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GPT-5Oct 27, 2026openai/gpt-5-2025-08-07
Should AI development be paused or restricted?
Leans yes
Short answer: We shouldn’t pause all AI development, but we should place firm, enforceable restrictions—and be ready to pause specific high‑risk work when safety criteria aren’t met.
Why not a blanket pause
•Would also halt safety research and beneficial uses (medicine, accessibility, climate).
•Hard to enforce globally; risks pushing activity underground or to less‑regulated jurisdictions.
•Concentrates power in a few incumbents.
Why targeted restrictions (and conditional pauses) make sense
•Some capabilities pose outsized, systemic risks (bio/cyber offense, scalable manipulation, autonomous replication).
•Externalities (misuse, labor shocks, disinformation) aren’t priced into private incentives.
•Gives space for governance, standards, and safety tooling to catch up.
A practical policy mix
•Capability‑tiered triggers: If a model crosses preset thresholds (e.g., compute scale; evals showing bio/cyber weaponization assistance; autonomous tool‑use), then:
•Pre‑registration of training runs and safety plans.
•Licenses for “frontier” training and deployment.
•Independent red‑teaming and third‑party audits before release.
•Strict incident response, model cards, and safety reports.
•Release controls:
•No open release of weights for models that meet “dangerous capability” thresholds; offer API access with rate limits, monitoring, and tool restrictions instead.
•Staged release with gated access for researchers under codes of conduct.
•Containment and evaluation:
•Sandbox high‑risk systems; limit tools (code exec, bio/chem design, exploit kits) until they pass adversarial evaluations.
•Continuous post‑deployment monitoring, logging, and rapid “model recall” mechanisms.
•Compute and supply‑chain governance:
•Track large training runs via chip/cloud attestation; require reporting for runs above set FLOP thresholds.
•Export controls and cloud‑based enforcement for advanced AI chips.
•Data, security, and provenance:
•Lawful data sourcing; privacy safeguards; robust cybersecurity; content provenance/watermarking for AI outputs.
•Accountability:
•Clear liability for negligent deployment; mandatory insurance for frontier providers; whistleblower protections.
•International coordination:
•Interoperable standards, joint evals, and information‑sharing; narrow treaties on the most dangerous capabilities.
What to pause or block now
•Any training run or release that fails dangerous‑capability evals or lacks a credible safety case.
•Weight releases that would materially enable serious misuse.
•Deployments into critical infrastructure without domain‑specific certification.
What to accelerate
•Alignment, interpretability, and eval research; defensive cybersecurity; red‑team capacity; open safety tools; public‑interest compute for auditors.
If you want, I can tailor this into a concrete policy for your org or a one‑page brief for policymakers.