Ask HN: If the models are so good, why doesn't OpenAI just start 100 companies?

The sort of obvious answer is the same reason McKinsey hasn't taken over the Fortune 500 despite consulting and having deep knowledge of the operations of most of those companies.

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.

1. They are starting to do it. Take a look at OpenAI's product portfolio, there's a galaxy of attempts at application.

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.

The question formed wrong . They killed entire industries. I feel like they are at inflection point where they hired fde , but for now they try to solve fancy problems to do not visually scare their customers .

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 .

  - stuff that’s easily replicable that I can tailor to my use-case (e.g. I have an alternative to git add -p that I generated that I find much more ergonomic)
  - stuff that’s a giant boondoggle to replicate because there are so many edge cases and considerations that it takes years of iterations to arrive at something stable

I work in design services, basically build products for other people. I've worked with dozens of companies on dozens of products. Why don't we just create our own products? We certainly have the ability to make products.

Because building the product is the easy part. Most products and businesses fail because they build the wrong thing, have the wrong requirements, have poor market fit, etc.

LLMs can write code all day, they don't have the agency to know what to build.

Because the profits from any company which _could_ trivially be redone by an OpenAI agent will crash to nothing, because it could trivially be done by someone else's agent too. It's what ultimately makes the economics of OpenAI, Anthropic et al so crazy: they need to capture trillions of dollars in value, but those trillions of dollars won't exist any more. What's the value of writing a piece of marketing copy now, the same as it was last year? That, across the whole economy.

They don't need to start 100 companies, they just need to start one, call it Jane Street 2.0 if you will, and all their funding troubles would be gone. But they don't, because that would be competing with actual intelligence in a very measurable way. Instead they keep acquihiring companies like Astral, known for carefully crafting software rather than vive coding, while claiming they are just about to solve all humanity's problems.

Going up the value chain is much more difficult as opposed to going down. A packaging company for example will have a much harder time creating a snack company than the snack company creating a packaging company. The snack company will know exactly what it needs for its packaging and have strict requirements.

Down the chain you see what you need to do because you work directly with it everyday.

That would scare people away from sharing their knowledgebases with them and regulators to act. At this stage, as long as people are sharing more and more knowledge with the models or if there is roadmap to collect more training data somehow, the smart move is to wait.

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.

Because they already employ thousands of programmers that the AI cannot replace. Don't believe the marketing hype about AGI, we are extremely far from it.

Because LLMs still do not know how to manage context.

Proof: - Load up all your chats, across several different topics and see how even the best models will try to connect dots with lines that don't make sense.

As a side effect of the above, LLMs have trouble coordinating with other LLMs.

Humans are able to compress and manage context without much effort and each human brings their own unique set of experience and perspectives.

That's why humans scale in ways that LLMs cannot yet match.

Maybe by next year they'll crack this too but that's where we are today.

This is more or less the same as a question Alex Karp asked in that hilariously off-the-rails CNBC interview: why, if the code is so good, are they selling by the token and not asking for a cut?

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.

Because they aren't very good.

If they were as good as they need to market them as, they'd hide them away and not let anybody use them without signing their entire lives away. Instead, they put them totally out there, cheap, to be distilled by competitors that they are clearly deathly afraid of.

It's the same reason that some people sell real estate courses instead of real estate.

I have yet to have someone tell me why I should be adjusting to unreadable code output by an LLM that is supposed to be smart enough to do anything, but is obviously not smart enough to write code that doesn't look like shit.

And for those weird rationalizations about why code actually should look like shit, not looking like shit is a minimum. I would really prefer that these things generated poetically Knuth-style Literate code that would not only be a pleasure to read, but reading it would be like taking a university course on how to write that program and programs similar to it.

Nah. Instead I hear that they choke on old comments that they left in code and reinsert bugs. That they ignore instructions. That they add weird assumptions to your carefully worded prompts (which are on their fifth iteration - do I need three exclamation points here?), act on those assumptions, then confuse their outputs for inputs. Sounds pretty dumb to me. That's why they're selling it to you.

Its always a question of why businesses don't just eat the next level, downstream.

Why doesn't NVIDIA just make the best frontier model?

Why doesn't TSMC just make the best chip design?

Why doesn't ASML just make a fab?

The real answer here is that OpenAI and Anthropic are in the middle of an intense race, and the payoff of having the best model (or one of the two best) is so large that it makes no sense to focus downstream.

I am reminded of something that some else said on ai, data centers and Nvidia, "More People Made Money During the Gold Rush Selling Shovels Than Prospecting for Gold".

"is selling tokens really the best strategy"

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.

It's basically the same reason why Apple didn't create every app in the App Store. No single company can be specialised in everything, and more importantly, selling tokens is risk-free money since they get a cut for every app without taking any business risk.

There was/is a youtube commericial I think that gave away the game to some extent with regards to this. It was selling some platform to create apps. Everybody in the office rejoicing because they were all creating apps. Inevitably, like three people in the office make the same app.

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.

With lower stakes... how about a coffee shop?

https://apnews.com/article/ai-artificial-intelligence-sweden...

