I think it depends on the interests of the researcher. If a researcher is comfortable being a “brain for hire” who is comfortable solving research problems that are driven by business needs and where there needs to be short-term or medium-term results, then I think there are plenty of opportunities at large companies, including the FAANGs. I find research more fun than software engineering, but researchers are far from immune from pressures to ship.

If a researcher is more interested in curiosity-driven work and who wants to work on a longer time frame, I’m afraid that there’s no place in industry, except for maybe Microsoft Research (which I’ve heard changed under Satya Nadella), that supports such work. The days of Bob Taylor-era Xerox PARC and Unix-era Bell Labs ended many decades ago, and while there were still curiosity-driven labs in industry well into the 2010s, I have witnessed the remainder of these old-style labs change their missions to become much more focused on immediate and near-immediate business needs.

The challenge for computer science researchers who have qualms about working for Big Tech is finding an alternative career path. Speaking from an American point of view, academia has always been competitive, and the immediate future of research funding is uncertain given the political climate. This uncertainty also extends to government labs. The challenge with industry research is that there are not a lot of non-Big Tech employers of computer science researchers. This leaves starting a business, but business is very different from research.

I’m a tenure-track professor at a community college in the Bay Area. While I’ll never be able to afford to purchase a home near my job, I am able to live well as a single man renting an apartment. I have a great career teaching and using my long summer breaks for research and side projects. I like not having to worry about “publish or perish,” and I enjoy teaching and mentoring students. While this might not be considered “successful” for some people who are aiming for a professorship at an R1 university or an industry job at a top company’s top lab, I love my job and believe it’s a fantastic route for someone who enjoys teaching and who also wants extended time during the summer for research and side projects.

In aggregate, sure, but no company today comes within an order of magnitude of the power an IBM of the ‘70s and ‘80s or a Microsoft of the ‘90s and ‘00s had over the tech landscape.

I feel generative AI is being imposed onto society. While it is a time-saving tool for many applications, I also think there are many domains where generative AI needs to be evaluated much more cautiously. However, there seems to be relentless pressure to “move fast and break things,” to adopt technology due to its initial labor-saving benefits without fully evaluating its drawbacks. That’s why I feel generative AI is an imposition.

I also resent the power and control that Big Tech has over society and politics, especially in America where I live. I remember when Google was about indexing the Web, and I first used Facebook when it was a social networking site for college students. These companies became successful because they provided useful services to people. Unfortunately, once these companies gained our trust and became immensely wealthy, they started exploiting their wealth and power. I will never forget how so many Big Tech leaders sat at Trump’s second inauguration, some of whom got better seats than Trump’s own wife and children. I highly resent OpenAI’s cornering of the raw wafer market and the subsequent exorbitant hikes in RAM and SSD prices.

Honestly, I have less of an issue with large language models themselves and more of an issue with how a tiny handful of powerful people get to dictate the terms and conditions of computing for society. I’m a kid who grew up during the personal computing revolution, when computation became available to the general public. I fell for the “computers for the rest of us,” “information at your fingertips” lines. I wanted to make a difference in the world through computing, which is why I pursued a research career and why I teach computer science.

I’ve also sat and watched research industry-wide becoming increasingly driven by short-term business goals rather than by long-term visions driven by the researchers themselves. I’ve seen how “publish-and-perish” became the norm in academia, and I also saw DOGE’s ruthless cuts in research funding. I’ve seen how Big Tech won the hearts and minds of people, only for it to leverage its newfound power and wealth to exploit the very people who made Big Tech powerful and wealthy.

The tech industry has changed, and not for the better. This is what I mourn.

I’ve been programming since 1998 when I was in elementary school. I have the technical skills to write almost anything I want, from productivity applications to operating systems and compilers. The vast availability of free, open source software tools helps a lot, and despite this year’s RAM and SSD prices, hardware is far more capable today at comparatively lower prices than a decade ago and especially when I started programming in 1998. My desktop computer is more capable than Google’s original cluster from 1998.

However, building businesses that can compete against Big Tech is an entirely different matter. Competing against Big Tech means fighting moats, network effects, and intellectual property laws. I can build an awesome mobile app, but when it’s time for me to distribute it, I have to either deal with app stores unless I build for a niche platform.

Yes, I agree that it’s never been easier to build competing products due to the tools we have today. However, Big Tech is even bigger today than it was in the past.

Look at how even the Posix ecosystem - once a vibrant cluster of a dozen different commercial and open source operating systems built around a shared open standard - has more or less collapsed into an ironclad monopoly because LXC became a killer app in every sense of the term. It’s even starting to encroach on the last standing non-POSIX operating system, Windows, which now needs the ability to run Linux in a tightly integrated virtual machine to be viable for many commercial uses.

