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I think the main problem with AI sustainability is that all this VC investment is burning through ungodly amounts of money to produce something that provides no

I think the main problem with AI sustainability is that all this VC investment is burning through ungodly amounts of money to produce something that provides no moat. Even if all AI investment froze tomorrow, I'd still have my 405B Llama 3.1 model, along with countless other smaller models, and I'd run them to do whatever the heck I felt like doing, with no commitment to any provider. Writing code with AI? I could swap to a local model. Costs nothing. Provides no revenue to any VC-backed company. Yes, bigger models will always command a premium for the highest end of reasoning. But you don't always need the best possible reasoning. GPT-3 and early GPT-4 were more than good enough for a ton of use cases last year. And we've seen the pace of development in the open source world these past two years. The open source community (Meta in particular) has completely obliterated the commercial value of these models. If there were no "weights available" models, OpenAI would have an incredible, unbeatable moat, and they would be worth truly astounding amounts of money. But as it stands, we all have free, unfettered access to local models far better and cheaper than models that were flagship 18 months ago, and close enough to the performance of current flagship models that it won't make a difference for a ton of use cases. There is no way to justify the current level of investment, with local models being freely available. The assumption I'm making, of course, is that this transformer technology won't ever lead to AGI - it will just be another tool in our ever-expanding tool-belt.

pmontra 2y ago
> Writing code with AI? I could swap to a local model. Costs nothing. Costs nothing? I'd have to buy a desktop computer with a good GPU to run that model. How much would that cost?
- nateglims 2y ago
  You need to own that or buy someone else's compute to run any LLM. The marginal cost of the open local models is free.
  - Arainach 2y ago
    You can get a LOT of compute credits, at the public rates the cloud providers charge (not OpenAI's discount) for the price of an NVIDIA 4xxx with 24GB or more of RAM. Enough to do what most people need AI to do for a long time.
- tabtab 2y ago
  I think the point is that anyone can enter the AI cloud market to compete with the fat cats if they get too pricy. NVIDIA's de-facto standards are the closest thing to a market moat. If AI ends up highly tied to NVIDIA's standards, then NVIDIA could become the Microsoft of AI. It's hard to know how tool chains will evolve to answer that though, it's new territory. (It could be all moot if the bots take over and eat us humans. I taste like chicken.)
- falcor84 2y ago
  You can get a second hand MacBook Air 16GB with Apple silicon from a couple of years ago for about $1,000 and get a decent performance from Llama 3.1
  - linotype 2y ago
    Right? Or a second hand desktop for $300 and put a $300 3060 in it and get even better performance. So many options.
- taneq 2y ago
  To run a 7b model? Not much. To run the 405b model mentioned, at interactive speeds, at reasonable quantisation? A lot.
nateglims 2y ago
It seems like OpenAI is trying to build a moat now, each generation appears to be less and less open to the point that you can't even see LLM reasoning steps. It's been a while since we've had a major tech company commodify/open source a major dependency (and not just layer some rent seeking pay/subscription service over existing assets). Meta seems to have the best long term plan for LLMs.
Animats 2y ago
"OpenAI will have to continue to raise more money than any startup has ever raised in history, in perpetuity, to survive." As of 2022, Uber had supposedly raised a total of $25.2B in funding over 32 rounds. So they may be ahead in the money-drain business. "There is no way to justify the current level of investment, with local models being freely available." The business problem OpenAI faces is that their systems aren't good enough that users can trust the results. Slightly crappier results are also available at much lower cost. Unless OpenAI can definitively fix the "hallucination" problem, and at least return "Don't know" when appropriate, this isn't going to work at OpenAI's price point. Chatbots are about as good as low-end outsourced call centers. They can sort of help with programming. The systems that generate pictures can do some impressive things. LLMs produce better blithering than most bloggers. It's really impressive. But, absent a major theoretical breakthrough, that's not enough to fund OpenAI.
- nateglims 2y ago
  One big difference is Uber only has a few peers and became instrumental before the realities of market size caught up to them. But every hyperscale data center owner is investing for themselves, major companies capable of the R&D are investing in their own science, and the market for LLMs is slow to materialize relative to the hype. That's money that might have gone to OpenAI if their situation was similar to Uber's.
- dartos 2y ago
  IIRC A proof was just published for the inevitability of hallucinations in the kinds of statistical models we use.
- roenxi 2y ago
  Humans hallucinate too though. A lot. A good tip for system design is to assume the human operator goes off the rails at some point and does something absurd and nonsensical. Hallucinations don't need to be fixed in one go as much as improved progressively. There is some magic threshold where they'll stop being an issue for specific tasks, or alternatively they simply become more reliable than humans. The problem will die with a whimper.
VirusNewbie 2y ago
interesting. I have not found Llama able to do any of the sort of 'reasoning' that the larger foundational models can do. It's too easily tricked up by small things. The larger models aren't perfect, but worlds apart from Llama.
seany62 2y ago
> Writing code with AI? I could swap to a local model. AWS only brings in 80B+ a year, must be from everyone self-hosting. I'm curious why people think this is different?
- ac29 2y ago
  AWS' moat, if they have one, is not in renting out generic compute. That's been done since before anyone ever heard the term "cloud". I think the point was that the things LLMs are useful for can increasingly be done with freely available models you can run on your own hardware, hardware you rent from someone else, or via a 3rd party's hosted service. Want to run Meta's models on Google's server? Nothing stopping you.
roenxi 2y ago
I like your comment, but it doesn't go far enough! The other issue here is the price of FLOPS is still dropping; so even if training the model weights was a moat it will probably evaporate quickly over the next few years if price trends hold. All this capital spend would have been much cheaper if deferred by a few years. If they aren't making massive profits right now then there may well be a problem.
- anjel 2y ago
  History is redolent with first-to-market fails followed by also-ran successes. This would appear to inform Apple's AI strategy, but not just since AI.
deleted 2y ago
[deleted]
klipklop 2y ago
Sounds like a good plan until the US and EU declare open-weight models "unsafe" and ban them. "It can help somebody create a bioweapon," etc. I would imagine lobbying efforts and scaremongering is already underway. This is a powerful technology that the general public (and small businesses) might not have access to in the near future.
elorant 2y ago
As it has been mentioned numerous times in the past, we are not the average user. I don't know anyone outside our bubble who would bother running a local LLM, or going into all the trouble building an x8 GPU rig to run one of the biggest models.
- bryanlarsen 2y ago
  Easy to run locally for geeks means easy to package up and sell to non-geeks.
  - elorant 2y ago
    Why would non-geeks want to run a local LLM? Electricity prices alone are quite high. Why not pay a subscription on a cloud service and be done with it.
    - xyc 2y ago
      Privacy could be one reason. There are a lot of cases where people do not want to send data to a cloud service.
    - bryanlarsen 2y ago
      My point is that you will have many cloud services to choose from if geeks can build them easily, which will keep prices down.
senko 2y ago
> If there were no "weights available" models, OpenAI would have an incredible, unbeatable moat Not really, as Claude is competitive both on quality and price. (agree with the rest of your comment)
raxxorraxor 2y ago
Not only that, but these models are at some point superior. Even if AI companies offer embedding services, which some do not or only recently added such capabilities, the best results are often a specifically tuned openly available model like llama. I do think this moat can still exist though, as providing a product is different than running a model somewhere, even if you have the compute power to allow prompting by a large amount of people. And making a model ready for the business user is quite a bit of work. Although true, the capabilities of commercial products will probably be eclipsed sooner rather than later. We saw that in image generation. Crowd sourcing the problem did produce better results. Generating videos is probably still locked behind enormous compute power (well, the 400+B model of Llama is too...) I think the focus of these companies isn't the performance of the model itself, it is providing interfaces to all kinds of systems and maybe solve a specific task.

