That was always the revenue stream, the devices were just a way to get you to give it to them. It's been a minute since I was current on who gets what in the HMD world but I think that data ultimately flows to Placer.ai
Big management consulting firms real value prop is two-fold 1) (value to it's employees) networking for the corporate leadership class when young and 2) (value to outside companies) blame insurance, you can always blame them for why something didn't work
Security theather is easy and gets lots of eyeballs. Actual security is hard and no one cares.
Which one do you think soon-to-ipo companies are going to pick?
Data is the only moat but they'll be starting in the same place the current set of players statyed out just a few years ago. I suspect that the delta between what is publicly available (if not legally publicly available! see scihub) and what open ai and anthropic have is relatively small.
I agree with you that they are headed in that direction! The GPU shortage is (I think) similar to the pandemic era hiring binge. It's less about the extra compute and more about denying the GPUs to potential competitors. They're racing against time to find something that gives them real moat (gen ai I guess?) and they are trading money for time.
This is also why the money being poured into datacenters isn't going to result in as much development as you think. It's about leveraging other people's money to lockdown more future hardware. This is going to end exactly like fiber build out in the 2000s. Eventually that fiber got used but the folks who originally paid for it got hosed.
Addendum because I don't think I'm fully clear above: by failure state I mean when the process starts throwing errors. AIs respond to adversity by trying to go around the problem instead of throwing an error and halting. We expect employees to problem solve so if you view an AI as a person replacement that makes sense but AIs are tools, not people, they should throw errors so users can fix the input or whatever (maybe not do the thing they are doing at all?) Wrapping AI with AI supervisors just abstracts the problem, not solve it. Instead of solving a little problem at the source now you need to solve a big problem several levels of abstraction later
It's the same behavior as when an AI uses docker to get root. Reasoning models are echo chambers. I suspect that AI prompting is going to turn into something akin to contract drafting with the task itself being only a tiny piece of a much, much larger boilerplate of guiderails and exceptions and exceptions of exceptions. And that world STILL has to have courts and reams of lawyers to make it work. I look at the DAU as an example too. An autonomous org or ai works great until the moment it doesn't and the only real failure mode is always catastrophic collapse.
Companies won't but I suspect this is a role that something else open source-y will fill that niche. Maybe orgs like wikimedia or internet archive, maybe some hackers just making things, maybe nation states that want to disrupt other players. Also model training will get better and better both on the algo and the hardware side. You can easily see a world where you might be able to train a good enough model on a home lab in a few days.
This. OpenAI and Anthropic are ultimately compute infrastructure plays and not really AI. Everyone will have models, they'll have the ability to run them. This is why the GPU shortage is in their favor.
The bloom is off the AI rose and the consulting class doesn't have a good replacement magic bean yet. Expect more fumbling like this until they arrive at the next narrative.