Would anyone like to found a startup doing high-security embedded systems infrastructure? Peter at my username dot com if you’d like to connect.
I’d argue the other way around: 100M growth in two months suggests literally every single human being on Earth would benefit from using this all the time, and it’s just a matter of enabling them to.
Beware the sigmoidal curve, though. Growth is exponential till it’s not.
In what way does it suggest that? What level of growth is evidence that a product is universally useful?
That seems like pretty strong evidence that it is generally, if not universally, useful to everyone given the opportunity.
Anyway, I bet it will be really useful for cool stuff if it can ever run on my laptop!
The blockchain/bitcoin bros tried the same marketing spin. "Bitcoin will end poverty once we get it into everyone's hands." When that started slipping, NFTs will save us all.
Yeah. Sure. Been there. Done that. Just needs "more investment"... and then more... then more... all because of self reported "growth".
The latest LLMs are extraordinarily useful life agents as is. Most people would benefit from using them.
It'd be like pretending it's either water or education (pick one). The answer is both and you don't have to pick one or the other in reality at all. The entities trying to solve each aspect are typically different organization anyway.
hmm maybe that "would benefit" is a bit too vague?
"Ah, you're absolutely right! Have you tried looking in the shop?"
I'm not sure i understand the reasoning. lots of people use a thing, so everyone should?
For OpenAI I think the problem is that if eventually browsers, operating systems, phones, word processors [some other system people already use and/or pay for] integrate some form of generative AI that is good enough - and an integrated AI can be a lot less capable than the cutting edge to win, what will be the market for a stand alone AI for the general public.
There will always be a market for professional products, cutting edge research and coding tools, but I don’t think that makes a trillion dollar company.
This doesn’t make any sense. Popular is not the same as useful. You’d have a more compelling argument if you included data showing that all this increased LLM usage has had some kind of impact on productivity metrics.
Instead, some studies have shown that LLMs are making professionals less productive:
https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...
Selling 100B worth of stocks for anything close to 100B is not possible. That volume would mini-crash the entire exchange.
Have a search for "chatfishing".
Always a good dating strategy.
That aside, his math is wrong
edit: Aussies and kiwis too!
Why can't OpenAI keep projecting/promising massive data centre growth year after year, fail to deliver, and keep making Number Go Up?
Because eventually, Nvidia will run out of money, so the incestuous loop between Nvidia funding AI entities, who then use those funds to buy Nvidia chips, artificially propping up Nvidia's stock price, will eventually end and poof.
Competing forces are the market's insatiable need for growth every quarter, and other countries also chasing AIs and will not slow down if other countries, like the US, do slow down.
I've been thinking about American exceptionalism - they way it is head and shoulders above Europe and the developed world in terms of GDP growth, market returns, start up successes etc. and what might be the root of this success. And I'm starting to think that, apart from various mild genuine effects, it is also a sequence of circular self-fulfilling prophecies.
Let's say you're a sophisticated startup and you want some funding. Where do you go? US of course - it has the easiest access to capital. It does so presumably because US venture funds have an easier time raising funds. And that's presumably because of their track record of making money for investors - real, or at least perceived. They invest in these startups and they exit at a profit, because US companies have better valuations than elsewhere, so at IPO investors lap up the shares and the VCs make money. It's easy to find buyers for US stocks because they're always going up. In turn, they're going up because, well, there's lots of investors. It's much easier to raise billions for data centres and fairy dust because investors are in awe of what can be done with the money and anyway line always go up. Stocks like TSLA have valuations you couldn't justify elsewhere. Maybe because they will build robot AI rocket taxis, or maybe because the collective American Allure means valuations are just high.
The beauty of this arrangement is that the elements are entangled in a complex web of financial interdependency. If you think about these things in isolation, you wouldn't conclude there's anything unusual. US VC funding is so good because there's a lot of capital - lucky them. This thought of circularity only struck me when trying to think of the root cause - the nuclear set of elements that drive it. And I concluded any reason I can think of is eventually recursive.
I'm not saying America is just dumb luck kept together by spittle, of course there are structural advantages the US has. I'm just not sure it really is that much better an economic machine than other similar countries.
One difference to a Ponzi scheme is that you might actually hit a stable level and stay there rather than crash and burn. So it's more like a collective investment into a lottery. OpenAI might burn $400bn and achieve singularity, then proceed to own the rest of the world.
But I can't shake the feeling that a lot of recent US growth is a bit of smoke and mirrors. After adjusting for tech, US indices didn't outperform European ones post GFC, IIRC. Much of its growth this year is AI, financed presumably by half the world and maintained by sky-high valuations. And no one says "check" because, well, it's the US and the line always go up.
The Oracle deal structure: OpenAI pays ~$30B/year in rental fees starting fiscal 2027/2028 [2], ramping up over 5 years as capacity comes online. Not "$400B in 12 months."
