I am sure I am missing something obvious here, but why is that valuable? Like, what kinds of projects are there where you need to classify stuff but are unable to make a bespoke model targeting the specific problem?
On top, most EMs wouldn’t take a risk on an exploration of something “unknown” (to them) and couldn’t get buy-in from a PM.
I say this as an EM. Interview hundreds of people and, while, yes, some people don’t interview well, you might be shocked at the level of creative thinking. Even when “creative” is narrowly scoped to “this is a solved problem in a related domain.
It's more likely the product is focused on something else, but a classification model could come in handy...
What I said will only make sense if you take yourself out of your current context and think entirely from the perspective of someone who knows little-to-nothing about ML.
It’s the same mistake folks on HN made when Dropbox launched, drawing comparisons to rsync and other Unix tools as if they were somehow equivalent.
In practice, that's enough of a barrier to not even try the approach on a number of cases where it might potentially be useful.
I wouldn't be surprised if Jev turned out to be a "gateway drug" that validates approach on a use case, the team gathers experience and labeled data, and switches to an in house locally tuned model to minimize costs.
There is a fixed cost (and some maintenance) to e.g. fine tuning ModernBERT.
Maybe once you include all of that it might be a half-day to a day of engineering time to set everything up in a maintainable fashion.
For Jev, it takes all of 30 seconds of prompting. And it's not that much more expensive to deploy vs. a BERT model.