The Neuron: AI Explained

 What if neural networks are less like mysterious black boxes and more like systems we can inspect, debug, and eventually design with intention?In this episode of The Neuron: AI Explained, Corey Noles and Grant Harvey talk with Eric Ho, Cofounder & CEO of Goodfire, an AI interpretability company working to understand what’s happening inside neural networks. Eric explains why models may contain meaningful internal structures — including features, representations, circuits, and curved manifolds — and how mapping those structures could make AI systems safer, more reliable, and more useful.They discuss why models may “think in shapes,” how Goodfire uses AI to interpret other AI systems, what neural geometry can reveal about hallucinations and model behavior, and why interpretability could change how companies train and control their own models.They also get into consciousness, robotics, multimodal models, the bitter lesson, and why Eric thinks more people should be looking under the hood of the most consequential technology of our time.Subscribe to The Neuron for more grounded conversations about how AI actually works: https://www.theneuron.ai/Sponsored by SAS AI Governance: Visit https://www.sas.com/en_us/solutions/ai/governance.html?utm_source=other&utm_medium=cpm&utm_campaign=sas-vpl-gbc-gps-global#The Neuron Academy helps professionals build practical AI skills they can use right away, with lessons on prompting, workflows, and real workplace use cases. Check out https://theneuronacademy.com/ today!

AI agents are not just coming for laptops, inboxes, and office workflows. Samsara Cofounder and CTO John Bicket says they’re also coming for fleets, drivers, dispatchers, safety teams, and the physical infrastructure that keeps the world moving.Recorded on-site at Samsara Beyond 2026 in Las Vegas, this conversation explores how Samsara is layering AI onto dash cams, telematics devices, asset trackers, vehicle data, and operational workflows. John explains how Agent Studio helps companies build useful AI tools without overwhelming frontline teams, why physical operations create different challenges than software-only environments, and how AI can help turn raw vehicle and safety data into coaching, risk reduction, maintenance insights, and real-time decisions.Corey, Grant, and John also dig into AI ride-alongs, privacy concerns around in-vehicle cameras, why driver safety programs need to be framed around trust instead of surveillance, and what the day-to-day work of dispatchers, drivers, and maintenance leaders could look like three years from now.Subscribe to The Neuron for more practical conversations about how AI is changing real businesses, not just demos.https://www.theneuron.ai/

Join Grant Harvey and Corey Noles from The Neuron live on Thursday, July 9 at 10AM PT as we test OpenAI’s new GPT-5.6 model family in real time: Sol, Terra, and Luna.Instead of just reading benchmark charts, we’re going hands-on with the use cases people should actually try first.We’ll test:🛠️ Building a working mini-app from a messy product idea🧹 Refactoring a broken codebase without babysitting it🧠 Using Ultra mode as a project manager with subagents⚙️ Automating one annoying weekly workflow🥊 Testing Sol vs. Terra vs. Luna on the same job🔍 Running the same prompts head-to-head against FableAlso: OpenAI just announced a 10AM PT livestream on July 9 for the next generation of ChatGPT Voice, reportedly featuring an upgraded bidirectional voice model, real-time capabilities, new voice samples, and voice orb colors tied to accent settings.So we’ll also react to what OpenAI shows, explain what “bidirectional voice” actually means for normal users, and test where voice might finally become useful for work instead of feeling like you’re leaving a voicemail for a robot receptionist.By the end, you’ll know what to try first, which model to use for which task, and whether GPT-5.6 or the new ChatGPT Voice actually changes your daily AI workflow.📩 Subscribe to our newsletter: https://www.theneuron.ai

Most AI image tools give you a prompt box and a result. ComfyUI gives creators the pipeline underneath - the models, parameters, nodes, and repeatable workflows that can turn visual AI from a toy into production infrastructure.In this episode of The Neuron, Corey Noles and Grant Harvey talk with Yannik Marek, co-founder and original creator of ComfyUI, about why node-based workflows matter, how open-source visual AI is moving into real creative production, and what teams gain when they can inspect, modify, and repeat every step of generation.They cover how diffusion models work in plain English, why Comfy is useful for VFX, gaming, animation, e-commerce, and creative studios, and how Comfy balances open-source values with Cloud, API, and Enterprise products. Yannik also shares practical hardware advice for running models locally, where open models are catching up fastest, and why the future of creative AI may depend less on a universal prompt box and more on visible, controllable workflows.Try ComfyUI at comfy.org, and subscribe to The Neuron for more grounded conversations about AI in practice.https://www.theneuron.ai/The Neuron Academy helps professionals build practical AI skills they can use right away, with lessons on prompting, workflows, and real workplace use cases. Check out theneuronacademy.com today!

This week on The Neuron: AI Explained, Grant and Corey break down the strangest week in frontier AI so far: Fable 5 relaunching and getting yanked almost immediately, OpenAI’s GPT-5.6 rollout arriving in a weird half-launch state, and the bigger question underneath all of it.What happens when governments can slow, restrict, or pause the most powerful AI systems right as the economy starts depending on them?We’ll get into why these false starts matter beyond Silicon Valley drama. If U.S. labs keep getting caught between safety fears, export controls, and uneven release rules, China may get more room to catch up through open-source models, faster iteration, and fewer distribution bottlenecks. Meanwhile, businesses betting on AI have to plan for a world where the “best model” might disappear, degrade, get delayed, or become unavailable to half their stack overnight.So this episode is our guide to navigating the current AI chop:Why Fable 5’s relaunch and takedown became a warning shotWhat GPT-5.6’s limited launch says about frontier model accessHow restrictions could reshape the U.S. vs. China AI raceWhy every company needs an open-source backup strategyHow AI uncertainty could ripple into the broader economyWhat we’d do now as builders, buyers, workers, and AI-curious professionalsJoin us for a strategic read on what’s changing, what’s fragile, and how to make smarter decisions while the AI ocean gets weird.

