The Neuron: AI Explained

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.

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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

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📖 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