Why Google Bid $10 Million for a Failed Airline’s Data

Spirit Airlines stopped flying in May; now the AI industry has come knocking. —Rebecca Noble—Getty Images What would a company like Google want with data from a failed airline? 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.

The cache includes 100 million emails and 500 million Microsoft Teams messages, and around 30 million lines of code.

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.

“Buying Spirit Airlines—that's not centrally a coding agent purchase,” says Nick Heiner, the head of reinforcement learning environments at Surge, a data company that supplies the frontier AI labs.

“That's a purchase if you believe that your agent is going to generalize into the rest of the economy.” Reinforcement learning 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.

Anthropic alone has discussed spending more than $1 billion in a year on so-called reinforcement learning (RL) environments, which are essentially simulations of commonly used software, where AI agents can take strings of actions independently.

If these actions lead to a desired outcome, the AI is rewarded and will be more likely to take similar actions in the future.

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.) But AI companies’ long-term goal is to automate large swathes of the economy.

That’s where Spirit’s data likely comes in.

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.