Category: AI News
-
Connor Leahy argues that advanced AI safety is a question of public accountability, not just engineering. His case raises difficult questions about control, corporate incentives and where regulation should begin.
-
AI depends on a healthy supply of reliable information. Model collapse, copyright disputes and pressure on publishers raise a shared question: who will pay to keep producing it?
-
GPU sales measure hardware purchases—not working data centers or profitable AI services. The gap between those milestones is where power constraints, debt and aging equipment can turn expansion into financial risk.
-
Informal discussions about buying data from struggling or closed startups point to a potential AI resource—but ownership, privacy and usefulness could complicate any deal.
-
Chip sales and data-center announcements do not tell us how much usable AI computing capacity exists. The gap matters for investors—and for judging whether the infrastructure boom is sustainable.
-
A bipartisan proposal would require powerful AI systems to have emergency controls. The challenge is making those controls effective, safe and enforceable—not just available on paper.
-
Competition between the US and China makes AI cooperation difficult—but more necessary. The challenge is to protect useful innovation while setting enforceable limits on dangerous capabilities.
-
An AI chatbot is not a legal person, but its developers and operators can face scrutiny when conversations precede real-world harm. The central questions are about duty, design, knowledge and causation.
-
A former Google whistleblower argues that AI safety should rest on verifiable rules, not corporate promises. Tracking the computing power behind advanced models could help—but it is no simple fix.
-
The AI policy debate is not just about partisan gridlock. Disputes over competitiveness, overlapping committee responsibilities and the role of states make a federal agreement harder to reach.