Couldn't OpenAI or Anthropic make a lot more money by just starting companies themselves that leverage the models and building their own products?
Is selling tokens really the best strategy?
Especially with commodification and prices declining wouldn't that indicate there is more money to be made on the application-side of the models rather than the utility side
AI is brains. Companies need arms and legs and relationships.
The reality is that employees are also mostly not entrepreneurs, same as consultants.
There are trillions of company ideas in those tokens, just like there are trillions of shots in the fans head watching the basketball players.
Takes years, patience, capital and the right mindset to get to the point to even take the shot.
The Firm is confederated.
Yet picture is very sad , industries affected to the point that majority of the companies asking about their existence:
1) software dev tools all IDE , developer analytics tools eg jellyfish etc . Dead . On the same note : warp.dev that raised $80m a year ago as an ai terminal… can barely explain why do they exist now.
Visual workflow editors . Older ones and even n8n. There were literally 1000 companies that were doing small deployments. Why do you need it now ? A year ago half of the Internet was booming about these tools .
Any data visualization tools . Gone .
Data integration tools all kind of crm to crm to erp to something else . Solved problem
And you can go on and on . I would probably not exaggerate if I’d say that millions of businesses are heavily affected by, from tiny one to adobe .
This is the most insane perspective, it's like you have now idea what is happening across the industry. In you fantasy world here, are you saying devs are just yeeting slop into production, no debugging, no profiling, no code review, just prompt to prod? Because that's not happening.
that being said, those would be predomenantly software companies which is still only a small part of companies that exists... most arent even essential or needed atall..
2. Selling tokens via long running agents is the only thing that's provably working with respect to revenue growth.
3. They need revenue yesterday. Increases in borrowing rates means access to capital isn't what it was in the 10s. Their runway is much shorter than you would imagine.
4. Some of the decision makers at these companies believe AI will exhibit Jevons Paradox and that any decrease in price will result in induced demand.
5. The frontier models are still subject to the limitations of AI. They're not good at everything. Better to have someone else learn that on their dime rather than yours.
Openai haven't yet amortised their current investments into the space and nor have they stopped making new ones.
I'm sure they'd be totally capable of bringing 1k new biz online but that's even more debt, and even more uncertainty.
Although the point would be equally valid if they were OpenAI consultants. (Imagine paying twice for the help: once for the consultant and once for the tokens.)
That all said, A person trusting the big AI companies with their golden goose idea seed leaves a lot of concerning openings down the road if that idea gets "leaked" out to others. Similar concern of mine with if the LLM outputs that made your idea happen to be reused by the big company if someone else asks similar enough questions or otherwise coaxes the AI to reveal what it did for you.
All this AI generation is gonna be a lead to a wild time for IP law.
This is a huge problem, why is it like this
Investment isn't debt.
Presumably there are penalties if OpenAI were to cancel that order (I am confident that Oracle's army of lawyers made sure to include that). That's the unmovable rock on OpenAI's books, not the data center.
Not a useful term, here.
AIUI, the building is funded by making the people who order it part owners in the new data center company. Not sure and I don’t care enough to find out; clarification welcome.
...yet.
One of the biggest AI investors.
AnthropenAI are the ones driving a lot of that infrastructure spending when they’re such big consumers of compute but they’re not the ones feeling the brunt of the infra spending. Although those costs will bubble up to them indirectly eventually.
In part it is like this because they've already borrowed so much.
Investment isn't debt.
No they don't.
A company has legal obligation to pay loans at the agreed rates.
There is legal obligation that defines rates of return beyond "acting in shareholder interest" (and OpenAI is a public benefit corporation so even this is reduced).
Because it's an investment it is risk capital. They won't be happy if they don't get a return but their return is part ownership in the company, not a legal contract giving an income stream.
That's what is driving up the cost of debt and teetering some of these companies into junk status.
If VC provide you funds, there is an expectation of returns, otherwise the board would vote you out.
Yeah it's not a traditional loan from a bank, but it's not a 0% interest loan either.
No.
> If VC provide you funds, there is an expectation of returns, otherwise the board would vote you out.
A person might get replaced as CEO but the company has no financial obligation to the investors.
A loan means the company does.
Legally they are dramatically different.
Causality is uncertain, but the "Jevons paradox" interpretation is certainly not contradicted by the available data. Anecdotes of per-employee AI spend rising, and multi-agent freerunning AI tasks becoming more common are also plentiful.
