Computer science as a lost art (2015)

Self-driving cars (controls, perception, planning, safety, sensor design, localization, mapping, integration, security, etc.), human-competitive NLP/image classification, advanced robotics (repeat list from cars here but for things in the air, off-road, on the water, in the water, in orbit, in deep space, ...).

Those are all examples of real things that real people get to work on every day.

Operating systems topics don't even scrape the top 20 of stuff I think of when I think of deep expertise.

And even if we limit ourselves to the sort of things you mention, most people who work deeply on languages, tools, and standards view these as manifestations of their deep exploration rather than the focus or subject of the dive.

For example, TensorFlow. The framework very much is the product. But even if some other framework won the day tomorrow, the people who worked on TensorFlow would not have "wasted" their time thinking deeply about how to build the system.

This is why researchers whose original contributions were made in the 70s and 80s none-the-less continue to establish themselves as desired experts in new technology trends (e.g., Leslie Lamport and cloud computing or Martin Abadi and ML frameworks). Because they were focused on ideas and fundamental problems. The problems never disappeared, just changed form. And ideas have a lot more staying power than their manifestation in code.

Most people working deeply on systems today are not "revolving around POSIX in some way". See the proceedings of OSDI. And most deep experts choose other topics, most of which your post doesn't mention: graphics, programming languages (making them and analyzing programs written in them), compilers, security, robotics, user interfaces, NLP, ...

Your definition of "expert" seems to revolve around using things, mostly things based on ideas and techniques that were well-understood already 20 years ago and that are related to building a particular type of software system. Which, if anything, seems to deepen the author's point.

Someone gets to fill the AI research labs, staff the self-driving car companies, work at NASA, build core infra at large tech companies, and build the foundation for the next 20 years of trendy growth areas.

It's possible to get to those places without a degree, of course, but a degree is by far the path of least resistance. And in most of these cases, learning the material from the degree isn't optional; you're probably going to have hard time doing that controls engineering job at a self-driving car company if you never made your way through a calc sequence, some physics, and an algorithms course.

It's also worth noting that very often, building wordpress plugins pays more than doing all of those things I mentioned. I guess it's all about what you want to spend your life doing, which is exactly what the author says at the end of the article.

She's currently on her second internship. They're both at employers that don't screen candidates on actual programming ability (they just looked at GPA, resume/application, and then a soft interview), and the current one has a reputation for being a very meh internship, though good resume padding. The last time I helped her, her code was fine for someone who had just started learning two years ago, but I don't think she is going to progress to the point that you'd expect someone with a Master's to be at simply because she isn't doing her own homework.

I don't have a CS BS or MS, but there have been a few times where I feel like I need to get one just in case the market tanks again and they become a significant hiring criteria. But at the same time, I have to wonder just how many people currently enrolled in MS CS programs throughout the nation are doing something similar, and devaluing the worth of the degree (on paper, to potential employers) to the point that some could even look at it negatively.

There's plenty of programming work out there that doesn't require any deep understanding of CS. You're not going to be creating algorithms when using existing frameworks to write yet another web thing, phone app, or internal businessy database-based system.

A bootcamp can get you started doing practical things. Yes, you won't have deep knowledge, but really you don't need deep knowledge for most employable work. Code doesn't need to be hyper-optimized at the scale you're working, and it's easy to learn common pitfalls & best practices from applied practice, reading, and mentorship.

And I say all this as an oldish fart who understands the chain from designing bespoke high level language environments down through to transistors. We don't need to count bytes & clock cycles anymore; people can let the machine & its provided environment simply work for them and learn the top-level interface.

It's not orthogonal, but rather highly correlated. Combinatorics, graph theory, computer architecture, etc., will all be part of a university CS curriculum. Someone who has a CS degree will probably know those things (or at least recall them after a brief refresher).

In the years since the author graduated, there has been a shift in some CS degrees, to meet market needs. A key difference is that then, a CS degree wasn't about programming.

Those CS degree were just the starting point, letting you know what is out there, and a taste of how to approach it. Part of that was contact with research academics. (Though some recent graduates miss the point and believe that because they now know something, they now know everything.)