That was an excellent, well thought-out article. Everyone should read it and not just rely on the summary on Slashdot.

> There's little interest in interrogating the downsides of generative AI, such as the environmental impact, the data theft impact, the treatment and exploitation of data workers

Since when has anyone (not counting the people downstream, the theft victim, or the workers themselves worried or cared about that?

> There's little interest in considering the extent to which, by incorporating generative AI into our teaching, we end up supporting a handful of companies that are burning billions in a vain attempt to each achieve performance that is a scintilla better than everyone else's.

I fail to see how this is any different than now or at any other point in CS education since at least the 1980s and possibly before.

> There's little interest in thinking about what's going to happen when the LLM companies decide that they have plateaued, that there's no more money to burn/spend, and a bunch of them fold

Same thing that happened the last dozen times. Everyone will be chasing after the next wave to ride instead of caring about AI. Does anyone even remember, much less actively think about all of the blockchain companies?

The promises started long before the technology could fulfill them. Who is going to do the vibe-cleanup coding if it takes a decade or three for the tech to catch up to the hype?

People who understand how to write reliable maintainable code, of course... But the world seems poorly positioned to produce more of those.

The tricky thing is that LLMs are actually pretty good at implementing homework assignments. It's when you need code beyond that scope that the illusion of competence starts to fall apart.

If I were in charge of a Computer Science curriculum at a university, I would address the LLM problem like this:

I would offer a class (third or fourth year class) that starts from the basics of Neural Networks, and by the end of the class the students have built their own LLM. By building their own LLM, they will deepen their understanding, have a solid foundation, and avoid a lot of the nonsense that gets propagated about LLMs. The amount of code involved is not huge, it's actually quite doable.

And every time I mention it, some angry soul mods me down.

This is exactly why universities can seem aloof to industry needs. Industry decides they need everything to be focused on *insert most recent shiny thing*, and suddenly every company is demanding that every new employee aBsOlUTeLY MuST have at least 10 years experience in something that basically didn’t exist 4 years ago. And they blame universities for a “disconnect between the ivory tower and the real world”. The universities roll their eyes, and start making noises about catering to current needs, knowing full well that the bubble will pop and the focus will be replaced by the next shiny thing.

Universities need to be prepping people to use AI, for sure but it’ll be one skill among many. You know what else employers like to see in their new hires? Decent speaking skills, decent writing skills, an understanding if basic professionalism, the ability to work on a team, basic mastery of the standard CS topics developed over the last 25 years, an ability to work with non-CS types. And, yes, some skill with upcoming AI/ML/LLM tools. Oh, and all this has to be taught in 4 years. Any university that actually listens to the industry screaming about AI will dump all those other skills, implement 4 full years of AI-centeric content, and their CS program will crater like the tunguska event when the AI bubble pops.

No, LLM models are NOT the start of the singularity. Sam Altman and all the other AI-tech-bros want you to believe that because they want investors to cough up all-the-dollars so they can play in the big leagues of the most recent computing fad.

Maybe I’m wrong and the world will blast past me while I gumble about people on my lawn. I acknowledge that AI will have a significant impact. But, 30 years from now, workplaces will look a lot like they do right now. The main difference is that people will have one more useful tool in their belt to use.

No real CS student (aka hacker in the vernacular sense) is ever concerned with A let alone B or C. They are only interested in exploration of technology and how to make it do things that the designers and gatekeepers never intended. Read Steven Levy's book.

It seems that downsides of AI as discussed by the professor appears to be mostly about social or economics impact. While these are valid points, they looked like discussion by a social science professor instead of a computer science professor. "Exploitation of (data) workers" is exactly the kind of words commonly spoken by social science professors. It would be helpful to CS education if there are more discussion about pitfalls of AI from a computer science standpoint. I can think of two main problems after

I got my CS degree in 1988, just as the personal computer revolution was washing over the world. There was a lot of hand-wringing back then too, about how the computer would take away people's ability to think and do things on their own.

But I didn't make something of my career by wringing my hands about the down sides of the new computer technologies. I was *excited* about the possibilities I could see, and dove headlong into it. The result was a very fun, long, and exciting, not to mention well-paying, car