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.
Is it just me or is this comment extremely difficult to parse? Besides the lack of any links/evidence the assertions are full of jargon "decently sized", "scope and imagination", "productionalize their own fine-tunes", "This whole notion... is ridiculous". It is currently at the top of HN and I hope that is just because it's a Sunday afternoon in many places.
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
I hope we can quickly escape these tasks and the focus on coding. It’s all very superficial. There are a ton of serious AI workloads imaginable and I haven’t seen anyone pushing those frontiers, at least not publicly. Nvidia had a shiny keynote about their digital twin of the world for solving large problems but i guess that one died already.
Only use cloud for backups of local client encrypted blobs of vectors:data
If you get rid of a lot of the suspect semantics hallucinated up over decades of software development it's not hard to see the geometry of an electronic snowflake. All the language just obfuscates the elegance. Crude meat suit grunts and clicks.
Streamline it all to management of geometric states and access control and put the semantics on the presentation layer. What if we don't need python and go and ruby anymore? Made sense in a pre-gpu everywhere reality. Could just be high school stats classes to generate sets of values. Let go of the obscure linguistic chants.
The data centers are just to serve surveillance purposes. Obfuscated behind politically correct memes of creating jobs.
Chip away at the monolith and atomize the topology
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.
I’m not a heavy user, but use them routinely to do dumb translations and refactors etc. For my money the models are mostly indistinguishable at achieving long term objectives. Every time a new one drops I’m a part of a number of communities that go insane trying to “max” on it, waiting for resets etc. I just don’t get it, and if it turns out my view it rational, then it seems like training new models will become a thing of the past at some point. It’s just not economical other than for maxing hype.
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.
Why do people continue to use them despite the existence of so many open weights models? This is "priced in." Short of stealing the checkpoints straight from the servers, it's unlikely to change.
I think it’s mostly a marketing, branding thing. Most people haven’t heard of the Chinese AI companies and models, but everybody heard of OpenAI and Anthropic. The US companies have sales team, a constant media presence. It’s just a question of time for open models to gain market shares.
Just look at how OpenAI has lost market share over the past year
Anyone who remotely pays attention to AI would know who DeepSeek is, given the shockwave it sent in early 2025, impacting the tech stocks. News was full of "Kimi moment" recently too.
Sure but a few weeks of coverage is nothing compared to OpenAI and Anthropic. Claude and ChatGPT are household names you can mention to your parents, it’s not the case (yet) for the Chinese models
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.
It is the same business model, just at a grander scale. There is no sustainable growth business underneath it, only vibes and hype, and the "future promise" of some miraculous breakthrough. It's becoming clearer by the minute that no such miracle is forthcoming.
[edit] one small difference from web3 is that AI does seem to be at least marginally useful in some narrow verticals (devtools, infosec). That's pretty inconsequential, though. Those verticals can't supply the capital needed to sustain the industry. So on a macro scale that isn't relevant.
When the dust settles I hope we end up in a place where we no longer accept lies and grift as pitchdecks and product briefs. If you have a technology, you demonstrate it, and only after a successful demonstration do you expect funding. We don't need to throw money at vaporware anymore, that's proven itself to be idiotic behavior over and over again.
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?
High growth companies often have significant negative cashflow during the early high growth era, followed by positive cashflow in the years later down the line.
This phenomenon is known as the J-curve[1], and Uber is a good example of how this can turn out absolutely fine. To some extent, the entire Venture Capital industry exists to finance precisely this dynamic!
Nb. I'm not suggesting OpenAI is fairly valued, or that they will definitely become profitable, but "OpenAI is losing billions of dollars" doesn't really mean anything in and of itself.
> High growth companies often have significant negative cashflow during the early high growth era, followed by positive cashflow in the years later down the line.
