Stay ConnectedThe Neuron is a daily newsletter with 700,000+ readers that covers the latest AI developments, trends and research; this is our podcast, hosted by Grant Harvey and Corey Noles. We aim to create digestible, informative and authoritative takes on AI that get you up to speed and help you become an authority in your own circles. Available Wednesdays and Sundays on all podcasting platforms and YouTube.
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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.ai
Relevant links:
NVIDIA Build (try Nemotron): https://build.nvidia.com
Nemotron on Hugging Face: https://huggingface.co/nvidia
Open Router: https://openrouter.ai
Kari'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/about
Subscribe 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 them
If 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.io
LTX-2 on Hugging Face: https://huggingface.co/Lightricks/LTX-2.3
LTX Desktop Repo: https://github.com/Lightricks/LTX-Desk
For 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 acquisition
Try Canva AI at https://canva.com/ai
Special 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=podcast
For 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 ships
Ryan 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 beyond
To 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 benchmarks
3x faster than 2.5 Pro
Less than 1/4 the cost of Gemini 3 Pro
1M token context window
Advanced visual and spatial reasoning with code execution
78% on SWE-bench Verified (agentic coding)
Rolling out globally in Gemini app, AI Mode in Search, and developer platforms
Logan 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 profile
Where 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 choices
The real-world way to evaluate models in production: offline evals + the gold-standard A/B test
Stefano also shares what’s next on Mercury’s roadmap—especially around stronger planning and reasoning for agentic use cases.
Try Mercury + learn more: inceptionlabs.ai
For 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 future
This 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
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