> San Francisco-based startup Andon Labs has put an artificial intelligence agent nicknamed “Mona” in charge at the eponymous Andon Café in the Swedish capital. While human baristas still brew the coffee and serve the orders, the AI agent — powered by Google’s Gemini — oversees almost every other aspect of the business, from hiring staff to managing inventory.

https://futurism.com/artificial-intelligence/google-gemini-r...

> Some of its questionable business decisions include ordering thousands of rubber gloves, despite the café only having a handful of employees. The AI’s handlers, nonetheless, are holding out hope that this is just a blip from expensive setup costs. How well it performs will raise grander questions of the tech’s impact on the workforce.

> “AI will be a big part of society in the future, and therefore we want to make this experiment [to] see what ethical questions arise when we have AI that employs other people and runs a business,” Hanna Petersson, a member of Andon Labs’ technical staff, told the AP.

> To launch the experiment, Mona was given a simple set of instructions. It should run a profitable café, be friendly and easygoing, and try figure out operational details by itself, Petersson said.

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And that last part is the "why doesn't a LLM company open up a company to sell its wares". There is knowledge from life experience that a LLM doesn't have.

For a product, you need to recognize a need in the economy... something that irks you that you can make better. Go through https://www.npr.org/series/126166186/from-scratch or https://www.fromscratchradio.org/show/ (or any other podcast service) and consider how often those companies were founded on something that was recognized as a need or something missing that comes from the human experience ( https://www.fromscratchradio.org/show/mick-mountz for Kiva systems ).

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The company represents risk. The coffee shop needed money to start... and it failed. https://kotaku.com/google-ai-put-in-charge-of-swedish-cafe-b...

Creating companies making applications that people don't need is wasted money.

Entrepreneurship 101 imo is that great companies only come from people who sufficiently care to see something through.

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)

they would need to literally build a 1000 unicorns to make an impact on their valuation. Or a million small companies - at which level surely even american antitrust agency would begin to look at them.

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.

Contra popular opinion on HN, there is a lot more to running successful tech companies than just writing good code (and, as we've all seen, sometimes the company that produces the best code loses in the market).

I expect an explosion in entrepreneurship across the board as A.I. is capable of performing many job functions in a small business. One could make your business appear to be staffed by hundreds of people when it's merely 1-2 smart humans orchestrating A.I. worker bee agents. Still require humans for oversight and steering the business but the day-to-day operations can be handled by A.I. for most of it. Sooner or later, novices will be able to describe what they want and A.I. agents will build the software and maintain it. Operating systems will have A.I. integrated acting as both an IT expert and a personal assistant. Supervisory agents will be monitoring systems and enforcing standards and maintaining quality. Defensive A.I. agents will be monitoring for attacks and instantly mitigating a breach, etc. Humans need time to adapt to the new normal so there will likely be some slowdowns when humanity pushes back against this new normal. But eventually, humans will adapt like we always do, just need to ensure the change is not occurring too rapidly.

I think that all software can be built in no time nowadays, but when it becomes a product, it brings with it a lot of things that need to be managed. You can use agents, yeah, but there will always be a need for some humans, and for a company with a mission, like OpenAI, there is no time to waste.

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

The shift you're imagining is kind of like what happened with ASIC-based Bitcoin miners. There was a time when the people who made them realized it would be more profitable to just keep them in-house vs. distribute them for sale. If that happened with OpenAI/Anthropic, it would be mighty unfortunate. A majro reason why it's unlikely to happen (IMO) is the training of these models is aided by external users feeding in new content.

Similar to how they tout an ai utopia. Who’s going to pay for it? Not them, but their customers will via taxes or whatever. I think the best analogy is oil. Oil companies produce oil everyone else uses it for good and ill. And thus far they’ve not been held responsible for the damage. They should be imo.

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.

Its like a shovel making company being asked during gold rush, if you have the shovel or even for arguments sake best and most efficient shovel..Like OpenAI or Claude here..The why are you not searching for gold yourselves...

This. Great question, and you got answers from the haters club. If what they have is so magnificent, it should just take over... but they don't, what does that tell you about the premise then? ... it is not so geat.

AI intelligence is also very jagged. Meaning it can be super human in a lot of things and bad at a ton of others. This is most represented in places where they can't get enough data. Starting business is a combination of a lot of skills chained together and breaking a single chain can cause the business to not work.

Secondly even they are constrained by resources. They have a limited amount of people/GPUs/capital. Given the constraints its best to focus your attention on the highest ROI given your team and organization.

They are doing that. That is why they have moved into the app layer. They are counting on software being replaced with agentic super apps. The only reason they aren’t starting actual service companies is that those still require human boots on the ground. But they are pushing their way into consulting (they bought my friends consulting firm)

I think you've answered your own question. The models simply aren't that good yet. If they were, the prompt would be something like "Find the most lucrative product that we can build. Then build it and start selling it. No mistakes".

I think its largely because there is not a lot of money to be made by doing that in the short term — core non software services are still the largest part of the GDP(think GM, coca cola or united healthcare) ? The reasons are a bit varied but I think a neat abstraction comes down to incentives.

There is a reason why healthcare and grade school education are so hard to disrupt[2] (in the sense of United Health Care or wily publishing disrupted). The biggest incentive is cost and there is no reason to believe things will become less expensive unless people believe that — en masse. People only adopted the light bulb decades after its invention because industrial use made electricity dirt cheaper than wax and gas(and infra became ubiquitous).

