Expert Opinion Research Agent

SELECTION RATIONALE: These experts were selected by deconstructing the core research question into key themes: labor economics, corporate strategy, human-computer interaction, and skills development. The list prioritizes influential thought leaders who bridge academic theory with real-world business application and have a strong public presence, ensuring they can provide substantive, interview-ready insights from diverse and often contrasting viewpoints. TOP RECOMMENDATIONS (10): Erik Brynjolfsson - Director, Digital Economy Lab @ Stanford University Ethan Mollick - Professor of Management @ The Wharton School Daron Acemoglu - Institute Professor of Economics @ MIT Fei-Fei Li - Co-Director, Institute for Human-Centered AI (HAI) @ Stanford University Michael Chui - Partner, McKinsey Global Institute @ McKinsey & Company Jaime Teevan - Chief Scientist & Technical Fellow @ Microsoft Kai-Fu Lee - Chairman & CEO @ Sinovation Ventures Paul Daugherty - Group Chief Executive, Technology & CTO @ Accenture Josh Bersin - Global Industry Analyst & CEO @ The Josh Bersin Company Thomas H. Davenport - Distinguished Professor of IT and Management @ Babson College ADDITIONAL EXPERTS TO CONSIDER: Andrew McAfee - Co-Director and Principal Research Scientist @ MIT Initiative on the Digital Economy David Autor - Ford Professor of Economics @ MIT Manuela Veloso - Head of AI Research @ J.P. Morgan Chase (Emeritus) John Maeda - VP of Design and Artificial Intelligence @ Everbridge Stuart Russell - Professor of Computer Science @ UC Berkeley Ellyn Shook - Chief Leadership & Human Resources Officer @ Accenture David Luan - Co-founder & CEO @ Adept AI Labs Mark Muro - Senior Fellow, Policy Director @ Brookings Metro

TABLE OF CONTENTS 1. 01 Executive Summary 2. 02 Research Questions & Expert Insights 3. 03 Interview Sources 4. 04 Expert Profiles 5. 05 Additional Insights & Emerging Themes HUMAN ROLES AND AGENCY IN THE AI-AUGMENTED WORKFORCE 👨‍💻 BUILT BY SHAALIN @ INDUSTRY GTM Finding this analysis useful? If you're interested in more agents created by me or would like to work together on a deeper research project, connect with me on LinkedIn or check out my website: * LinkedIn [https://linkedin.com/in/shaalin] * Newsletter [https://go.industrygtm.com] 01. EXECUTIVE SUMMARY The expert interviews reveal that the future of work in an AI-augmented world will be shaped less by technological inevitability and more by human agency, organizational choices, and policy interventions. While AI is already delivering measurable productivity gains—such as a 26% speed and 40% quality improvement for consultants—experts caution that these benefits are not automatically captured at the organizational or societal level. Instead, the integration of AI is exposing new challenges: the "secret cyborg problem," declining trust between employees and management, and the risk of job polarization and inequality if proactive measures are not taken. Experts emphasize that humans will remain indispensable in roles requiring creativity, critical thinking, and collective decision-making. The most significant, non-obvious insight is that the value of human work will increasingly derive from agency, collaboration, and the ability to shape technology’s direction—rather than from routine or easily automated tasks. Contrarian viewpoints challenge the dominant narrative of inevitable productivity booms, with some experts predicting only a modest 1% GDP increase over a decade, far below popular forecasts. Emerging trends include the rise of AI as a creative partner, the need for new economic measurement frameworks (such as GDP-B), and the growing importance of democratic participation and labor negotiation in shaping AI’s impact. Experts predict that the next 1-3 years will see a shift toward organizations redesigning roles, investing in reskilling, and adopting more participatory models for technology governance. The report highlights that the future of work is not predetermined by AI’s capabilities, but by the collective choices of workers, organizations, and policymakers. 02. RESEARCH QUESTIONS & EXPERT INSIGHTS WHAT ROLES WILL HUMANS PLAY IN AN AI-AUGMENTED WORKFORCE? The consensus among experts is that humans will occupy roles that leverage uniquely human strengths—such as creativity, critical thinking, social intelligence, and collective decision-making—while AI handles routine, analytical, and repetitive tasks. Ethan Mollick’s research demonstrates that AI can outperform average humans in creativity tests and significantly boost productivity, but human oversight, strategic direction, and refinement remain essential. Daron Acemoglu stresses that human agency and collective learning are critical for ensuring that AI augments rather than replaces meaningful work. Erik Brynjolfsson highlights the importance of measuring not just economic output, but also the consumer surplus and welfare benefits that arise from digital goods and AI-enabled services. Experts provide concrete examples: AI as a “co-founder” in entrepreneurship, AI-generated marketing strategies completed in minutes, and the use of large language models to simulate consumer behavior. However, they caution that the integration of AI requires organizational redesign, new training paradigms, and a shift in management culture to fully realize these benefits. "AI can complement human skills, but it can also substitute for them. The outcome depends on how AI is integrated into the workplace." — Daron Acemoglu, MIT 🔄 DIFFERING EXPERT PERSPECTIVES Experts diverge on the extent and