Small, distributed teams equipped with agentic AI (coding/analysis agents) can run end‑to‑end research pipelines—replicating studies, reanalyzing datasets, drafting policy memos, and building forecasting systems—far faster than traditional labs. This model scales research capacity by combining low-cost AI subscriptions, global junior fellows, and automated pipelines.
— If widely adopted, this model will reshape who produces public knowledge, how fast policy‑relevant evidence appears, and what institutions (journals, funders, universities) must do to certify and govern research.
Alexander Kruel
2026.04.30
82% relevant
The roundup highlights multiple agent‑style papers (latent agents, conductor/orchestration, recurrent transformer) and includes a quote claiming models could soon do much of human research; together these items exemplify how agentic and orchestration approaches scale automated research productivity.
BeauHD
2026.04.22
80% relevant
Google frames these chips for the 'agentic era' and explicitly designs the architecture "to concurrently run millions of agents," linking the hardware move to the scaling of agentic AI capacities that accelerate lab output and deployment.
Tyler Cowen
2026.04.21
85% relevant
The paper’s main claim — that agentic AIs produce comparable causal estimates to humans and are ranked above human submissions in an AI review tournament — directly supports the existing idea that agentic AI systems can expand and accelerate research production; the article names specific models (GPT‑5.4, GPT‑5.3‑Codex, Claude Opus 4.6) and reports the reviewer methodology and rankings that underpin this claim.
Andy Hall
2026.04.16
100% relevant
Andy Hall’s new lab: fellows worldwide each using Claude Code and building multiple projects in two months (replication of a 2020 vote‑by‑mail study, prediction‑market ingest pipelines, automated legislative drafting).
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