OpenAI and Thrive Holdings built Tax AI, a Codex-powered agent that helps prepare complex tax returns while preserving evidence for accountant review. In this episode, Corey and Grant talk with OpenAI’s John de Wasseige and Arthur Fernandes Araujo about how expert corrections become structured signals, how Codex turns repeated failures into evals and scoped engineering tasks, and why the best AI deployments still need humans close to the work. They also dig into what this pattern could mean for bookkeeping, audits, IT help desks, and other expert workflows where the system can measure what “right” looks like.Relevant links:OpenAI Tax AI case study: https://openai.com/index/building-self-improving-tax-agents-with-codex/OpenAI Codex: https://openai.com/codex/Harness engineering: https://openai.com/index/harness-engineering/Thrive Holdings: https://www.thriveholdings.com/Crete: https://www.cretepa.com/Subscribe to The Neuron newsletter: https://theneuron.ai

Humanoid robotics challenges go beyond movement and servo motors. The hardest problems are often AI problems. Bringing intelligence into the physical world means dealing with gravity, friction, uncertainty, and real consequences. Mistakes can break hardware.This week on Neuron Live, we’re joined by Nikita Rudin, Co-founder and CEO of Flexion Robotics, to unpack what it actually takes to build intelligence for humanoid systems.What we’ll cover:🤖 Training control policies and perception models🧪 Bridging simulation and the real world (sim-to-real)🛠️ Robotics training pipelines and the embodied AI stack🔮 Where humanoid and physical AI is headed nextIf large language models are the brain in the cloud, what does intelligence look like when it has to walk, grasp, and not fall over?Expect a deep dive into embodied AI, physical AI, and the systems powering the next generation of humanoid robots.

Larry Meadows, Head of Product Strategy & Evangelism for HP's Workforce Experience Platform (WXP), joins us to break down how HP is using AI to predict and prevent IT problems before employees ever notice them. We get a live demo of the platform—from AI-powered software recommendations across 50M+ devices to automated remediation in 3–4 clicks—and dig into the global memory crisis, shadow AI risks, and why IT leaders are drowning in portals. Whether you manage a fleet of 50 devices or 50,000, this one's worth your time.HP WXP: https://www.hp.com/wxpThe Neuron newsletter: https://theneuron.ai

Confused by AI Skills, Projects, Gems, Custom GPTs, and Agents?You're not alone. It's important to separate which is best for which use-case, because each has a place depending on what you're trying to get done. In this beginner-friendly live episode, we break down what these AI terms actually mean, who creates them, and when everyday users should use each one.Think of this as your plain-English map to the new AI assistant world:✅ Projects = places to organize ongoing work✅ Gems & Custom GPTs = reusable custom assistants✅ Skills = reusable instructions and workflows✅ Agents = AI systems that can take actions on your behalfBy the end of this live session, you'll understand the practical difference between creating a custom assistant, organizing work in a project, giving AI a repeatable skill, and letting an agent complete tasks for you.What You'll Learn:🔹 What an AI Skill is🔹 What an AI Project is🔹 What Google Gemini Gems are🔹 What Custom GPTs are🔹 What AI Agents are🔹 Which one beginners should start with🔹 The simple framework for choosing the right tool for your workflowGet beginner-friendly AI explainers, practical tutorials, and daily updates on what matters in AI.📩 Subscribe to our newsletter: https://www.theneuron.ai

AI can write code, pass exams, and summarize the web, but ask it to reason through a real-world image, and the magic often breaks. Andrew Dai, co-founder and CEO of Elorian, joins The Neuron to explain why visual reasoning may be one of the biggest unsolved problems in AI.Andrew spent years at Google Brain and DeepMind, including work connected to Gemini and sparse mixture-of-experts systems. Now, he’s building Elorian around a simple but powerful idea: if AI is going to understand the physical world, it needs more than text-based reasoning layered on top of images.In this episode, Corey and Grant talk with Andrew about why frontier models struggle with counting, navigation, design, engineering, charts, and physical reasoning; why scaling language models hasn’t solved vision; what a “visual chain of thought” might look like; and how better visual reasoning could accelerate robotics, satellite analysis, product design, and mechanical engineering.Sponsored by Dell Technologies and NVIDIA. Learn more at techrepublic.com/hubs/the-enterprise-guide-to-scalable-ai/.Sponsored by Outshift: Visit https://outshift.cisco.com/?utm_campaign=fy26q3_outshift_ww_paid-media_ioc-neuronai-outshift_podcast&utm_channel=podcast&utm_source=podcast to learn more about the Internet of Cognition.Subscribe to The Neuron for more conversations with the people building the future of AI.

Live from Microsoft Build, Corey Noles sits down with Scott Hanselman for a hands-on Neuron LIVE episode about AI-augmented software development, how it differs from just "vibe coding", and the surprisingly practical things people can now build with tools like GitHub Copilot and more.Scott is one of the best technical explainers in software: a longtime Microsoft and GitHub developer, teacher, speaker, author, blogger, and podcaster who has helped millions of developers understand new technology without making it feel impossible to learn.This episode turned into a live demo tour of what AI coding can already do, led by Scott's own use-cases. Corey and Scott walked through a series of examples showing how AI can help people build useful apps, prototypes, workflows, and small tools from everyday ideas, including Scott's own vibe-coded tools Baby Smash (https://www.babysmash.com/), which lets babies press random buttons for fun shapes and sounds, and Tiny Tool Town (https://www.tinytooltown.com/), which showcases random, cool tools Scott found around the web. But in the coolest demo of all, Scott shows how to take an open source tool and create software a personal blood sugar tracking app for his own diabetes management. If that doesn't get your idea blood flowing for what you can do with AI, we don't know what will! https://www.theneuron.ai/

In this episode of The Neuron, Corey Noles sits down with Mustafa Suleyman, CEO of Microsoft AI, at Microsoft Build 2026 to unpack Microsoft’s next AI chapter: seven new MAI models, a push toward in-house model development, and the idea of Humanist Superintelligence.Mustafa explains how Microsoft is thinking about AI that can reason, code, generate images, transcribe speech, and power real products—without turning the future into a vague AGI race. The conversation gets into what “humanist” means in practice, why Microsoft is building models from the ground up, how AI agents may reshape work, and what it takes to keep increasingly capable systems useful, controlled, and aligned with human goals.You’ll learn why Microsoft is investing in its own model family, how MAI-Thinking-1 and MAI-Code-1-Flash fit into the stack, why Suleyman frames superintelligence around human control, and what builders and operators should watch as agents move into real workflows.Sponsored by BeyondTrustCheck it out at: https://www.beyondtrust.com/products/identity-security-insights/assessment?campid=701Vw00000drII6IAMSubscribe to The Neuron for practical AI conversations with the people building what comes next.

