Ablative Software

Two years ago a company called Cognition sold an AI software engineer named Devin for $500 a month. Today it sells the same idea for $20. Nobody at Cognition failed. Revenue grew almost sevenfold after the price cut. What happened is simpler and stranger than failure: the thing they built was consumed by the thing it was built on. The models underneath got better, and 96 percent of the price burned off.

This has now happened to enough companies, in enough sectors, over enough years, that it cannot be called an accident. It is the normal life cycle of software built on frontier models. And yet the industry has no honest word for it. So let me offer one.

Ablative software: software built to fill the gaps in the current generation of models, and consumed when the next generation closes them.

The words we use instead

It is worth pausing on the vocabulary, because the vocabulary is doing a lot of quiet work.

When an investor asks a founder what happens when the model improves, the founder says “moat.” When a founder builds a product that patches a model’s weakness, a critic says “wrapper,” and the founder says “workflow layer.” When a lab releases a feature that destroys a category of startups, the startups call it “platform risk,” as if it were weather. Every one of these words exists so that nobody has to say the plain thing: this software has a decay rate, and the party that controls the decay rate is not the party that owns the software.

Watch what the plain version sounds like. Winston Weinberg runs Harvey, the most valuable legal AI company in the world, worth eleven billion dollars. Asked about his position, he said: “If the models get really, really good, all traditional moats go away.” He also said his largest competitor is, indirectly, OpenAI. OpenAI wrote Harvey’s first check. There is no euphemism in either sentence, which is why both sentences are worth more than most investor memos on the subject.

The record, set down plainly

Consider what has actually happened, case by case, since 2022. I will keep the numbers few and the pattern visible.

The models could not write marketing copy well, so Jasper sold copywriting on top of them and was worth a billion and a half dollars. Then ChatGPT gave the same ability away free, on the same underlying models. Within nine months Jasper was cutting forecasts and staff.

The models could not answer homework, so Chegg’s answer library was worth fourteen billion. One sentence from its own CEO, admitting students were switching to ChatGPT, cut the stock nearly in half in a single day in May 2023. The company is now worth about one percent of its peak.

The models could not remember, so an industry of retrieval and vector databases grew up to remember for them. Pinecone raised at $750 million two weeks before the first hundred-thousand-token context window shipped. Context windows are now measured in millions, the most successful coding agent in the world ships with no vector index at all, and the CEO of Elastic said last year that vector databases “were never a business.”

The models could not reason step by step, so we taught them to in the prompt, and a small literature of technique grew up around the teaching. Then OpenAI moved the reasoning inside the model, and the literature became a checkbox. The models could not fill out forms or click buttons, so Adept raised $415 million to build hands for them. Amazon bought the founders out four months before Anthropic shipped the same capability natively. When that native capability arrived it could barely use a computer, scoring 15 percent on the standard test. Eleven months later it scored 61. Every product whose business was compensating for the 15 lost its reason to exist inside one funding cycle.

Copy, answers, memory, reasoning, hands. Five different companies, five different sectors, one identical story. Software was built to supply what the model lacked. The model stopped lacking it. The software did not fail; it was spent. That is the meaning of ablative. The material does its job by being consumed.

The only real mistake, in every case, was one of accounting. Each of these companies, and each of their investors, booked the software as an asset. It was a consumable. It had a burn rate the whole time, and the burn rate was set in a lab in San Francisco by people they had never met.

What the laboratory sees

Now stand where the frontier lab stands, because from there the whole thing looks different, and more orderly.

A lab ships a model with gaps. It cannot see well, remember long, or act reliably. Around those gaps an ecosystem forms overnight, and the ecosystem does the lab an enormous unpaid favor: it maps exactly where the value is. Every popular tool built on a model is a confession of what the model cannot yet do. Browsing tools confessed the model could not see the web. Memory layers confessed it forgot. Agent frameworks confessed it could not plan. The lab reads these confessions, and the next training run answers them.

The record here is public and the rhythm is almost mechanical. Plugins, then custom bots, then built-in memory, then computer use, then research agents, then coding agents, then industry products for finance, medicine, and law. Sam Altman told founders the truth in April 2024, in words nobody could mistake: build on the model’s current shortcomings and the next model will “steamroll you.” He said something like 95 percent of startups should bet on the model getting better, not on it staying broken. Most quoted the line. Few acted on it.

The interesting part is that the labs are not exempt from their own process. In the summer of 2025, nearly half of Anthropic’s API revenue came from two coding companies, Cursor and GitHub Copilot, at the very moment Anthropic’s own coding product was competing with both. A lab’s revenue today is a measure of the gaps it intends to close tomorrow. Its best customers are its next casualties. Nobody in this arrangement is being dishonest. The arrangement itself is the message, and everyone who reads their own invoices can see it.

The stock market took until this year to do the arithmetic. When Anthropic shipped an office-work product in January, the software index reportedly lost a quarter of its value in six weeks. When OpenAI announced its enterprise agent in July, Workday, Atlassian, and HubSpot fell ten to thirteen percent in two days. The market was not repricing what those companies sell today. It was repricing the gap they live in.

What the science already knew

Here is the part that should embarrass us, because the evidence was published before most of the losses were booked.

