Chain of Thought

Conor Bronsdon
  • 5.0 (34)
  • TECHNOLOGY
  • UPDATED BIWEEKLY

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. MAR 10

    I Started r/AI_Agents and Now I'm Launching a VC Fund

    Yujian Tang started the r/AI_Agents subreddit in April 2023. For the first year, it barely moved. Then it hit 9,000 members, he went on vacation, came back to 36,000, and now it's approaching 300,000. In this episode, Yujian talks about how that community grew alongside his event business (Seattle Startup Summit, 900+ attendees last year), his two failed startups, and why he just filed paperwork to launch his own venture fund. Conor and Yujian dig into the mechanics of starting a fund from scratch (Delaware PO boxes, EIN numbers, lawyers), why AI startup valuations have doubled in the last two years, whether a one-person unicorn is realistic, and what failed founders learn that successful ones sometimes miss. Chapters: (0:00) Cold Open: The Subreddit Growth Explosion (0:21) Intro and Meet Yujian Tang (1:06) From AI Research to Community Building (7:26) Where AI Applications Are Headed (10:03) The AI Bubble and a Valuation Reset (10:39) Getting Deal Flow Through Community Events (14:02) Filing the Fund: The Boring Side of VC (16:04) How r/AI_Agents Went from Crickets to 300K (18:39) Building an Accidental Empire (26:37) What Two Failed Startups Taught Him (29:52) Why Pre-Seed Valuations Are Out of Control (37:37) The One-Person Unicorn Debate (39:50) Seattle Startup Summit 2026 (42:17) What Chain of Thought Should Cover Next (43:25) Outro About the Guest: Yujian Tang is the founder of Seattle Startup Summit, the largest startup event in the Pacific Northwest. He created the r/AI_Agents subreddit (now nearly 300K members), runs hackathons and developer events across Seattle and the Bay Area, and is launching an early-stage AI venture fund. Guest Links: Seattle Startup Summit: seattlestartupsummit.com Reddit: reddit.com/r/AI_Agents Show Links: Chain of Thought Podcast: https://chainofthought.show Newsletter: https://newsletter.chainofthought.show/LinkedIn: https://www.linkedin.com/in/conorbronsdon/X/Twitter: https://x.com/ConorBronsdon Sponsor: Thanks to Galileo. Download their free 165-page guide to mastering multi-agent systems at galileo.ai/mastering-multi-agent-systems

    44 min

  2. MAR 4

    I Built an AI Coworker That Runs 90% of My Day

    Sterling Chin stopped thinking of AI as a tool and started treating it like a junior employee. Onboarded it with context, corrected its mistakes, and gave it writing rules. Forty days later, MARVIN was handling 90% of his workday. In this episode of Chain of Thought, Sterling (Applied AI Engineer and Senior Developer Advocate at Postman) walks through live demos of MARVIN, his personal AI assistant built on Claude Code. From pulling meeting transcripts and updating Jira tickets to drafting blog posts and managing his calendar, MARVIN runs as a full-time AI chief of staff. We cover: How MARVIN bookends Sterling's workday from first login to the end of the dayPersonality, sub-agents, and writing rules that make MARVIN an effective co-workerAutomating meeting notes to Jira ticketsWhy DIY assistants outperform big tech alternativesHow Sterling onboarded 12+ colleagues at Postman, including non-technical knowledge workersWhat the compute crunch means for open source AIConnect with Sterling: LinkedIn: https://www.linkedin.com/in/sterlingchin/Twitter/X: https://x.com/SilverJaw82MARVIN Template: https://github.com/SterlingChin/marvin-template Connect with Conor: Newsletter:⁠ ⁠https://conorbronsdon.substack.com/Twitter/X:⁠ https://x.com/ConorBronsdon⁠LinkedIn:⁠ https://www.linkedin.com/in/conorbronsdonYouTube:⁠⁠ https://www.youtube.com/@ConorBronsdon⁠⁠ 🔗 More episodes:⁠⁠ https://chainofthought.show⁠⁠ Timestamps: (0:00) Intro (0:28) Meet Sterling Chin and the MARVIN AI Assistant (9:10) Live Demo: How MARVIN Bookends Your Workday (16:04) Personality, Sub-Agents, and Writing Rules (22:00) Automating Meeting Notes to Jira Tickets (29:30) Why DIY AI Assistants Outperform Big Tech (40:55) Treat Your AI Like a Junior Employee (46:41) How to Get Started with MARVIN (55:36) The Compute Crunch and Open Source Future Thanks to Galileo — download their free 165-page guide to mastering multi-agent systems at galileo.ai/mastering-multi-agent-systems

    1h 2m

  3. FEB 26

    How Intercom Cut $250K/Month by Ditching GPT for Qwen

    Intercom was spending $250K/month on a single summarization task using GPT. Then they replaced it with a fine-tuned 14B parameter Qwen model and saved almost all of it. In this episode, Intercom's Chief AI Officer, Fergal Reid, walks through exactly how they made that call, where their approach has changed over time, and how all of their efforts built their Fin customer service agent. Fergal breaks down how Fin went from 30% to nearly 70% resolution rate and why most of those gains came from surrounding systems (custom re-rankers, retrieval models, query canonicalization), not the core frontier LLM. He explains why higher latency counterintuitively increases resolution rates, how they built a custom re-ranker that outperformed Cohere using ModernBERT, and why he believes vertically integrated AI products will win in the long term. If you're deciding between fine-tuning open-weight models and using frontier APIs in production, you won't find a more detailed decision process walkthrough. 🔗 Connect with Fergal:  Twitter/X: https://x.com/fergal_reid LinkedIn: https://www.linkedin.com/in/fergalreid/ Fin: https://fin.ai/ 🔗 Connect with Conor: YouTube: https://www.youtube.com/@ConorBronsdon Newsletter: https://conorbronsdon.substack.com/ Twitter/X: https://x.com/ConorBronsdon LinkedIn: https://www.linkedin.com/in/conorbronsdon/ 🔗 More episodes: https://chainofthought.showCHAPTERS 0:00 Intro 0:46 Why Intercom Completely Reversed Their Fine-Tuning Position 8:00 The $250K/Month Summarization Task (Query Canonicalization) 11:25 Training Infrastructure: H200s, LoRA to Full SFT, and GRPO 14:09 Why Qwen Models Specifically Work for Production 18:03 Goodhart's Law: When Benchmarks Lie 19:47 A/B Testing AI in Production: Soft vs. Hard Resolutions 25:09 The Latency Paradox: Why Slower Responses Get More Resolutions 26:33 Why Per-Customer Prompt Branching Is Technical Debt 28:51 Sponsor: Galileo 29:36 Hiring Scientists, Not Just Engineers 32:15 Context Engineering: Intercom's Full RAG Pipeline 35:35 Customer Agent, Voice, and What's Next for Fin 39:30 Vertical Integration: Can App Companies Outrun the Labs? 47:45 When Engineers Laughed at Claude Code 52:23 Closing Thoughts TAGSFergal Reid, Intercom, Fin AI agent, open-weight models, Qwen models, fine-tuning LLMs, post-training, RAG pipeline, customer service AI, GRPO reinforcement learning, A/B testing AI, Claude Code, vertical AI integration, inference cost optimization, context engineering, AI agents, ModernBERT reranker, scaling AI teams, Conor Bronsdon, Chain of Thought

    54 min

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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
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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
    52
  • Rating
    Clean
  • Copyright
    © Conor Bronsdon
  • Show Website
    Chain of Thought

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