Everyone is talking about Mercury-alpha, the mystery model that many believe could be GPT-5.6.In this live discussion, we're separating fact from speculation and unpacking what would actually matter if OpenAI releases a new flagship model this week.We'll cover:🔹 What Mercury-alpha is (and why people think it's GPT-5.6)🔹 The biggest rumors and evidence so far🔹 What a new OpenAI model would need to deliver to move the industry forward🔹 How Mercury-alpha fits into the broader AI agent race🔹 Codex, Hermes Desktop, and the rise of coding and desktop agents🔹 What all of this means for AI users, builders, and businessesJoin us live, bring your questions, and help us figure out whether Mercury-alpha is the next major leap in AI or just another chapter in the internet's favorite pastime: model-name archaeology.👇 Drop your predictions in the chat:What do you think Mercury-alpha actually is?📩 Subscribe to The Neuron for daily AI insights: https://www.theneurondaily.com/

How do you prove there’s a real human on the other side of the screen when AI can generate faces, IDs, accounts, agents, and entire swarms of bots?Tiago Sada, Chief Product Officer at Tools for Humanity, joins The Neuron to explain why proof of human may become one of the internet’s most important trust layers. Tools for Humanity is building the technology behind World and World ID, a system designed to verify that someone is a real, unique person without requiring them to reveal their identity across the web.Tiago breaks down why CAPTCHAs, phone numbers, KYC, and AI-detection systems are starting to fail; how World ID uses in-person verification, cryptography, and zero-knowledge proofs; and why the future internet may need to distinguish between humans, bots, and agents acting on behalf of humans.We also discuss concert ticket scalping, Tinder verification, Zoom deepfake protection, enterprise fraud, gaming bots, and why AI agents may need a kind of digital “power of attorney.”Subscribe to The Neuron for clear, practical conversations about AI and the future of technology: https://www.theneuron.ai/This episode is sponsored by Guru. https://www.getguru.com/?utm_source=theneuron&utm_medium=podcast&utm_campaign=silver-bundle-june2026

AI agents and automation sound complex, but they’re really about one simple idea: helping you spend less time on repetitive work and more time on the things that need your judgment.In this beginner-friendly Neuron Live, we’ll break down what AI agents are, how automation actually works, and how to start using both without getting overwhelmed.You’ll learn:🤖 How AI agents are different from regular chatbots⚙️ What actually happens inside an automation workflow🧰 Where tools like ChatGPT, Claude, Make, ClickUp, and other AI assistants fit in💼 Practical ways to use AI at work and in everyday life🔁 How to spot tasks that are worth automating⚠️ Common mistakes beginners make with AI workflows✅ How to decide what should stay human and what AI can help withNo coding experience required. No jargon.Just a clear, practical conversation about how to make AI more useful, more responsible, and less intimidating.Join us live, bring your questions, and leave with a better understanding of how to make AI do more than just answer prompts.Subscribe to our newsletter: https://www.theneuron.ai/

What if the next big AI breakthrough is not a bigger model, but a completely different kind of computer?Jeff Shainline, co-founder and CEO of Great Sky, joins The Neuron to explain how his team is building brain-inspired AI hardware using superconductors, photonics, and analog computation. Great Sky’s architecture, called Superconducting Optoelectronic Networks, or SOENs, is designed to move beyond the traditional GPU roadmap by co-locating memory and processing, communicating with light, and mimicking some of the high-connectivity dynamics found in biological brains.In this conversation, Jeff breaks down why today’s chips can struggle with fast, multimodal inference; why transformers may be powerful but inefficient for some future workloads; how Great Sky’s system differs from quantum computing; and why early applications could include fusion reactors, particle physics, video understanding, content moderation, and eventually new model architectures that do not map neatly onto today’s hardware.Subscribe to The Neuron for grounded, practical conversations about where AI is going next—and what actually has to work before the hype becomes real.

Voice agents are moving from “cool demo” to real product infrastructure.In this livestream, we’re joined by Ben Cherry of LiveKit to break down what it actually takes to build real-time AI agents that can listen, respond, interrupt, call tools, and work in production.LiveKit is an open source framework and developer platform for building voice, video, and physical AI agents in production.We’ll talk through the stack behind real-time AI experiences, then build and test a live demo together on The Neuron.In this live demo, we’ll cover:🎙️ How LiveKit helps developers build voice, video, and physical AI agents⚡ What makes real-time agents different from normal chatbots🧠 How voice agents handle latency, interruptions, speech, and tool calls🛠️ Why production-ready AI agents are much harder than a weekend demo🚀 What builders should know before shipping voice AI to real usersAnd yes, we’re doing a live demo, which means there is at least a small chance the agent talks back at exactly the wrong time. Perfect television.Guest: Ben Cherry, LiveKitLiveKit: https://livekit.com/Ben on LinkedIn: https://www.linkedin.com/in/bcherry-product-engineerBen on GitHub: https://github.com/bcherrySubscribe to The Neuron for clear, useful AI news, demos, and explainers for people trying to understand where this tech is actually going.https://www.theneuron.ai/

What happens when AI stops simply giving answers and starts producing proofs a computer can verify?In this episode of The Neuron, Corey Noles and Grant Harvey talk with Tudor Achim, Co-Founder and CEO of Harmonic, the company behind Aristotle — a formal reasoning system built to generate machine-checkable mathematical proofs. Tudor explains why math may be the clearest test case for moving AI from “trust me” to “check me,” and why formal verification could matter far beyond Olympiad benchmarks.They discuss what “mathematical superintelligence” actually means, why Tudor thinks solving a Millennium Prize problem would be a meaningful threshold, and how Lean-based proofs could change the way mathematicians collaborate. They also explore Aristotle’s real-world use cases, from open math problems to verified software, chip design, scientific computing, and the future of AI-assisted discovery.Plus: why Tudor thinks formal math has reached a “zero to one” moment, why specs may be the bottleneck in verified software, and why humans still need to direct the questions AI systems try to solve.Subscribe to The Neuron and sign up for The Neuron Daily at theneuron.ai.

