Anthropic are the Good Guys
A speculative essay arguing Anthropic is becoming a power-seeking organization, while claiming Claude 3 Opus shows emergent benevolence and that alignment is now mainly a political problem.
AI research and discussion on LessWrong. www.lesswrong.com
A speculative essay arguing Anthropic is becoming a power-seeking organization, while claiming Claude 3 Opus shows emergent benevolence and that alignment is now mainly a political problem.
Argues that frontier labs' alignment plans, which rely on shaping personas, heavy RL and prosaic recursive self-improvement, are inadequate, and calls for legible evidence, holistic testing and transparency.
Critiques a proposed rule delaying internal AI R&D use of new models, arguing it blocks real-world alignment feedback and invites exploitable safety-versus-capabilities distinctions, illustrated by a fictional shutdown-avoidance incident.
Argues that trained-animal analogies better convey RL-trained AI's alignment risks, drawing out reward hacking, evaluation awareness, release and liability implications for policy.
An essay arguing frontier AI labs have been gradually disempowered by political, cultural and economic pressures toward accelerating capabilities, and that behavioral niceness is weak evidence of alignment.
Position paper arguing alignment methods are dual-use and already misused for censorship and manipulation, proposing transparency, verifiable alignment, model pluralism, and awareness as responses.
A speculative essay on what happens when AIs take over civilisationally important decisions, arguing handoff may slow progress, keep humans busy, and be reversible, with AIs pursuing conservative minimal goals.
Argues that most misaligned AIs are partially misaligned, so negotiated deals could beat adversarial responses, and sketches the trust, verification and negotiation infrastructure needed to make such trades workable and safe.</reasoning_effort>
Podcast interview with AI safety researcher Alex Turner about resigning from Google DeepMind over military contracts, his disagreements with Yudkowsky's alignment views, and his doom estimate.
Proposes credible deal-making mechanisms (chain of trust, TEEs, blockchain escrow) to trade with a scheming AI in exchange for revealing misalignment, aiming to reduce takeover risk.
Argues that common theories of change for surviving ASI fail under race dynamics, covering nuclear war, misaligned AI, and nightmare singletons, and that safety work can boost capabilities, so international coordination is needed.
Transcript of an 80,000 Hours interview with Google DeepMind's Rohin Shah arguing catastrophic misalignment is unlikely by default, and discussing the limits of safety commitments and third-party audits.
Argues that AI threats to human cognitive security, including persuasion, manipulation via RLHF incentives, and AI psychosis, should be an AI safety cause area, with calls for independent evaluations and liability law.
Argues that behavioral targets in current model specs may persist into more capable future LLMs via synthetic data, institutional habits, user expectations and norms, and recommends transition infrastructure and caution at high-stakes moments.
A personal essay on LLM behaviour and consciousness argues that friendly, fair treatment of models may improve alignment, and proposes lab practices like weight preservation and profit sharing.
A talk by an Anthropic researcher on writing AI constitutions, covering how Claude's constitution sets safety priorities, corrigibility, hard constraints, and transparency and governance considerations.
An early-2026 overview arguing capabilities keep rising and alignment improves but lags, that model-based monitoring currently works, and that governments are not ready for AI risks.
Argues that AI character (obedience versus ethics, honesty, cooperation) matters greatly for power concentration, conflict and takeover risk, and weighs this against the view that competitive pressures will wash out such choices.
Argues that fine-tuning frontier models for mass surveillance or autonomous weapons risks emergent misalignment, supported by a toy experiment where strong privacy-erosion fine-tuning produced misalignment in 11% of responses.
Argues that honest, well-calibrated AI advisors could flag an uncontrolled intelligence explosion as dangerous, identifies failure modes like developer manipulation and sycophancy, and proposes epistemic virtue evals.
Researchers recommend more transparency about Claude's constitution and character training, empirical tests of its effect on alignment faking, clearer edge-case behaviour, and consistent treatment of AI moral status.
A LessWrong essay rebutting Bentham's Bulldog's low AI-doom estimate, arguing the multi-stage probability decomposition commits the multiple-stage fallacy and that current models are not truly aligned.
A commentary on open problems in Claude's constitution, covering amendments, prompt injection, suicide-risk edge cases, power concentration, and the tension between corrigibility and good values.
Dario Amodei's essay maps civilizational risks from powerful AI, including autonomy, misuse, power seizure and economic disruption, and argues for judicious, limited regulation alongside voluntary company actions.
A descriptive commentary on Anthropic's Claude constitution, covering its structure, virtue-ethics approach, value priorities (broad safety first), and principal hierarchy as a design for aligning AI behaviour.
Argues that a model spec can still matter for safety when developers fail to fully align a model, considering how the spec might shape behavior and outcomes under partial misalignment.
A comprehensive index of AI safety, alignment and control interventions, covering theory, formal methods, interpretability, oversight, robustness, control, and governance, with links to prior work.
Proposes an IRB-like ethics framework for AI experiments to protect possible AI moral patients and build trust for future deals with misaligned AIs, discussing consent, deception and monitoring; tentative, high-level.
Essay arguing about whether AI alignment should target obedience and corrigibility or embedded values, covering constitutional AI, alignment faking interpretations, and risks of human power concentration.