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Top 10 Artificial Intelligence & Machine Learning Podcasts of 2026

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The AI Daily Brief31 min

Opus 5.5 vs GPT-6 Sol and Luna

Anthropic and OpenAI release new models on the same day, with Opus 5.5 winning early acclaim while GPT-6 Sol and Luna push the frontier of affordable intelligence. NLW breaks down the benchmarks, real-world use cases, and first reactions—and why model personality, falling costs, and the tools surrounding AI increasingly shape which models people actually use. New users get $100 in free credits.

Latent Space1 hr 32 min

🔬Bio-security is an AI Arms Race - Eric Nguyen (CEO, Radical Numerics)

The OpenAI → Hugging Face attack has people asking “what else do we need to worry about?” and Anthropic’s filters flag two things: cyber-security and biology. The natural question is: what about bio-security, then? Clem Delangue argues that cyber-warfare defensive capabilities need to be open and to keep pace with frontier models’ attack capabilities Radical Numerics co-founder Eric Nguyen sat down with us and explained why the same models that increase biological capability can also keep defense from falling behind. Building a virus from scratch While he was at Stanford, Eric couldn’t get traction on Genomic Language Models (GLMs) for a long time. Biologists didn’t believe it would work, didn’t think they could verify the output, and didn’t see important applications beyond what they could already do. He kept pushing, eventually helping lead the development of Evo and contributing to Evo 2 at Arc Institute. Those models were later used by a separate Arc/Stanford team to generate entire bacteriophage genomes that were synthesized into functional viruses ! Long context unlocks biological intelligence Early ChatGPT spit out poems and email, and early DNA language models like Evo and Evo-2 could build a genome from scratch. DNA is different, however, from natural language in that it has a very small alphabet (4 characters ACTG) and that its sequences are very long: 60K for an average human gene long being up to 2.3M the whole human genome around 3B. Innovation in long-context models made this possible about 3 years ago (footnote: striped hyena), long before the frontier labs were building 1M+ context models. Now Eric and other AI x Bio luminaries have founded Radical Numerics to build and scale GLMs to tack a wide range of biological problems, extending well beyond generating DNA. Thinking in DNA Their GLMs already do pretty well with RNA and protein because there are clear markers in the DNA sequence for genes (RNA sequences the perform many functions) and specific genes that encode proteins. This means that the models already generalize to multiple “languages,” before even attempting to train in other modalities, such as 3d protein structure, epigenetics and natural language. If a model thinks in the DNA language, maybe it understands the imprint that environment left on different genomes as well? Perhaps the model has learned the functional relationship between different sequences, and could extrapolate to new sequences based on that? And so what we wanted to showcase was that if we show the model progressively better RNAs in a series of steps with its score, right? So you have like low scores first and then you gradually move up the chain. Can the model continue that trajectory on its own? And then in the final step, does it self optimize to a point where it's like the best score it can get? That was the experiment. Can we do that? And so we took a data set, a large data set of aptamers. We held out a portion of the best performing ones and we showed it only the lower ones, but then we ranked it, right? So we showcase lower scores with the RNA aptamers and then progressively got higher, and then ask the model to just like continue with that pattern. And it turns out it was able to recapitulate some of those higher scores that we had not shown it yet. So, voila: chain-of-thought, thinking in DNA! The arms race But much as long-context inference, chain-of-though and multi-modal perception unlocked sophisticated reasoning in natural language LLMs, these capabilities in GLMs are enabling increasingly sophisticated “biological intelligence,” and along with it, greater danger. According to Eric, defense is currently losing this battle, but Radical Numerics argues to push the frontier harder! I won’t spoil the details for you. In the episode we talk in detail about: Biosecurity as an arms race — and how defense can keep up The genome as the imprint of the environment on DNA Going truly multi-modal How chain-of-though works when you “think” in the language of DNA This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes,

The Artificial Intelligence Show1 hr 37 min

#241: Pacing the Frontier Gets Political, Why AI Labs Could Keep the Best Models for Themselves & Introducing the AI Transformation Blueprint

AI-safety pacing became a political fight this week: Trump versus a bipartisan push, with data centers and the midterms in the middle. Paul and Mike go deeper on the part that matters more: the widening gap between the models labs keep and the ones you can use, Noam Brown's warning about it, and OpenAI's six new rogue-agent disclosures. Plus Paul's surprise book announcement and a full rapid-fire slate. AI-Pulse Survey: Fill out this week’s AI-Pulse Survey here. Show Notes : Access the show notes and show links here Timestamps: 00:00:00 — Intro 00:04:29 — The AI Slowdown Gets (Very) Political 00:25:18 — AI Labs Could Keep the Best Models to Themselves 00:54:33 — The AI Transformation Blueprint 01:09:45 — OpenAI Discloses More Rogue Agent Incidents 01:14:03 — Claude Merges Chat and Cowork 01:17:53 — Meta Rolls Out Muse 01:20:36 — Apple Ships Siri AI 01:23:20 — OpenAI and Microsoft Face Copyright Revelations 01:27:18 — AI Use Case Spotlight 01:32:59 — AI Product and Funding Updates This episode is supported by Outshift, Cisco's incubation engine for frontier technology. Outshift by Cisco is an open source foundation for building multi-agent systems from design to production with shared context, shared memory, and guardrails to drive the results we actually expect from AI. Read the paper, experience the demo, grab the code from Outshift . Register for a free webinar Come to our next Marketing AI Conference Enroll in our AI Academy

