I think this whole premise is ignoring the point that most decently sized companies want to and eventually will be running their own LLM workloads. Currently use cases are limited by scope and imagination, and predominantly focused on cost efficiency. Once a use case becomes a top line revenue driver budgets will become essentially only limited by ROI. There are some inference workloads that are unacceptable to send to the frontier labs for privacy reasons. Most of it will come from firms who want to productionalize their own fine-tunes. In any case, the market for inference is less than 1% of what it will be in 5-10 years. This whole notion that GPUs will be sitting idle en masse is ridiculous. People will just start running GPU databases if it becomes cost efficient.
but this is just an absolute fantasy, what, exactly, will this compute be doing? Our lives are already deeply entwined with technology and use barely any compute. How could our lives become 100x more dependent on compute?
You can be thrilled by this exciting technology and all the possibilities it brings without thinking it is going to require this huge capital investment in GPUs. You could radically change billions of lives with a few dozen GPUs.
Push everybody to buy hardware they don’t need to constantly run agents for pretty basic tasks, such as processing your daily emails and sending you little summaries notifications. So, the most inefficient software tools ever produced, but that keeps the whole industry alive. That’s pretty much the vision Jensen Huang is selling to ensure NVIDIA continues to grow
Is anyone really running GPU databases in prod at scale? Or are you assuming that the GPU compute from a crash will become so cheap that it makes this niche scale?
You can like the character or not, but there is a trend I’m following ( heavily vested in NVIDIA, so tongue in cheek when I say this ) that might be highly align with Zitron. Looking at the moves from NVIDIA ( Groq )and AMD ( Taalas ) which are pure inference plays. I believe this shows that the impetus to train a better-bigger model might be coming to a level of maturity that might merit a serious threat to the frontier labs.
For frontier labs, their fund-train-new model play might not be as effective, and a shift of spent of compute cost moving away from training to inference might be a tell-tell sign of the LLM as we know it plateauing out as scale is just not as effective. Open models might also be placing a major pressure on meeting then revenue targets need to sustain the model, lots of customer hosting their own inference to mitigate costs.
If you only move the needle just slightly in the direction of inference, frontier labs will soon loose their alphas. Becoming just another SaaS for inference might not be as attractive unless you are Google/MSF ( IMHO ).
Should this pan out, it could be a scenario where the NeoClouds could soon loose their biggest customers, so I tend to agree with that aspect of Zitron’s view.
Ai revenue or datacenter/compute revenue? There's a lot of circular financing right now and there's a pretty obvious bubble for sure but I haven't been able to figure out what exactly this guy's argument is after seeing it a few times recently. Like yeah most ai datacenter spend is those two companies - that's pretty standard monopoly (or monopsony for the cloud providers) dynamics. If you're saying some major percentage of all ai related spending is openai and anthropic spending money on compute - well that's not exactly right because Google etc are also spending money on building datacenters (that's not ai spend in this definition? WTF is ai revenue exactly?) - and that's just pretty much indicating it's a frothy market with two unprofitable companies at the center with huge cogs? We knew that already.
Feels like they want to make a clean headline grabbing argument about how "70% of all the spending is actually just these two companies" and are ending up with a really muddled headline that's just like yeah that's how monopoloies and duopolies work. When there's a lot more insidious circular complicated shenanigans going on that gets collapsed by this framing.
The general concern (from him and others) is that the cloud providers are making extremely large capex commitments (borrowing, going cashflow negative) to satisfy demand from a small number of customers who may not be able to pay them.
>> When there's a lot more insidious circular complicated shenanigans going on that gets collapsed by this framing.
All that is discussed in the video, plus those distinctions. And most important, that AI revenue would not exist...if OpenAI and Anthropic would not be funded, by the same Amazon, Google and Microsoft they are buying it from!
One interpretation is that so called profitability is one 15 minute phone call between OpenAI and Anthropic to increase prices. If you think that antitrust is the reason that this won't happen, you've stated a speculation. Not a certainty.
there isn't enough demand to raise prices. OpenAI announced price CUTS recently and they are hilariously unprofitable already. Why would they be cutting prices if they have the ability to raise them? outside of this website and the ownership class everyone hates AI
And I am not even sure ownership class have any real understanding or opinion on AI. Just that it has been sold to them as something that will save wages and make money... If it does not or the narrative turns they will abandon it like any other idea they have been sold.
