The Race for AI Supremacy Raises Safety Concerns
THE SO WHAT
When a handful of labs and executives drive toward AGI, safety becomes a governance and concentration question, not just a technical one. Boards relying on these systems should be asking vendors for concrete safety practices, escalation paths, and external oversight—not just capability roadmaps.
READ THE SOURCE
MORE FROM THE WIRE
Applied AIUK can’t rely on AI that could be switched off, Turing chief tells Guardian
Sovereign AI is moving from talking point to design constraint for national labs and critical industries. If you sell into UK public or regulated sectors, expect procurement to start asking where models run, who can turn them off, and how inspectable they are.
Applied AIDoes a VPN protect your privacy when using ChatGPT or Claude? What network encryption can and can’t hide from AI
VPNs hide your location, not what you type into AI tools. If staff are pasting sensitive data into assistants, you need data governance and DLP at the app and API layer, not just another network tunnel.
Applied AIAnthropic is cutting off its internal evaluations from the internet
Cutting eval models off the open internet is a quiet admission that red-teaming frontier systems now carries real-world risk, not just bad PR. If you’re running agentic evals or sandbox tests, treat them like live-fire exercises—segmented networks, synthetic environments, and clear incident playbooks, not just a staging cluster.
Applied AITrump’s AI Liability Push Opens Blame Game for Models Gone Rogue
A liability regime that can pin blame on either model builders or deployers turns every AI integration into a legal allocation problem. If you’re shipping AI into customer-facing workflows, you need explicit contracts on responsibility, logged decision trails, and insurance conversations now—not after the first lawsuit.