Alex Imas: AI is reshaping job roles towards social skills, the emergence of AGI marks a pivotal shift, and Claude code enhances AI’s practical applications | Odd Lots

AI advancements are reshaping job markets, emphasizing roles that require social skills and personal branding.

Listen on Odd Lots

Share

Add us on Google by Editorial Team Apr. 18, 2026

Key takeaways

• AI is reshaping the job market, emphasizing roles that require social skills and personal branding.
• The generality of AI technologies has significantly increased, enabling them to perform basic cognitive tasks effectively.
• The concept of AGI emerged to address the limitations of specific AI technologies.
• The release of Claude code marked a pivotal moment in AI development, enhancing its utility and perception.
• Economists predict moderate productivity growth due to AI, with a limited impact on the labor market by 2030-2050.
• Job exposure to AI is assessed by the percentage of tasks AI can perform within a job.
• The impact of AI on jobs is influenced by the specific tasks automated and their interrelationships.
• Task complementarity plays a crucial role in determining automation’s effect on the labor market.
• Understanding consumer demand elasticity is essential for analyzing productivity and wage dynamics.
• The demand for software engineers might not be as elastic as assumed, potentially leading to sector downsizing.
• AI advancements may shift job roles towards more human-centric tasks.
• The evolution of AI capabilities underscores the transition from narrow to general AI applications.
• Future job landscapes may prioritize performative and interpersonal skills.
• AI’s impact on productivity is expected to be substantial but not transformative in the short term.
• The relationship between automated tasks and job performance is critical for understanding AI’s labor market impact.

Guest intro

Alex Imas is the Roger L. and Rachel M. Goetz Professor of Behavioral Science, Economics, and Applied AI at the University of Chicago Booth School of Business. His research examines how AI reshapes productivity, labor markets, and creative work. Previously, he was the William S. Dietrich II Assistant Professor of Behavioral Economics at Carnegie Mellon University.

The future of work with AI

• The job market is shifting towards roles that emphasize social skills and personal branding.

• I actually think that’s kind of where we might be heading where like the sort of social skills… becomes more important so the future is performative humanity.

  — Alex Imas

• AI advancements are likely to create new job categories that focus on human interaction.
• The performative aspect of jobs may become more prominent as AI handles routine tasks.
• The evolving nature of work will require adaptability and continuous skill development.
• Human-centric roles may gain more significance in an AI-driven economy.
• The integration of AI in workplaces will necessitate a reevaluation of job roles and responsibilities.
• Workers may need to focus on enhancing their interpersonal and communication skills.

The explosion of AI capabilities

• The generality of AI technologies has exploded, allowing them to perform basic cognitive tasks effectively.

• Once you started using it… it was able to kind of do basic cognitive tasks to a decent degree… the generality of the technologies just exploded and to me that was that was a huge deal.

  — Alex Imas

• This shift in AI capabilities is crucial for understanding its impact on various sectors.
• The transition from narrow to general AI applications marks a significant technological advancement.
• AI’s ability to perform a wide range of tasks enhances its utility across industries.
• The expansion of AI capabilities may lead to more innovative applications and solutions.
• Understanding the evolution of AI is essential for anticipating future technological trends.
• The increased generality of AI technologies may redefine industry standards and practices.

The emergence of AGI

• The term AGI emerged as a response to the limitations of specific technologies in AI development.

• The whole term agi the general part of it… was because in response to these very specific technologies that were being developed got it which were by design not general.

  — Alex Imas

• AGI represents a conceptual evolution in the field of artificial intelligence.
• The distinction between AGI and specific AI technologies is crucial for understanding AI development.
• AGI aims to address the limitations of narrowly focused AI applications.
• The pursuit of AGI reflects the desire for more versatile and adaptable AI systems.
• The development of AGI may lead to breakthroughs in various scientific and technological domains.
• Understanding AGI’s potential impact is essential for shaping future AI policies and strategies.

The impact of Claude code

• The release of Claude code marked a significant shift in how AI is perceived and utilized.

• The moment when things seem to get very serious was the release with claude code and at that point you went from like okay the model could not just tell you things but it could actually do things for you.

