Live with Tim O’Reilly

O'Reilly

A series of candid conversations designed to slow down, dig deeper, and share real insights you can build on. Get the story behind the show here: https://www.oreilly.com/radar/more-slowly/

  1. 3d ago

    Making AI Relatable: Harper Carroll Live with Tim O’Reilly

    Harper Carroll is a computer scientist who built machine learning systems at Meta but she also describes herself as "born an actress from Manhattan." She’s combined those disparate parts of her background into a unique role as an AI educator, where she uses her magic superpower of making sense of AI to reach half a million people on social media and beyond. She has a knack for explaining how models actually work, covering concepts like optimization and token distributions and the math behind them in terms that land for people who've never opened a Python notebook. Harper sat down with Tim to talk about how she makes technical complexity incredibly relatable, but they also thought through some of the more comprehensive challenges the industry is facing. Those ranged from the technical, as Harper explained why fine-tuning a small open source model beats prompting even the best closed-source model when you're trying to capture voice, to cultural considerations like the need to shift the narrative from fearing AI to explaining how AI can expand ambition both for individuals and for organizations, why we should treat AI as a medium like photography or writing, and why open source AI is a much bigger story than open source models. And in keeping with both Harper’s and Tim’s focus on learning, they discussed the skills everyone in the workforce will need to have to use AI effectively. That’s a social problem to the extent that we’ll need to ensure that everybody learns enough about AI so we don't end up with AI haves and have-nots. But it’s also a recognition that AI education is becoming a critical part of the path to success for all kinds of jobs. "The people who are really going to struggle," Harper told Tim, "are the people who are not willing to accept that AI is coming and are not willing to learn it."

    59 min

  2. Jun 3

    Data Access and Other Bottlenecks in Enterprise AI Adoption: DJ Patil Live with Tim O’Reilly

    DJ Patil co-coined the term "data scientist," served as America's first chief data scientist under President Obama, was chief scientist at LinkedIn, and has spent the past decade on the founding team at Devoted Health, where he's built the kind of data infrastructure that most organizations are still struggling to create. DJ’s been on a listening tour. Wherever he travels, he finds a local university, holds office hours, and asks whoever shows up—students, faculty, hospital administrators, executives—what they're actually experiencing with AI. What he's hearing about the anger, angst, and the “impasse of dialogue” between AI boosters and skeptics is helping him refocus his thoughts about AI adoption. He joined Tim for a wide-ranging conversation about what's working right now, what's broken, and where the real bottlenecks are. DJ and Tim started with students, who are feeling that the social contract of “go to college and start a career” is broken, and DJ's plan to launch a makerspace-style program for those who didn’t land internships this summer to learn and demonstrate their skills. Then they went deep on why the organization is the bottleneck to transformation, using the healthcare industry as an example of the entrenched challenges and what’s possible when you get the infrastructure right. DJ walked through how Devoted Health built its data foundation before LLMs existed, why that tidy house is now a compounding advantage, and the change we can make when transforming our healthcare system is, as he put it, "like walking, chewing gum while balancing bowling balls on your head and on a unicycle." "We're [both] this giant human LLM," DJ told Tim, "summarizing and distilling what we're hearing from a lot of people." What they're hearing is that the chief constraint is whether our institutions can build the organizational and economic infrastructure to actually deploy what we've built.

    1 hr

  3. May 27

    Inside the Code Factory: Ryan Carson Live with Tim O’Reilly

    Ryan Carson has spent 25 years building developer communities, conferences, and Treehouse, which taught over a million people to code. His latest company, Untangle, is an AI-powered divorce assistant—and he’s building it entirely alone. Just $2 million in seed funding, his guidance, and a team of agents running while he sleeps. Ryan sat down with Tim to walk through the “code factory” powering Untangle: a system where agents write and review the code, run the tests, triage error reports, and monitor the production environment under his oversight. In their conversation, they covered the Ralph Wiggum loop (Geoffrey Huntley’s deceptively simple technique for giving agents large goals across multiple context windows) and the power of primitive loops, how Ryan used Claude Design and a human designer to build a full design system he can now reproduce with AI, what attorneys really think about Untangle, the economics of running a company of agents, why the narrative that programming is going away gets the abstraction story exactly backwards, and why, even when you can automate nearly everything else, you still can’t automate the judgment call about what to build. “There isn’t a magic wand still,” Ryan told Tim. “You can build faster, but whether you’re building the right thing, and doing it better, is something [else].” Read Tim’s takeaways from the conversation, plus clips, on Radar.

    1 hr

  4. May 26

    A Conversation with Computer Programmer Steve Yegge

    Longtime software engineer Steve Yegge has lately been exploring the limits of vibe coding with projects like Beads, his coding agent memory system, and Gas Town, a proof-of-concept agent orchestrator so complicated, expensive, and chaotic that he warned those interested NOT to use it. As Tim O’Reilly points out in his takeaways from this episode, “Steve has always been one of the most provocative thinkers in our industry.” Steve joined Tim for an insightful and entertaining conversation on coding with AI that touched on everything from computer graphics in the ’90s to desire paths and why getting riled up is the key to writing a good blog post. Steve and Tim spent a lot of time discussing the wider context around Gas Town, including why Steve sees it as an enterprise tool—and as an executive assistant who takes on the mundane work so you can focus on the important problems. They also covered agent orchestration and the evolution of coding, using Steve’s eight-level framework; AI vampires and how exhausting it is to work with a stable of agents (Steve’s taking two naps a day!); why you might find yourself on the wrong side of the bitter lesson, and what to do about it; the reason Steve “wouldn’t touch [OpenClaw] with somebody else’s 10-foot pole”; why he’s adamant that developers need to overhaul their mental models from structure and framework cognition to just letting AI do its thing—and why he’s not even looking at the code anymore; and much, much more. “The big takeaway,” Steve told Tim, “is that there’s always more work. It doesn’t matter how superhumanly good your helpers get; you’re just going to want to do something bigger. Our ambition will always outstrip our compute.” Check out Tim’s takeaways from the conversation, plus clips, on Radar.

