Ablative Software - by Chetan Conikee
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Ablative Software
Chetan Conikee<br>Aug 02, 2026
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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.<br>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.<br>Ablative software: software built to fill the gaps in the current generation of models, and consumed when the next generation closes them.<br>The words we use instead
It is worth pausing on the vocabulary, because the vocabulary is doing a lot of quiet work.<br>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.<br>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.<br>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.<br>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.<br>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.”<br>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.<br>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.<br>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.<br>What the laboratory sees
Now stand where the frontier lab stands, because from there the whole thing looks different, and more orderly.<br>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...