Sam Altman on OpenAI, Government and AI Infrastructure (X) (twitter.com)

I couldn’t retrieve the X thread because the page content was blocked (JavaScript disabled). Please enable JavaScript or paste the thread text; below is a provisional summary of the kinds of points Sam Altman typically raises on OpenAI, government, and infrastructure so you know what to expect. Provisional summary: Altman usually frames regulation and public engagement as complementary to technical safety: he advocates targeted government standards for high-risk systems, transparency around deployment, and regulatory sandboxes that let researchers test safeguards without stifling innovation. On infrastructure, he stresses the need for resilient, secure compute—more efficient model architectures, tighter hardware–software co-design, supply-chain redundancy for GPUs/accelerators, and investment in data-center capacity and networking to support large-scale training and inference. Technical implications include stronger model auditing and provenance, standardized APIs for safer third-party integrations, emphasis on cost/energy efficiency to lower barriers to experimentation, and industry–government collaboration on monitoring and incident response. If you paste the thread or enable access, I’ll produce a precise 2‑3 paragraph summary that quotes key lines and highlights exact technical takeaways.