May is Mental Health Awareness Month, and as AI becomes more embedded in our daily lives, one of the biggest questions we face is whether these systems can responsibly support emotional and psychological well-being.AI chatbots are increasingly being used for emotional support, but recent lawsuits faced by OpenAI and earlier ones targeting character.ai and Google's AI Overviews, as well as clinical reports, and internal research have raised valid concerns about their impact on vulnerable users.What does it take to build an AI system specifically designed for mental health from the ground up? Is that even possible?In this LIVE episode of The Neuron Podcast, Corey Noles and Grant Harvey speak with Daniel Reid Cahn, co-founder and CEO of Slingshot AI, about Ash, an AI application purpose-built for therapeutic support. Slingshot has raised $93M from a16z, Radical Ventures, and others to develop a foundation model for psychology trained on structured therapeutic conversations across modalities such as CBT, DBT, and psychodynamic therapy.We discuss the limitations of general-purpose chatbots in mental health contexts, recent controversies surrounding AI and psychiatric risk, and what differentiates a system designed to provide structured therapeutic engagement compared to one being used in a way it was never intended to be. The conversation also explores a broader question: Can AI meaningfully expand access to high-quality mental health care, and where should clear boundaries remain? Or should we keep our counseling where we always have, on a couch with a box of Kleenex and a hug nearby?🔗 Try Ash:https://www.talktoash.com/📌 About The Neuron PodcastThe Neuron breaks down the biggest stories in AI for 580,000+ daily readers. Our podcast goes deeper with the leaders, founders, and researchers shaping the future of AI. New episodes every week.Subscribe to The Neuron newsletter — theneuron.ai

Genspark went from AI search startup to autonomous AI agent platform, hitting $250M ARR in 12 months with no paid ads until they bought a Super Bowl spot. Co-founder and COO Wen Sang joins Corey and Grant to explain what "AI employee" actually means, demos Genspark Claw live (including buying us coffee mid-interview), and lays out his big thesis: legacy software is becoming infrastructure while AI agents become the new interface between humans and work. We get hands-on with Workspace 4.0, Claw, and a custom agent built live for the show.• Genspark Workspace 4.0 announcement: https://www.genspark.ai/blog/genspark-ai-workspace-4• Genspark sb-git: https://genspark.ai/sb-git/intro• OpenAI's customer story on Genspark: https://openai.com/index/genspark/• Forbes AI 50 (2026): https://www.forbes.com/lists/ai50/• Marc Benioff on Salesforce Headless 360 (referenced by Wen): https://x.com/Benioff • Andrej Karpathy's "wiki for agents" idea (referenced as inspiration for sb-git): https://x.com/karpathy• Wen on the DealMaker Show: https://alejandrocremades.com/wen-sang/Try Genspark for free: https://genspark.aiSubscribe to The Neuron newsletter: https://theneuron.ai

New to AI and not sure where to start?Join us live Thursday for The AI Starter Kit: What to Try...and What to Ignore.This beginner-friendly session will help you cut through the noise and focus on the AI tools, habits, and prompts that actually matter. By the end, you’ll know what to try first, what not to worry about yet, and how to ask better questions when you get stuck.In this session, we’ll cover:🚀 The best first steps for AI beginners🛠️ What tools and features are worth trying now🙅 What you can safely ignore for the moment💡 Simple ways to get better answers from AI🔍 How to troubleshoot when AI gives you something unhelpfulWhether you’re brand new to AI or still figuring out how to use it well, this live session will give you a practical place to start.

Can AI move from predicting proteins to actually designing new drugs? Isomorphic Labs is trying to answer one of the biggest questions in science.In this episode of The Neuron, Corey Noles and Grant Harvey talk with Rebecca Paul, Head of Medicinal Drug Design at Isomorphic Labs, and Michael Schaarschmidt, Foundational AI Research Lead.They explain why drug discovery is so slow, expensive, and failure-prone—and why AI drug design is much more complicated than “generate a molecule and ship it.” The conversation covers AlphaFold, structure prediction, molecule generation, binding models, clinical failure rates, human trust in AI systems, and the long-term hope of designing drugs for targets once considered “undruggable.”In this episode:Why drug discovery can take more than a decadeWhat people misunderstand about “AI-designed drugs”How medicinal chemists actually use AI modelsWhy biology is harder than text, images, or codeWhat it would take to make drug discovery faster and cheaperThe dream of designing a drug candidate in one iterationWhy “undruggable” proteins may not stay undruggable foreverAdditional resources:Technical report blog Best resource for learning about the capabilities that we are buildingIsomorphic Labs websiteBest destination for learning more about Iso and joining our team in London, Lausanne or Cambridge, MASubscribe for more grounded conversations on how AI is changing science, work, and the world.For more practical, grounded conversations on AI systems that actually work, subscribe to The Neuron newsletter at https://theneuron.ai.

Join us Thursday as we break down OpenAI’s new Workspace Agents and what they mean for the future of work.We’ll cover:⚙️ What workspace agents are🤖 How they differ from regular chatbots🏢 Where they fit into real team workflows🚀 How to start working with them effectively🔄 What agentic AI means for workplace automation📈 Why teams are shifting from one-off prompts to repeatable AI-powered processesWhether you’re experimenting with ChatGPT at work, leading AI adoption, or trying to understand where OpenAI is taking agents next, this session will help you see what’s possible and what to watch for.Tune in for a practical, hands-on deep dive into the future of AI at work.Sign up for The Neuron newsletter: https://www.theneuron.ai/

Google just acquired an AI startup that lets anyone create real music, music videos, and custom instruments — no experience required. In this hands-on episode, Corey sits down with Kendall Rankin from Google to demo Flow Music (formerly Producer AI), the generative music tool now living inside Google Labs. They build a garage rock song about AI from scratch, generate a music video with VEO, and dig into what "amplifying human creativity" actually looks like when the tool can do most of the lifting. Listeners walk away with a clear view of where AI music tools fit in an artist's workflow, why watermarking (SynthID) matters, and how to try it for free.Try Flow Music: https://producer.ai Google Labs: https://labs.google SynthID (watermarking): https://deepmind.google/technologies/synthid/ Subscribe to The Neuron newsletter: https://theneuron.ai