Uber is the antithesis of OpenAI, it’s not a good example. Uber was burning money on acquiring customers. OpenAI is burning money to provide their service (and the R&D they need to continue to have valuable models). They cannot just stop and turn profitable like Uber. The money they burn isn’t invested, it won’t yield a multiple of revenue in the future. It’s consumed for compute and that’s it loo
If the leaked data is to be believed, OpenAI is spending 40% of revenue on sales and marketing, which is not the OPEX profile of a product-led technology company
Broadly speaking, companies that spend 40%+ of revenue on sales and marketing end up being a bit of a drag on society. Eg. Salesforce’ product quality is far lower than winners in other sectors that sit closer to 10-15% of revenue on sales and marketing
Maybe - just as how the city of Sao Paolo implemented a ban on billboards - we can implement a law where a 3 year rolling average of sales and marketing spend cannot exceed 20% of revenue in that period
> They cannot just stop and turn profitable like Uber.
Of course they can. They could just stop training new models and milk the existing ones. A billion users check in ChatGPT weekly. Software developers wouldn't stop using Codex.
OpenAI is not unlike any other startups who try to build their marketshare early on. No matter how much money they lose, they would be fine as long as they could raise more money than they spend. Uber is exactly the same. HN during 2015-2020 were full of comments predicting Uber's demise.
I don’t think you understand how bad OpenAI economics are. The company is burning billions just to operate. They cannot stop the training treadmill due to competitive pressure, but assuming they do that would only reduce their expanses, not increase their revenue. They would still be in the negative. We are talking about a company that has more than >$750B of infrastructure expenditure commitment for 2030.
The number of users they have checking weekly is irrelevant, most of them are free users, unless they find a way to make money from them, but their ads business has been a flop so far.
For context: Uber losses were $12B over 5 years. AWS was $5B invested over 7y.
OpenAI is projected to lose more than $14B just this year!!!
First and foremost, it's about competent reporting. You can think a company is doomed and still expect people to report on it accurately.
Zitron continually presents things in ways that create a hyperbolic narrative. Turning routine consolidation accounting into an unexplained mystery hinting at some sort of fraud is the perfect example of that. He does it so often and in such a way that I truly believe he just doesn't understand accounting.
It doesn't take a rocket scientist to understand that OpenAI is losing money. But the question ("where's the profit?") assumes that profit is the thing being optimized for. It isn't.
There are basically three buckets here: cost of serving a query, cost of training and capex for capacity.
The first one is unit economics. The other two are bets on the future that get expensed against present revenue. A company can serve every query at a healthy gross margin (OpenAI has improved margins considerably) and still have a $9 billion loss because it spent $12 billion training a model that generates $0 this year.
Zitron constantly blends everything into a pithy "they lose money on everything" narrative, which just isn't accurate. The thing is that OpenAI could have a very different P&L if it chose to, say, stop training the next model.
The problem, obviously, is that if you stop training the next model, the competition might eat you. So right now you have a situation where the the frontier model you spent $12 billion on depreciates in about 18 months, the GPUs depreciate on a schedule nobody agrees on, and you seemingly can't stop the cycle without risking your position in the market.
This is the legitimate bear case, but the problem with Zitron is that he doesn't make it using an argument that is coherent and honest as far as the accounting is concerned. And the accounting is everything.
Can you explain which partly owned subsidiaries lost $3.74 billion and why it is legitimate to exclude that from their losses in a non-handwavy fashion? Your condemnation leaves me none the wiser and this does matter.
This can of course be used to distort the financial picture and this is a significant amount, almost 50% of losses. Is this from the ‘non-profit’ which used to be OpenAI or something else?
Smells like creative accounting to me and the CEO was accused by his board of dishonesty.
I get that not everyone is an accountant or has had to become educated in accounting matters as part of their work, but you really shouldn't say "smells like creative accounting to me" if you don't have a basic understanding of the subject.
This is like the least interesting thing about OpenAI's financials, and Zitron framing it as some sort of mystery hinting at fraud is one of the least effective ways to make a point given that it's absolutely a nothingburger.
No loss is disappearing or being hidden. This is by-the-book consolidation accounting.
The accounting mechanics are uninteresting, lots of normal accounting rules are abused for nefarious purposes (see Enron et al). What is interesting is why this was done.
Say Parent Co. controls Subsidiary LLC and owns 60% of it. Minority Corp. owns the other 40%. Subsidiary LLC loses $10 billion.
