OpenAI Just Filed For Its IPO. The Real Story Isn't The Trillion Dollars.

OpenAI and Enthropic are both moving toward IPOs and most of the conversation is going to collapse into one question. Are these companies worth the numbers people are putting on them? And I think that in some ways is the least useful place to start. I know we're all asking the trillion dollar question, but I think the better question is what are public investors actually being asked to believe? And I think the answer is pretty simple. They're being asked to believe that OpenAI and Enthropic can do two things at the same time. One, they can make intelligence cheap enough to serve at massive scale. And two, they can build the layer around that intelligence fast enough that companies rent the whole system instead of building it themselves. That is the bet. Cheap tokens and proprietary harnesses equal a trillion dollars. If that sounds abstract, I'm going to make it very concrete. A token is raw intelligence, right? It is the thing you buy by the meter. A harness is everything that turns that raw intelligence into work. the files the models can see, the tools it can use, the permissions it has, the memory it keeps, the evals that check the output, the routing between a cheap model and an expensive model, and the workflow that tells the system what done needs. Codeex is a harness. Claude code is a harness. Chat GPT is becoming a harness, and inside companies, every serious AI project is a harness project. And that is why this IPO story matters. The question is not just whether OpenAI has better models. The question is whether OpenAI and Enthropic can own the work layer that sits above the models. There's an analysis circulating today that tried to estimate the notional API value of the $200 AI plans from Enthropic and OpenAI. I think it was by semi analysis. The rough claim was that a heavy Open AI user would be getting $14,000 in value for a 200 buck plan and a heavy Claude user would get $8,000 in value for a 200 buck plan. And the obvious reaction is these companies are lighting money on fire. And maybe for some users they are. But I think the sharper read is that API prices are not an internal cost. API prices are retail. It includes markup. It includes margin. It reflects the price charged to developers, not necessarily the cost the lab pays to serve the token internally. So the question is not how much API value did the user get. The question is what did that usage actually cost OpenAI or Anthropic to serve. And those are very different questions. If the API price includes 70 or 80% gross margins and the internal cost is far below the public sticker price and if the labs are improving inference efficiency and model routing and caching and batching and distillation and chip utilization and everything else that lets them squeeze more intelligence out of the same hardware, then the 200 buck plan may not be as irrational as it looks from the outside. It might be a subsidy. It could also be a strategy. They may be letting power users consume huge amounts of intelligence while they race the cost curve down underneath that usage. They are effectively saying we can afford to serve intelligence closer to cost now because we believe the cost of serving is going to keep falling. And this changes the IPO frame because if you think the models are hitting a wall and token costs are going to stay high, the business is much harder to run. But if you think the labs can keep making inference cheaper, then the story becomes much more interesting. Open AI and Anthropic are not only selling intelligence, they're trying to make intelligence abundant enough that the real business moves somewhere else. And that is the key turn. If tokens get cheap, raw intelligence becomes way less defensible. That doesn't mean intelligence stops mattering. To be clear, electricity matters, bandwidth matters, compute matters. But once an input becomes widely available, the value often moves to what people build around the input. So if intelligence gets cheaper, the question becomes who owns the layer that makes it useful and that layer is the harness. This is where the open AI and anthropic bet becomes much much clearer. They do not want to be just API companies forever. They do not want to sell raw intelligence forever because raw intelligence is going to get compared and routed and priced down and substituted. They want to sell the work surface. They want to sell the operating layer. They want to sell the thing that makes the intelligence useful before the customer has to understand how any of it works. Codeex is the cleanest example. Codeex is not impressive only because the underlying model is smart. It is actually impressive because the model is sitting inside a harness that understands the job. It can see the repo and edit files and run tests and inspect errors and keep track of changes and use the computer and move through the loop of software and knowledge work. The product is not just a model that knows code. The product is a system that can participate in general purpose knowledge work. That is a huge difference. A model gives you intelligence and a harness gives you work. And the IPO question is whether OpenAI and Enthropic can build those harnesses faster than companies can build their own. Because companies have one enormous advantage the labs do not have private context. Open AAI does not know how your company works. Anthropic does not know where the real documents live. They do not know which Salesforce fields matter to you. They don't know which approval step is real and which one everyone ignores. They don't know who can approve the exceptions. They don't know which spreadsheet is a fake source of truth and which one is the real source of truth. They don't know the internal history that explains why the workflow is broken. The labs have models and they have infrastructure and product talent and usage data and they have speed. Companies have context. That is a powerful information asymmetry and the whole fight is over which side can turn its advantage into the better harness. This is why the forward deployed engineering move matters. The simplest version is oh open AAI is becoming a consulting company. I do think there's something to that but it's not the deepest point. The deeper point is that forward deployed engineering is how the labs try to overcome the context problem. They cannot know your company from the outside. So they send people inside. They map