Could AI Slow Science? Confronting the Production-Progress Paradox
A commentary piece from AI Snake Oil explores the potential paradox whereby AI tools increase scientific output volume while simultaneously slowing genuine scientific progress. The piece examines how AI-assisted research production may prioritize quantity over quality, potentially crowding out deeper, slower-moving inquiry. This raises structural concerns about how AI integration into research workflows could reshape the incentive landscape of science.
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Is AI Progress Slowing Down?
A commentary piece from the AI Snake Oil newsletter examines recent claims and trends around whether AI progress is decelerating. The article appears to analyze the evidence for and against a slowdown in frontier AI development. As a tier-2 commentary source, it likely synthesizes public signals rather than presenting original research.
Scientists should use AI as a tool, not an oracle
This commentary critiques the feedback loop between AI hype and scientific research, arguing that scientists who treat AI systems as oracles rather than tools produce flawed research that in turn amplifies further hype. The piece examines how uncritical adoption of AI in scientific workflows can compromise research integrity. It calls for a more epistemically disciplined approach to AI use in science.
Simon Willison on the asymmetric time pressures facing AI enthusiasts vs. skeptics
Simon Willison publishes a commentary framing the AI debate as two groups facing different temporal pressures: enthusiasts racing against time to realize transformative potential before momentum stalls, and skeptics racing against entropy as AI systems proliferate and become harder to constrain. The piece is an opinion/strategy essay from a respected practitioner voice. It contributes to ongoing discourse about AI trajectories and the structural dynamics of the optimist-pessimist divide.
Your AI Use Is Breaking My Brain
Simon Willison comments on the phenomenon of AI-generated or AI-assisted content degrading the quality of online discourse and information environments. The piece reflects on how widespread AI use is affecting the experience of consuming internet content. This is a commentary piece from a prominent developer/blogger on the social and epistemic effects of AI proliferation.
Fact checking Moravec's Paradox
A commentary piece from normaltech.ai argues that Moravec's paradox — the observation that tasks easy for humans are hard for AI and vice versa — is neither empirically accurate nor conceptually useful. The piece appears to challenge a foundational heuristic that has shaped AI capability expectations for decades. Given recent advances in robotics, vision, and language models, the argument likely draws on contemporary evidence to reframe how practitioners should think about AI difficulty gradients.
Real AI Agents and Real Work
A commentary piece from One Useful Thing examining the practical deployment of AI agents in real work contexts, framing the tension between human-centered work and AI-generated productivity outputs. The piece appears to analyze how autonomous AI agents are changing knowledge work workflows. Published by a Tier 2 source known for applied AI analysis aimed at practitioners and researchers.
AI Scaling Myths
A commentary piece from normaltech.ai argues that AI scaling will eventually hit limits, framing the debate as a question of timing rather than whether limits exist. The piece appears to challenge prevailing optimism around continued scaling returns. Given the minimal body text, the depth of argument is unclear, but the topic directly engages the scaling laws debate central to frontier AI development.
Why AI hasn't replaced software engineers, and won't
A commentary piece from the AI Snake Oil / Normal Tech newsletter argues that coding agents should be understood as normal technology rather than transformative replacements for software engineers. The piece examines why AI has not displaced software engineering roles despite significant capability advances. This is a skeptical industry analysis relevant to ongoing debates about AI's impact on software development labor.