I totally agree, but I feel like some comments are making the mistake of stating "this bubble will pop, which means it was all smoke and mirrors to begin with".
The dot com bubble popped, but it's not like the Internet technologies that were launched then (and companies like Amazon and Google) weren't hugely impactful on all of society since then.
I think the AI bubble will pop, and while I think there is a lot of nonsense hype about AI I still think AI's societal impact will only grow.
No one who is saying the bubble will pop thinks there is nothing behind it. That’s the definition of a bubble: you always need soap and water to make it, but soap and water are commodities, not this special unicorn that will change the world.
Beanie Babies, Tulips, NFTs, Web3 tokens were all obviously not going to change the world. The bubble was pure emotion and greed. All the cash inflows were speculation.
Nvidia made 18 billion in profit last quarter, and expects to make 20 next quarter. That isn't speculation.
I mean, how much money did the NFT companies and Tulip vendors make? Nvidia isn't OpenAI. They're selling products that the bubble is built on, not the bubble itself.
How much money is OpenAI or Anthropic making? Because that's what people are thinking is speculative value.
My position has always been that Gemini/ChatGPT/Claude are all pretty great at a cost of Free, and grow increasingly questionable past that. My work is already limiting how many ChatGPT users we can afford with their price increases, and I'm pretty sure OpenAI is still not profitable. If ChatGPT is $50/month as a breakeven cost for them, how many people/companies will buy it then? Most jobs I've been at won't pay for JetBrains licenses that cost way less per head.
I feel like the best comparison is something like Uber or AirBnB where it's easy to be excited about it when all the services are crazy discounted by free VC money, but when they have to start turning a profit, they're back to actually competing with other tools.
The cost to run models of a specific quality goes down by about 90% a year. So the unprofitable $50/month cost becomes profitable in about 6 months.
But the big deal isn't OAI being a profitable company. The big deal is that GPT6 will be 100x more useful in doing productive work.
Tulips did not have cash flow like this. It was only people selling to speculators who hoped to sell again to another speculator.
> The big deal is that GPT6 will be 100x more useful in doing productive work.
Citation extremely needed. There's a lot of people and companies downstream of OpenAI speculating on that 100x that are gonna be in a lot of trouble if it's even just 10x, let along 5x.
Again, not saying that none of this has any value, just that the value may well never live up to the cost. Uber's not a worthless company or service, but they're far from the values or profits they were pitching 10 years ago.
I would point you to tow pieces of data.
1. Drive a new Tesla with the latest Supervised FSD and measure how often you have to intervene to stop a crash.
2. Go back and look at your own expectations around AI two years ago. Did thing progress the way you expected or did they progress further?
1. The friend I know with FSD has had it nearly kill him twice in the last year, but it does seem notably better, but in the sort of incremental way I'd expect. They keep it more as a novelty than a functional service.
2. If anything, GPT4 has turned out to be less of an advancement over 3.5 than either OpenAI was claiming and what I'd expected. 2 years ago, people were all but promising AGI by now. Even the folks I know working in the GenAI space are telling me they're using Copilot/ChatGPT less now than a year or so ago. My work has actively cut back on spending in the area and investors have been asking our board questions to make sure we're not overinvesting in it.
I want to be clear, I'm not a doomer at all about this. I use these tools a fair bit and find value in them. But the value that GPT3 and 3.5 brought to me versus what GPT 4 has brought certainly isn't 100x. GPT4 isn't even 100x better than me using Google Search most of the time.
1. You are comparing 2 very different things. GPT6 is a generative LLM. Tesla's FSD is machine learning.
2. I have no expectations for 'AI' because the term is a nonsense label. I have followed and been excited by machine learning for a good number of years, and my expectations of progress were pretty much on par. The progress with LLMs has taken me a little by surprise, but I am also cognisant that their progress is being massively over-hyped presently, not least by ppl who call them 'AI' and then, even more foolishly, go on to talk about 'AGI' (a nonsense upon a nonsense).
I agree that AGI is a meaningless term.
I'm intentionally including FSD and LLMs under the same category of technologies that will have a huge impact. The point of this thread is that the demand for inference is going to skyrocket because AI is going to get a lot more useful.
Putting aside my (trenchant) philosophical issues with the term 'AI', I also don't think just pragmatically that it's a good categorical label.
We both appear to agree that Machine Learning is a very powerful technology that will have huge impacts. Machine Learning requires (and will continue to require) a lot of compute and thus large costs but will also, almost certainly, produce great profits in some domains (FSD being one).
It's a lot less clear to me that LLMs will 1) continue to require lots of compute beyond the short term (languages can get close to being 'solved') or 2) that LLMs will generate substantial profits because a) the model can escape capture from a monopoly player far more easily and b) while useful for translation, pulling summarised data from a corpus, recognition of voice commands, etc, none of these applications actually make for the kind of profound impacts that ML is capable of, because none of them transcend human ability like ML has the power to do.
Reasoning and MultiModal are emerging out of the larger LLMs. That opens up more use cases, which then drive demand for inference. And that also drives demand for more research. It is hard to say exactly which use cases are going to be huge in a year but it seems very likely that more use cases will open up given how widely you could apply even a small amount of visual reasoning with robotics.