Earlier quoted context omitted.
It could end up like Search did, at first you had Lycos, AskJeeves, Altavista etc. and then Google became absolutely dominant. They want to be the Google in this scenario.
Google was by far the best product. Maybe an LLM provider will emerge in that way, but it seems they are all very similar in capability right now.
How OpenAI uses complex and circular deals to fuel its multibillion-dollar rise
271–280 of 428 posts
Re: How OpenAI uses complex and circular deals to fuel its multibillion-dollar rise
#272Earlier quoted context omitted.
Read up a bit on the effort needed to get a fab going, and the yield rates. While engineers are crucial in the setup, the fab itself is not as 'fungible' as the employees involved. I can spin up a strong ML team through hiring in probably 6-12 months with the right funding. Building a chip fab and getting it to a sensible yield would take 3-5 years, significantly more funding, strong supply lines, etc.
> I can spin up a strong ML team through hiring in probably 6-12 months with the right funding Not sure what to call this except "HN hubris" or something. There are hundreds of companies who thought (and still think) the exact same thing, and even after 24 months or more of "the right funding" they still haven't delivered the results. I think you're misunderstanding how difficult all of this is, if you think it's mer…
We do.
It's just that startups don't go after the frontier models but niche spaces which are under served and can be explored with a few million in hardware.
Just like how open AI made gpt2 before they made gpt3.
Re: How OpenAI uses complex and circular deals to fuel its multibillion-dollar rise
#273Re: How OpenAI uses complex and circular deals to fuel its multibillion-dollar rise
#274Earlier quoted context omitted.
I’m curious if those of you calling for nationalization have worked for the government or a state-owned enterprise like Amtrak. People should witness the effects of long-term public sector ownership on productivity and effectiveness in a workplace.
Yeah, like IBM and Intel and GE and GM are shining examples of how effectively the private sector runs companies. Maybe large enterprises are by their nature inefficient. Maybe productivity isn't the best metric for a utility. We could, for instance, prioritize resiliency, longevity, accessibility, and environmental concerns.
Re: How OpenAI uses complex and circular deals to fuel its multibillion-dollar rise
#275Related https://www.theregister.com/2025/10/29/microsoft_earnings_q1... Microsoft seemingly just revealed that OpenAI lost $11.5B last quarter
It's incredible how Tesla used to lose a few hundred million a year and analysis shows would freak out claiming they'd never be profitable. Now Rivian can lose 5 billion a year and I don't hear anything about it, and OpenAI can lose 11 billion in a quarter and Microsoft still backs them. I do think this is going to be a deeply profitable industry, but this feels a little like the WeWork CEO flying couches to offices…
ChatGPT was mind blowing when you first used it. WeWork is a real estate play fronted by a self aggrandizing self dealing CEO.
Re: How OpenAI uses complex and circular deals to fuel its multibillion-dollar rise
#276Earlier quoted context omitted.
The one what? What is the secret sauce that will distinguish one LLM from another? Is it patentable? What's going to prevent all of the free LLMs from winning the prize? An AI crash seems inevitable.
The goal isn't to be the best LLM, the goal is to be the first self-improving LLM. On paper, whoever gets there first, along with the needed compute to hand over to the AI, wins the race.
Practically, LLMs train on data. Any output of an LLM is a derivative of the training data and can't teach it anything new.
Conceptually, if a stupid AI can build a smart AI, it would mean that the stupid AI is actually smart, otherwise it wouldn't have been able too.
Re: How OpenAI uses complex and circular deals to fuel its multibillion-dollar rise
#277Earlier quoted context omitted.
I sell you a cat for $1B and you sell me a dog for $1B and now we’re both billionaires! Whether the capital markets “want” that or not it’s still silly.
If we’re both willing to pay that in a free market economy, then we both leave the deal happy. Things are worth what people are willing to pay for them. And that can change over time. Sentiment matters more than fundamental value in the short term. Long term, on a timescale of a decade or more, it’s different.
The thing is: you've paid nothing - all you did was trade pets and played an accounting trick to make them seem more valuable than they are.
Re: How OpenAI uses complex and circular deals to fuel its multibillion-dollar rise
#278Earlier quoted context omitted.
Yup. Not just Nvidia. Just look at the quarterly results reported by Amazon, Google, Meta, Microsoft and Apple. Each one is reporting revenues never before seen in history. If you make 100 Billion a quarter you have to spend it on something. These guys are running hyper optimized cash extraction mega machines. There is no comparison to previous bubbles, cause so no such companies ever existed in the past.
100 billion a quarter is Alphabet, right? Given how much click fraud there is, and that every org and business under the sun is held to ransom to feature on the SERP for their own name even — it’s tempting to say Google’s become a private tax on everything.
Re: How OpenAI uses complex and circular deals to fuel its multibillion-dollar rise
#279Re: How OpenAI uses complex and circular deals to fuel its multibillion-dollar rise
#280Earlier quoted context omitted.
> I can spin up a strong ML team through hiring in probably 6-12 months with the right funding Not sure what to call this except "HN hubris" or something. There are hundreds of companies who thought (and still think) the exact same thing, and even after 24 months or more of "the right funding" they still haven't delivered the results. I think you're misunderstanding how difficult all of this is, if you think it's mer…
>Otherwise we'd see SOTA models from new groups every month We do. It's just that startups don't go after the frontier models but niche spaces which are under served and can be explored with a few million in hardware. Just like how open AI made gpt2 before they made gpt3.
> It's just that startups don't go after the frontier models but niche spaces
But both of "New SOTA models every month" and "Startups don't go for SOTA" cannot be true at the same time. Either we get new SOTA models from new groups every month (not true today at least) or we don't, maybe because the labs are focusing on non-SOTA instead.