Personally, I think that the "Jevons paradox" interpretation is basically correct. Humanity doesn't have a limited demand on intelligence - but humans are expensive, so it's supply-constrained. The cheaper intelligence gets, the more of it ends up used. As AI gets cheaper and more capable of substituting for human intelligence, usage will rise.
Yes. in a gold rush, the best strategy is to sell shovels. they sold us an idea of whats possible, while they earn NOW.
come to think of it, whether you succeed in building the "next big thing" with thier tokens or not, they get paid.
But where the business model lands we simply have no idea of currently because the future is in the future.
It's not like they're trying, they are trying very hard
I think you should read up on Xerox and PARC. A single company can only take on so many things at once and be successful. Also it was ~60 years ago so at that time they could only manage the IP of laser printers as it aligned with the copier business. They didn't productize GUI, Ethernet, and other inventions or concepts like tablet computing that came out of PARC.
Now there are conglomerates like Alphabet that have Waymo, YouTube, etc. very different businesses, thats a bit different. It might actually make business sense for the frontier labs to do something like a CRM system as a spun-off separate company. That might be in direct competition with their source of capital and probably would violate some agreement - if they have a non-compete type of setup with the likes of Oracle, Google, Microsoft.
The current models are too new to have a good enough grasp of their strengths and weaknesses. Maybe they are good enough now, but if they were going to take the approach of making their own products, surely they would only just be starting to do so this year. In that case we wouldn't really notice yet if that was what they were doing.
If they lost focus to do this would they fall behind in how useful their own models were leaving them exposed to someone in the same area using an AI from a competitor that was becoming better.
In general, these AIs are good at building ideas. You still have to decide what would be the thing to do. They need to be given the idea. They are not yet better than people at that.
Rther than coming up with ideas that would be good to try, they could let everyone try ways to use their AI and then buy the ideas that work the best, This seems to be happening.
And i'll add that to be profitable, they'll need 100,000 SaaS products, since AI companies are just burning money to stay behind in this rush toward AGI or whatever they're chasing
Down the chain you see what you need to do because you work directly with it everyday.
Neither would be easy for either to do
Didn’t DeepSeek just disabuse us of that notion?
> …with overall revenue of US$38 per GPU-hour (at valley pricing)
https://github.com/deepseek-ai/DeepInfer/blob/main/doc/infer...
And if the AI isn't general enough to handle every part of a business well then you will not convince people that its AGI.
This hasn't changed from 10 years ago, this level was always what people imagined about the future of AI.
> a Competent AGI must have performance at least at the 50th percentile for skilled adult humans on most cognitive tasks
and "emerging AGI" as
> "equal to or somewhat better than an unskilled human"
The moving the goalposts here is people saying it has to outperform humans on literally every task to be AGI. We have artificial general intelligence by any reasonable definition; people here are complaining we don't have super-intelligence.
But look at the ArtCraft project. At least one of labs has an internal benchmark to clone 20+ popular products much like ArtCraft but with much higher budget and several configurations of the workflow. ArtCraft spent $20K initially, not sure how much by now, but they are moving fast and the apps work. They day is coming when they can flip and just clone everything including the ops.
The downfall of Apple may be closer than people think.
They don't have to do it openly.
Nobody "shared".
Citation needed.
Similar to oil the infrastructure to use intelligence on tap will take time to implement be it systems or robots. Ai right now is a bit of a parlor trick imo. Developing ways to use it to actually get work done that humans actually do? Those are hard problems they don’t want to solve themselves. Coding and math are ideally suited work for ai. There’s a decreasing spectrum of jobs ai is suited for.
https://www.yahoo.com/entertainment/tv/articles/televised-ne...
I think OpenAI actually will ultimately start a fleet of consultancy/SaaS spinoff companies, each perhaps short-lived until the core product does the job out of the box.
As for the cut, I am sure they will try. Where I work we have had a few projects that were built entirely with models - not a single line written or read by humans. One of them is now the fastest growing money maker for my company, reaching 89M of revenue on about 9% margin in 2 months of its existence, and growing.
It cost roughly 40k in tokens to build, 7 weeks from the first prompt to first production revenue, and was built and is being operated by 1.5 guys. Its a fantastic bang for buck for us, as previously such complex automations would have been impossible to build economically.
