The 26 Best AI Podcasts (2026)

Noah Kravitz has been hosting the NVIDIA AI Podcast for over seven years now, and the show has quietly built itself into one of the most consistent interview series in the AI space. With nearly 300 episodes and over 6 million listens, it pulls in researchers, startup founders, and engineers from across the industry for conversations that typically run 30 to 50 minutes -- long enough to get into substance but short enough that you can finish one on a commute.

What sets this show apart from a lot of corporate-backed podcasts is that it doesn't feel like a product demo. Sure, NVIDIA is behind it, but the guest list ranges from disaster relief organizations using satellite imagery and AI to construction companies automating job sites with Caterpillar. One week you'll hear about autonomous vehicle safety testing, the next it's AI-powered holograms for live entertainment. The breadth is genuinely surprising.

Noah's interviewing style leans informational. He asks clear, grounded questions and lets guests explain their work without too much interruption. If you're looking for hype or hot takes, this probably isn't your thing. But if you want to understand how AI is actually being applied across industries right now -- retail, healthcare, sustainability, infrastructure -- this is a solid pick. The weekly cadence means there's always something new, and the back catalog is deep enough to keep you busy for months. Rated 4.4 stars with over 330 ratings on Apple Podcasts, it's well-regarded among listeners who appreciate substance over spectacle.

Latent Space is the podcast that AI engineers actually talk about at work. Hosted by Swyx (Shawn Wang) and Alessio Fanelli, it sits at the intersection of building real software and understanding where foundation models are headed. These two bring genuine technical chops -- Swyx from his developer advocacy background and Alessio as a venture-backed founder at Kernel Labs -- and it shows in the caliber of conversations they pull off.

Most episodes run between an hour and 90 minutes, which gives guests room to go deep. And they do go deep. The show has featured leaders from OpenAI, Anthropic, Google DeepMind, and Meta, alongside founders from companies like Sierra, Databricks, and Modular. A recent episode had Jeff Dean from Google talking about the future of AI research. They record mostly in person, which gives the conversations a more natural, less scripted feel than your average Zoom-recorded pod.

Fair warning: this show assumes you already know your way around terms like MCP, RAG, and mixture-of-experts. If you're brand new to AI, you might feel lost in the jargon. But if you're an engineer, researcher, or technical PM working with AI tools daily, there's really nothing else like it. Over 10 million readers and listeners found their way to Latent Space in 2025 alone. The companion Substack newsletter adds written depth to each episode, making it a two-for-one resource. Rated 4.6 stars on Apple Podcasts across 93 ratings.

Nathan Labenz and Erik Torenberg run one of the more ambitious AI interview shows out there. The Cognitive Revolution, part of the Turpentine podcast network, takes on a wide mandate: understanding how AI is reshaping not just technology but biology, law, cybersecurity, education, and basically everything else. With over 310 episodes in the catalog, they've built up a serious archive.

The format is long-form conversation, and they mean it. Episodes regularly stretch past 90 minutes, with some clocking in over two hours. That length isn't filler -- Nathan in particular has a knack for asking follow-up questions that push guests past their rehearsed talking points. Recent episodes have included Helen Toner discussing automated AI R&D, Mark Zuckerberg and Priscilla Chan on AI-powered biology, and deep explorations of AGI timelines and AI safety.

Nathan brings a unique perspective as someone who genuinely uses AI tools in his own work and isn't shy about sharing what he thinks is overhyped or underappreciated. Erik's venture background adds a business lens that keeps conversations grounded in what's actually getting funded and built. Together they strike a balance between technical depth and strategic thinking that's hard to find elsewhere.

The show publishes roughly twice a week. It's best suited for listeners who want to sit with big ideas for a while rather than skim headlines. At 4.5 stars from 93 ratings on Apple Podcasts, it has earned a loyal following among the seriously AI-curious.

If the name doesn't give it away, Practical AI is obsessed with making AI useful in the real world. Hosts Chris Benson and Daniel Whitenack, produced by Changelog Media, have been at this for nearly 350 episodes, and their focus has stayed remarkably consistent: strip away the hype and figure out what actually works.

Episodes run about 40 to 50 minutes and typically follow one of two formats. Some are interview episodes where they bring in guests like security expert Bruce Schneier to discuss AI's impact on democracy, or a Waymo VP to break down autonomous vehicle research. Others are just Chris and Daniel riffing on a topic together, like their year-end reviews where they sort through what mattered in AI and what was just noise. Both formats work well because these two have genuine chemistry and don't talk past each other.

The show deliberately targets a broad audience. You don't need a PhD to follow along, but experienced practitioners won't feel like they're being talked down to either. They cover everything from MLOps and neural networks to the Model Context Protocol and how AI is reshaping hiring practices. Their recent episodes on AI startup funding and the ethics of AI-assisted coding show they're not afraid to tackle uncomfortable questions alongside the technical stuff.

Published weekly and rated 4.4 stars across 190 ratings on Apple Podcasts, Practical AI has built a reputation as the show you recommend to someone who keeps asking you to explain what's going on with AI.

