AI adoption, financial decision-making, learning, market behaviour, and the economics of new technologies.
Credit support shapes both the financing of AI infrastructure and the distribution of its downside exposure. We combine a 30-project powered-land inventory, a purposive register of 15 financing instruments across four parent…
Personal demand for generative AI connects workplace adoption to household budgets. We study this connection in eleven weekly ConsumerInsight surveys from South Korea, comprising 8,800 records and a main occupational sample …
Better predictions and broader allocation flexibility create complementary economic value. I measure their returns in 25 Chicago food-inspection queues from 2013 to 2026, covering 27,637 inspections and 6,473 failures. Predi…
AI expands candidate drug design faster than validation capacity. I develop a bottleneck-relocation framework in which experimental, manufacturing, and clinical capabilities become scarce complements to computational search.…
Adaptive financial AI creates a system-level question that single-model evaluation cannot answer: what happens when multiple agents learn from the same experience? I identify this externality with a marginal-preserving exper…
Experiments on shared learning histories must distinguish cross-agent dependence from changes in the learning task assigned to each agent. We describe a block construction that varies exact public-history overlap while prese…
Public discussion of artificial intelligence and employment has split between a jobs apocalypse and an AI jobs boom. Both claims are easier to make than to count because creation is often measured with broad infrastructure e…
Agentic trading systems are usually evaluated by the accuracy of individual forecasts. That evaluation misses a distinct systemic-risk channel: several agents can be individually well calibrated while forming correlated post…
Machine-learning volatility forecasts are usually judged by average out-of-sample accuracy, but their practical value depends on whether relative performance survives stress. This paper studies state-dependent forecast ranki…
A futures contract has a legal maturity, but it can lose economic relevance before that date. This paper develops the concept of contract half-life for derivatives written on rapidly evolving technological vintages. Contract…
Financial AI is commonly evaluated at the model or forecasting level, while its economically consequential effects arise through portfolio construction, market interaction, and stress-state behavior. Evidence at these layers…
Shared technology suppliers can create common operational exposure across banks, but public evidence rarely identifies when that exposure becomes a cascade or financial contagion. This paper develops a layered, disclosure-ba…
This study examines how far public information allows external observers to move from a bank's visible artificial-intelligence (AI) use to disclosure relevant to dependency resilience. We audit all 29 banks on the Financial …
Benchmark design shapes whether a derivatives market forms and how much disruption it can withstand. We develop a participation model of compute derivatives in which benchmark granularity improves exposure matching but divid…
Artificial intelligence can improve each institution's risk forecast while reducing the financial system's ability to diversify forecast errors. This paper separates these two objects. Individual accuracy is a marginal prope…
Artificial intelligence can shorten investors’ responses to public news without making their desired positions more alike. This paper isolates the consequences of changing when a fixed information-driven order reaches the ma…
Boards derive value from plural judgment, yet conventional independence measures observe directors rather than the information infrastructures that shape their decisions. This paper develops a theory of synthetic consensus: …
U.S. financial statements contain no standardized “AI investment” line item, although market commentary routinely compares firms’ AI spending. This paper studies the resulting measurement problem using 40 earnings events for…
This document establishes the Artificial Intelligence Regulatory Uncertainty (AIRU) Standard, an open and version-controlled measurement standard for institutional uncertainty in AI governance. AIRU treats regulatory transit…
The rapid integration of artificial intelligence across financial markets has generated a heterogeneous literature whose findings are frequently compressed into conflicting partial verdicts about whether AI stabilizes or des…
The past two decades have witnessed a structural transformation in financial market microstructure: human traders have been displaced by algorithmic agents operating at microsecond timescales. This paper provides a systemati…
This paper documents a two-stage volatility transmission chain running from sovereign bond market stress, through the core equity assets of the artificial intelligence (AI) super-cycle, into physical energy infrastructure ma…
The period from January 2023 to July 2026 witnessed an unprecedented surge in investor enthusiasm surrounding generative artificial intelligence (AI), propelling technology-sector equities—most notably NVIDIA Corporation (NV…