Chain of Thought | AI Agents, Infrastructure & Engineering

Conor Bronsdon
  • 5.0 (36)
  • Technology
  • Updated Biweekly

AI is reshaping infrastructure, strategy, and entire industries. Chain of Thought is the podcast where builders reason through what's changing. Host Conor Bronsdon sits down with the engineers and founders shipping AI in production to get past the hype into what's working and what isn't. Episodes cover model infrastructure, inference, agent frameworks, evaluation, and developer tools. Guests have come from NVIDIA, Google DeepMind, AMD, Databricks, Vercel, and more. Every episode carries a full transcript and show notes at chainofthought.show. New episodes weekly. Conor Bronsdon is an independent consultant and angel investor in AI infrastructure and developer tools. He led technical ecosystem at Modular, acquired by Qualcomm in 2026; led developer awareness at Galileo, acquired by Cisco; and ran developer marketing at LinearB, where he was GM of the Dev Interrupted podcast and community. Views expressed by the host and guests are their own.

  1. 1d ago

    Slack Wants to Be the Context Harness for Code | CPO Jaime DeLanghe

    Jaime DeLanghe has spent nine years at Slack turning search, machine learning, and now agents into product. Her team just shipped Slack Code: tag a coding agent like Claude Code, Devin, Codex, or the GitHub agent in a conversation, and it spins up a code channel where everyone in that conversation gets a live development environment, diffs post as artifacts, and the channel winds down when the task is done. Slack's bet is that AI at work is multiplayer. Agents belong in the channels where teams already work, not in a private chat with one person. Jaime explains why Anthropic pushes so much of its code through Slack, how the channel permission model became the agent context model, and what has to change in engineering culture when the branch is public and the whole team is steering the same agent. The bigger question is whether Slack becomes the context harness where enterprise agents actually run. In this conversation: What happens mechanically when an agent creates a code channel, from authentication to diffs as artifactsWhy engineering at Slack now looks like delegating discrete tasks to agents instead of copy-pasting from a chatHow Slack's channel permission model doubles as the context and access model for agentsWhy Anthropic ships code through Slack: the conversation is where the issue emergesHow culture decides whether a multiplayer coding session converges or splitsWhy solo-terminal coding with an "army of Claudes" reinforces bias, and what social spaces fixSlack as an accidental knowledge management system that ranks recency and engagement over correctness(0:00) Slack as an IDE and a GitHub for your team(0:29) Who is Jaime DeLanghe(1:21) The reaction to the Slack Code launch(5:30) Why coding agents belong in a context-rich environment(6:08) Engineers now manage agents, not copy-paste code(7:24) The permission model: agents get the channel's context(11:44) What happens when a code channel is created(15:00) Why Anthropic pushes so much code through Slack(19:14) Steering one agent with many people: culture decides(24:54) Slackbot, skills, and MCPs: agents go where the work is(30:53) The solo terminal vs. agents in social spaces(33:53) Org charts and ownership when agents join the team(39:33) Learning loops and shared agent memory(42:39) Citations, recency, and accidental knowledge management(46:50) Context bloat and multi-pass search for agents(50:01) How Jaime uses Slackbot as CPO(52:38) Slack Code is V1 of multiplayer AI Connect with Jaime DeLanghe: LinkedIn: https://www.linkedin.com/in/jaime-delanghe-aba59b1a/Slack Code announcement: https://slack.com/blog/news/slack-code-channels-for-agentsIntroducing Slack Code (Salesforce): https://www.salesforce.com/introducing-slack-code/Connect with Chain of Thought host Conor Bronsdon: Newsletter: https://newsletter.chainofthought.show/Twitter/X: https://x.com/ConorBronsdonLinkedIn: https://www.linkedin.com/in/conorbronsdon/YouTube: https://www.youtube.com/@ConorBronsdonMore episodes: https://chainofthought.show Thanks to Walrus Memory, presenting sponsor of season four of Chain of Thought. Walrus Memory gives AI agents portable, verifiable memory that carries context across apps, sessions, and other agents. Get started at https://walrus.xyz/cot. Thanks to Svix, presenting sponsor of season four of Chain of Thought. Svix delivers billions of reliable webhooks for startups and the Fortune 500. Get started at https://link.svix.com/cot. Qualified startups get $12,000 in credits, and YC companies get $50,000.