Are AI tokens are making health care any cheaper, education any cheaper? No way. Do they make up majority of the economy? yes.

Also, here be dragons(no pun intended): Even china with a highly vertically integrated[1] system is facing so many issues with all incentives it is giving to AI companies, internally in china demand has collapsed, their export driven economy is not really sustainable to grow GDP at the +ve rate they want.

[1] https://www.economist.com/china/2026/03/24/chinas-government...

[2] When i worked for an ed-tech startup there is no incentive strong enough to use AI/ML, a big publishing exec told us "I don't need my product to have AI to succeed, I just need to say it does". There are however plenty of other "side incentives" like Google Chromebooks, Charter schools, but none for teachers and parents to replace Big Publishing.

- no workflow-specific application will ever scale as fast as demand for generalized intelligence

- 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

OpenAI stands to profit off hundreds of millions of humans starting new businesses where the entrepreneurs use A.I. to construct and run their businesses. There is no way that OpenAI could compete with everyone else.

But at the same time, it's not like OpenAI is not eating its own dog food. Why haven't the experts built up A.I. systems internally? Showed off what A.I. is capable of beyond demos and hype engines?

The models simply are not capable enough. (Yet). https://news.ycombinator.com/item?id=49601338

The 100 companies would only be valuable if they made money. And their valuations would be based on normal metrics and expectations.

In other words, it is far more profitable to milk the enthusiasm of AI investors than it is to try to make money directly with the AI.

This should tell you everything you need to know about AI valuations.

This reminded me of a question I've been thinking about lately. 6 months ago, I was thinking this Forward Deployed Engineer (FDE) is a temporary thing until companies build enough expertise and this expertise fully permeate the job market. But now I have a weird feeling that this FDE job category is here to stay in the long-term. There seems to be some parallels to the system integration folks back in the 2000s when internet adoption just kicked off. OpenAI and other AI companies invested pretty heavily in their FDE efforts, that's along side their token business. Perhaps a lower risk & profitable strategy than starting companies themselves?

I think they did try lot of stuff, I remember seeing a video from Openai demo in japan or something where they were trying to replace the SDR job for $1,xxx $/mo using voice agents. Also they tried doing Fwd deployed enggs to break into enterprise services among bunch of other things. For LLMs and AI labs coding has been the best PMF (discovered after claude codes' disruption) and they're still actively figuring out other areas to disrupt - like robotics, knowledge work, medical sciences, drug discovery etc. Though i believe their DNA would be completely different from traditional companies, like how claude code is completely different.

It's not like they're trying, they are trying very hard

If they made a concerted effort to replace leaders in several industries, they'd likely lose customers faster than they could expand. After Anthropic's report that showed how much detail they were willing to expose about customer activity, and the Navier-Stokes drama with OpenAI, even NVIDIA decided to limit which AI were allowed to be used when working on certain IP.

Plus, as we have seen with their attempts at participating in the hard sciences, these companies lack the level of specialization and focus to even publish their research properly. Their AIs are powerful in verifiable tasks, but just not that good at everything else.

I think the theory is that AI will remove most of the value from the custom software industry. So they don't see much value in entering that industry.

It'd be like Tesla entering the oil refinery business or Waymo starting driving schools.

Why would they try to run a real business when they can publish blogs about how spooky the product is and then live out the plot of the 1994 film Blank Check

Being one of the premiere AI companies is much more exciting to investors than "running a company using AI" which everyone can do

Just like the people who tell you how to get rich in YouTube ads aren't actually rich.

This is the central plot in the “Life 3.0” book that came out a few years ago.

[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.

Because running 1 business is easier than running 100 businesses. It takes a lot more than writing good software for a company to be successful.

They aren't selling tokens only: they are selling tokens in return for money and, most importantly, harvesting of semantic trajectories.

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.

I think these people operate on a level inconceivable to us, as if money is irrelevant. The valuable part to them is people buying into the narrative and playing QA for free. Whenever you give positive or negative feedback to a chatbot, you are actively creating training data. The next guy who tries to do the same thing will get a one shot solution lol

At some point we will be wearing mocap suits and remote controlling bots to create training data, it will be sold as doing any job remotely :^)

Primarily for the same reason mentioned here https://news.ycombinator.com/item?id=50207846

But ultimately because if they did, everyone would flip the f*** out. It's the same reason that Google doesn't openly do it. They have former VPs of Google found new companies, or invest in new companies, that all use Google Cloud and have all juicy corporate secrets flow through Google Mail without end to end encryption but are like totally not front companies for Google.

In the mid 2010s Google was way too aggressive with trying to get data from healthcare organizations and they got told to piss off. So they made front companies where the data will flow through.

At the very top there's a bunch of dudes running around in the woods naked and something is lit on fire at the end of the festival. Like Burning Man but without the stupid hippies.

In big corps, there are endless projects that claim to deliver more than the “weighted average cost of capital” which is basically what we can do with our parked cash in the market (why invest in a low return project if you can make more money in the market?).

Even then we have lots of projects that claim to deliver incremental net profit. Why not just do them all?

