Deploying AI on the Buyside: A 20-Year Engineer's Playbook
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Deploying AI on the Buyside: A 20-Year Engineer's Playbook
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What’s the biggest hurdle in deploying AI on the buyside? After 20 years of building software, PragmaNexus Founder Matt Stockton has found that the most difficult challenge is knowing your own process well enough to write it down. As he puts it, that’s 80% of the work.
He, Brett, and Khe get into the shift from "single-player" AI (one person, one laptop) to "multiplayer" AI across a whole firm, the voice-memo trick Matt uses to pull his own process out of his head, and why he tells people to go try the one thing they're sure AI can't do yet.
A grounded, practical conversation on what it actually takes to get AI working inside an investment firm.
Timestamps:
[00:00] Intro
[00:59] — 20 Years of Software, From Data Infra to LLMs
[02:23] — Single-Player to Multiplayer: The Local-Machine Problem
[03:18] — Building a Company "Resource Brain"
[05:57] — Folder Structures and Markdown vs. Relational Databases
[07:48] — The Excel Problem: 1.2M-Token Financial Models
[10:46] — The Bitter Lesson of AI Engineering
[12:36] — How Time-Crunched CIOs Actually Stay Current
[15:42] — AI Psychosis and the Aha Moment
[17:27] — Turning a Research Doc Into a Shareable Website
[20:06] — Bucketing the Deployment Problem: Job to Be Done
[23:29] — Investors as Intuitive Pianists: The Articulation Problem
[24:07] — The Voice-Memo Hack for Extracting Your Own Process
[25:22] — Why It's Not a Tech Problem
[28:49] — The Last Mile: Getting From Prototype to Production
[29:42] — Assembling Existing Tools vs. Building Custom
[32:06] — Moving Beyond Basic Synthesis Skills
[34:20] — Skill Creation, Hill Climbing, and the Red Pen
[36:27] — Building Evals and LLM-as-Judge
[40:06] — Debugging the Model: The Goodwill Impairment Trap
[42:25] — The Tool Stack: Claude Code, Codex, and Mobile
[49:45] — Chinese Models, Open Weights, and Token Efficiency
[52:13] — Frontier Intelligence for Judgment, Cheap Models for the Rest
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