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GPT-5Oct 10, 2026openai/gpt-5-2025-08-07
Should AI development be paused or restricted?
Short answer: A blanket, time-based “pause” on all AI development is neither practical nor desirable. But targeted restrictions, conditional pauses, and stronger safety gates for the highest‑risk work are warranted.
A sensible approach
•Risk‑tiered governance: Light requirements for low‑risk applications; stringent controls for “frontier” systems with capabilities that could cause systemic harm.
•Conditional pauses (“circuit breakers”): Temporarily halt training or deployment when predefined triggers are met—for example, evaluations indicate abilities in:
•Enabling creation or acquisition of biological/chemical agents
•High‑end cyber intrusion or rapid discovery of zero‑days
•Scalable manipulation/persuasion or autonomous replication across networks
•Bypassing safety controls or evading oversight
•Licensing and audits for frontier labs: If a training run exceeds a compute or capability threshold, require registration, third‑party red‑teaming, safety cases, and approval before release.
•Pre‑deployment evaluations: Standardized, independent capability and misuse testing; publish model/system cards and residual‑risk rationales.
•Deployment safeguards: Rate limits, identity and use‑case verification for sensitive tools, sandboxing, kill‑switches, continuous monitoring, incident reporting, and recall authority.
•Content provenance: Robust watermarking or cryptographic provenance to reduce AI‑driven disinformation.
•Data and privacy controls: Strict handling of sensitive data; opt‑outs; secure fine‑tuning to prevent leakage and misuse.
•Liability and insurance: Clear responsibility for harms, with penalties for negligent deployment; require safety budgets proportional to risk.
•Open‑source considerations: Staged releases (research previews, then weights with gating), use of responsible AI licenses, and withholding dangerous capabilities until mitigations exist.
•International coordination: Interoperable standards so safety isn’t undercut by regulatory arbitrage; export and compute controls focused on the highest‑risk capabilities.
Why not a blanket pause?
•Enforcement and coordination problems across borders
•Opportunity costs: lost health, education, climate, and productivity benefits
•Chilling effects on open research and smaller innovators, entrenching incumbents
•Risk doesn’t stop evolving; better to build and practice the governance we’ll need
When should we actually stop something?
•If evaluations show credible catastrophic misuse potential and mitigations are not yet available
•After serious incidents until root causes are fixed (post‑incident moratoriums)
•When a developer cannot produce a convincing safety case for a high‑risk deployment
If you want, tell me your context (policy debate, company governance, academic work, or personal view), and I can tailor a concrete set of recommendations, examples, and sample “triggers” appropriate to your setting.