At my institution (Ohlone College in Fremont, California), we teach the intro courses in C++. However, for discrete math, a sophomore-level course, each instructor is allowed to choose the language. I chose Haskell the last time I taught the class, and I plan to use Haskell again, since I believe it’s a great vehicle for coding discrete math, plus I believe it’s a great thing teaching undergraduates functional programming early on.

Yet despite this head start, the Turing machine formalism became the dominant framework for CS theory—complexity classes, computability, formal verification. Whether that's path dependence, historical accident, or something deeper about how humans reason about computation, I'm not sure.

But it does make me wonder: if the imperative, state-based model proved more tractable even for theorists, maybe FP's learning curve isn't purely about unfamiliarity. There might be something genuinely harder about reasoning in terms of pure functions and recursion vs. "do X, then Y, update Z."

Fully acknowledge this is handwavy—curious if others have thoughts.

(Or even - and I know this is a spicy take - assembly language. An intro-level course that takes students through games like TIS-100 and Human Resource Machine might have a lot of pedagogical value.)

What I’ve observed is that people who have had to spend some time working in a language that maintains a clean separation between functions and data tend to be better at domain modeling, which ultimately enables the to produce designs that are simpler and easier to maintain. OOP can be a powerful mechanism, but it seems like, perhaps ironically, people who only know OOP tend to have a harder time reasoning about information flow, control flow and state. Perhaps because object-oriented language features are mostly meant as a way to abstract over those concerns. Which does have some value, but maybe also discourages learners from thinking about them too deeply.

The issue is that "CS 110" gets used by a lot of STEM students in other departments that require computational methods and statistical tools, and that require you get to a CS course taught by actual computer scientists. I don't necessarily think that it's a bad thing that you'll need CS 101 before a higher-level biology elective that needs R!

Anyways, cool to see another alum here! I hope the department keeps it around for a while longer :)

I have a feeling we’ll have to revisit this topic in the next five years as college and university CS departments grapple with the implications of generative AI coding tools such as Claude Code, but that’s another story…

I work closely with Northeastern CS students (via co-op program) and haven't heard anything but negative opinions about Racket.

There's also a very long history in America of laws and law enforcement being targeted against poor people and minorities. Vagrancy laws (https://en.wikipedia.org/wiki/Vagrancy#Post-Civil_War) and modern anti-homeless laws effectively criminalize homelessness, and the War on Drugs has had a major negative impact on poor people and minorities. Yes, in this situation those who have been imprisoned due to such laws did violate the law, but such laws, in my opinion, serve the function of kicking people while they are down rather than addressing the root causes of their poverty.

There's a good argument that having a system of convict labor creates a perverse incentive to fill that labor pipeline, similar to how well-meaning traffic laws (such as speed limits) can be abused (for example, "speed traps").

While the Mac ecosystem did get the Omni Group and some other companies who leveraged OpenStep/Cocoa to build great software, the Mac ecosystem was (and still is) dominated by major vendors (e.g., Microsoft and Adobe) shipping very large apps (e.g., Office and the Creative Suite) developed by very large teams.

I believe ease of development, whether through new APIs or through generative AI, isn’t enough. Competitors to entrenched software companies need to deal with network effects, proprietary file formats and protocols, and the “long tail” of features in large software packages that deter users from switching to smaller, less feature-rich applications.

I feel similarly about the classic Mac OS: excellent interface and UI guidelines hampered by its cooperative multitasking and its lack of protected memory.

Windows XP and Mac OS X were major blessings, bringing the NT kernel and Mach/BSD underpinnings, respectively, to home computing users.

Trump learned during his first term that he can bypass checks and balances by making sure the GOP is thoroughly MAGA. People who stood up to Trump have been sidelined, such as Justin Amash, Mitt Romney, and, most famously, Mike Pence, who stood up to Trump on January 6 and paid a heavy political price for it. That’s why Vance, not Pence, is the current VP.

Of course, the challenge is convincing the electorate in red states that Trump’s antics regarding Greenland are catastrophic enough to warrant his removal, given the stranglehold MAGA has on the Republican electorate.

Protests. Riots. Strikes.

Y'know, the sort of thing that toppled Yanukovych in Ukraine, lotsa Middle Eastern dictators during the Arab Spring, British rule in India, Soviet control over the Baltics, etc etc etc.

Your politicians are use- and spineless. It's time for your people to step up.

Plus, I just saw a little segment on Fox News where it was portraying this whole Greenland deal as a way to "help Greenlanders boost their economy, each person will get X cash, blah blah". So anyone only watching Fox is probably convinced we're doing Greenland a favor, liberating them from Danish oppression, just like we recently liberated Venezuela from oppression (in case you didn't know!).

For the sake of the country, I hope that this is finally the red line that will get enough Republicans representatives to finally have the courage to rein in Trump, at least on this issue.

I’m a Californian. It’s one thing for me to write Alex Padilla or Adam Schiff; they’d vote to convict if they have the chance. But they won’t get a chance unless people like Ted Cruz and Lindsay Graham say “enough is enough,” but I don’t live in those states.