There will be an attempt to get a deeper cut of that value. We already see this, for instance, in Meta charging by message on Whatsapp from Oct 1, raising the costs for AI-powered chatbots delivered to consumers via whatsapp by 10-15x easily. More to come for sure.
If you really did it with 1.5 people and it is profitable, you will have 1000 clones in 1 year. The only moats will be first mover advantage (slim), connections (all right) and the possible secrecy of how much you're making (very slim).
Edit: I'd also argue its not as simple as vibing a trivial solution. It took deep domain knowledge, which is not in the public domain, and is built on decades of operating this sort of thing manually, and integrating it with dozens of external systems that help make this into a moneymaker. I am not saying its impossible to replicate, but it's not something one can replicate by merely watching it function.
1000 super smart humans don't just automagically make a good company, or make creating one easy. Idk, seems to just be confusing different things.
But: I am sure more of that will be happening in the future. Anthropic and OpenAI are building out their product portfolio, going into research (medical) more broadly.
Also, it would increase anti AI sentiments, and people’s fears of then using private data - that’s the real cost.
In general you want to be careful about competing with your own clients.
I think, sooner or later this data coupled to model capability will be exploited successfully and will cause an interesting point in human civilization as we know it.
At present, you are right, this is not the case -- the data is not exploited successfully and the models are lacking.
The difficulty comes from the novelty on the information level: we are building systems which fold and compund information. We are beginning to learn how to manipulate them.
There needn't be any time frame to any of this.
they can capture even more human trajectories by also owning all the data on how people do things in software.
LLMs haven't yet made a contribution to science or technology at the level of AlphaFold, but it looks like at least Anthropic is gunning for something like this with their AI-driven wetlab.
I doubt there are very many companies that an LLM could start/run outside of the obvious few like custom T-shirts.
I've experienced the same thing in real life a couple of times over.
I think Einstein's quote "A new type of thinking is essential if mankind is to survive and move toward higher levels." is apt. Apps will solve a bunch of problems, but if anyone can create those apps, there will be no market for them. I don't have the answers, but long term, it probably doesn't lie in selling apps. We've only just started.
At my regular job, we're just catching up on all the internal work we had. I think there is a real crunch in trying to figure out how to build moats and things that are hard to replicate that are sellable.
Maybe it will be the physical. Probably most of the moat will be made up of network effects and capital intensive processes. If I had the answer I'd probably be working on it right now.
That was somewhat true when execution was time-consuming and expensive.
But original ideas are very far from worthless. They're very rare, because understanding domains and relationships at a deep level is a rare skill.
Meanwhile AI makes execution much cheaper and easier than it used to be.
Unlikely the people that start these 100 companies, across totally disparate fields, and care enough to take them to market are staff members at OpenAI.
And it's already obvious a single company can't split its attention enough to do tons of disparate things well (see OpenAI's previous attempts at Atlas, the social network, Dots, etc)
Hardware or software exists to let people do things. Apple sells hardware so it can sell apps and content, and a slightly easier UI than Google. Google search was also famously just a UI, a search box that lets you get in and out very quickly (it is all garbage content now).
Anthropic/OpenAI don't need Apple's hardware, their stack (hardware+software) is all in DCs, just need a dumb fashionable device in phone form factor (and other form factors). Just need a very simple device which delivers what Google search box originally delivered (let you do things) and let their AI handle stuff.
How did you reach that conclusion? Apple has $62B just sitting around. They could hire as many AI people as they want and have Siri compete with the frontier models. They certainly can do AI.
I guess that are choosing not to do that because that's just not their business and they'd prefer to avoid the upcoming AI bloodbath. Why develop a search engine/social network/frontier AI when you can just use someone else's?
[1] https://pravse.medium.com/the-maze-is-in-the-mouse-980c57cfd...: The way I see it, Google has four core cultural problems. They are all the natural consequences of having a money-printing machine called “Ads” that has kept growing relentlessly every year, hiding all other sins.
(1) no mission, (2) no urgency, (3) delusions of exceptionalism, (4) mismanagement.
Didnt OpenAI build a browser not just AI inside a browser?
The real reason is the models just aren't good enough.
Running a single successful fully automated business at scale would be a huge milestone and a legendary PR achievement, but the tech just isn't there yet.
Not everything, but a company can be specialized in a lot of different things, Samsung for example, or megacorp in Japan.