Consolidation accounting requires Parent Co. to report the full $10 billion loss, as if it owned all of Subsidiary LLC, which it doesn't. Then, on the next line, it attributes the portion belonging to the other owner (Minority Corp.). There are two lines and one loss. Nothing is removed and nothing hidden. This is the definition of disclosure, not obfuscation.
Note that the trigger for applying this is actually control, not ownership interest. My understanding is that OpenAI Foundation controls the public benefit corporation with 26%. And for an LLC, the loss split isn't automatically pro-rata by ownership. It follows the profit-sharing terms in the operating agreement, which is why the there's a Hypothetical Liquidation at Book Value method. Under an old capped-profit waterfall, nobody can verify the exact figure without looking at the operating agreement.
"I can't verify the split" is a very different statement than "it's unclear what this means."
Your "why" question: the reason it works this way is that consolidated statements are meant to show the business as an operating whole because that's what a controlling parent actually runs. You can't operate 60% of a data center or sign 60% of a compute contract. Every line (be it revenue, expenses, assets, etc.) comes in at 100% for that reason. The noncontrolling interest line then answers the separate question of how much of that whole belongs to the parent's own shareholders. One statement answers two questions (what does this enterprise look like versus how much of it is ours).
As for your "who" question, the answer is simple: the other equity holders. Again, OpenAI doesn't own 100% of the entity. My understanding is that Microsoft is one of the other equity holders.
"Also as of closing of the recapitalization, Microsoft holds roughly 27% of OpenAI Group, and the remaining 47% is held by current and former employees and investors."
Maybe I am little confused here but what you just described here doesn't sound great either? What smaller entities who OpenAI is parent to lost 3.74 billion dollars?
Or maybe just can you point to some primary sources about this? I am not too bright about this stuff.. I guess I always thought it was usually about having more money than when you started? Or at least about having a story of how you will have more money? Is that not right?
OpenAI isn't a single company. I haven't followed all the details with its structure change/recapitalization, but it's (I believe) a parent sitting on top of an LLC that outside investors like Microsoft hold a large minority stake in.
The rules say that the parent has to report 100% of the LLC's revenue and expenses as if it owned everything and then, at the end, back out the share of the loss that economically belongs to the minority holders.
So $8.84 billion is the whole loss, $3.74 billion is approximately the outside members' proportional share of it, and $5.09 billion is what's left for the parent. Nothing disappeared or was hidden. It's one number presented two ways because two sets of people own it.
OK interesting. Who specifically are these other subsidiaries that clearly account for this loss though? I get it's just a trueism for those in the know like you, but it's pretty fascinating for me at least! Like is there one example company we can point to here? Even if there were like 20 subsidiaries and the combined loss was 3 billion, that would feel noteworthy on its own?
Like to be absolutely honest, this point ends up just sounding more alarmist than the claim you took issue with originally. But perhaps I am just misunderstanding.
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.
Ed wrote a post back in 2024 where he claimed OpenAI would fail in 2 years if they didn’t do the, seemingly impossible at that time, things like raising more money than any company before etc.
And OpenAI did all of that and is still alive today. Would be good to know this context because if you are out there boldly making doomer predictions month after month then you should also rate your previous ones.
Because one day Ed will be right and he’ll go around and take a victory lap while ignoring he has basically not been right before.
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.
Is it just me or is this comment extremely difficult to parse? Besides the lack of any links/evidence the assertions are full of jargon "decently sized", "scope and imagination", "productionalize their own fine-tunes", "This whole notion... is ridiculous". It is currently at the top of HN and I hope that is just because it's a Sunday afternoon in many places.
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
I hope we can quickly escape these tasks and the focus on coding. It’s all very superficial. There are a ton of serious AI workloads imaginable and I haven’t seen anyone pushing those frontiers, at least not publicly. Nvidia had a shiny keynote about their digital twin of the world for solving large problems but i guess that one died already.
Ditch the servers.
Do and sync over client to client.
Keep data local again.
