Recommended readings: economics of AI, Merging LLMs and economics research, defi ecosystems, World’s Electronics

This edition explores how AI is reshaping the economics of business and research, while highlighting the challenges of measuring economic activity in increasingly complex digital financial ecosystems. It also examines the concentration of global digital hardware capacity in East Asia.

I. The economics of AI: AI is getting more expensive for companies. As rising token usage and workflow complexity make costs harder to predict, leaders need new ways to manage AI economics (McKinsey).

II. An LLM Workflow That Reproduces, Improves, and Extends Published Economics Research: We introduce an open-source workflow that enables an LLM to reproduce, improve, and extend an economics article using the article’s published replication package. First, the workflow attempts to reproduce the original calculations, checks for discrepancies with published findings, and performs automated sensitivity analysis. Across 4,452 published replication packages for five economics journals, the workflow flags discrepancies in 3,460 articles or their appendices. Second, the workflow improves the original calculations by using a different implementation or algorithm. In 496 articles, the workflow is able to reduce a calculation’s computation time, at similar or greater accuracy, by more than a factor of 10. Third, the workflow extends the original analysis. In 923 articles, the workflow develops an extension that does not appear in the original article and that is aligned with the original article’s goals and assumptions (NBER).

III. Hidden by complexity? Measuring stablecoin, crypto and decentralised finance ecosystems: Decentralised finance data presents a distinctive paradox: while every data point is publicly recorded and accessible, deriving meaningful insights is obscured by the scale, fragmentation and complexity of the ecosystem. Key metrics illustrate that the rapidly evolving DeFi ecosystem introduces unique challenges for economic and financial research in accurately capturing financial activity in DeFi. These challenges stem from protocol architecture and the technical execution of transactions that complicate deriving economic meaning. Leveraging blockchain data for Bitcoin, Ethereum and Tron, the paper illustrates three structural sources of measurement divergences: (1) the economically meaningful aggregation of Bitcoin transaction values, (2) the challenge of programmability and proliferation of spurious smart contracts and (3) comparability of use cases across different chains, exemplified by stablecoins. While the examples relate to specific chains and layers of technical execution, the measurement challenges generalise to all blockchains and DeFi ecosystems relying on a similar technical underpinning. We propose measurement approaches based on granular, data bounded estimates that incorporate explicit assumptions, technical classification and disaggregation to align technical execution with economic meaning. The paper demonstrates that, despite the transparency of public blockchains, widely used indicators of cryptoasset and DeFi activity are highly dependent on methodological choices and underlying assumptions that warrant careful interpretation. The findings imply that on-chain indicators should be treated as noisy approximations rather than direct measures of economic activity (BIS).

IV. China, Taiwan, South Korea, and Japan account for 80.5% of global digital hardware capacity

Source: Visual Capitalist

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