AI Labs Buy Data from Failed Startups as Shutdowns Climb

A New Market in Failed Startups' Data

When Keelan Miskell began winding down his crypto startup this year, he did not think its data was worth much. He has since netted roughly $25,000 for the code base and expects another $15,000 or so for workspace data stored in Google, Notion, Figma, and Slack. The sale was brokered by SimpleClosure, part of a growing sector of businesses catering to dying startups. Estimates from Carta and AngelList show shutdowns rising between 26% and 56% annually in recent years, and an end-of-life ecosystem of coaches, "death doulas," and firms handling legal, tax, and operational headaches has emerged.

SimpleClosure added data sales to its services in April. Its Asset Hub platform helps founders sell code and workspace data, including emails, chats, and documents. Buyers include foundation AI labs, vendors that build simulated workspaces for AI training, and mature tech companies developing their own models. Asset Hub has handled over 150 deals and expects to return more than $10 million to startups by the end of 2026. CEO Dori Yona says 47% of the company's deals now include code or data sales, transactions once considered rare and "somewhat taboo." Rival Sunset also runs a rapidly growing data resale business, valuing assets by head count, years in operation, data volume, and how teams used their tools.

Why AI Labs Want Real Archives

Demand for defunct companies' data emerged suddenly last year as AI training labs hit the limits of public and synthetic datasets. Surge AI buys data to build "reinforcement-learning gyms" where clients' AI agents practice tasks such as debugging complex IT architecture or drafting appellate briefs. Nick Heiner, who leads that work, says archives capturing disagreements and tricky judgment calls are especially valuable, and one company's Slack history can be worth more than another's Slack, email, and calendars combined. Real archives also avoid errors common in synthetic worlds, such as references to dates that do not exist, though no company has released tests comparing performance gains.

Pricing remains highly uncertain. Buyers quote wildly different numbers because valuations rest on "research theses" estimating how many useful tasks come from, say, 100,000 lines of code. That has not stopped larger deals. In August, as part of Spirit Airlines' bankruptcy, Google outbid AI data company Mercor to buy the carrier's decades of data, including 100 million emails, 500 million Teams messages, and 176,000 employee records, for $10 million. Bloomberg reported in September that SpaceX's AI unit is weighing buying data from struggling startups.

Legal and Ethical Questions

Spirit's flight attendants' union objected to the sale on privacy grounds, and attorney Greg Ewing, who advises AI startups, has called such data "legally radioactive." Suppliers employ data scientists to scrub personally identifiable information and exclude HR records, and buyers say they scrub as well. But Ewing notes that emails and calendars can contain doctor appointments, school pickup arrangements, and date-night plans, describing a "Wild West of AI training" that is only starting to give way to licensing agreements and vetted use.

There is also the question of who profits. Proceeds typically flow to senior creditors and investors, not employees. Ifeoma Ajunwa, a law professor at Emory University and author of The Quantified Worker, calls this a form of "captured capital." She says employers should have express consent before selling workers' data, but no federal law bans such sales. Founders also have little visibility into who is buying their data or what they plan to do with it, and little assurance of a fair offer. Data prices will likely stabilize in the coming year, unless an AI bubble pops first.

Sources: Mit Technology Review Ai