There is some gold, but it’s a fairly small amount compared to the ATM discussed by AI companies (basically software generation, software security, audio transcripts, translation, image processing, image generation, search, etc). So it’s not like there won’t be an AI industry in the future but it won’t be absolutely everywhere the way the AI boosters are projecting. Inference will be a low margin industry, training will be capex constrained and likely low margin too. And some services on top will have decent margins. But nothing like the datacenters full of PHDs Amodei and Altman are dreaming about.
NVIDIA, Micron, SK Hynix, and other hardware manufacturers, and also hyperscalers are the ones making the hard cash (unless you’re Oracle, that company is more than fucked)
I know, but NVIDIA is also the main beneficiary from that whole scheme, what they get out of it is actual cash. They hyperscalers have a more complicated role though
> Wasn't it always seen as a "winner takes all" business?
No. It was always a pyramid scheme marketed as a "winner takes all" business. But there's no winning. The only winners are those who cash out before it collapses. This is literally Web3 2.0.
Hilarious. You can agree or disagree with Zitron but he really has no business talking about Goldman Sachs. His posts are littered with evidence he has no ability to perform the type of financial analysis he thinks he does.
> Additional factors – including interest income and interest expense – left it with a net loss of $8.84 billion. It then marked $3.74 billion of losses as “net loss attributable to noncontrolling members capital,” leaving the net loss attributable to the company as $5.09 billion.
> It’s unclear what this means, nor how OpenAI reconciled the removal of $3.74 billion in costs. I will not speculate further.
It is very clear what this means, and no speculation is required if you understand basic consolidation accounting, which you would expect someone in his position to understand.
It's not rocket science: when you have a parent company with entities it doesn't wholly own, the slice of losses belonging to the other equity holders is split out as "noncontrolling interests." Nothing is removed or hidden; the total loss is unchanged, it's just allocated to reflect that the parent company doesn't own the whole. Framing it as OpenAI removing costs implied something sketchy and requiring speculation where there's only routine GAAP accounting.
But it's even worse than this. So many of Ed's claims conflate the foundational R&D and capital expenditures these companies are incurring with the unit economics of their businesses. He seems woefully unable to understand that you could sped gobs of money on the former and still have positive gross margins that scale over time with the latter.
But how does that detract from the overall point that Open AI is losing billions of dollars? If there's an insatiable demand for AI compute, where's the profit?
One example - he often compares current revenues to capex being spent on future capacity to claim that AI companies aren't profitable. (See this post for example: https://www.wheresyoured.at/am-i-meant-to-be-impressed/ .)
But this ignores that the capex spent to build more capacity is expected to generate additional future revenue. You don't need to recoup your capex immediately. A better approach would be to amortize the capex and compare revenues to that.
Clearly he assumes revenue won't increase enough to recoup this level of capex (and it's very possible it won't) but IMO it's either a miscalculation of how the financing works or a deliberately misleading framing to compare current small revenues to a big scary capex number.
I'm sure the above is simplified by the way, but I am confident that people who work at Goldman understand the relevant details extremely well.
I think this whole premise is ignoring the point that most decently sized companies want to and eventually will be running their own LLM workloads. Currently use cases are limited by scope and imagination, and predominantly focused on cost efficiency. Once a use case becomes a top line revenue driver budgets will become essentially only limited by ROI. There are some inference workloads that are unacceptable to send to the frontier labs for privacy reasons. Most of it will come from firms who want to productionalize their own fine-tunes. In any case, the market for inference is less than 1% of what it will be in 5-10 years. This whole notion that GPUs will be sitting idle en masse is ridiculous. People will just start running GPU databases if it becomes cost efficient.
but this is just an absolute fantasy, what, exactly, will this compute be doing? Our lives are already deeply entwined with technology and use barely any compute. How could our lives become 100x more dependent on compute?
You can be thrilled by this exciting technology and all the possibilities it brings without thinking it is going to require this huge capital investment in GPUs. You could radically change billions of lives with a few dozen GPUs.