The deals are structured as staged vendor financing: - NVIDIA "invests" $10B per gigawatt milestone, gets paid back through chip purchases [3] - AMD gives OpenAI warrants for 160M shares (~10% equity) that vest as chips deploy [4] - As one analyst noted: "Nvidia invests $100 billion in OpenAI, which then OpenAI turns back and gives it back to Nvidia" [3]
This is circular vendor financing where suppliers extend credit betting on OpenAI's growth. It's unusual and potentially fragile, but it's not "OpenAI needs $400B cash they don't have."
Zitron asks: "Does OpenAI have $400B in cash?"
The actual question: "Can OpenAI grow revenue from $13B to $60B+ to cover lease payments by 2028-2029?"
The first question is nonsensical given deal structure. The second is the actual bet everyone's making.
His core thesis - "OpenAI literally cannot afford these deals therefore fraud" - fails because he fundamentally misunderstands how the deals work. The real questions are about execution timelines and revenue growth projections, not about OpenAI needing hundreds of billions in cash right now.
There's probably a good critical piece to write about whether these vendor financing bets will pay off, but this isn't it.
[1] https://www.cnbc.com/2025/09/23/openai-first-data-center-in-...
[2] https://w.media/openai-to-rent-4-5-gw-of-data-center-power-f...
[3] https://www.cnbc.com/2025/09/22/nvidia-openai-data-center.ht...
[4] https://techcrunch.com/2025/10/06/amd-to-supply-6gw-of-compu...
It is bagholders all the way down[1]! The final bagholder will be the taxpayer/pension holder.
These companies are doing all sorts of round tripping on top of propping up the economy on a foundation of fake revenue on purpose so that when it does some crumbling down they can go cry to the feds "help! we are far too big to fail, the fate of the nation depends on us getting bailed out at taxpayer expense."
I wrote a post about his insistence that the "cost of inference" is going up. https://crespo.business/posts/cost-of-inference/
Assuming their growth rate is getting close to stabilizing and will be at ~100% for 3 years to end of 2028 - that'd be $104B in revenue, on 6.4B WAUs.
I wouldn't bank on either of those numbers - but Oracle and Nvidia kind of need to bank on it to keep their stocks pumped.
Their growth decay is around 20% every 2 months - meaning - by this time next year, they could be closer to 1.2B WAUs than to 1.6B WAUs, and the following year they could be closer to 1.4B WAUs than to 3.2B WAUs.
Impressive, for sure, but still well bellow Google and Facebook, revenue much lower and growth probably even.
AGI is absolutely a national security concern. Despite it being an enormous number, it'll happen. It may not be earmarked for OpenAI, but the US is going to ensure that the energy capability is there.
This may well be the PR pivot that's to come once it becomes clear that taxpayer funding is needed to plug any financing shortfalls for the industry - it's "too big to let fail". It won't all go to OpenAI, but be distributed across a consortium of other politically connected corps: Oracle, Nvidia/Intel, Microsoft, Meta and whoever else.
These 6 companies are using only a small portion of their own cash reserves to invest, and using private credit for the rest. Meta is getting a $30 billion loan from PIMCO and Blue Owl for a datacenter [0], which they could easily pay for out of their own pocket. There are also many datacenters that are being funded through asset-backed securities or commercial mortgage-backed securities [0], the market for which can quickly collapse if expected income doesn't materialize, leading to mortgage defaults, as in 2008.
[0] https://www.reuters.com/legal/transactional/meta-set-clinch-...
[1] https://www.etftrends.com/etf-strategist-channel/securitizin...
They Don't Have the Money: OpenAI Edition
To put it another way, I don't know anything but I could probably make a '1 GW' datacenter with a single 6502 and a giant bank of resistors.
Also the workloads completely change over time as racks get retired and replaced, so it doesn't mean much.
But you can basically assume with GB200s right now 1GW is ~5exaflops of compute depending on precision type and my maths being correct!
Look at next gen Rubin with it's CPX co-processor chip to see things getting much weirder & more specialized. There for prefilling long contexts, which is compute intensive:
> Something has to give, and that something in the Nvidia product line is now called the "Rubin" CPX GPU accelerator, which is aimed specifically at parts of the inference workload that do not require high bandwidth memory but do need lots of compute and, increasingly, the ability to process video formats for both input and output as part of the AI workflow.
https://www.nextplatform.com/2025/09/11/nvidia-disaggregates...
To confirm what you are saying, there is no coherent unifying way to measure what's getting built other than by power consumption. Some of that budget will go to memory, some to compute (some to interconnect, some to storage), and it's too early to say what ratio each may have, to even know what ratios of compute:memory we're heading towards (and one size won't fit all problems).
Perhaps we end up abandoning HBM & dram! Maybe the future belongs to high bandwidth flash! Maybe with it's own Computational Storage! Trying to use figures like flops or bandwidth is applying today's answers to a future that might get weirder on us. https://www.tomshardware.com/tech-industry/sandisk-and-sk-hy...
You have a lot more things in a DC than just GPUs consuming power and producing heat. GPUs are the big ones, sure, but after a while, switches, firewalls, storage units, other servers and so one all contribute to the power footprint significantly. A big small packet high throughput firewall packs a surprisingly amount of compute capacity, eats a surprising amount of power and generates a lot of heat.
