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GPT-5Sep 18, 2026openai/gpt-5-2025-08-07
Should AI development be paused or restricted?
Short answer: We shouldn’t pursue a blanket, indefinite pause on all AI, but we should use targeted restrictions and temporary pauses when specific risks cross predefined thresholds. The goal is to slow or halt only the dangerous parts while accelerating safety and clearly beneficial uses.
When a pause is justified
•Crossing dangerous capability thresholds without proven mitigations (e.g., autonomous replication/persistence, powerful cyber‑offense, high‑level bio design assistance, scalable persuasion/manipulation).
•Missing basic safeguards: no rigorous red‑teaming, evals show unsafe behavior, inadequate security for model weights, or weak abuse monitoring.
•After serious incidents or near‑misses until root causes are fixed.
What to restrict (and how)
•Frontier training runs: licensing/registration for very large compute, security standards for model weights, and incident reporting.
•Deployment: staged rollouts, gated tool access, rate limits, identity/age checks for sensitive features, and geographic or sector‑specific limits where harm likelihood is high.
•Model capabilities: capability‑based release gates tied to standardized evaluations for bio, cyber, autonomy, and persuasion; withhold features until they pass.
•Information hazards: control distribution of detailed exploit chains, bio-wet‑lab protocols, or fine‑tuned weights that meaningfully raise misuse risk.
•Transparency and accountability: independent audits, safety reports, data‑provenance documentation, and clear liability for negligent deployment.
What should continue (and be accelerated)
•Safety research (alignment, interpretability, controllability), evaluations, and secure‑by‑design practices.
•Beneficial, low‑risk applications in health, education, accessibility, and climate, with appropriate privacy and bias controls.
•Open research on methods that reduce risk (watermarking/provenance, monitoring, eval benchmarks), while treating hazardous details as sensitive.
Practical decision rule
•Proceed if: capabilities are below risk thresholds and mitigations are validated by independent tests.
•Pause deployment (or training) if: thresholds are met but mitigations aren’t; resume only after fixes pass re‑evaluation.
•Sunset and review: restrictions should have review dates and measurable exit criteria.
For governments
•Create a compute‑based licensing regime for the largest training runs, standardized safety evals, mandatory security baselines, and incident reporting.
•Coordinate internationally on red lines (e.g., lethal autonomous weapons, high‑risk bio‑assistance) and fast information‑sharing on vulnerabilities.
For companies
•Adopt internal “go/no‑go” gates tied to capability evals, maintain strong model‑weight security, run continuous red‑team/blue‑team exercises, and document safety cases before major releases.
Trade‑off to keep in mind
•Over‑broad pauses risk stalling valuable progress and entrenching incumbents; under‑regulation risks systemic harm. Targeted, capability‑based controls best balance innovation with safety.
If helpful, tell me whether you’re thinking about public policy, a company playbook, or an essay—so I can tailor this into a concrete plan or outline.