I was intensely stressed through high school and undergrad, then later in grad school, postdoc, full time job as computer scientist, then as a SWE. It wasn't pleasant but it drove me to achieve at far higher levels, in ways that paid off well in the long run (like financial stability and earlier retirement). Note: in undergrad, I specifically went to a school that did not have grades but still felt all the stress to perform.

I suspect that as unpleasant as stress is, it ultimately can make us better people.

For AI I use python+rust. I develop code in python (mostly AI-driven these days) and then have it port to rust for speed. As always, extensive testing (much of which is manually inspected to make sure it's logically consistent with the design and contract).

They aren't being anti-science and it's low-quality to suggest they are.

That objective then gets loaded into an ML model that spits out an experimental protocol. A protocol can be as simple as: "make 1 million test tubes, each with the protein, and in each, a custom molecules, and look for test tubes that show some reaction of interest". It can be a lot more complicated (for some reason, biologists who run these systems always try to do the most challenging experiments first, while I tend to spend all my time demonstrating the system can pass basic controls first). The protocol is then loaded into a robotic work cell which has access to protein-making machines and drug making machines, and then it handles all the experimental details (which previously would have been done by a technician). It scales up far larger than individual technician, is much more reliable, and faster (in theory- all of these are aspirational goals right now). T he results of those experiments are used to fine tune the experimental protocol and run another round. You run this in a loop and the result is better drugs faster (again- in theory.)

This is already an active area of research with more resources going to into it every day. The fact that Jeff and Sanjay have chosen to bet on this approach should be no surprise. In many ways, this is exactly what I intended when I wrote the documents inside Google (15 years ago) that motivated Jeff and Sanjay to work on scientific computing problems, and my current company is already trying to figure out how to work with Discovery Loop.

I don't get this weird rejection of AI from a socialistic perspective. Or rather, I do, but I don't think it's healthy.

Also, the companies are required to do this by the government/law, they are not doing this because they want to destroy old knowledge. They are making that knowledge more widely available in a digital form.

Who cares how big the graph is in CSV? That's not the representation you operate over in big data.

All of this would have easily fit in memory on any reasonable modern system.