My students frequently ask me what LLMs mean for them as future software developers and data scientists. With little exaggeration it often comes across something along the lines of “low-key, are we cooked?”. The last one, if you are not one of my students, translates in millennial to “good esteemed professor, tell me true, are we f#@ked?” While I’ve given various off-the-cuff answers, I feel inspired to be more thoughtful in putting down more complete thoughts.

Some personal background

I want to start by giving a little personal history and just saying I understand the anxiety. I started my freshman year at CalPoly San Luis Obispo in Computer Science in September 1999. Like many of us older millennials that got into tech, I had been programming since elementary school (QBasic!) and computer science seemed a natural path. I always loved reading philosophy though and I seriously considered getting a philosophy degree instead. It was a choice between something I figured I was pretty decent at and could make money doing (computer science) and something that I was personally invested in but probably couldn’t make money with (philosophy). Earning a living won out over passion. I stuck with computer science, but I took as many philosophy classes as I could get into. To the extent that I was put on academic probation, not because my grades were too low, but because in the words of the admin “stop taking so many philosophy classes and just graduate!”. Good times…

Within a year of starting my degree, the tech market fell out. March 2000, we saw the dotcom bust, and here I was a computer science student, kind of doing it for the money, kind of not, and my sure bet didn’t seem so sure anymore. We also saw a revival of the perennial bugaboo for American software developers: outsourcing. Every decade brought fresh panic that all programming jobs would move to India, that American developers were too expensive, that we’d all be obsolete. I had to eat and had done a combination of construction and IT jobs up until that point and I was quickly burning through the savings I had built up from working. Luckily, I was able to convince one of my professors, Dr. Clint Staley, to whom I am forever grateful for many reasons, to let me interview for a startup he was running. Working that part-time while I went to school I was able to pay for myself, and momentum carried me forward to finishing my degree.

Are we cooked?

The best part about teaching in a university is you get to ramble. It is the single most defining characteristic of professors. But I’m sure at this point my students are asking: can you get to the point, are we cooked or not? I consider myself a skeptical optimist at heart. Meaning, I’m not inclined to believe that change is bad, but I’m also more cautious about predicting the future than others. Straightforwardly, that leads me to an answer of no, I don’t think you are cooked, but that doesn’t mean I can tell you with great certainty how things will play out. What I can do is point you towards the toolkit for how to make better decisions here.

Embracing uncertainty

Life is filled with uncertainty. Many people react irrationally to uncertainty, avoiding it too much or betting too much on luck. Learning how to deal rationally with uncertainty can give you an advantage throughout your life.

From 2010 to 2016, I built and then led the supply chain and capacity planning systems for AWS Infrastructure. My biggest lesson is dollar for dollar, people are overly biased towards investing in prediction when they are often better suited to invest in flexibility. Time series forecasting tools take the past and extend it out to the future. The further out you go, the more variance you get. And black swan style events, like when Thailand becomes flooded and you lose a healthy portion of the world’s hard-drive manufacturing capacity, are not frequent enough to learn from in a predictable way. Better to get a good enough forecast, but instead focus on shortening your lead-times, making your supply fungible—meaning interchangeable and adaptable to different uses—and late-binding your decisions as much as possible.

The parallel to career planning is direct. You can spend a lot of time trying to accurately predict where LLMs will take the industry and the job market. But that will quickly hit diminishing returns. I would instead approach the question from the other angle: what skills are most likely to be durable and fungible—that is, transferable and valuable across different contexts—in a wide variety of potential outcomes? Going whole hog into “I’m going to build my career around being a React developer” is betting on one very specific outcome. If it pays off, great, you can probably command a premium if you turn out to be one of the world’s best React developers. But what happens when React joins jQuery in the graveyard of once-essential frameworks?

An Interlude about Koalas

When I graduated from college, I went to work for Lawrence Livermore National Labs as a computer scientist. I was working on translating large-scale semantic graph algorithms into usable interfaces for intelligence analysts. I had personally received an award from the Secretary of Homeland Security. We had the academic freedom to explore whatever angles we wanted. There was little pressure to meet deadlines. It felt like a safe and secure job for life working in my little niche. My former professor and boss, Dr. Staley, called me up and said his new startup was just acquired by some struggling online bookseller called Amazon. I wasn’t super interested, as I could see existing in my current niche for my whole life.

He convinced me to join by telling me a story about koalas. Koalas primarily subsist on eucalyptus leaves. Most other animals don’t eat eucalyptus, because they have little to no nutrition and they are kind of toxic. But koalas have built their entire evolutionary strategy around being the ones to eat eucalyptus leaves. This has been a great and successful strategy for koalas. But what happens if the eucalyptus forest goes away? Koalas are screwed. Does that mean koalas are actually in danger? No, but it does mean their fate is entirely bound to that one food source existing, while an animal like a rat can happily live and thrive in many ecosystems and is thus much more resistant to shocks in any given ecosystem.

For some reason, that story convinced me to give Amazon a chance. Rather than focusing on a more niche area as defining “what I did” like “I’m the person who designs usability for mathematically intensive applications,” I instead built my career around solving hard technical problems regardless of the area.

What are those fungible skills?

When I look back at the skills I learned in university, many of the specific technologies I learned never got used. I learned all about expert systems, but never built an expert system. I learned all about OpenGL, never used it. What I learned from my computer science courses that stuck was the more fundamental ideas of how to think about hard technical problems and create simple, workable solutions to them. For this reason, I often recommend to students who ask me which classes to take that it is more important to take a class that is difficult with a high degree of rigor that challenges you than to focus on any particular domain. Surprisingly, in retrospect, I’ve gotten as much use out of the philosophy classes I took—that CalPoly tried to kick me out for taking too many of—as I did my computer science classes. Learning critical thinking skills, how to navigate difficult ethical situations, how to communicate difficult ideas. When Amazon asked me to design a system that could fairly allocate scarce resources across competing teams, it wasn’t my coding skills that mattered most—it was my ability to think critically about the problem space and use data to understand and communicate trade-offs to executives who each thought their project was most important. What I’d say is my computer science skills were 95% of what initially got me in the door, but it was my liberal arts skills that dominated my later career.

So my answer is, whatever you do, take on challenging problems, regardless of the area, so you can learn the meta-cognitive skills to understand how you learn and face up to these challenges. Learn critical thinking and how to tear apart problems to turn them from intractable to tractable. And don’t neglect the human-side of building your ability to communicate and deal ethically and fairly with others.

Yes, LLMs are different from outsourcing or the dot-com bust. They can actually write code—not just cheaper, but instantaneously. And yes, I’ve seen the headlines: 27% of programming jobs are gone, engineers facing hundreds of rejections. While I’m skeptical that this disruption is solely due to LLMs (e.g., a mix of post COVID overhiring, interest rate hikes, and broader economic shifts) there’s no doubt that a painful market correction is underway. But remember: every technological disruption feels unprecedented while it’s happening. The telephone operators watching automatic switches get installed thought their world was ending. They were right about their specific job—wrong about their ability to adapt. The question isn’t whether LLMs will change things—they will. The question is whether you’ll be a koala or a rat when they do.

So no, you’re not necessarily cooked. But you might be if you specialize too narrowly in whatever framework or language seems hot today. The jobs that are available will be different, and many of the existing software roles will not exist, at least in their current form. Build skills that transfer. Solve hard problems. Learn to think, not just code. The future needs people who can work with AI, not be replaced by it. And that future is built on the same foundation it always was: adaptability, critical thinking, and the uniquely human ability to navigate uncertainty with wisdom rather than fear.