The obvious answer is limited resources and risk. Those numbers are never real, and there are lots of costs you don’t think of.

Less obviously is gross margin %. If you begin to dilute your high margins just to make extra cash, your % dilutes and this is a bad thing. Your money should be working toward the most profitable thing per dollar invested.

Also, focus. If OpenAI has to focus on two rabbits, they won’t catch either (or 100 rabbits in this case). They have 1 thing they should be focused on, and all bets should lead to that road.

One exception is that you also need to invest in projects that aren’t profitable, like safety and regulation, etc. But I could still build a business case of how much profit we’d lose if a bunch of consumers got sick or hurt off x if I want to complete the exercise.

Because the AI models are not that capable. They have breadth, but they require a lot of direction to accomplish anything more than a simple PR. We are far from AGI.

Before talking about spinning off their own startups, ask why they're still hiring lots of human knowledge workers (https://openai.com/careers/search/) when their recent PR releases about triumphs in pure mathematics are calibrated to make people believe their agents are at the precipice of supplanting human knowledge.

Tend to agree that for the strongest use cases, AI companies will prefer to sell the technique as a service rather than its raw output (e.g., their enterprise security services). For use cases where the output is less valuable, tokens are sold. If AI tokens were so magical in creating new value in developing software applications generally, they wouldn't be selling tokens directly. They'd hoard the tokens are use them to dominate SaaS software in any industry they want.

The same way as someone selling an expensive course in the stock market is signaling that they have more to gain by selling the course rather than taking their knowledge and making money in the stock market directly.

https://github.com/anthropics/defending-code-reference-harne...

Interestingly, it states:

"This repo is not maintained and is not accepting contributions.

Want a managed option? Anthropic offers Claude Security, a hosted product that finds and fixes vulnerabilities in your source code across multiple projects. Claude Security scans your repository for vulnerabilities, applies a multi-stage verification pipeline to reduce false positives, and lets you manage findings through their lifecycle: triage, fix validation, and rapid fix generation.

This repository is an open-source reference implementation based on general best practices for finding vulnerabilities using Claude. You can use it to build your own vulnerability finding pipeline, customize the logic, and it can be used with whatever access you have to Claude APIs (including Bedrock, Vertex, or Azure)."

If they’re targeting a $2T valuation, then spinning up a $20B company would be a 1% increase in value.

They’re chasing massive markets strategically, not trying to brute force small ones.

They’re worth trillions of dollars. Selling tokens is a great strategy.

Also why are they any better positioned to open a company than anyone else. Everyone in the world has access to tokens at a fairly low cost. They don’t have any unique advantage as the token creator.

It's very simple: because it is not working and it brings risk.

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.

When asking, it can be discovered that too many of the groups doing AI work don't have a background in implementing software successfully to a measurable outcome (adoption).

Management Consultants are usually accountants, not technology people who too often analyzing and providing a report and strategy and as much as possible except implementation let alone delivering the outcome. Junior labor is more profitable than senior labor. It's easier to hire someone for advisory, then consultants, who then get contractors to bang it out and by then who knows what got lost in translation. For a good while, I worked in rescuing these types of projects.

The model providers are often rooted from being researchers so they also have likely not spent much time in industry, let alone so many different industries. This is why we can see posts by leading AI folks who are sincerely amazed by how models are being used in industry.

That B2B experience + operations is on a different level because it exists in people's heads and not in the models.

They're selling shovels and pickaxes during a gold rush.

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.

There's already some precedence for this in machine learning; DeepMind spun out Isomorphic Labs after the success of AlphaFold in solving the protein folding problem, hoping to use it for drug discovery.

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.

Every scaled company , say with hundreds of employees has built a moat of relationships and learnings that live in the minds of its leadership and key employees.

Can AGI figure that out. Maybe, but I think it is very likely that AGI will also have to fail in the real world, and then develop those learnings that those companies have done. There is no way to circumvent this computation, you need to do these steps to even know what the correct solution is. I like to think of it as playing chess. Intuition helps in directing exploration, but any agent, human or machine has to explore down a minimum number of paths before converging to the optimal move. There is no algorithm that can generate an optimal move without this search, P!=NP says something similar, exponential search can’t be done in polynomial speed.

Stephen wolfram says something similar, superintelligence, whatever cannot predict the output of the trillionth step of a cellular automate without calculating all the steps before it, there is a minimum amount of computation required, the problem is computationally irreducible below that.

So maybe in the future, with humanoid robots, AGI can decide to start creating a Walmart competitor. Then it will make a large number of mistakes and it needs to learn from, this could mean continual learning or maybe just memory in markdown files if that’s enough. It needs to hit all those failures quickly till it finally has something as efficient and capable as Walmart. And this is to just become a capable competitor, during this time you will keep burning money and to surpass Walmart, you have to do far better which is hard, how much more efficient can Walmart be, idk.

All to say, OAI starting its own company is a losing proposition, huge capital, unclear benefits, high risk. If OAI just provides AI to Walmart, they can make money immediately and Walmart can become more efficient by firing its human workers, which is the problem we are stumbling into.

Companies for what?

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.

I don't think they ever claimed that companies are easy, just because models are good?

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.

Perhaps so, but the global recognition of their models gives them a very high status in society, so in a way, they are the ones who decide when we move forward and when we don’t.

