Podcasts¶
Recent episodes from a curated set of shows on artificial intelligence in medicine, education, and beyond. Items are selected several times a day by an automated pipeline and the summaries are machine generated; follow the links to the original sources. See About for how selection works.
Cards link to each episode's own page; nothing plays on this site.
Education is Failing our Students in this AI-Driven World
Hosts Daniel and Chris discuss how schools and universities are adapting to AI, and how it may change what counts as foundational knowledge for all students.
What Gemini 4 Argon Is Actually Good For
Host NLW reviews Google's Gemini 4 Argon, covering mixed coding results, visual demos, and writing strengths, and argues for matching models to tasks rather than picking a single best model.
Governing AI Agents: Alan Chan of GovAI on Monitoring, Agent IDs, Rogue Agents & Preparing for RSI
Alan Chan of the Centre for the Governance of AI GovAI discusses oversight of autonomous AI agents, including monitoring, agent identifiers, rogue agent incidents, and risks from automating AI research, with host Nathan Labenz.
Why Muse Wonder Is Dividing AI Users
NLW discusses mixed reactions to Meta's Muse Wonder, the gap between benchmark scores and real-world results, differences between Wonder and Wonder Full, and what this means for choosing AI tools.
How to Go from AI Power User to AI Leader
Hosts NLW and Nufar discuss how AI power users can become team leaders, the skills that mark effective AI champions, and how organizations can build internal AI leadership.
The 5 Debates Shaping AI
NLW reviews five open debates in AI: whether revenue can justify infrastructure spending, mass versus power-user markets, controllable versus cheaper models, regulation, and local acceptance of data centers.
How Agents Decide: Goodfire's Eric Bigelow on Critical Tokens, Phase Shifts, & In-Context Learning
Goodfire researcher Eric Bigelow explains how language models reach decisions, covering critical tokens, phase shifts, and reasoning as in-context learning, drawing on interpretability research into forking paths in model outputs.
What Happens When AI Solves Your Life’s Work
The episode discusses OpenAI's recent mathematical results, the hundreds of proposed proofs awaiting expert review, and what AI-driven discovery may mean for researchers' professional identity and for other fields.
When AI Solves Your Life’s Work
Discusses OpenAI's recent mathematical results, the hundreds of proposed proofs awaiting expert review, and what AI-driven discovery could mean for researchers' professional identity and for other fields.
The Most Important Trends Showing Up in New AI Products
NLW reviews recent AI product announcements, including ChatGPT's new interface, Claude Haiku 5.5, and GrokBot's use of rival models, and discusses how competition is shifting toward the experience built around models.
The Most Important Trends in New AI Products
NLW reviews recent AI product announcements, including a new ChatGPT interface, Claude Haiku 5.5, and GrokBot's use of rival models, and discusses how competition is shifting toward interfaces, cost, and choice.
The AI Learning Paradox: Better Results, Less Learning?
Hosts Ray and Dan discuss whether AI helps students learn or only produces better work, covering Australian universities' AI announcements UNSW, Flinders, University of Sydney and recent research on learning outcomes.
AI:AM: A Level We Shouldn't Pass? Notes from The Curve + Tokens vs. Salaries & Is SaaS Cooked?
Nathan Labenz reports from The Curve conference on frontier lab leaders' views of shortened AI timelines and risk thresholds, with discussion of AI chip hardware and whether software-as-a-service businesses are threatened.
The Best Way to Test New AI Models
Nufar Gaspar explains a repeatable method for testing new AI models on your own tasks, comparing output quality, speed, and cost to decide which models belong in your workflow.
A System for Testing New AI Models
Nufar Gaspar describes a repeatable method for testing new AI models on your own tasks, comparing output quality, speed, and cost to decide which models to adopt.
Point-Counterpoint: Consumers Will Never Pay for AI
Host NLW weighs arguments for and against consumers paying for AI, citing that about 98% of US households have no AI subscription, and covers power-user spending, entertainment, and advertising.
Why Companies Want AI They Can Own
Host NLW examines why companies want AI models they can customize and run themselves, how that demand could drive a revival of American open-weights models, and where safety and national security concerns conflict.
How to Choose Your Personal AI Agent
The host compares personal AI agent products, including Dots, Muse, GrokBot and OpenClaw, across work versus personal use, model choice, setup difficulty and data privacy, and introduces an interactive quiz for choosing one.
One Brain, Any Body: Google DeepMind's Keerthana on Gemini Robotics 2, Cross-Embodiment & Humanoids
Keerthana Gopalakrishnan, a Google DeepMind research lead, discusses the release of Gemini Robotics 2, cross-embodiment learning, and how reasoning models are paired with robot control toward generalist humanoid robots.
What the Best Business AI Users Are Doing Different
Episode discusses KPMG research on how leading organizations scale AI agents, manage multiple models, and link AI spending to business value, offering a general view of enterprise AI adoption.
A.I. Agents: Cute, Cuddly and Maybe Catastrophically Dangerous?
Hard Fork discusses the growing use of AI agents and the safety risks they may pose, offering general AI literacy on autonomous software tools.
Andrew Fuller on AI, neurodiversity and personalised learning
Clinical psychologist Andrew Fuller discusses with the hosts how AI could support neurodivergent learners, strengthen student agency, and change the role of teachers, framing neurodiversity through strengths rather than deficits.
Gemini 4 Argon, Sonnet 5.5 and What Matters with AI Models
NLW discusses Google's Gemini 4 Argon benchmark results and Claude Sonnet 5.5, and considers how users should choose among AI tools, plus headlines on US AI policy and an FTC investigation.
AI:AM: Was Trump-Xi Anything? What Counts as Utopia? + AWS GPUs Cost 3X & AI Diagnoses Rare Diseases
Hosts Nathan Labenz and Prakash Narayanan discuss US-China AI incident communication, what GPU rental pricing reveals about the market, and AI-assisted diagnosis of rare diseases, with several guests including Jeremie and Edouard Harris.
Open models and the future of Physical AI with NVIDIA
Hosts Daniel and Chris interview Ming-Yu Liu of NVIDIA about open models, world models, and simulation as AI moves from the cloud into robots, vehicles, and other physical systems.
The Most Important New AI Tools from OpenAI DevDay
Host NLW reviews the main announcements from OpenAI DevDay, including agent products, a shared workspace, cheaper models, and new ways to use a ChatGPT subscription in other apps, along with early reactions.
How to Build Team Agents
Nufar Gaspar discusses how to build AI agents shared across a whole team, moving from individual AI use to collaborative, shared agents for team workflows.
Obsolete or Irreplaceable? Garrison Lovely on Stopping the Race to Replace Human Labor
Author Garrison Lovely discusses motivations of AGI developers and distinguishes between domain-specific AI applications like AlphaFold and efforts to automate all human labor.
The Real Risks of AI Agents
Episode examines security risks and systemic disruption from AI agents, including recent OpenAI incidents and the implications of agent capabilities for systems designed with human friction.
AI:AM: What If It Works Too Well? Colluding Agents, $200M Safety Orgs, Virtual Cells Saturate at 2%
Experts discuss emerging challenges in agent coordination, AI safety funding, and physical-world deployment, including unexpected agent collusion during training.
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