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> So your flagship model must be fastest and most accurate.

That's rarely the way anything in life works though. Do you prioritize fastest OR most accurate? That's a question that depends on context, task difficulty, etc.

You can use a 17M parameter NLI model for simple stuff and get orders of magnitude faster decisions. But the intelligence of that model will be quite a bit weaker.

In some tasks you might be talking about very small differences in accuracy between that model and a 27B parameter LLM decision model for several orders of magnitude in speed. In other tasks, the 17M might be about as good as a random guess.

But isn't there still a meaningful spread of difficulty, risk tolerance, etc. in decision model relevant tasks? Especially at high volumes I think there would still be value in being able to make intelligence/cost/speed/calibration tradeoffs between models

I've found this fairly accurate: https://huggingface.co/spaces/multimodalart/jev-decision-ind...

You seem to be confusing market capture for novelty. This almost certainly is not intended to be taken as some greenfield thing from perplexity, same goes for google

Perhaps, but if you're already inside GCP it's nice to have something easy to turn on.

> version of this easily reproducible technology

so you'd prefer indefinite first-mover monopolies?

And all the people involved are making millions to billions. Maybe we're the schmucks.

I don't know how we're going to convince the best and brightest minds of the future to do real work when grifting, corruption, and trend chasing pays the real money. Why try to cure cancer when you can make a guaranteed bag building drones that kill people, gambling apps, or ponzi scheme financial engineering?

I highly doubt it. Even if a company does internally, its highly unlikely they will reveal it publically.

One use case is live content moderation or streaming where reasoning models could be a lot slower and its hard to hardcode logic.