Chain of Thought

Conor Bronsdon

AI is reshaping infrastructure, strategy, and entire industries. Host Conor Bronsdon talks to the engineers, founders, and researchers building breakthrough AI systems about what it actually takes to ship AI in production, where the opportunities lie, and how leaders should think about the strategic bets ahead. Chain of Thought translates technical depth into actionable insights for builders and decision-makers. New episodes bi-weekly. Conor Bronsdon is an angel investor in AI and dev tools, Head of Technical Ecosystem at Modular, and previously led growth at AI startups Galileo and LinearB.

  1. JAN 21

    How Block Deployed AI Agents to 12,000 Employees in 8 Weeks w/ MCP | Angie Jones

    How do you deploy AI agents to 12,000 employees in just 8 weeks? How do you do it safely? Angie Jones, VP of Engineering for AI Tools and Enablement at Block, joins the show to share exactly how her team pulled it off. Block (the company behind Square and Cash App) became an early adopter of Model Context Protocol (MCP) and built Goose, their open-source AI agent that's now a reference implementation for the Agentic AI Foundation. Angie shares the challenges they faced, the security guardrails they built, and why letting employees choose their own models was critical to adoption. We also dive into vibe coding (including Angie's experience watching Jack Dorsey vibe code a feature in 2 hours), how non-engineers are building their own tools, and what MCP unlocks when you connect multiple systems together. Chapters: 00:00 Introduction 02:02 How Block deployed AI agents to 12,000 employees 05:04 Challenges with MCP adoption and security at scale 07:10 Why Block supports multiple AI models (Claude, GPT, Gemini) 08:40 Open source models and local LLM usage 09:58 Measuring velocity gains across the organization 10:49 Vibe coding: Benefits, risks & Jack Dorsey's 2-hour feature build 13:46 Block's contributions to the MCP protocol 14:38 MCP in action: Incident management + GitHub workflow demo 15:52 Addressing MCP criticism and security concerns 18:41 The Agentic AI Foundation announcement (Block, Anthropic, OpenAI, Google, Microsoft) 21:46 AI democratization: Non-engineers building MCP servers 24:11 How to get started with MCP and prompting tips 25:42 Security guardrails for enterprise AI deployment 29:25 Tool annotations and human-in-the-loop controls 30:22 OAuth and authentication in Goose 32:11 Use cases: Engineering, data analysis, fraud detection 35:22 Goose in Slack: Bug detection and PR creation in 5 minutes 38:05 Goose vs Claude Code: Open source, model-agnostic philosophy 38:17 Live Demo: Council of Minds MCP server (9-persona debate) 45:52 What's next for Goose: IDE support, ACP, and the $100K contributor grant 47:57 Where to get started with Goose Connect with Angie on LinkedIn: https://www.linkedin.com/in/angiejones/ Angie's Website: https://angiejones.tech/ Follow Angie on X: https://x.com/techgirl1908 Goose GitHub: https://github.com/block/goose Connect with Conor on LinkedIn: https://www.linkedin.com/in/conorbronsdon/ Follow Conor on X: https://x.com/conorbronsdon Modular: https://www.modular.com/ Presented By: Galileo AI Download Galileo's Mastering Multi-Agent Systems for free here: https://galileo.ai/mastering-multi-agent-systems Topics Covered: - How Block deployed Goose to all 12,000 employees - Building enterprise security guardrails for AI agents - Model Context Protocol (MCP) deep dive - Vibe coding benefits and risks - The Agentic AI Foundation (Block, Anthropic, OpenAI, Google, Microsoft, AWS) - MCP sampling and the Council of Minds demo - OAuth authentication for MCP servers - Goose vs Claude Code and other AI coding tools - Non-engineers building AI tools - Fraud detection with AI agents - Goose in Slack for real-time bug fixing