Craig S. Smith spent years as a New York Times correspondent before turning his attention to AI, and that journalism background gives Eye On A.I. a distinctly different texture from most tech podcasts. This isn't a show where two engineers geek out over benchmarks. It's a journalist methodically working through what AI means for the world, one conversation at a time.

With over 310 episodes published biweekly, Craig has built an impressive roster of guests spanning AI security, healthcare, construction, cybersecurity, and conversational AI. Recent episodes have featured founders from companies like Poolside exploring reinforcement learning for software development, Abnormal AI tackling behavioral cybersecurity, and CureWise applying AI to medical decision-making. The range reflects Craig's genuine curiosity -- he's not locked into any single corner of the AI world.

Episodes tend to run between 50 and 70 minutes, sometimes stretching longer when the conversation warrants it. Craig's interviewing style is calm, measured, and genuinely curious. He asks the kind of questions a thoughtful outsider would ask, which means guests have to explain things clearly rather than hide behind jargon. Listeners consistently praise this quality in reviews, and it's probably why the show holds a 4.7 star rating on Apple Podcasts -- the highest among AI-focused podcasts in this group.

The show works particularly well for people who want to understand AI's broader implications without needing an engineering degree. Craig puts individual advances into context, connecting dots between different industries and research directions in a way that feels genuinely insightful rather than forced.

Jordan Wilson breaks down AI news and tools in a way that doesn't require a computer science degree. Daily episodes cover new releases, practical use cases, and interviews with people actually implementing AI in their businesses. The production is tight and Jordan has a knack for cutting through hype to find what's genuinely useful. If you want to stay current on AI developments without drowning in jargon, this is probably your best bet. The daily format means you won't fall behind.

Sam Charrington has been covering machine learning and AI since before it was cool (or terrifying, depending on your perspective). Deep technical interviews with researchers, engineers, and practitioners who are building the actual systems. Episodes run long but the depth is the whole point. If you work in tech or want to understand the research driving AI progress, TWIML delivers consistently excellent conversations. One of the most respected shows in the space for good reason.

Straight from one of the world's leading AI research labs. Scientists and engineers at DeepMind explain their work on everything from protein folding to game-playing agents. Obviously there's a promotional element - it's a corporate podcast - but the scientific content is genuinely fascinating and well-presented. Episodes don't oversimplify, which is refreshing. Great for understanding what cutting-edge AI research actually involves beyond the headlines.

Covers the rapidly evolving landscape of AI tools - ChatGPT, Claude, Midjourney, and whatever dropped last week. They test tools, compare capabilities, and discuss practical applications for everyday work. The pace matches how fast the field moves, which means episodes stay relevant longer than you'd expect. Good for people who want to actually use AI tools better rather than just argue about whether they'll take everyone's jobs.

Structured educational content about AI concepts and applications. Episodes build on each other, creating something closer to a course than a typical podcast. Good for people who prefer systematic learning over random topic coverage. The production quality is high and explanations are clear without being dumbed down. If you're trying to develop a solid foundational understanding of AI, this gives you more structure than most shows.

World Wide Technology's podcast exploring enterprise AI implementation - the messy reality of deploying AI in actual businesses. Less theoretical, more practical. They cover real deployment challenges, infrastructure decisions, and how organizations are navigating the gap between AI hype and AI reality. Useful if you're involved in technology decisions at work and need to separate genuine capabilities from vendor marketing.

A straightforward AI discussion podcast covering developments, applications, and implications across industries. Episodes are digestible and well-structured, making complex topics accessible. The hosts maintain a balanced perspective on both opportunities and risks, which is harder to find than it should be in AI media. Solid entry point if you're building your understanding of the field.

Targeted at business leaders and startup founders trying to figure out how AI fits into their operations. Episodes are practical and strategic - less about the technology itself and more about what to do with it. Interviews feature founders who've successfully integrated AI and are honest about what worked and what didn't. Short enough to fit into a lunch break.

Meta in the best way - a podcast about using AI to make better podcasts. Covers tools, workflows, and techniques for content creators who want to integrate AI into their production process. Niche but extremely practical if you're a podcaster looking to work smarter. Episodes are focused and actionable.

James Taylor explores the intersection of AI and human creativity. How are artists, musicians, and innovators using AI tools? Where does human creativity remain irreplaceable? Episodes feature creative professionals who are embracing AI rather than fighting it, offering a refreshingly optimistic perspective. Good for anyone in creative industries wondering what their future looks like.

Explores how AI is reshaping different aspects of society - healthcare, education, work, relationships. Each episode picks a domain and examines how AI is changing it, for better and worse. The approach is balanced and thoughtful, avoiding both utopian and dystopian extremes. Good for big-picture thinkers who want to understand AI's broader impact beyond the tech industry.