    58 min

  5. May 25

    A Conversation with Google Cloud AI Director Addy Osmani

    Addy Osmani should be a familiar face to the O’Reilly community. He’s the cohost of our AI Codecon events, the author of Beyond Vibe Coding, Leading Effective Engineering Teams, The Effective Software Engineer, Web Performance Engineering in the Age of AI, Learning JavaScript Design Patterns, and Building Web Apps with Bolt, and a prolific blogger on Radar and with his own newsletter, Elevate. He’s also a longtime Googler who’s spent nearly 14 years building developer experiences in Chrome and is now helping developers and businesses succeed with Gemini. As Tim O’Reilly put it in the introduction to this episode, “Addy Osmani is one of those people who is really grounded but also really able to think big and see the future.” Addy sat down with Tim to chat about the state of the industry as it moves toward the orchestration of multi-agent workloads. In their wide-ranging conversation, they covered the tension between creativity and productivity, and balancing velocity with long-term technical maintenance and reliability—particularly from the enterprise perspective. Larger organizations can’t just let the agents rip. As Addy explained, “The real frontier for business is not necessarily having hundreds of agents for a task just for its own sake. It’s about orchestrating a modest set of agents that solve real problems while maintaining control and traceability.” And then there’s the as-of-yet unsolved problem of making everything work together as smoothly as possible. Along the way, they considered the distinction between “feeling” productive and “being” productive (h/t Will Manidis); how to keep up-to-date on the latest trends and conversations; why being able to explicitly define the architecture and the purpose of what you’re building will matter more than how fast an AI tool can build it; why it’s still a good moment for young students to become software engineers; how MCP and A2A complement each other; why YOLOing token use is probably not the best strategy for most people; and more. Watch now, or read Tim’s takeaways from the conversation (with clips) on Radar.

    1h 4m

  6. May 22

    Ask Me Anything

    In the year’s final Live with Tim O’Reilly, Tim hosted an AMA to pull together some of the threads that have emerged during his conversations with guests who, as he put it, “know way more than me”—and think through how they may play out next year. “With AI, regardless of what happens with scaling, we now have a set of new capabilities that really challenge our thinking and our understanding about the role of human expertise,” he explained in his introduction. In his audience-guided talk, Tim explored the ramifications on our jobs and responsibilities, open source, and building participatory markets writ large. Watch now to hear Tim’s perspective—illustrated by examples and anecdotes drawn from his long involvement in the tech industry—on why we’re still waiting to see an explosion of software productivity; developing (and sharing) skills and products as a side-hustle; what’s surprised him about AI this year; whether we’re moving toward personalized software; how to keep up-to-date with the speed of innovation; the growing AI backlash; how new technologies like MCP are informing the evolution of the AI industry; grappling with the bitter lesson; and much more.

    58 min

  7. May 21

    A Conversation with Author and Programmer Kent Beck

    Kent Beck’s career-long mission has been to improve software development. He created Extreme Programming back in the ’90s to address some of the issues that were slowing productivity. More recently, he’s been working on a series to help “tidy” development. Kent joined Tim to share his philosophy on tidying not just code but also the very human tasks of collaboration and teamwork, as well as his approach to coding with AI. Kent offered takeaways from collaborating with the “genie”—his term for AI tools, which may grant your wish but usually not in the way you wanted—focusing on the benefits beyond the ability to generate more code faster. Check it out to learn why Kent thinks inhibition is the key to building reliable systems out of unreliable components like generative AI; what carries over from Agile and XP and pair programming to AI; the things old programmers know that new programmers still need to learn; why the deepest insights come the furthest into maintenance (hat tip to Ward Cunningham); and the importance of fostering a sense of shared responsibility throughout the organization as AI coding tools accelerate the pace at which code can be produced. Kent wrapped things up by discussing the joy augmented coding brings him and the knock-on effects that come from “engineer playtime” with GenAI tools. As he explained, “It’s not what [developers] produce; it’s how much faster…they can learn and how much richer their thought processes can be. And that benefits everybody.”

    1h 1m

  8. May 20

    A Conversation with Independent Researcher Anjali Shrivastava

    If the assumption that we’ve lived with for years that scale brings lower marginal costs isn’t true, we really have to rethink a lot of things. Anjali Shrivastava is an independent researcher and data scientist looking into the unit economics of AI’probing how the seemingly simple token unit of cost for generative AI services is actually highly variable, leading to hidden margin risks that undermine conventional subscription or usage pricing models. Anjali joined Tim to talk through her recent work, particularly as it relates to code-generation tools like Cursor and Claude Code. Over the course of a very interesting hour, they got into the the distinctions between input tokens and output tokens and between reasoning tokens and output tokens, and why they matter; the challenges providers face in instituting systems to offer visibility for users, and even for themselves’and why it’s not only a business problem to solve but one at the heart of AI products as they currently exist; how AI service providers differ from traditional SaaS businesses in ways that make the latter’s pricing model obsolete (even though as of now, it’s the model that AI providers continue to use); and how AI providers can balance, and even temper, demand as use scales. They also discussed the efficiency opportunities new hardware may provide, one potential future where AI gets much pricier, why efficiency alone won’t solve the pricing issue, how external bottlenecks, like electricity, influence pricing, and much more.

    58 min
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A series of candid conversations designed to slow down, dig deeper, and share real insights you can build on. Get the story behind the show here: https://www.oreilly.com/radar/more-slowly/

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