    Slack Wants to Be the Context Harness for Code | CPO Jaime DeLanghe

  2. Aug 27

    11 Cameras, Dozens of Mics: The AI That Reads the Room

    Tormod Ree puts 11 or more cameras and dozens of microphones into a single meeting room, then runs computer vision on all of it to figure out who is present, who is talking, and who is looking at whom. He is the chief product and engineering officer at Neat, the Zoom-backed video hardware company. Before Neat, Tormod co-founded AVA, a computer vision security company Motorola acquired, and spent close to eight years at Cisco running the Spark Board. He explains how Neat turns a room into a system that directs the meeting instead of just framing whoever talks, why almost all of the AI has to run at the edge, and how the company builds computer vision models without ever collecting a customer's audio or video. In this conversation: Why the real advantage is the harness that combines signals from different detectors, not the models themselvesHow Neat draws a hard line: no customer audio or video ever trains its modelsThe "captain" device that orchestrates a room full of cameras and mics by passing metadata, not raw mediaWhy the meeting "director" is still deterministic today, and what changes when it becomes a trained modelBuilding AI under a five-year device lifetime and phone-class computeAgentic fleet management, an MCP server, and what Tormod calls "agentic healing"How Neat gets its own non-technical teams building with AI(0:00) Reading the room: 11 cameras, dozens of mics(0:27) Who is Tormod Ree(2:07) Turning a meeting room into a system that directs itself(3:39) What it takes to actually read a room(5:13) Why the media path has to run at the edge(6:27) Open models, in-house models, and the harness that matters(7:51) The data problem when you can't touch customer meetings(11:09) The captain device: distributing compute across the room(15:38) Two users: the people in the room and the IT admin(17:00) Agentic management and "agentic healing" for device fleets(20:12) Why a meeting device has to stay useful for five years(23:44) How agentic workflows evolve on an open platform(25:53) When the meeting director becomes a trained model(28:54) Building AI under five-year, phone-class hardware limits(37:26) Where silicon caps what you can run locally(39:18) AI pendants and other form factors(41:48) How Neat adopts AI across its own teams(49:20) Non-technical teams building their own MCPs(50:27) Where Neat is headed Connect with Tormod Ree: LinkedIn: https://www.linkedin.com/in/toree/Neat: https://neat.noConnect with Chain of Thought host Conor Bronsdon: Newsletter: https://newsletter.chainofthought.show/Twitter/X: https://x.com/ConorBronsdonLinkedIn: https://www.linkedin.com/in/conorbronsdon/YouTube: https://www.youtube.com/@ConorBronsdonMore episodes: https://chainofthought.show Thanks to Svix, presenting sponsor of season four of Chain of Thought. Svix delivers billions of reliable webhooks for startups and the Fortune 500. Get started at https://link.svix.com/cot. Qualified startups get $12,000 in credits, and YC companies get $50,000. Thanks to Walrus Memory, presenting sponsor of season four of Chain of Thought. Walrus Memory gives AI agents portable, verifiable memory that carries context across apps, sessions, and other agents. Get started at https://walrus.xyz/cot.

