Jevon’s Paradox is simple: when a resource becomes more efficient to use, we tend to use more of it—not less. Originally observed in the 19th century when improvements in coal-burning steam engines led to increased coal consumption, it has since become a powerful lens for understanding how efficiency drives demand. And today, it’s one of the most useful ways to think about the future of market research.
AI is making the production of insight vastly more efficient. Research is faster, cheaper, and higher quality. Automation handles the tedious tasks: data cleaning, QA, tabulations, charting, reporting. NLP unlocks open-ended feedback. Predictive models surface emerging trends. But instead of replacing the need for research, this efficiency is triggering more demand. It’s not killing curiosity—it’s fueling it.
Across this series, we’ve seen how AI enables teams to:
- Know It Faster: collapsing turnaround times, enabling real-time iteration.
-Know It Cheaper: democratizing access to insight across roles and functions.
-Know It Better: augmenting analysis, improving depth, and strengthening confidence.
-Know It All: connecting signals at scale, moving from snapshots to streams.
Each of these represents a meaningful shift. But together, they suggest something bigger: research is moving from the periphery to the core. From an occasional, outsourced function to a continuous capability embedded across the business. And the driver behind that shift is AI-powered efficiency.
This has real consequences for how we think about the role of research.
First, it decentralizes insight. When the cost and complexity of running a study drop, more people start to participate. Product teams, marketers, sales, customer success—everyone can run lightweight studies, ask targeted questions, and gather rapid feedback. That doesn’t eliminate the need for expert researchers, but it does change their role. Researchers become enablers, curators, and strategic advisors rather than gatekeepers or bottlenecks.
Second, it raises the standard. As AI enables deeper, more nuanced analysis at scale, the expectations around what constitutes a “good” piece of research increase. Toplines and dashboards are no longer enough—stakeholders want interpretation, synthesis, and narrative. They want to understand the why, not just the what. That’s where skilled researchers come in, translating data into meaning.
Third, it shifts the cadence. Traditional research operated on long cycles—quarterly trackers, annual brand studies, campaign pre-tests. Now, insights can flow in continuously, with AI surfacing real-time changes in sentiment, brand perception, product feedback, or competitor positioning. This makes research not just faster, but ambient—always running, always watching, always ready.
And finally, it changes the posture of research. Instead of waiting for a question to be asked, AI-powered systems can identify anomalies, highlight weak signals, and suggest areas for exploration. Research becomes proactive, anticipatory. We move from reacting to market shifts to spotting them before they land.
Jevon’s Paradox reminds us that efficiency doesn’t mean reduction. In research, it means expansion. More curiosity. More participation. More impact.
AI won’t replace researchers—it will multiply them. It will extend their reach, speed up their process, and elevate their role. The future of market research is not just smarter—it’s everywhere. And it starts with embracing the paradox.