The Economic Value of AI Capability Gains

Research question:

What is the economic value of an increment in AI model capability — today, and over the next 5–10 years?

The best open-weight models trail the frontier by less than a year, yet frontier labs charge large premiums for marginally better models, and their revenues have grown explosively (Anthropic reportedly ~10× year-on-year). Underneath sits a puzzle with three faces:

1. Value is heterogeneous: A casual chatbot user may barely notice the difference between free and premium models. A software developer may value the frontier highly: a slightly more capable model completes much longer tasks under the same supervision. And in domains that hinge on the balance between offense and defense — cybersecurity is the canonical case — even a small capability advantage can be decisive.

2. Revenue growth is not identification: Does explosive revenue growth mean the marginal value of capability is rising exponentially — or mostly that adoption is catching up to capability that already existed? These stories have very different implications; disentangling them — even partially, from the footprints they leave in pricing and adoption data — is one of the project's central empirical tasks.

3. The map moves: A static "value of automating today's tasks" picture is incomplete. As easily automated tasks become abundant and cheap, the tasks not yet automated become bottlenecks and their value grows — a manager creates more value when supervising more employees. Further out, entirely new tasks will emerge.

What we build: The central object is a value-of-capability map: how much economic value each level of AI capability unlocks. The frontier premium is then simply the value unlocked by the frontier model but not yet by its best open-weight follower — yet the map itself is left unspecified in current debates. This project will construct and stress-test that map, keeping explicit a distinction the debate usually blurs: private willingness-to-pay (what labs can monetize — the relevant object for profitability) versus social value (the relevant object for welfare and governance; in offense–defense domains the two can diverge sharply).

Concretely, the team will combine three lines of attack:

• Sector value maps — for a few sectors (e.g. software engineering, customer support, cybersecurity), which tasks become automatable at which capability thresholds, and what value sits behind them (wages, contract values, seat pricing, incident costs) — anchored where possible in benchmarks that price AI performance in units of real paid work, such as the Remote Labor Index, where the share of end-to-end freelance projects the best model completes has risen from 2.5% to 16% in under a year, with the leader roughly doubling the runner-up;

• Revealed preference — pricing ladders across API tiers and open-weight alternatives; decomposing observed revenue growth into an adoption margin and a willingness-to-pay-per-capability margin;

• A simple formal model of how the shape of the value map evolves as automation proceeds and bottlenecks shift, disciplined by the empirical pieces.

How the work is divided will depend on who joins; the aim is a small team whose skills complement one another.

Why it matters:

Whether frontier labs can eventually harvest returns on trillion-dollar-scale investment — or broader competition from open-weight models commoditizes the surplus — depends directly on the shape of this value map. The question emerged from my CAIS Fellowship work on when frontier labs can stay profitable as open-weight models catch up; this project develops the value-of-capability map in its own right.

Outputs:

A working paper; a blogpost distilling the findings for a broader audience; and an interactive widget that lets readers vary the assumptions and see how the conclusions change. Any claim about the next 5–10 years rests on contestable assumptions; the widget makes them explicit instead of burying them in a single headline number.

What success looks like:

The committed core is analytical. At a minimum, the project will deliver a precise operational definition of the value of a capability increment, an account of why lab revenue growth may overstate or understate that value, and an explicit statement of the assumptions on which the answer hinges — adjustable in the widget. Where the data allow, the project goes further: defensible empirical bounds in at least one sector. Finally, the analysis will propose leading indicators — the observables that would first reveal that the value of additional capability has begun to flatten, whether that happens in two years, in five, or never.

Whether frontier labs end up profitable or commoditized shapes racing incentives, model-release decisions, and the resources available for safety. All of these debates lean on a poorly measured object: how much additional AI capability is actually worth, to whom, and over what horizon.

A clearer value-of-capability map helps distinguish "capability growth is explosively valuable" from "revenue growth is mostly adoption catching up" — informing investment and profitability forecasts, governance priorities (identifying domains where thin capability gaps are dangerous), release policy, and expectations for private safety spending. Keeping private willingness-to-pay separate from social value further helps target governance at domains where the two diverge most.

The outputs — a working paper, a blogpost, and an interactive widget — are intended to feed into ongoing conversations in the AI governance, forecasting, and economics-of-AI communities, replacing hand-waving with explicit, contestable assumptions. Even a partial answer over three months improves a key input to all of these decisions.

Mentees work mostly independently, each owning a substantial workstream — literature, data, and drafting — end to end. I set the research question, help find a suitable formal model for the analysis, and give regular feedback on direction and drafts in weekly check-ins; I expect mentees to come to meetings with proposals, not only status updates.

How the work is divided depends on the team: mentees may each first attack one subquestion (sector value mapping, revealed-preference evidence, the formal model) and then join forces on a unified output, or collaborate throughout — what matters is that team members complement one another. Strong contributors will coauthor the working paper.

Required: clear technical writing; comfort with economic or formal reasoning; ability to work independently.

Valued (any of — a diverse, complementary team works best): econometrics / causal inference; an economics or other quantitative social-science background; familiarity with AI capability evaluations and benchmarks.

Remote; I will be in Central European Time (Barcelona). Flexible on meeting times.