Well there is a very specific architecture “rut” the LLMs people use have fallen into, and even small attempts to break out (like with Jamba) don’t seem to get much interest, unfortunately.
Sure, but LLMs aren’t the only AI being used, nor will they eliminate the other forms of AI. As people see issues with the big LLMs, development focus will change to adopt other approaches.
There is real risk that the hype cycle around LLMs will smother other research in the cradle when the bubble pops.
The hyperscalers are dumping tens of billions of dollars into infrastructure investment every single quarter right now on the promise of LLMs. If LLMs don’t turn into something with a tangible ROI, the term AI will become every bit as radioactive to investors in the future as it is lucrative right now.
Viable paths of research will become much harder to fund if investors get burned because the business model they’re funding right now doesn’t solidify beyond “trust us bro.”
Sure, but those are largely the big tech companies you’re talking about, and research tends to come from universities and private orgs. That funding hasn’t stopped, it just doesn’t get the headlines like massive investments into LLMs currently do. The market goes in cycles, and once it finds something new and promising, it’ll dump money into it until the next hot thing comes along.
There will be massive market consequences if AI fails to deliver on its promises (and I think it will, because the promises are ridiculous), and we get those every so often. If we look back about 25 years, we saw the same thing w/ the dotcom craze, where anything with a website got obscene amounts of funding, even if they didn’t have a viable business model, and we had a massive crash. But important websites survived that bubble bursting, and the market recovered pretty quickly and within a decade we had yet another massive market correction due to another bubble (the housing market, mostly due to corruption in the financial sector).
That’s how the market goes. I think AI will crash, and I think it’ll likely crash in the next 5 years or so, but the underlying technologies will absolutely be a core part of our day-to-day life in the same way the Internet is after the dotcom burst. It’ll also look quite a bit different IMO than what we’re seeing today, and within 10 years of that crash, we’ll likely be beyond where we were just before the crash, at least in terms of overall market capitalization.
It’s a messy cycle, but it seems to work pretty well in aggregate.
Sure, but those are largely the big tech companies you’re talking about, and research tends to come from universities and private orgs.
Well, that’s because the hyperscalers are the only ones who can afford it at this point. Altman has said ChatGPT 4 training cost in the neighborhood of $100M (largely subsidized by Microsoft). The scale of capital being set on fire in the pursuit of LLMs is just staggering. That’s why I think the failure of LLMs will have serious knock-on effects with AI research generally.
To be clear: I don’t disagree with you re: the fact that AI research will continue and will eventually recover. I just think that if the LLM bubble pops, it’s going to set things back for years because it will be much more difficult for researchers to get funded for a long time going forward. It won’t be “LLMs fail and everyone else continues on as normal,” it’s going to be “LLMs fail and have significant collateral damage on the research community.”
The scale of capital being set on fire in the pursuit of LLMs is just staggering.
I’m guessing you weren’t around in the 90s then? Because the amount of money set on fire on stupid dotcom startups was also staggering. Yet here we are, the winners survived and the market is completely recovered now (took about 15 years because 2008 happened).
I just think that if the LLM bubble pops, it’s going to set things back for years because it will be much more difficult for researchers to get funded for a long time going forward
Maybe. Or if the research is promising enough, investors will dump money into it just like they did with LLMs, and we’ll be right back where we are now with ridiculous valuations.
I’m guessing you weren’t around in the 90s then? Because the amount of money set on fire on stupid dotcom startups was also staggering.
The scale is very different. OpenAI needs to raise capital at a valuation far higher than any other startup in history just to keep the doors open another 18-24 months. And then continue to do so.
There’s also a very large difference between far ranging bad investments and extremely concentrated ones. The current bubble is distinctly the latter. There hasn’t really been a bubble completely dependent on massive capital investments by a handful of major players like this before.
There’s OpenAI and Anthropic (and by proxy MS/Google/Amazon). Meta is a lesser player. Musk-backed companies are pretty much teetering at the edge of also rans and there’s a huge cliff for everything after that.
It’s hard for me to imagine investors that don’t understand the technology now but getting burned by it being enthusiastic about investing in a new technology they don’t understand that promises the same things, but is totally different this time, trust me. Institutional and systemic trauma is real.
(took about 15 years because 2008 happened).
I mean, that’s kind of exactly what I’m saying? Not that it’s irrecoverable, but that losing a decade plus of progress is significant. I think the disconnect is that you don’t seem to think that’s a big deal as long as things eventually bounce back. I see that as potentially losing out on a generation worth of researchers and one of the largest opportunity costs associated with the LLM craze.
