Chain of Thought | AI Agents, Infrastructure & Engineering

Loïc Houssier leads engineering at Superhuman, the email client Grammarly acquired for ~$825 million in July 2025. Before Superhuman he was CTO of OpenTrust (acquired by DocuSign), ran engineering at ProductBoard, and started his career in applied cryptography for France's defense industry, including work on nuclear submarine systems. Loïc joined Superhuman in early 2024 and within 30 days was leading a six-week sprint to ship AI Inbox.
Superhuman's brand is built on speed: every interaction under 100 milliseconds. LLMs do not run in 100 milliseconds. So Loïc walks Conor through how his team retrofitted AI into a product that was already winning without it: pre-caching context for the mobile voice feature, starting every feature on the smartest available model and only then fine-tuning down to cheap dedicated infrastructure, treating "look foolish" as a P0 bug class, and refusing to auto-send any email even when their agents could.
This is a practitioner's tour of what it actually takes to put AI on top of a product that has to stay fast, stay quiet, and never embarrass the user.
We cover:
The model-routing strategy: Opus and frontier models to prove a feature, then fine-tuned BERT classifiers on dedicated inference
Pre-caching voice and tone context separately from dictation to keep the mobile voice feature feeling fast
Why eval engineering at Superhuman is owned by PMs, and how a single "how much time did I spend in Waymo last month" query exposes the eigenvectors a feature has to cover
Why "look foolish" is a P0 bug class, and where the boundary between agent agency and agent laziness actually sits
How Superhuman's pod structure (PM, tech lead, designer) and a central AI platform team support aligned autonomy
Hiring for AI fluency: how interview questions are changing and what self-augmenting engineers look like
Pattern detection as the leadership skill that transfers from nuclear submarines to AI email
Chapters:
(00:00) Cold open: pattern detection beats new tools
 (00:18) Loïc's path: cryptography, OpenTrust, ProductBoard, Superhuman
 (02:13) Retrofitting AI into a 100ms product
 (04:08) Voice on mobile: pre-caching LLM context to keep the feel fast
 (07:46) Frontier first, then fine-tune: model strategy across features
 (11:04) The "double-dipping" trick that worked on GPT-4 and stopped working
 (12:25) Cognitive load and staying current as a leader
 (16:59) Balancing YC founder urgency with peer CTO grounding
 (19:28) Pods, AI Guild, and aligned autonomy
 (23:15) Managing models vs. managing people: delegation in reverse
 (28:27) The Waymo example: eigenvectors of evaluation
 (32:15) Day 30 onboarding: leading the AI Inbox sprint
 (35:04) Why email is the killer agent use case
 (38:51) Auto-draft, never auto-send
 (39:57) Agent agency vs. agent laziness
 (43:07) Hiring for AI fluency
 (45:55) Pattern detection is the leadership skill
 (47:21) Nuclear submarines as engineering reference points
 (48:37) Closing thoughts
 (49:38) Superhuman is hiring
Connect with Loïc:
LinkedIn: https://www.linkedin.com/in/houssier/
Superhuman careers: https://superhuman.com/careers
Superhuman: https://superhuman.com
Connect with Conor:
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 Galileo — download their free 165-page guide to mastering multi-agent systems at galileo.ai/mastering-multi-agent-systems

Job applications are up 239% since ChatGPT launched, tech layoffs show no signs of slowing down, and the market for technical talent is a topsy turvy mess. 
Greenhouse has a unique vantage point to understand all of this: they process 22 million job applications a month across 7,500+ companies including HubSpot, Anthropic, Coinbase, and the NFL. CEO Daniel Chait has had a front-row seat to the strangest hiring market in decades, and he's here to advise us all on how to navigate it.
Daniel coined the term "AI doom loop" for what's happening: applications up 239% since ChatGPT launched, resume hacks like white-fonting and prompt injection up 500%, and 75% fewer applications reaching the hire stage. 91% of recruiters have spotted candidate deception. 38% of job seekers walk away from processes that include an AI interview.
It's the worst job market for candidates and the hardest hiring market for recruiters.
Daniel explains how technical talent can break the loop.
We cover:
Why software engineers, according to Greenhouse data, are the worst auto-appliers and what to do instead
The North Korean infiltration problem: deepfakes, laptop farms, and why companies are flying candidates in for in-person interviews again
How AI screener interviews open up the funnel when companies are transparent about using them, and break it when they aren't
Greenhouse Dream Jobs: how a single high-signal application a month converts at 5x the rate
Why take-home assignments don't survive contact with AI and what Greenhouse uses instead
What a coding interview looks like when leetcode is dead and engineers run 10+ Claude Code sessions in parallel
The case for killing the resume entirely and rebuilding hiring around AI conversations
Chapters:
(00:00) Cold open: 239% more applications, 75% fewer hires
(02:14) Galileo
(03:05) The AI doom loop, defined
(04:01) How we got here: remote work, ZIRP, and ChatGPT
(07:51) Are software engineering jobs really in trouble?
(12:46) The trust crisis: 91% of recruiters spot deception
(15:52) North Korean spies, deepfakes, and laptop farms
(19:34) Can AI fix the problem it created?
(20:52) AI screener interviews and the uncanny valley
(26:33) Greenhouse Dream Jobs: one signal, 5x conversion
(28:31) Why auto-apply doesn't work (and what does)
(30:18) Communities, building in public, and the early-mover advantage
(37:08) Gen Z lost trust, and the bias problem
(39:04) Kill the resume: rethinking hiring from scratch
(43:34) How Greenhouse changed its own interview process
(48:47) Coding interviews in the agent era: leetcode is dead
(51:33) Predictions: more proof, more conversations, less noise
(54:34) Where job seekers and hiring teams should start
Connect with Daniel:
Greenhouse: https://www.greenhouse.com
My Greenhouse (for job seekers): https://www.mygreenhouse.com
LinkedIn: https://www.linkedin.com/in/dhchait/
Connect with Conor:
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 Galileo — download their free 165-page guide to mastering multi-agent systems at galileo.ai/mastering-multi-agent-systems

