Mapping Cost Trajectories of AI-Adjacent Technologies

Motivation: AI macrostrategy needs an empirical base. Most strategic disagreements about transformative AI — timelines, takeoff speed, which capabilities diffuse when, where intervention is possible — reduce to disagreements about rates of technological progress. Yet these debates run almost entirely on anecdote and single-series extrapolation. We know from the innovation literature that technologies differ enormously and persistently in their improvement rates (solar PV ~10%/yr, many technologies ~0%), and that these rates are among the most predictable regularities in economics (Farmer & Lafond 2016). This means the strategic questions are, in principle, empirically tractable: if we measured the cost trajectories of AI's complements — the hardware stack that feeds it and the physical systems that will deploy it — we could replace priors with measured learning rates. Almost none of these trajectories have been systematically measured. Lafond, Farmer & Roser call existing data on technological change "crude and incomplete"; The larger project is building the infrastructure to fix this.

Which complements of AI are on fast improvement curves and which are stagnant — and therefore, where do the binding constraints on AI development and diffusion sit, and where does policy have leverage? For example is robotics improving fast enough for AI capabilities to translate quickly into physical-world capacity?

Each mentee owns one technology, selected in weeks 1–2 against the Observatory's purpose-based sampling logic, from two classes: Over 12 weeks: (1) define the product universe and inclusion criteria; (2) collect verbatim specifications from manufacturer and archival pages and quantify them; (3) recover historical prices quarterly (e.g. via online archives); (4) identify key perfomance mtrics to construct; (5) estimate price trajectories and write a short report.

We have completed test runs of the full pipeline at our lab, most recently on civil drones. Mentees will be provided with documented protocols and a working database schema; the intellectual work is in adapting them to a new technology and interpreting the resulting trajectories, which requires careful consideration and a geniuine interest in the technology at hand.

Farmer, J. Doyne, and Francois Lafond. "How predictable is technological progress?." Research policy 45.3 (2016): 647-665.

Macrostrategy for transformative AI currently rests on unmeasured empirical claims. Whether AI capabilities translate into deployed economic and military capacity depends on the cost trajectories of complements — compute, memory, robotics, sensing, power. Whether compute governance remains a viable lever depends on how fast the hardware bottleneck erodes. Whether we get gradual diffusion or discontinuity depends on which complements are stagnant. Today these parameters are set by intuition; measured learning rates would let strategists and governance actors (a) stress-test timeline and takeoff models against data, (b) anticipate diffusion of dual-use capabilities before they arrive (drones are the live example), and (c) identify which bottlenecks are durable enough to regulate through. Technological improvement rates are persistent and forecastable (Farmer & Lafond 2016).

The datasets are public goods contributed to the Technology Observatory (Lafond & Farmer, INET Oxford) — infrastructure explicitly designed to make forecasts of major technological disruptions empirically grounded, and to give the AI strategy community the same empirical footing the energy-transition debate now has.

High autonomy within a fixed methodology. Each mentee owns one technology end-to-end: product-universe definition, spec extraction, archival price recovery, clustering, trend estimation, and write-up. Documented protocols and a working database schema from our lab's prior test runs serve as the playbook; deviations from it are discussed in group meetings. I set direction, review data-quality audits and drafts, and arbitrate methodological choices; I will not be doing hands-on data collection. Mentees should expect to independently debug scraping/archival issues (with the pilot's documented protocols as reference) and to present progress at weekly group meetings.

-Proficient in Python (pandas; comfortable writing scrapers or using Playwright/requests with guidance from documented example scripts). -Comfortable with careful, tedious data work: manual verification of archival sources, documenting provenance, coding missing data correctly. This project is 70% data construction, 30% analysis. -Basic econometrics/statistics: OLS regression, log transforms; familiarity with PCA or clustering is a plus, not required. -Interest in economics of innovation or technology forecasting; prior exposure to experience/learning curves is a plus. -Sufficient weekly time (8+ h) — data collection cannot be batched into occasional sprints without quality loss.

No location preference. Must be available for one weekly group call compatible with CET afternoons/evenings