Only use cloud for backups of local client encrypted blobs of vectors:data
If you get rid of a lot of the suspect semantics hallucinated up over decades of software development it's not hard to see the geometry of an electronic snowflake. All the language just obfuscates the elegance. Crude meat suit grunts and clicks.
Streamline it all to management of geometric states and access control and put the semantics on the presentation layer. What if we don't need python and go and ruby anymore? Made sense in a pre-gpu everywhere reality. Could just be high school stats classes to generate sets of values. Let go of the obscure linguistic chants.
The data centers are just to serve surveillance purposes. Obfuscated behind politically correct memes of creating jobs.
Chip away at the monolith and atomize the topology
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?
TIL too, but probably what GPUs are good at: optimizing for throughput while hiding latency. Probably better suited for OLAP than OLTP.
[dead]
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.
[dead]
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?
I’m not a heavy user, but use them routinely to do dumb translations and refactors etc. For my money the models are mostly indistinguishable at achieving long term objectives. Every time a new one drops I’m a part of a number of communities that go insane trying to “max” on it, waiting for resets etc. I just don’t get it, and if it turns out my view it rational, then it seems like training new models will become a thing of the past at some point. It’s just not economical other than for maxing hype.
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.
Like DeepSeek's?
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
Why do people continue to use them despite the existence of so many open weights models? This is "priced in." Short of stealing the checkpoints straight from the servers, it's unlikely to change.
I think it’s mostly a marketing, branding thing. Most people haven’t heard of the Chinese AI companies and models, but everybody heard of OpenAI and Anthropic. The US companies have sales team, a constant media presence. It’s just a question of time for open models to gain market shares.
Just look at how OpenAI has lost market share over the past year
Anyone who remotely pays attention to AI would know who DeepSeek is, given the shockwave it sent in early 2025, impacting the tech stocks. News was full of "Kimi moment" recently too.
Sure but a few weeks of coverage is nothing compared to OpenAI and Anthropic. Claude and ChatGPT are household names you can mention to your parents, it’s not the case (yet) for the Chinese models
>Why do people continue to use them despite the existence of so many open weights models?
Because of the low prices lol.
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
Most tech revolutions end up making a fair bit of money for successful businesses.
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
They also get sales, which prop up the extraordinary share price. That seems to be the main point of the circular deals.
When somebody's spending someone else is making money.
With circular deals it’s hard to tell.
If nobody is gaining, nobody is losing.
Except when it's that same someone else's money being spent.
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.
AI isn't Web3.
It is the same business model, just at a grander scale. There is no sustainable growth business underneath it, only vibes and hype, and the "future promise" of some miraculous breakthrough. It's becoming clearer by the minute that no such miracle is forthcoming.
[edit] one small difference from web3 is that AI does seem to be at least marginally useful in some narrow verticals (devtools, infosec). That's pretty inconsequential, though. Those verticals can't supply the capital needed to sustain the industry. So on a macro scale that isn't relevant.
When the dust settles I hope we end up in a place where we no longer accept lies and grift as pitchdecks and product briefs. If you have a technology, you demonstrate it, and only after a successful demonstration do you expect funding. We don't need to throw money at vaporware anymore, that's proven itself to be idiotic behavior over and over again.
"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?
High growth companies often have significant negative cashflow during the early high growth era, followed by positive cashflow in the years later down the line.
This phenomenon is known as the J-curve[1], and Uber is a good example of how this can turn out absolutely fine. To some extent, the entire Venture Capital industry exists to finance precisely this dynamic!
Nb. I'm not suggesting OpenAI is fairly valued, or that they will definitely become profitable, but "OpenAI is losing billions of dollars" doesn't really mean anything in and of itself.
[1] https://www.uark.vc/blog/breaking-down-the-j-curve-the-journ... (many other similar such articles exist)
> High growth companies often have significant negative cashflow during the early high growth era, followed by positive cashflow in the years later down the line.
