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.
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
What are the advantages of a GPU database?
And probably some decently sized states too. Not only commercial actors are up to the job.
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?
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.
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
Because of the low prices lol.
Just like any other technology
With circular deals it’s hard to tell.
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-...
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.
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.
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)
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.
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.
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.
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.
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.