This publication contains my latest reading suggestions, i.e., academic papers and articles I enjoyed reading in July 2024. You can follow me on X (@ProfSchrepel) or LinkedIn (here) to be notified of similar articles on a more regular basis. The Network Law Review is also available on X (@NetworkLawRev) and LinkedIn (here).
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Antitrust:
- The Multi-Purpose Approach and Article 102 TFEU (Maggiolino – Network Law Review)
- Antimonopoly Antitrust Metrics (Hovenkamp – Network Law Review)
- The Long Future of the Neo-Brandeisian Movement, in Three Parts (Teachout – Network Law Review)
- The Legacy of Neo-Brandeisianism: History or Footnote? (Auer & Radic – Network Law Review)
- Neo-Brandeis and Antitrust: an Olive Branch for Consensus (Chirita – Network Law Review)
- Software Crash Exposes Tensions Between Security and Competition (Josephine Wolff – FT)
- A Text-based Measure of Antitrust Policy Beliefs and Attitudes (Mueller – Stanford)
- Defining Relevant Markets with NLP (Wohak – Stanford) 🎙️
- The Antitrust and Privacy Interface: Lessons for Regulators from the Data (Pinto, Sokol & Zhu – SSRN)
- Big Tech Acquisitions and Product Discontinuation (Gautier & Maitry – JCLE)
- The EU Should Be Careful How It Takes On Big Tech (Bloomberg Editorial Board)
Artificial Inteligence:
- Decoding the AI Act: A Critical Guide for Competition Experts (Schrepel – SSRN)
- The Decade Ahead (Leopold Aschenbrenner – Situational Awareness)
- OpenAI Scale Ranks Progress Toward `Human-Level’ Problem Solving (Rachel Metz – Bloomberg)
- Artificial intelligence and shapeshifting capitalism (Grilli, Mariotti & Marzano – JEE)
- Consent in Crisis: The Rapid Decline of the AI Data Commons (Longpre et al. – MIT Data Provenance)
- Meta won’t release its multimodal Llama AI model in the EU (Weatherbed – The Verge)
- Benchmarking Commercial Large Language Models for Political Bias (Batzner, Stocker, Schmid & Kasneci)
- On the modification and revocation of open source licences (Gagnon et al. – Arvix)
- Demand for Artificial Intelligence in Settlement Negotiations (Joshua S. Gans – NBER)
- OECD launches pilot to monitor application of G7 code of conduct on advanced AI development (OECD)
Digital:
- The E.U. Goes Too Far (Ben Thompson – Stratechery)
- OpenAI announces SearchGPT, its AI-powered search engine (Kylie Robison – The Verge)
- The Diffusion of New Technologies (Kalyani et al. – Inet)
- The who’s who of the Parliament’s tech committees (Julia Tar & Théophane Hartmann – Euractiv)
- Is Software Eating the World? (Sangmin Aum & Yongseok Shin – NBER)
- Superstars or Supervillains? Large Firms in the South Korean Growth Miracle (Choi et al. – NBER)
- Switzerland federal government requires releasing its software as open source (Vaughan-Nichols – ZDNet)
- Connecting the dots between democracy and innovation (Yoon, Boudreaux & Kim – Research Policy)
- The Little Tech Agenda (Marc Andreessen & Ben Horowitz – a16z)
Blockchain:
- Against choosing your political allegiances based on who is “pro-crypto” (Vitalik Buterin)
- How AI Models are Optimized Through Web3 Governance (Wulf A. Kaal – SSRN)
Econ:
- The Fascinating World of Increasing Returns (Michael Mauboussin – Scaling Theory) 🎙️
- Trends In Competition In The United States: What Does The Evidence Show? (Shapiro & Yurukoglu – NBER)
- Industrial Organization and The Rise of Market Power (Nathan H. Miller – NBER)
- Technology and Costs (Rajiv Sethi & Zarghame – Interactive Economics)
- Do Mergers and Acquisitions Improve Efficiency? (Demirer & Karaduman – NBER)
- Lessons from the Biggest Business Tax Cut in US History (Chodorow-Reich, Zidar & Zwick – NBER)
- The Relationship Between Capitalism and Political Freedom (Benzecry et al. – SSRN)
- Industrial Policy in the Global Semiconductor Sector (Goldberg, Juhász, Lane, Lo Forte & Thurk – NBER)
- Price Effects of Horizontal Mergers (Annika Stoehr – Plamadiso) 🎥
Other:
- Haruki Murakami (David Senra – Founders) 🎙️
- BYTE Magazine (Archive.org)
- The Employment Effects of a Guaranteed Income (Vivalt et al. – NBER)
- The Authoritarian Side of Effective Altruism Comes for AI (Neil Chilson – Reason)
Thibault Schrepel
@ProfSchrepel
