AI Economics & Policy

I designed identification strategies for five open questions regarding AI’s economic footprint: the enterprise productivity J-curve, capability overhang and non-linear risk emergence, automation-versus-augmentation wage divergence, the economics of malicious use, and cross-jurisdiction regulatory arbitrage.

Breaking the Proposals First

Before running anything on real data, I wrote a design simulation for each protocol that deliberately attacked its own proposal against a known truth. I reported the proposals failing:

• A J-shape naturally arises with probability ~1 under a zero-effect process.
• A Poisson count regression on reported cyber incidents rejected a true null in 100% of draws, falsely attributing +160% to open-weight diffusion when the true attack count was constant.
• A compute-location coverage bias flipped an estimate from −40% to +64%, with the analyst completely unable to sign it from inside the data.

Catching Specification Errors Early

This verification-first approach caught specification and algebra errors that no sample size could ever repair. For instance, conditional quantile regression recovered only 0.3% of a wage compression that an RIF estimator recovered in full.

I ultimately rebuilt each design around what free, public data can actually identify: nested multi-frequency architectures, IRT latent indices over heterogeneous indicators, and measured rather than assumed coverage functions—unified by a shared measurement layer and power analysis across all five studies.