Alex Ratner co-founded Snorkel AI out of Chris Ré's Stanford lab and helped establish data-centric AI as a field. Today, Snorkel is a $1.3B company shipping thousands of data sets and environments a week to frontier labs and vertical AI teams like Harvey.
In this conversation, he argues our ability to build AI agents has outpaced our ability to measure them. That gap is what's keeping most enterprise agents stuck in demo purgatory.
If you can't measure it, you can't improve it. And you can't deploy it.
In this conversation:
The three axes of the evaluation gap: input complexity, autonomy horizon, and output complexity
Big Law Bench: how Snorkel and Harvey benchmarked legal agents on deep-research tasks that take lawyers 10-15 hours
What Snorkel's $3M Open Benchmarks Grant is funding, and why "benchmaxxing" critiques don't kill the case for public benchmarks
Why 40-50% of Snorkel's data work is still review and labeling, even with the best models in the loop
The "expert-agentic" era, where domain expertise (law, finance, coding, even woodworking) is the new bottleneck
Why self-supervision is a dead end outside narrow cases like distillation
The false dichotomy between data and environments, and why pure-environment vendors miss how AI actually works
Chapters
(00:00) Intro: Alex Ratner and Snorkel AI
 (02:50) What the evaluation gap actually is
 (06:05) Moravec's paradox and the jagged frontier
 (08:46) Where AI agents fall down in enterprise work
 (10:40) Big Law Bench: benchmarking Harvey's legal agents
 (12:00) The three axes: input, autonomy horizon, output
 (18:31) Snorkel's $3M Open Benchmarks Grant
 (22:33) From "janitorial" to epicenter: 15 years of data-centric AI
 (29:26) The expert-agentic data era
 (34:54) The false dichotomy between data and environments
 (40:05) DoorDash Tasks and expert data at scale
Connect with Alex Ratner:
X/Twitter: https://x.com/ajratner
Snorkel AI: https://snorkel.ai
Connect with Conor:
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 Galileo — download their free 165-page guide to mastering multi-agent systems at galileo.ai/mastering-multi-agent-systems