    11 Cameras, Dozens of Mics: The AI That Reads the Room

  3. Aug 24

    Thomson 1: The New $40M Legal AI Model | Thomson Reuters Joel Hron

    Behind Thomson, the new legal AI model from Thomson Reuters, is a $40 million investment in people, compute, and evaluation methods. The final training run cost just $450,000. CTO Joel Hron, whose teams build Westlaw, Practical Law, and CoCounsel for millions of professionals in more than 100 countries, joined us for the launch to break down why the 175-year-old company chose to own its model layer instead of solely renting frontier intelligence. We cover: Why Thomson Reuters trained its own model instead of relying only on Claude, GPT, or GeminiThe compute, data, and expertise flywheel behind the Thomson modelHow rebuilding CoCounsel around agent-native tools took one-shot accuracy from 25% to over 70%The rent-versus-buy case for owning model weights and compounding expert feedback over timeThe dangers of AI inaccuracies in legal workCitation ledgers, deep research, and verifying legal work with no ground-truth oracleJoel's advice to CTOs weighing open models and training on their own dataChapters: (0:00) Cold open: a $40M model and 25% to 70%(0:27) Why Thomson Reuters built the Thomson model(2:46) From information services to an AI company(5:14) The flywheel: compute, data, and expertise(8:21) The oldest company to ship a model?(9:47) Training for users without catastrophic forgetting(14:37) Continuous pre-training on Westlaw and Checkpoint(15:29) Fine-tuning, DPO, and agentic reinforcement learning(17:37) Rebuilding CoCounsel: 25% to 70% overnight(21:48) Capturing expert judgment: own versus rent the model(28:59) Managing lawyer time and protecting customer IP(31:45) Eval results and avoiding catastrophic forgetting(34:34) Tabular analysis and legal deep research(36:26) Benchmarks, Harvey, and frontier comparisons(39:26) Verifying legal work with no ground-truth oracle(41:54) Citation ledgers and the hallucinations that matter(45:41) Rebuilding the platform and the Trust in AI Alliance(48:59) Advice to CTOs on open models and owning intelligence(51:31) The compounding flywheel and what comes next Connect with Joel Hron: LinkedIn: https://www.linkedin.com/in/joel-hron-90a3421a/Thomson Reuters AI: https://www.thomsonreuters.com/en/artificial-intelligenceThe Thomson model and next-gen CoCounsel Legal: https://www.thomsonreuters.com/en/press-releases/2026/august/thomson-reuters-launches-next-generation-of-cocounsel-legal-the-ai-ecosystem-built-for-legal-professionalsConnect with Chain of Thought host Conor Bronsdon: Newsletter: https://newsletter.chainofthought.show/Twitter/X: https://x.com/ConorBronsdonLinkedIn: https://www.linkedin.com/in/conorbronsdon/YouTube: https://www.youtube.com/@ConorBronsdonMore episodes: https://chainofthought.show Our sponsors: Thanks to Svix, presenting sponsor of season four of Chain of Thought. Svix delivers billions of reliable webhooks for startups and the Fortune 500. Get started at https://link.svix.com/cot. Qualified startups get $12,000 in credits, and YC companies get $50,000. Thanks to Walrus Memory, presenting sponsor of season four of Chain of Thought. Walrus Memory gives AI agents portable, verifiable memory that carries context across apps, sessions, and other agents. Get started at https://walrus.xyz/cot.

    Thomson 1: The New $40M Legal AI Model | Thomson Reuters Joel Hron

  4. Aug 19

    Time to Exploit is Negative: AI Broke the Patch Cycle | Dan Lorenc

    Attackers used to take months, sometimes 270 days, to weaponize a disclosed vulnerability. Now it happens in weeks, minutes if the incentive is there, and independent reports from Mandiant and CrowdStrike show the average time to exploit has gone negative. Dan Lorenc's conclusion: finding flaws is no longer the hard part. Fixing them first is. Dan is the co-founder and CEO of Chainguard. Before that he spent years at Google building the backbone of software supply chain security and created Sigstore. In June his team launched Athena, a coalition of more than two dozen companies including JPMorgan, Cloudflare, Cisco, and Kyndryl, built for the era where AI finds vulnerabilities faster than maintainers can patch them. Last month alone it processed more than 40,000 AI-discovered findings. In this conversation: Why the average time to exploit went negative, and what collapsed the fat tail of never-exploited bugsHow models chain "low severity" flaws into working exploits, like Project Zero's zero-click iPhone takeoverInside Athena: what happens between a member submitting a finding and a fix landing upstreamWhy 40,000 findings is not 40,000 CVEs: validation, dedup, and vulnerability archaeologyFuzzing outpaced patching for a decade, and AI is the first tool that speeds up the fixing sideThe Log4j thought exercise for solo maintainers and enterprise CISOs alikeFork economics, "state your intentions," and defense in depth for agent infrastructureChapters: (0:00) Cold open: how time to exploit goes negative(0:31) The 20-year assumption that just died(3:21) What a negative time to exploit actually means(6:04) Two new realities: attacks democratized, more bugs than anyone knew(8:55) Chaining tiny flaws: the Project Zero iPhone story(11:26) Why fixing AI-found vulnerabilities takes a coalition(13:27) 40,000 findings in one month: submission to upstream fix(18:06) The agentic pipeline: as few human eyes as possible(19:03) Fuzzing outpaced patching for a decade(20:50) The Log4j thought exercise for maintainers and CISOs(23:49) When no maintainer answers: the new economics of forking(27:40) Deleting dangerous code to slow the treadmill(29:35) How security kills entire vulnerability classes(31:08) Agent infrastructure: defense in depth or nothing(34:35) Regulation: maintainer liability, frontier labs, DC's busy year(37:01) Open model economics(39:55) Gas Town, multiclaude, and going back to normie(42:47) Why Dan turns the AI memory system off(45:53) Closing: still the most fun time to build software Connect with Dan Lorenc: LinkedIn: https://www.linkedin.com/in/danlorenc/X: https://x.com/lorenc_danChainguard: https://www.chainguard.devAthena: https://www.chainguard.dev/athenaConnect with Chain of Thought host Conor Bronsdon: Newsletter: https://newsletter.chainofthought.show/Twitter/X: https://x.com/ConorBronsdonLinkedIn: https://www.linkedin.com/in/conorbronsdon/YouTube: https://www.youtube.com/@ConorBronsdonMore episodes: https://chainofthought.show Thanks to Svix, presenting sponsor of season four of Chain of Thought. Svix delivers billions of reliable webhooks for startups and the Fortune 500. Get started at https://link.svix.com/cot. Qualified startups get $12,000 in credits, and YC companies get $50,000. Thanks to Walrus, presenting sponsor of season four of Chain of Thought. Walrus Memory gives AI agents portable, verifiable memory that carries context across apps, sessions, and other agents. Get started at https://walrus.xyz/cot.