the workflows. They connect the tools. They learn which use cases are real. They adapt the product to the customer. They turn the generic harness into a company specific harness. And if that works, the customer is no longer just buying tokens. The customer is reorganizing work around the lab system. That's much more valuable. It's also much stickier because once your workflow is rebuilt around open AIS or Enthropics harness, switching gets harder. Even if the model underneath is replaceable, another model might be cheaper. Another model might be better for one task. An open model might be good enough, but your process is now wrapped around one company's way of doing the work. That is the lockin. It's not the model. So from a company's perspective, the strategic question is not should we use open AI or anthropic. Of course, you should use them. The question is, are we renting the harness or are we owning the harness? Owning the harness does not mean training a frontier model. To be clear, almost no company should do that. Owning the harness means owning the layer that decides which model gets used for which job. It means owning the context, the evals, the permissions, the workflow definition, the review process and the routing logic. It means open AI and enthropic and Google and DeepSeek and open source models are going to have to compete to serve your work. If you own the harness, the labs are suppliers. If the lab owns the harness, the lab becomes the operating layer. That is the fork in the road. And this is also where recursive self-improvement becomes more practical than mystical. The dramatic version of recursive self-improvement or RSI is that AI improves AI, intelligence explodes and everything changes. Well, maybe. But for the IPO, the more practical version is enough. If better models help OpenAI and Enthropic improve their own products faster, then recursive self-improvement becomes an iteration advantage. They can improve code faster. They can improve eval faster. They can tune routing faster. They can optimize inference faster. They can compress models faster. They can make the harness better faster. And that is what matters for the business. Not just whether the model gets smarter in the abstract, but whether the lab can convert smarter models into cheaper tokens and better harnesses faster than customers can respond and build their own. So the bullcase for OpenAI and Anthropic becomes very clean in that world. Open AAI and Enthropic can manage token costs. They can compete with open-source models on price over time. They can use their scale to push down the cost of inference. They can use their models to improve their own products. And they can build harnesses so good that most companies decide not to build their own. That's a real thesis. And honestly, they have a shot. After all, most companies are slow. Most companies don't understand their own workflows. Most companies can't write down what done means. Most companies won't build routing logic. Most companies won't maintain evals. Most companies will not create a clean internal AI layer. they will just buy the product that works. And if Codex is a sign of where this is going, the labs are getting very good at making products that work. But the bare case is also really clear. If companies learn to own their harnesses and the labs become suppliers of intelligence rather than owners of the work layer, they may still be huge companies. They may still make a ton of money, but the valuation changes because the most valuable layer is no longer fully theirs. the company will capture the workflow value in that scenario and the lab is stuck with a token margin. And if token prices keep falling, that is a much less dominant position to be in. And that's what I would look for when the S1's are finally released for anthropic and open AI. Not just revenue, not just user growth, not just cash burn, not just the valuation number. I'm sure it will be in the trillions. I would want to know whether heavy users are getting cheaper to serve over time. I would want to know whether gross margin improves as usage grows. I would want to know whether enterprise customers are buying scalable software or custom deployment labor. I would want to know whether customers are building real workflows inside the product. And I would want to know whether forward deployed engineering is a bridge to product or a permanent requirement for the product to work. Those are the numbers that should tell you what kind of business this actually is. But if you're not an investor, the practical question is even simpler. Are you building your own harness or are you letting someone else own it? By all means, use the tools, use open AI, use anthropic, use codeex, use cloud code, use whatever works. But do not confuse using AI with having an AI strategy. An AI strategy is knowing what work should run where. It's knowing which tasks need a frontier model and which tasks need very cheap, reliable intelligence. It's owning the context. It's having eval. It's having a review path. It's being able to swap models without breaking a workflow. That's the company version. The individual version is the same thing at a smaller scale. The valuable skill is not prompting. Prompting is thin. Now the valuable skill is harness building. Can you take a recurring job and define it clearly? Can you give the model the right context? Can you connect the right files and tools? Can you check the output? Can you make the system better next week? That is where the leverage is because cheap intelligence is coming either way. The question is who knows how to use it. So the open AI and anthropic IPOs are not just stories about whether these companies are worth a trillion dollars. They're the first public test of a cleaner thesis. Can the labs make tokens cheap enough and build harnesses fast enough to own the work layer of AI? Or will companies use cheaper tokens to build their own harnesses and keep more of the value themselves? Sheep intelligence is the input that makes the token economy possible. The harness is the engine that makes the token economy valuable. So, whoever controls the harness has the dominant position in the token economy of the future. And that that is the trillion dollar question I'm watching. And yes, I do think we'll get clues to that when those S1s leak, as they inevitably will for Open AI and for Anthropic. Stay tuned. And of course, I'll be digging in as soon as we get more information. Cheers.