The Neuron: AI Explained

The Neuron

The Neuron covers the latest AI developments, trends and research, hosted by Grant Harvey and Corey Noles. Digestible, informative and authoritative takes on AI that get you up to speed and help you become an authority in your own circles. Available every Tuesday on all podcasting platforms and YouTube. Subscribe to our newsletter: https://www.theneurondaily.com/subscribe

  1. 1D AGO

    2026 AI Predictions: Who Wins, Who Loses, and What Changes Everything

    AI is moving fast — and 2026 is shaping up to be a turning point.In this livestream, Corey and Grant from The Neuron break down our biggest AI predictions for 2026, including: 🏆 Which companies, tools, and model types are most likely to come out on top 📉 Who could lose ground (and what’s driving the shift)🎲 The wildcards most people aren’t factoring in yet👀 What to watch across AI policy, agents, open source, and consumer adoption 🧠 The skills and strategies that will matter most in 2026Join us live for audience Q&A and a real-time debate on the hottest AI takes — then drop your prediction in the comments: what’s the biggest AI surprise coming in 2026? 🔮 Subscribe for weekly AI coverage from The Neuron and more livestreams like this.🎙️ https://theneuron.ai

    2h 41m

  2. 4D AGO

    Inside Google Labs: 3 AI Tools That Will Change How You Create

    In this special episode, we go hands-on with three cutting-edge AI tools from Google Labs. First, Jaclyn Konzelman (Director of Product Management) demos Mixboard, an AI-powered concepting board that transforms ideas into visual presentations using Nano Banana Pro. Then, Thomas Iljic (Senior Director of Product Management) shows us Flow, Google's AI filmmaking tool that lets you create, edit, and animate video clips with unprecedented control. Finally, Megan Li (Senior Product Manager) walks us through Opal, a no-code AI app builder that lets anyone create custom AI workflows and mini-apps using natural language. Subscribe to The Neuron newsletter: https://theneuron.ai Links: Mixboard: https://mixboard.google.com  Flow: https://flow.google  Opal: https://opal.google  Google Labs: https://labs.google

    1h 57m

  3. JAN 27

    This AI Agent Builds Better Code Than Most Developers (Factory AI)

    Autonomous coding agents are moving from demos to real production workflows. In this episode, Factory AI co-founder and CTO Eno Reyes explains what "Droids" really are—fully autonomous agents that can take tickets, modify real codebases, run tests, and work inside existing dev workflows. We dig into Factory's context compression research (which outperformed both OpenAI and Anthropic), what makes a codebase "agent-ready," and why Stanford research found that the ONLY predictor of AI success was codebase quality—not adoption rates or token usage. Whether you're a developer curious about autonomous coding tools or just want to understand where AI engineering is headed, this episode is packed with practical insights. 🔗 Try Factory AI: https://factory.ai 📰 Subscribe to The Neuron newsletter: https://theneuron.ai 📖 Resources mentioned: • Factory's compression research: https://factory.ai/news/evaluating-compression

    56 min

  4. JAN 23

    OpenAI Researcher Explains How AI Hides Its Thinking (w/ OpenAI’s Bowen Baker)

    AI reasoning models don’t just give answers — they plan, deliberate, and sometimes try to cheat. In this episode of The Neuron, we’re joined by Bowen Baker, Research Scientist at OpenAI, to explore whether we can monitor AI reasoning before things go wrong — and why that transparency may not last forever. Bowen walks us through real examples of AI reward hacking, explains why monitoring chain-of-thought is often more effective than checking outputs, and introduces the idea of a “monitorability tax” — trading raw performance for safety and transparency. We also cover: Why smaller models thinking longer can be safer than bigger models How AI systems learn to hide misbehavior Why suppressing “bad thoughts” can backfire The limits of chain-of-thought monitoring Bowen’s personal view on open-source AI and safety risks If you care about how AI actually works — and what could go wrong — this conversation is essential. Resources: Title URL Evaluating chain-of-thought monitorability | OpenAI https://openai.com/index/evaluating-chain-of-thought-monitorability/ Understanding neural networks through sparse circuits | OpenAI https://openai.com/index/understanding-neural-networks-through-sparse-circuits/ OpenAI's alignment blog: https://alignment.openai.com/ 👉 Subscribe for more interviews with the people building AI 👉 Join the newsletter at https://theneuron.ai

