The doom-calling headlines are relentless:"Knowledge Work That Cost $10,000 a Year Ago Now Costs a Dollar or 10 Cents","AI Could Wipe Out 50% of Entry-Level White-Collar Jobs","Investors Predict AI Is Coming for Labor in 2026".
Much of these predictions come from techno-optimists and AI company leaders, the very people building and investing in the technology. Brilliant innovators for sure, but not necessarily labour market economists who've studied how technological transitions actually reshape work over decades.
So I decided to investigate what the experts in economic theory have to say. And what this might mean for the Market Research industry....
Three established economic theories, developed by actual labour economists studying decades of automation and the impact on the labour market, give me hope that Market Research isn't dying but might have a bright future. It's very likely evolving into something more critical, more strategic, and more human than ever before.
Theory #1. Jevons Paradox: More efficiency = More demand
The theory:In 1865, economist William Stanley Jevons observed that when coal-powered steam engines became more efficient, Britain's coal consumptionincreasedrather than decreased. Making something cheaper and easier leads to exponential growth in usage.
Knowledge work example:Automated dashboards and BI tools. When creating reports became "push-button" with Tableau and Power BI, organizations didn't need fewer reports, they demanded customized dashboards for every department, real-time metrics for every stakeholder, and alerts for every KPI. One analyst who used to produce 5 monthly reports now oversees 50 automated dashboards.
Why this matters for MR:When AI makes research cheaper and faster, organizations won't conduct fewer studies but they will conductexponentially more. Every micro-segment gets tested. Every iteration needs validation.And it will significantly increase the number of companies that can now afford to conduct (quality) research, where before they didn't have the resources The bottleneck isn't generating the data / insights anymore; it'ssynthesizing the avalanche of data into strategic decisions. We're drowning in data , starving for sense-and decision making.
Theory #2. O-Ring Theory: Quality multiplies across every step
The theory:MIT economist Michael Kremer's 1993 model showed that in complex processes, total value equals themultiplicationof all component qualities. Named after the O-ring that caused the Challenger disaster, it shows how one small failure can destroy an entire system's value.
Knowledge work example:A very recent example of how this is relevant is Deloitte Australia's AI failure: in a report produced for the government human reviewers failed to catch fabricated academic citations before delivery. That single validation failure (at just one step in the research chain) destroyed the value of a $442,000 contract and made international headlines. One missed quality check contaminated everything.
Why this matters for MR:One hallucinated trend, one biased sample, one misinterpreted correlation anywhere in the chain can sink a million-dollar product launch. This is why human validation becomesexponentiallymore valuable as AI handles more steps. When AI does six research tasks, you don't need 6x less human involvement but you needhigher-qualityhuman involvement because a mistake anywhere destroys everything. The firms that win will know how to use AI tools without letting quality collapse at any step in the chain.
Theory #3. The reinstatement effect: Automation creates new tasks
The theory:MIT economists Daron Acemoglu and Pascual Restrepo demonstrated in 2019 that automation doesn't simply displace workers but it "reinstates" them into newly created tasks requiring human judgment (Source: Journal of Economic Perspectives, 2019).
Knowledge work example:Excel eliminated bookkeeping clerks but created Financial Analysts who run scenario modeling and strategic forecasting, higher value work that didn't exist before at scale.
Why this matters for MR:Increasingly we're being "reinstated "from data gathers toinsight ochestratorsanddecision enablers. The work becomes more strategic, more consultative, and more valuable. And with a strong increase in demand for MR (see #1) follows a similar increase for this type of work:
OK - so seniors are safe, but we won't need juniors anymore?
Yes it's true that a lot of the tasks that entry level & junior roles typically perform can (and to large extent will) be automated. But that's been the case for at least the last 40 years. I actually believe that junior researchers matter MORE in an AI-native world, and that the industry can't afford to eliminate entry-level roles:
1. Seniors become single points of failureWhen only senior researchers can validate AI outputs, interpret complex results, and spot methodological flaws, you've created an organizational bottleneck. One senior takes parental leave or moves to a competitor, and your entire insights engine stalls. AI amplifies this risk.... the faster insights flow, the more catastrophic when your lone expert can't keep up.
2. The pipeline collapsesIf juniors don't get hands-on experience managing AI-assisted projectsnow, where do tomorrow's senior researchers come from? You can't train someone to orchestrate 50 AI studies by only letting them read PowerPoints. We need to double down on junior development, teaching them to be AI Quality Auditors, bias detectors etc. on real projects with real stakes.
3. Scale demands distributed expertiseOne senior researcher can't review 200 AI-generated concept tests per quarter. You need a team of trained juniors who understand both the methodologyandthe business context to act as quality gates at scale.And no this is not about dumbing down the work.... it's evolving our operating model.
So i truly believe the future is rosy, but we need to keep investing in the next generation. I will spend some more time thinking about what this will require in practice.
What this means for you
So my current thinking is that individuals and organizations that will thrive in this new environment are the ones that can:
So yes, I am positive about our future. Transformation creates opportunities for those who understand the economics and not just the hype.
But hey... "this time it's different" some will say. Maybe, maybe not. "Only time will tell" as the Black Crowes would sing.
