professors stood their ground then somehow students will be motivated to study doesn't pan out in practice, though. There is already a significant number of students who are perpetually struggling. They are missing basic prerequisites, and instead of catching up on them, they repeated try and fail at learning the same materials, passing only when they got a lenient instructor. The problem compounds because failing brings helplessness and exacerbates their mental issues, which brings more failing. The university cannot sit on their high ground and watch these students struggle, especially if their number reaches a critical mass.

I think you inferring my motivation for the rant and creating a strawman yourself.

I do agree that directing my rant at the generic "fans" is not productive. The article Tao wrote was a good example of communicating the result. I should direct my criticism at specific instances of bad communication, but not the general "fans".

This is a really good example of how to use the current capabilities of LLM to help research. The gist is that they turned math problems into problems for coding agents. This uses the current capabilities of LLM very well and should find more uses in other fields. I suspect the Alpha evolve system probably also has improvements over existing agents as well. AI is making steady and impressive process every year. But it's not helpful for either the proponents or the skeptics to exaggerate their capabilities.

1) Tacit knowledge. In many fields, there are important information only accessible from having a mentor like heuristics, insider information, in-lab techniques etc.

2) Investment opportunities. A good adviser is often good at spotting opportunities for their students. It's also common for an academic adviser to share their most valuable opportunities with their students.

It's clear to me that the ideal of meritocracy (talent and hard work leads to success) does not hold in academia, and maybe not anywhere. Having a good mentor gives you extremely valuable information that contribute to success. On the other hand, I am not sure this can be fixed or even needs fixing. I think it's healthy for academics to be partially siloed, so that they can develop their unique approaches and maintain a healthy diversity for the field.

On a side note, recently the government of Manitoba in Canada removed requirement for maths teachers to take university maths courses. This is being pushed strongly by the education departments of university, which shows how much these maths teachers hate maths.

Fair enough.. at least they think they can add value by improving efficiency.

This is why I think the most effective way is to empower good actors. Ensure open debate, limit the power of individuals, and prevent over concentration of power in a small group. These efforts are harder to implement than you think because they run against our desire to have scientific superstars and celebrities, but I think they will go a long way towards building a healthy community.

For the first type, I argue it's simply the resistance to a new mental model. The article's example of epsilon-delta language is a perfect example. It's a new way of thinking that takes time (and it did historically) to sink in. Competing on how fast you grasp this new concept is stupid. When the new mode of thinking becomes natural, it won't care how long you took to adapt to it.

For the second type, it's simply an impossible standard to reliably have eureka moments. Clearly, smarter people will have more of these than the average people, but no one can do this reliably. On the other hand, while it takes more work for us mortals to have these insights than a genius, there are plenty of ways to get there that don't require a super high IQ. Teachers should try to foster these moments because they are huge confidence builders, but try to minimise the impact of someone showing off their brilliance.

AI has no emotional barrier to wasted effort, which make them better reasoners than their innate ability would suggest.