Can you recommend AI safety research blogs and newsletters for a graduate student learning emerging risks and mitigations?

Absolutely — here’s a curated list of AI safety / AI governance research blogs and newsletters that are especially useful for a graduate student trying to understand emerging risks, threat models, and mitigations.

High-signal blogs / research hubs

1. AI Alignment Forum

• Why follow: One of the most important places for technical AI safety discussion.
• Best for: Alignment theory, agent foundations, interpretability, scalable oversight, corrigibility.
• Style: Research-y, often dense, but very high value.
• Link: https://www.alignmentforum.org

2. LessWrong

• Why follow: Broader than pure AI safety, but many key discussions on alignment, risk, and reasoning show up here.
• Best for: Conceptual AI safety, rationality, governance-adjacent arguments, forecasting.
• Style: Essays and discussion posts, variable depth.
• Link: https://www.lesswrong.com

3. Distill (archived)

• Why follow: Historically excellent for mechanistic interpretability and ML concepts explained clearly.
• Best for: Understanding how neural networks work and why interpretability matters.
• Note: No longer actively publishing, but still worth reading.
• Link: https://distill.pub

4. Redwood Research blog

• Why follow: Strong technical safety work, often on training methods, oversight, and evals.
• Best for: Scalable oversight, model evaluation, training against deception, adversarial robustness.
• Link: https://redwoodresearch.org/blog

5. Anthropic research / engineering blog

• Why follow: Frequent publication on safety, interpretability, evaluations, and alignment-related methods.
• Best for: Frontier-model safety, red-teaming, Constitutional AI, model behavior studies.
• Link: https://www.anthropic.com/research

6. DeepMind / Google DeepMind safety research

• Why follow: Frontier-lab perspective with work on robustness, interpretability, agentic risks, and safety evals.
• Best for: Technical and governance-facing frontier risk research.
• Link: https://deepmind.google/research/

7. OpenAI research and preparedness updates

• Why follow: Useful for understanding frontier-model risk framing, preparedness, and evaluation practice.
• Best for: Model capability risk, evals, policy-process intersections.
• Link: https://openai.com/research

8. ARC Evals / METR

• Why follow: Focus on evaluating dangerous capabilities and assessing frontier-model risk.
• Best for: Concrete mitigation/testing approaches and eval design.
• Links:
  • ARC Evals: https://evals.alignment.org
  • METR: https://metr.org/blog

9. NIST AI / AI safety-related publications

• Why follow: More policy/standards oriented, but excellent for risk management and evaluation framing.
• Best for: Risk taxonomies, measurement, standards, governance.
• Link: https://www.nist.gov/ai

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Newsletters worth subscribing to

1. The Gradient

• Why follow: Good mix of technical ML and AI-safety-relevant discussion.
• Best for: Surveys, essays, emerging technical and societal issues.
• Link: https://thegradient.pub

2. Import AI

• Why follow: One of the best historically for AI policy, strategy, and safety news.
• Best for: Governance, labs, regulation, frontier model developments.
• Note: Check current publication status/archives if needed.
• Link: https://jack-clark.net

3. The Batch by Andrew Ng

• Why follow: Less AI-safety focused, but useful for keeping up with the field’s direction and deployments.
• Best for: Broad AI developments and practical context.
• Link: https://www.deeplearning.ai/the-batch/

4. State of AI Report / newsletter

• Why follow: Excellent annual and newsletter-style updates on trends in AI capabilities, industry, and safety.
• Best for: Big-picture awareness of the field.
• Link: https://www.stateof.ai

5. Cold Takes

• Why follow: Forecasting and AI policy commentary; useful for strategic thinking.
• Best for: AI governance, risk assessment, industry trends.
• Link: https://www.cold-takes.com

6. 80,000 Hours AI newsletter

• Why follow: Helpful for structured overviews of AI safety, career paths, and key issues.
• Best for: High-level orientation and links to deeper resources.
• Link: https://80000hours.org/tag/artificial-intelligence/

7. AI Impacts

• Why follow: Research-oriented updates on AI timelines, risks, and governance.
• Best for: Surveys, expert elicitation, risk framing.
• Link: https://aiimpacts.org

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If you want to focus on “emerging risks and mitigations”

A good reading stack would be:

Technical risk / mitigation

• Alignment Forum
• Redwood Research blog
• Anthropic research
• METR blog
• Distill archive

Governance / frontier-risk monitoring

• Import AI archive/newsletter
• NIST AI publications
• Cold Takes
• AI Impacts
• State of AI

Broad synthesis / accessibility

• The Gradient
• 80,000 Hours AI pages/newsletter
• LessWrong (selectively)

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Suggested approach for a graduate student

If you’re just starting, I’d recommend:

1. Subscribe to 3–5 newsletters rather than many.
2. Pick one technical blog and one governance blog to read consistently.
3. Track recurring themes, such as:
   • deception / situational awareness
   • evals for dangerous capabilities
   • interpretability
   • scalable oversight
   • robustness / adversarial behavior
   • model theft / misuse
   • autonomous agents and tool use
   • policy and standards for frontier deployment

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A compact starter bundle

If you want the shortest possible high-quality set, I’d start with:

• Alignment Forum
• Anthropic research blog
• Redwood Research blog
• METR blog
• The Gradient
• AI Impacts
• Import AI / archives

If you want, I can also give you:

1. a ranked list by technical depth,
2. a beginner-friendly reading order, or
3. a list focused specifically on governance/policy vs technical alignment.