You’ve felt it. That quiet, nagging panic when the code compiles perfectly on the first try, or the copy lands flawlessly, and you realize you have absolutely no idea why it worked.
We’ve been told LLMs are superpowers. They open up entirely new fields, letting backend engineers hack together frontend code or letting non-programmers build complex automations. But there’s a trap hiding in the syntax. When an LLM spits out an error, you don’t sit with it. You don’t trace the logic. You just copy the stack trace, paste it back into the chat, and let the model fix it.
Convenience and competence have become inversely correlated. The faster you ship, the less you actually know.
We thought the danger of AI was hallucinations or factual inaccuracies. The real danger is that it never lets you fail long enough to learn. As machine learning engineer Paolo Galeone recently pointed out, LLMs are actively eroding our savviness. He can spot the AI’s mistakes because he has years of hard-won experience. But the next generation won’t. They are skipping the struggle entirely.
Think about the old-school carpenters. When power tools and orbital sanders hit the market, woodworkers didn’t forget how to build chairs. But there’s a massive difference between using a sander to finish a piece of wood, and outsourcing the structural engineering of the chair to a machine. If you never have to plane the wood yourself, you never learn how the grain behaves.
If you never have to sit in the discomfort of your own mistakes, you aren’t losing your savviness—you are preventing it from forming in the first place.
This isn’t a philosophical debate about the future of work. It’s a biological reality about how humans build mental muscles. Expertise is forged in a specific feedback loop: effort, error, diagnosis, and correction. When you outsource the diagnosis and correction to an omnicompetent cloud API, you break the loop. You turn yourself into a quality assurance checker for a machine, rather than the author of your own work.
The quiet fear you feel every time you blindly copy-paste a block of code is completely justified. It’s the fear that your hard-won expertise is becoming optional. And the more you rely on the tool to fix your blind spots, the less you trust your own judgment when the tool is inevitably wrong.
The real division isn’t between those who use AI and those who don’t. It’s between those who use it to compress the learning loop, and those who use it to skip the loop entirely.
If you’re a coder, a writer, or an analyst, you are currently deciding whether your expertise survives the decade. When the tool breaks, or when the prompt hits the limits of the model’s training data, will you be able to catch the error? Or will you just keep hitting regenerate until something looks right?
The tool is here to stay. But your judgment is not a passive asset; it requires active defense. Keep the struggle. Sit with the bug for ten minutes before you ask the bot to fix it. Diagnose the error in your own logic before you outsource the correction. Because the day you stop trusting your own diagnosis is the day your expertise dies.
FAQ
Q: Isn't this just the same argument people made about calculators?
A: No. A calculator does the math for you, but you still have to set up the formula and understand the logic. LLMs generate the formula, write the logic, and fix the errors when they arise. They don't just compute answers; they remove the diagnostic struggle required to build expertise.
Q: How do I use AI without losing my edge?
A: You have to deliberately retain the struggle. Treat the AI as a peer reviewer, not an oracle. Do not let it fix errors blindly. Force yourself to diagnose the bug, understand why it happened, and only then use the AI to assist with the correction.
Q: If AI can do it faster, why does my 'savviness' even matter?
A: Because AI is probabilistic, not omniscient. When the model hallucinates or produces subtle logical flaws, you need deep domain expertise to catch it. If you outsource the creation, you won't have the expertise required to spot when the machine is confidently lying to you.