Friends who know me well are aware that I'm deeply passionate about using AI to boost real-world productivity — and I've had solid results. But after a year or two of practice, while I've genuinely enjoyed the productivity gains, I've also unwittingly fallen into a trap. This trap has fundamentally limited my career growth, all while pushing me — relentlessly and unconsciously — toward the absurd goal of making myself easier to replace with AI. What follows is an analysis of this deeply ironic trap: why the people who are best at using AI to actually get things done are the ones most vulnerable to AI damaging their careers. And how we can break out of it.
An Absurd Career Story
Let me share my story. As an engineer at a mid-sized company, my AI-driven productivity boost looked like a massive success on the surface. Since the end of 2024, I've basically stopped writing code by hand the old-fashioned way, yet my output and rating have consistently been among the highest in the org (my VP described my pace as "superhuman"). This earned me a biweekly meeting with the CEO, CPO, and CTO (CXO for short) for over a year. And because I could reliably deliver solid results every two weeks while using AI to polish my slides, my presentations were some of the smoothest in the org.
Then my promotion failed. I tried again — failed again. Promotions are inherently somewhat random, and the real reasons are rarely the official ones. My promotion required C-suite approval, and getting shot down repeatedly was baffling — every meeting had gone so smoothly. My manager and I did a retrospective and came up with some possible explanations. But then I quit, and the whole thing was shelved.
Recently, as I've been furiously micromanaging AI to build things every day, something suddenly clicked. I realized there's one question that may have been the key factor holding my career back: in the eyes of my boss and the C-suite, was I their hand or their brain?
Being a "hand" means this: a CXO has an idea they want to test quickly, so they call me over and say, "Hey, go try this." Then I use AI to crank out a week's worth of work in a day, and two weeks later I've built an insanely impressive demo. The CXO sees it and says, "Damn, Yage, you're amazing — now try this other thing." Sometimes the new idea is related to the old one, sometimes not, depending on whatever the company's most valuable direction happens to be at the moment. In a perfect world, the CXO would keep my career development as an IC in mind and give me work with some coherent thread. In reality, the "most valuable direction" is rarely stable or even connected. This created an objective problem: every two weeks I delivered a ton of stuff, but the direction shifted every month or two — sometimes firefighting, sometimes new experiments. In the end, it was nearly impossible to tell a coherent story about what I had accomplished over the year. Both my VP and I noticed this, but it was hard to push back against. The mandate of heaven is hard to defy (jk).
Here's the irony: if I weren't so fast, I wouldn't have been put in the passive position of being a "hand" in the first place. I was simply too useful, with too little friction. Eventually the CXO arrived at the same conclusion I had about using AI: strike is cheaper than probe. Instead of spending time in meetings thinking through whether something can be built or how good it would be, just have Yage hand-build a prototype — it's faster and more accurate than debating it in a room. If it fails, no big deal, the guy's fast. Worse still, this rapid iteration tempts bosses into micromanagement, which meant my projects were more fragmented, had higher failure rates, and churned faster than anyone else's. When promotion time came, telling a coherent story became nearly impossible — a major liability. (Caveat: not the only reason.)
So here's the thing: my speed, my skill, my AI-amplified productivity — all of it ended up slotting me into a pure execution role in my bosses' eyes. The projects I got were fragmented and ever-shifting, putting me at a fundamental disadvantage for career growth. And this wasn't a "bad boss" problem. My VP and CXOs were genuinely reasonable and insightful. This isn't something you solve by switching managers. It's rational behavior. Think about how we use AI: we throw the fast model at anything just to see what happens, and fast models are easy to micromanage. And here's the even more absurd part — did you catch that I'm naturally using AI as an analogy for my own situation? That's because my execution role is eerily similar to an AI's. My boss sees me the same way I see AI. Now guess who, in the boss's eyes, is the easiest to replace with AI? In other words, the very fact that I'm good at using AI objectively pushed me into more execution work, which made me easier to replace with AI.
This is a fatal trap. And the cruelest irony: only people who are genuinely good at using AI fall into it. If your AI productivity gains are mediocre and your boss doesn't notice how useful you are — you won't even have this problem.
A Powerful Tool and a Fatal Trap: Incentive Structures
To understand this trap, we need to talk about a management concept: incentive structure. In plain terms, it's the reward system — what gets rewarded and under what conditions. Promotions and raises, like the ones I was chasing, are incentives. Companies use them as carrots to encourage specific behaviors. Amazon has its Leadership Principles, Facebook emphasizes Impact — people who embody these get promoted. That's the incentive structure.
This is everywhere once you start noticing it. In games, a certain class or tactic gets "nerfed" and suddenly no one plays it anymore — the incentive structure changed, so player behavior changed. National laws and policies are incentive structures too: tax breaks for EVs push people toward electric cars. Even AI model training works this way. We don't teach models parameter by parameter. Instead, we design a reward system, give the model a score, tell it where the gap is, and let it optimize itself to get higher scores. Sound familiar? It's not so different from career progression.