Push everybody to buy hardware they don’t need to constantly run agents for pretty basic tasks, such as processing your daily emails and sending you little summaries notifications. So, the most inefficient software tools ever produced, but that keeps the whole industry alive. That’s pretty much the vision Jensen Huang is selling to ensure NVIDIA continues to grow
TIL about GPU databases. I did a bit if research but only found this one: https://github.com/bakks/virginian
What are the advantages of a GPU database?
Is anyone really running GPU databases in prod at scale? Or are you assuming that the GPU compute from a crash will become so cheap that it makes this niche scale?
> most decently sized companies want to and eventually will be running their own LLM workloads
And probably some decently sized states too. Not only commercial actors are up to the job.
You can like the character or not, but there is a trend I’m following ( heavily vested in NVIDIA, so tongue in cheek when I say this ) that might be highly align with Zitron. Looking at the moves from NVIDIA ( Groq )and AMD ( Taalas ) which are pure inference plays. I believe this shows that the impetus to train a better-bigger model might be coming to a level of maturity that might merit a serious threat to the frontier labs.
For frontier labs, their fund-train-new model play might not be as effective, and a shift of spent of compute cost moving away from training to inference might be a tell-tell sign of the LLM as we know it plateauing out as scale is just not as effective. Open models might also be placing a major pressure on meeting then revenue targets need to sustain the model, lots of customer hosting their own inference to mitigate costs.
If you only move the needle just slightly in the direction of inference, frontier labs will soon loose their alphas. Becoming just another SaaS for inference might not be as attractive unless you are Google/MSF ( IMHO ).
Should this pan out, it could be a scenario where the NeoClouds could soon loose their biggest customers, so I tend to agree with that aspect of Zitron’s view.
Thoughts?
Inference time compute is the new scaling.
I don't know much about this guy other than every time I see him on here he has an axe to grind about AI
You could start a new business selling grindstones to the axe grinders. Like selling pickaxes in a gold rush.
Ai revenue or datacenter/compute revenue? There's a lot of circular financing right now and there's a pretty obvious bubble for sure but I haven't been able to figure out what exactly this guy's argument is after seeing it a few times recently. Like yeah most ai datacenter spend is those two companies - that's pretty standard monopoly (or monopsony for the cloud providers) dynamics. If you're saying some major percentage of all ai related spending is openai and anthropic spending money on compute - well that's not exactly right because Google etc are also spending money on building datacenters (that's not ai spend in this definition? WTF is ai revenue exactly?) - and that's just pretty much indicating it's a frothy market with two unprofitable companies at the center with huge cogs? We knew that already.
Feels like they want to make a clean headline grabbing argument about how "70% of all the spending is actually just these two companies" and are ending up with a really muddled headline that's just like yeah that's how monopoloies and duopolies work. When there's a lot more insidious circular complicated shenanigans going on that gets collapsed by this framing.
The general concern (from him and others) is that the cloud providers are making extremely large capex commitments (borrowing, going cashflow negative) to satisfy demand from a small number of customers who may not be able to pay them.
> to satisfy demand from a small number of customers who may not be able to pay them
Because a lot of isn't real demand, e.g. given away for free or very very cheap.
That’s really secondary, they don’t have the money and it’s not clear they will.
>> When there's a lot more insidious circular complicated shenanigans going on that gets collapsed by this framing.
All that is discussed in the video, plus those distinctions. And most important, that AI revenue would not exist...if OpenAI and Anthropic would not be funded, by the same Amazon, Google and Microsoft they are buying it from!
We now have several voices saying the same:
"Aswath Damodaran: Big Tech Has No Idea How AI Pays Off" - https://news.ycombinator.com/item?id=49229981
"Why Wall Street Is Ignoring Big Tech's Debt" - https://news.ycombinator.com/item?id=49230630
One interpretation is that so called profitability is one 15 minute phone call between OpenAI and Anthropic to increase prices. If you think that antitrust is the reason that this won't happen, you've stated a speculation. Not a certainty.
They are pressured by open models to reduce prices, not increase
there isn't enough demand to raise prices. OpenAI announced price CUTS recently and they are hilariously unprofitable already. Why would they be cutting prices if they have the ability to raise them? outside of this website and the ownership class everyone hates AI
And I am not even sure ownership class have any real understanding or opinion on AI. Just that it has been sold to them as something that will save wages and make money... If it does not or the narrative turns they will abandon it like any other idea they have been sold.
