Chain of Thought | AI Agents, Infrastructure & Engineering

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 by passing metadata, not raw media
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.

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