> For all the desperate founders rushing to train their models to convince their investors for their next $100 million round.
I would say Meta has (though not a startup) justified the expenditure.
By freely releasing llama they undercut every a huge swath of competition who can get funded during the hype. Then when the hype dies they can pick up what the real size of the market is, with much better margins than if there were a competitive market. Watch as one day they stop releasing free versions and start rent seeking on N+1
Right, but that is all predicated that, when they get to the end, having spent tons of nuclear fuel, container shiploads of GPUs and whole national GDPs on the project, there will be some juice worth all that squeeze.
And even if AI as we know it today is still relevant and useful in that future, and the marginal value per training-dollar stays (becomes?) positive, will they be able to defend that position against lesser, cheaper, but more agile AIs? What will the position even be that Llama2030 or whatever will be worth that much?
Like, I know that The Market says the expected payoff is there, but what is it?
With all these billions upon billions in AI hardware screaming along, are ads actually that much better targeted than they used to be?
I imagine admongers like Meta and Google have data that shows they are right to think they have a winning ticket in their AI behemoths, but if my YouTube could present any less relevant ads to me, I'd be actually impressed. They're intrusive, but actually they're so irrelevant that I can't even be bothered to block them, because I'm not going to start online gambling or order takeaways.
A better question, with a growing push for privacy, how can they keep ads from regressing?
There’s a lot more that goes into the ad space than just picking which ad to show you, and it’ obviously depends on who wants to reach you. For example, probabilistic attribution is an important component on confirming that you actually got the ad and took the action across multiple systems.
Also, since you mentioned it, TV ads tend to be less targeted because they’re not direct-action ads. Direct action ads exist in a medium where you can interact with the ad immediately. Those ads are targeted to you more, because they’re about getting you to click immediately.
TV ads are more about brand recognition or awareness. It’s about understanding the demographic who watches the show, and showing general ads to that group. Throw a little tracking in there for good measure, but it’s generally about reaching a large group of people with a common message.
You ask a great question, and I wonder how the push for more privacy will pan out (pardon the gold mining analogy). I am almost done with the very good new book The Tech Coup by Marietje Schaake, and I have also read Privacy is Power and Surveilance Capitalism. I think more of the public is waking up to the benefits of privacy.
All that said, I am an enthusiastic paying customer of YouTube Prime and Music, Colab (I love Colab), and sometimes GCP. For many years I have happily have told Google my music and YouTube preferences for content. I like to ask myself what I am getting for giving up privacy in a hopefully targeted and controlled way.
> Ironically, by supporting the LLM community with free compute-intense models, they’re decreasing demand (and price) for the compute.
For other people that that sentence didn't make sense for at first glance: "by supporting the LLM community with free compute-intense models [to run on their own hardware] they’re decreasing demand (and price) for the compute [server supply]."
They’re decreasing demand for expensive GPUs that would be required to train a model. Fine-tuning and inference are less compute intense, so overall demand for top-end GPU performance is decreased even if inference compute demand is increased.
Basically, why train an LLM from scratch, and spend millions on GPUs, when you can fine tune LLAMA and spend hundreds instead.
How fungible is that compute though? Having even a single H100 is different than having a bunch of 4090's, nevermind a properly networked supercomputer of H100s.
That’s the point. You can run inference on a 4090 but training is better on a H100. If you use llama, you don’t need to train on an H100, so you can free that supply up for meta.
I haven't been following llama closely but I thought the latest model was too big for inference on 4090's, and that you can't fine tune on 4090's either, but furthermore, the other question is if the market is there for running inference on 4090s.
Well, (1) there are a ton of GPUs out there of various specs, and you can also use an inference provider who can use a H100 or similar to serve multiple inference requests at once. (2) there are a ton of LLAMA sizes, from 1b, 2b, 8b, 70b, and 400b. The smaller ones can even run on phone GPUs.
It will be primarily gas, maybe some coal. The nuclear thing is largely a fantasy; the lead time on a brand new nuclear plant is realistically a decade, and it is implausible that the bubble will survive that long.
> there will be some juice worth all that squeeze.
Without the squeeze there'd be a risk for some AI company getting enough cash to buy out Facebook just for the user data. If you want to keep status quo it's good to undercut someone in the cradle that could eventually take over your business.
So it might cost Meta pretty penny but it's a mitigation for existential risk.
If you climbed up to the top of wealth and influence ladder you should spend all you can to kick off the ladder. It's gonna be always worth it. Unless you still fall because it wasn't enough.
I would say Meta has (though not a startup) justified the expenditure.
By freely releasing llama they undercut every a huge swath of competition who can get funded during the hype. Then when the hype dies they can pick up what the real size of the market is, with much better margins than if there were a competitive market. Watch as one day they stop releasing free versions and start rent seeking on N+1