What happens when intelligence gets 100X cheaper?
@shashankgoyal95 sits down with @deedydas from @MenloVentures to unpack a few truths about AI economics:
- Token prices are down 40%, while usage has grown 4X.
- Optimizing costs too early can be the wrong move.
- The best AI products will increasingly be built across multiple models.
The conversation goes beyond cost: how fast model cycles are changing, why benchmarks only tell part of the story, and what happens when AI usage is still nowhere near its ceiling.
This is New Defaults, our event series exploring the shifts reshaping how AI gets built and used.
Watch the full episode on Youtube: https://t.co/V5IKrP0d6S
inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
Why it matters
Gives a working picture of where inferenceRunning a trained model to get answers — the phase where AI is actually used, as opposed to trained.Full definition → cost is heading, plus an argument for deferring cost optimization and designing products across several models rather than committing to one.