nature of AI’s impact on human roles. While some predict a collaborative future where AI augments human creativity and expertise, others warn of job polarization and the risk of AI reinforcing existing inequalities. * Ethan Mollick: Advocates for AI as a creative partner and productivity enhancer, emphasizing the need for organizations to innovate and redesign roles to harness AI’s potential. * Daron Acemoglu: Warns that without proactive policy and organizational choices, AI could substitute for human labor, exacerbate inequality, and diminish worker agency. HOW SHOULD ORGANIZATIONS AND POLICYMAKERS RESPOND TO AI’S WORKFORCE IMPACT? Experts agree that a proactive, participatory approach is essential. Acemoglu calls for democratic engagement, regulatory frameworks, and the empowerment of labor unions to negotiate not just wages, but also working conditions and technology use. Mollick emphasizes the need for organizations to foster a culture of experimentation, transparency, and trust, moving away from the “small path” of downsizing toward expanding workforce capabilities. Brynjolfsson advocates for engaging with industry and government to ensure that research and policy are grounded in real-world practices. Concrete recommendations include investing in reskilling and training, adopting codetermination models (as in Sweden), and developing new metrics (like GDP-B) to capture the true value of digital and AI-enabled goods. Experts highlight the importance of balancing automation with the creation of new tasks and roles, and ensuring that AI deployment is aligned with societal goals. "We need a different mindset for regulation... one that creates incentives for the development of beneficial AI." — Daron Acemoglu, MIT 🔄 DIFFERING EXPERT PERSPECTIVES There is disagreement on the sufficiency of market-driven approaches versus the need for strong regulation and collective action. * Erik Brynjolfsson: Emphasizes industry engagement and practical relevance, suggesting that collaboration between researchers, industry, and government can drive positive outcomes. * Daron Acemoglu: Argues that leaving AI development to market forces is inadequate and potentially dangerous, advocating for robust regulatory intervention and democratic oversight. WHAT ARE THE MAIN CHALLENGES AND MISCONCEPTIONS IN INTEGRATING AI INTO THE WORKFORCE? Experts identify several challenges: declining trust between employees and management, the “secret cyborg problem” where employees use AI without disclosure, and the risk of overestimating AI’s capabilities. Mollick notes that organizations often fail to capture AI’s productivity gains due to lack of communication and innovation in management practices. Acemoglu warns against techno-optimism and the assumption that AI will automatically boost productivity and prosperity. Brynjolfsson highlights the limitations of traditional economic metrics and the need for new frameworks to measure digital value. Misconceptions include the belief that AI will inevitably lead to massive productivity gains (contradicted by Acemoglu’s estimate of only a 1% GDP increase over 10 years) and the idea that AI can fully replace human expertise and judgment. Experts stress the importance of human oversight, ethical considerations, and the need for continuous skill development. "The experience we have so far is not really encouraging on the productivity gains... you cannot just leave it to the market." — Daron Acemoglu, MIT 🔄 DIFFERING EXPERT PERSPECTIVES Experts differ on the pace and scale of AI-driven transformation, as well as the best strategies for measurement and integration. * Ethan Mollick: Focuses on rapid experimentation and the need for organizations to develop their own benchmarks for AI effectiveness. * Daron Acemoglu: Urges caution, realism, and the need for regulatory safeguards to prevent negative societal outcomes. HOW CAN HUMAN AGENCY AND COLLECTIVE LEARNING BE PRESERVED AND ENHANCED IN THE AI ERA? Experts converge on the importance of preserving human agency through democratic participation, collective bargaining, and the redesign of AI to serve as an informational tool that empowers workers. Acemoglu advocates for a multi-pronged approach: integrating diverse skill sets, fostering collective learning, and ensuring that AI provides better information rather than simply automating tasks. He highlights the need for global collaboration, especially to give developing countries a voice in AI governance. The creation of new tasks, investment in training, and the strengthening of unions are seen as vital strategies for maintaining human agency and ensuring that AI contributes to human flourishing. "AI should be thought of as something that provides information... to boost the expertise of the workers." — Daron Acemoglu, MIT 03. INTERVIEW SOURCES Erik Brynjolfsson on Measuring the Digital Economy and AI’s Workforce Impact - YouTube Featuring expert(s): Erik Brynjolfsson Watch Video [https://www.youtube.com/watch?v=SstOWPTg7hk] Ethan Mollick on AI, Creativity, and Organizational Change - YouTube Featuring expert(s): Ethan Mollick Watch Video [https://www.youtube.com/watch?v=I5BBhyT5eV8] Daron Acemoglu: ECINEQ 2025 Keynote on AI and Labor Dynamics - YouTube Featuring expert(s): Daron Acemoglu Watch Video [https://www.youtube.com/watch?v=1xrbv9VIdZk] Daron Acemoglu on Human Agency and AI Regulation - YouTube Featuring expert(s): Daron Acemoglu Watch Video [https://www.youtube.com/watch?v=fxYN2sHtS2A] Ethan Mollick: AI Productivity Experiments and the Future of Work - YouTube Featuring expert(s): Ethan Mollick