Every few months a new frontier model arrives, bigger and faster than the last. The benchmarks climb and one number climbs quietly alongside: the cost. Training runs now reportedly run into the hundreds of millions of dollars, and serving these models at scale is not far behind. Intelligence has become abundant but expensive - which raises an uncomfortable question. Do we actually need smarter AI, or do we need to get smarter about using the AI we already have?
We already have more than we use
Consider where we already are. Today's models can review, write, and verify dozens of documents in the time it takes to make coffee. They can read a sprawling codebase, propose a change, run the tests, and check their own work against the result. They can ingest a stack of contracts, flag the clauses that matter, and cross-reference them against policy - without a human babysitting each step.
This isn't a hypothetical future; it's a Tuesday. The frontier has moved so fast that most organizations are nowhere near using the current generation fully, let alone needing the next one. They keep buying a faster car every year and never drive above thirty.
The cost curve and the real bottleneck
