This weekly digest tracks what is NEW or CHANGED in AI-economics research. For the cumulative state of evidence on any topic, see the /syntheses pages. A single study rarely overturns a body of evidence.

Strengthened: platform attention is reallocated, not just scaled, with an 8.56M-user Netflix randomized controlled trial (RCT) diffusing consumption from superstars to the middle tail while a preregistered search RCT finds AI overviews pull clicks on-platform and away from publishers.

Better measured: the publisher and user-experience costs of AI-answer search are now quantified causally (-18.8 percentage points in external click-through rate (CTR), small declines in trust and sessions).

Challenged: name-inference-driven equity fixes are not uniform, with new evidence suggesting that benefits concentrate on Western-legible women's names and often miss culturally ambiguous ones.

Confirmsestablished

Recommendation quality and the concentration of consumption: Experimental evidence from Netflix

Guy Aridor, Winston Chou, Nathan Kallus, Antoine Scheid, Allen Tren, Kevin Zielincki; RCT, high evidence

In a 60-day randomized holdback on a global platform with 8,559,252 subscribers, improved recommendations increase engagement and shift recommendation share away from superstars toward the middle tail, lowering title-level concentration by about 5.7 percent. This independently corroborates the standing view that recommender upgrades can broaden consumption rather than amplify hits, at scale.

So what: If this holds, attention and revenue risk sits with superstar-heavy catalogs and contracts, not just with the long tail.

Full numbers

Confirmsestablished

AI in search reduces publisher referrals without improving user experience: Experimental evidence

Stephanie T. Wang, Jeffrey Gleason, Yakov Bart, Christo Wilson, Danae Metaxa; RCT, high evidence

A preregistered randomized controlled trial (N=1,100) finds AI-answers mode cuts click-through to external sites by 18.8 percentage points, reduces news and Reddit clicks, and slightly lowers trust and sessions versus current search; removing AI overviews modestly raises referrals without perceived quality gains. This tightens causal estimates around publisher harm and neutral-to-worse UX under AI-answers, aligning with prior concerns about on-platform answer capture.

So what: If this generalizes, publisher revenue exposure to search format shifts is larger and less offset by UX gains than many models assume.

Full numbers

Extendssuggestive

Beyond automation: AI and the human value of sell-side analysts

Devin Shanthikumar, Il Sun Yoo; quasi-experiment, high evidence

Using difference-in-differences and event studies around US 10-K inline XBRL (iXBRL) adoption and bank AI investments, analysts at AI-invested banks are associated with timelier, bolder, and more accurate forecasts and expanded coverage, consistent with a shift from public-data processing to private information gathering. This extends augmentation evidence into high-skill finance, indicating reallocation toward higher-value tasks rather than displacement.

So what: If this holds, earnings-season information production could become more uneven across institutions, raising model and market-structure risk for firms relying on slower or thinner coverage.

Full numbers

Extendsestablished Whose doctor does the AI recommend? An algorithm audit of reputation and demographic signals in large language model-assisted physician choice (Syeda Anshrah Gillani, Mirza Samad Ahmed Baig)

Newestablished Event-time confounding under bursty human dynamics (Michael Iannelli, Alan Ai)

Extendsestablished Targeting support using job seekers' biases: A randomized experiment (Bruno Crépon, Aurélien Frot, Christophe Gaillac)

Newdescriptive UpgradeBench: A decision-centric benchmark for upgrading fine-tuned LLM specialists (Ye Chen, Weining Zhang)

Extendsestablished TRACE: Agentic catalog enrichment with multi-source evidence grounding (Rohan Kumar, Steven Xu, Kyle MacDonald, Matthew Long, Bernice Chow, Mac VanRenterghem, Sudeep Das)

Extendsestablished The legibility gap: How gender equity interventions redistribute recognition across cultures (Binglu Wang, Jose Cervantez, Jiahui Xue, Katherine L. Milkman, Dashun Wang)

Newframework The order of binary experiments under endogenous stopping (Zihao Li)

Extendssuggestive Auditing self-evolution in financial agents: Capability gains, security drift, and execution-interface mismatch (Jialong Li, Jialing Zhu)

Extendssuggestive Science under sanctions: The impact of the entity list on Chinese academic research (Xiaodie Pu, Xintong Wang, Di Tong, Alain Yee Loong Chong)

Tensionsuggestive Stranded credentials: how a skill-signaling market absorbed generative AI (Song Yao)

Newdescriptive Pricing the risk of runtime compression: Anytime-valid admission and a served-output law for compressed serving state (Fanzhe Wei, Li Liu)

Extendssuggestive Governing generative AI in organizations: a design theory and quasi-experimental field study of sociotechnical guardrails (Maikel Leon)

