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Spirit's data auction shows what office AI is missing. Google ($GOOGL) opened at $5m and was selected at $10m for Spirit Airlines' internal data in August. Mercor was the $7.5m backup. Then micro1 proposed $12.5m. The sale still awaits court approval, with a hearing scheduled for November 6. The material includes emails, Teams conversations and business records. Google's counsel confirmed that it wants the data for AI training. I think this competition for records of real work will keep pushing up the value of enterprise data. This fits our earlier argument about why coding agents advanced faster than office automation. Coding has public repositories and tests that provide fast feedback. In office work, the context behind a decision is often scattered across private documents and conversations. Knowing how to use a spreadsheet is only part of the job. Knowing why an exception was approved, and what happened afterward, is much harder to learn from the public internet. My read is that Google is preparing for that gap. Internal records could help models learn how work moves through an organization, provided those records can be turned into tasks with outcomes that can be checked. Also it remains an open opportunity for $MNDY https://t.co/Rmzyg6DE0y, and other AI-native productivity startups to tacle. Gemini 4 Carbon already leads Artificial Analysis' AutomationBench-AA, which tests simulated workflows across business applications. Google is competitive in the kind of work this data could help models learn. Anthropic's IPO filing also puts adoption in perspective. As of September, over 50% of the Fortune 500 generated at least $100,000 each in trailing-year revenue for Anthropic. Among the publicly listed customers in that group, spending on Anthropic averaged under 0.1% of total expenses in 3Q26. We still think enterprise AI is falling short on office automation. Having access to a model tells us little about how much work a company can delegate to it. I find Google's enterprise opportunity more compelling over time than Meta's emphasis on personal AI. Google already has Workspace and Cloud as routes to customers. If better training on real work makes its models reliable enough to complete office tasks, it has a way to sell that capability. The unlock still requires models that can carry a task through the office environment with limited supervision. That remains some distance away. I think acquiring real work histories now could be a fruitful long-term bet for Google.

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