Uber is the antithesis of OpenAI, it’s not a good example. Uber was burning money on acquiring customers. OpenAI is burning money to provide their service (and the R&D they need to continue to have valuable models). They cannot just stop and turn profitable like Uber. The money they burn isn’t invested, it won’t yield a multiple of revenue in the future. It’s consumed for compute and that’s it loo
If the leaked data is to be believed, OpenAI is spending 40% of revenue on sales and marketing, which is not the OPEX profile of a product-led technology company
Broadly speaking, companies that spend 40%+ of revenue on sales and marketing end up being a bit of a drag on society. Eg. Salesforce’ product quality is far lower than winners in other sectors that sit closer to 10-15% of revenue on sales and marketing
Maybe - just as how the city of Sao Paolo implemented a ban on billboards - we can implement a law where a 3 year rolling average of sales and marketing spend cannot exceed 20% of revenue in that period
> They cannot just stop and turn profitable like Uber.
Of course they can. They could just stop training new models and milk the existing ones. A billion users check in ChatGPT weekly. Software developers wouldn't stop using Codex.
OpenAI is not unlike any other startups who try to build their marketshare early on. No matter how much money they lose, they would be fine as long as they could raise more money than they spend. Uber is exactly the same. HN during 2015-2020 were full of comments predicting Uber's demise.
I don’t think you understand how bad OpenAI economics are. The company is burning billions just to operate. They cannot stop the training treadmill due to competitive pressure, but assuming they do that would only reduce their expanses, not increase their revenue. They would still be in the negative. We are talking about a company that has more than >$750B of infrastructure expenditure commitment for 2030.
The number of users they have checking weekly is irrelevant, most of them are free users, unless they find a way to make money from them, but their ads business has been a flop so far.
For context: Uber losses were $12B over 5 years. AWS was $5B invested over 7y.
OpenAI is projected to lose more than $14B just this year!!!
First and foremost, it's about competent reporting. You can think a company is doomed and still expect people to report on it accurately.
Zitron continually presents things in ways that create a hyperbolic narrative. Turning routine consolidation accounting into an unexplained mystery hinting at some sort of fraud is the perfect example of that. He does it so often and in such a way that I truly believe he just doesn't understand accounting.
It doesn't take a rocket scientist to understand that OpenAI is losing money. But the question ("where's the profit?") assumes that profit is the thing being optimized for. It isn't.
There are basically three buckets here: cost of serving a query, cost of training and capex for capacity.
The first one is unit economics. The other two are bets on the future that get expensed against present revenue. A company can serve every query at a healthy gross margin (OpenAI has improved margins considerably) and still have a $9 billion loss because it spent $12 billion training a model that generates $0 this year.
Zitron constantly blends everything into a pithy "they lose money on everything" narrative, which just isn't accurate. The thing is that OpenAI could have a very different P&L if it chose to, say, stop training the next model.
The problem, obviously, is that if you stop training the next model, the competition might eat you. So right now you have a situation where the the frontier model you spent $12 billion on depreciates in about 18 months, the GPUs depreciate on a schedule nobody agrees on, and you seemingly can't stop the cycle without risking your position in the market.
This is the legitimate bear case, but the problem with Zitron is that he doesn't make it using an argument that is coherent and honest as far as the accounting is concerned. And the accounting is everything.
Can you explain which partly owned subsidiaries lost $3.74 billion and why it is legitimate to exclude that from their losses in a non-handwavy fashion? Your condemnation leaves me none the wiser and this does matter.
This can of course be used to distort the financial picture and this is a significant amount, almost 50% of losses. Is this from the ‘non-profit’ which used to be OpenAI or something else?
Smells like creative accounting to me and the CEO was accused by his board of dishonesty.
I explained this is a different comment. This is basic consolidation accounting per ASC 810-10-45.
https://dart.deloitte.com/USDART/home/codification/broad-tra...
I get that not everyone is an accountant or has had to become educated in accounting matters as part of their work, but you really shouldn't say "smells like creative accounting to me" if you don't have a basic understanding of the subject.
This is like the least interesting thing about OpenAI's financials, and Zitron framing it as some sort of mystery hinting at fraud is one of the least effective ways to make a point given that it's absolutely a nothingburger.
No loss is disappearing or being hidden. This is by-the-book consolidation accounting.
You have avoided answering the question.
