We study whether common ownership affects the direction of technological change. We develop a task-based model with multiple local labor markets in which commonly owned firms internalize wage externalities from portfolio rivals when hiring from the same labor pool, increasing incentives to automate. We establish causality by exploiting institutional investor mergers in a dynamic DiD design, using U.S. data on institutional ownership, establishment level employment, and text-classified automation patents. Increases in common ownership among local labor-market rivals raise firms’ automation propensity by 22.7 percentage points and reduce employment growth. The effect disappears when firms do not compete within labor markets.

We propose that AI automation is a continuum between: (i) crashing waves where AI capabilities surge abruptly over small sets of tasks, and (ii) rising tides where the increase in AI capabilities is more continuous and broad-based. We test for these effects in preliminary evidence from an ongoing evaluation of AI capabilities across over 3,000 broad-based tasks derived from the U.S. Department of Labor O*NET categorization that are text-based and thus LLM-addressable. Based on more than 17,000 evaluations by workers from these jobs, we find little evidence of crashing waves (in contrast to recent work by METR), but substantial evidence that rising tides are the pri- mary form of AI automation. AI performance is high and improving rapidly across a wide range of tasks. We estimate that, in 2024-Q2, AI models successfully complete tasks that take humans approximately 3-4 hours with about a 50% success rate, increasing to about 65% by 2025-Q3. If recent trends in AI capability growth persist, this pace of AI improvement implies that LLMs will be able to complete most text-related tasks with success rates of, on average, 80%–95% by 2029 at a minimally sufficient quality level. Achieving near-perfect success rates at this quality level or comparable success rates at superior quality would require several additional years. These AI capability improvements would impact the economy and labor market as organizations adopt AI, which could have a substantially longer timeline.

We present causal evidence on the rent-sharing elasticity of German manufacturing firms. We develop a new firm-level Bartik instrument for firm rents that combines the firms’ predetermined energy input mix with national energy carrier price changes. Instrumental variable estimation yields a rent-sharing elasticity of approximately 0.20 implying that a 10% change in rents leads to a 2% change in wages. Rent-sharing induced by energy price variation is asymmetric and driven by energy price increases, such that, on average, workers do not benefit from energy price reductions but are harmed by price increases. Reduced-form evidence shows that a 10% increase in firm-level energy prices depresses firm-level wage growth by 0.34%.