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.
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.
AI slashed the cost of shipping features but not the cost of reliability, so it amplifies whatever your organization already rewards. Most reward the wrong thing.
Stack Overflow's question volume fell 78% in a year. As the public corpus freezes, competitive advantage shifts to the knowledge your company still writes down itself.
Microsoft's engineers merged 24% more pull requests with AI. The constraint didn't vanish; it moved to review, and most teams are still counting the wrong thing.
Public coding benchmarks have decoupled from real work. The teams getting value from AI build a small evaluation from their own merged pull requests instead.
AI agent loops have made code fast and cheap to generate. The hard part, knowing what to build and staying in ownership of it, is still entirely yours.
SpaceX just paid $60 billion for a coding tool. The price is interesting. The justification -- a $26 trillion addressable market -- reveals more about how we price AI right now than about what it's actually worth.