    50 min

  2. JAN 14

    Gemini 3 & Robot Dogs: Inside Google DeepMind's AI Experiments | Paige Bailey

    Google DeepMind is reshaping the AI landscape with an unprecedented wave of releases—from Gemini 3 to robotics and even data centers in space. Paige Bailey, AI Developer Relations Lead at Google DeepMind, joins us to break down the full Google AI ecosystem. From her unique journey as a geophysicist-turned-AI-leader who helped ship GitHub Copilot, to now running developer experience for DeepMind's entire platform, Paige offers an insider's view of how Google is thinking about the future of AI. The conversation covers the practical differences between Gemini 3 Pro and Flash, when to use the open-source Gemma models, and how tools like Anti-Gravity IDE, Jules, and Gemini CLI fit into developer workflows. Paige also demonstrates Space Math Academy—a gamified NASA curriculum she built using AI Studio, Colab, and Anti-Gravity—showing how modern AI tools enable rapid prototyping. The discussion then ventures into AI's physical frontier: robotics powered by Gemini on Raspberry Pi, Google's robotics trusted tester program, and the ambitious Project Suncatcher exploring data centers in space. 00:00 Introduction 01:30 Paige's Background & Connection to Modular 02:29 Gemini Integration Across Google Products 03:04 Jules, Gemini CLI & Anti-Gravity IDE Overview 03:48 Gemini 3 Flash vs Pro: Live Demo & Pricing 06:10 Choosing the Right Gemini Model 09:42 Google's Hardware Advantage: TPUs & JAX 10:16 TensorFlow History & Evolution to JAX 11:45 NeurIPS 2025 & Google's Research Culture 14:40 Google Brain to DeepMind: The Merger Story 15:24 Palm II to Gemini: Scaling from 40 People 18:42 Gemma Open Source Models 20:46 Anti-Gravity IDE Deep Dive 23:53 MCP Protocol & Chrome DevTools Integration 26:57 Gemini CLI in Google Colab 28:00 Image Generation & AI Studio Traffic Spikes 28:46 Space Math Academy: Gamified NASA Curriculum 31:31 Vibe Coding: Building with AI Studio & Anti-Gravity 36:02 AI From Bits to Atoms: The Robotics Frontier 36:40 Stanford Puppers: Gemini on Raspberry Pi Robots 38:35 Google's Robotics Trusted Tester Program 40:59 AI in Scientific Research & Automation 42:25 Project Suncatcher: Data Centers in Space 45:00 Sustainable AI Infrastructure 47:14 Non-Dystopian Sci-Fi Futures 47:48 Closing Thoughts & Resources - Connect with Paige on LinkedIn: https://www.linkedin.com/in/dynamicwebpaige/ - Follow Paige on X: https://x.com/DynamicWebPaige - Paige's Website: https://webpaige.dev/ - Google DeepMind: https://deepmind.google/ - AI Studio: https://ai.google.dev Connect with our host Conor Bronsdon: - Substack – https://conorbronsdon.substack.com/ - LinkedIn https://www.linkedin.com/in/conorbronsdon/ Presented By: Galileo.ai Download Galileo's Mastering Multi-Agent Systems for free here!: https://galileo.ai/mastering-multi-agent-systems Topics Covered: - Gemini 3 Pro vs Flash comparison (pricing, speed, capabilities) - When to use Gemma open-source models - Anti-Gravity IDE, Jules, and Gemini CLI workflows - Google's TPU hardware advantage - History of TensorFlow, JAX, and Google Brain - Space Math Academy demo (gamified education) - AI-powered robotics (Stanford Puppers on Raspberry Pi) - Project Suncatcher (orbital data centers)

    51 min

  3. 12/19/2025

    Explaining Eval Engineering | Galileo's Vikram Chatterji

    You've heard of evaluations—but eval engineering is the difference between AI that ships and AI that's stuck in prototype. Most teams still treat evals like unit tests: write them once, check a box, move on. But when you're deploying agents that make real decisions, touch real customers, and cost real money, those one-time tests don't cut it. The companies actually shipping production AI at scale have figured out something different—they've turned evaluations into infrastructure, into IP, into the layer where domain expertise becomes executable governance. Vikram Chatterji, CEO and Co-founder of Galileo, returns to Chain of Thought to break down eval engineering: what it is, why it's becoming a dedicated discipline, and what it takes to actually make it work. Vikram shares why generic evals are plateauing, how continuous learning loops drive accuracy, and why he predicts "eval engineer" will become as common a role as "prompt engineer" once was. In this conversation, Conor and Vikram explore: Why treating evals as infrastructure—not checkboxes—separates production AI from prototypesThe plateau problem: why generic LLM-as-a-judge metrics can't break 90% accuracyHow continuous human feedback loops improve eval precision over timeThe emerging "eval engineer" role and what the job actually looks likeWhy 60-70% of AI engineers' time is already spent on evalsWhat multi-agent systems mean for the future of evaluationVikram's framework for baking trust AND control into agentic applicationsPlus: Conor shares news about his move to Modular and what it means for Chain of Thought going forward. Chapters:00:00 – Introduction: Why Evals Are Becoming IP01:37 – What Is Eval Engineering?04:24 – The Eval Engineering Course for Developers05:24 – Generic Evals Are Plateauing08:21 – Continuous Learning and Human Feedback11:01 – Human Feedback Loops and Eval Calibration13:37 – The Emerging Eval Engineer Role16:15 – What Production AI Teams Actually Spend Time On18:52 – Customer Impact and Lessons Learned24:28 – Multi-Agent Systems and the Future of Evals30:27 – MCP, A2A Protocols, and Agent Authentication33:23 – The Eval Engineer Role: Product-Minded + Technical34:53 – Final Thoughts: Trust, Control, and What's Next Connect with Conor Bronsdon:Substack – https://conorbronsdon.substack.com/LinkedIn – https://www.linkedin.com/in/conorbronsdon/X (Twitter) – https://x.com/ConorBronsdon Learn more about Eval Engineering:⁠https://galileo.ai/evalengineering⁠ Connect with Vikram Chatterji:LinkedIn – ⁠https://www.linkedin.com/in/vikram-chatterji/⁠

