To credibly identify the causal effect of AI on productivity, we develop a novel instrumental variable strategy, inspired by Rajan and Zingales’ (1998) seminal work on financial dependence and growth. Their key insight was that industry characteristics measured in one economy – where they are arguably less affected by local distortions – can serve as an exogenous source of variation when applied to other countries.

We extend this logic to the firm level. For each EU firm in our sample, we identify comparable US firms – matched on sector, size, investment intensity, innovation activity, financing structure and management practices. We then assign the AI adoption rate of these matched US firms as a proxy for the EU firm’s exogenous exposure to AI. Because US firms operate under different institutional, regulatory and policy environments, their adoption patterns capture technological drivers that are plausibly independent of EU-specific factors. Rigorous propensity-score balancing tests confirm that our matched US and EU firms are virtually identical across key observable characteristics, validating the identification strategy. Our analysis draws on survey data from EIBIS combined with balance sheet data from Moody’s Orbis.

If you lie down with dogs, you will wake up with fleas.

If they don't publish their code, you can't read it.

The company's Windows Platform engineer Logan Iyer said the move was prompted by applications increasingly overriding user settings, installing unwanted software, and modifying core Windows experiences without permission.

The proposed pact, which is not final and could be subject to change, is framed as a voluntary agreement...