Researchers have a routine practice called ablation: switch off one part of a system at a time and measure what each part was actually worth. In the last year, two teams ran this practice on agent harnesses, the scaffolding of memory, perception, and control that engineers build around models. They found three things.

First, the scaffolding can matter enormously; in one game environment it multiplied the model’s score more than four times over. Second, much of it does not matter at all, and some of it makes the agent worse; several carefully engineered control layers lowered performance the moment they were switched on. Third, and this is the finding that prices everything else: a module’s worth is not a property of the module. It is a property of the gap between what the module supplies and what that particular model lacks. Measure the same scaffolding against a stronger model and the value shrinks, vanishes, or goes negative.

There is no permanent number in that table. There is only a spread, and the spread closes from the model’s side. The researchers ran the experiment on a benchmark. The market runs it on your company. The only difference is who holds the switch.

The honest counterargument

If everything above were the whole truth, the software built on models would be a graveyard by now, and it is not. It is the fastest-growing layer in the industry. Enterprise spending on model APIs more than doubled in six months last year. Cursor, a code editor built entirely on other companies’ models, the exact thing the steamroll speech condemned, went from $100 million to a reported two billion in annual revenue in thirteen months. Harvey tripled. OpenEvidence says four in ten American doctors now use it every day. Aaron Levie argues the applied layer is worth more than the wrapper insult implies, and on the growth numbers he is simply right.

So the doomsday version of this essay is false, and I want to be precise about which version is true.

The companies that died held on to their scaffolding. The companies that grew burned it first. Cognition cut its own price 96 percent the moment the market said the premium was gone, and grew straight through the cut. Cursor rebuilt its product around every model generation and publicly apologized its way through the pricing chaos that came with it. Compare Windsurf, a fine product with $100 million in revenue, which discovered in June 2025 that a model provider could shut off its supply in a week; within six weeks its revenue had sagged and the company had been carved up between Google and Cognition. The difference between Cursor and Windsurf was not talent or taste. It was that one of them treated its own software as fuel and the other was still holding it when the fire arrived.

The value of the application layer is real and growing. It just does not live where people keep looking for it. It does not live in the code.

Where the value actually sits

Ask the vertical founders themselves, the ones building AI for hospitals and law firms, because they have thought about this longer and harder than anyone, and the honest ones all give versions of the same answer.

Shiv Rao, who runs Abridge, the company that writes clinical notes for a quarter of a million doctors’ patients, says up to 60 percent of his product’s model outputs come from in-house work in a given week, and in the same conversation says that the moment his company works against the grain of model progress, “we’re screwed.” Both halves matter. Build over the model, sharpen it for your domain, and hold none of it sacred.

Bret Taylor at Sierra went further and moved the pricing itself. Sierra charges per resolved customer case, about a dollar fifty, not per seat and not per token. His stated reason is the cleanest sentence in this entire subject: reducing token usage for the same outcome “is your problem, not your customer’s.” Read that twice. Under outcome pricing, every improvement in the model widens Sierra’s margin instead of erasing its product. He is the one founder in the industry who has arranged his business so that ablation pays him.

And Harvey, having said the plain thing about moats, acted on it: it began training its own open models to encode law firm workflows, moving its value out of the scaffolding and into something a frontier lab cannot ship in a keynote: the accumulated knowledge of how legal work actually flows, who is liable when it goes wrong, and what correct looks like in a domain where correct is argued about for a living.

That is the pattern, and it is short. What survives is what cannot be trained away: rights to data the labs cannot buy. Distribution the labs cannot shortcut. Liability the labs will not accept. Judgment about correctness in domains where correctness is contested. Everything else, the parsing and routing and checking and formatting, all the clever machinery we are proudest of, is fuel. It was always fuel.

Build things worth burning

If you build on models, the conclusions are not complicated. They are only uncomfortable.

Run the experiment on yourself before the market runs it on you. Every time a new model ships, switch your own machinery off piece by piece against it and see what still earns its keep. The answer will get worse every year. You want to hear it first.

Write the funeral into the design doc. Every piece of scaffolding you build should carry, from the day it ships, a note saying which model capability makes it unnecessary. When that capability arrives, deleting the module is not a setback. It is the plan working.

Let the model take the reasoning. The parts that die first, in the research and in the market, are the parts that second-guess the model: the checkers, the routers, the elaborate control flow. The parts that die last are the interfaces to the world: the data going in, the accountability coming out. Own the edges. Rent the middle.

Charge for the outcome, not the machinery. If your price is attached to your scaffolding, model progress is your competitor. If your price is attached to the result, model progress is your raise.

And keep an honest ledger. Call the software what it is: a consumable with a burn rate set by someone else. The companies in the graveyard were not built by fools. They were built by good engineers whose accounting had one wrong line, the line that said the software was an asset.

The industry will keep inventing softer words for all this, because soft words are easier to raise money with. But the mechanism does not care what we call it. The model gives, and the model takes away, and the taking is not a malfunction of this era of software. It is the era.

Ablation is not the risk to your roadmap. Ablation is the roadmap.

Build things that are worth burning. Then strike the match yourself.