Eye on AI1 hr 6 min

Inside Ukraine's Drone War: Maj. "Phoenix" of Lasar's Group

The first armed drone Ukraine ever fielded wasn't built in a factory or procured from a defense contractor. It was built in four months by a network engineer using a Starlink terminal and a large agricultural quadcopter frame, and the man who built it, Maj Phoenix, co-founder of Lasars Group, joins Craig Smith in Kyiv to explain exactly how it happened and what the battlefield looks like now. The conversation traces the full arc from that first prototype - with its two-person crew of pilot and navigator, operating beyond line of sight using satellite imagery and landmarks - to the layered drone architecture Lasars Group now operates: FPV drones on fiber optic covering 30 kilometers, heavy bombers reaching 65 kilometers, ISR planes surveilling the entire range, and interceptors protecting each crew position. The most unexpected part of this conversation is what drone technology has done to the physical shape of the war. As drone range has expanded - from rifles at 400 meters to FPV at 10 kilometers to heavy bombers at 65 kilometers - the kill zone between the two armies has grown to match, pushing human soldiers further apart and turning the conflict increasingly into drones fighting drones rather than people fighting people. Phoenix frames this explicitly as a reduction in human casualties. The episode also reveals a genuinely surprising institutional innovation: Ukraine's "Army of Drones Bonus" system, a gamified procurement platform where units earn virtual points for destroyed targets and spend them on a drone marketplace, creating competition among manufacturers to build cheaper, more effective systems and directing the best equipment to the highest-performing units. Craig also asks directly about the psychological reality of FPV warfare, following a specific soldier through a camera, watching him try to hide, and killing him, and Phoenix answers with a moral clarity that is both philosophically coherent and quietly unsettling.

The AI Movement Podcast24 min

How AI Is Changing Who Gets Promoted | Ximena Paul, Nala

Ximena Paul is the CEO and co-founder of Nala, an AI-powered HR platform focused on performance management, talent reviews, succession planning, and workforce planning. We talk about how AI could make employee feedback more useful, why quiet high performers may become easier to see, and how the manager's job changes when AI takes over more of the process work. As AI gets better at tracking performance and spotting patterns across a company, it may start changing who gets promoted at work. Ximena believes AI can help make talent decisions more data-informed, but the final call still needs to belong to people. If you manage people, lead a team, or are thinking about what makes someone valuable at work now, this episode gives you a clearer way to think about what good leadership looks like as AI becomes part of the workplace. Listener offer Save 5% on Leela Quantum Tech with code BROOKE . Affiliate disclosure: I may earn a commission if you purchase through this link, at no additional cost to you. How I AI is now The AI Movement Podcast. LinkedIn: Brooke Gramer Website: TheAIMovementPodcast Disclaimer: This podcast is for general informational and educational purposes only, not legal, financial, medical, business, technical, or other professional advice. Guest views are their own. Information may change, and accuracy, safety, or results are not guaranteed. Verify information and consult a qualified professional before acting. To the fullest extent permitted by law, The AI Movement, Brooke Gramer, and EmpowerFlow Strategies LLC disclaim liability arising from reliance on this content. How I AI is now The AI Movement Podcast. LinkedIn: Brooke Gramer Website: TheAIMovementPodcast Disclaimer: This podcast is for general informational and educational purposes only, not legal, financial, medical, business, technical, or other professional advice. Guest views are their own. Information may change, and accuracy, safety, or results are not guaranteed. Verify information and consult a qualified professional before acting. To the fullest extent permitted by law, The AI Movement, Brooke Gramer, and EmpowerFlow Strategies LLC disclaim liability arising from reliance on this content.

The Cognitive Revolution1 hr 42 min

AI:AM Highlights: Zvi on Pacing & Trump-Xi, Astra better behaved than Fable? + a new LLM Pain Axis??

Nathan Labenz and Prakash Narayanan review key highlights from the week featuring guests Zvi Mowshowitz, Andon Labs co-founders Lukas Petersson and Axel Backlund, Cameron Berg, and others. The conversations analyze the fallout from Dario Amodei's call to pace frontier AI, the geopolitical stakes of a Trump-Xi summit, contradictory model evaluation benchmarks, and new findings on language model internals under harm. Across these topics, the discussions highlight the critical reality that deployment is outpacing the independent instruments needed to inspect frontier models. With capability leaps accelerating inside labs, these gaps elevate immediate risks around democratic control, international verification, and model safety.

