AI Labs Buy Data From Dead Startups as Shutdowns Rise

When Keelan Miskell began winding down his crypto startup this year, he did not think its data was worth much: the users were anonymous, and the company was just a few years old. He has since netted roughly $25,000 for his code base and a still-pending amount, likely around $15,000, for workspace data in Google, Notion, Figma, and Slack. “At least all that stuff we were doing for two and a half years is worth something to someone somewhere,” he said.

The sale was brokered by SimpleClosure, part of what MIT Technology Review describes as a new growth sector in tech: 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—including coaches, “death doulas,” and firms that handle legal, tax, and operational headaches—has emerged. Now tech undertakers are selling the data remains to AI labs.

SimpleClosure added data sales to its services in April. Its Asset Hub platform helps founders sell code and workspace data such as emails, chats, and documents. Buyers include foundation AI labs, vendors that construct simulated workspaces for AI training, and mature tech companies building their own models. Asset Hub has handled more than 150 deals and expects to return over $10 million to startups by the end of 2026. CEO Dori Yona has seen these transactions, once rare and “somewhat taboo,” become increasingly routine, with 47% of SimpleClosure deals now including code or data sales.

Industry rival Sunset also has a rapidly growing data resale business. Founder and CEO Brendan Mahony says Sunset values a dying client’s assets by head count, years in operation, volume of data, and how the team used its tools. He compares the material to particle board: “This operational exhaust data is like the wood shavings that everyone thought had no value, but it actually has a ton.”

Demand for defunct companies’ data emerged suddenly last year, as AI training labs hit the limits of public and synthetic datasets, according to the report. Surge AI, for example, buys data to build “reinforcement-learning gyms” where clients’ AI agents practice tasks, such as debugging a complex IT architecture or drafting appellate briefs. Nick Heiner, who leads the company’s work on these environments, says he seeks data archives reflecting a range of situations, from disagreements to tricky judgment calls. One company’s Slack history, if it captures conversations with variety and depth, can be worth more than another’s Slack, email, and calendars combined, he said. Real archives avoid common errors in synthetic worlds, such as references to dates that do not exist, but the extent of performance gains remains unclear. No company has yet released tests comparing results of real-world data training with the alternatives. Pricing reflects that uncertainty: buyers quote wildly different numbers, Heiner says, because valuations are based on “research theses” estimating how many useful tasks come from, for example, 100,000 lines of code.

Larger deals are already happening. This past August, as part of Spirit Airlines’ bankruptcy, Google outbid the 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. The price tag was $10 million. Bloomberg reported in September that SpaceX’s AI unit is weighing buying data from struggling startups.

Legally, the market could face problems. Spirit’s flight attendants’ union objected to the sale on privacy grounds, and Greg Ewing, an attorney 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 too. But Ewing points out private details that lurk in emails and calendars: doctor appointments, school pickup arrangements, date-night plans. He describes a “Wild West of AI training” that is only starting to give way to proper licensing agreements and vetted use.

There is also the question of who profits. Proceeds typically flow to senior creditors and investors—not employees. To Ifeoma Ajunwa, a law professor at Emory University and author of The Quantified Worker, this represents a form of “captured capital.” Legal precedent allows for monitoring to help workers do their jobs, but once a firm is out of business, that justification vanishes. “Employers should have express consent before selling workers’ data,” Ajunwa says, though she adds that there is no federal law banning such sales.

Many see the new market as a chance for failing businesses to recover some value. The wind-down sales also have limitations: founders have little to no visibility into who is buying the data and what they plan to do with it, and there is little assurance they are getting a fair offer.

Editor's Summary

AI labs are buying code and workplace data from defunct or shutting-down startups, according to MIT Technology Review, as brokers such as SimpleClosure and Sunset turn failed companies’ archives into training assets. Deals range from one founder’s roughly $25,000 code sale and a pending workspace-data payment to Google’s $10 million purchase of Spirit Airlines data out of bankruptcy. The market remains legally unsettled, with privacy objections and questions about whether employees should share in proceeds.