Sarah Guo and Elad Gil bring something rare to the AI podcast space: genuine insider access combined with sharp, unfiltered opinions. Guo founded the AI-focused venture firm Conviction, and Gil has backed companies like Airbnb, Stripe, and multiple AI startups. Together they sit down weekly with the people actually building the future of AI, from frontier lab researchers to startup founders deploying models in production.

The format is straightforward interview-style, usually running 30 to 60 minutes. What makes it stick is the quality of guests and the hosts’ willingness to push back. They had Dario Amodei discussing the limits of exponential scaling, RJ Scaringe from Rivian talking autonomous vehicle architecture, and Dave Baszucki on AI-native gaming worlds. The conversation tends to move fast, skipping past surface-level hype to get at what is actually working and what is not.

Guo and Gil are both investors, so there is a natural business lens here. They care about market dynamics, competitive moats, and where capital is flowing. But they also geek out on technical details when the guest warrants it. You will hear them dig into training compute economics one week and SaaS disruption patterns the next. With 153 episodes and a 4.3-star rating from 125 reviews on Apple Podcasts, No Priors has become a go-to for anyone who wants the VC perspective on AI without the empty buzzwords.

If you want a single podcast that reliably covers everything important happening in AI each week, Last Week in AI is hard to beat. Andrey Kurenkov and Jeremie Harris have been doing this since 2020, and they have built up a rhythm that works: they scan through dozens of AI news stories, research papers, and industry announcements, then distill it all into a weekly discussion that is informative without being exhausting.

The show has racked up 274 episodes, and recent ones cover everything from new model releases like Sonnet 4.6 and GPT-5.3-codex to industry drama around Anthropic and the economics of AI training runs. Kurenkov comes from an academic AI research background, while Harris brings more of a startup and policy perspective. That combination means they can explain technical breakthroughs clearly while also contextualizing why a particular business move or policy decision matters.

Episodes typically run about an hour, and the pace is brisk. They do not belabor any single topic, instead giving you enough context to understand the significance and moving on. With a 4.6-star rating from over 300 reviews, it is clearly resonating with a technically literate audience. Perfect for anyone who follows AI professionally and needs efficient, well-curated weekly updates.

Lex Fridman’s podcast has become almost synonymous with long-form AI conversation. An AI researcher with ties to MIT, Fridman started the show in 2018 and has since produced nearly 500 episodes, pulling in guests from across science, technology, philosophy, and beyond. The AI episodes remain the beating heart of the show.

The format is simple: Lex sits down with one guest for an extended conversation, often running two to four hours. He has talked with virtually every major figure in AI, from Demis Hassabis and Yann LeCun to Sam Altman and Ilya Sutskever. His interviewing style is patient and methodical. He asks big, open-ended questions and then lets people talk. Some listeners love the contemplative pace; others wish he would push back more. Either way, the depth is unmatched.

Recent episodes include a sprawling State of AI in 2026 discussion covering LLMs, scaling laws, agents, and AGI timelines. The show carries a 4.7-star rating from over 12,000 reviews on Apple Podcasts, making it one of the most popular podcasts in any category. For AI specifically, the back catalog alone is an incredible resource.

Dwarkesh Patel has quickly become one of the most respected interviewers in the AI space, largely because of his preparation. Before each conversation, he does an almost absurd amount of research, reading papers, studying his guests’ previous interviews, and building question frameworks that go well beyond the usual prompts. The result is conversations that consistently extract new information, even from guests who have been interviewed hundreds of times.

Dario Amodei sat down with Patel in early 2026 to discuss whether we are approaching the end of exponential AI progress. Elon Musk talked about putting AI compute in space. Adam Marblestone explored what current AI systems are fundamentally missing about how brains work. These are not softball conversations. Patel is polite but persistent, and he has a knack for framing questions that force guests into genuinely novel territory.

With 117 episodes and a 4.5-star rating from over 500 reviews, the podcast has grown from a niche interview show to a must-listen for anyone serious about understanding where AI is headed. Episodes vary from quick 12-minute reflections to nearly three-hour deep sessions. Patel built his audience through substance rather than polish.

Dr. Jon Krohn has been running Super Data Science since 2016, and the show just crossed 970 episodes. That kind of longevity is rare, and it reflects both Krohn’s genuine enthusiasm and the practical value listeners keep coming back for. The focus is squarely on machine learning, AI, and data science as practiced in the real world.

Krohn alternates between two episode formats. The longer ones, running about an hour, are interview-based. He brings on researchers, engineers, and executives to talk about specific projects, tools, or career paths. Recent guests include Fireworks AI’s Lin Qiao discussing private data infrastructure and Samsara’s Praveen Murugesan on AI for physical-world applications. The shorter episodes, around 15 minutes, are solo commentary where Krohn shares his own takes on trending topics.

The tone is educational but approachable. Krohn has a PhD in neuroscience and works at the intersection of deep learning research and practical deployment, so he can translate complex ideas without dumbing them down. The show publishes twice a week. It carries a 4.6-star rating from 300 reviews on Apple Podcasts. If you are a data scientist, ML engineer, or anyone building with AI tools, this podcast consistently delivers useful, actionable insights.