A Race to Nowhere : Was Labor Ever the Problem?

When supervision becomes productivity

Companies are increasingly turning to artificial intelligence to improve productivity, but what if labor was never the problem? What if businesses have become so focused on monitoring employees that they've confused supervision with productivity?

Employers trust people enough to hire them, yet often rely on monitoring systems to determine whether they're working. Meanwhile, AI is being introduced to evaluate employees, complete assignments and generate performance reports.

Businesses should give employees greater autonomy while holding both human workers and AI to consistent, independently verified standards. Otherwise, the pursuit of efficiency risks rewarding measurements rather than meaningful results.

Meeting targets isn't the same as succeeding

Consider the familiar story of Soviet nail factories allegedly producing enormous nails to satisfy weight quotas or tiny nails to meet quantity quotas. Whether historically accurate or not, the story illustrates a problem: Meeting a target doesn't necessarily mean accomplishing its purpose.

Splunk's explanation of Goodhart's law describes how performance measures can lose their usefulness when organizations prioritize hitting numerical targets.

The same concern applies when employers mistake time spent at a computer for valuable work.

What the research reveals

A 2025 study published in The Quarterly Journal of Economics examined 5,172 customer support agents and found that AI assistance increased issues resolved per hour by approximately 14%. Less-experienced employees benefited most, demonstrating how technology can support human expertise.

However, a 2025 experiment by research organization METR found that 16 experienced software developers took 19% longer to complete assigned tasks when AI tools were available, despite believing they worked faster.

Workplace monitoring also deserves scrutiny. A 2025 Organization for Economic Cooperation and Development report found widespread adoption of software-based employee management tools. Researchers also identified concerns about accountability and transparency.

Together, these findings raise questions about whether workplace technology is being evaluated as critically as the employees using it.

The argument for efficiency

Businesses have legitimate reasons to monitor performance and adopt automation. Accurate measurements can identify problems, reduce costs and improve customer service.

However, faster doesn't automatically mean more productive, just as expensive doesn't automatically mean better. Comparing monitored employee activity against AI-generated output ignores differences in what is being measured.

Technology can improve efficiency, but its benefits shouldn't be assumed simply because a system reports them.

Measuring what matters

Companies should establish clear expectations, give employees reasonable autonomy and independently evaluate the quality, usefulness and total cost of completed work.

AI performance should face the same scrutiny, including the time and expense required to review and correct its output.

Technology should improve how people work, not become another reason to distrust them. If businesses continue celebrating efficiency without questioning what it accomplishes, what exactly are they racing toward?