Growth In The Age Of AI: One Computer Scientist’s Reckoning

As a computer scientist, I have a big dilemma with AI. It has sharply increased my productivity and accelerated the pace at which I can innovate, design, and ship my abstract ideas into real-world products. But using it every day for the last two years has raised an uncomfortable question.

For 20+ years, my skillset has run the whole gamut: ideation, research, prototyping and experimentation, algorithm design, system engineering, software implementation, testing and evaluation. I got good the only way anyone does, by figuring things out myself, or with collaborators, over and over. Reasoning through the minute details of an algorithm to be sure it worked correctly under every condition. Chasing a null pointer dereference for days through the memory module of my file system in C, until at some ungodly hour it finally made sense. Digging through the API documentation of one python ML library after another to be sure my model was doing what I thought. Working through every test case to cover all the ways my software could fail. That daily practice, and the friction that came with it, is how my skills sharpened and my understanding of computing deepened.

So what happens to that growth when the hard part becomes optional?

To be clear, my fear is not that AI is taking my job. On the contrary, I am now doing more than ever, exploring multiple ideas simultaneously, and converting ideas to products in hours or days instead of months. I could not be happier to stop poring over stack traces chasing memory bugs or reading confusing API documentation. It is closer and more personal than that. It is that AI slowly erodes my daily practice, the friction that made me good in the first place. So, the question for me isn’t whether I keep my job. It’s whether I keep growing.

My goal here is to explore this honestly for myself first and hopefully generate insights that may help others in my situation. I am not here to tell you AI will make us all obsolete, and I am not here to tell you it’s fine and we will all just move up the value chain. Both of those seem too simplistic in my opinion. I keep circling something more uncomfortable: the same tool that is making me more productive may be quietly eroding the very skills and understanding that made me good in the first place, and I can already feel it happening.

There are several angles to explore here. In this series I want to start with the question as it applies to me: a professional with two decades of experience watching the ground beneath my feet shift quickly. In a later article I will explore the harder version of it, i.e. what this means for the younger generation (my kids, 10 and 14) growing up with AI as a given, who never had to build the skills before the tool arrived to replace them.

My initial attempt at exploring this question quickly filled several pages, which would have created one boring long post. Instead, I decided to break it down into four posts, each exploring a topic connected to my main question. Each stands on its own but builds on the last.

Part 1: How AI is different. Why doesn’t AI feel like every other tool I have adopted in my 20+ years? I think it’s the first tool to automate the end-to-end reasoning itself, not just execution, and that’s what makes it different.

Part 2: How I Use AI Day to Day. Before I can judge what AI is doing to me, I have to be honest about how thoroughly it has woven into my work as an applied cybersecurity researcher, touching nearly everything from literature surveys to writing code to red-teaming systems. (coming soon)

Part 3: How AI Is Reshaping My Skills. The heart of it: where AI is making me genuinely better, where I can feel myself quietly eroding, and what the research on skill atrophy in other fields, pilots, surgeons, drivers, tells me about which of my own fears are real. (coming soon)

Part 4: How I Plan to Grow in the Age of AI. The practical stance I’m taking, the skills I’m choosing to protect and the new ones I need to build, and an honest accounting of how I’ve folded AI into my own workflow, including writing this very series, and whether I stayed on the right side of my own argument. (coming soon)

My goal with this series is to understand, in real time, how AI is reshaping the way I grow. I have a clear view of the problem and some sense of what to do about it, but I don’t pretend to have it all figured out. If you are someone in the same boat, I would genuinely like to hear where your experience matches mine and where it doesn’t.