AI Economy Institute​

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Cohort 3: AIEI Senior Fellows

Cohort 3 is open to researchers affiliated with accredited universities or research institutions worldwide. Up to two Principal Investigators (PIs) are accepted per proposal, and PIs must hold a PhD or equivalent terminal degree (e.g., MD, JD, DrPH) and must have held that degree for at least five years at the time of application.

Cohort 3: Research focus

AIEI 3^rd CFP: Frontier Firms and the Transformation of Work in the AI Economy

The Microsoft AI Economy Institute (AIEI) invites proposals for its third global research call, centered on understanding how frontier firms – those firms adopting and deploying AI at scale – are reshaping the organization of work and the broader economic landscape.

These firms sit at the leading edge of technological diffusion, providing early evidence of how AI changes job design, skill demands, productivity, and regional economic development. Analyzing frontier firms allows researchers to examine both upstream, firm level transformations and downstream, economywide impacts. Upstream, these firms are adapting internal structures, from workforce skills and job design to innovation processes and decision-making workflows, to fully leverage AI. Downstream, their adoption of AI influences labor markets, industry standards, supply chains, and regional economic patterns, offering early signals of broader structural change.

We welcome research offering empirical rigor, comparative insight, and practical guidance for policymakers, educators, employers, and workers. We especially welcome studies that document firm level experimentation, labor market adjustments, and early indicators of broader structural change. Priority will be given to studies examining organizational change, occupational restructuring, and identifying the early signals that precede broader labor market transformation. We invite proposals addressing any of the following themes:

Priority research themes

• Productivity at the Frontier and Firm-Level Transformation: How AI Is Reshaping Production and Organizational Design
  • How are frontier firms restructuring tasks, workflows, roles, teams, and organizational structures, including managerial systems to integrate AI into processes whether internal or production?
  • Which occupations or tasks show the earliest measurable productivity gains, and under what organizational or technological conditions? What strategies help firms mitigate “workslop,” where individual productivity gains create collective losses?
  • What complementary investments—across talent, governance, culture, data infrastructure, process redesign, and compute—are necessary for firms to fully realize productivity improvements? And how is the integration of AI transforming collaboration dynamics, team structure, and decision‑making, and do these shifts lead to measurable improvements in business performance?
  • Are frontier firms exhibiting shifts in productivity – positive or negative – that may not yet appear in aggregate statistics, and how should these effects be measured to determine whether a modern productivity paradox is emerging?
  • What indicators reliably signal movement along the J curve from early friction to sustained lift?
• Occupational Change, Leadership Expectation, and Workforce Transformation
  • How are frontier firms reorganizing work – tasks, team structures, cross functional responsibilities, and required‑functional responsibilities, and required skill mixes – when integrating AI into their operations? And how do these patterns differ between “legacy” firms transitioning into frontier status and “AI native” firms that were “frontier-native” firms that were frontier from inception?
  • How does AI adoption alter the task structure of occupations, particularly in software engineering and other technical fields, and what shifts are emerging in the composition, allocation, and sequencing of work?
  • How is AI reshaping entry-level work (roles, tasks, and expectations) and what does this mean for career ladders, early career mobility, and the development of core judgment and domain expertise? What evidence do we have on which training pathways most effectively support early career workers in this transition?
  • What new hybrid roles or AI-augmented occupations are emerging inside frontier firms?
• Economic Geography and Diffusion: Regional and Market Level Spillovers
  • How does the presence of frontier firms shape emerging regional patterns, such as new AI hubs, talent clustering, job and startup growth, and risks of geographic inequality, and which place based strategies can support more inclusive diffusion of AI capabilities and opportunities?
  • What risks of regional divergence arise as AI activity concentrates in specific metropolitan areas, and which place based strategies (infrastructure, education, incentives) most effectively broaden the diffusion of AI capabilities and opportunities?
  • How do frontier firms shape their sectors—through the standards they set, the capabilities they diffuse, and/or the competitive dynamics they influence across supply chains and partner ecosystems?
  • Are there observable global diffusion patterns – for example, rapid advancements in markets like China (e.g., the DeepSeek phenomenon) – that illuminate how frontier capabilities spread internationally?
  • What downstream effects are visible in labor markets (job postings, wage adjustments, internal mobility, separations)?
• When History Rhymes: GPT Analogues and Structural Shifts
  • Are we seeing parallels to earlier general-purpose technologies (electricity, PCs, the internet), where productivity improvements first appear locally before diffusing more broadly?
  • How does today’s AI frontier resemble or differ from earlier technology shifts such as electrification, the semiconductor revolution, personal computing, or the internet?
  • Under what historical conditions did general purpose technologies expand economic activity, jobs, and wages, and are those conditions present with AI?
  • What can be learned from recent industry restructuring (mergers, alliances, and the emergence of new entrants) about the trajectory of AI-driven transformation?
  • Which historical analogues best guide expectations for labor market adjustment, occupational evolution, or firm reorganization today?
• Forecasting the Frontier: Capabilities, Diffusion, and Labor Market Signals
  • How can we model likely trajectories of AI capability improvement—both rate (“how fast”) and functional ceiling (“how good”)—to anticipate productivity, growth, and labor market impacts?
  • What empirically grounded forecasts (performance curves, scaling laws, cost curves) can be incorporated into economic models of task substitution, task creation, or occupational redesign?
  • Which labor market signals adjust first when AI diffuses: job postings, hiring flows, separations, wage offers, hours, or contracted types? What features of these market signals change first and how do they relate to labor market signals?
  • How can we measure and forecast AI’s evolving task level capabilities across occupations, and which metrics best link these capability changes to projected organizational redesign, sectoral transformation, and institutional adaptation?