Incentive structures matter for two reasons. First, they're high leverage — incredibly efficient. Controlling what a hundred people think one by one is impossible. Convincing them all to "bias for action" through individual heart-to-hearts would take forever. But set up a reward system — achieve X and get recognition plus material rewards — and you move mountains with a light touch. People naturally slip into test-taker mode, optimizing for the score. And the beauty is, if the optimization target is well-designed, people's self-interested efforts end up achieving exactly what you wanted — often without them even realizing it. Which brings us to the second reason: this system's invisibility. Most people never realize the incentive structure was deliberately designed. They think it's just natural — work hard, get promoted. That very stealth is what makes it so powerful. This is why a manager's core responsibility is designing and maintaining these incentive systems, so team members unconsciously but actively pursue the outcomes the company wants.
This tool is purely rational, and we've already been arbitraging it without realizing. The dominant incentive structure in tech companies was designed in the pre-AI era: the more you deliver, the more you're rewarded. But AI has made delivery dirt cheap, and that shift created an arbitrage opportunity. So I changed my behavior and went all-in on delivery. But as we've seen, this arbitrage also created our predicament. The problem is that, unlike the examples above where influence only flows downward from superior to subordinate, here subordinates influence superiors right back. Take "strike is cheaper than probe." In the pre-AI era, writing code was slow and expensive, so the system objectively rewarded thinking before acting. Now, AI writes code so fast that while you're still deliberating, AI has already built every plausible implementation for you to pick from. This shift in the underlying cost structure is a change in the incentive system, and it alters the optimal strategy — stop thinking, just go for it. And here, AI is the subordinate and we're the superior. Our behavior is being shaped by our subordinates.
Be Your Own Manager: Design Your Boss's Incentive Structure
Back to my story — the same dynamic was at play. From my bosses' perspective, in the pre-AI era, assigning work to me was as annoying as assigning it to anyone else. They had to convince me the task mattered, sell the vision, keep an eye out for slacking. In the AI era, I became frictionless. No convincing needed — point me at something and I'd crush it, fast. This shift in the incentive structure led my bosses to unconsciously arbitrage it too, optimizing relentlessly: they'd increasingly throw me the dirty, thankless work nobody else wanted. Every decision in this chain was rational. My boss found a highly useful pair of hands and put them to good use. I felt like I was arbitraging the system — using AI to deliver more. But here's the problem: both my boss and I were merely reacting passively to this organic incentive structure, each finding our own local optimum. The result was a waste of resources: my boss wasn't extracting my full value, and my career growth was stunted.
If everyone's decisions are rational, how do we break out of this? Or more broadly: in the AI era, how do we prevent skilled AI users from being pigeonholed into execution roles and becoming more replaceable? The answer is simple: be a good manager. Actively manage the incentive structures around you.
Concretely: when making decisions, don't just react passively to the incentive structures around you — "the system rewards more output, so I'll use AI to output more." Go proactive: design the incentive structures you present to others. Sure, we can't give our boss a raise or a promotion. Our lever is friction. Unconditionally high-quality delivery creates the impression that throwing grunt work at me is easy. So I need to first judge whether a task is grunt work, whether it's even worth doing, and push back when it's not. Locally, this might seem counterproductive — even harmful. I'd be contradicting my boss more, which would definitely annoy him. Meetings would get bumpy. My boss might push back against my pushback, and we might waste more time arguing than if I'd just shut up and done the work. But in the long run, his rational decision-making might actually tilt toward the win-win outcome we both want.
Notice I said "might." Management has many nuances. Randomly resisting won't stop the grunt work from coming — it'll just make your boss think you're unreliable and difficult. Pushback only works if you genuinely have good judgment: you need to push back on genuinely bad ideas and pour your energy into the good ones, so your boss comes to see you as decisive and accountable. There are many variables and subtleties here that I won't dive into, mainly because once we adopt this perspective, we're no longer discussing a new problem at all. This is a centuries-old phenomenon: why workhorses don't get ahead. AI or no AI, being a workhorse — being your boss's hands — will never pay as well as being your boss's brain. With AI, more people can deliver fast, the workhorse population has exploded, and workhorses have become even cheaper. People who can discuss things as equals with their boss, push back, spot blind spots, and build genuine alignment — those people are rarer and more valuable than ever.
So the right way to arbitrage with AI is not to react to the existing incentive structure by cranking out more output. That's drinking poison to quench thirst — it naturally creates an incentive structure where our interests and our boss's are in conflict. The boss will unconsciously optimize against us, and the more they optimize, the more passive we become. No matter how hard we or our boss try, this is a losing battle. The better path is to actively manage the incentive structures we present to others, so that in those systems, people rationally and spontaneously place us in the most important position. What counts as "most important" is subjective — less work, more money, faster promotion, often contradictory. Finding your sweet spot is a personal choice. But whatever your goal, expecting AI-boosted productivity and hard delivery to get you there is unrealistic. We need to be good managers: proactively design and maintain the incentive structure, then let others work toward our goals of their own accord. That's the high-leverage, low-effort approach. And designing this system is something AI can accelerate too. Compared to using AI to crank out more code, sharpening judgment and building systems around people — that's a far more efficient use of AI.
To sum it up: using AI to mindlessly output code is the path of least resistance. Using AI to think deeply and produce judgment — that's the high-leverage, competitive career move.