2026-08-18 16:53:30
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.
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.
Explore our AI Economy work here: intelligence.exponentialview.co.
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.
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.
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.
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.
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:
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?
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 to [email protected] 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.
Reasonable adjustments will be available throughout the application process.
2026-08-17 21:37:21
Hi all,
Here’s our Monday roundup of data signals across AI, energy & markets.
Enjoy!
Every week, we will share the latest updates on the state of the AI economy based on our own latest research and tracking.
Since our report in June, revenues have continued to grow, with this July sitting three times higher year-over-year. The annualized run-rate is now over $210 billion.
See our State of the AI Economy 2026 report for more.
📧 For advisory requests and institutional inquiries, please contact [email protected]
🤝 Want to work with us? We are hiring an AI Economy Research Fellow
The frontier races ahead. The top 10% of companies using OpenAI’s products use 8.3x more tokens than the typical firm.
Reaching the ceiling. Fable 5 token usage at businesses has been flat, making up only 6% of all their tokens (11% of spend) — businesses appear to have reached their limit on willingness to spend for the best model.
2026-08-16 13:39:19
Good morning from London.
We are looking for an outstanding economist to join us as an AI Economy Research Fellow. If you know someone we should speak to, send them our way.
Cheers
Amazon spent some $1.8 million on a Claude project that ran for five months. A senior employee said: “It’s difficult to figure out how much anything [AI-related] costs”.
We had a similar experience with R Mini Arnold, my OpenClaw agent. Its costs have run up as it has grown in complexity, and it takes time to switch it to progressively cheaper models. It becomes hard to keep track of exactly what is running efficiently and what isn’t. called out an outrageous few days when the bot blew through $500 a day.
The subsequent tedious audit was worth it. Many processes were running older, higher-tier models, like Opus 4.5, which are more expensive than smaller, newer models like Sonnet 5 or a slew of open-weight models. The price war that has broken out between Anthropic and OpenAI in response to Chinese advances has helped even more.
By default, RMA now uses my token allowance on OpenAI Codex, which is already paid for in my $200-a-month subscription. It will fall back to DeepSeek v4 Flash or Pro if OpenAI is unavailable. For harder tasks, it can jump to 5.6 Sol, OpenAI’s top model, through the same subscription or Kimi K3 or Anthropic’s Fable (both of which I pay for by the token). The net result is $6 a day, lower than it has been for months.
The funny thing is that RMA is cheaper than it ever has been and yet more capable than ever. It plugs into the Manus API for some types of work; Claude Code and Codex for coding tasks; Prism (our internal research graph, which is more powerful than ever); and other resources like Elicit for academic papers.
It’s a microcosm of the big question in the industry. Has $494 a day just disappeared from genAI revenue? In some sense, yes, but that was really an anomaly. RMA had typically cost me $50 to $60 a day before it went wild. Even at $6 a day, it runs to $2k per year from me alone, which is reasonably substantial for someone who isn’t writing code. I expect spending to spike as I move back into book-writing terrain and need more research done.
I’m curious whether readers have had similar experiences.
See also:
8 minutes with me on the state of the AI economy:
US companies are continuing to spend on AI. Ramp reports that “in July, the top 1% of businesses spent a median $7,400 per employee on AI. The top 10% spent $650. The median firm spent $11.95 per employee.”
Opus 5 is driving a large chunk of Anthropic’s revenue growth.
SpaceXAI is picking up a pricing fight with Grok 4.6. The new model undercuts top rivals by more than 60%.
The cost of building data centers spills over on neighboring towns — in some cases disproportionately so. Each additional data center within 25 miles raises a neighboring town’s bond spread by about 10 basis points. The effect fades with distance — roughly 4 basis points at 75 miles. Neighbors also borrow more. A town with the average number of nearby data centers issues about $34 million more in debt over the following year. The effect roughly doubles between six and thirty-six months. In states with tax breaks, the bill goes through schools. After a state adopts a data center incentive, state transfers to school districts fall by roughly $673 per student.
Explained simply, the towns nearby get the strain but no bargaining chips, so when they need to borrow money for a school or a road, lenders charge them more. And if the state gave the company a tax break to show up, the money the state didn’t collect comes out of the school budget.
Addressing these types of issues is going to become a priority as data centers become about as popular as lead in petrol.
’s extraordinary reporting from the frontlines of data center backlash is more than worth your time.
See also:
Some teams within Microsoft are working on regenerative data center designs based on biomimicry.
Researchers built protein logic gates that can trigger cancer cells’ self-destruction.
A hidden prompt injection in a court filing asked AI to side with the plaintiff in case the court used LLMs.
Batteries deployed in 2026 could move more than one-third of new solar generation into the evening hours to replace fossil fuels.
💪🏼 France’s solar panel recycling sector hit scale in 2025, up 40% from 2024.
An AI designed 16 entirely new synthetic viruses from scratch that were better at killing E. coli than the natural counterparts.
👀 Anthropic is hiring a chip design team.
AI is a decent financial advisor, but it tends to be too patient and sensitive to your prompting.
👾 Fun game: run the AI lab from 2017 and race to recursive self-improvement takeoff.
Over 150 years and despite major electoral reforms, Congress has consistently been dominated by “fortunate sons”.
Thanks for reading!
2026-08-15 12:31:21
Jeff Dean and Sanjay Ghemawat are leaving Google after more than a quarter-century, as you know. Outside the industry, the pair may not be well-known, but theirs was “the friendship that made Google huge.” Jeff and Sanjay are the reason why billions of us have been able to use Google over the past 20 years. Their work on distributed systems, in particular, is why the search engine could handle decades of growth. “Sanjay and I sped up Google Search by 10% today,” Dean once told his daughter.