Newdescriptive No task fails every time: Why one-shot audits are structurally blind to agent damage (Shiven Khurdi)

Newdescriptive Frontier AI forecasting has a measurement problem: An audit of progress evidence (Fabricio F. Costa)

Tensiondescriptive AI with authority, from application to silicon (Jason Hickey)

Tensiondescriptive BC-Bench: Evaluating agentic engineering in a domain-specific language for ERP (Haoran Sun, Klaus Marius Hansen)

Newframework Foundation models for partial causal identification (Alexis Bellot, Anish Dhir)

Tensiondescriptive ContractScrub: A benchmark for final review of legal contracts (Yejin Bang, Kirsty Fielding, Brandan Oliver, Brian Birke, Nabeel Seedat, Andrew M. Bean)

Newsuggestive Credit without ground truth: Auditing step-level credit assignment in LLM agents against executed replay (Haiyue Zhang)

Extendssuggestive Platform labour participation and the division of household labour: evidence from the 2023 Chinese Social Survey (Mingzhe Cui, Han Wang, Chang Li, Dingxuan Wang, Yingying Zheng)

Newframework Beyond weaponized interdependence: the corporate chokehold in frontier semiconductor governance (Nik Hynek)

Extendsdescriptive CentaurBench: Benchmarking LLM capabilities on augmenting vs. automating real-world work tasks (Pattaraphon Kenny Wongchamcharoen, Kris Gulati, Min Min Fong, Abhishek Nagaraj)

Extendssuggestive A jagged frontier: Evaluating robustness of code agents to semantics-preserving transformations (Hasan Najib Mahmud, Shreya Gupta, Isha Chaudhary, Nathaniel Enis, Ravi Mangal, Gagandeep Singh, Corina Pasareanu)

Extendsdescriptive StagedWorkspace: A versioned workspace for knowledge-work agents (Yining Hua, Hongbin Na, Yifan Zhou, Akshay Kalose, Cyrus Ayubcha, Levi Lian)

Confirmsdescriptive ASI-Bench: At the dawn of artificial superintelligence (Junwei Zhou et al.)

Extendssuggestive Institutional empowerment: How national pilot zones for innovative development of artificial intelligence affect corporate ESG performance? (Kunzhe Yuan)

Newdescriptive China's diffusion-forward AI strategy: The “AI race” in political economic context (Hao Chen, Meg Rithmire)

Concentration dynamics in recommendations: Against the long-running worry that recommenders amplify superstars, the 8.56M-user Netflix RCT shows diffusion toward the middle tail with higher engagement. Relative to earlier mixed correlational evidence, this is a clean causal push toward lower concentration when quality improves.

Intermediary traffic under AI-answer search: A preregistered RCT quantifies a large drop in outbound clicks and small UX declines with AI answers, strengthening the case that on-platform synthesis reallocates rents away from publishers without clear user-perceived gains in this setting. This contrasts with platform claims of improved satisfaction and pushes the debate toward quantifying publisher compensation mechanisms.

Task reallocation in finance analytics: Quasi-experimental evidence from US 10-K cycles and bank AI investments extends augmentation findings to sell-side analysts, with faster, bolder, more accurate forecasts and expanded coverage. This shifts weight toward AI complementing expert judgment rather than replacing it in information-intensive roles.

Equity via name inference: Experimental and observational results show a legibility gap that advantages Western-signaling names, challenging the idea that name-based gender interventions benefit women uniformly. This narrows the scope of evidence for name-inference benefits and spotlights cross-cultural blind spots in citation and recognition tools.

Methods and measurement: A general caution on event-time designs argues self-timed AI events can mechanically produce post-event lifts, implying some past click- or engagement-based evaluations may overstate treatment effects. Editors' inference: expect re-estimation and design changes in upcoming digital-product studies.

Do platform recommenders amplify or diffuse concentration?

Are AI-answers in search net beneficial for users and publishers?

Does AI augment or displace high-skill analysts?

Can name-based equity interventions deliver across cultures?

Do self-evolving agents net raise utility without raising risk?

Massive production holdback RCT (Netflix paper): An 8.56M-user, 60-day randomized holdback on a live platform provides rare, economy-relevant causal estimates of how recommender upgrades change engagement and concentration.

Repeated, state-diff grounded audits (One-shot audits paper): By replaying and diffing world states across runs, the method reveals stochastic, irreversible agent damage that single-run audits systematically miss.

Longitudinal upgrade durability benchmarking (UpgradeBench): Evaluating specialist adapters across actual base-model release sequences, not one-off hops, informs real upgrade-vs-retrain trade-offs practitioners face.

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