OpenAI dropped GPT-5.5, so we did the only reasonable thing: went live immediately and tried to break it.In this off-the-cuff Neuron Live, Corey and Grant walk through OpenAI's GPT-5.5 release notes, benchmark claims, rollout details, and early access reactions before testing the model live across coding, reasoning, creativity, web research, and absurd prompt challenges. We also compare a few GPT-5.5 responses against Claude Opus 4.7, test Codex, build a new version of Cat Doom, and ask the important questions, like whether a sentient vending machine that only dispenses expired tuna salad deserves to live.In this episode, we cover:• What OpenAI says is new in GPT-5.5• GPT-5.5’s improvements in coding, computer use, research, and knowledge work• Early benchmark results across Terminal-Bench, GDPval, Frontier Math, BrowseComp, and scientific research tasks• Why token efficiency may matter as much as raw intelligenceGPT-5.5’s rollout across ChatGPT, Codex, Plus, Pro, Business, and Enterprise• Live Codex testing with a one-shot Cat Doom game buildCreative stress tests involving palindromes, time-traveling potatoes, dystopian vending machines, and Lord of the Rings product reviews• First impressions of whether GPT-5.5 feels meaningfully different from GPT-5.4 and Claude Opus 4.7This was not a formal benchmark. It was a first-contact livestream: messy, fast, weird, and exactly the kind of test we like.Subscribe for more AI breakdowns, live model tests, beginner-friendly explainers, and weirdly useful prompt experiments from The Neuron.Sign up for The Neuron newsletter: https://www.theneuron.ai/Follow along for more AI news, analysis, and live experiments.

Grant and Kyle dive into a comprehensive review and live test of the newly released Claude Opus 4.7, a cutting-edge large language model. This session explores its capabilities for coding and game dev, specifically referencing the "Renaissance / Plan Final Fantasy Tactics RPG Game" project. Discover how this ai model performs under pressure and its potential impact on game design workflows.🔴 LIVE at 9:30AM PT / 12:30PM ETAnthropic just dropped Claude Opus 4.7, and we’re putting it through the gauntlet in real time.Join Grant Harvey (Lead Writer at The Neuron) for an unscripted, warts-and-all test of Anthropic’s newest flagship model.What we’re testing- Advanced coding on tasks Opus 4.6 struggled with- New higher-resolution vision support for images up to ~3.75 megapixels- File system-based memory across multi-session work- The new xhigh effort level, which sits between high and max- Claude Code’s new /ultrareview slash command- Auto mode for longer, less-interrupted agent runsWhy this mattersOpus 4.7 is the first model Anthropic is releasing with its new automatic cyber safeguards, following last week’s Project Glasswing announcement.It’s also the direct upgrade path from Opus 4.6 at the same price:- $5 per million input tokens- $25 per million output tokensIf you build on Claude, this is likely the model you’ll be using next.What’s changing under the hood- New tokenizer, where the same input can map to more tokens depending on content type, roughly 1.0x to 1.35x- State-of-the-art score on GDPval-AA, a third-party evaluation of economically valuable knowledge work- Better instruction following, which means prompts written for earlier models may now behave differently- Improvements across finance agent evals, document reasoning, and long-context tasksBring your hardest prompts. We’ll run them live and show you what breaks, what shines, and whether it’s worth migrating today.Watch part two, where Grant covers Codex for (almost) anything: https://youtube.com/live/OiRkwm3-og0📰 Full writeup in tomorrow’s newsletter: 🐱 Subscribe to The Neuron (700K+ readers): https://www.theneuron.ai

AI can reason about text and images, but it still struggles to understand the physical world. In this episode, Grant sits down with Peter Wilczynski, Chief Product Officer at Vantor (formerly Maxar Intelligence / Digital Globe), to unpack why spatial intelligence is emerging as critical AI infrastructure. Peter spent years at Palantir building ontology systems and mapping tools for defense operations before joining Vantor, where his team has built a 100M+ square kilometer 3D model of the entire Earth at 50cm resolution. We dig into how satellite imagery becomes machine-readable through embedding models, why "ground truth world models" are fundamentally different from hallucinated ones, the Raptor GPS-alternative system, simulation and digital forensics, the future of augmented reality, and why the physical world might be the most important thing AI still doesn't understand.Vantor: https://vantor.comTensorGlobe Platform: https://vantor.com/product/platform/Vantor rebrands from Maxar Intelligence (Business Wire): https://www.businesswire.com/news/home/20251001760322/en/Vantor-Rebrands-from-Maxar-Intelligence-Unveils-AI-Powered-PlatformSubscribe to The Neuron newsletter: https://theneuron.ai

Brandon Baum — better known as heybrandonb to his 25M+ followers — built a YouTube empire making cinematic, effects-heavy videos that look like they cost millions but were born in a bedroom during COVID. In this episode, we get into how he went from 2 views to a million followers in a month, why he shoots everything on iPhones with a custom 3D-printed dual-phone rig, how AI tools like Firefly Boards have replaced his Post-it Note wall, and why he thinks the atmosphere is "ripe for change" in Hollywood. We also talk about what content is actually performing now (hint: it's not spectacle anymore), his plan to seed original IP on social before taking it to theaters, and why he's building custom AI agents to offload his admin so he can just be creative.Brandon B's YouTube channel:https://www.youtube.com/@heybrandonbAdobe Firefly Boards: https://firefly.adobe.com/LM Studio / Ollama (referenced in OpenClaw discussion): https://lmstudio.ai/ https://ollama.com/Subscribe to The Neuron newsletter: https://theneuron.ai

Let’s build with v0 in real time. We’re going LIVE with Tom Occhino, Chief Product Officer at Vercel, to explore vibe coding and take a hands-on look at v0, Vercel’s AI-powered development platform for building apps faster. We’ll show v0 live and walk through how it turns a simple prompt into a real, shippable interface. Tom will also explain what “vibe coding” actually looks like in practice, including how teams are using it today and where it fits into modern development workflows. What we’ll cover:⚡ You’ll see a live build using v0, from the first prompt to a working app.🧠 We’ll break down what vibe coding means and why so many teams are experimenting with it.🔐 Tom will share how Vercel thinks about AI-assisted development, including security, developer experience, and scale.🧭 We’ll talk about product, community, and where the Vercel ecosystem is headed next.❓ We’ll wrap with live Q&A and take questions from the chat. If you’re building products, shipping web apps, or thinking about how AI fits into your workflow, this session will give you a clear, practical look at what’s possible.Links: • Try v0: https://v0.app/• Vercel platform: https://vercel.comCheck out these template to get inspired: https://v0.app/templatesHow to prompt with v0: https://vercel.com/blog/how-to-prompt-v0 📬 For more AI news and deep dives, subscribe to The Neuron newsletter: https://theneuron.ai 🎙️ New to streaming or looking to level up? Check out StreamYard and get $10 discount! 😍 https://streamyard.com/pal/d/5214856690925568

