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