    55 min

  5. JAN 20

    The Hidden Cost of AI Agents No One Talks About

    Everyone is rushing to build AI agents — but most companies are setting themselves up for failure. In this episode of The Neuron, Darin Patterson, VP of Market Strategy at Make, explains why agentic AI only works if your automation foundation is solid first. We break down when to use deterministic workflows vs AI agents, how to avoid fragile automation sprawl, and why visibility into your entire automation landscape is now mission-critical. You’ll see real examples of building agents in Make, how Model Context Protocol (MCP) fits into modern workflows, and why orchestration — not hype — is the real unlock for scaling AI safely inside organizations. Subscribe to The Neuron newsletter for more interviews with the leaders shaping the future of work and AI: https://theneuron.ai

    1h 1m

  6. JAN 13

    Why IBM Wants AI to Be Boring

    IBM just released Granite 4.0, a new family of open language models designed to be fast, memory-efficient, and enterprise-ready — and it represents a very different philosophy from today’s frontier AI race. In this episode of The Neuron, IBM Research’s David Cox joins us to unpack why IBM treats AI models as tools rather than entities, how hybrid architectures dramatically reduce memory and cost, and why openness, transparency, and external audits matter more than ever for real-world deployment. We dive into long-context efficiency, agent safety, LoRA adapters, on-device AI, voice interfaces, and why the future of AI may look a lot more boring — in the best possible way. If you’re building AI systems for production, agents, or enterprise workflows, this conversation is required listening. Subscribe to The Neuron newsletter for more interviews with the leaders shaping the future of work and AI: https://theneuron.ai

    53 min

  7. JAN 6

    This AI Grows a Brain During Training (Pathway’s AI w/ Zuzanna Stamirowska)

    Imagine an AI that doesn’t just output answers — it remembers, adapts, and reasons over time like a living system. In this episode of The Neuron, Corey Noles and Grant Harvey sit down with Zuzanna Stamirowska, CEO & Cofounder of Pathway, to break down the world’s first post-Transformer frontier model: BDH — the Dragon Hatchling architecture. Zuzanna explains why current language models are stuck in a “Groundhog Day” loop — waking up with no memory — and how Pathway’s architecture introduces true temporal reasoning and continual learning. We explore: • Why Transformers lack real memory and time awareness • How BDH uses brain-like neurons, synapses, and emergent structure • How models can “get bored,” adapt, and strengthen connections • Why Pathway sees reasoning — not language — as the core of intelligence • How BDH enables infinite context, live learning, and interpretability • Why gluing two trained models together actually works in BDH • The path to AGI through generalization, not scaling • Real-world early adopters (Formula 1, NATO, French Postal Service) • Safety, reversibility, checkpointing, and building predictable behavior • Why this architecture could power the next era of scientific innovation From brain-inspired message passing to emergent neural structures that literally appear during training, this is one of the most ambitious rethinks of AI architecture since Transformers themselves. If you want a window into what comes after LLMs, this interview is essential. Subscribe to The Neuron newsletter for more interviews with the leaders shaping the future of work and AI: https://theneuron.ai

    49 min

  8. 12/30/2025

    This 24-Year-Old Raised $64M to Build an AI Smarter Than the World's Best Mathematicians

    Carina Hong dropped out of Stanford's PhD program to build "mathematical superintelligence" — and just raised $64M to do it. In this episode, we explore what that actually means: an AI that doesn't just solve math problems but discovers new theorems, proves them formally, and gets smarter with each iteration. Carina explains how her team solved a 130-year-old problem about Lyapunov functions, disproved a 30-year-old graph theory conjecture, and why math is the secret "bedrock" for everything from chip design to quant trading to coding agents. We also discuss the fascinating connections between neuroscience, AI, and mathematics. Lean more about Axiom: https://axiommath.ai/  Subscribe to The Neuron newsletter: https://theneuron.ai

    59 min
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About

The Neuron covers the latest AI developments, trends and research, hosted by Grant Harvey and Corey Noles. Digestible, informative and authoritative takes on AI that get you up to speed and help you become an authority in your own circles. Available every Tuesday on all podcasting platforms and YouTube. Subscribe to our newsletter: https://www.theneurondaily.com/subscribe

Information

  • Creator
    The Neuron
  • Years Active
    2024 - 2026
  • Episodes
    58
  • Rating
    Clean
  • Copyright
    © The Neuron
  • Show Website
    The Neuron: AI Explained

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