  — Alex Imas

• Claude code’s release highlights a pivotal moment in AI development.
• This development enhances user interaction and expectations from AI technologies.
• Claude code’s capabilities may lead to more practical and efficient AI applications.
• The perception of AI has evolved from a theoretical concept to a practical tool.
• Understanding the impact of Claude code is essential for anticipating future AI advancements.
• The release of Claude code may influence future AI research and development priorities.

AI’s impact on productivity and the labor market

• Economists predict moderate growth in productivity due to AI, with substantial capability increases but only a 2-3% impact on the labor market by 2030-2050.

• One of the things that you got from that survey was they’re very much aligned… they think there will be a big impact as far as capabilities and there will be some impact on the labor market not astronomical… we’re talking about like 2030 2050 and things like that there’s gonna be substantial capability increases but the growth is gonna be pretty moderate it’s like an extra two three percent.

  — Alex Imas

• AI’s impact on productivity is expected to be significant but not transformative in the short term.
• The labor market may experience gradual changes due to AI advancements.
• Understanding the survey’s methodology is crucial for assessing the reliability of these forecasts.
• The predicted impact of AI on the labor market underscores the need for strategic workforce planning.
• AI-driven productivity gains may lead to more efficient business operations.
• The anticipated moderate growth highlights the importance of managing expectations regarding AI’s economic impact.

Job exposure to AI

• The exposure of jobs to AI is determined by how many tasks within a job AI can perform at least 50%.

• It’s really important to understand what that number means… that number means that AI could do 50% of a task right and how many tasks are in the job that AI can do 50% or more of.

  — Alex Imas

• This metric provides a nuanced understanding of job exposure to AI.
• Assessing task exposure is critical for evaluating AI’s impact on various jobs.
• The focus on task exposure highlights the importance of job function analysis in an AI-driven economy.
• Understanding task exposure can help identify which jobs are most at risk of automation.
• The concept of task exposure underscores the need for continuous skill development.
• Evaluating job exposure to AI is essential for effective workforce planning and policy-making.

The role of task complementarity

• The impact of AI on jobs depends on which specific tasks are automated and how they relate to each other.

• The idea is that when we look at a job and we say look your job is exposed let’s say it’s 50 exposed it really really matters what tasks in your job are exposed and how these tasks relate to one another.

  — Alex Imas

• Task complementarity plays a crucial role in determining automation’s effect on the labor market.
• Understanding task relationships is essential for assessing AI’s impact on job performance.
• The interrelation of tasks influences overall job productivity in an AI-driven environment.
• Analyzing task complementarity can help identify opportunities for job redesign and optimization.
• The focus on task interrelation highlights the complexity of AI’s impact on the labor market.
• Task complementarity underscores the importance of a holistic approach to workforce planning.

Understanding consumer demand elasticity

• We need a significant effort to understand consumer demand elasticity in relation to productivity and wages.

• We need almost like a manhattan project level effort on this is the this is a term from economics called elasticity of consumer demand.

  — Alex Imas

• Understanding demand elasticity is crucial for analyzing economic dynamics.
• The relationship between consumer behavior and price fluctuations influences labor market trends.
• Extensive research on demand elasticity can inform policy decisions regarding productivity and wages.
• The focus on demand elasticity highlights the interconnectedness of economic factors in an AI-driven economy.
• Understanding demand elasticity is essential for anticipating future economic shifts.
• The emphasis on demand elasticity underscores the need for comprehensive economic research and analysis.

The future of software engineering

• The demand for software engineers may not be as elastic as previously thought, potentially leading to downsizing in the sector.

• Some people are arguing look we have seen historically pretty elastic demand and so we’re gonna potentially see a lot more hiring in that sector… other people are saying wait maybe it’s not as elastic as we as we think and people are gonna become so productive that we’re really are gonna see a down downsizing.

  — Alex Imas

• The tech sector may experience a shift in hiring trends due to increased productivity.
• Understanding historical demand elasticity is crucial for anticipating future employment trends in the tech sector.
• The potential downsizing highlights the impact of AI-driven productivity gains on employment.
• The focus on demand elasticity underscores the complexity of predicting future job market dynamics.
• The anticipated changes in the tech sector emphasize the need for adaptability and skill development.
• Understanding the future of software engineering is essential for effective workforce planning and career development.

Disclosure: This article was edited by Editorial Team. For more information on how we create and review content, see our Editorial Policy.