[devjab]

In document recognition they’re going to replace everything that came before. A couple of years ago you needed a couple of ML experts to setup, train and refine models that could parse through things like contracts, budgets, invoices and what not to extract key info that needed to be easily available for the business.

Now you need someone semi-proficient in Python who knows enough about deployment to get a local model running. Or alternatively skills to connect to some form of secure cloud LLM like what Microsoft peddles.

For us it meant that we could cut the work from 6-12 months to a couple of weeks for the initial deployment. And from months to days for adding new document types. It also meant we need one inexpensive employee for maybe 10% or their total time, where we needed a couple of expensive full time experts before. We actually didn’t have the problem with paying the experts, the real challenge was finding them. It was almost impossible to attract and keep ML talent because they had little interest in staying with you after the initial setups, since refining, retuning and adding new document types is “boring”.

As far as selling hardware goes I agree with you. Even if they have the opportunity to sell a lot right now it must be a very risk filled future. Local models can do quite a lot on very little computation power, and it’s not like a lot of use cases like our document one need to process fast. As long as it can get all our incoming documents done by the next day, maybe even by next week, it’ll be fine.

[olliej]

I mean I’ve not had any problems with whatever the macOS built in one is, but document recognition is a a tiny part of the industry and isn’t involved in the hype bubble at all.

The stuff with Python->traing->??->$$$ is what I don’t buy:

First: the “AI” stuff is “generate content with no obvious financial value to anyone”, chat bots (which no one I know actually seems to want), or “maybe better predictions”.

Second: the “person can do X with AI with less training” etc is not a value of AI, it’s just a product of improved libraries and UI for putting things together. It doesn’t mean the thing they’re doing with AI has any value outside of bandwagonning.

Third: the reason for AI start ups is just that training costs a tonne of capital - and VCs love throwing cash at bandwagons so there’s a pile of “AI” startups, all of which offering essentially the same thing below cost in the hopes that they’ll magically find a profit model.

Finally: there’s already near enough on device processing power on phones for most actual practical uses of “AI” so the need for massive gpu rigs will start to tank especially once the hype train dies off and people start asking what is actually useful in the giant AI startup buzz.

Each of these things is going to result in the valuation bubble for nvidia collapsing. Mercifully I don’t think there’s any real harm in the nvidia valuation bubble (congrats to the folk who made well on their RSUs!), but I still don’t think the valuation has significant longevity.