Meta's mandate for employees to have their laptop activity tracked for AI training, followed by AI-driven layoffs, creates a new labor paradigm. Workers are compelled to provide the very data that makes their roles obsolete, turning the workforce into the raw material for their own automation.

An Indian company, Objectways, pays thousands of workers to wear headset cameras while performing manual tasks. This footage is sold as training data for humanoid robotics companies like Tesla's Optimus, effectively paying humans to accelerate their own obsolescence.

The new paradigm for knowledge workers isn't about using AI as a tool, but as a team of digital employees. The worker's role evolves into that of a manager, assigning tasks and reviewing the output of autonomous AI agents, similar to managing freelancers.

AI's potential for rapid growth is creating a new moral calculus. Practices like tracking every employee keystroke for CRM automation, once controversial, are becoming standard. This trend suggests that as companies chase exponential gains, they will increasingly justify and normalize actions, from mass layoffs to invasive monitoring, that were previously considered unacceptable.

Instead of repeatedly performing tasks, knowledge workers will train AI agents by creating "evals"—data sets that teach the AI how to handle specific workflows. This fundamental shift means the economy will transition from paying for human execution to paying for human training data.

The most valuable data for training enterprise AI is not a company's internal documents, but a recording of the actual work processes people use to create them. The ideal training scenario is for an AI to act like an intern, learning directly from human colleagues, which is far more informative than static knowledge bases.

Knowledge work will shift from performing repetitive tasks to teaching AI agents how to do them. Workers will identify agent mistakes and turn them into reinforcement learning (RL) environments, creating a high-leverage, fixed-cost asset similar to software.

Because Meta is using raw employee computer usage for AI training, its models may learn to replicate common human inefficiencies. This could lead to AI agents that browse social media or watch videos instead of working, mirroring the actual behavior of their human trainers.

To build coordinated AI agent systems, firms must first extract siloed operational knowledge. This involves not just digitizing documents but systematically observing employee actions like browser clicks and phone calls to capture unwritten processes, turning this tacit knowledge into usable context for AI.