Sam Altman isn’t exactly asking everyone to relax about AI. But the OpenAI CEO is making a fairly blunt argument about where society should draw the line: some bad things are going to happen, and the benefits of AI could still make accepting those risks worthwhile.
In a new interview with POLITICO’s Decoded, Altman highlighted a fundamental difference between OpenAI and rival Anthropic over how AI should be regulated. Asked where he disagrees with Anthropic CEO Dario Amodei, Altman said:
“we believe that the world should accept some bad things happening for the benefits of this technology and people having the agency.”
The argument isn’t that AI should become a free-for-all. Altman said OpenAI supports tougher regulations in some areas and agreed with Amodei’s recent call to slow the development of the most advanced AI systems. However, he favors what he describes as a lighter-touch approach, particularly when regulation could restrict people’s ability to use increasingly capable AI tools.
That’s where the disagreement gets interesting. Anthropic has generally taken a more cautious position on AI regulation, while OpenAI has increasingly moved toward supporting safety requirements as concerns around increasingly autonomous AI systems have grown. Altman nevertheless appears unwilling to accept a future where control over powerful AI is concentrated among a small number of organisations or governments.
Altman isn’t arguing that AI should get a free pass. He draws the line at “the really catastrophic risks”, while betting that people will ultimately do “orders of magnitude more good stuff than bad stuff” with the technology. That becomes a particularly important distinction as AI moves beyond chatbots and starts taking real-world actions, where even relatively small failures can have much bigger consequences.
For Altman, the choice isn’t AI at any cost or slam the brakes entirely. It’s about keeping people in control, accepting that some things will inevitably go wrong, and making sure the benefits aren’t locked away from everyone except a handful of gatekeepers. Whether that’s a sensible trade-off or a dangerously optimistic one may be one of the defining arguments of the AI era.
AI companies spend enormous amounts of time convincing us their technology is safe and secure. But what happens when those same companies become targets of increasingly sophisticated AI-powered attacks? Anthropic, the company behind Claude, is now facing that question. According to a Washington Post report, cybersecurity startup Cathedral discovered vulnerabilities that let its researchers access sensitive information in Anthropic's internal systems.
The researchers used AI agents to identify potential entry points in less than 12 hours. Interestingly, they didn't need to crack Claude itself. Instead, they exploited weaknesses in third-party business applications that Anthropic uses for everyday operations. Anthropic's security testing program disclosed the findings, which allows outside researchers to identify and report vulnerabilities.
Military working dogs charge into places the rest of us would flee. So, I understand why anyone would want to breed tougher, healthier dogs for the job.
Warhound, a new startup from Colossal Biosciences co-founder Ben Lamm and Anduril co-founder Palmer Luckey, has stepped out of stealth with $200 million to clone and gene-edit working dogs for American troops, police departments, and emergency services (via TechCrunch).
AI can describe a picture, identify objects, and answer questions about what a camera sees. But when a blind user needs a quick answer, getting an entire spoken essay about the scene isn't exactly helpful. Researchers at the University of Maryland (UMD) have found that AI-powered visual assistance can leave blind users doing too much work to get the information they actually need.
The study, titled Say It My Way: Exploring Control in Conversational Visual Question Answering with Blind Users, examined how people interact with Be My AI, an AI-powered feature of the Be My Eyes app. The researchers found that users often received lengthy descriptions that didn't adequately address their immediate goals, forcing them to ask follow-up questions or find ways to steer the AI towards a more useful answer.