The New Economics of Research
A decade ago, the idea of the "10× engineer" became part of the tech industry's vocabulary. The point was that a handful of exceptional people consistently produced dramatically more value than their peers through a combination of technical skill, experience, judgment, creativity, and systems thinking.
AI is changing that equation. Many of the activities that once consumed enormous amounts of time—writing code, exploring alternatives, debugging, documenting, and prototyping—can now be accomplished far more quickly. That doesn't make expertise obsolete. If anything, it makes expertise more valuable because it can be applied more broadly and at much greater speed.
The same forces are now reshaping research. For decades, much of a research organization's capacity has been devoted to the mechanics of conducting research. Recruiting participants, scheduling interviews, drafting questionnaires, coding open-ended responses, preparing reports, and synthesizing findings all required considerable time and expertise. None of this work was incidental. It was the necessary cost of producing reliable evidence.
AI is changing those economics. Researchers who learn to work effectively with these tools can complete many of these activities in a small fraction of the time they once required. The productivity gains are real, but I think the more interesting question isn't how much labor businesses can eliminate. It's how they choose to reinvest the capacity AI creates.
One way to think about this is that the bottleneck is moving. Collecting, organizing, and processing information are becoming dramatically less expensive. The scarce resource is increasingly human judgment: asking the right questions, choosing the right methodology, recognizing meaningful patterns, and deciding what to do with the answers.
Some organizations will almost certainly view AI primarily as a cost-reduction tool, conducting the same amount of research with fewer people. That may improve efficiency in the short term. A more interesting possibility is that organizations will use those productivity gains to conduct more and better research: test more ideas, spend more time understanding customers, iterate more quickly on products and services, and shorten the cycle between learning and decision-making. The opportunity isn't simply to work faster. It's to become a fundamentally more curious organization.
Ironically, as the operational costs of research decline, the activities that remain become more valuable. Framing the right business question, designing an appropriate methodology, recognizing bias, probing unexpected findings, and connecting evidence to business decisions have always distinguished exceptional researchers from competent ones. Those are precisely the capabilities that should command a greater share of researchers' time in the years ahead.