    37 min

  4. 11/26/2025

    Debunking AI's Environmental Panic | Andy Masley

    AI is destroying the planet—or so we've been told. This week on Chain of Thought, we tackle one of the most persistent and misleading narratives in the AI conversation. Andy Masley, Director of Effective Altruism DC, joins host Conor Bronsdon to fact-check the absurd AI environmental claims you've heard at parties, in articles, and even in bestselling books. Andy recently went viral for discovering what he calls "the single most egregious math mistake" he's ever seen in a book—a data center water usage calculation in Karen Hao's NYT Bestseller, Empire of AI, that was off by a factor of 4,500. In this conversation, Andy and Conor break down the myths around AI’s water and energy usage and explore: The viral Empire of AI error and what it reveals about the broader debate Why most AI water usage statistics are misleading or flat-out wrong How one ChatGPT prompt represents just 1/150,000th of your daily emissions Trade-offs around data center cooling + decision making Why "tribal thinking" about AI is distorting environmental activism Where AI might actually help the climate through deep learning optimization If you've ever felt guilty about using AI tools, been cornered at a party about AI's environmental impact, or simply want to understand what the data actually says, this episode, and Andy’s deep dive articles, arm you with the facts. Chapters: 00:00 – Introduction: The Party Guilt Problem 01:54 – Andy's Background and What Sparked This Work 03:50 – The 4,500x Error in Empire of AI 06:39 – Breaking Down the Math: Liters vs. Cubic Meters 10:39 – The Unintended Consequence: Air Cooling vs. Water Cooling 12:51 – Karen Hao's Response and What's Still Missing 19:08 – Why Environmentalists Should Focus Elsewhere 21:41 – The Danger of Tribal Thinking About AI 25:49 – What Is Effective Altruism (And Why People Attack It) 29:15 – EA, AI Risk, and P(doom) 34:31 – Why Misinformation Hurts Your Own Side 37:39 – Using ChatGPT Is Not Bad for the Environment 42:14 – The Party Rebuttal: Practical Comparisons 45:23 – Water Use Reality: 1/800,000th of Your Daily Footprint 48:27 – The Personal Carbon Footprint Distraction 53:38 – Data Centers: Efficiency vs. Whether to Build 55:13 – AI's Net Climate Impact: The Positive Case 59:34 – Deep Learning, Smart Grids, and Climate Optimization 1:03:45 – Final Thoughts Key references IEA Study: AI and climate change - https://www.iea.org/reports/energy-and-ai/ai-and-climate-change#abstract  Nature: https://www.nature.com/articles/s44168-025-00252-3  The Empire of AI Error: https://andymasley.substack.com/p/empire-of-ai-is-wildly-misleading  Using ChatGPT isn’t bad for the environment: https://andymasley.substack.com/p/a-short-summary-of-my-argument-that https://andymasley.substack.com/p/a-cheat-sheet-for-conversations-about  Connect with Andy Masley:  Substack – https://andymasley.substack.com/ X (Twitter) – https://x.com/AndyMasley Connect with Conor Bronsdon:  Substack – https://conorbronsdon.substack.com/ LinkedIn – https://www.linkedin.com/in/conorbronsdon/ X (Twitter) – https://x.com/ConorBronsdon