I mean… it is machine learning.
It’s also neural networks, and probably some other CS structures.
AI is a category, and even specific implementations tend to use multiple techniques.
Well there is a very specific architecture “rut” the LLMs people use have fallen into, and even small attempts to break out (like with Jamba) don’t seem to get much interest, unfortunately.
Sure, but LLMs aren’t the only AI being used, nor will they eliminate the other forms of AI. As people see issues with the big LLMs, development focus will change to adopt other approaches.
There is real risk that the hype cycle around LLMs will smother other research in the cradle when the bubble pops.
The hyperscalers are dumping tens of billions of dollars into infrastructure investment every single quarter right now on the promise of LLMs. If LLMs don’t turn into something with a tangible ROI, the term AI will become every bit as radioactive to investors in the future as it is lucrative right now.
Viable paths of research will become much harder to fund if investors get burned because the business model they’re funding right now doesn’t solidify beyond “trust us bro.”
Well you say that, but somehow crypto is still around despite most schemes being (IMO) a much more explicit scam. We have politicans supporting it.
Sure, but those are largely the big tech companies you’re talking about, and research tends to come from universities and private orgs. That funding hasn’t stopped, it just doesn’t get the headlines like massive investments into LLMs currently do. The market goes in cycles, and once it finds something new and promising, it’ll dump money into it until the next hot thing comes along.
There will be massive market consequences if AI fails to deliver on its promises (and I think it will, because the promises are ridiculous), and we get those every so often. If we look back about 25 years, we saw the same thing w/ the dotcom craze, where anything with a website got obscene amounts of funding, even if they didn’t have a viable business model, and we had a massive crash. But important websites survived that bubble bursting, and the market recovered pretty quickly and within a decade we had yet another massive market correction due to another bubble (the housing market, mostly due to corruption in the financial sector).
That’s how the market goes. I think AI will crash, and I think it’ll likely crash in the next 5 years or so, but the underlying technologies will absolutely be a core part of our day-to-day life in the same way the Internet is after the dotcom burst. It’ll also look quite a bit different IMO than what we’re seeing today, and within 10 years of that crash, we’ll likely be beyond where we were just before the crash, at least in terms of overall market capitalization.
It’s a messy cycle, but it seems to work pretty well in aggregate.
Well, that’s because the hyperscalers are the only ones who can afford it at this point. Altman has said ChatGPT 4 training cost in the neighborhood of $100M (largely subsidized by Microsoft). The scale of capital being set on fire in the pursuit of LLMs is just staggering. That’s why I think the failure of LLMs will have serious knock-on effects with AI research generally.
To be clear: I don’t disagree with you re: the fact that AI research will continue and will eventually recover. I just think that if the LLM bubble pops, it’s going to set things back for years because it will be much more difficult for researchers to get funded for a long time going forward. It won’t be “LLMs fail and everyone else continues on as normal,” it’s going to be “LLMs fail and have significant collateral damage on the research community.”
I’m guessing you weren’t around in the 90s then? Because the amount of money set on fire on stupid dotcom startups was also staggering. Yet here we are, the winners survived and the market is completely recovered now (took about 15 years because 2008 happened).
Maybe. Or if the research is promising enough, investors will dump money into it just like they did with LLMs, and we’ll be right back where we are now with ridiculous valuations.
The scale is very different. OpenAI needs to raise capital at a valuation far higher than any other startup in history just to keep the doors open another 18-24 months. And then continue to do so.
There’s also a very large difference between far ranging bad investments and extremely concentrated ones. The current bubble is distinctly the latter. There hasn’t really been a bubble completely dependent on massive capital investments by a handful of major players like this before.
There’s OpenAI and Anthropic (and by proxy MS/Google/Amazon). Meta is a lesser player. Musk-backed companies are pretty much teetering at the edge of also rans and there’s a huge cliff for everything after that.
It’s hard for me to imagine investors that don’t understand the technology now but getting burned by it being enthusiastic about investing in a new technology they don’t understand that promises the same things, but is totally different this time, trust me. Institutional and systemic trauma is real.
I mean, that’s kind of exactly what I’m saying? Not that it’s irrecoverable, but that losing a decade plus of progress is significant. I think the disconnect is that you don’t seem to think that’s a big deal as long as things eventually bounce back. I see that as potentially losing out on a generation worth of researchers and one of the largest opportunity costs associated with the LLM craze.