Why Technical Skills Matter More in the Age of AI
There’s a story people like to tell about AI.
It goes like this: once machines can write code, technical skill stops mattering. Everyone can build. The playing field levels. Engineers become optional.
That story sounds reasonable, if you think programming is about producing code.
It isn’t.
What AI actually did was remove friction from execution.
The part of the job that used to take the most visible effort now takes the least. You can spin up code, tests, scripts, and scaffolding faster than you can decide what you even want.
When execution becomes easy, the advantage shifts to people who can steer.
Anyone can ask an agent to write code. Far fewer people can recognize when that code is wrong, incomplete, unsafe, or quietly harmful.
Not everyone knows:
- where a system will crack under load
- what will fail silently
- which shortcut turns into long-term tax
- what “working” means in production, not in a demo
Those instincts come from building systems, watching them fail, and cleaning up the mess.
AI magnifies that experience.
Someone who understands systems can now test ideas at a speed that used to be impossible. Someone who doesn’t just ships faster mistakes.
Output may have exploded but understanding didn’t. This is the part most people miss.
AI massively increased output, but it didn’t increase comprehension at the same rate. Code is easier to generate than to reason about.
That creates a new bottleneck: judgment.
When you can produce more than you can review, the skill that matters is the ability to quickly tell if code is correct, safe, worth keeping, and moving us forward or just adding weight.
Great engineers are skilled in exactly that.
Not because they’re better typists, but because they’ve internalized how systems behave.
The more you use AI in domains you understand deeply, the clearer it is that AI is a tool that produces both terrible and exceptional results at the same time.
The difference isn’t the model. It’s the steering.
Without a strong mental model of the system, you can’t tell which outputs are dangerous, which are shortcuts, and which are genuinely good. With one, AI becomes a force multiplier instead of a liability.
This is why technical skills have more leverage in the age of AI, not less.
You’re going to see great product teams moving at a pace that feels unreasonable. They can try ideas quickly, discard bad ones cheaply, and compound learning instead of effort.
Other teams will also generate mountains of code and still feel stuck.
Same tools. Very different outcomes.
Coding agents don’t primarily help you build more software. Writing code was never the main bottleneck.
They help you build better software, because you can experiment faster, explore alternatives, and invest more in quality: tests, CI, monitoring, and correctness.
There’s never been a better time to learn software engineering and develop a product sense.
Not because AI makes it easy but because it removes the dead time. You can iterate faster, see failures sooner, and build intuition in months that used to take years.
Deep understanding now compounds at a rate it never did before.