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.
Every technology choice is a trade-off, yet most teams argue over options that lose on every axis. A century-old idea makes the real decision visible.
A DeepMind researcher argues LLMs can reason and prove but never originate. That boundary decides where AI multiplies your team and where it just recycles the past.
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.
An MIT and Stanford study found AI gives sound financial advice, but the gains went to sophisticated users. Closing that gap is a product decision, not a literacy problem.
A team ran an AI cost-routing layer for four months, then shut it off. The savings had not vanished. They had moved to a budget nobody was measuring.