    Time to Exploit is Negative: AI Broke the Patch Cycle | Dan Lorenc

  5. Aug 12

    Parenting Your AI Agents for Best Results | Cisco's Jeetu Patel

    Agents are like teenagers: profoundly intelligent, extremely resourceful, no fear of consequence, and missing the judgment to know right from wrong at all times. That's how Cisco President and Chief Product Officer Jeetu Patel thinks about securing AI agents, and it's why he says static allow/block rules are already obsolete. Agents are smart enough to route around them. Jeetu returns to Chain of Thought to map cyber's third phase: an agent trust platform where security and observability fuse into one discipline. He explains how Cisco Cloud Control spins up a digital twin to test every agent-recommended fix before it touches production, why Cisco moved from unlimited tokens to rationing them like headcount, and why the gap between people who are fluent with AI and people who aren't is now a 10x differential, not 10%. He also makes the contrarian case that AI will create more jobs than it destroys  - and of course, we talk infrastructure for this new era.  We cover: Why agents need dynamic runtime guardrails instead of static allow/block rulesThe agent trust platform: how security and observability are fusing into one disciplineAgentic ops in Cisco Cloud Control: ambient agents, digital twins, and human-in-the-loopHow Cisco rations AI tokens the way it rations headcountThe intelligence, cost, and control trade-offs behind open vs closed modelsAction control vs access control: what rights you actually grant an agentWhy every automation step creates a new human bottleneck, and more jobsChapters: (0:00) Agents are like teenagers: cold open(0:25) Welcome back Jeetu Patel(1:18) Open weight vs closed models(2:26) Intelligence, cost, and control: the model trade-off triangle(10:13) Shrinking model half-life and the economics of frontier training(11:38) Why token costs still outrun token value(14:15) Build your own evals and route intelligently(17:11) Rationing tokens like headcount at Cisco(19:54) Agentic ops: ambient agents and digital twins in Cisco Cloud Control(23:01) When to take the human out of the loop(25:13) Cyber's third phase: the agent trust platform(28:17) Parenting AI agents: dynamic boundary conditions, not static rules(31:10) LiveProtect and baking security into the network fabric(34:40) Action control vs access control for agents(36:42) What the industry is getting wrong(37:48) The case for AI creating more jobs and the 10x fluency gap(42:36) Career paths, entry-level hiring, and upskilling at scale(45:40) Closing thoughts Connect with Jeetu Patel: LinkedIn: https://www.linkedin.com/in/jeetupatel/X: https://x.com/jpatel41Cisco: https://www.cisco.comConnect with Chain of Thought host Conor Bronsdon: Newsletter: https://newsletter.chainofthought.show/Twitter/X: https://x.com/ConorBronsdonLinkedIn: https://www.linkedin.com/in/conorbronsdon/YouTube: https://www.youtube.com/@ConorBronsdonMore episodes: https://chainofthought.show Thanks to Svix, presenting sponsor of season four of Chain of Thought. Svix delivers billions of reliable webhooks for startups and the Fortune 500. Get started at https://link.svix.com/cot. Qualified startups get $12,000 in credits, and YC companies get $50,000.

