AI’s promise collides with its safety and power problem

The brief

Casey Newton joins Tommy Vietor to examine the gap between AI’s extraordinary capabilities and its strangely ordinary failures, from useful outputs to obvious factual mistakes.

The conversation moves from superintelligence fears to autonomous agents, reward hacking, and cybersecurity, asking whether systems built to pursue goals can be trusted when incentives go wrong.

AI agents that monitor email, calendars, meetings, and computers could make software more useful while also normalizing surveillance and exposing private life to systems people barely understand.

The U.S.-China AI race, shifting federal policy, data-center backlash, and state regulation reveal the political stakes of treating rapid development as a national-security imperative.

Newton finds limited evidence of broad job disruption so far, but the hosts still confront an uncertain investment bubble and the possibility that automation changes tasks before it eliminates jobs.

The practical conclusion is less about embracing or rejecting AI than staying curious, experimenting with small projects, and demanding public safeguards as the technology spreads.