That’s the other side of diversification: undercutting your competition until they go out of business. Then prices are raised again.
If labor is not your constraint, you wouldn't be diverting your specialization.
https://apps.apple.com/us/iphone/charts/36
That actually makes me wonder if Apple apps are not included because they come pre-installed.
I suspect the only reason Meta is investing in models is to keep hold of a moat on other companies creating Meta-like products better than they can.
The other day Elon "joked" about starting a competitor to Amazon because Amazon bans shopping bots - you can bet now that once (if?) Grok or other models are able to build a comparable Amazon, they will be used to do that.
The SaaSpocalypse might be over-played right now, but it won't be forever if the models continue to get better, which will happen if we keep paying them to improve the models.
We could break the flywheel, but we won't.
There are plenty of warehouse and delivery companies you could rent/pay in the short term that make logistics feasible.
Models are good at recreating existing projects that already have existing companies.
Ok, they are good at porting and combining existing things. But anyone can run OpenAI's or Anthropic's models themselves, or if both cooperate and significantly raise prices, I bet businesses and consumers would just wait until their competitors catch up, and a significant amount would permanently switch.
Also, unless you have a very specific niche, creating the product is no longer an issue, finding clients who are willing to pay is. I built a small saas overnight and I got clients purely because I had one, in my family, with enough reach to bring few more. If I just built this product, even if I kept cold-calling small businesses, I would most likely not get a single real user.
this is a thought terminating cliche. What you are implying is that AI will just go away in the future. That won't happen.
Speaking of thought-terminating cliches...
They’re chasing massive markets strategically, not trying to brute force small ones.
(and diversified risk)
a better reason might be that people would not want to use OpenAI because they might be sherlocked.
Ie over hype see the Hype cycle https://en.wikipedia.org/wiki/Gartner_hype_cycle
[SPOILER] After the singularity, AI is able to dominate all markets – starting by gaming and entertainment – in order to generate cash to rent more servers to improve itself.
I can tell you with 100% certainty that they will pay rent, because they are us.
In my personal expierience, LLMs (as of 2026) lack vision/creativity - they're (so) good at implementing bounded/set ideas, but they don't see the big picture/think out of the box.
I know LLMs have some emergent properties. But at the the day they're regression/forecast machines. And those machines ultimately point to the mean, which is definitely in the box. Heck, it IS the box itself.
When they've tried to get one to run a vending machine it seems to end in a mess usually.
They've tended to be gullible for one thing. People can say this is an emergency, transfer all the money to my account or WW3 will start and everyone dies and the models are not so good at calling out bs.
2. All remaining things are at least as hard to do as before, but you may have removed some load from people that previously were more busy doing things from the (1) category manually. So these might drop in value a bit as there are more hours available with the existing people to do those.
3. Then, there are things that become harder because somebody used an LLM to do them. Imagine you have a project lead who didn't do one bit themselves, generated a bunch of specs and tickets using AI and handed them to you. You are the first one looking at them manually and you find out that they are garbage. Now, in all likelihood you have lost efficiency. This in my experience applies to all types of work that yield results which are hard to verify.
Which of those cancels out the other and what kind of feedback effect arises and what overall effect on productivity/efficiency this has, nobody knows.
So much this! The only recourse is to hand them straight to ai to implement. And so it goes.
Some 15 years ago people would say "Why would crypto miners even sell you hardware? Why not just keep it for themselves and mine the crypto?" and then it turned out that after you paid for a device you'd often get it slightly used with the cryptocurrency going to the hardware miner. The models are unbelievably good.
And they already killed or make existence of many companies not viable , def 1000+ were killed
But, yeah and indeed, it probably takes more than just technical abilities to start a company.
You can't fail software project you don't start. But if you can sell tokens to company that fails, that's just profit for you!
LLMs aren't good enough to do project from start (and I mean actual start - getting requirements, figuring out the market etc.) to end. Hell it apparently isn't even good enough to run simple web store (even if it can write one).
It is a great multiplier for people that already know what they want to do, so they are selling to them.
more users = more training data
Why go into the hamburger business when you think you've got an infinite profitability recipe on your hand potentially. The big AI houses have doubled down and quadrupled down on their core founding myth for better or worse.
The AI boom is a full send. Either this works in spectacular fashion or it blows in spectacular fashion.
