Intelligence-per-Token: Why AI's Cost Problem Is Forcing a Reckoning in 2026

Running large models is expensive. Everyone in the industry knew this, but for a while it was someone else's problem — a future problem, once revenue caught up. In 2026, the bill has come due. The phrase circulating now is "intelligence-per-token." Not capability in the abstract, but useful output per dollar of inference spend. It's an unglamorous metric, and that's kind of the point. After years of chasing benchmarks, labs are being forced to ask whether what they're building is actually economically viable to serve. TurboQuant Google's recent answer to this is TurboQuant, a compression algorithm built specifically for long-context inference. Feeding a model 100K+ token prompts — the kind of input needed for serious document analysis — has always been memory-intensive. At scale, serving those requests gets expensive fast. Quantization itself isn't new. Reducing the numerical precision of model weights to cut memory and compute overhead has been standard practice for a while. What Goog