What happens when a VP of AI Software at a major chip company goes all-in on AI coding agents for his own team's work?
Anush Elangovan runs 10–12 Claude Code agents across three machines, burns 6.5 billion tokens a week, and rewrote a 25-year-old project (Slurm → Spur in Rust) in a single night.
He does it all on dangerously-skip-permissions.
About Anush
Anush Elangovan is Corporate VP of AI Software at AMD. He founded Nod.ai, where his team built SHARK and was a primary contributor to Torch-MLIR and IREE. AMD acquired Nod.ai in 2023, and Anush now leads AI software strategy across AMD's full silicon portfolio. Before Nod.ai, he shipped the graphics stack on the first ARM Chromebook and led Chrome OS's migration to Gentoo.
We cover:
How Anush runs 10–12 parallel agents with a geo-distributed AMD hardware rig
Why the test harness is the new code review (and why agents are "sneaky and dumb")
Rewriting a 25-year-old project in Rust overnight, without opening the editor
Why every new project is in Rust specifically because he refuses to learn it
The "HR partner fixing engineering bugs" moment and what it says about upskilling
Why normal SDLC is dead and speed is the only durable moat
AMD's fully open-source software stack and how community contributions are accelerating ROCm
"Software is just tokens" and what that means for AMD's bet against CUDA lock-in
Connect with Anush
LinkedIn: linkedin.com/in/anushelangovan
Twitter/X: @AnushElangovan
AMD AI blog: amd.com
AMD AI Developer Program: amd.com/developer
Connect with Conor
Newsletter: newsletter.chainofthought.show
Twitter/X: @ConorBronsdon
LinkedIn: linkedin.com/in/conorbronsdon
YouTube: @ConorBronsdon
More episodes: chainofthought.show
Chapters
0:00 Cold open
0:21 Welcome + guest intro
3:43 250K lines a week, 10–12 parallel agents
7:34 Agent architecture + geo-distributed test rig
9:57 When does AI-generated code become a liability?
14:12 80% tests first: the test harness philosophy
18:24 Dangerously-skip-permissions + testing as code review
19:52 "Normal SDLC is dead in the agentic world"
20:44 Advice for engineers and leaders who feel behind
24:51 Tokens, throughput, and what happens next
26:29 Block layoffs, uneven AI gains, the 25-year Slurm rewrite
32:55 Galileo sponsor break
34:24 When agents go off the rails: sneaky and dumb
37:52 Orchestrator agents vs. focused multi-threading
40:45 Open source, ROCm, AMD's software bet
44:19 "Software is just tokens"
45:24 AMD Developer Program + community contributions
47:09 Where to start with AMD
48:39 Heterogeneous compute
50:13 Outro
Thanks to Galileo. Download their free 165-page guide to mastering multi-agent systems at galileo.ai/mastering-multi-agent-systems
Full show notes: newsletter.chainofthought.show
Disclaimer from our host: All views, opinions and statements expressed on this account are solely my own and are made in my personal capacity. They do not reflect, and should not be construed as reflecting, the views, positions, or policies of my employer. This account is not affiliated with, authorized by, or endorsed by my employer in any way.

Sudhir Hasbe is President and Chief Product Officer at Neo4j, the graph database company powering 84 of the Fortune 100 (Walmart, Uber, Airbus) at $200M+ ARR and a $2B+ valuation. Before Neo4j, he ran product for all of Google Cloud's data analytics services: BigQuery, Looker, Dataflow, and led the Looker acquisition.
His thesis: the hallucinations we blame on AI models are really a data architecture problem. LLMs weren't trained on your enterprise knowledge, so handing them a data lake with 10,000 disconnected tables and asking them to reason is the wrong design. The fix is knowledge graphs: feeding the model a structured map of relationships, entities, and context so it can reason over meaning, not just vector similarity.
Sudhir breaks down the five capabilities knowledge graphs unlock for enterprise AI: GraphRAG (moving accuracy from 60% to 97%), semantic mapping across siloed systems, context graphs, agent memory, and multi-hop reasoning. He explains three architecture patterns customers are actually shipping, why giving an LLM hundreds of tools makes it worse, and what Uber, EA Sports, Klarna, and Novo Nordisk are doing differently.
This is the case for treating knowledge as infrastructure.
We cover:
Why enterprise AI needs a different playbook than consumer AI
The five data asset types every agentic system needs: system of record, historical, memory, context, and reference
How GraphRAG combines vector search and graph traversal to move from 60% accuracy to 95%+
Three architecture patterns: semantic layer only, semantic map plus domain data, full consolidation (the Klarna/Kiki model)
What context graphs capture that Salesforce doesn't: the Slack and email negotiation behind every deal
Why giving an LLM hundreds of tools drops accuracy, and how Uber uses knowledge graphs as a business validation layer
What Neo4j's Aura Agent, MCP server, and A2A support mean for developers starting today
Chapters:
(0:00) Why building a self-driving car is hard
(0:22) Intro
(2:03) Hallucinations as a data architecture problem
(4:31) From models-as-core to systems-of-knowledge
(6:13) Why data lakes fail AI agents
(9:15) The five data asset types enterprise agents need
(11:46) Where basic RAG breaks down: the Spotify metadata lesson
(16:00) GraphRAG: 3x accuracy, easier development, explainability
(18:47) Semantic mapping across the enterprise estate
(19:23) Three knowledge-graph architecture patterns
(22:42) Context graphs: capturing the "why" behind decisions
(25:33) Individual vs. organizational agent memory
(28:40) Multi-hop reasoning for fraud rings and AML
(31:52) Why there are no shortcuts in enterprise AI
(36:38) What happens when you give an LLM 100 tools
(39:19) The Uber example: knowledge graph as business validation
(44:42) First mile of a 26-mile marathon
(48:32) Aura Agent, MCP server, and the A2A protocol
(50:43) Where developers should start
Connect with Sudhir Hasbe:
LinkedIn: https://www.linkedin.com/in/shasbe/
Neo4j: https://neo4j.com/
Neo4j Aura: https://neo4j.com/product/auradb/
Connect with Conor:
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 Galileo — download their free 165-page guide to mastering multi-agent systems at: 
galileo.ai/mastering-multi-agent-systems