The accounting mechanics are uninteresting, lots of normal accounting rules are abused for nefarious purposes (see Enron et al). What is interesting is why this was done.
Which subsidiary owns the loss and why?
Here's the logic since it's not that hard.
Say Parent Co. controls Subsidiary LLC and owns 60% of it. Minority Corp. owns the other 40%. Subsidiary LLC loses $10 billion.
Consolidation accounting requires Parent Co. to report the full $10 billion loss, as if it owned all of Subsidiary LLC, which it doesn't. Then, on the next line, it attributes the portion belonging to the other owner (Minority Corp.). There are two lines and one loss. Nothing is removed and nothing hidden. This is the definition of disclosure, not obfuscation.
Note that the trigger for applying this is actually control, not ownership interest. My understanding is that OpenAI Foundation controls the public benefit corporation with 26%. And for an LLC, the loss split isn't automatically pro-rata by ownership. It follows the profit-sharing terms in the operating agreement, which is why the there's a Hypothetical Liquidation at Book Value method. Under an old capped-profit waterfall, nobody can verify the exact figure without looking at the operating agreement.
"I can't verify the split" is a very different statement than "it's unclear what this means."
Your "why" question: the reason it works this way is that consolidated statements are meant to show the business as an operating whole because that's what a controlling parent actually runs. You can't operate 60% of a data center or sign 60% of a compute contract. Every line (be it revenue, expenses, assets, etc.) comes in at 100% for that reason. The noncontrolling interest line then answers the separate question of how much of that whole belongs to the parent's own shareholders. One statement answers two questions (what does this enterprise look like versus how much of it is ours).
As for your "who" question, the answer is simple: the other equity holders. Again, OpenAI doesn't own 100% of the entity. My understanding is that Microsoft is one of the other equity holders.
What is actually interesting here is which subsidiary, what it does, and how much they own and are liable for.
If they own 90% and it is core to the business that’s very different from owning 2% say.
https://openai.com/our-structure/
"Also as of closing of the recapitalization, Microsoft holds roughly 27% of OpenAI Group, and the remaining 47% is held by current and former employees and investors."
Maybe I am little confused here but what you just described here doesn't sound great either? What smaller entities who OpenAI is parent to lost 3.74 billion dollars?
Or maybe just can you point to some primary sources about this? I am not too bright about this stuff.. I guess I always thought it was usually about having more money than when you started? Or at least about having a story of how you will have more money? Is that not right?
You can look up ASC 810-10-45.
OpenAI isn't a single company. I haven't followed all the details with its structure change/recapitalization, but it's (I believe) a parent sitting on top of an LLC that outside investors like Microsoft hold a large minority stake in.
The rules say that the parent has to report 100% of the LLC's revenue and expenses as if it owned everything and then, at the end, back out the share of the loss that economically belongs to the minority holders.
So $8.84 billion is the whole loss, $3.74 billion is approximately the outside members' proportional share of it, and $5.09 billion is what's left for the parent. Nothing disappeared or was hidden. It's one number presented two ways because two sets of people own it.
OK interesting. Who specifically are these other subsidiaries that clearly account for this loss though? I get it's just a trueism for those in the know like you, but it's pretty fascinating for me at least! Like is there one example company we can point to here? Even if there were like 20 subsidiaries and the combined loss was 3 billion, that would feel noteworthy on its own?
Like to be absolutely honest, this point ends up just sounding more alarmist than the claim you took issue with originally. But perhaps I am just misunderstanding.
Microsoft owns a significant minority stake in the LLC to my understanding.
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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.
Ed wrote a post back in 2024 where he claimed OpenAI would fail in 2 years if they didn’t do the, seemingly impossible at that time, things like raising more money than any company before etc.
And OpenAI did all of that and is still alive today. Would be good to know this context because if you are out there boldly making doomer predictions month after month then you should also rate your previous ones.
Because one day Ed will be right and he’ll go around and take a victory lap while ignoring he has basically not been right before.
> Because one day Ed will be right
Bold prediction.
Sounds like his prediction in 2024 was spot on.
So the core of your comment is...."the bubble has not burst yet, therefore there is no unsustainable bubble" ?