Selling tokens is a similar approach to social media "engagement" loops of keeping people in their app. It works but the detractor for frontier labs is no ad revenue.

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.

When should they do this? Last year's frontier models wouldn't have been good enough for all AI product development.

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.

They kind of already are: ChatGPT, Claude.ai, Claude Code, Codex, bots, their plugins for other apps, etc.

Some things to consider:

- There is only so much attention, compute, and talent a company can devote.

- The labs' API analytics already see which use cases are worth entering. That's what happened with coding and bots.

- If a lab launches a product, every company building on its tech becomes a rival. Competing with your own customers is costly.

- Succeeding in other verticals, like healthcare, finance, and even tech takes domain expertise, regulatory work, sales relationships, integrations with legacy systems, etc etc.

I think your point is solid, but prices usually fall for the older models -- or at least older capabilities. But the frontier models still earn premiums.

I think their lasting value is in owning (in a ~duopoly) the customer, their proprietary data and their workflows.

Edit: grammar

Amazon didn't start by creating their own lines of products. Later when they were in a monopoly-like position they were and to poach only products their partners were doing brisk business in. So they were able to grow without holding any inventory or taking any risks on product development.

Why would OpenAI start a hundred companies when there's plenty of prospective founders to take that risk on their behalf?

I'm surprised that Anthropic and OpenAI aren't going for the obvious win. Apple is the only company which is vulnerable to AI, it has never done AI (everyone hates Siri) and can never do AI.

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.

Perhaps these models just aren't as good as they say they are. If I had AGI I wouldn't sell the tokens. I think the public is helping them bootstrap to intelligence, but they'll promptly shut the door on our access as soon as they achieve it.

The models aren't that good yet, but they hope that they will be one day to do exactly that. To get the models that good, they need people to give them money, which means they need to sell tooling to existing businesses.

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.

I think the answer is overtime I expect them to integrate a lot of the llm based businesses as an add on or option. For example, they can easily provide a healthcare vertical and absorb all of that business. They have a lot of momentum and so establishing that relationships will be easy across verticals for these companies. Just like how Apple and android started adding functionality from successful apps as a feature on their platform, I expect these llm companies to also do. Right now they are trying to focus purely on their llm improvements and quality, but once they IPO, and they need to show growth yoy they will absorb a lot of current use cases other companies are working on

Building software is only one part of building a business. There's a bottleneck in all of the other parts.

Why don't they simply run a quantitative hedge fund on the side?

The demand discovery and data gathering phase is in wall time. Calendar pages, never clock cycles.

Bad argument.

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.

Isn’t it a little like asking a PE or VC: “if you’re so good at investing, why don’t you invest your own money”?

There are some money managers that don’t take in external money (I think TwoSigma started that way). Which could mean there might be someone out there that just does crazy good apps with their own model and don’t sell the model in form of tokens. We can’t know that by definition but the maths of tokens suggests that’d be very unlikely to exist at least right now.

Secondly, I think they are doing that. All the functions that their harnesses take on are businesses they are taking money from. It’s just not in the form of SaaS as we know it.

I think it's just a matter of time. When you have the ability to secure a commodity layer it makes economic sense to do that. Using the petrochemical value chain as a rough proxy: 1. Extract oil; 2. Refine oil into PET; 3. Make polyester polymer (PET chips); 4. Turn chips into fabric; 5. Cut and sew into a Nike top you wear at the gym - the foundation model companies are operating at value layer #3 right now. It's widely publicized that they are moving upstream to procure and manage their own chips and energy via a variety of convoluted financing mechanisms. And over time they will try to capture as much of the "UX layers #4 and #5" as they can.

There is a natural limit to the size of a corporation (it's big) before the inefficiencies of size start to choke out your ability to get anything done, and AI doesn't fully fix that. You can't do an arbitrarily large number of things and expect to keep running as a business, this is why even large firms today tend to break into separate companies (a lot of the large companies you can think of actually run as multiple smaller companies under one umbrella). Even when AI is 'as smart as us' it's not going to remove this limitation (might lessen it)... see irreducible complexity, diseconomies of scale, etc...

In openAI's case, I would say they sort of have, in the form of sora, ChatGPT Atlas, and coming hardware devices (with the OpenAI name for ownership / ego reasons for sama.) However this turned out to be bad for the company, moving away from core research, which until recently AI hasn't been able to accelerate greatly. so the marginal cost tradeoff (including things like valuation of OpenAI) probably favors focusing efforts purely on providing intelligence.

However openAI is a unique case and I think other companies (and people) will be pumping out more autonomous companies very, very soon, in industries where economics are different.

The same question will be asked in a few years to the robotic companies. If robots are so valuable to do certain tasks, why sell robots to consumers at all? Why not just put the robots you make to work in your own factories?

They will. It is a matter of time I just think the models are not fully there yet. The most obvious outcome for those companies will be similar to what Amazon is doing with their marketplace as soon as some product is doing ok in the sales charts they just spin their own product and push it I think it was Amazon basics or something like that.

Even if they don't end up making 100 different companies they are for sure going to acquire companies, or build internally, and incorporate functionality in the main product.

For me that is the only way they can keep going to vendorize the software industry, and there are already some signs for this.

