The Ground Is Moving

Here's How One Engineer Is Walking on It

I want to be upfront about what this post is and isn't. It isn't a list of five things you should do to stay relevant in the age of AI. I've read plenty of those, and I've tried to write one myself more than once. Every time, it came out sounding like advice I didn't actually have the standing to give. So this is the other kind of post — a description of how one person is muddling through, written precisely because I don't have the playbook.

The ground moved under me

The moment it became real for me was working with Claude Code on an internal project. I came in expecting to do what I've always done: tell the tool, in fine detail, how to accomplish a thing. Instead I found myself describing what I wanted and why, and handing off the details. The shift was quiet, almost embarrassingly small in the moment, and yet it kept nagging at me afterward.

I've lived through paradigm shifts before. Web 2.0. Mobile. Each one had its share of "I need to go do the cool new thing for clients." But those were fundamentally about learning a new tool. This one feels different to me. It isn't asking me to learn a new framework. It's asking me to re-evaluate my place in the market and what my job even is. I work as a consultant who helps clients understand and adopt emerging technology, which means I don't get to sit this out in some established niche. The ground is moving, and I'm standing on it.

The part I can't resolve

Here's the scariest thing, and I'd rather say it plainly than tuck it away: after nearly thirty years of building precise, complex systems, I don't know whether all that experience is an advantage or a trap.

I'd love to tell you that three decades of deconstructing hard problems gives me a head start on structuring intent for an AI. Some days I believe that. Other days I wonder whether my deterministic instincts — the habits that made me good at this — are exactly the reflexes I now have to unlearn. I genuinely can't tell yet. Both stories fit the facts I have.

That unresolved feeling is the reason this post exists at all. The anxiety got loud enough that I sat down and interviewed myself through an AI just to get the thoughts out of my head and into words. Which, I'll admit, is its own small act of walking forward without a map.

What actually changed, and how loosely I'm holding it

The phrase that's stuck with me is language is the new code. It crystallized while I was building agentic skills — the files an AI harness uses to take on new behavior without new source code. My first instinct was to write those skill files myself, in my own voice. Then it hit me how silly that was: I was already talking to the model to tell it what to do. Why not just describe the result I wanted and let it write the skills?

I'm leaning into that idea because it's interesting, not because I've decided it's the final truth. My working method is to treat it like a poorly documented API — try things, watch what happens, react. I could try to dress this up with a rigorous theory of how you "debug" natural language aimed at a probabilistic system, but I'd be pretending to a confidence I don't have. The honest version is: I'm still figuring out the rules, same as you.

Why I hope I can adapt (call it anecdata)

I want to be careful here, because my training is in data and the distinction matters. What follows isn't evidence. It's anecdotal — anecdata, if you'll let me. It's the pattern I see in my own history that makes me hope I'll be okay, offered as exactly that and nothing firmer.

Years ago I spent a pile of hours learning Erlang. It was commercially useless to me. I've never earned a single dollar writing Erlang. But Erlang forces you into a pure functional paradigm — you can't fall back on procedural or object-oriented habits the way you can in most languages. And when rich functional JavaScript came along, that transition was easy for me in a way it wasn't for a lot of my peers. Erlang never paid a dime. JavaScript bought my house. I believe the useless thing built the muscle that made the useful thing easy — though I'll be honest that I also just chose Erlang because it was fun, and I can't fully separate the two motives.

There's a related thread I've carried my whole career. I've fought the "jack-of-all-trades" label for as long as I can remember — the industry's quiet suspicion that a generalist isn't really an expert at anything. I used to joke that I was "a jack-of-all-trades and a master of many." Then I finished a master's in data science right before the LLM wave broke, and suddenly a background in CS, math, and data looked like brilliant foresight for a world of probabilistic systems. It wasn't foresight. I got lucky. I followed what I found interesting, and the timing happened to work out. I'm not going to dress that up as a plan.

The one boundary I'll stand behind

Consulting runs on timesheets and billable deliverables, and there's a permanent tension between that and the undirected play that keeps you sharp. Employers would love for you to predict the future and learn on your own time. I won't pretend I've solved that. But I have settled on one boundary that's genuinely mine.

I do learn off the clock. I'll own that. The condition is that off-the-clock learning happens strictly on my terms, on topics I actually enjoy. The moment an employer wants to direct what I learn, those hours come back on the clock and get paid. Wanting control over my off-hours means they aren't off-hours anymore. That's a negotiation I can have with an employer — even if there's no negotiating with the broader pressure of the industry itself.

The reason I hold that line connects straight back to the thing I can't resolve. I can't be sure my experience will save me. So I keep the one lever I do control: staying sharp. And the only learning I reliably stick with is the learning I enjoy. A dull mind can't pivot, and there is always a next thing to pivot to.

Doing things the hard way, with intent

So here's where I've landed, for now. If you feel like you're drowning in the new, you may not have to swim upstream after every client-driven framework. You can pick a different direction entirely — toward something that interests you, even if it looks low-value on paper.

But I don't want you to hear "just do something different." The point isn't difference for its own sake. The way I actually think about it is doing things the hard way, with intent. I take that partly from strength training: you can always make an exercise harder, and if a movement gets too easy, you're probably not creating enough stress to drive any adaptation. The deliberateness is the whole point. I don't want you walking away saying "this is different." I want you saying "I am picking a different direction to swim, with clear intent."

This post is itself an example of that. Writing a blog post by interviewing myself through an AI, then arguing with the model about the narrative arc, is an absurdly inefficient way to produce something that almost nobody will read. But the actual problem underneath it — how do you write in your own voice with an AI without letting the model flatten you into its voice? — is one I find genuinely interesting. Working through it might teach me something about countering model bias in my real work. Or it might not. Only time will tell, and I'm fine with that.

That's the most honest thing I can offer. The ground really is moving. A lot of us feel it. I don't know how it shakes out, and anyone who tells you they do is selling something. Forging ahead clear-eyed anyway — picking your direction on purpose, and giving yourself permission to not know — is, as far as I can tell, the whole job right now. It's just how I'm handling it. I'm not claiming to be right. But asking the hard questions instead of pretending to have the answers is how I keep the burnout at bay, and maybe that permission to not-know is the most useful thing I can pass along.