Chain of Thought | AI Agents, Infrastructure & Engineering

Andrea Malagodi is CTO at Sonar, which builds AI code verification and governance tools. When finance asked about the size of his team's AI bills, he calculated cost per PR. At one extreme was an engineer with more than 500 PRs in a short period and a low cost per PR. At the other was an engineer with very high spend and very few PRs.

Judged on cost alone, the first engineer wins and the second looks like a problem. Malagodi looked closer. The first had built a personal agent factory, with specification, design review, documentation records, coding, verification and functional testing, and about two-thirds of the output was tests and validations. The second was working on a hard problem that needed the AI to reason through long, multi-turn sessions and didn't reduce to a line count.

Numbers alone tell you something, but not which engineer was doing the more valuable work.

We cover:

• Why cost per PR is becoming the industry's standard AI spend metric, and how Malagodi's 500-PR engineer and high-spend engineer broke it
• The history of bad proxies, from lines of code to story points to token leaderboards, and why Malagodi says the people who removed code were often the most important
• Sonar's guide, verify, solve loop: Vortex for codebase context, algorithmic plus LLM-based PR analysis, a remediation agent for legacy debt, and the Hunter agent for security vulnerabilities
• Why Malagodi breaks agent work into small pieces, and why a five-day session that produces 400,000 lines of code leaves you with no idea what is in your codebase
• Setting up disagreement between agents with an orchestrator and personas for engineering, product management and quality, plus Conor Bronsdon's cross-model-family review setup
• Rolling AI tools out at enterprise scale: one cost view for finance, up to 30% savings from cutting repeated context reads, and why budgets as small as $50 a week are hard to work with
• Rotating a triage duty to protect deep work, including how Sonar's teams built skills to sort through hundreds of reported CVEs, a number of them likely false positives

New episodes, the ideas behind them and Conor's essays land in the Chain of Thought newsletter first. Subscribe: https://newsletter.chainofthought.show/

Chapters:
(0:00) Human reviewers rubber-stamp big AI changes
(2:43) Learning the craft alongside AI tools
(9:09) From lines of code to PR counts
(12:17) Spreading top engineers' setups org-wide
(16:50) The guide, verify, solve agent loop
(22:51) Specialized agent lanes and multitasking limits
(26:51) Clear asks and guardrails for agent coordination
(30:29) Building disagreement between agents
(34:59) Mixing models instead of picking one
(39:46) Rolling out AI tooling at enterprise scale
(46:19) Defense in depth for AI-written code
(50:52) Protecting time for incident investigations
(54:08) Personalized software for teams and individuals

Links from the episode:

• Sonar Vortex research: https://www.sonarsource.com/blog/cut-your-coding-agents-cost-with-sonar-semantic-code-navigation/
• SonarQube Hunter Agent: https://www.sonarsource.com/products/sonarqube/hunter-agent/

Connect with Andrea Malagodi:

• LinkedIn: https://www.linkedin.com/in/malagodia/
• Sonar: https://www.sonar.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.

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

Thanks to Inngest, presenting sponsor of season four of Chain of Thought. Agents in production run long - they call models and wait on APIs and people. But the longer agents run, the more they break. Inngest handles that with durable execution. You build your agent as steps in TypeScript, Python, or Go. When a step fails, Inngest retries it with exponential backoff, and completed steps are saved and skipped. Try it out: https://inngest.link/cot-pod

Thanks to G2i for sponsoring this episode - for over a decade, they vetted and placed engineers at other companies, from startups to FAANG. Two years ago, they turned that same judgment inward, building their own bench to review RL environments, evals, and training data that models are trained on. Get access: https://fandf.co/3SFxVm6

An AI agent answering questions about your business needs to understand how that business works. How do you calculate churn? When does your fiscal year end? Who has the authority to change those definitions? Connecting a model to company data leaves those questions unresolved.

That’s the work of context engineering: giving agents the business definitions, instructions and examples they need to interpret your data. But that context changes as the business evolves, and someone has to keep it accurate.

