{"id":6041,"date":"2026-08-26T11:08:08","date_gmt":"2026-08-26T11:08:08","guid":{"rendered":"https:\/\/crosscountrymovingteams.com\/?p=6041"},"modified":"2026-08-26T11:08:08","modified_gmt":"2026-08-26T11:08:08","slug":"alex-karp-was-right-you-dont-own-your-data","status":"publish","type":"post","link":"https:\/\/crosscountrymovingteams.com\/?p=6041","title":{"rendered":"Alex Karp was right: you don\u2019t own your data"},"content":{"rendered":"
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OpenAI and Anthropic tell commercial customers they will not train on their data. After I recently moved my company off Anthropic, following a Supply-Chain Risk designation, I read the actual agreements and found a hole big enough to drive the entire AI industry through.<\/p>\n

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The OpenAI Services Agreement says: \u201cOpenAI will not use Customer Content to develop or improve the Services, unless Customer explicitly agrees to such use.\u201d Reasonable, until you check the definitions. \u201cCustomer Content\u201d means the Input and the Output. \u201cInput\u201d is what the customer sends the model. \u201cOutput\u201d is what comes back based on the Input. Here\u2019s why that\u2019s vaguer than it sounds.<\/p>\n

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Early large language models generated answers token by token without scaling their effort to the difficulty of the question. Humans don\u2019t work that way. Ask someone what 2 + 2 is and they\u2019ll answer instantly. Ask them to plan a family reunion, and they\u2019ll think it over first.<\/p>\n

Newer reasoning models do the same thing, spending computation on intermediate steps before returning a final answer, breaking a hard question into parts and working through each one. That intermediate reasoning produces real data on the way to the final output, which raises the question: is it actually capital-O Output, legally? Nothing in these agreements says so.<\/p>\n

The definitions matter because another clause ties ownership directly to them. OpenAI\u2019s agreement states that the customer \u201cretains all ownership rights in Input\u201d and \u201cowns all Output,\u201d with OpenAI assigning its interest in Output to the customer.<\/p>\n

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Picture handing a consultant your confidential financial forecast and asking for next year\u2019s headcount budget. The consultant reasons by filling a notebook with calculations, then hands you a one-sentence answer, and keeps the notebook. Your contract with the consultant covers the question and the answer. It says nothing about the notebook.<\/p>\n

That\u2019s what happens every time you use a reasoning model. It generates intermediate reasoning tokens before returning a final answer: a digital scratchpad of facts pulled from your prompt, intermediate conclusions, and newly derived insights about your business. The labs know it\u2019s valuable. OpenAI has said it hides raw chains of thought for reasons that include safety, user experience, and competitive advantage. Anthropic bills customers for full internal thinking even when none of it is ever shown to them.<\/p>\n

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So you pay for the production of this data. You never see it. And no one will say whether it\u2019s legally yours. OpenAI\u2019s no-training promise covers Input and Output, but the notebook fits neither definition. If reasoning tokens are Output, say so, and extend the protections to cover them. If they\u2019re not, OpenAI has created a third category of data its agreement never defines and never protects.<\/p>\n

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Anthropic has the same problem. Its API bills for reasoning tokens that never appear in the visible response, and returns the raw reasoning encrypted, so only Anthropic can decode it. So do you own it? This isn\u2019t a drafting oversight. It\u2019s a convenient ambiguity, given how much this data is worth.<\/p>\n

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The labs can\u2019t dismiss reasoning tokens as meaningless computational exhaust. Their own conduct proves otherwise. Anthropic says DeepSeek, Moonshot, and MiniMax generated more than 16 million Claude exchanges to help train competing models, calling it industrial-scale distillation, a shortcut to capabilities that would otherwise take enormous time and money to build. Labs protect outputs aggressively because outputs transfer intelligence, and raw reasoning tokens are an even richer record of how a model reaches an answer. When OpenAI launched o1, it said this directly:<\/p>\n

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\u201cAfter weighing multiple factors including\u2026 competitive advantage\u2026 we have decided not to show the raw chains of thought to users.\u201d<\/p>\n

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They want it both ways: bill you for the reasoning, hide it because it\u2019s strategically valuable, and decline to say whether it\u2019s legally your Output.<\/p>\n

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The double standard is hard to miss. This industry was built on the argument that a company can ingest someone else\u2019s protected work, transform it through training, and own the resulting asset. Apply that logic to your enterprise data. You provide confidential material as Input. The model transforms it into reasoning tokens that aren\u2019t identical to your Input but are valuable, newly generated material derived from it. Why would anyone expect the labs to resolve that gray area against their own interests?<\/p>\n

As Karp put it, \u201cthe jig is up.\u201d You don\u2019t own your alpha. The guy everyone called erratic was trying to tell you. Even Zero Data Retention doesn\u2019t close the gap: both OpenAI and Anthropic offer it, but it requires separate approval and doesn\u2019t guarantee reasoning data gets discarded rather than retained. If not keeping your data takes a special request, retention is the default for a reason.<\/p>\n

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I expect intermediate reasoning tokens to be assigned to me, the same way I\u2019d want my own notebook back. I may need to reason from it again. The labs haven\u2019t clearly assigned those rights, because doing so means giving up the value. And if reasoning tokens aren\u2019t Customer Content, it\u2019s entirely possible they\u2019re being used to train the next model. Nothing in the labs\u2019 conduct gives me confidence otherwise.<\/p>\n

Sam Altman and Dario Amodei can resolve this in a sentence each. Until they do, Karp was right. You own the prompt. You own the answer. They keep the notebook.<\/p>\n

Adam Fish is the CEO and co-founder of Ditto, an edge data platform built for unstoppable operations. Its peer-to-peer sync technology keeps applications running with no cloud dependency, in U.S. defense programs including Special Operations Command and for commercial brands like Chick-fil-A.<\/em><\/p>\n

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The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of <\/em>Fortune.<\/em><\/p>\n

Read more Why Dubai has become a city of retail traders<\/a><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Generative AI companies say you own your “Inputs” and “Outputs.” Their legal agreements leave out who owns the reasoning tokens generated between them.<\/p>\n","protected":false},"author":1,"featured_media":6040,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[493],"tags":[],"class_list":["post-6041","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-palantir"],"yoast_head":"\nAlex Karp was right: you don\u2019t own your data - Cross Country Moving Team<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/crosscountrymovingteams.com\/?p=6041\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Alex Karp was right: you don\u2019t own your data - Cross Country Moving Team\" \/>\n<meta property=\"og:description\" content=\"Generative AI companies say you own your "Inputs" and "Outputs." 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