Watch Video [https://www.youtube.com/watch?v=1X6YJi17Swo] Daron Acemoglu on Collective Knowledge, Democracy, and AI - YouTube Featuring expert(s): Daron Acemoglu Watch Video [https://www.youtube.com/watch?v=nqeFL9_0L_c&pp=0gcJCf0Ao7VqN5tD] Daron Acemoglu: Realism vs. Techno-Optimism in AI Economics - YouTube Featuring expert(s): Daron Acemoglu Watch Video [https://www.youtube.com/watch?v=DYg3CBeXneY] Ethan Mollick: AI Strategy, Benchmarking, and Collaboration - YouTube Featuring expert(s): Ethan Mollick Watch Video [https://www.youtube.com/watch?v=KEQjwE7hDjk] Daron Acemoglu: Human Flourishing, Agency, and AI - YouTube Featuring expert(s): Daron Acemoglu Watch Video [https://www.youtube.com/watch?v=88N3qyWVBNo] Daron Acemoglu: Algorithmic Governance and the Swedish Model - YouTube Featuring expert(s): Daron Acemoglu Watch Video [https://www.youtube.com/watch?v=Gri2WdPD0tU] 04. EXPERT PROFILES ERIK BRYNJOLFSSON Director, Stanford Digital Economy Lab Areas of Expertise: * Economic measurement of digital goods and AI * Consumer surplus and welfare analysis * AI’s impact on productivity and labor markets Relevant Experience: Brynjolfsson has pioneered new frameworks for measuring the value of digital goods, such as the GDP-B approach, and has led cross-country studies on the economic impact of AI. His work bridges academic rigor with practical relevance, engaging directly with industry and government stakeholders. Key Insights: "Traditional GDP often overlooks non-priced goods, which are increasingly significant in the digital economy." ETHAN MOLLICK Associate Professor, Wharton School, University of Pennsylvania Areas of Expertise: * AI-driven organizational change and innovation * Human-AI collaboration and creativity * Entrepreneurship and productivity benchmarking Relevant Experience: Mollick conducts hands-on experiments with AI in business and education, including controlled studies on AI’s impact on consulting, entrepreneurship, and creativity. He is known for advocating rapid experimentation and the development of new organizational benchmarks for AI adoption. Key Insights: "AI consistently beats average humans in creativity tests, although definitions of creativity are often poorly constructed." DARON ACEMOGLU Institute Professor, Massachusetts Institute of Technology (MIT) Areas of Expertise: * Political economy of technology and labor * AI, automation, and job polarization * Democratic governance and regulatory policy Relevant Experience: Acemoglu is a leading voice on the societal impacts of AI, drawing on historical and global perspectives. He has developed influential theories on automation, labor dynamics, and the role of human agency, and is recognized for his critical stance on techno-optimism and market-driven narratives. Key Insights: "There is nothing inevitable about the direction of progress... The choices we make today about AI will determine its impact on society." 05. ADDITIONAL INSIGHTS & EMERGING THEMES REDEFINING ECONOMIC MEASUREMENT IN THE DIGITAL AGE Brynjolfsson’s GDP-B framework and the use of large language models for simulating consumer behavior represent a shift toward measuring the true value of digital and AI-enabled goods. This approach captures consumer surplus and welfare benefits that traditional GDP misses, especially in poorer regions where free digital goods have outsized impact. The adoption of such metrics could reshape policy priorities and resource allocation, but also raises questions about data validity and the risk of LLM-generated survey responses. ORGANIZATIONAL INNOVATION AND THE “SECRET CYBORG PROBLEM” Mollick’s identification of the “secret cyborg problem”—where employees use AI tools without organizational acknowledgment—highlights a critical gap in current management practices. This theme underscores the need for transparent, trust-based cultures and the redesign of roles to fully leverage AI’s capabilities. The next wave of organizational innovation will likely focus on integrating AI into workflows, developing internal benchmarks, and fostering R&D mindsets rather than relying solely on external consultants. DEMOCRATIC PARTICIPATION AND LABOR NEGOTIATION IN AI GOVERNANCE Acemoglu’s advocacy for democratic engagement, codetermination, and the strengthening of unions points to a future where workers have a greater say in how AI is deployed. The Swedish model of labor negotiation and the emphasis on training and reskilling are emerging as best practices. However, experts note that global disparities in voice and influence persist, particularly for developing countries, and that more research is needed on effective models for participatory AI governance. BALANCING AUTOMATION WITH NEW TASK CREATION The balance between automation and the creation of new tasks is a recurring theme. Acemoglu warns that overemphasis on automation can erode worker power and exacerbate inequality, while a balanced approach can enhance both productivity and job quality. This insight suggests that future research and policy should focus on incentivizing the development of new, meaningful roles alongside technological advancement. RISKS OF TECHNO-OPTIMISM AND THE NEED FOR REALISM Experts caution against the prevailing narrative of inevitable, large-scale productivity gains from AI. Acemoglu’s estimate of a 1% GDP increase over a decade stands in stark contrast to more optimistic forecasts, highlighting the need for realism and critical evaluation of AI’s actual impact. This theme calls for more rigorous, context-sensitive research and a willingness to challenge mainstream assumptions.