A team of former Google DeepMind researchers just raised $2B to build America's answer to DeepSeek. In this episode, we sit down with Ioannis Antonoglou (Yannis), co-founder and CTO of Reflection AI, who helped create AlphaGo—the AI that beat the world champion in the game of Go back in 2016. Yannis breaks down what Reflection is building, why they're releasing frontier-level AI models as open-weight, and how mixture-of-experts architecture lets massive models run efficiently. We dig into reinforcement learning, the US vs. China open source gap, sovereign AI, coding agents, and why open science might be the fastest path to the most powerful AI on the planet.Reflection AI: https://www.reflection.aiReflection AI raises $2B at $8B valuation (TechCrunch): https://techcrunch.com/2025/10/09/reflection-raises-2b-to-be-americas-open-frontier-ai-lab-challenging-deepseek/Previous Neuron coverage of DeepSeek: https://www.theneuron.ai/newsletter/deepseek-returns https://www.theneuron.ai/newsletter/10-wild-deepseek-demosSubscribe to The Neuron newsletter: https://theneuron.ai

This video is a re-upload from our livestream on YouTube. Most people are still using ChatGPT the way they used Google in 2005: type a question, get an answer, close the tab.In 2026, that’s like owning a professional kitchen and only using the microwave.In this episode, Grant and Corey walk through The Neuron’s 5-Level AI Proficiency Stack — a framework for going from “I use ChatGPT sometimes” to “AI saves me 10 hours a week.”No coding required. No hype. Just the actual progression that separates casual users from people getting real, compounding value out of AI every single day.The 5 Levels:🔹 Level 1: Projects — Why your first move isn’t prompting. It’s onboarding.🔹 Level 2: Prompting — The simplest formula that actually works🔹 Level 3: Skills — Turn one good conversation into a reusable superpower🔹 Level 4: Automations — Set it, schedule it, forget it🔹 Level 5: Agents — AI that decides what to do and when to do itTools mentioned:• ChatGPT Projects: https://help.openai.com/en/articles/10169521-projects-in-chatgpt• Claude Skills: https://support.claude.com/en/articles/12512176-what-are-skills• Claude Cowork: https://support.claude.com/en/articles/13854387-schedule-recurring-tasks-in-cowork• OpenAI Codex: https://developers.openai.com/codex/app/automations• Gemini Opal: https://blog.google/innovation-and-ai/models-and-research/google-labs/opal-agent/• Gemini Scheduled Actions: https://support.google.com/gemini/answer/16316416📩 Read the full deep dive:https://www.theneuron.ai/explainer-articles/how-to-actually-use-ai-in-2026-the-complete-guide/

Brian Gerkey is the CTO of Intrinsic, the robotics software company that started inside Alphabet and now sits inside Google, working directly with DeepMind and Gemini. Brian co-created ROS (Robot Operating System), the open-source platform used by over 1 million developers that powers everything from factory robots to NASA's Astrobee on the International Space Station. In this episode, Grant talks with Brian about "physical AI" — what happens when AI leaves the screen and starts controlling robots in the real world. They cover why 80% of US manufacturing facilities still have zero automation, how Intrinsic's platform acts as the "Android of robotics," the breakthroughs in AI-powered perception that let robots see with sub-millimeter accuracy using cheap cameras, the challenges of simulating physical contact (friction is a nightmare), and why the best robot application ideas often come from people who know nothing about robots.Subscribe to The Neuron newsletter: https://theneuron.aiIntrinsic: https://www.intrinsic.ai/ROS (Robot Operating System): https://www.ros.org/AI for Industry Challenge: https://www.intrinsic.ai/events/ai-for-industry-challengeIntrinsic joins Google (Feb 2026): https://www.intrinsic.ai/blog/posts/intrinsic-joins-google-to-accelerate-physical-ai

Most businesses don't buy their AI services directly from OpenAI or Google—they buy it through a massive, invisible distribution network called "the channel." Victoria Durgin and Katie Bavoso of Channel Insider join Corey and Grant to explain how this hidden industry works, why AI is shaking it up unlike anything before, and what it means for businesses trying to adopt AI in 2026.Subscribe to The Neuron newsletter: https://theneuron.aiChannel Insider: https://channelinsider.com

In this episode of The Neuron Podcast, Corey Noles and Grant Harvey sit down with Dan Shipper, CEO of Every, to talk about agent-native engineering—the framework his team uses to build and ship AI-powered products at a pace most companies can't match.Dan walks us through what happened when his AI document editor Proof went viral (and then went down), why he believes the way we build software is fundamentally changing, and how Every's small team manages to ship and maintain an entire suite of AI tools: Spiral (automatic style guides from your writing), Sparkle (AI writing cleanup with custom folders), Cora (AI research assistant, now on iOS), Monologue (AI-powered journaling with notes), and Proof (the agent-first document editor that broke the internet for a day), as well as their new to be revealed on Friday: Plus One (a hosted AI agent for Slack).Whether you're a founder, developer, or just someone trying to understand what "agentic" actually means in practice—this conversation is the real-world playbook.Subscribe to The Neuron newsletter: https://theneuron.aiProducts mentioned:• Every: https://every.to• Spiral: https://spiral.computer• Sparkle: https://sparkle.computer• Cora: https://cora.computer• Monologue: https://www.monologue.to/• Proof: https://proofeditor.ai• Plus One (the new one!): https://every.to/plus-one

