Comment by ainch
6 hours ago
I think the main argument would just be that because the model is general, you don't need to retrain it from scratch for a new problem - just tweak the input prompt. For a typical classifier there's a lot more hassle - collecting the data, training it yourself, retraining under distribution shift... In that sense Jev seems great for prototyping or small-scale use cases.
Counterargument: this works for quick prototyping, but for any serious business, you will eventually develop a benchmark/eval to track how well the general model is working, and once you have that dataset, you might as well train a specific model
Jev's bet is that if it works well enough for random use cases that nobody complains, then management won't feel a need to develop a benchmark/eval, and they won't need to employ all those data science guys.
I'd also add that they're hoping Jevon's Paradox also leads to a whole new segment of users who would have never reached for a classifier in the first place, given the barrier to entry.
2 replies →
Or not. And replace the generalist with the next generalist that gets you +15% on that benchmark for the same price, or gives you the same benchmark performance for half the price.
One advantage of using generalist models is that the generalists are improving - regardless of whether you're doing anything about it.
Training a classification model is trivial these days, even for a number far bigger than what Jev can do.