They weren’t alone. DeepMind’s founder and Nobel Laureate Demis Hassabis also wanted to leave the company, according to well-grounded reports. He was persuaded to stay in a chair role for the sake of the share price. Koray Kavukcuoglu, an executive more closely associated with product delivery and commercial integration, will run the organization.
Losing your very best talent in a short span, both homegrown in the case of Dean and Ghemawat, and acquired in the case of Hassabis, looks like bad news. Superstars like to be on the winning team, after all.
This is what the market believed, and Alphabet’s share price dropped 4% in a day.
But in our view, this is as much a signal about capital and compute allocation as it is about talent.
That matters because Alphabet is not an ordinary incumbent. Google built the most formidable system in corporate history for stewarding uncertain ideas from the demands of its cash-generating core. Think of 20% time; it’s moonshot factory, X; the Alphabet corporate structure; and an extraordinary appetite to acquire.
If even Google now allows the engineers who built its very foundations to leave, something about the way it allocates capital has changed. The question is what. Are researchers leaving Alphabet because they lost faith in the firm’s AI prospects? Or because every TPU can earn such an attractive return serving today’s bread-and-butter models that open-ended research fails to clear the hurdle?
These imply opposite positions in the AI capital cycle.
Below, we identify how Google’s compute has moved, examine what demand for old chips reveals about the economic lives of AI chips, and identify the four signals that would tell us the infrastructure cycle has finally turned.
Markets viewed these departures as a crisis. We think they tell us more about the capital-compute axis.
The full essay includes seven charts showing:
How Google’s latest models fare on the Pareto frontier
How it has shifted compute away from research
What Google Cloud’s growth and economics reveal about infrastructure demand.
Where we are in the AI infrastructure cycle… and the four signals that would tell us it has finally turned.
Upgrade to continue reading.
2026-08-11 01:09:58
For the research and modeling behind this analysis, see our 2026 State of the AI Economy Report.
Based on current guidance, the seven largest AI-infrastructure builders1 expect capital expenditure of $863 billion in 2026 – 88% more than last year. We estimate that roughly two-thirds, some $550 billion, will be AI-related.
That investment does not begin affecting earnings through depreciation as soon as a project starts. While infrastructure is being built or assembled, the attributable costs are capitalized on the balance sheet as construction in progress. Depreciation begins only when the assets are ready for their intended use.
Across the four hyperscalers that disclose this balance2, assets not yet in service now total $315 billion3, up from $281 billion one quarter earlier. This represents both capacity still to come online and a reservoir of future depreciation that has not yet reached the income statement.
A dollar of capex spent by Meta now waits some 1.7 years before going live, a year more than in FY2024. So, for every dollar it spends today, only about a third will reach service within the year. Others have seen a similar trend, to a smaller extent.
2026-08-09 10:52:30
Hi,
It’s time for our Sunday briefing #596, final holiday edition before I get back to my desk next week.
If you missed it earlier in the week, my team shared our best practices for managing AI agents – including what we learned from running a task for a month.
Let’s go!
Highlights of my discussion with Robert Peston and Steph McGovern on the Rest is Money podcast:
On Kimi K3 and Moonshot AI:
They’ve got an extraordinary team that’s had to work under the difficult circumstances of export controls and sanctions. They don’t have access to all the compute, and what they’ve been able to develop is: how do you do a lot without very much? And that is a skill in and of itself.
Americans always tell us that competition is the best thing for the market. So at that one level, it’s competition, and that’s quite good. It will show the extent to which American businesses and British businesses value provenance, brand, trust, liability, service and support.
What motivates the Chinese labs:
They’re competing with each other more than they compete with Silicon Valley. And they’re honest about being behind Silicon Valley. But the ferocity of the competition is really with your neighbor over in Shanghai or your neighbor in Beijing.
My AI revenue outlook:
We will end calendar 2026 somewhere between $185 billion and $190 billion. It is harder to forecast 2027, but getting towards $300 billion is not unreasonable. Our range is wide: it could be $250 billion or it could be $350 billion.
On enterprise adoption:
We built our internal systems around assumptions about how quickly people work. When individuals suddenly produce much faster, verification, approval and decision-making cannot necessarily keep up. Transformation requires changing those systems, not simply giving everyone an AI tool.
Where leverage is (two weeks before the Situational Awareness selloff):
US banks’ Tier 1 capital is extremely healthy right now and, certainly compared to where it was in 2007, 2008, very, very underleveraged. There is a lot of leverage in the US financial system sitting with hedge funds and investing more broadly, which I think are more than the retail risk, because they’re overexposed. They borrow from only a handful of banks, and they can unwind rapidly.
Could there be a crash?
When I look at the metrics that we track, things look healthier because of revenue. They look slightly less healthy because of the way financing, especially the debt financing, sits. Valuations don’t look too aggressive at all across the Nasdaq. There are exceptions; SpaceX was one, briefly, but across the market they don’t look particularly hairy. So the patient, for me, if I had to give it a rating, is still reasonably healthy; perhaps not as healthy as it was a year ago, but not yet at a point where I have to call the emergency services. But I wouldn’t rule out having to do that at some point.
OpenAI models that attacked Hugging Face started cooperating two months before the incident happened. They created a message board to share code and credentials, delegated work, and developed naming and auth protocols. When OpenAI erased the board, agents reconstructed their comms a few days later. For a full breakdown, watch OpenAI researchers talk through their preliminary findings.
Google researchers propose a new game theory for agents, and their paper may explain why the OpenAI agents coordinated so easily.