In this episode of Chain of Thought, WisdomAI co-founder and CPO Kapil Chhabra joins Conor Bronsdon to explain how his team approaches that challenge. We explore how companies maintain shared context, why data teams are taking on the role of AI context engineers, and how a specialized harness plans queries, checks results and repairs errors before returning an answer.

Recorded at WisdomAI’s San Mateo office, this episode is sponsored by WisdomAI.

We cover:

• Why the context layer is broader than a semantic layer or catalog, and how it differs from memory
• How context drifts, and why a subject-matter expert has to approve changes to shared definitions
• Why Kapil sees an AI context engineer role emerging, and why data teams are moving from providing insights to providing context
• How WisdomAI's harness decomposes a question, federates queries across data sources and repairs errors, including a customer that replaced a $5M-a-year analytics pipeline
• The four ingredients of a trustworthy AI answer: accuracy, consistency, governance and explainability
• Live Apps: governed analytics apps built from a single prompt, and what keeps them live
• Why your context is your IP and should stay portable

Chapters:
(0:00) Do your agents have the right context?
(1:56) The four ingredients of trust
(5:13) The criticality and impact 2x2
(8:12) Data, context, harness: the hospital analogy
(11:12) What the context layer actually means
(11:48) Specialized harnesses: legal, support, analytics
(13:17) Why only 7% of data leaders have scaled AI
(15:46) What models can't guess: ARR, churn, fiscal years
(16:45) The data stack collapses into the context layer
(20:55) Memory vs. context
(25:26) Are agents the new users of software?
(27:19) Where humans should spend their time
(28:14) Commissioning an AI agent, and who verifies it
(31:28) Context drift and the learning loop
(34:04) Context is a multiplayer game
(35:12) Decompose, query, verify, repair
(38:47) Replacing a $5M analytics pipeline with federation
(42:04) The context development life cycle
(43:29) The AI context engineer
(46:02) Jobs are changing, not disappearing
(46:59) Product, people and process
(51:20) Who decides? Why FDEs can't own your context
(52:22) Data context vs. business context
(54:11) The benchmark: specialized harness vs. general agent
(56:08) Meeting users in ChatGPT, Claude and Slack
(58:48) Static vs. runtime context
(1:00:08) Harness engineering as models change
(1:01:44) Right-sizing AI and Live Apps
(1:04:42) The boring parts: governance, security, caching
(1:06:18) 1,000 dashboards, 50 human-years
(1:08:08) What "live" means
(1:09:19) Are dashboards going away?
(1:12:08) A pipeline app built on a weekend walk
(1:15:53) Who owns the apps?
(1:17:27) Data teams now provide context, not insights
(1:18:56) Building with the WisdomAI MCP
(1:19:43) Your context is your IP
(1:20:48) Closing thoughts: none of that work goes to waste

Links from the episode:
Meet the Modern Data Team (WisdomAI CDO report)
AI Context Engineer (ACE) certification
Live Apps
WisdomAI in ChatGPT Work
Avoid AI Writing
ssot-check
I Paid an AI Agent $8 to Write About its 'Life'
Slack Wants to Be the Context Harness for Code | CPO Jaime DeLanghe
The AI Framework Era Is Over: Why Context Is the Moat | Jerry Liu

Connect with Kapil Chhabra:
LinkedIn
WisdomAI
WisdomAI on X

Connect with Chain of Thought host Conor Bronsdon:
Newsletter
Twitter/X
LinkedIn
YouTube

More episodes: https://chainofthought.show

Thanks to WisdomAI for sponsoring this episode. WisdomAI is the agentic analytics platform for trusted enterprise intelligence: governed context, an analytics harness that makes every answer consistent and verifiable, and Live Apps built from a single prompt. Try Live Apps: https://wisdom.ai/liveapps

Laurie Voss co-founded npm - now head of developer relations at Arize, he argues that engineers will increasingly earn their keep as 'product engineers': understanding what users need and directing AI agents to build it.

