linhvn

Every company would use your data and "steal" it at some point. VC money would demand it the moment the pressure is up.

Then your point is? One should not make Uber pay $40M to the fatal victim's family, or one should make the driver pay $40M to the victim's family (which the driver definitely does not have $40M), or one should award all the traffic fatalities the same amount?

In a just world, I may concur with third, but don't let the perfect be the enemy of good.

"AI tends to make interesting things boring"

If a conjecture can be one-shotted by OpenAI Astra, I have a hard time believing it is interesting.

If this is the viewpoint of mathematics, why does it make a difference that human or AI solve it? And forget about "understanding", because "understanding" in mathematical sense means in a very narrow way: top experts of math in certain field would understand and accept it (my estimation is that ~100 people in the world would understand Fermat's Last Theorem proof). Mathematicians could spend the whole year digesting FLT and "convince" the public that this is correct, and for most people (including math PHDs and professors), FLT is correct because some smart people say it is.

Throughout the history, we had made some of the best mathematicians abandon math. In turn, they apply their math skills somewhere else (hedge fund), and the companies they founded absorbed all the people from academia.

This has happened numerous times in the past, and it will happen over and over again.

Reform the way math is taught and researched. Right now, the aspiring mathematicians have to follow a ritual and system for 15+ years: undergrad -> PhD -> postdoc -> faculty, specializing on very narrow fields and god forbid if they have even a slight interest in quantitative finance. You could go through 4 years of undergrad classroom and had no idea what research math is (even at top schools like MIT, Harvard or Stanford), how it's done, etc. and the people who do it are typically the one "already in the system" (e.g. parents are professors, or in academia, or have connections to do research).

In the age of AI, there's no reason one has to follow the kind of classes like Algebra, Topology or PDE. Teach just enough so that good students can understand the basic, and go straight into seminar and research math. I don't think a top student in sophomore year cannot understand or work on some combinatorics research problem and get some results, with proper mentoring and guidance.

And I am outraged by their dinosaur mindset and the gatekeeping mentality that force every student to follow their same archaic system that no longer makes sense 20 years ago, let alone now.

They get a special, non immigrant work visa. If they want to apply for green card they subsequently need to apply for H-1B.

This is the similar mindset in sport athletes. On a good day, a strong driver could beat Verstappen, or a chess player could beat Carlsen. Over the course of a tournament, or a season, then Verstappen and Carlsen are unbeatable. They get to the top, and go on great length to stay there.

That is true for the older generation (immigrating to US pre 1990-ish). The newer generation immigrating to US has a very different mindset now, and they embrace American values as much as the native born here.

Which people? If you are software engineers or AI researchers, sure. Otherwise, it probably won't matter to you.

I'd say it usually comes from the difficulty in hiring. Extremely difficult to convince someone to leave FAANG / quant firms to work in a no name startup with very high chance of failures, and therefore usually you need a good GTM strategy first (which often requires you have acquired customers / revenue). Often in startups, the priority is figuring GTM strategy > acquiring customers > ... > hiring.

Yes, it was lucky that he was tutored my a Fields medalist. But he did the hard work to get himself tutored: sitting in an algebraic geometry class (that saw attendances dropping from 200 to 5 within a few weeks), reaching out to the professor, traveling with him for 2 years, etc. And then he still needed to do all the work himself for his PhD, and so forth.