pmontra 2y ago

> Writing code with AI? I could swap to a local model. Costs nothing. Costs nothing? I'd have to buy a desktop computer with a good GPU to run that model. How much would that cost?

nateglims 2y ago
You need to own that or buy someone else's compute to run any LLM. The marginal cost of the open local models is free.
- Arainach 2y ago
  You can get a LOT of compute credits, at the public rates the cloud providers charge (not OpenAI's discount) for the price of an NVIDIA 4xxx with 24GB or more of RAM. Enough to do what most people need AI to do for a long time.
tabtab 2y ago
I think the point is that anyone can enter the AI cloud market to compete with the fat cats if they get too pricy. NVIDIA's de-facto standards are the closest thing to a market moat. If AI ends up highly tied to NVIDIA's standards, then NVIDIA could become the Microsoft of AI. It's hard to know how tool chains will evolve to answer that though, it's new territory. (It could be all moot if the bots take over and eat us humans. I taste like chicken.)
falcor84 2y ago
You can get a second hand MacBook Air 16GB with Apple silicon from a couple of years ago for about $1,000 and get a decent performance from Llama 3.1
- linotype 2y ago
  Right? Or a second hand desktop for $300 and put a $300 3060 in it and get even better performance. So many options.
taneq 2y ago
To run a 7b model? Not much. To run the 405b model mentioned, at interactive speeds, at reasonable quantisation? A lot.

nateglims 2y ago

It seems like OpenAI is trying to build a moat now, each generation appears to be less and less open to the point that you can't even see LLM reasoning steps. It's been a while since we've had a major tech company commodify/open source a major dependency (and not just layer some rent seeking pay/subscription service over existing assets). Meta seems to have the best long term plan for LLMs.

