Why AI doesn’t make companies more productive

• Job applicants can use AI to apply for far more positions, burdening hiring managers and slowing the hiring process. (LinkedIn data shows applications per job posting in the US have roughly doubled since spring 2022.) AI that’s supposed to make it easier to find a job actually makes it harder.

• AI is emboldening more people to represent themselves in court while also speeding up the work of lawyers. A 2026 study by researchers at MIT and USC drew on 4.5 million federal civil cases filed between 2005 and 2026, applied an AI-text detector to a random sample of 1,600 complaints, and found that the share containing AI-generated text rose from 1% in 2023 to 18% in early 2026. That burdens judges, who now have to comb through long, complex filings for citations to nonexistent cases and other hallucinations, slowing justice. AI that’s supposed to make court cases quicker makes them slower.

• Students increasingly use AI to produce longer essays, and a growing number of instructors report leaning on AI tools to keep up with grading, a loop in which the writing and the reading are both partly automated.

AI-pilled chatbot enthusiasts who believe the technology is solving all their problems are judging AI through a narrow personal lens. Likewise, analysts and AI companies look at one person’s productivity gains, extrapolate across thousands of employees, and wrongly conclude that the gains will scale without considering the impact of that output on the productivity of others. 

Understanding the problem at scale

The idea that productivity-enhancing AI might reduce productivity sounds paradoxical, so consider this oversimplified thought experiment. Suppose AI enables you to write three times as many emails as before (say, 30 a day instead of 10). Your email-writing productivity has tripled, making you more valuable to the company. 

The problem is that every additional email you send lands in someone else’s inbox. Your colleagues, who once got 10 emails from you daily, now get 30. Multiply that across an organization: if 10 people triple their email output, the team gets 300 emails a day instead of 100. If 100 people do the same, that figure blows up to 3,000, three times the reading burden. The AI that makes email writing easy makes email reading hard for everyone else.