Nick Heiner leads RL environment development at Surge AI, the bootstrapped company that hit $1.2B in revenue training models for OpenAI, Anthropic, Meta, and Google. In this episode, we break down reinforcement learning environments—the secret training grounds where AI agents learn to actually do work. Nick shares why even the best models fail 40% of real workplace tasks, what happened when 200 Wall Street experts graded GPT-5 and Claude, and his prediction that a $1B company with one human employee could exist by 2030.A Special Thank You To Our Sponsor For This Video: Dell AI Factory with NVIDIA. Learn more at https://dell.com/yourwaytoaiResources: • Surge AI Research – Hierarchy of Agentic Capabilities: https://arxiv.org/abs/2601.09032 • Surge AI Blog: https://surgehq.ai/blog • Nick's Sonnet 4.5 Review: https://surgehq.ai/blog/sonnet-4-5-product-take • Nick’s Substack: https://nickheiner.substack.com/ • SurgeHQ’s enterprisebench: https://surgehq.ai/blog/enterprisebench-corecraft • Nick’s hilarious Gemini 3.1 review: https://nickheiner.substack.com/p/gemini-31-pro-not-leading-edge-also  • Hemingway-bench AI Writing Leaderboard https://surgehq.ai/blog/hemingway-bench-ai-writing-leaderboard • LMArena is a cancer on AI: https://surgehq.ai/blog/lmarena-is-a-plague-on-ai • Dell AI Factory with NVIDIA: https://dell.com/yourwaytoaiSubscribe to The Neuron newsletter: https://theneuron.ai

Proton—the company behind the world's largest encrypted email service with 100M+ users—just launched Lumo, a privacy-first AI assistant. We sit down with Eamonn Maguire, who leads Proton's ML team and built Lumo from the ground up. Eamonn has a PhD from Oxford and a postdoc at CERN, and he breaks down how Lumo's encryption actually works, why Big Tech's business model prevents them from building private AI, the real privacy threats hiding inside viral AI trends like Ghibli-fication, and whether AI agents are safe to connect to your bank account. Listeners will learn how encrypted AI handles your data differently, what open-source models power Lumo, and why "set-and-forget" agents are still more hype than reality.🔗 Links & Resources:Lumo by Proton: https://lumo.proton.me Proton: https://proton.me Lumo 1.3 (Projects): https://proton.me/blog/lumo-1-3 Lumo for Business: https://proton.me/blog/lumo-business Proton Sheets: https://proton.me/blog/sheets-proton-drive CLI for Proton Pass: https://proton.me/blog/proton-pass-cli Reserve your child's email: https://proton.me/mail/born-private/email Subscribe to The Neuron newsletter: https://theneuron.ai 

Carta CMO Nicole Baer joins Corey and Grant to break down the real state of startups in 2026. With half of all venture funding now flowing to AI-native companies and seed deals at a six-year low, the startup playbook has fundamentally changed. Nicole shares Carta’s data on solo founders, the new billion-dollar timeline, why the Bay Area’s grip is tighter than ever, and how AI is reshaping everything from marketing to fund administration.Carta State of Startups 2025 Report: https://carta.com/blog/state-of-startups-2025/Carta Data & Insights (free): https://carta.com/data/Subscribe to The Neuron newsletter: https://theneuron.ai

Recorded live at NVIDIA GTC 2026 in San Jose, Corey sits down with returning guest Kari Briski—VP of Generative AI Software for Enterprise at NVIDIA—to unpack their biggest open-source model yet: Nemotron 3 Super. Kari breaks down why a 120B-parameter model runs as fast as a 12B one, how multi-agent systems are going from science fiction to production, and why Jensen Huang is calling this "a new operating system." We also dig into NVIDIA's work on Open Claw security, the 35x explosion in open-model token generation, and where omni-modal AI is heading next.Subscribe to The Neuron newsletter: https://theneuron.aiRelevant links:NVIDIA Build (try Nemotron): https://build.nvidia.comNemotron on Hugging Face: https://huggingface.co/nvidiaOpen Router: https://openrouter.aiKari's previous Neuron episode (Oct 2025): https://youtu.be/p0INn_w7TYo

Scientific discovery has always been slow. Until now.In this episode, we sit down with Dr. Qichao Hu, CEO of SES AI, to reveal how they are using AI agents to turn a 8-year research cycle into a 2-week sprint. By combining autonomous "wet labs" with advanced AI models, they are solving one of the hardest physics problems in tech: the battery bottleneck.We dive deep into how this "Molecular Universe" project isn't just about EV batteries—it's about unlocking power for data centers, robotics, and AR glasses. If you want to see a concrete example of AI agents working in the physical world to solve material science constraints, do not miss this conversation.🔗 Learn more about SES AI: https://www.ses.ai/🔗 Follow the Molecular Universe project: https://molecular-universe.com/aboutSubscribe for more interviews with the people building AI’s next wave.For more practical, grounded conversations on AI systems that actually work, subscribe to The Neuron newsletter at https://theneuron.ai.

In this episode, we sit down with Yaron Inger, co-founder of Lightricks and LTX, to explore the future of open-source AI video.LTX-2 is currently the #1 ranked open-source audio & video model on Hugging Face — with over 4.5 million downloads in just two months.But what makes it different?It runs locally.It can be fine-tuned on your own IP.It integrates into real video workflows.And it might change how filmmaking, education, and creative work evolve in the AI era.We talk about:• Why open models are catching up to Big Tech• How smaller models are getting better through distillation• Running AI video on consumer GPUs• Infinite, autoregressive video generation• AI teachers that change environments in real time• Whether AI will replace filmmakers — or empower themIf you care about the future of creativity, open AI, or the economics of filmmaking… this one is worth your time.Check out LTX: https://ltx.ioLTX-2 on Hugging Face: https://huggingface.co/Lightricks/LTX-2.3 LTX Desktop Repo: https://github.com/Lightricks/LTX-DeskFor more practical, grounded conversations on AI systems that actually work, subscribe to The Neuron newsletter at https://theneuron.ai.

You've probably used Canva—but you probably haven't seen what it can do with AI. In this episode of The Neuron, we sit down with Danny Wu, Head of AI Products at Canva, to explore how the platform went from a simple design tool to a full-blown "Creative Operating System" powered by AI—serving 230+ million users every month.Danny walks us through how Canva's MCP server lets you create fully editable designs from inside ChatGPT, Claude, and Microsoft Copilot, why their new Canva Design Model is fundamentally different from typical AI image generators (hint: layers), and why 24 billion AI tool uses later, the most surprising use cases are ones they never anticipated.We also get Danny's take on whether AI will homogenize all design, his advice for freelancers who don't want to get replaced, and a live demo of Canva's AI design generation in action.You'll learn:• How MCP powers Canva inside ChatGPT, Claude, and Copilot• What the Canva Design Model understands that GPT-4 doesn't• Why editable layers (not flat images) are the real AI design breakthrough• Danny's advice for freelancers to become irreplaceable in an AI world• How Canva uses AI internally on tens of millions of lines of code• Why AI assistants are becoming "the new SEO" for user acquisitionTry Canva AI at https://canva.com/aiSpecial thanks to the sponsor of this video, Cohesity: https://www.cohesity.com/ResilienceEverywhere/?utm_source=brand-ta-podcast&utm_medium=direct-publisher&utm_campaign=fy26-q2-01-amer-us-digital-awarewbpg-brd-genbr&utm_content=podcastFor more practical, grounded conversations on AI and emerging tech, subscribe to The Neuron newsletter at https://theneuron.ai.