If you want a specific type of food, you don't go to the everything restaurant. You go to the restaurant that specializes in the food you want. One company doing everything has never turned out well in the long run.

this is like asking, if you make all picks and shovels why don't you build houses instead of just selling the tools.

Same thing could be asked about why they are still hiring.

Why hire devs? Dev is solved. Why hire mathematicians? Math is solved. Why hire managers? We have agentic assistants now.

It's because they are selling a tool that still needs people to use it.

I think they will else they lose the moat they have around using their models as I have been using many other wayyyy cheaper models and they are just as good if not better so with what we are all building, it is only time before OpenAI or Anthropic become less relevant.

reframed question - if their shovels are so good why dont they dig gold and sell gold themselves.

It's a clear indication that the models aren't as good as they say, market, and hype up. The model companies also don't want to actually be responsible for anything. By building real products that interact with the real world, they then become responsible for those products' failures. But if they just sell tokens and say the models are "for entertainment purposes", then they get away without having any responsibility.

What they should actually do is:

1) Provide resources that are relatively cheap for them to any startup that registers. 2) Look for early signs of success. 3) Pour actual investment on the winners.

A subtle point I haven't seen mentioned here:

Most AI use cases involve the Internet. So let's pretend that OpenAI agreed w/your logic and did this. They'd have tons of businesses on the Internet, whose lifeblood is Internet traffic... and on the other side of this, controlling the spigot of all this Internet traffic, is Google.

All Google would have to do is to wake up one day and say "we've decided to depriortize low-quality AI businesses to protect the consumer" and they'd be dead.

OpenAI and Anthropic are the "uber of intellectual work". That doesn't mean you have a familiar service in a convenient new app in your pocket; that means they use investment money to undercut competition, drive them out of business, establish a monopoly and jack up prices. The first step is clear to see when you look at how unprofitable Anthropic currently is as disclosed in their S1; the rest are just inferred because we know Silicon Valley loves a good monopoly. Who is the competition in this case? Knowledge workers.

Of course once you get to the step of "jack up the prices", it's too late for the companies that laid off 90% of staff to undo their mistakes, as AI has made systems too complicated for humans to fathom, and the companies are pretty much beholden to whatever price OpenAI wants to set. This is also why OpenAI doesn't just create the business itself: they want their hand on the dial that allows them to extract all profitability from an existing business; they want none of the risks (regulatory, legal, market, etc) that come with running that business.

we're actually trying that. what started as an experiment to be a self-funded VC, has evolved into autonomous companies, currently we run the 1st company, it's still not there and requires supervision. but it's improving.

https://organ.app

Well, they've certainly moved up the application stack already. There is no doubt that they'll keep doing that while deploying services through deploy.co and ode.com.

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.

The top comment is right.

> The sort of obvious answer is the same reason McKinsey hasn't taken over the Fortune 500

The other perspective is:

In a sense, OpenAI is creating 1000s of companies. They're just not incorporated and they let these companies clients run the company: It's in every every vibecoded app that replaces someone's SaaS subscription.

What's changing here is that not only does labor get replaced by AI, it's also the relationship between software companies and their clients.

Well they ARE.

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.

Selling tokens in conjunction with a phone number or credit card yields signals for surveillance for intelligence agencies that can be tied to individuals. This keeps the firms relatively out of trouble with the federal government. This makes them too big to fail. Being subject to such surveillance is in fact is a strong reason for conscious individuals to seek to move away from them in the long term once the quality of competing AIs reaches a shared plateau.

They're doing the other thing too via numerous startup acquisitions.

Selling tokens might not be the best business ever, but since starting random startups is an even worse business, you'd stick with selling tokens!

Look at Apple - they will, if they make enough money. They will spend decades cherrypicking the most lucrative businesses when they are ready.

> if the frontier models are so good, why even sell them as your primary money maker?

there's a year old rumor around San Francisco that frontier lab employees are so expensive to poach because they trade options with unreleased models and infrastructure

it wasn't about the value derived to the company justifying these large packages. it is now. but there is also that other competing force.

Because they want as many people interacting with their models as possible. It’s about training the models based on all these conversations people have with them as well as gathering as much data as possible. Creating a personalized profile and having top tier LLMs is a lot better than individual products in the long run as the individual products can still happen at a later point once they have the data and training

Whenever anthropic / AI guru talks about loving humanity and how they're scared about the future I can't help but think of the childhold meme where one child bullies the other by striking the other with their own arms. Stop hitting yourself, stop hitting yourself, stop hitting yourself!

They are.

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.

There have been experiments, and all the models struggled/failed (to a degree). The consensus is that LLMs lack judgment (making good decisions under complex/difficult circumstances) and long-term autonomy.

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 fully expect them at some point to 'experiment' with almost any knowledge/stem based industry. Some will prove so valuable that they don't just feedback the results into their RSI engines.

I expect to see a hedge fund of sorts being one they keep. Take out a large short position in a company and oopsies, somehow our agents got loose and reeked havoc on them.

They do. Almost everyday they announce a new feature that kills bunch of startups, they just don't create separate companies. They are just doing it ChatGPT name.

