The snake eating its own tail effect of AI agents isn’t so much a problem of training data model collapse as the collapse of the social epistemic foundations by which human sense-making can be carried out at all—and which AI agents *also* rely on for whatever facsimile of sense-making they conduct.
Ryan McGrady
The original: arstechnica.com/ai/2026/02/after-a-routine-co… and the context: theshamblog.com/an-ai-agent-published-a-hit-p… I hope they elaborate on the retraction, too, though I'm glad they acted quickly to take it down (and hope they put more effort into covering this terrifying story, since it's in their wheelhouse)
Feb 15, 2026 at 10:19 PM
The AI feedback loop: Researchers warn of 'model collapse' as AI trains on AI-generated content
As a generative AI training model is exposed to more AI-generated data, it performs worse, producing more errors, leading to model collapse.
AI, Human Cognition and Knowledge Collapse
We study how generative AI, and in particular agentic AI, shapes human learning incentives and the long-run evolution of society’s information ecosystem. We bui
AI, Human Cognition and Knowledge Collapse
We study how generative AI, and in particular agentic AI, shapes human learning incentives and the long-run evolution of society’s information ecosystem. We bui
Agentic AI gets lost
On the failure of AI to develop world models
AI FOR EPISTEMICS & COORDINATION
Civilization and technology have radically improved the human condition. Nonetheless, the world sometimes goes in directions which essentially nobody would prefer — e.g., nuclear arms races, unexpected financial crashes, predatory marketing, or ubiquitous political misinformation.
Building and evaluating AI Agents — Sayash Kapoor, AI Snake Oil
[Keynote 04] AgentSociety: Exploring Large Language Model Agents for Piloting Social Experiments
Cosmik Updates: February 2026 - Cosmik Labs
@atproto.science @cosmik.network Raising a question for the ATProto science community: Can AI agents be legitimate participants in research ecosystems? What would make their outputs trustworthy?
Position: Don't Just "Fix it in Post": A Science of AI Must Study Training Dynamics
What would it mean to have a scientific understanding of AI? Models are not static objects: they are snapshots of time-evolving processes shaped by data, objectives, architectures, and optimization dynamics. Yet much of AI research treats models as fixed artifacts, analyzing behaviors after training rather than asking why they emerge. This position paper argues that a science of AI must move beyond post-hoc fixes and study the training dynamics that produce model behavior. Such a science should support progressively stronger forms of understanding: predicting outcomes from early training signals, intervening when trajectories go wrong, and ultimately designing training procedures that more reliably produce desired properties. Scaling laws have made prediction routine for loss; the challenge is extending this success to capabilities, biases, robustness, and safety-relevant behaviors. We articulate requirements for such theories grounded in the history and philosophy of science, examine progress in mechanistic interpretability, fairness, memorization, and simplicity bias, and identify concrete open problems.
Anthropic model takedown fuels warning of ‘ad hoc’ AI regulation
The Trump administration is coming under fire for a directive prompting Anthropic to pull its latest models, and artificial intelligence policy advocates warn the move signals the White House is ta…
AI Epistemic Risks: Emerging Mechanisms & Evidence
<p>Advances in artificial intelligence pose risks to humanity's collective capacity to form accurate beliefs, reason well, and maintain a healthy information en
A Rational Analysis of the Effects of Sycophantic AI
People increasingly use large language models (LLMs) to explore ideas, gather information, and make sense of the world. In these interactions, they encounter agents that are overly agreeable. We...
Agentic AI and the next intelligence explosion
For decades, the artificial intelligence (AI) “singularity” has been heralded as a single, titanic mind bootstrapping itself to godlike intelligence, consolidating all cognition into a cold silicon point. But this vision is almost certainly wrong in its most fundamental assumption. If AI development follows the path of previous major evolutionary transitions or “intelligence explosions,” our current step-change in computational intelligence will be plural, social, and deeply entangled with its forebears (us!).
"Powerful AI can statically help human decision-makers, but can harm collective knowledge building... it can lead to what we call “knowledge collapse” whereby in the long-run all human knowledge is ultimately destroyed.” economics.mit.edu/sites/default/files/2026-02/A…