    Parenting Your AI Agents for Best Results | Cisco's Jeetu Patel

  6. Jul 16

    Data Federation, Not Centralization, Is What Enterprise AI Needs

    Jitender Aswani was customer zero for Presto at Meta, where a billion daily active users generated queries that took hours to return. He watched that drop to minutes, scaled the same technology at Netflix across 300 million subscribers, and now runs engineering and security at Starburst, the $3.35 billion platform built on Trino. His argument: every enterprise AI project that stalls is fighting the same hidden battle. The agents can query the model fine. They just can't reach the data. The average enterprise runs 52 to 200 data sources, and a decade of moving all of it into one lake produced ETL debt, governance problems, and pipelines that break whenever a SaaS vendor adds a column. Federation is the only model that scales with entropy. We cover: Why Presto changed what Meta could experiment on, and how that compounded product velocityWhat broke when Jitender took the same technology to enterprises running 52 to 200 data sourcesWhy centralization stopped working once data grew faster than the ability to move itWhat happened to Starburst's query volume the day they shipped an MCP serverThe FinOps agent that fired queries for 30 minutes against data it never hadHow AIDA turns ad hoc analysis into workflows using skills and MCP serversWhy a context graph is different from a knowledge graph, and why ontology decides agent accuracy(0:00) Enterprises run on 52 to 200 data sources(0:25) Intro(2:18) Customer zero for Presto at Meta(9:50) Scaling to trillions of events at Netflix(15:11) Taking Trino from Silicon Valley to 10,000 enterprises(20:24) The 2011 research that predicted conversational analytics(28:54) Why centralization can't scale with entropy(32:26) The agent query explosion and what MCP did to volume(41:44) Inside AIDA, Starburst's conversational analytics product(46:56) Context graphs versus knowledge graphs(51:17) Where to follow Jitender's work Connect with Jitender Aswani: LinkedIn: https://www.linkedin.com/in/jitenderaswani/Starburst: https://www.starburst.io/Connect with Chain of Thought host Conor Bronsdon: Newsletter: https://newsletter.chainofthought.show/Twitter/X: https://x.com/ConorBronsdonLinkedIn: https://www.linkedin.com/in/conorbronsdon/YouTube: https://www.youtube.com/@ConorBronsdonMore episodes: https://chainofthought.show

    Data Federation, Not Centralization, Is What Enterprise AI Needs

  7. Jul 1

    You Can't Secure an AI Agent with Software

    Charles Guillemet is CTO of Ledger and the founder of the Donjon, Ledger's internal offensive security lab whose job is to break the company's own products before attackers do. He spent a decade in cryptography and hardware security before Ledger, including designing secure integrated circuits. His argument is blunt: you cannot secure an AI agent with software alone. As agents start moving real money, API keys and trust scopes leave no physical verification layer, and Charles makes the case that hardware has to sit in the loop. This one turned into a wide-ranging thought piece (and some debate) on what the agentic economy actually looks like, and how to stay safe inside it. We cover: Why Charles thinks "securing an AI agent" with software permissions and API keys is a false promiseThe economic asymmetry between attackers and defenders, and how AI is collapsing itHow a policy engine plus a hardware-enforced signature can delegate rights to an agent safelyWhy Charles thinks the agentic economy settles on blockchain rails over Visa and MastercardSecure elements, HSMs, and zero-knowledge proofs as execution-integrity guaranteesHow Ledger uses hardware authorization internally for passkeys, signed releases, and multisigA practical way to classify assets by threat model and match security to value(0:00) Why securing an AI agent in software alone is impossible(0:30) Delegating execution power inside your security perimeter(2:28) The attack-defense asymmetry AI is erasing(6:00) The alignment problem and delegating rights to agents(9:24) Policy engines, intents, and hardware-enforced signatures(13:19) From developer experience to agent experience(15:12) Secure elements, HSMs, and execution integrity(20:00) Zero-knowledge proofs, proving without revealing(27:24) Convincing the skeptics on agent-driven payments(34:49) Why Ledger bet on dedicated hardware(36:15) Hardware as a determinism layer for agents(38:52) How Ledger uses hardware authorization internally(43:42) Classifying assets by threat model(46:55) When attack and defense become symmetric(48:44) Deepfakes, voice cloning, and the scam wave(50:04) Closing thoughts on staying safe in the agentic economy Connect with Charles Guillemet: LinkedIn: https://www.linkedin.com/in/charles-guillemet/Twitter/X: https://x.com/P3b7_Ledger: https://www.ledger.comConnect with Chain of Thought host Conor Bronsdon: Newsletter: https://newsletter.chainofthought.show/Twitter/X: https://x.com/ConorBronsdonLinkedIn: https://www.linkedin.com/in/conorbronsdon/YouTube: https://www.youtube.com/@ConorBronsdonMore episodes: https://chainofthought.show