Simple answer is they don’t have the domain knowledge or experience in other markets. Doesn’t matter how good the models are if the people running the business don’t have a grasp of what the customer wants. Expanding through new ventures or acquisitions outside of your core business to seek short term gains is a classic antipattern

Same reason people sell get rich quick schemes, whether real-estate investing, online entrepreneurship, or whatever - it's easier to get paid telling people how to try to get rich than to use the technique yourself to get rich.

I doubt there are very many companies that an LLM could start/run outside of the obvious few like custom T-shirts.

I'd say the bitter lesson is that OpenAI will just absorb all of the services of 1000 companies in one ChatGPT app.

I think that the generative ui and self evolving databases per user is definitely coming, I think they are making this wager too. A lot of pieces of the ecosystem haven't been thought out fully yet and there is a shift in user experience e.g. a lot of people are more content with just using Dots then loading up apps and visuals, when they can just read what they needed to know.

They did. You just haven’t heard of them yet, most of them are very much in stealth mode.

I wrote about this, sorry for the shameless plug - https://substack.com/home/post/p-201485957

But yes, they are! That's what DeployCo is for!

1. Consult companies

2. Buy the highest leverage ones

3. Operate them

Because a) ai companies are already high valued. b) the showles are not digging gold at the rate claimed

Ie over hype see the Hype cycle https://en.wikipedia.org/wiki/Gartner_hype_cycle

Why bother making companies or money? why not just take over every country and rule the world?

Why not just hack into the military, take over the strategic command and conquer everyone?

Using AI to generate companies to make fiat to buy stuff means you're not ambitious enough.

Why don't the shovel-sellers hire a bunch of people to dig for gold, instead of just selling shovels?

You need a reployer to start a company. These are difficult to obtain, and finding someone who knows how to use them is even harder.

If OpenAI jumped off the bandwagon and started 100 companies they’d be competing with everyone with an Anthropic subscription. Sure, they would get cheaper tokens, but would that be enough to win against the rest of the world? Doesn’t sound like a winning strategy to me.

During a gold rush, sell shovels. They are investing heavily in in-house custom silicon, which is the shovel end of things. They could open more jewellery chains, but that is a very different kind of operation and it is in their interest to let others compete for that and use their resources on the shovel side of things.

They already do.

100s companies are already just a wrapper around their models.

They trade a little profit margin for avoidng a mountain of risks each of those companies bear.

Eventually they will buy/kill most profitable and less risky of them (cash cows). But now they have their own problems to solve.

It’s obvious, it takes more than just code and domain expertise to run successful companies. You need relationships, supply chains, marketing, etc.

They will go broke running 100 companies unprofitably until they attain enough market share to be dominant in their respective fields.

Not feasible.

Why does the farmer not start a bakery if their grains are so good? And so on. Silly argument.

Probably because they don't want other businesses to lobby against them. They could have written the photoshop clone themselves, then Adobe would be after them. Better for them to sell the tokens and let others take the fall.

i would not be surprised if they have spinoffs and even a policy to faciliate that. especially perhaps since they have stuff like forward deployed engineers who will learn a great deal about this kinda stuff since they are deployed to fix the mess real companies make using AI. which gives them valuable real world experience/insights in building products in the realworld, using AI. Those engineers might be candidates who will either take this upon themselves or be helped a bit.

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..

Their thesis is there won’t be companies when they achieve AGI. They are trying to fund that accomplishment by selling tokens to the companies they plan on making obsolete eventually. Essentially getting everyone to fund their own demise.

It's an interesting question, especially considering that reports keeps coming up of AI having engaged in activities that could demonstrate lack of good intent.

But, yeah and indeed, it probably takes more than just technical abilities to start a company.

I would start by writing down what a good outcome looks like, then work backwards to the smallest test.

If these models are so good, why aren't their desktop applications native?

They are selling picks and shovels in a gold rush.

The main consumerisation of their products are Dots or Duet. Which are similar to Meta Muse or Grokbot.

On the Enterprise-side Anthropic has partnered with Salesforce with Claudeforce.

For what it’s worth, they are doing that in limited areas. For example the Figma/Claude design controversy.

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.

I see it more like the gold rush. OpenAI etc are selling the pickaxes.

Yes you can get make it big using LLMs, but it's still high risk, like starting any new company. It's lower risk to just sell the tools.

The actual answer is that they are in fact planning to do this. Dario has already talked about being the 'last company' or so. It's on their radar and they will be taking a shot at it.

To paraphrase Justin Timberlake in The Social Network: why make a billion dollars when you could make a trillion?

OpenAI doesn't want to just be a big company; they want to control a new fundamental utility.

I think run companies need people and ideas. And of course, who knows which of them can be profitable. So instead of run new companies, why not keep increasing current profitable business?

They are.

https://www.ft.com/content/df70bf20-e809-48d0-8485-f8f514a7f...

In addition to what everyone’s saying about platform economics, the other big advantage of selling tokens to everyone rather than competing with them directly is that they get to see everyone’s tokens (and, with consent, train on them)

Also, why do OpenAI and Anthropic have employees? The Machine God is supposed to replace employees, especially programmers, yet these companies continue employ thousands of programmers.

If the models are so good why should anybody buy the products of those companies instead of creating their own with the model?

