I'd like to give my perspective as a computer science professor at Ohlone College, which is a two-year community college located in Silicon Valley. I used to work as an AI researcher in industry (but not in large language models) before becoming a tenure-track instructor in Fall 2024.

Our core computer science curriculum consists of five courses: (1) an introductory programming course taught in a procedural subset of C++, (2) an object-oriented programming course taught in C++, (3) a data structures and algorithms course taught in C++, (4) a discrete mathematics course, and (5) an assembly language course that also covers basic computer architecture. Students who pass all five courses are prepared to transfer to a four-year university to complete their undergraduate computer science programs. The majority of our students transfer to either San Jose State University or California State University East Bay, though many of our students transfer to University of California campuses, typically UC Davis, UC Santa Cruz, UC Merced, and UC Irvine.

Because I teach introductory freshman- and sophomore-level courses, I feel it is vital for students to have a strong foundation with basic programming and basic computer science before using generative AI tools, and thus I do not accept programming assignments that were completed using generative AI tools. I admit that I'd have a different, more nuanced stance if I were teaching upper-division or graduate-level computer science courses. I have found that students who rely on generative AI for programming tend to struggle more on exams, and they also tend to lack an understanding of the programming language constructs the generated program used.

With that said, I recognize that generative AI tools are likely to become more powerful and cheaper over time. As much as I don't like this brave new world where students can cheat with even less friction today, we professors need to stay on top of things, and so I will be spending the entire month of June (1/3rd of my summer break) getting up to speed with large language models, both from a users' point of view and also from an AI research point of view.

Whenever my students are wondering whether it's worth studying computer science in light of the current job market and anxieties about AI replacing programmers, I tell them two things. The first thing I tell them is that computers and computation are very interesting things to study in their own right. Even if AI dramatically reduces software engineering jobs, there will still be a need for people to understand how computers and computation work.

The second thing I tell them is that economic conditions are not always permanent. I was a freshman at Cal Poly San Luis Obispo in 2005, when computer science enrollment bottomed out in the United States. In high school, well-meaning counselors and teachers warned me about the post-dot com bust job market and about outsourcing to India and other countries. I was an avid Slashdot reader, and the piece of advice I kept reading was to forego studying computer science and earn a business degree. However, I was a nerd who loved computers, who started programming at nine years old. I even wrote an essay in high school saying that I'd move to India if that's where all of the jobs are going to end up. The only other things I could imagine majoring in at the time were mathematics and linguistics, and neither major was known for excellent job prospects. Thus, I decided to major in computer science.

A funny thing happened while I was at Cal Poly. Web 2.0, smartphones, cloud computing, and big data took off during my undergraduate years. My classmates and I were able to get internships at prestigious companies, even during the economic crisis of 2008-09. Upon graduation, I ended up doing an internship in Japan at a major Japanese tech company and then started a PhD program at UC Santa Cruz, but many of my classmates ended up at companies like Microsoft, Apple, and Google, just in time for tech industry to enter an extended gold rush from roughly 2012 when Facebook went public until 2022 when interest rates started to go up. Many of my classmates made out like bandits financially. Me? I made different choices going down a research/academic path; I still live in an apartment and I have no stock to my name. I have no regrets, except maybe for not getting into Bitcoin in 2011 when I first heard about it.... Though I'm not "Silicon Valley successful", I'm living a much better life today than I was in high school, qualifying for Pell Grants and subsidized student loans to help pay for my Cal Poly education due to my parents' low income.

I still believe in the beauty of an undergraduate curriculum that encourages critical thinking and developing problem-solving skills, as opposed to merely learning industry topics du jour. Specific tools often come and go; my 2005 Linux system administration knowledge didn't cover systemd and Wayland since they didn't exist at the time, but my copies of Introduction to Algorithms by Cormen et al. and my Knuth volumes remain relevant.