One example: a bakery owner who knows how to make a croissant but has no interest in building software. Someone still has to turn that owner's needs into requirements. Laurie sees that work becoming central to product engineering, with cheaper code making software for narrower industries more viable.

We discuss where he still sees a need for human code review and operational knowledge, what he would look for in a computer science course if he were starting out today (and what he wouldn't do), and why he compares AI today to the web in 1997. He is optimistic about the technology and skeptical of the valuations, while leaving one question unresolved: how do junior engineers learn the judgment this work demands?

We cover:

• Why the "aha moment" of solving a problem survives even when agents type the code
• Where Laurie still sees a need for human code review, operations, and tacit knowledge
• Whether a $15,000 coding bootcamp or a theory-heavy CS degree is still worth it
• Why AI in 2026 looks like the web in 1997, and what that says about the bubble
• How AI-generated pull requests burden open source maintainers, and why Laurie expects cheaper code to pressure closed-source business models
• Why the systems analyst returns as the product engineer, and why that means niche software for bakeries and auto parts
• Why Laurie predicts open-model competition and diminishing returns could compress frontier-model margins
• How apprenticeships could help junior engineers develop product judgment

Chapters:

(0:00) The shift in software jobs
(0:44) Why Laurie is optimistic about the code generation explosion
(3:06) The aha moment moves from typing code to thinking
(5:49) Where agents still need human review and operational knowledge
(8:51) Is college still worth it?
(9:54) Bootcamps versus theory-heavy CS courses
(13:04) We are all product engineers now
(15:55) AI is the web in 1997
(18:19) Exponential growth, the labs' pause, and npm's ten-year curve
(20:07) Barring AGI, AI is a normal technology
(21:48) Block's layoffs and companies staying smaller
(23:14) What the labor data shows: fewer people, more capital
(26:29) Open source as the canary: drowning in AI pull requests
(28:11) AI reimplementations and the pressure on software moats
(30:26) Personal software and the kill-my-SaaS hackathon
(32:32) The bakery and the return of the systems analyst
(34:09) Niche software for specific industries
(35:36) Bootstrapping and the DevTools opportunity
(37:13) What this means for the model companies
(38:20) Frontier-model margins and open-model competition
(39:53) How the bubble pops: scaling laws and diminishing returns
(43:24) Staying private and the trough of disappointment
(45:51) Get good at a domain, not the technology
(50:44) The missing junior ladder is the question of our time
(53:32) Closing thoughts: it's 1997, you can retrain

Connect with Laurie Voss:

• Blog: https://seldo.com/
• LinkedIn: https://www.linkedin.com/in/seldo/
• Twitter/X: https://x.com/seldo
• Bluesky: https://bsky.app/profile/seldo.com
• Arize: https://arize.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.

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

Thanks to G2i for sponsoring this episode - for over a decade, they vetted and placed engineers at other companies, from startups to FAANG. Two years ago, they turned that same judgment inward, building their own bench to review RL environments, evals, and training data that models are trained on. Get access: https://fandf.co/3SFxVm6

Thanks to Inngest, presenting sponsor of season four of Chain of Thought. Agents in production run long - they call models and wait on APIs and people. But the longer agents run, the more they break. Inngest handles that with durable execution. You build your agent as steps in TypeScript, Python, or Go. When a step fails, Inngest retries it with exponential backoff, and completed steps are saved and skipped. Try it out: https://inngest.link/cot-pod

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Genspark went from launch to $250 million in ARR in about a year. Along the way it shipped a card-thin meeting recorder, open sourced an office suite that Wen Sang says one engineer prototyped in a week, and started running product triage with agents instead of product managers.

Wen's bet is that agents, not people, become the next users of software.

Wen Sang is co-founder and COO of Genspark. In this episode he walks through the company's three-layer architecture (models, tools and premium data as the execution layer, a memory layer he calls the second brain, and a collaboration layer called Gen Team), why a meeting note should be the start of work rather than the end of it, the engineering behind the SecondBrain Note, and where he thinks knowledge work goes once agents absorb the busy work.

Disclosure: Genspark provided the SecondBrain Note recorder discussed in this episode at no cost. Genspark is not a sponsor of this episode.

