Chain of Thought | AI Agents, Infrastructure & Engineering

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

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 promise
The economic asymmetry between attackers and defenders, and how AI is collapsing it
How a policy engine plus a hardware-enforced signature can delegate rights to an agent safely
Why Charles thinks the agentic economy settles on blockchain rails over Visa and Mastercard
Secure elements, HSMs, and zero-knowledge proofs as execution-integrity guarantees
How Ledger uses hardware authorization internally for passkeys, signed releases, and multisig
A 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.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

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 it
The token max trap that "use AI everywhere" mandates create, and how to drive efficient use instead
Why former managers make the best operators of agent fleets
How Laurel lets PMs, designers, and customer success ship features end to end
The bottom-up plus top-down playbook for re-architecting a team around AI
Why technology moats are falling away while brand and data moats endure
Laurel'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/jz
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

Most of the web will never get APIs for AI agents. School district sites, small business pages, government offices, and the long tail of e-commerce were built for humans, and they will keep working that way for years. So how do agents actually get things done across the web?
Dhruv Batra is co-founder and chief scientist of Yutori, the company building specialized browser and computer-use agents. He previously led embodied AI at Meta's FAIR lab, training robots in simulation and shipping the image question-answering model on Ray-Ban Meta glasses. His bet: the web is a shared roadway, much like roads split between human drivers and self-driving cars, and agents will be built to use it the way people already do.

Pixels in, clicks out. That is the API.
In this conversation:
Why the long tail of the web won't re-architect itself for agents
How Yutori's Navigator perceives pixels and writes JavaScript on the fly to shorten task trajectories
Why Navigator runs 2-3x faster and 4-5x cheaper than Opus 4.7 and GPT-5.5 on browser tasks
Learning from live websites, and using URL query parameters as privileged verifiers instead of cloning sites
What the shift from American to Chinese open-weight models means for startups
How smart glasses and robots share the same perception-action loop
Why demand for inference compute is pushing models smaller and onto devices
Chapters:
(00:00) Pixels in, clicks out
 (01:37) Why most of the web will never get APIs
 (08:47) Aggregation, specialization, and human friction
 (11:39) Digital niches and specialized models
 (16:41) The web's heavy tail and where browser agents win
 (20:40) Inside Yutori's Navigator and Scouts
 (24:08) N1.5: writing JavaScript to cut trajectory length
 (27:45) Training on live websites
 (33:29) Open source: FAIR's legacy and the Chinese frontier
 (37:22) Agent frameworks: OpenClaw, Hermes, heartbeats
 (40:57) How non-technical users adopt agents
 (44:25) Smart glasses, robotics, and embodied AI
 (50:57) Compute demand and smaller on-device models
 (53:12) Why the company is called Yutori
Connect with Dhruv Batra:
LinkedIn: https://www.linkedin.com/in/dhruv-batra-dbatra/
X/Twitter: https://x.com/DhruvBatra_
Yutori: https://yutori.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

Kristin "Kris" Lovejoy has spent her career inside the systems the global economy runs on: banks, hospitals, energy grids, governments. Today she is Global Head of Strategy at Kyndryl, the world's largest IT infrastructure services provider, working with mission-critical enterprises across more than 60 countries. Before that she ran security businesses at EY and IBM, founded the AI security company BluVector (acquired by Comcast), and now sits on the board of Dominion Energy.
Her prediction: the first fully autonomous AI attack, where an AI takes down an enterprise network with no human driving it, lands within 18 months.
Conor and Kris dig into why 62% of enterprise AI initiatives are still stuck in pilots even as spend climbs 33% year over year, why attackers chaining low-risk vulnerabilities changes the patching math, and why she has a fraught relationship with policy as code.
We cover:
The electricity analogy: we can build the models, but the transmission lines for industrial AI don't exist yet
Productivity AI vs mission-critical AI, and why banks and healthcare systems aren't running agentic AI at production scale
Why deterministic policy as code clashes with autonomous systems, and "human on top" vs human in the loop
The 18-month prediction: chaining low-risk vulnerabilities, outcome-oriented agents that take systems down by accident, and insiders armed with AI attack tools
The data center build-out from a Dominion Energy board member: PJM load forecasts that miss by double digits every year, water use, density, and rack optimization
Privacy as a double-edged sword: data combinations that suddenly become PII and the shift to continuous compliance
What's next: open source everywhere, sovereignty as control, autonomous robotics, and quantum
Chapters:
(00:00) Meet Kris Lovejoy: Kyndryl, EY, IBM, and Dominion Energy
 (02:09) Why 62% of AI initiatives are stuck in pilots
 (03:07) The electricity analogy: models without transmission lines
 (04:23) Productivity AI vs mission-critical AI
 (06:53) Vintage systems, hybrid data, and the risk gap
 (11:03) Policy as code and "human on top"
 (16:25) Data centers, energy, and the grid build-out
 (24:44) Data center design: density, cooling, rack optimization
 (26:54) Privacy, continuous compliance, and sovereignty as control
 (32:06) The first fully autonomous AI attack: 18 months away
 (38:06) Predictions: open source, robotics, and quantum
 (42:32) Control planes for agentic AI: closing thoughts
Connect with Kris Lovejoy:
LinkedIn: https://www.linkedin.com/in/klovejoy/
Kyndryl: https://www.kyndryl.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