AI Economy Fellowship

Exponential View is appointing its first Research Fellow.

We are looking for an economist who can connect frontier economic thinking to messy, real-world evidence and reach useful judgments with the foresight Exponential View is renowned for.

The Fellow will investigate how AI is changing economic value, work, firms and markets. The questions may include but are not limited to:

• How should AI companies, infrastructure and capabilities be valued from first principles?

• Where in the AI value chain is the economic surplus created, and who is capturing it?

• What is AI’s impact on wages, employment, productivity and worker bargaining power long-term?

• What are the microeconomic effects inside firms?

• What counts as transformative AI and which leading indicators would reveal the state of the transition?

We don’t expect the Fellow to arrive with settled answers. But we do expect the Fellow to know how to turn our questions into testable economic mechanisms, assumptions and back-of-the-envelope estimates that build towards further empirical work.

The position is based in London, UK.

About Exponential View

Exponential View is an independent research organisation founded by Azeem Azhar. We study how AI and other general-purpose technologies change economic value, institutions and the distribution of power.

Our AI Economy programme tracks the physical, financial and organisational build-out of AI. We research semiconductors, energy and data centres, model capabilities and economics; corporate investment and adoption; firm-level performance; labour markets; and the distribution of value across the stack. We combine original datasets, company and sector analysis, economic reasoning, and direct engagement with stakeholders to produce evidence-based analyses that stand the test of time.

Our work is written for people making consequential decisions in business, investment, technology and public policy. It is rigorous, empirical and explicit about uncertainty. We don’t wait for consensus, and we rarely care for it.

The Fellowship

This is a paid six- or twelve-month applied economics Fellowship with substantive responsibility across Exponential View’s research and publications.

Our proposition is as ambitious as it is simple. Inter alia, the Fellow will translate economic theory and frontier research into useful insights that might inform decision-making. They will maintain the standards of serious academic work while learning to operate at the speed of a live technological and economic transition. They will develop scenarios, measures, and leading indicators for transformative AI, connecting the work of leading economists to our own methods and models.

The Fellow will work closely with Azeem Azhar and EV's research team, contribute to the core AI Economy programme, and develop one substantial research output of their own. Where the work warrants it, contributions will be publicly credited or bylined.

Academic projects can run for months or years. At Exponential View, we often need a good answer within hours or days. That requires tightly framed questions, intelligent use of imperfect data, visible assumptions, distinguishing between evidence and inference, and the willingness to revise or abandon a view quickly.

The Fellow will be augmented by as much AI as they need and is expected to actively build their AI research tools and skill set to become an even better researcher.

Who we are looking for

At minimum, you will have an outstanding master’s-level qualification in Economics, including an MPhil. You may be a current doctoral student in economics seeking a period of intensive applied work.

Relevant specialisms may include applied microeconomics, labour economics, industrial organisation, productivity and growth, innovation economics, financial economics or the economics of technological change.

The Fellow must demonstrate:

• Serious prior engagement with AI, automation, technological change or a closely related economic question. A generic interest in AI is not enough.

• Strong applied economics and empirical judgement. We are less interested in theory for its own sake than in the ability to use theory to structure an answerable question.

• Experience working directly with difficult real-world data, including knowing when it is incomplete, endogenous, inconsistently defined or simply wrong.

• The ability to translate frontier models and academic arguments into observables, datasets, tests and an intelligible view of the world.

• Comfort with ambiguity, compressed deadlines and changing priorities.

• The ability to write clearly for an economically literate audience without hiding behind academic language.

• You work well independently, can make rapid progress on your own, and identify the few decisions that genuinely require senior input.

• You are intellectually honest and can distinguish what is observed, estimated, inferred, and unknown; you change your mind when the evidence changes.

This role will fit an economist who wants to become faster, more empirical, and more effective without surrendering rigour.

Right to work in the UK is required.

Who this is not for:

• This is not a role for a general quantitative researcher from an unrelated discipline or for someone who wants to learn economics on the job.

• It is also unlikely to suit someone seeking a conventional academic postdoctoral rhythm or whose overriding objective is the next journal publication.

What the Fellow will gain

The Fellow will leave with:

• A body of applied, publicly visible work on the AI economy.

• Experience moving from an open-ended question to a defensible empirical judgement on a compressed timescale.

• Feedback on research design, analytical judgement, visual explanation and writing.

• One substantial, independently owned research output.

• Exposure to EV's network of economists, technologists, business leaders, investors and policymakers, including relevant academic collaborations.

Terms

• Duration: Six or twelve months

• Commitment: Full-time preferred; a substantial part-time arrangement may be possible for an exceptional candidate

• Location: London, UK / hybrid, with regular in-person work

• Application deadline: 6 September 2026

• This is a paid Fellowship.

How to apply

Please submit:

• Your CV.

• A note of no more than 200 words explaining why you want this Fellowship and what you want to improve during the appointment.

• One or two substantial examples of empirical economic work.

  • For collaborative work, a precise description of your own contribution.

• Please answer the following questions in no more than 500 words each:

1. Choose one important claim about AI's effect on fundamental value, wages, productivity, or market structure. What is the economic mechanism? How would you test it with real-world data, and what result would cause you to revise or reject the claim?

2. Take one proposition from the economics of transformative AI – for example, from the work of Anton Korinek, Daron Acemoglu or another serious economist – and translate it into observable indicators over the next two years. Which existing data would you use, what is missing, and what new measure could you create?

• Please confirm you have the right to work in the UK. Evidence will be required at a later stage.

Send your application via email by 23:59 BST on 6 September 2026.

We will invite shortlisted candidates to a first interview. Owing to the volume of applications, we may be unable to respond to every applicant individually.

Following the interviews, selected candidates will complete a paid, tightly time-boxed research exercise using public or synthetic data. It will test the ability to frame an economic question, work quickly with imperfect evidence, produce a defensible analysis and communicate it clearly. It will not be used as unpaid production work.

We set the offer against the same criteria we use to assess the application, and we tell shortlisted candidates where in the range we expect to land before we make an offer. You will not need to negotiate to arrive at a fair figure.

Reasonable adjustments will be available throughout the application process.