Possible

Only 36% of AI-aware Americans expect AI to mostly help their country, according to a Gallup and Microsoft survey. Reid traces that unease to three forces: fear that the future will be worse than the present (which AI amplifies when prosperity has not reached much of the country), an instinct to protect what Americans have, and doubts about who will share in AI’s gains. With Aria Finger, he makes the case for winning at AI while spreading its prosperity, including through AI and robotics in manufacturing. They also examine HackerRank’s Chakra AI interviewer and why hiring should remain a team decision. Finally, OpenAI’s plan to test visual ads in ChatGPT raises another question: can a free AI assistant earn trust while carrying advertising? Reid calls for a clear separation between ads and answers, enforceable commitments, and third-party audits. For more info on the podcast and transcripts of all the episodes, visit https://www.possible.fm/podcast/

Reid Hoffman sees promise in an AI buildout and wants its financial risks in the open. He and Aria Finger unpack a Financial Times report on up to $300 billion in AI infrastructure guarantees largely absent from the balance sheets of technology companies. They explore California Governor Gavin Newsom’s push for independent lab auditors and an AI kill switch, and discuss what effective federal oversight could look like. Reid explains how these proposals differ from the 2024 bill he opposed and makes the case for an international cyber treaty. A Truth Social poll to rename AI also prompts a debate about why “artificial” sells the technology short.

Reid Hoffman and Parth Patil argue that if your AI experiments always work, you’re not pushing hard enough. Wrapping up the Tokens to the Future series, they revisit seven grantees’ experiments with $1,000 a week each in tokens. Their conversations span Dungeons & Dragons worldbuilding, an iOS game built in a weekend, and personal agents whose usefulness compounds over years. They explore how AI can shift more people from execution-focused “cog jobs” to creative “spark jobs,” and why expertise can sometimes make new possibilities harder to see. Throughout, they ask what to delegate and where story, pacing, and judgment still demand human direction. Plus, Parth’s example of ChatGPT picking seven for a “random” number raises a bigger question: how do you get surprise from a system built to predict?