ai-podcast
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AI has a demo. Who has a business?
Unsupervised Learning puts the people building AI in the interview chair. Redpoint’s podcast asks what happens after the impressive demo - when customers, costs and awkward trade-offs arrive.
The podcast that sends you back to work
The Cognitive Revolution turns long AI conversations into useful working knowledge. Its most telling review came from a founder who heard an interview and changed what his team tried next.
Eye on AI asks the next question
Before AI became an everyday conversation, Craig S. Smith was interviewing the people building it. Eye on AI turns those conversations into a public record of what the technology promises - and what still needs explaining.
AI Today: Make AI earn its keep.
Before the chatbot came the business problem. AI Today turned that simple ordering into a podcast, a teaching business, and a place inside the Project Management Institute.
Everyday AI makes learning a daily habit
Jordan Wilson built a media and consulting company around a simple invitation: watch AI work, ask a question, then try it yourself. The interesting part is what happens after the broadcast.
Last Week in AI makes room to think
A researcher’s answer to AI overload became a newsletter and a long-running podcast. Its most useful lesson may be the work it learned to leave behind.
The AI news is endless. Your attention isn’t.
The Artificial Intelligence Show turned two colleagues’ reading habit into a weekly guide to AI at work. Its most useful lesson is how to make time to think before buying another tool.
No Priors opens the room where AI gets built
Sarah Guo and Elad Gil turn their access to AI founders into a weekly public conversation. The useful part is hearing which assumptions the builders are willing to defend.
The AI Daily Brief makes room to think
Nathaniel Whittemore built an AI news business around a daily listening habit. Its revealing move was deciding which news product to stop making.
Latent Space gave AI builders a place to belong.
A newsletter became a meeting place for people turning AI models into working software. Now Latent Space faces the same problem as its readers: how to scale without losing what made it useful.