Chain of Thought | AI Agents, Infrastructure & Engineering

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.

Chain of Thought | AI Agents, Infrastructure & Engineering is a business podcast hosted by Unknown Host, with 74 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Unknown Host hosts Chain of Thought | AI Agents, Infrastructure & Engineering, a business show with 74 episodes published.

Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.

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  Episode #72

  Tuning GPU Performance with AI Agents | AMD’s Anush Elangovan on ROCm 10

  Sep 9, 202653 minS4

  Watch the full conversation on YouTube AI agents can keep tuning GPU workloads after you step away from the keyboard. Anush Elangovan, Corporate VP of AI Software at AMD, returns to Chain of Thought to explain how that works with Hyperloom and ROCm 10. Anush and Conor Bronsdon trace the process from installing ROCm through Claude Code or Codex to profiling workloads, finding slow kernels, and testing optimizations while preserving numerical accuracy. Anush shares a Hyperloom run spanning 14,000 models and explains why clear goals and feedback matter when agents are doing the tuning. They also explore what comes next for engineers: keeping skills and frameworks reliable, managing the security and accountability of autonomous agents, and applying AI to the last mile of useful software. We cover: How agents help install ROCm and serve models through natural language How Hyperloom profiles workloads and uses LLMs to explore optimizations The role of GEAK in tuning kernels while preserving numerical accuracy Anush’s account of optimizing 14,000 models in one pass How software improvements get more performance from existing GPUs Keeping agent skills current and testing across AI frameworks Security, accountability, and the next bottlenecks in agent-driven development Chapters: (0:25) A decade of ROCm, now agent native (3:03) What agentic ROCm looks like in practice (6:17) Installing ROCm then versus now (9:14) An order of magnitude more CI across every framework (10:44) Anush’s workflow: agents and deployment (12:21) Speed is the moat (15:00) Success is a stranger who cannot spell ROCm serving an LLM (17:09) Keeping agent skills from going stale (21:38) Co-designing kernels with the frontier labs (24:06) Hyperloom, GEAK, and 14,000 models in one pass (26:45) Managing autonomous agents: control and liability (32:21) Security at the speed of agent swarms (36:03) ROCm performance gains on the same hardware (37:36) Where enterprises hit walls in production (40:24) Why coding was the right reward function for AI (44:42) Which industries get the next software scale unlock (47:02) The last mile of AI (50:31) Closing thoughts Connect with Anush Elangovan: LinkedIn: https://www.linkedin.com/in/anushelangovan/ Twitter/X: https://x.com/AnushElangovan ROCm.AI: https://rocm.ai AMD AI blog: https://www.amd.com/en/blogs/by-author/anush-elangovan.html AMD AI Developer Program: https://www.amd.com/en/developer/ai-dev-program.html Connect with Chain of Thought host Conor Bronsdon: Newsletter: https://newsletter.chainofthought.show/ Twitter/X: https://x.com/ConorBronsdon LinkedIn: https://www.linkedin.com/in/conorbronsdon/ YouTube: https://www.youtube.com/@ConorBronsdon More 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

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  Episode #71

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

  Sep 2, 202655 minS4

  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 artifacts Why engineering at Slack now looks like delegating discrete tasks to agents instead of copy-pasting from a chat How Slack's channel permission model doubles as the context and access model for agents Why Anthropic ships code through Slack: the conversation is where the issue emerges How culture decides whether a multiplayer coding session converges or splits Why solo-terminal coding with an "army of Claudes" reinforces bias, and what social spaces fix Slack 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-agents Introducing 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/ConorBronsdon LinkedIn: https://www.linkedin.com/in/conorbronsdon/ YouTube: https://www.youtube.com/@ConorBronsdon More 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.

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  Episode #70

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

  Aug 24, 202654 minS4

  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 Gemini The compute, data, and expertise flywheel behind the Thomson model How 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 time The dangers of AI inaccuracies in legal work Citation ledgers, deep research, and verifying legal work with no ground-truth oracle Joel's advice to CTOs weighing open models and training on their own data Chapters: (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-intelligence The 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-professionals Connect with Chain of Thought host Conor Bronsdon: Newsletter: https://newsletter.chainofthought.show/ Twitter/X: https://x.com/ConorBronsdon LinkedIn: https://www.linkedin.com/in/conorbronsdon/ YouTube: https://www.youtube.com/@ConorBronsdon More 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, 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 .

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  Episode #67

  AI Agents Need Parenting, Not Static Rules | Cisco's Jeetu Patel

  Aug 12, 202646 minS4

  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 rules The agent trust platform: how security and observability are fusing into one discipline Agentic ops in Cisco Cloud Control: ambient agents, digital twins, and human-in-the-loop How Cisco rations AI tokens the way it rations headcount The intelligence, cost, and control trade-offs behind open vs closed models Action control vs access control: what rights you actually grant an agent Why every automation step creates a new human bottleneck, and more jobs Chapters: (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/jpatel41 Cisco: https://www.cisco.com Connect with Chain of Thought host Conor Bronsdon: Newsletter: https://newsletter.chainofthought.show/ Twitter/X: https://x.com/ConorBronsdon LinkedIn: https://www.linkedin.com/in/conorbronsdon/ YouTube: https://www.youtube.com/@ConorBronsdon More 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.

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  Episode #66

  Data Federation, Not Centralization, Is What Enterprise AI Needs

  Jul 16, 202653 minS3

  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 velocity What broke when Jitender took the same technology to enterprises running 52 to 200 data sources Why centralization stopped working once data grew faster than the ability to move it What happened to Starburst's query volume the day they shipped an MCP server The FinOps agent that fired queries for 30 minutes against data it never had How AIDA turns ad hoc analysis into workflows using skills and MCP servers Why 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/ConorBronsdon LinkedIn: https://www.linkedin.com/in/conorbronsdon/ YouTube: https://www.youtube.com/@ConorBronsdon More episodes: https://chainofthought.show

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