Animats 2y ago

"OpenAI will have to continue to raise more money than any startup has ever raised in history, in perpetuity, to survive." As of 2022, Uber had supposedly raised a total of $25.2B in funding over 32 rounds. So they may be ahead in the money-drain business. "There is no way to justify the current level of investment, with local models being freely available." The business problem OpenAI faces is that their systems aren't good enough that users can trust the results. Slightly crappier results are also available at much lower cost. Unless OpenAI can definitively fix the "hallucination" problem, and at least return "Don't know" when appropriate, this isn't going to work at OpenAI's price point. Chatbots are about as good as low-end outsourced call centers. They can sort of help with programming. The systems that generate pictures can do some impressive things. LLMs produce better blithering than most bloggers. It's really impressive. But, absent a major theoretical breakthrough, that's not enough to fund OpenAI.

nateglims 2y ago
One big difference is Uber only has a few peers and became instrumental before the realities of market size caught up to them. But every hyperscale data center owner is investing for themselves, major companies capable of the R&D are investing in their own science, and the market for LLMs is slow to materialize relative to the hype. That's money that might have gone to OpenAI if their situation was similar to Uber's.
dartos 2y ago
IIRC A proof was just published for the inevitability of hallucinations in the kinds of statistical models we use.
roenxi 2y ago
Humans hallucinate too though. A lot. A good tip for system design is to assume the human operator goes off the rails at some point and does something absurd and nonsensical. Hallucinations don't need to be fixed in one go as much as improved progressively. There is some magic threshold where they'll stop being an issue for specific tasks, or alternatively they simply become more reliable than humans. The problem will die with a whimper.

VirusNewbie 2y ago

interesting. I have not found Llama able to do any of the sort of 'reasoning' that the larger foundational models can do. It's too easily tricked up by small things. The larger models aren't perfect, but worlds apart from Llama.

seany62 2y ago

> Writing code with AI? I could swap to a local model. AWS only brings in 80B+ a year, must be from everyone self-hosting. I'm curious why people think this is different?

ac29 2y ago
AWS' moat, if they have one, is not in renting out generic compute. That's been done since before anyone ever heard the term "cloud". I think the point was that the things LLMs are useful for can increasingly be done with freely available models you can run on your own hardware, hardware you rent from someone else, or via a 3rd party's hosted service. Want to run Meta's models on Google's server? Nothing stopping you.

roenxi 2y ago

I like your comment, but it doesn't go far enough! The other issue here is the price of FLOPS is still dropping; so even if training the model weights was a moat it will probably evaporate quickly over the next few years if price trends hold. All this capital spend would have been much cheaper if deferred by a few years. If they aren't making massive profits right now then there may well be a problem.

anjel 2y ago
History is redolent with first-to-market fails followed by also-ran successes. This would appear to inform Apple's AI strategy, but not just since AI.

deleted 2y ago

[deleted]

klipklop 2y ago

Sounds like a good plan until the US and EU declare open-weight models "unsafe" and ban them. "It can help somebody create a bioweapon," etc. I would imagine lobbying efforts and scaremongering is already underway. This is a powerful technology that the general public (and small businesses) might not have access to in the near future.

elorant 2y ago

As it has been mentioned numerous times in the past, we are not the average user. I don't know anyone outside our bubble who would bother running a local LLM, or going into all the trouble building an x8 GPU rig to run one of the biggest models.

bryanlarsen 2y ago
Easy to run locally for geeks means easy to package up and sell to non-geeks.
- elorant 2y ago
  Why would non-geeks want to run a local LLM? Electricity prices alone are quite high. Why not pay a subscription on a cloud service and be done with it.
  - xyc 2y ago
    Privacy could be one reason. There are a lot of cases where people do not want to send data to a cloud service.
  - bryanlarsen 2y ago
    My point is that you will have many cloud services to choose from if geeks can build them easily, which will keep prices down.

senko 2y ago

> If there were no "weights available" models, OpenAI would have an incredible, unbeatable moat Not really, as Claude is competitive both on quality and price. (agree with the rest of your comment)

raxxorraxor 2y ago

Not only that, but these models are at some point superior. Even if AI companies offer embedding services, which some do not or only recently added such capabilities, the best results are often a specifically tuned openly available model like llama. I do think this moat can still exist though, as providing a product is different than running a model somewhere, even if you have the compute power to allow prompting by a large amount of people. And making a model ready for the business user is quite a bit of work. Although true, the capabilities of commercial products will probably be eclipsed sooner rather than later. We saw that in image generation. Crowd sourcing the problem did produce better results. Generating videos is probably still locked behind enormous compute power (well, the 400+B model of Llama is too...) I think the focus of these companies isn't the performance of the model itself, it is providing interfaces to all kinds of systems and maybe solve a specific task.