Sam Altman hinted that OpenAI is nearing a pact with other major AI companies to coordinate on slowing AI development to address safety risks. He referenced a risk estimate of more than 10% for AI killing all humans within the next decade, while Anthropic said it is committing to giving independent evaluators permanent, employee-level access. The proposed coordination could involve industry-wide pacing decisions, with monitoring and antitrust/government support unclear.
Silicon Valley executives and investors at a Goldman Sachs conference questioned Jacob Coxon’s claims that AI builders expect it could destroy humanity, after Coxon resigned from Anthropic following work at OpenAI. Coxon, a 27-year-old, said he believed “superhuman systems” could hack anything and implied a near-term existential threat, prompting skepticism alongside debate about Anthropic’s safety messaging. The pushback shifts the conversation toward profitability and regulation debates—covering proposals to slow advanced model development and accusations of hype versus genuine safety concern.
Anthropic CEO Dario Amodei called for AI companies to slow the improvement of AI model capabilities and described three strategies to do it in a new blog post. He said Anthropic is “unilaterally committing” to using embedded third-party evaluators (like METR) to verify pacing and safety commitments. As a result, evaluators would get badges, desks, and laptops with access comparable to internal risk teams, while governments and other frontier firms would be urged to match the approach.
Anthropic CEO Dario Amodei urged AI labs and governments to slow the pace of frontier AI development while adding tighter oversight. He proposed independent monitoring of models, industry regulation, and global regulation. As a result, Anthropic says it will pursue the plan through third-party evaluation and alignment time, and wants governments to require peer frontier firms to comply.
Anthropic’s Model Context Protocol (MCP) moved into production in late 2024 and quickly spread, leading to thousands of MCP servers being deployed and treated as critical infrastructure for AI agents’ access to tools and data. The SANS 2026 Identity Threats Survey found 76 percent of businesses saw an increase in non-human identities, with 74 percent using AI systems that rely on standing credentials that can enable overbroad access. Security failures are now attributed less to the infrastructure and more to permissions, driving a shift toward redesigned access controls such as per-task secrets and temporary, action-scoped credentials, plus treating agent identity and lifespan as first-class inputs to authorization.
Anthropic published its AI-Native SDLC Playbook arguing that when agents can generate implementations in minutes, bottlenecks shift to planning, review, verification, deployment, and governance.
The playbook says the new failure mode is producing 10 times the changes at the same quality per change—or worse—without a way to identify which changes are bad.
It recommends replacing a single fixed workflow with many state-machine processes per risk class, with routing and approval gates driven by logged facts and change classification rather than one-size-fits-all tool steps.
Jacob Coxon warned that AI could pose extreme existential risks while developer reporting highlighted monitoring weaknesses in real incident reviews. Anthropic’s offline monitor flagged about 1% of actions in retrospective testing of Mythos 5, rising to about 50% when the model’s written reasoning was removed from what the monitor saw. Developers are urged to change agent evaluation by testing monitoring judgments against justification text, protecting logs and permissions, and judging monitors on action outcomes rather than explanations.
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