This data sale extends to internal digital records like Slack chats, emails, Jira tickets, and Google Drive archives, described as generating revenue from an operational exhaust.

Defunct companies are selling former employees' internal communications—emails, Slack and Teams messages—as training data for AI language models, effectively monetizing an operational footprint.

Industry players report strong demand from AI firms, with transactions often totaling six figures and founders seeing sizable payouts.

Data is valued because public internet data不足; real-world records capture authentic decision-making and workflows that public sources cannot provide.

Buyers typically anonymize sender details, yet the material often contains identifiable information, raising privacy concerns.

Privacy advocates warn that employee privacy is at risk when internal communications are repurposed for AI training, noting employees rarely anticipated such use.

The trend sits within broader AI industry dynamics, with influential voices framing policy debates around maximizing AI benefits while safeguarding privacy.

Proponents like SimpleClosure emphasize stripping out personally identifiable information to anonymize data, aiming to perfect this before broader deployment.

Policy attention is rising, including calls for FTC scrutiny of new AI data practices and stronger safeguards for personal data.

A broader issue highlighted is the scarcity of real-world internal company communications as valuable training data compared with publicly available content.

Cielo24’s wind-down yielded significant sums from selling its digital footprint via SimpleClosure, which now monetizes data assets through Asset Hub.

Legal and regulatory implications loom, with GDPR in Europe cited as unlikely to permit similar data trades.