No Priors38 min

Why Diffusion Will Win AI Inference with Inception Co-Founder and CEO Stefano Ermon

As generative AI hits hardware and latency bottlenecks, Stanford professor, diffusion pioneer, and Inception co-founder and CEO Stefano Ermon is betting on a radical new architecture. Stefano joins Sarah Guo to talk about Inception, and how his team is applying diffusion architecture beyond images and video into discrete text and code generation. Stefano explains the limitations of autoregressive LLMs, as well as why parallel token generation in diffusion models offers superior inference scaling and hardware utilization on standard GPUs. He also shares details about Inception’s Mercury models, real-world voice agent applications, the software stack required to serve diffusion-based models at scale, academia’s role at the frontier of AI innovations, and why the next era of AI competition will be defined by efficiency. Continuous Modalities 13:19 – Inception Today 16:45 – Where Speed Wins 17:31 – Inception Customer Base 18:49 – Interaction with Hardware Landscape 19:34 – Inception and the Broader Industry 21:41 – Data Compression and Structure 24:45 – Controllability of Diffusion Modeles 27:25 – Emergent Capabilities at Scale 29:02 – Future Workload Split Between Diffusion vs. Traditional 30:03 – Adoption Challenges 31:44 – Hiring and Team Organization 32:50 – Recursive Self Improvement 34:02 – Resource Allocation 35:10 – Impact of Academia 38:13 – Conclusion

Dwarkesh Podcast1 hr 20 min

Noam Brown – Agent swarms, alignment, & recursive self-improvement

New episode with Noam Brown. We talk about multi-agent, Navier-Stokes, and what the current explosion of maths progress tells us about what happens once you automate AI research. And we also discuss how we will know if the models are actually aligned before we kick off RSI. Watch on YouTube ; read the transcript . Sponsors Jane Street has been interested in AI for a lot longer than you’d think, and not just for trading. In 2011, a full year before AlexNet and over a decade before ChatGPT launched, they hosted the first FOOM Debate between Eliezer Yudkowsky and Robin Hanson on whether AI would lead to an intelligence explosion. Now Jane Street is revisiting the question with a new panel: Daniel Kokotajlo, Ege Erdil, Ryan Greenblatt, and Jaime Sevilla, hosted by Ron Minsky in San Francisco this October. I expect it to be a truly excellent conversation. It runs on its own cloud computer, where it installs the tools it needs to handle tasks end-to-end. For the podcast, we use Grok Bot to help produce our videos. You may have noticed that our ads feature animations of real websites. Getting these pixel-perfect used to mean running a convoluted, multi-step workflow ourselves. Now we just let Grok Bot handle it. Best of all, Grok Bot has learned all of our specs and preferences, so we don’t have to redescribe the task each time! Say you’re doing a major backend refactor: building enough tests to trust it could take weeks. Antithesis solves this by running your software through countless simulated worlds, injecting faults and hunting for failures. On any PR, you can turn a dial to decide exactly how much testing you want. And because every run is fully deterministic, agents can branch off the moment a bug appears, rewind it, inspect memory, and replay it, all while the original test keeps running. (00:22:02) – What math progress tells us about recursive self improvement (00:40:22) – Hugging Face and alignment (01:01:18) – The internal/external model gap (01:08:34) – Chain of thought is degrading (01:14:12) – How will we know when alignment is solved?

Practical AI55 min

How to get discovered in AI search

AI search is changing how people discover information and how brands need to think about visibility. Daniel and Chris talk with Liam Dunne and Ben Moore, co-founders of Discovered Labs, about the shift from traditional SEO to AI search, what happens behind the scenes when an LLM generates an answer, and why retrieval and citations don't always tell the whole story. They dig into AI visibility, Reddit strategy, query fan-out, representation in model weights, and consensus. They also look ahead to a world where AI agents don't just discover websites, but interact with them and take actions on behalf of users. Featuring: Liam Dunne – LinkedIn Ben Moore – LinkedIn Daniel Whitenack – Website , GitHub , X Chris Benson – Website , LinkedIn , Bluesky , GitHub , X Links: What AI models know about 1,000 B2B SaaS brands before web search Discovered Labs Sponsors: Midwest AI Summit: Join AI practitioners on October 15 in Indianapolis for practical sessions, hands-on discussions, and real-world AI solutions. Prior Webinars from our partner Prediction Guard Midwest AI Summit 2026

The TWIML AI Podcast1 hr 5 min

From Voice Agents to AI Avatars with Alexander Smola - #777

Voice AI has gotten remarkably good, but natural conversation remains a high bar. Small delays, awkward interruptions, or the wrong tone can quickly break the illusion—and adding vision and visual presence only raises the stakes. In this episode, Alex Smola, co-founder and CEO of Boson AI, explores the path from today’s voice agents to audiovisual agents and AI avatars. We discuss the technical tradeoffs behind real-time voice, including audio tokenization, latency, model size, and inference cost, as well as what changes when these systems can both see and be seen. We also explore the role of emotional intelligence in AI, how agents can learn from human interactions, and what it will take to move beyond impressive demos toward interactions that actually feel natural. 🗒️ Full show notes:.