    59 min

  5. 11/19/2025

    The Critical Infrastructure Behind the AI Boom | Cisco CPO Jeetu Patel

    AI is accelerating at a breakneck pace, but model quality isn’t the only constraint we face.. There are major infrastructure requirements, energy needs, security, and data pipelines to run AI at scale. This week on Chain of Thought, Cisco’s President and Chief Product Officer Jeetu Patel joins host Conor Bronsdon to reveal what it actually takes to build the critical foundation for the AI era. Jeetu breaks down the three bottlenecks he sees holding AI back today:  • Infrastructure limits: not enough power, compute, or data center capacity  • A trust deficit: non-deterministic models powering systems that must be predictable  • A widening data gap: human-generated data plateauing while machine data explodes Jeetu then shares how Cisco is tackling these challenges through secure AI factories, edge inference, open multi-model architectures, and global partnerships with Nvidia, G42, and sovereign cloud providers. Jeetu also explains why he thinks enterprises will soon rely on thousands of specialized models — not just one — and how routing, latency, cost, and security shape this new landscape. Conor and Jeetu also explore high-performance leadership and team culture, discussing building high-trust teams, embracing constructive tension, staying vigilant in moments of success, and the personal experiences that shaped Jeetu’s approach to innovation and resilience. If you want a clearer picture of the global AI infrastructure race, how high-level leaders are thinking about the future, and what it all means for enterprises, developers, and the future of work, this conversation is essential. Chapters: 00:00 – Welcome to Chain of Thought 0:48 - AI and Jobs: Beyond the Hype 6:15 - The Real AI Opportunity: Original Insights 10:00 - Three Critical AI Constraints: Infrastructure, Trust, and Data 16:27 - Cisco's AI Strategy and Platform Approach 19:18 - Edge Computing and Model Innovation 22:06 - Strategic Partnerships: Nvidia, G42, and the Middle East 29:18 - Acquisition Strategy: Platform Over Products 32:03 - Power and Infrastructure Challenges 36:06 - Building Trust Across Global Partnerships 38:03 - US vs. China: The AI Infrastructure Race 40:33 - America's Venture Capital Advantage 42:06 - Acquisition Philosophy: Strategy First 45:45 - Defining Cisco's True North 48:06 - Mission-Driven Innovation Culture 50:15 - Hiring for Hunger, Curiosity, and Clarity 56:27 - The Power of Constructive Conflict 1:00:00 - Career Lessons: Continuous Learning 1:02:24 - The Email Question 1:04:12 - Joe Tucci's Four-Column Exercise 1:08:15 - Building High-Trust Teams 1:10:12 - The Five Dysfunctions Framework 1:12:09 - Leading with Vulnerability 1:16:18 - Closing Thoughts and Where to Connect Connect with Jeetu Patel: LinkedIn – https://www.linkedin.com/in/jeetupatel/  X(twitter) – https://x.com/jpatel41 Cisco - https://www.cisco.com/ Connect with ConorBronsdon   Substack – https://conorbronsdon.substack.com/  LinkedIn – https://www.linkedin.com/in/conorbronsdon/ X (twitter) – https://x.com/ConorBronsdon

    1h 18m

  6. 11/12/2025

    Beyond Transformers: Maxime Labonne on Post-Training, Edge AI, and the Liquid Foundation Model Breakthrough

    The transformer architecture has dominated AI since 2017, but it’s not the only approach to building LLMs - and new architectures are bringing LLMs to edge devices Maxime Labonne, Head of Post-Training at Liquid AI and creator of the 67,000+ star LLM Course, joins Conor Bronsdon to challenge the AI architecture status quo. Liquid AI’s hybrid architecture, combining transformers with convolutional layers, delivers faster inference, lower latency, and dramatically smaller footprints without sacrificing capability. This alternative architectural philosophy creates models that run effectively on phones and laptops without compromise. But reimagined architecture is only half the story. Maxime unpacks the post-training reality most teams struggle with: challenges and opportunities of synthetic data, how to balance helpfulness against safety, Liquid AI’s approach to evals, RAG architectural approaches, how he sees AI on edge devices evolving, hard won lessons from shipping LFM1 through 2, and much more. If you're tired of surface-level AI takes and want to understand the architectural and engineering decisions behind production LLMs from someone building them in the trenches, this is your episode. Connect with ⁨Maxime Labonne⁩ : LinkedIn – https://www.linkedin.com/in/maxime-labonne/ X (Twitter) – @maximelabonne About Maxime – https://mlabonne.github.io/blog/about.html HuggingFace – https://huggingface.co/mlabonne The LLM Course – https://github.com/mlabonne/llm-course Liquid AI – https://liquid.ai Connect with ⁨Conor Bronsdon⁩ : X (twitter) – @conorbronsdon Substack – https://conorbronsdon.substack.com/ LinkedIn – https://www.linkedin.com/in/conorbronsdon/ 00:00 Intro — Welcome to Chain of Thought 00:27 Guest Intro — Maxime Labonne of Liquid AI 02:21 The Hybrid LLM Architecture Explained 06:30 Why Bigger Models Aren’t Always Better 11:10 Convolution + Transformers: A New Approach to Efficiency 18:00 Running LLMs on Laptops and Wearables 22:20 Post-Training as the Real Moat 25:45 Synthetic Data and Reliability in Model Refinement 32:30 Evaluating AI in the Real World 38:11 Benchmarks vs Functional Evals 43:05 The Future of Edge-Native Intelligence 48:10 Closing Thoughts & Where to Find Maxime Online

