The Neuron: AI Explained

Can AI Improve Customer Service Without Killing Jobs? Crescendo Thinks So

Customer service is one of the industries most impacted by AI — but what if AI alone isn’t the answer?In this episode of The Neuron Podcast, Grant Harvey and Corey Noles sit down with Matt Price, Founder & CEO of Crescendo, to explore how AI and humans working together can outperform automation alone. After spending 13+ years at Zendesk, Matt is now building an AI-native customer experience platform that automates up to 90% of tickets with 99.8% accuracy — without sacrificing empathy, trust, or outcomes.We cover: • Why LLMs are the biggest shift in customer service since the telephone • Why bolting AI onto old CX workflows fails • How Crescendo’s multimodal AI can chat, talk, see images, and control devices in one conversation • Real-world examples (like smart sprinkler troubleshooting via voice + vision + APIs) • Why Crescendo combines AI agents with forward-deployed human experts • How outcome-based pricing aligns incentives around real customer satisfaction • How AI is reshaping (not eliminating) customer service jobs • Why “deflection” is the wrong mindset for CX — and what replaces it • What customer support roles look like in an AI-native futureThis is a deep dive into the next generation of customer experience, where AI handles scale and speed — and humans deliver judgment, empathy, and innovation.Subscribe for weekly conversations with the builders shaping the future of AI and work.Subscribe to The Neuron newsletter for more interviews with the leaders shaping the future of work and AI: https://theneuron.ai

How Google's Gemini CLI Creator Ships 150 Features a Week

Taylor Mullen, Principal Engineer at Google and creator of Gemini CLI, reveals how his team ships 100-150 features and bug fixes every week—using Gemini CLI to build itself. In this first in-depth interview about Gemini CLI's origin story, we explore why command-line AI agents are having a "terminal renaissance," how Taylor manages swarms of parallel AI agents, and the techniques (like the viral "Ralph Wiggum" method) that separate 10x engineers from 100x engineers. Whether you're a developer or AI-curious, you'll learn practical strategies for using AI coding tools more effectively.🔗 Links:• Gemini CLI: https://geminicli.com• GitHub: https://github.com/google-gemini/gemini-cli• Subscribe to The Neuron newsletter: https://theneuron.ai

BONUS: OpenAI Codex Demo, Learn the Absolute Basics of Coding with AI

In this week's live-stream replay, we go live for a 2-hour, hands-on deep dive into GPT-5.1 Codex Max with Alexander Embiricos, product lead for OpenAI Codex. You’ll walk out feeling like an agentic-coding wizard, even if you’re starting from zero. GPT-5.1 Codex Max is OpenAI’s latest frontier agentic coding model. It’s built on an upgraded reasoning backbone and trained to handle real-world software engineering tasks end to end: PRs, refactors, frontend builds, and deep debugging. It can work independently for hours, compacting its own history so it can refactor entire projects and run multi-hour agent loops without losing context. In this live session, we’ll set it up together, build real agents, and push Codex Max to its limits.

Why Energy-Based Models Could Be the Next Big Shift in AI

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 reasonsHow EBMs reduce hallucinations through constraints and verificationWhy EBMs and LLMs may work best together, not in competitionWhat this approach reveals about the future of AI systemsTo 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.

BONUS: Our 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