From the AI ROI Series, recorded 25 August 2026. Anthropic is going public, and it is shaping up to be the largest listing in history, bigger than SpaceX, which raised $75 billion back in June. The Financial Times reports that investors are targeting $2 trillion for the company. Almost all of the coverage stops at that number, and the arithmetic underneath it points straight at your own budget, so that is where I want to go.
A headline valuation is a function of how much stock changes hands
When a company lists, it does not sell itself. It sells a small piece of itself, and that piece sets the price of everything else. SpaceX sold 4.2% to raise its $75 billion. Anthropic wants to beat that raise and clear $2 trillion. So put $100 billion into $2 trillion and you get about 5% changing hands, which means 95% of the company is being priced by the 5% that sells.
That is the Wall Street half, and I am done with it, because we are buyers here rather than investors. What I care about is what has to be true for $2 trillion to hold, since the answer to that question arrives on your invoice. Two things have to hold, and they pull against each other.
First, the revenue
Bankers expect Anthropic past $100 billion annualised by year end. They started the year at around $9 billion. That is roughly an elevenfold move inside twelve months, and the thing to be clear about is where it comes from. That revenue is enterprises buying tokens. Their revenue forecast is your AI budget, and the growth has to come out of somebody’s line item, which means the plan you are writing for 2027 is on the other side of the same equation.
It is worth being precise about what that implies, because it is easy to read as rhetoric. A vendor growing from $9 billion to $100 billion in a year is not doing it on new logos alone at that scale. A large share has to come from existing customers spending more, which is the same creeping invoice I described in the agent portfolio piece: adoption spreads, usage climbs, and the bill climbs with it, always with a good reason attached. From the vendor’s side that curve is the growth story underwriting the listing. From your side it is next year’s budget variance.
Second, the margin, and this is the fragile one
Gross margins run around 44%. The valuation is priced on 40% to 50%, sustained for years. Anything under 35% and the analysis says most of the valuation goes with it. So the whole structure rests on a band of about ten points, in a business whose input costs are being set by the compute market I have been complaining about all year.
| Gross margin | What it implies |
|---|---|
| 40% to 50%, sustained | The band the $2 trillion valuation is priced on |
| ~44% | Where margins actually run today |
| Below 35% | Most of the valuation goes with it |
Now hold that next to the capability chart
Ten frontier models, 16 points of capability between them, priced 12 times apart. The top two, the most capable and the most expensive, are Anthropic’s. Sixteen points of capability across a twelve-fold price range means the premium buys real capability, and buys it at a rate that gets harder to defend the further you get from the tasks that genuinely need it. So the rational move for any buyer is to push work down the curve wherever the job allows it, and I am seeing a great deal more of that, seriously, among our own customers.
Here is the part I think is genuinely underappreciated. Every enterprise that pushes work down the curve takes a point of somebody’s margin. The buyer behaviour that is rational for you individually is the same behaviour that presses on the one variable the valuation cannot afford to lose. That is my thesis, and it is why enterprise buyers are the variable in this arithmetic rather than the audience for it. Routing work to the cheapest model that finishes the job stopped being purely a cost tactic somewhere in the last year.
You can only make that call if you can see it
Pushing work down the curve sounds like a procurement decision and is actually a measurement one, because the phrase “wherever the job allows it” is doing all the work in that sentence. Deciding which jobs allow it means knowing, per task type, what the cheaper model finishes and what it does not, which is a question about your own workload rather than about any leaderboard. Get that wrong in the cautious direction and you pay the premium on everything forever. Get it wrong in the aggressive direction and you cut the bill while quietly degrading the output, which I have written about at some length after getting the unit wrong myself.
None of the above is actionable without visibility across all three of the places AI now runs: your coding AI, your assistive AI, and the autonomous agents most organisations are piloting for next year. One record, rather than three vendor consoles and a cloud bill you reconcile by hand in January. Capture everything, attribute it to a team, an agent, and a model, and then it can answer a question you had not thought of when you started collecting.
Not a dashboard, though. A dashboard is just a view, and a view only shows what somebody already collected, which in practice means vanilla metrics chosen before anyone knew what would matter. That distinction is the same one behind a falling rate card and a rising bill, and behind an agent portfolio nobody had split by agent. In both cases the number that mattered existed only after somebody kept the record that could produce it.
2027 budgets are being written right now
That is the window, and it closes. As always, check my math and tell me if I have this wrong, because I welcome that all day long. But the questions I would want answered before you sign next year’s number are these. Do you have one source of truth, or several? Are you capturing every AI interaction across coding, assistive, and agentic use, or only the ones a vendor happens to report to you? Can you make sense of it at scale without a project to do so? And are you collecting continuously, so that the AI ROI question can be answered with evidence rather than reconstructed under deadline?
I’m Paul, co-founder of Olakai. Olakai is the system of record for enterprise AI: one record across every tool, every agent, and every token, in your own environment. Your AI is an investment, so let’s measure it like one.