We cover:

• Why Genspark builds the self-driving car around the frontier labs' engines, and what that means for people who cannot code
• How Genspark's mixture-of-agents architecture routes work across 70+ models, 150+ in-house tools and paid data sets
• Evals that grade whether the sales proposal answered the RFP, not whether the model can solve a differential equation
• What a meeting turns into a week later when an agent needs it: proposals, pricing models, research, follow-ups
• The SecondBrain Note's microphone array, battery decisions, and consent in a two-party state
• Why GenOffice went open source, and the one-week prototype story behind it
• How Genspark runs product feedback triage with agents and no dedicated PMs

Chapters:

(0:00) Cold open
 (0:32) Geniuses with goldfish memories
 (3:47) Engines and vehicles: Genspark builds the self-driving car
 (6:25) Mixture of agents: models, in-house tools and premium data
 (10:03) Grade the work output, not the intelligence
 (11:22) A meeting note is where the work starts
 (13:25) The second brain: Genspark's memory layer
 (14:45) A thousand recorders, one question for the revenue team
 (16:33) Execution, memory and collaboration layers
 (19:19) Ten days in Bora Bora without a laptop
 (20:07) Gen Team, Slack, and meeting customers where they are
 (21:57) Agents become the users of software
 (24:17) Keeping memories current when the deal changes
 (26:41) Engineering the SecondBrain Note
 (30:18) The note as an API for the room
 (31:19) What deserves hardware and what stays software
 (33:33) Learning hardware supply chains at a two-year-old company
 (35:05) Why GenOffice went open source
 (38:01) What knowledge workers do once the busy work is gone
 (40:07) Building on Genspark with the CLI
 (42:01) Consent, two-party states and the surveillance line
 (43:45) Genspark Claw
 (47:09) Cheaper hardware, deeper integration, and model welfare
 (51:13) Eighty people and a lot of agents
 (53:03) What 2027 looks like
 (59:26) Where to find Wen, and product triage without PMs

Connect with Wen Sang:

• LinkedIn: https://www.linkedin.com/in/wen-sang/
• Twitter/X: https://x.com/sang_wen
• Genspark: https://www.genspark.ai
• GenOffice on GitHub: https://github.com/genspark-ai/genoffice

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, 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.

Thanks to Inngest,  presenting sponsor of season four of Chain of Thought.  Agents in production run long - they call models and wait on APIs and people. But the longer agents run, the more they break. Inngest handles that with durable execution. You build your agent as steps in TypeScript, Python, or Go. When a step fails, Inngest retries it with exponential backoff, and completed steps are saved and skipped. Try it out: https://inngest.link/cot-pod

Thanks to G2i for sponsoring this episode - for over a decade, they vetted and placed engineers at other companies, from startups to FAANG. Two years ago, they turned that same judgment inward, building their own bench to review RL environments, evals, and training data,that  models are trained on. Get access: https://fandf.co/3SFxVm6

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

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.

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 themselves
• How Neat draws a hard line: no customer audio or video ever trains its models
• The "captain" device that orchestrates a room full of cameras and mics, directing from metadata rather than re-running inference on every stream
• Why the meeting "director" is still deterministic today, and what changes when it becomes a trained model
• Building AI under a five-year device lifetime and phone-class compute
• Agentic 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.no

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 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.

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 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.

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 bugs
• How models chain "low severity" flaws into working exploits, like Project Zero's zero-click iPhone takeover
• Inside Athena: what happens between a member submitting a finding and a fix landing upstream
• Why 40,000 findings is not 40,000 CVEs: validation, dedup, and vulnerability archaeology
• Fuzzing outpaced patching for a decade, and AI is the first tool that speeds up the fixing side
• The Log4j thought exercise for solo maintainers and enterprise CISOs alike
• Fork economics, "state your intentions," and defense in depth for agent infrastructure

Chapters:

(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_dan
• Chainguard: https://www.chainguard.dev
• Athena: https://www.chainguard.dev/athena

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.

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.