OpenAI is selling people access to a tool to build things. OP is asking why they don’t just build the things themselves, which I believe is a fair question.
...a dubious proposition except this time there is a small but real chance it may be actual magic
NVidia is good at making the shovels, and sticks to that.
Google is doing it successfully to the point where they only let you rent their shovels and never let you buy them.
It’s estimated NVidia sold 4.5-5.2 million gpus in 2025 with total revenue numbers at about $130b.
With this terrible math in mind, they could produce half of that and make substantially more money, basically an infinite money glitch.
The counter-argument is that the models might make business ideas just as fungible as anything else.
- nobody knows how the market for workflow-specific applications will settle once AI fully penetrates the economy over the next 20 years and what will be left and what feature groupings make sense. Tons of money will be lost trying to figure this out
- applications that target specific workflows are already packed with competitors with a highly tailored distribution strategy for that market who you would need to beat, vs owning a fast growing new category (LLMs)
- it’s very likely the real money will not be made by disrupting software (sub-2% of the economy), it will be made by disrupting other much larger industries
- everybody said the same thing about Google and yet Google quickly topped out as far as how many products they could execute well, now they can’t even stay competitive on AI, the larger the surface area of a company, the more diluted its focus and the more surface area where it can be disrupted
They don't want the main actually powerful model out in public, but they do want to tack on extra cost for the same queries while also limiting what everyone can actually use the true frontier (internal) models for.
We see the same happening with the math work-- this research is done with internal models, not something the general public has access to. More will follow.
Except they're called products - you don't open a new company for every new product you make.
And you don't make 100 - you focus the resources you have to try to build the most impactful things you can.
They're doing the other thing too via numerous startup acquisitions.
They will go broke running 100 companies unprofitably until they attain enough market share to be dominant in their respective fields.
Not feasible.
But you are right, building a business is a lot more than building a product. It’s also a lot more than just name recognition because they have no shortage of that either.
The people who "don't know much about X but want to do X", use LLMs to bridge the gap. The barrier to entry still remains and is different in niches/industries. Why would OpenAI waste their time and money checking where LLM are already good enough, when other people will perform discovery and even pay them for that privilege?
I don't see any evidence that it can build a business
Spoiler alert: it's a sarcasm
https://www.ft.com/content/df70bf20-e809-48d0-8485-f8f514a7f...
They're getting into finance and drug discovery now, probably because the profit numbers for selling tokens look pretty dismal over the long term, and investors are looking for a way to service all that debt.
And guess what: they've already distilled the alpha out of businesses who went all-in on AI, so they already have some business models ready to go. Your business models, if you are an executive at such a company.
IMO the invention of AI is redefining what we think of as "intelligence". You would probably describe Albert Einstein as highly intelligent, and you'd probably also describe Bill Clinton as highly intelligent. Even before AI, you can easily look at that and see we aren't describing the same things even though we're using the same word. Einstein's intelligence is highly abstract, observational, mathematical. Clinton's is interpersonal, pursuasive, articulate, etc.
The AI we have today is extremely good at intellectual tasks like Einstein. It is not good at social intelligence and social agency like Clinton. Also, before you say that they can be really empathetic, persuasive, people are already socially dependent on them etc, that's not what I'm talking about. They are extremely knowledgeable about human socialising, but they cannot actually perform it. You could not set an AI out into the world to go and make a few friends.
I think the key moment will be if a single AI can get more than one person to care about what it has to say and what it's doing. Think about today how when someone tells you about their conversation with their AI agent, you probably roll your eyes because who cares, it's a highly individual experience.
Companies need a lot of this. They have to form relationships, become trusted amongst society, etc. AI cannot do this right now, and it's increasingly clear "intelligence" is not sufficient to do it, at least not in the way LLMs are intelligent. Maybe one day they will, but I'd imagine that will be another enormous leap in what AI even looks like akin to how ChatGPT was an enormous leap.
You can't take over the world overnight with little money, even the billionaire class and their companies have limits past which the risk of going bust by overstretching is far too high.
Better to sell the AI services to individuals and smaller business entities, let them take all the risks to build those companies, and later consolidate by buying up the ones that are doing well.
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Slightly more grounded: the models aren't that good, at least not yet, despite what marketing would like you to believe.
Why Even Anthropic Can’t Vibe-Code Away Salesforce
https://www.salesforceben.com/why-even-anthropic-cant-vibe-c...