Google won a bankruptcy auction this month with a bid to spend $10 million on the corporate data of Spirit Airlines, which stopped flying in May. Google’s offer beat a $7.5 million bid for the data from an AI data company, Mercor.
AI companies have been battling it out this year to make the best AI coding agent—and they have benefited from an abundance of publicly available code to train these models. But teaching agents to do other kinds of white-collar work may require data that is largely private, buried in corporate emails and chats. If that effort succeeds, it could send ripples across the labor market.
One of the biggest trends in AI over the last 18 months or so has been the rise of reinforcement learning from verifiable rewards—a method of AI training that has led to rapid improvements in models that can work independently for long periods of time.
Until now, the biggest jumps from this type of training have come from coding models, mostly because code has a useful property: it either works or it doesn’t, meaning that the reward signal is immediate, so improvement can happen in a fast loop. (It’s also helpful that there was plenty of coding data already out there on the internet, meaning models were good coders to begin with.)
What makes the data useful
RL environments are only as good as the data that populates them, says Heiner of Surge AI. Companies like Surge and Mercor often hire human workers who are tasked with populating these environments with realistic data, either from scratch or in partnership with AI tools. “But even that is a little bit removed from literally having actual data that was used in the real world,” Heiner says. “That's where deals like Spirit come in.”
But unlike coding, which can be quickly and easily determined to either work or not, office tasks are fuzzier. Human experts can still provide that feedback—a service that companies like Mercor and Surge offer to AI companies—but that work is relatively expensive, time-consuming, and subjective. It’s therefore unclear that even populating RL environments with real-world data like Spirit’s will allow AI companies to move into white-collar fields as quickly as they have ripped through the software industry.
Privacy drama
After Google submitted its bid, a union of flight attendants filed an objection, saying that although the deal’s terms would result in personal data being removed, the dataset would maintain “referential integrity,” meaning that links between one type of data and another would be kept. That integrity is what makes the data useful for training AI, but the union said it may also allow for even anonymized information about its members to be reconstructed. The group is seeking additional privacy protections; a judge is set to rule on the matter on Sept. 9.
Whichever way the deal goes, it looks like the AI industry is setting its sights on white-collar work. “Google is willing to pay $10 million for a failed budget airline, because if you believe this is really generalizable, you see a path to all these different industries—not just the ones that Spirit was involved in—being something you can replace pretty quickly,” Heiner says.
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