AI’s New Training Data: Your Old Work Slacks And Emails

Key Takeaways

• Cielo24 sold its 13‑year Slack and email archive for hundreds of thousands.
• SimpleClosure’s Asset Hub lets winding‑down firms monetize internal data.
• AI labs seek real‑world work data to train agentic models.
• Demand for corporate exhaust data described as a “gold rush.”
• Data scrubbing removes personal identifiers before AI consumption.

Pulse Analysis

By late 2024, AI developers had mined most publicly available text—from Reddit threads to digitized books—leaving a diminishing pool of novel training material. The next frontier is corporate digital exhaust: the massive troves of Slack messages, Jira tickets, emails and code that accumulate in everyday business operations. These artifacts capture the nuance of decision‑making, workflow bottlenecks, and collaborative problem‑solving, offering a richer substrate for training agentic AI that can perform real‑world tasks rather than merely generate text.

Enter SimpleClosure, a niche startup that has turned company wind‑downs into a data marketplace. Its new Asset Hub platform connects defunct firms with AI labs eager to purchase sanitized datasets. The process involves rigorous removal of personally identifiable information, a technically demanding step that the company is perfecting before a broader rollout. For founders like Shanna Johnson, the sale of Cielo24’s multi‑terabyte archive not only provided a financial cushion but also gave the company’s operational knowledge a second life in emerging AI systems.

The surge in demand for workplace data signals a strategic shift in the AI industry. Access to authentic, task‑level examples can dramatically shorten model development cycles and improve performance in sectors such as finance, legal services, and project management. However, the commoditization of internal communications raises regulatory and ethical questions around privacy, consent, and data ownership. As more firms monetize their digital remnants, policymakers and AI developers will need clear frameworks to balance innovation with responsible data stewardship.