AI Economy Institute

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Cohort 4: AIEI Senior Fellows

Cohort 4 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).

Cohort 4: Research focus

AI and Jobs: designing for augmented work

Microsoft AIEI invites proposals for its fourth global research call, centered on understanding the organizational choices that shape where AI complements human work, where it substitutes for human work, and where it creates entirely new forms of economic activity. These outcomes are not fixed properties of a model; they emerge from choices about workflow design, team structure, governance, incentives, accountability, and expertise development.

This call therefore asks researchers to investigate the organizational conditions under which AI augments people, supports meaningful and sustained engagement with work, enables new forms of work, and creates durable value. Research can also examine the circumstances in which AI displaces labor, erodes capability, or creates new oversight burdens, particularly when those findings help explain when augmentation succeeds, when automation dominates, and what makes the difference.

We welcome research that offers empirical rigor, comparative insight, and practical guidance for policymakers, educators, employers, and workers.

We invite proposals addressing any of the following themes:

Priority research themes

• The Everyone-a-Manager Shift: Delegation, Judgment, and Accountability
  As AI systems perform more execution, workers may spend more time defining goals, decomposing work, directing systems, evaluating outputs, and deciding when to intervene. Functions historically concentrated in formal management roles may become increasingly distributed throughout organizations, leading to questions such as:
  • Which managerial functions are diffusing?
  • How does AI reshape worker agency, authority, credit, responsibility, and organizational hierarchy?
  • How does supervising AI differ from supervising people, and how should organizations select, train, support, and compensate workers for these new managerial demands?
• Designing for Augmentation and Innovation: Tasks, Skills, Roles, and New Work
  Whether AI augments or substitutes for human labor depends not only on technical capability but also on how organizations implement the technology and redesign work around its capabilities. Open questions include:
  • At what level should augmentation be measured, and what metrics can capture the interdependencies across these levels of measurement?
  • How do organizational objectives shape automation-augmentation choices?
  • Under what conditions does AI raise the marginal value of human judgment and expertise or increase labor demand or employment?
  • What new capabilities, tasks, or outputs arise through human-AI complementarity?
• Designing Organizations and Workflows People Embrace
  AI adoption becomes economically consequential when organizations integrate AI, learn from its use, and adapt their systems over time. Yet organizations differ substantially in their ability to adapt technologies to their needs, build complementary capabilities, and capture durable value from AI investments, leaving many questions unexplored, such as:
  • What should organizations optimize for beyond time saved—for example, quality, meaningful work, learning, or customer value?
  • What complementary investments and implementation choices enable workers to embrace redesigned workflows rather than merely comply?
  • How do AI practices diffuse within and across teams, occupations, organizations, supply chains, and geographies?
  • Under what conditions do open-weight models broaden access, control, and confidentiality, and when do they create new barriers through the technical expertise and infrastructure costs required to deploy them locally?
  • How do open models increase or decrease risks—for example, financial, informational, or technical risks—compared with closed models? To what extent do open models mitigate the reverse information paradox, in which organizations pay for AI capabilities while simultaneously contributing proprietary knowledge that may enhance the provider’s models?
  • How do degrees and forms of model openness—including access to model weights, modifiability, local deployment, and interoperability—shape organizations’ ability to develop technical, governance, and learning capabilities internally, as well as their breadth of deployment and approaches to redesigning work?
• Building Expertise and the First Rung
  If AI performs much of the junior and routine work through which people have historically learned, organizations may gain near-term efficiency from AI use while weakening the pathways that produce future experts and managers. Some organizations have signaled reductions in entry-level hiring, while others plan to increase it, but it is not clear what the equilibrium or impact will be. This leaves many open questions, including:
  • Who bears the cost of developing future experts and managers when AI increasingly performs the work that once trained them?
  • What are the long-term consequences for organizations that invest in talent development and those that do not?
  • Which apparently routine tasks carry hidden learning, socialization, or signaling value?
  • How can AI accelerate learning, build expertise, and upskill human workers instead of producing performance without transferable skill (deskilling or never-skilling)?
  • What new skills are needed?
  • What alternatives can replace lost learning pathways, and how effectively do they develop the new skills required for long-term success?
• Designing for AI-Mediated Discovery, Markets, and Value Creation
  As AI systems increasingly mediate how people discover information, evaluate alternatives, and conduct transactions, they may change how firms interact with customers and capture economic value. Measures such as website traffic and search ranking may become less informative with AI in the mix. This leads to questions such as:
  • How do purchasing decisions, pricing, product differentiation, and market concentration change when AI agents increasingly act and transact on behalf of consumers and organizations?
  • What metrics best capture AI visibility—for example, inclusion in AI-generated answers, citations, or recommendations—to help explain its impact on business outcomes?
  • How does the effect of AI-mediated discovery differ by business size, age, and geography, and under what conditions does it broaden market access or reinforce incumbent advantages?

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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, AIEI leadership and other Microsoft leaders. During this phase, AIEI Senior 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, AIEI Senior 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.

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 and PIs and co-PIs must hold a PhD or equivalent terminal degree (e.g., MD, JD, DrPH) at the time of application.
• Each proposal may include up to two Principal Investigators (PIs), but only one award will be issued. Interdisciplinary teams are encouraged, and all PIs must meet the eligibility requirements.

Award and support

• Each selected proposal will receive a single research gift of $75,000.
• 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
   • 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.