I expect that all mathematicians are going to be working with power tools, so they might as well learn about that. They will still need to do math exercises by hand to learn the material.
But I feel the need to point out - the goalposts for "does AI work" shift daily.
If people use AI for libraries, OSs, and mission critical software, the apparent productivity gains would have to be weighed against the reliability and performance hits that bubble up to the things that are built on them and rely on them.
I think the Bun port is a great example where testing enabled a very successful implementation. (Both the original tests themselves and runtime comparisons to the previous implementation.)
Tests can show you problems, if you can find them, but they cannot show that there are no problems. Property based testing or fuzzing gets your more coverage, and is a good step, but it is still nothing compared to proving things or understanding how something is built and that it is solid. Testing works towards checking for reliability and robustness, but often it's only 10% (?) of the job.
I'm curious - is there any data on the Bun port error rate? I think that would be very indicative of how successful or not the testing is.
"Corruption is not going away so people will have to get used to it. The alternative is making less money than people who are happy to take bribes."
Socrates famously made the opposing side of the argument against writing. Which is why we mostly know of him through Plato, who did believe in writing.
And to your corruption example. If you live in a society where corruption is normal and expected, you will be worse off if you are unwilling to be corrupt. It is indeed a local optima. But we are all, of course, better off if we live in a society where corruption is punished. To me, the worst thing about modern US politics, is that it's encouraging us to see ourselves as living in a world where corruption exists and is tolerated.