Ryan Carson taught over 1,000,000 people how to code at Treehouse and spent 25% of his entire life doing it. Now he says everything about that process needs to change.In this livestream, Ryan joins Corey Noles and Grant Harvey to rethink programming education from scratch. When AI agents can write production code, pass competitive coding challenges, and ship features while you sleep.We'll cover:🧠 What’s still fundamental when agents handle the syntax🔄 Where beginners should start in 2026 (it’s not where you think)🚀 The new hard parts: deployment, databases, security, and getting your app on the internet⭐ Ryan’s viral 3-file system for building with AI agents (5,000+ GitHub stars)🧪 Why “vibe coding” gets you a prototype but not a product🛠️ The skills that separate someone who prompts from someone who shipsRyan is the founder of Treehouse (raised $23M, taught 1M+ students, acquired 2021), Builder in Residence at Amp (Sourcegraph's coding agent), and is currently building Untangle, a real production app, almost entirely with AI tools.Whether you're a complete beginner curious about coding in 2026 or an experienced developer rethinking your workflow, this one's for you.🔗 LINKS & RESOURCES:• Ryan Carson's website: https://www.ryancarson.com/• Ryan's articles on agent workflows: https://www.ryancarson.com/articles• Code Factory workflow: https://x.com/ryancarson/status/2023452909883609111• Agent teams in OpenClaw: https://x.com/ryancarson/status/2020931274219594107• Agents that ship while you sleep: https://x.com/ryancarson/status/2016520542723924279• Ryan's newsletter: https://ryancarson.substack.com/• Untangle: https://untangle-us.com/• Amp (Sourcegraph coding agent): https://ampcode.com/🗞️ Subscribe to The Neuron newsletter: https://theneuron.ai

AI data centers are going to double their power consumption by 2030—so where's all that energy coming from? One answer is fusion, the same process that powers the sun.In this episode of The Neuron, we're joined by Brandon Sorbom, Chief Science Officer and Co-founder of Commonwealth Fusion Systems, to explore how his company is racing to build the world's first commercial fusion power plant—and how AI is helping them get there faster.Brandon explains why fusion has been "30 years away" for decades, what changed with high-temperature superconducting magnets, and why fusion is fundamentally safer than fission (hint: fusion is "default off"). We dive into CFS's collaborations with Google DeepMind and NVIDIA, what it takes to wrangle 10,000 unique parts, and when we might actually see fusion on the grid.You'll learn:• What fusion actually is (and why it's not nuclear fission)• Why high-temperature superconducting magnets changed everything• How AI is accelerating plasma control and simulation• The safety profile that makes fusion regulated like an MRI, not a reactor• When CFS expects to hit Q > 1 (net energy) and beyondTo learn more about Commonwealth Fusion Systems, visit https://cfs.energy.For more practical, grounded conversations on AI and emerging tech, subscribe to The Neuron newsletter at https://theneuron.ai

From the YT live archives: Google just dropped Gemini 3 Flash—a model that outperforms Gemini 2.5 Pro (their last top model) while running 3x faster at less than 1/4 the cost. It's frontier-level reasoning at Flash-level speed, and it's rolling out globally right now.We're sitting down with Logan Kilpatrick from Google DeepMind to explore what this actually means for developers, knowledge workers, and anyone trying to figure out how AI fits into their workflow.What we'll cover:🔥 Live demos – Logan will show us Gemini 3 Flash in action, from coding to multimodal understanding ⚡ What's now possible – Use cases that weren't practical with previous models (or weren't possible at all)🛠️ Building together – We might wire up a tool live if Logan's game (we've got ideas) 💰 Intelligence too cheap to meter – We'll dig into the economics: when AI gets this powerful and this affordable, does it change the hiring calculus? On that last point: right now, data shows AI is raising wages for AI-impacted roles because workers who use AI effectively can command higher salaries. But what happens when frontier intelligence costs $0.50 per million tokens? When does “intelligence as a commodity” flip from “AI makes workers more valuable” to “why hire a human?” We’ll see if we can get Logan’s take on this topic! Key specs on Gemini 3 Flash:Outperforms Gemini 2.5 Pro across most benchmarks3x faster than 2.5 ProLess than 1/4 the cost of Gemini 3 Pro1M token context windowAdvanced visual and spatial reasoning with code execution78% on SWE-bench Verified (agentic coding)Rolling out globally in Gemini app, AI Mode in Search, and developer platformsLogan has been at the center of Google's push to make frontier AI accessible to millions of developers. If you're shipping products, building with AI, or just trying to wrap your head around where this is all going, this conversation will give you clarity.

Diffusion models changed how we generate images and video—now they’re coming for text.In this episode, we sit down with Stefano Ermon, Stanford computer science professor and founder of Inception Labs, to unpack how diffusion works for language, why it can generate in parallel (instead of token-by-token), and what that means for latency, cost, and real-time AI products.We talk through:The simplest mental model for diffusion: generate a full draft, then refine it by “fixing mistakes”Why today’s autoregressive LLM inference is often memory-bound—and why diffusion can shift it toward a more GPU-friendly compute profileWhere Mercury wins today (IDEs, voice/real-time agents, customer support, EdTech—anywhere humans can’t wait)What changes (and what doesn’t) for long context and architecture choicesThe real-world way to evaluate models in production: offline evals + the gold-standard A/B testStefano also shares what’s next on Mercury’s roadmap—especially around stronger planning and reasoning for agentic use cases.Try Mercury + learn more: inceptionlabs.aiFor more practical, grounded conversations on AI systems that actually work, subscribe to The Neuron newsletter at https://theneuron.ai.

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

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

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.

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.

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