So, about 2 years worth of operations based on alleged $14 billion burn rate projected for 2026.
What an absurd amount of money - if only this was invested in energy sector scientific research and development, or healthcare or anything else practical.
I really hoped to see compact molten salt nuclear reactors in operation before 2030.
I think it should be 70 billion, scratch that, 125 billion, scratch that - 180 trillion. I'd rather have pictures of crocodile Trump on the Moon than a warm house, scratch that, rather than kids, scratch that, rather than any decent future at all. Great move, guys, can't get enough of it.
> as part of the startup’s [OpenAI's] efforts to raise up to $100 billion
At this stage, why not go public? Yes, they would need to manage quarterly financial reports and answer to shareholders, but they have reached a size where they are in the top 20 range on the NASDAQ. These public companies doing well, so it seems like a logical next step.
I'm pretty sure I could become a billionaire just by meeting Masayoshi Son and having a chat for 15 minutes. I've never seen a better case for a fool and his money being parted.
It can be also just a trade part of a deal; like in YC companies, where investor Y buys company A from investor Z, and in exchange investor Z buys company B from investor Y, so the choo-choo train keeps running.
All of these things can be simultaneously true (and I would say, are true):
1) We are in a huge investment bubble right now and it's going to burst.
2) LLMs are extremely useful right now for certain niche tasks, especially software engineering.
3) LLMs have the potential to transform our world long-term (~10 yr horizon), on the order of the transformations wrought by the internet and mobile.
4) LLM's don't lead directly to AGI (no continuous learning), and we're not getting AGI any time soon.
This is an extremely obvious point, but bears repeating. I feel the assumption of an implicit link (in both truth or falsehood) between these fairly independent assertions can cause people to talk past each other about the really important questions in play here.
Regarding The Great Bubble, I am very very bearish about OpenAI in particular. They've had a good run for three years with consumer mindshare due to their first-mover advantage, but they have no moat, trouble monetizing most of their users, not much luck building out products that stick among consumers that aren't chatbots, and their models are no better than Anthropic's, Google's, or even the best Chinese open weight models 6 months later.
My bet would be on Google and Apple together (with Gemini powering Siri, for now) destroying OpenAI in the consumer AI market over the next 2-3 years. Google has first-rate models... but more than that, both Google and Apple have the enormous advantage of owning underlying platforms that they can use to put their own AI chat in front of consumers. Google has a mobile OS, the leading browser, and search. Apple has the premium hardware and the other, premium, mobile OS. They also have the advantage of the current regulatory climate being less antitrust than it was. And they don't have to monetize their AI offerings (no ads in gemini; ChatGPT is adding them) and can run them at a loss for as long as it takes to eat up OpenAI's market share. If they partner up, as they seem to be doing, OpenAI should very very afraid.
At ~$100B in AI funding, the question isn’t capital—it’s physical constraints.
Data centers need power (H100s are ~700W each), and recent capacity additions were mostly pre-allocated. Chip supply is also constrained by CoWoS packaging, not fab capacity, and expansions take years.
If power, packaging, and GPUs are fixed in the near term, does $100B mostly drive inflation in AI infrastructure prices rather than materially more deployed compute? Are we seeing the real cost of a usable GPU cluster rise faster than actual capacity?
Has anyone modeled what $100B actually buys in deployable compute over the next 2–3 years given these constraints—and whether that figure is shrinking as more capital piles in?
Oh it is driving inflation alright, inflation for everyone else because OpenAI are buying things like a years supply of DRAM with money they don't even have yet (surely that should be an investigation?).
Sure, let's throw more wild amounts of money at a wildly unprofitable company with no clear roadmap to profitability any time in the near-term, and in the long-term it's very probable that any arguably-useful use cases for LLM-based technology will be a commodity anyone can run anywhere.
My gosh, this bubble can't burst soon enough. It's a form of torture to keep waiting on the pain we all know is coming…
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[ 0.12 ms ] story [ 224 ms ] threadWhat an absurd amount of money - if only this was invested in energy sector scientific research and development, or healthcare or anything else practical.
I really hoped to see compact molten salt nuclear reactors in operation before 2030.
At this stage, why not go public? Yes, they would need to manage quarterly financial reports and answer to shareholders, but they have reached a size where they are in the top 20 range on the NASDAQ. These public companies doing well, so it seems like a logical next step.
1) We are in a huge investment bubble right now and it's going to burst.
2) LLMs are extremely useful right now for certain niche tasks, especially software engineering.
3) LLMs have the potential to transform our world long-term (~10 yr horizon), on the order of the transformations wrought by the internet and mobile.
4) LLM's don't lead directly to AGI (no continuous learning), and we're not getting AGI any time soon.
This is an extremely obvious point, but bears repeating. I feel the assumption of an implicit link (in both truth or falsehood) between these fairly independent assertions can cause people to talk past each other about the really important questions in play here.
Regarding The Great Bubble, I am very very bearish about OpenAI in particular. They've had a good run for three years with consumer mindshare due to their first-mover advantage, but they have no moat, trouble monetizing most of their users, not much luck building out products that stick among consumers that aren't chatbots, and their models are no better than Anthropic's, Google's, or even the best Chinese open weight models 6 months later.
My bet would be on Google and Apple together (with Gemini powering Siri, for now) destroying OpenAI in the consumer AI market over the next 2-3 years. Google has first-rate models... but more than that, both Google and Apple have the enormous advantage of owning underlying platforms that they can use to put their own AI chat in front of consumers. Google has a mobile OS, the leading browser, and search. Apple has the premium hardware and the other, premium, mobile OS. They also have the advantage of the current regulatory climate being less antitrust than it was. And they don't have to monetize their AI offerings (no ads in gemini; ChatGPT is adding them) and can run them at a loss for as long as it takes to eat up OpenAI's market share. If they partner up, as they seem to be doing, OpenAI should very very afraid.
Data centers need power (H100s are ~700W each), and recent capacity additions were mostly pre-allocated. Chip supply is also constrained by CoWoS packaging, not fab capacity, and expansions take years.
If power, packaging, and GPUs are fixed in the near term, does $100B mostly drive inflation in AI infrastructure prices rather than materially more deployed compute? Are we seeing the real cost of a usable GPU cluster rise faster than actual capacity?
Has anyone modeled what $100B actually buys in deployable compute over the next 2–3 years given these constraints—and whether that figure is shrinking as more capital piles in?
My gosh, this bubble can't burst soon enough. It's a form of torture to keep waiting on the pain we all know is coming…