AI Agents and Market Efficiency: Impacts, Risks, Inequality
AI Agents and Market Efficiency
Why Agents Matter for Market Efficiency: AI agents help improve how markets work by acting as extensions of human decision-making. They assist people in making faster and smarter economic choices.
How Agents Help
How they help:
- They optimize decisions that humans might make less efficiently (due to limited attention or data).
- They can also make “good enough” decisions — quick, low-cost, and effective for everyday use.
Immediate Results
The results: Higher efficiency thanks to lower search and coordination costs. More dependence on algorithmic advice in daily decisions. New market behaviours driven by agent-to-agent interactions rather than human-to-human exchanges.
Market Transformation and New Challenges
Market Transformation and New Challenges (Shahidi et al., 2025)
As AI agents become more common, digital markets will change deeply, following two main paths of evolution. Adaptation: Existing markets start by using AI agents as assistants, and later as replacements. Human roles will focus more on judgment, supervision, and managing relationships. Reorganisation: Completely new agent-first markets may appear — where agents trade and make decisions directly for humans.
Economic Effects
Economic effects: Efficiency gains from cheaper searching, communicating, and contracting. Possible problems: congestion, unclear prices, or collusion between algorithms. Lower costs of collecting user preferences and verifying identities → new ways to design markets. New regulatory challenges: Ensuring accountability, transparency, and fairness in agent-based markets.
By reducing transaction costs, they can enable new market designs and improve efficiency. At the same time, their power and low costs can disrupt existing market structures. The final impact depends on how agents are designed, how markets are organized, and how they are regulated. Economists and policymakers will play a key role in shaping whether AI agents improve welfare or mainly redistribute rents.
Inequality Risks and Mechanisms
Why inequality can increase (even with weak productivity growth) Displacement affects routine and often lower-paid workers. Reinstatement may be slow or limited. Productivity gains are concentrated among high-skill workers. Composition effects may shift activity toward automated sectors. Result: higher inequality, even if overall productivity growth is modest.
Interpretation (Aksoy et al., 2021) The main idea is that robotization is not gender-neutral. Robots and automation do not affect men and women in the same way. Instead, they reinforce existing inequalities in the labor market. Digital and robotic technologies tend to benefit male-dominated jobs more, because these roles are often better paid and more protected. As a result, technology works as a multiplier of inequality, not as a tool to fix it.
Interpretation (Abraham et al., 2025) The main idea is that the digital gender divide is structural, not based on personal choice. Women do not use less technology because they are less interested. Instead, workplaces give women fewer chances to use digital tools and gain digital skills. Because of this, digitalization does not reduce the gender pay gap. In fact, it can make the gap bigger, because digital benefits are not shared equally. This is similar to robotization: Digital technologies can increase inequality instead of reducing it, when access and opportunities are uneven.
Both studies show that technology is not an equalizer. Whether through digitalization or robotization, technology multiplies existing gender inequalities instead of correcting them, because access and rewards are unevenly distributed.