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About the AIEI Senior Fellows Program

This section describes the structure, expectations, and opportunities associated with participation in the AIEI Senior Fellows Program.

The AIEI Senior Fellows experience

The Senior Fellows Program follows a structured, cohort‑based model designed to support rigorous research, peer exchange, and public dissemination.

Senior Fellows will:

• Participate in bi‑weekly virtual workshops with fellow researchers and subject‑matter experts
• Engage in a multi‑day, in‑person workshop with Microsoft subject matter experts and AIEI leadership
• Contribute a chapter to an edited trade book on the AI economy
• Submit a research manuscript to a high‑quality, open‑access academic journal

Researchers are recognized as AIEI Senior Fellows and may be invited to present their findings at industry and policy forums.

The experience outlined above unfolds through a structured, multi‑phase program model, outlined below.

Program structure and timeline (general)

The AIEI Senior Fellows Program follows a structured, cohort‑based model spanning approximately 12 months. The program is organized into three sequential phases designed to support rigorous research, peer exchange, and public dissemination.

Phase 1: Proposal and selection

• During the first phase, AIEI issues a Call for Proposals outlining priority research questions. Submitted proposals are reviewed by academic and industry subject‑matter experts, and selected Principal Investigators are invited to join the Senior Fellows cohort.

Phase 2: Cohort engagement and draft development

• Senior Fellows participate in bi‑weekly virtual convenings and an in‑person workshop with fellow researchers and AIEI and Microsoft leadership. During this phase, Fellows refine their research questions and methods while concurrently developing an initial draft of a chapter for an edited volume on the AI economy.

Phase 3: Revision, publication, and research completion

• In the final phase, Fellows revise their chapter based on peer and editorial feedback, contribute to the publication and public launch of the edited volume, and complete their research projects, culminating in submission of a manuscript to a high‑quality academic journal.

Specific dates and deadlines for this Call for Proposals are provided below.

Priority considerations

All else equal, preference will be given to proposals that:

• Are cross‑, multi‑, or interdisciplinary
• Apply novel methods, including AI‑enabled approaches
• Use existing datasets or leverage established relationships to collect data efficiently
• Compare or contrast country and/or regional perspectives
• Deliver actionable insights to help people and institutions

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Methodological scope

Qualitative, quantitative, theoretical, and mixed methods approaches are all acceptable, provided proposals align with the stated research priorities.

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Application details

The following sections outline eligibility requirements, application materials, review criteria, funding

Eligibility

• Open to researchers affiliated with accredited universities or research institutions worldwide
• Up to two Principal Investigators (PIs) per proposal – only one awardee; interdisciplinary teams encouraged. All PIs must meet eligibility requirements.
• PIs must hold a PhD or equivalent terminal degree (e.g., MD, JD, DrPH) and must have held that degree for at least five years at the time of application.

Award and support

• Each selected proposal will receive a single $75,000 research grant
• Each selected proposal is eligible for one travel allowance to support attendance at the in‑person workshop:
  • $7,500 for awardees based in the United States, Canada, or Mexico
  • $20,000 for all other awardees

Application components

Applications consist of two parts:

1. Applicant Biosketch (11-point font)
   • Personal statement (≤300 words)
   • One page professional summary
   • Up to three contributions to science
   • Current research support and citation metrics (H-index, total number of citations, and I-index)
2. Project Overview (≤1 page, 11-point font)
   • Research question, methods, and contribution
   • Policy relevance and applicability
   • Up to three target journals

Review criteria

Proposals will be evaluated on four equally weighted criteria:

• Scientific strength (25%) – Rigor, appropriateness, and quality of methods; replicability and generalizability where applicable.
• Feasibility (25%) – Likelihood the project can be completed within the proposed timeline and scope.
• Pragmatic applicability (25%) – Potential to generate actionable insights relevant to AI-driven economic transformation.
• PI productivity (25%) – Evidence the PI can successfully execute and publish the proposed research.