Machine Intelligence and Human Judgment

Source details

AI could reverse the widening inequality driven by technology, or aggravate it.
The article explores how AI’s effects on prediction and judgment will shape markets, wages, and the distribution of power.
Publication: F&D Magazine, June 2025.
Authors: AJAY AGRAWAL, JOSHUA GANS, AVI GOLDFARB.

Prosperity scenario:
- Productivity and economic growth could soar.
- Industries from health care to education to technology could be revolutionized.
- Office tasks handled with flawless efficiency could free people to pursue more meaningful endeavors and lower service costs, raising living standards.
Instability scenario:
- Knowledge workers and professionals could face mass unemployment as geniuses perform tasks at a fraction of the cost.
- Eroded wages and job security could collapse the middle class and deepen inequality.
- Concentration of geniuses under a few corporations or nations could monopolize wealth and power, stifling innovation and increasing geopolitical tensions.
- Loss of social value for human creativity could produce widespread unrest and existential questions of purpose.

Roldán Monés (Esade Business School): generative AI in a university debate competition
- Higher-ability debaters were 12 percent more likely to win debates when using generative AI.
- Little change to the outcome for lower-ability debaters.
- Interpretation: AI amplified advantages for high-ability debaters by aiding credibility, rhetoric, and rebuttal (judgment-related gains).
Brynjolfsson et al. (Stanford) on call center employees:
- AI tools increased productivity (measured by number of problems resolved per hour) by 14 percent on average.
- Novice and low-skilled workers saw a 34 percent improvement.
- Minimal impact on productivity of experienced and highly skilled workers.
- Interpretation: AI substituted for human prediction, disproportionately aiding lower-skilled workers and reducing productivity gaps.

Decision theory framing: prediction assigns probabilities to outcomes; judgment assigns values to consequences.
Where differences between workers are prediction-based:
- AI prediction substitutes for human prediction.
- Lower-skilled workers benefit disproportionately; income disparity in that industry may decrease.
- Example consequence: back-office and call center wages may increase in India relative to the US.
Where differences are judgment-based:
- AI augments higher-skilled workers who better identify promising suggestions.
- AI amplifies rewards for judgment, widening productivity differences and income disparity.
- Potential geographic concentration of high-value work in regions that supply judgment-intensive talent (e.g., US innovation hubs).

Regions with more skilled workers, stronger research institutions, and advanced technological infrastructure will likely capture disproportionate economic benefits.
If AI’s amplification of judgment-intensive, high-value tasks outweighs its equalizing effect on prediction-intensive tasks, global economic inequality will deepen.
Potential long-term consequences:
- Greater concentration of wealth and influence in select cities or countries.
- Growing disparity in technological leadership, research funding, and geopolitical influence.
- Redefinition of which forms of judgment remain scarce, dependent on regional adaptation of the workforce.

Expand access to high-quality education and training that emphasizes complex decision-making skills to sharpen judgment across regions.
Promote global talent mobility and knowledge exchange to distribute judgment necessary for effective AI use more broadly.
Create incentives to spread the ability to generate valuable AI predictions beyond traditional power centers through funding, infrastructure, and AI adoption incentives.

AI is advancing rapidly, but complementary factors (management practices, infrastructure, education, regulations, customer demand) change slowly, limiting short-term impact.
Long-term global economic impact will be significant; economic stability depends on managing the transition.
Computer scientists raced ahead developing the technology; economists and policymakers must catch up with research to guide policy toward global stability and prosperity.

Editor’s Note (June 16, 2025): Article updated to remove references to “Artificial Intelligence, Scientific Discovery, and Product Innovation” by Aiden Toner-Rogers, a paper that MIT has said should be withdrawn from public discourse because it has no confidence in the research.
The authors and affiliations:
- AJAY AGRAWAL — Geoffrey Taber Chair in Entrepreneurship and Innovation, University of Toronto’s Rotman School of Management.
- JOSHUA GANS — Jeffrey S. Skoll Chair in Technical Innovation and Entrepreneurship, University of Toronto’s Rotman School of Management.
- AVI GOLDFARB — Rotman Chair in Artificial Intelligence and Healthcare, University of Toronto’s Rotman School of Management.
Citation highlights in the article: Agrawal, Gans, and Goldfarb 2018; Brynjolfsson, Li, and Raymond 2023; Roldán Monés 2024.

Source: Machine Intelligence and Human Judgment — F&D Magazine, June 2025