- cross-posted to:
- technology@lemmy.world
- cross-posted to:
- technology@lemmy.world
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O no, you mean the AI hype is another bs tech bubble?!
The funniest part is that all the AI hype is focused on all the wrong things. There are absolutely great AI tools that get very little mention.
For example, I’m visually impaired and use AI tools A fair bit to help me get around the internet and such. Especially when it comes to using AI I to generate descriptions of images.
O that’s very cool! I also heard it’s super good for auto subtitles (which admittedly is a bad thing for people doing that for a living 😿).
A 100+ billion dollar valuation.
Absurd.
It’s just as absurd as when WeWork got a 40 billion dollar valuation.
These VC’s are insane and should be paying a high-as-fuck tax rate instead of having a bazillion dollars to drop on boondoggles.
Ed is getting good at lobbing these darts at hype bubbles.
The thing that this writeup ignores is that the object isn’t to show short-term revenue, but to put all competitors out of business, be the last one standing, and create a monopoly. Either that or get bought out so the investors can move on to the next thing. But at $150B valuation, only MSFT or Nvidia can afford to buy them outright.
Google, Meta, and Amazon burned through cash for years, but they eventually outran all competition and then monetized the users who had nowhere else to go.
See that it’s never going to make money, go public, hand the keys over to someone else, and then try again with a wallet full of cash and a reputation for making billion dollar businesses.
Meanwhile people say way too personal stuff to chatgpt, copilot, bing, jetbrains, apple intelligence, etc.
I’m suspecting this might get sold off to data brokers
One thing I’d push back on in the article is:
That cost-per-user doesn’t decrease as you add more customers. You need more servers. More GPUs.
This is assuming constant use, which is not the case. If I have a server handling LLM prompt requests, and for illustrative purposes each request uses 100% of the single discrete GPU in it, and I only have 1 customer, but that one customer only uses it 5% of the day (which would actually be pretty high in real terms), I can still add additional customers without needing to buy additional servers. The question is whether the given revenue of a single server outweighs its cost to run.
And when it comes to training, that is an upfront cost, that you could (if you get a model to where you want it) stop having to pay whenever you want. I’m pretty surprised they haven’t been really leaning into training models for medical diagnoses, because once you have a model that can e.g. spot a type of tumor with n% accuracy beyond a human, you don’t really have to refine it further if you don’t want to (after all, it’s not like the humans can choose to do it better themselves at that point, like they can with writing prompts).
I’d say they’ve probably long reached the point where they have enough customers around the world to hold the load on their servers fairly constant. The example with one user only taking 5% of a servers load only works for low customer counts, similar to how you can’t count on one wind turbine or solar plant to provide all of your energy but if you have enough of them you can provide a base line of fairly constant energy
Not even a third of the way through… Holy crap.
And a few people will get extremely rich by siphoning off a percent or two, circle of life baby /s