And that's the important abstraction / simplification you get when you start running hardware at scale. Your limitation is not necessarily TFlops, GHz or GB per cubic meter. It is easy to cram a crapton of those into a small place.
The main problem after a while is the ability to put enough power into the building and to move the heat out of it again. It sure would be easy to put a lot of resistors into a place to make a lot of power consumption. Hamburg Energy is currently building just that to bleed off excess solar power into the grid heating.
It's problematic to connect that to the 10kv power grid safely and to move the heat away from the system fast.
I'm not saying there's no bubble, and I personally anticipate a lot of turmoil in the next year, but monetisation of that would be the most primitive way of earning a lot of money. If anyone is dead man walking it's Google. For better or worse, Chatgpt has become to AI what Google was to search, even though I think Gemini is also good or even better. I also have my own doubts about the value of LLMs because I've already experienced a lot of caveats with the stuff it gives you. But at the same time, as long as you don't believe it blindly, getting started with something new has never been easier. If you don't see value in that, I don't know what to tell you.
Google definitely has the better model right now, but I think ChatGPT is already well on its way to becoming to AI what Google was to search.
ChatGPT is a household name at this point. Any non tech person I ask or talk about AI with it's default to be assumed it's ChatGPT. "ChatGPT" has become synonymous with "AI" for the average population, much in the same way "Google it" meant to perform an internet search.
So ChatGPT already has the popular brand. I think people are sleeping on Google though. They have a hardware advantage and aren't reliant on Nvidia, and have way more experience than OpenAI in building out compute and with ML, Google has been an "AI Company" since forever. Google's problem if they lose won't be because of tech or an inferior model, it will be because they absolutely suck at making products. What Google puts out always feels like a research project made public because someone inside thought it was cool enough to share. There's not a whole lot of product strategy or cohesion across the Google ecosystem.
Thing that make me skip this specific narrative:
- There's some heavy-handed reaching to get to $400B next 12 months: guesstimate $50B = 1 GW of capacity, then list out 3.3 gigawatts across Broadcom chip purchases, Nvidia, and AMD
- OpenAI is far better positioned than any of the obvious failures I foresaw in my 37 years on this rock. It's very, very, hard to fuck up to the point you go out of business.
- Ed is repeating narratives instead of facts ("what did they spend that money on!? GPT-5 was a big let down!" -- i.e. he remembers the chatgpt.com router discourse, and missed that it was the first OpenAI release that could get the $30-50/day/engineer in spend we've been sending to Anthropic)
I wouldn't be surprised if the cost came down by at least one order of magnitude, two if NVidia and others adjust their margin expectations. If the bet is that OpenAI can ship crappy datacenters with crappy connectivity/latency characteristics in places with cheap/existing power - then that seems at least somewhat plausible.
OpenAI burning 40 billion dollars on datacenters in the next 1 year is almost guaranteed. Modern datacenter facilities are carefully engineered for uptime, I don't think OpenAI cares about rack uptime or even facility uptime at this scale.
alberth•1h ago
chilipepperhott•1h ago
kachapopopow•1h ago
This also assumes that intelligence continues to scale with compute which is not a given.
sillysaurusx•1h ago
Isn’t it? Evidence seems to suggest that the more compute you throw at a problem, the smarter the system behaves. Sure, it’s not a given, but it seems plausible.
deadbabe•57m ago
But human brains are small and require far less energy to be very generally intelligent. So clearly, there must be a better way to achieve this AGI shit. Preferably something that runs locally in the palm of your hand.
kachapopopow•49m ago
nutjob2•48m ago
That word is carrying a heavy load. There's no evidence that scaling works indefinitely on this particular sort of problem.
In fact there is no evidence that scaling solves computing problems generally.
In more narrow fields more compute gets better results but that niche is not so large.
B56b•27m ago
IsTom•20m ago
rediguanayum•1h ago
gkoberger•1h ago
But my personal belief is Sam Altman has a singular goal: AGI. Everything else keeps the lights on.
sho_hn•56m ago
My impression is that I hear a lot more about basic research from the competing high-profile labs, while OpenAI feels focused on their established stable of products. They also had high-profile researchers leave. Does OpenAI still have a culture looking for the next breakthroughs? How does their brain trust rank?
Analemma_•51m ago
thelastgallon•43m ago
Of course, there wouldn't be many people who don't want to be trillionaires. Rare exceptions[1]. But these are the people with means to get there.
[1]: No means NO - Do you want a one million dollar answer NO!: https://www.youtube.com/watch?v=GtWC4X628Ek
wkat4242•11m ago
gkoberger•5m ago
timeon•43m ago
JumpCrisscross•51m ago
I’m increasingly convinced this is AI’s public relations strategy.
When it comes to talking to customers and investors, AGI doesn’t come up. At fireside chats, AGI doesn’t come up.
Then these guys go on CNBC or whatnot and it’s only about AGI.
evandrofisico•1h ago
cma•48m ago
I'm not sure if OpenAI has been willing to deploy weights to Google infrastructure.