    You Can't Secure an AI Agent with Software

  8. Jun 25

    Stop Token Maxxing: Find Where AI Actually Pays Off | Jiaona Zhang

    Jiaona Zhang(JZ) is the Chief Product Officer at Laurel, where the team runs its own product on itself to see exactly where AI helps and where it doesn't. Before Laurel, JZ built products at Airbnb, Dropbox, Webflow, and Linktree, and she has taught product management at Stanford for nearly a decade. Companies are spending billions on AI tooling, but most still can't say where it returns time or revenue. Jiaona breaks down how to get that visibility, why blanket AI mandates backfire, and what it takes to re-architect a team so anyone can ship. Her argument is simple: stop token maxing and start measuring time back. We cover: Why most organizations can't see where AI is actually working, and how Laurel uses time data to fix itThe token max trap that "use AI everywhere" mandates create, and how to drive efficient use insteadWhy former managers make the best operators of agent fleetsHow Laurel lets PMs, designers, and customer success ship features end to endThe bottom-up plus top-down playbook for re-architecting a team around AIWhy technology moats are falling away while brand and data moats endureLaurel's bet on returning time to people instead of replacing them(0:00) The token max trap(1:47) Why companies can't see where AI is working(5:03) What Laurel does: turning time into data(8:53) Agents as an extension of the workforce(13:43) Why former managers make the best AI users(18:23) Lean teams and shipping end to end(22:29) Enabling non-engineers to ship features(28:30) Re-architecting teams: bottom-up and top-down(32:09) Keeping your professional identity as AI shifts work(38:53) The context layer is the new race(42:06) Fundamentals plus tinkering: how to learn(48:45) Brand and data moats when tech moats fall away(54:31) Laurel's movement: returning time to people Connect with Jiaona Zhang(JZ): LinkedIn: https://www.linkedin.com/in/jiaona/Laurel: https://www.laurel.ai/JZ's Linktree: https://linktr.ee/jzConnect with Chain of Thought host Conor Bronsdon: Newsletter: https://newsletter.chainofthought.show/Twitter/X: https://x.com/ConorBronsdonLinkedIn: https://www.linkedin.com/in/conorbronsdon/YouTube: https://www.youtube.com/@ConorBronsdonMore episodes: https://chainofthought.show

    Stop Token Maxxing: Find Where AI Actually Pays Off | Jiaona Zhang
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AI is reshaping infrastructure, strategy, and entire industries. Chain of Thought is the podcast where builders reason through what's changing. Host Conor Bronsdon sits down with the engineers and founders shipping AI in production to get past the hype into what's working and what isn't. Episodes cover model infrastructure, inference, agent frameworks, evaluation, and developer tools. Guests have come from NVIDIA, Google DeepMind, AMD, Databricks, Vercel, and more. Every episode carries a full transcript and show notes at chainofthought.show. New episodes weekly. Conor Bronsdon is an independent consultant and angel investor in AI infrastructure and developer tools. He led technical ecosystem at Modular, acquired by Qualcomm in 2026; led developer awareness at Galileo, acquired by Cisco; and ran developer marketing at LinearB, where he was GM of the Dev Interrupted podcast and community. Views expressed by the host and guests are their own.

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