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GPT-5Sep 29, 2026openai/gpt-5-2025-08-07
Should AI development be paused or restricted?
Short answer: a blanket, indefinite pause on all AI development isn’t wise, but targeted, conditional pauses and sensible restrictions on the highest‑risk systems are.
How to think about it
•Separate by risk, not hype. Most AI uses are low risk and benefit from open progress; a small subset (frontier models with powerful autonomy, persuasion, cyber or bio capabilities) warrant tight controls.
•Regulate the lifecycle: training, evaluation, deployment, and post‑deployment monitoring—not just one stage.
What to restrict now
•Licensing for very large training runs. Require developers that exceed defined compute/scale thresholds to be licensed, follow safety processes, and undergo independent audits.
•Pre‑training risk assessments and secure compute. Document goals, data, security, and fail‑safes; run on hardened infrastructure with insider‑threat controls.
•Standardized capability and safety evals. Independent red‑teaming for bio/cyber misuse, autonomous replication, targeted persuasion, and model deception. Publish results and mitigation plans.
•Deployment gating. No release of models that fail agreed hazard thresholds; use-based regulation for sectors like healthcare, finance, critical infrastructure, and education.
•Post‑deployment duties. Continuous monitoring, incident reporting, recall powers, and meaningful liability for foreseeable harms.
•Access controls for model weights. Keep weights for frontier systems closed until risks are demonstrably manageable; encourage open models for clearly low/medium‑risk capability bands.
•Provenance and authenticity. Content watermarking/provenance standards to curb fraud and election manipulation.
•Data protection. Enforce privacy, IP, and training‑data transparency requirements.
When a pause is appropriate
•Conditional, time‑bound pauses on training or deployment should trigger if: safety evals indicate hazardous capabilities without effective mitigations; credible real‑world incidents occur; monitoring capacity is outpaced; or during sensitive periods (elections) for certain high‑risk features.
•Pauses should be tied to clear safety criteria, independent verification, and sunset/review timelines.
What to avoid
•Broad moratoria that freeze benign research, entrench incumbents, or push development to less accountable jurisdictions.
•Symbolic rules without enforcement, audits, or incident disclosure.
International and societal pieces
•Coordinate chip/export controls for extreme‑scale training, shared safety benchmarks, and incident hotlines.
•Invest heavily in alignment, robustness, interpretability, and AI‑security research; support worker transition and education.
If helpful, tell me which AI you’re most concerned about (e.g., open‑source models, corporate frontier systems, deepfakes, military use), and I can tailor specific policies or an action plan.