On 16 June 2025, the best known AI coding tool in the market changed what its $20 Pro plan bought. The old plan gave 500 fast requests a month. The new one gave $20 of usage, billed at API rates, with credits to buy once you ran out. Within three weeks Cursor’s CEO, Michael Truell, published an apology and offered refunds to users who had been charged unexpectedly.
Read the apology as a margin document, not a communications failure. The newest Claude model cost more to run than the plan could carry, so the company handed the difference to its heaviest users.
Every model was supposed to be getting cheaper that year. It was. The margin still went the other way.
The thesis
When the model gets cheaper, the saving does not land in your gross margin. It lands in your customer’s price or in more tokens per task. A margin bridge built on the token deflation curve is a forecast of somebody else’s pricing decision, and investors are underwriting it as if it were an operating plan.
Founders raising this year are now asked the question directly: what happens to your gross margin when the model gets cheaper. The common answer is a chart. The honest answer, for most of them, is nothing.
Where the saving goes
The chart is real. Epoch AI measured it in March 2025: the price of reaching a fixed level of performance fell between 9x and 900x per year depending on the benchmark, and the price of GPT-4 level performance on PhD science questions fell about 40x a year. Gartner said in March 2026 that inference on a one trillion parameter model will cost providers more than 90% less in 2030 than in 2025.
The trouble is the word fixed. Nobody sells fixed performance. The saving leaks out through three holes, and I have not seen a pitch deck that plugs all three.
Leak one: the cost is common, so it gets competed away. Every competitor buys the same model at the same price on the same morning. A cost that every rival shares is not an advantage, it is a floor, and floors get passed through. Nicholas Charriere, founder of Mocha, told TechCrunch in August 2025 that variable costs across the code generation sector sit within 10% to 15% of each other, and that margins on those products were “either neutral or negative”.
Leak two: customers buy the frontier, and the frontier does not get cheaper. The price that falls is the price of last year’s capability. The model your customer asks for by name is this year’s. Tom’s Hardware, working from Artificial Analysis data in September 2026, put the newest flagship at $3.69 per benchmark task against $0.58 for a mid-tier model scoring about 10% lower. That is more than six times the cost for the model the buyer wants on the feature page.
Leak three: agents multiply the tokens. Gartner’s own release, the same one with the 90% forecast, says agentic models need 5 to 30 times more tokens per task than a chatbot, that provider savings will not be fully passed to enterprise customers, and that consumption will rise faster than unit costs fall. In the same document that promises the deflation, Gartner tells you total inference spend goes up.
All three leaks end in the same place: a flat margin. The only route to a wider one is something the company controls, and it is an engineering capability, not a market trend.
What it looks like in the numbers
The apology. Cursor reached $500 million in annual recurring revenue in June 2025, the same month as the pricing change. That is a company with scale, pricing power and a product people love, and it still could not hold a flat price when its preferred model got more expensive per task. Scale did not protect the margin. Repricing did.
The more autonomous agent. Sacra’s research on Replit puts its gross margin swinging between 36% and negative 14% during 2025, with the low in April, after a more autonomous agent shipped. The model price did not jump. The product started doing more work per request, and every extra step was billed to the company before it was billed to anyone else. The margin was back at 23% by July, after Replit changed how it charged for agent work. Again: repricing, not deflation, did the repair.
The survey. ICONIQ’s January 2026 State of AI, about 300 software executives building AI products, shows average AI product gross margin of 41% in 2024, 45% in 2025 and 52% projected for 2026. Over the same two years the price of a given capability fell by one or two orders of magnitude. An 11 point margin gain against a 90% plus input cost decline is not a pass through. It is a leak. The same survey shows consumption pricing at 35% of companies, up from 19%, and outcome pricing at 18%, up from 2%. Consumption pricing is how firms pass rising costs to customers. It is also, mechanically, how they pass falling costs to customers. You cannot have one without the other.
I build software for private capital and I pay model invoices every month. The line on those invoices that matters has never been the price per token. It is tokens per finished job, and that line only moves when somebody changes the product. The price list moves on its own. The margin does not. When I read a margin bridge, the first thing I look for is the line that assumes model prices fall, and then the name of the person who owns it. There is usually no name.
Where this breaks
Three objections, and the third is the strongest.
First, margins did rise. 41% to 52% in two years is real money, and Bessemer’s 2025 State of AI put its steadier cohort of AI companies at around 60%. True, and it supports the thesis rather than refuting it. An input cost that fell by orders of magnitude bought 11 points of margin. The rest of the saving went somewhere, and it was not to the shareholders.
Second, some products sit on capability that stopped moving. A document classifier that was good enough on a 2024 model does not need the 2026 flagship, and its owner keeps the whole saving. Correct. If the buyer cannot tell the difference between models, deflation flows to the seller. That describes a narrower set of companies than the decks claim, and it is a set whose moat is unlikely to be the AI.
Third, routing is the answer, and well run companies already do it. Send each task to the cheapest model that passes the evaluation, keep the frontier for the hard ten percent, and the margin expands while the price holds. I agree completely. That is the one route to a wider margin. But notice what it is: an engineering programme with an evaluation suite, a routing layer and an owner. It is not the deflation curve. A company that has built it should be valued for it. A company that points at the curve and says routing is on the roadmap is asking you to pay today for an asset it has not built.
What I would do on Monday
If you are underwriting an AI software company this quarter:
Ask for gross margin by month for the last 12 months with every model price change marked on the chart. If the margin did not move when Anthropic cut its flagship by two thirds on 24 November 2025, the deflation thesis has already been tested on the company’s own data, and it failed.
Ask for tokens per completed outcome, not cost per token. If the company cannot produce that number, it does not know where its margin goes.
Ask what share of revenue is consumption priced. That share passes every future price cut straight to the customer, by contract.
Ask what share of requests is served by something other than the flagship model, and to see the evaluation that permits it. No evaluation means no routing, whatever the roadmap says.
Strike the deflation line from the margin bridge in the model. Replace it with an engineering line that has a named owner and a date.
The apology
Truell’s apology was about how a change was communicated. The arithmetic underneath it was about who keeps the saving when the model moves, and in June 2025 the answer was nobody inside the company.
That has not changed. The model got cheaper. The customer noticed first.
I read AI margin bridges from both sides of the table, the one that builds the product and the one that funds it. If you are underwriting one this quarter, grab thirty minutes.