What do you mean the "first" big? 1929? 2001? 2008?
Do you mean 1929 wasn't a big financial crisis and that, this time, we'll have the first "real" big financial crisis?
I'm confused.
https://www.reuters.com/legal/litigation/openai-ipo-will-not...
They could potentially do another private bridge round, but for a company that was gearing up for the largest IPO in history a couple months ago, the reversal is a pretty bad sign. For investors that are looking for a fire sale, there's already smoke in the air.
National debt through the roof, inflation through the roof, PHD and research programs gutted, non-ai startups dead and unfunded for the last 4 years. These are just a few things that have been sacrificed on the altar of this bubble - there's far more I haven't recounted.
We're already in a widespread long term economic collapse, but the delusion just hasn't broken yet.
If the cutting edge OpenAI token prices are $80 per 1M token, and the open source tokens are $1 per 1M token, that's a huge gap of "this will never be able to make money under any scenario if the bubble bursts" that will catch a lot of these new datacenters. No one will run a datacenter that costs $5 per 1M token to sell at $1 per 1M token even if the debts are cleared.
That $5 per 1M token doesn't literally cost $5 per 1M token. It's more like they had to build a datacenter for $500M that can service 100T tokens over its lifetime. They did this by borrowing money on the capital markets, and now they have to pay interest to those bondholders, interest that they can recoup with their $80/1MT prices. But if it turns out they can't charge $80 and have to charge $1, they won't be able to make those interest payments. They enter bankruptcy, the court wipes the debt clean, and now they don't have to pay interest, only the actual operating costs, which may be more like 50c/1MT. The company gets recapitalized with the new owners being largely the bondholders, the existing equity holders get wiped out, and they can compete with the commodity producers now.
Also, Google and Facebook are still spending like drunken sailors. Nobody has stubbed their toe on hard limitations yet. So yes of course people will figure out how to optimize the cost of AI in their products. Just probably not this year.
This just doesn't pass the smell test.
This tracks the evolution of how we use cloud computing and GPUs over the last 20 years. Cloud computing was originally just about saving companies from needing to maintain their own server racks, but has unlocked previously unserviced demand by allowing people to build an app or service and scale it to meet rapidly rising use without needing to invest a ton upfront in hardware. Suddenly a hobbyist could spin something up in their spare time that previously required many thousands of dollars of investment. Or when someone wants to run a single large scale computation, they can do it without needing to waste capital on maintaining idling servers to meet an occasional demand spike, like when a company I used to work at moved from running atmospheric calculations on a server in a closet to the cloud and were able to achieve double digit accuracy increases with the increased scale, while spending less overall on batch computing jobs.
GPUs, as the name implies, were created for graphics, and primarily for gaming graphics, but then more or less accidentally ended up enabling the present AI boom, which depends on a scale of computation that would have been impossible with older CPU architectures. Maybe someone at some point predicted this, but I think for the vast majority of people, it was extremely surprising that a niche gaming product would enable an industrial revolution level technological leap forward.
LLMs are just one way that increased compute scale unlocks seemingly magical results, but they are far from the only example and I have no doubt that there are many unknown examples remaining to be discovered yet.
I mean the physical hardware will be fine but the owners and investors should be worried then right
Don't worry, the taxpayer will be on the hook for everything just like in 2008. This time the relief mechanism is already baked into the system.
https://prospect.org/2026/08/03/ai-bailout-could-be-baked-in...
Let's also not forget that even the open models are not getting smaller, they are getting larger. Of course, you can distill them down into something that will fit on smaller compute, but at the end of the day, the data centers of compute, still play a huge role.
There is no loyalty in AI. I can switch to another model at near zero cost. They absolutely do have to demonstrate value. The concept of "good enough" is a misnomer because we're talking about putting these products into the hands of people who need to generate value from them.
Also the decision makers who are signing off on things like ChatGPT Enterprise are at least 18 months behind the curve of what you can actually do with these things and how cheap they can be. They're still trying to figure out how to actually adopt the tech out of a sense of fomo, nevermind making nuanced decisions about hosting an open weights model. I see this firsthand in my own job.
I'm talking about adding facts to a model by modifying engrams or trying to bolster guardrails with J-washing, meanwhile they're still trying to figure out how to best prompt Copilot.
Give it a few years for everyone else to catch up, I'm barely able to catch my breath before there's some new development in the open source/weights space
Not only bigger, but smarter and more capable. From what I can tell, smaller are only getting smarter in very specific areas. There is a subtle difference there, that is extremely important.
> What I can do with an 8b used to require a 32b.
What exactly do you do with an 8b? I usually ask this question and either get no response or it is something that doesn't generate anything of value. So, please surprise me.
Seriously, do you believe the garbage you wrote?
I am a software engineer, and using this software in my personal and professional work has lead me to a very different conclusion, but everyone is entitled to their opinion.
There will be people who want to host things on-device. At some point, you could probably do most day-to-day tasks with a Siri-like agent, so you don't necessarily need it to be on a datacenter rack somewhere.
More complex tasks being run quickly opens up a choice: insanely beefy individual devices, on-prem hosting, or cloud hosting, whether that be some data center running FOSS models, or ones from people like Anthropic or OpenAI.
Beefy hardware for individual users? Not cost-effective. Could have people share that hardware by putting it in a data center. Do you want to operate that data center? For proven business cases, sure, why not? If you're still working out what your scale will be, maybe you ask the Googles, Amazons, or Microsofts of the world to rent you the hardware so you don't have wasted or too little capacity.
The real question is, how much value is there in a few companies that talk about how their eventual goal is to create AGI as opposed to just giving you enough intelligence to augment your current workers?
The answer is "probably not enough to justify more than one company having a valuation of over a trillion dollars, and that's generous".
something has to true to be surprising. your statement is false and nonsese
New datacenter builds are so far along the curve of diminishing returns it's absurd. No one is going to want to pay to run a datacenter that costs 10x as much to run for the same compute.
And the problem is with all these "freed" resources, the entire pipeline will be affected. No one will want to buy any new silicon if they can buy a B200 for $1000. We could potentially see a decade or more of stagnation in the chip sector, or even significant regressions in capabilities as foundries are shut down due to lack of demand.
The impact of what is coming scares me to my core. I don't think we're going to bounce back from this any time soon.
toomuchtodo•59m ago