    53 min

  7. 10/08/2025

    Architecting AI Agents: The Shift from Models to Systems | Aishwarya Srinivasan, Fireworks AI Head of AI Developer Relations

    Most AI agents are built backwards, starting with models instead of system architecture. Aishwarya Srinivasan, Head of AI Developer Relations at Fireworks AI, joins host Conor Bronsdon to explain the shift required to build reliable agents: stop treating them as model problems and start architecting them as complete software systems. Benchmarks alone won't save you.  Aish breaks down the evolution from prompt engineering to context engineering, revealing how production agents demand careful orchestration of multiple models, memory systems, and tool calls. She shares battle-tested insights on evaluation-driven development, the rise of open source models like DeepSeek v3, and practical strategies for managing autonomy with human-in-the-loop systems. The conversation addresses critical production challenges, ranging from LLM-as-judge techniques to navigating compliance in regulated environments. Connect with Aishwarya Srinivasan: LinkedIn: https://www.linkedin.com/in/aishwarya-srinivasan/ Instagram: https://www.instagram.com/the.datascience.gal/ Connect with Conor: https://www.linkedin.com/in/conorbronsdon/ 00:00 Intro — Welcome to Chain of Thought 00:22 Guest Intro — Ash Srinivasan of Fireworks AI 02:37 The Challenge of Responsible AI 05:44 The Hidden Risks of Reward Hacking 07:22 From Prompt to Context Engineering 10:14 Data Quality and Human Feedback 14:43 Quantifying Trust and Observability 20:27 Evaluation-Driven Development 30:10 Open Source Models vs. Proprietary Systems 34:56 Gaps in the Open-Source AI Stack 38:45 When to Use Different Models 45:36 Governance and Compliance in AI Systems 50:11 The Future of AI Builders 56:00 Closing Thoughts & Follow Ash Online Follow the hosts Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Atin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Conor⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Vikram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠Yash⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

    53 min
  8. The accidental algorithm: Melisa Russak, AI research scientist at WRITER

    10/01/2025

    The accidental algorithm: Melisa Russak, AI research scientist at WRITER

    This week, we're doing something special and sharing an episode from another podcast we love: The Humans of AI by our friends at Writer. We're huge fans of their work, and you might remember Writer's CEO, May Habib, from the inaugural episode of our own show. From The Humans of AI: Learn how Melisa Russak, lead research scientist at WRITER, stumbled upon fundamental machine learning algorithms, completely unaware of existing research — twice. Her story reveals the power of approaching problems with fresh eyes and the innovative breakthroughs that can occur when constraints become catalysts for creativity. Melisa explores the intersection of curiosity-driven research, accidental discovery, and systematic innovation, offering valuable insights into how WRITER is pushing the boundaries of enterprise AI. Tune in to learn how her journey from a math teacher in China to a pioneer in AI research illuminates the future of technological advancement. Follow the hosts Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Atin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Conor⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Vikram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠Yash⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Follow Today's Guest(s) Check out Writer’s YouTube channel to watch the full interviews. Learn more about WRITER at writer.com. Follow Melisa on LinkedIn Follow May on LinkedIn Check out Galileo ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Agent Leaderboard

    21 min
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About

AI is reshaping infrastructure, strategy, and entire industries. Host Conor Bronsdon talks to the engineers, founders, and researchers building breakthrough AI systems about what it actually takes to ship AI in production, where the opportunities lie, and how leaders should think about the strategic bets ahead. Chain of Thought translates technical depth into actionable insights for builders and decision-makers. New episodes bi-weekly. Conor Bronsdon is an angel investor in AI and dev tools, Head of Technical Ecosystem at Modular, and previously led growth at AI startups Galileo and LinearB.

Information

  • Creator
    Conor Bronsdon
  • Years Active
    2024 - 2026
  • Episodes
    49
  • Rating
    Clean
  • Copyright
    © Conor Bronsdon
  • Show Website
    Chain of Thought

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