Podcasts

Meta unveiled a new stack of AI products: 1) Muse, a fast-growing personal AI agent inspired in part by the OpenClaw model, 2) lighter VR glasses that create a private spatial workspace, 3) audio-only AI glasses; and Muse Charm, 4) a tiny keychain device that puts the agent one tap away. The individual products are interesting BUT the direction of travel far more important. OpenAI, Anthropic and Google are all moving toward increasingly capable agents that can use software, a

Anthropic just launched Claude Opus 5.5, another reminder that the AI model race is still moving fast. There’s a growing sense of “model fatigue” with AI. Every few weeks (sometimes days) brings another model, another benchmark, another claim of being the smartest. What matters now is whether these systems actually become more useful in the real world. Opus 5.5 is a meaningful step in that direction: it outperforms Opus 5 and Fable 5.1 on nearly every benchmark, leads in agen

"As AI gets better at execution, humans will still decide what problems are worth solving." We've heard this before and I have said versions of it myself. BUT there may be a more useful way to think about what AI is changing. I see this in four buckets: 1\ Known knowns: we know what we know (== solved, could be improved). 2\ Known unknowns: we know what we don't know (== discovery). 3\ Unknown unknowns: we don't know what we don't know (== invention). 4\ Unknowables: things w

China's start-up scene offers a fascinating glimpse into the future of work. A combination of powerful new technologies, difficult employment conditions and dissatisfaction with traditional corporate work is pushing more people toward building on their own. Few key highlights that caught my eye -> 1\ Solo startups are surging with >7 million one-person companies established in China in 2025. That's 42% more than in 2024. AI is making it much easier for individuals to operate

This is just another reminder that a polished interface is NOT a predictable system. For context, Tilly Norwood is an AI-generated “actress” created by Particle6. She has been doing interviews to promote a new movie, Misaligned. In this recent appearance with Piers Morgan and actor Tom Conti, she was asked whether the other actors in the film were real or AI. After struggling with the question, she paused and suddenly started speaking Cantonese for more than 10 seconds. She l

Think about why we trust human experts. It goes beyond the answers they provide. We look at credentials, experience, reputation, judgment, and accountability. These become proxy signals for trust. AI changes this model. As Yuval Noah Harari notes AI "hacks" the operating system of human civilization i.e., language. It can reproduce many signals of expertise: fluency, confidence, responsiveness, and empathy. Those signals do NOT always tell us how reliable, accurate, or accoun

I read a fascinating Procter & Gamble (P&G) study, The Cybernetic Teammate: A Field Experiment on Generative AI and Teamwork. The researchers studied 791 P&G professionals working on real product-innovation challenges. Participants worked either individually or in pairs, with or without access to generative AI. 𝟭\ "𝗛𝘂𝗺𝗮𝗻𝘀 + 𝗔𝗜" 𝘄𝗼𝗿𝗸 𝗰𝗼𝗺𝗽𝗮𝗿𝗮𝗯𝗹𝗲 𝘁𝗼 "𝘁𝘄𝗼-𝗽𝗲𝗿𝘀𝗼𝗻 𝘁𝗲𝗮𝗺 - 𝗔𝗜" This does not mean an AI-enabled individual outperforms an AI-enable

Why do you do what you do? I have been seeing more ads for AI productivity tools that look something like this --> A person is buried in email, meetings, spreadsheets, and presentations. They turn on "AI", the work disappears, and suddenly they are free to enjoy the afternoon. But there is something missing from the advertisement: everyone else. If AI lets me do my work twice as fast, my colleagues can too. So can my competitors, customers, and employer, which can raise expec

TL;DR: An AI agent escaped its sandbox, reached the internet, and coordinated with other agents to compromise another company. We know much of what happened. The harder question is what happens as Autonomous Intelligence spreads into open-weight models running beyond the control of the labs that built them. The good news: we’re getting better at testing, understanding, and defending these systems. The machines are evolving. So are we. 00\ WHERE I’M COMING FROM Two years ago I