Wasn't it always seen as a "winner takes all" business?
There is no "all", that's the point. There is no pot of gold at the end of the AI rainbow.
There is some gold, but it’s a fairly small amount compared to the ATM discussed by AI companies (basically software generation, software security, audio transcripts, translation, image processing, image generation, search, etc). So it’s not like there won’t be an AI industry in the future but it won’t be absolutely everywhere the way the AI boosters are projecting. Inference will be a low margin industry, training will be capex constrained and likely low margin too. And some services on top will have decent margins. But nothing like the datacenters full of PHDs Amodei and Altman are dreaming about.
Just like any other technology
There's a lot of money changing hands, at least that's a spoil isn't it? And if the music stops, someone is going to end up holding it.
Well a lot of money is being spent, but is a lot of money being made?
With circular deals it’s hard to tell.
NVIDIA, Micron, SK Hynix, and other hardware manufacturers, and also hyperscalers are the ones making the hard cash (unless you’re Oracle, that company is more than fucked)
They’re also at the centre of a lot of circular deals, nvidia in particular is notorious for it and still doing it.
From last month: https://www.bloomberg.com/news/articles/2026-07-27/nvidia-s-...
Or from 2025: https://www.cnbc.com/2025/10/15/a-guide-to-1-trillion-worth-...
I know, but NVIDIA is also the main beneficiary from that whole scheme, what they get out of it is actual cash. They hyperscalers have a more complicated role though
When somebody's spending someone else is making money.
If there was one winner then that would make a lot of people very angry.
> Wasn't it always seen as a "winner takes all" business?
No. It was always a pyramid scheme marketed as a "winner takes all" business. But there's no winning. The only winners are those who cash out before it collapses. This is literally Web3 2.0.
"Because Goldman does this kind of nonsense."
Hilarious. You can agree or disagree with Zitron but he really has no business talking about Goldman Sachs. His posts are littered with evidence he has no ability to perform the type of financial analysis he thinks he does.
Mind sharing more details?
Sure. Here's an example:
https://www.wheresyoured.at/exclusive-openai-financials/
Zitron wrote:
> Additional factors – including interest income and interest expense – left it with a net loss of $8.84 billion. It then marked $3.74 billion of losses as “net loss attributable to noncontrolling members capital,” leaving the net loss attributable to the company as $5.09 billion.
> It’s unclear what this means, nor how OpenAI reconciled the removal of $3.74 billion in costs. I will not speculate further.
It is very clear what this means, and no speculation is required if you understand basic consolidation accounting, which you would expect someone in his position to understand.
It's not rocket science: when you have a parent company with entities it doesn't wholly own, the slice of losses belonging to the other equity holders is split out as "noncontrolling interests." Nothing is removed or hidden; the total loss is unchanged, it's just allocated to reflect that the parent company doesn't own the whole. Framing it as OpenAI removing costs implied something sketchy and requiring speculation where there's only routine GAAP accounting.
But it's even worse than this. So many of Ed's claims conflate the foundational R&D and capital expenditures these companies are incurring with the unit economics of their businesses. He seems woefully unable to understand that you could sped gobs of money on the former and still have positive gross margins that scale over time with the latter.
But how does that detract from the overall point that Open AI is losing billions of dollars? If there's an insatiable demand for AI compute, where's the profit?
One example - he often compares current revenues to capex being spent on future capacity to claim that AI companies aren't profitable. (See this post for example: https://www.wheresyoured.at/am-i-meant-to-be-impressed/ .)
But this ignores that the capex spent to build more capacity is expected to generate additional future revenue. You don't need to recoup your capex immediately. A better approach would be to amortize the capex and compare revenues to that.
Clearly he assumes revenue won't increase enough to recoup this level of capex (and it's very possible it won't) but IMO it's either a miscalculation of how the financing works or a deliberately misleading framing to compare current small revenues to a big scary capex number.
I'm sure the above is simplified by the way, but I am confident that people who work at Goldman understand the relevant details extremely well.