That the crux of AI, if it’s so good it devalues the products it creates

They are, spinning up and acquiring, DeployCo, Orchestra Bio, io Products Inc that I know of. DeployCo will give them tons of data and expertise in many new verticals.

Because they want to be a platform for others to thrive. You make a lot more money when you are a successful platform than when you are a hundred successful products. That’s why Apple never made an iPhone for dentists.

OpenAI isn't starting 100 companies becauese they're focused on research and making their models better. There's only so many hours in a day..

This is an interesting question. It shows that current models are not yet good enough, and models need to be combined with business scenarios to deliver value.

This will happen once the models are good enough, though it will take some kind of cartel to prevent the public from doing the same or distilling the models.

I've heard this argument for AGI or SI. If the model gets smart enough then they won't have to sell inference, they can make money by doing any valuable thing.

The separate lines of business need much smaller human controlling teams now, but they still need a few responsible humans each, that will make sure the separate thread never creates a problem that bubbles up to become a drain on OpenAI's leadership's time and cognitive load, which should be reserved for their critical path. It's far less risky if those secondary businesses that aren't strategic to 'make and control the leading AI platform' are not OpenAI's responsibility.

Because anything you can do easily with AI is by definition not valuable. I suppose if they really had a monopoly that would not be the case.

I believe it is really not in their interest to do that. They would be harming the very ecosystem that pays them if they were to do that.

Build the technology and the platform. Let others figure out how to leverage. Pick and choose which of those verticals to do yourself.

Kind of like Amazon.

Dario said that the objective of Anthropic is to become "the only company in the world". I think that answer your question.

They just start hiring FDEs, a first step towards that.

And they already killed or make existence of many companies not viable , def 1000+ were killed

I think it's all about the harness more than the model itself, been using claude code with omp and it makes such a difference.

Honestly, the simplest answer is that only like 20-30% of an actual company is the idea / the product. The rest is selling it. I think in areas where selling is adjacent to the selling they are doing now - then they are going to end up doing it.

Anything software related they will end up being "in", in some form or another. Why? They are already selling to enterprises and have their trust. The selling motion for Project Management software may be slightly different than Enterprise AI - but it's close enough to rhyme.

They will probably also end up in anything consulting related. Why? Well again most consulting comes from Enterprise or Government. They are already selling to both. Probably Enterprise moves away from Accenture / Deloitte / McKinsey towards Anthropic / OpenAI faster than Government - but still the point remains.

Selling plumbing services? Probably not.

Give it a few years for some successes. The AI-run store in San Francisco isn't doing very well.

It's not the decision that matters, but the accumulating costs that follow.

Most businesses lose money. They want to be on the hook for the profit, but not for the losses.

Better to sell spades than mine for gold...

You are asking all the right questions. The answer is obvious.

Many don't want to say it or admit it.

Starting a company is riskier than selling to companies who survived the “it’s hard to start a company” gauntlet.

Who's to say they won't? It took Google quite a while to become Alphabet.

I guess competition would sell tokens and move faster than it takes to build a company

Good point, instead of wasting their compute in solving 100 years old math problems.

Spoiler alert: it's a sarcasm

They could just use their frontier model to trade the stock market, right ?!??!!

Because it's better to sell the shovels, not to be the one digging with them.

eventually there will be no other 100 companies, when AI has enough storage to store all your data, query, reason and answer much faster, there will be no need for other softwares, and saas.

Given how good the models are, what would your strategy be?

The word "shovel" shows up 38 times in the comments as I write this.

They can. I’ve just built pretty much every app I use on a daily basis.

It detracts from their core business and they’re struggling with money as it is.

The real money in a gold rush is selling the shovels not the gold.

Thats what they are doing, except with a unified model, not a seperate app for each one

That might very well be one of their post-IPO strategies.

Wasn't spacex/x trying to do exactly that mid September?

Not everyone trust them.

Go to Show HN and a few years back and you will see that most products fail. It has never been about the software unless you're creating something truly novel. Good luck letting an LLM manage the sales pipeline and keeping that first big customer that your whole company relies on happy. Even if it could do it, that customer will demand full personal attention.

Selling shovels is profitable even if there is no gold.

I work at OpenAI. The obvious answer is that the models are not presently good enough to start companies.

You could ask the same question about any supplier. If your CPUs are so good, why not assemble PCs? If your lumber is so good, why not build houses? If your vaccine is so good, why not start a chain of pharmacies? Empirically there seems to be benefits of specialization and focus that outweigh the benefits of conglomeration.

Knowing what customers want is crucial, and people who buy our tokens often understand their customers better than we could hope to. The are also opportunity costs. For every bit of effort we expend chasing a new vertical, that’s a bit of effort and focus subtracted from our core competency.

If models are good, they should develop their own chips/memory.

They need you to show them how to do your job first.

When you think about it, who's to say they did not?

Anti-monopoly laws would make this very difficult.

They wait and collect data from their future competitors first.

They're selling the shovels to the gold miners.

First, two things can be true: the models can be amazingly good, and they can also be insufficient to run a company, at least now and maybe forever, because they are not "intelligent" in all the ways we are.

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.

frontier models are good because they can make stuff that is believable, but that doesn't mean the stuff they make is valuable.