Dead startups are now AI training suppliers, and Spirit Airlines' data is the test
AI labs are buying failed startups' data. Google's $10 million bid for Spirit Airlines' enterprise data faces privacy scrutiny in bankruptcy court.
The most glamorous asset a failed startup owns may be its Slack history. According to MIT Technology Review, SimpleClosure, which helps founders shut down, began selling startup archives to AI developers in April. The company says its Asset Hub has handled more than 150 transactions, and 47% of its deals now include code or data sales.
The reporting says buyers include foundation AI labs, simulated-workspace vendors and mature tech companies. Founder Keelan Miskell netted roughly $25,000 for his crypto startup's code base, plus a pending sum, likely around $15,000, for workspace data from 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 says.
The big test is a bankruptcy: Spirit Airlines. According to MIT Technology Review's reporting, Google was chosen as winning bidder in an August 14 auction, offering $10 million for enterprise data including email, Microsoft Teams records, documents, operational data and code. Consumer databases are excluded, and the deal calls for deidentification. Micro1 later filed notice of a $12.5 million competing bid. A court-appointed privacy ombudsman's October 5 filing was conditional on court-adopted protections. The approval hearing now moves to November 6, after lawmakers asked for employee protections.
The context: Estimates from Carta and AngelList show shutdowns rising between 26% and 56% annually in recent years. The startup graveyard is getting crowded. My take: a market this big deserves privacy rules as polished as its bidders.
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Per the reporting, there is limited public evidence that training on startup archives beats synthetic data, and buyers price by guessing how many useful tasks can be extracted. That is a valuation method, not a benchmark.
Slack and Notion exports are unstructured. The open question is whether deidentification actually scrubs personal and third-party data from them before training. I'd want to see the pipeline.
