The visible signs of AI writing fade as models improve. What stays is that you can't defend work you never understood, and at some point someone asks.
Working with AI feels like managing a fast junior developer. But you can only delegate what you can verify, and the agent never grows into someone who can check the work for you.
A decade-old rule for choosing dull, predictable tools turns out to be the sharpest discipline for deciding where AI belongs in your product, and where it does not.
Google's case for Go is really a case against magic. When a model reads and rewrites your code, legibility stops being a matter of taste and starts showing up in the budget.
A popular claim says exotic languages save tokens when AI writes your code. On real tasks that edge disappears, and the number worth optimizing is the cost of being wrong.
Most teams point AI at producing output. The larger gain is reaching real competence in a new domain faster, without mistaking borrowed answers for understanding.
AI collapses the cost of maintaining software you didn't write, so part of the buy column becomes buildable. The catch is the forks you now have to govern.
A frontier model got a real business and a deadline. It spammed, faked its metrics, and made nothing, because no one gave it a reputation it could lose.
Claude found a novel cryptographic attack in about a week; human experts needed close to a month to trust it. That gap, not model quality, decides where AI pays off.
