The Neuron: AI Explained

Modern AI has been dominated by one idea: predict the next token. But what if intelligence doesn’t have to work that way?

In this episode of The Neuron, we’re joined by Eve Bodnia, Founder and CEO of Logical Intelligence, to explore energy-based models (EBMs)—a radically different approach to AI reasoning that doesn’t rely on language, tokens, or next-word prediction.

With a background in theoretical physics and quantum information, Eve explains how EBMs operate over an energy landscape, allowing models to reason about many possible solutions at once rather than guessing sequentially. We discuss why this matters for tasks like spatial reasoning, planning, robotics, and safety-critical systems—and where large language models begin to show their limits.

You’ll learn:

• What energy-based models are (in plain English)

• Why token-free architectures change how AI reasons

• How EBMs reduce hallucinations through constraints and verification

• Why EBMs and LLMs may work best together, not in competition

• What this approach reveals about the future of AI systems
  

To learn more about Eve’s work, visit https://logicalintelligence.com.

For more practical, grounded conversations on AI systems that actually work, subscribe to The Neuron newsletter at https://theneuron.ai.

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 2026
Join 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

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 

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

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/

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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

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

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

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

Nick Talken started a 3D printing materials company in a trailer lab in his co-founder's backyard, sold it to a 145-year-old German chemical giant, then spun out an AI platform that's now transforming R&D for Fortune 100 companies. Albert Invent's foundational AI model—trained on 15 million molecular structures—is helping scientists at companies like Kenvue (maker of Tylenol, Neutrogena, and Listerine) compress projects from 3 months to 2 days. We dig into how enterprises train bespoke AI models on proprietary data, why you can't just use ChatGPT for chemistry, and what becomes possible when AI can "think like a chemist."

Subscribe to The Neuron newsletter: https://theneuron.ai

Albert Invent website: https://www.albertinvent.com

Kenvue partnership announcement: https://www.businesswire.com/news/home/20251014240355/en/