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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The AI Accelerator is Fundamental Edge's 6-month cohort for investors who want repeatable AI workflows. Learn More below:
https://www.fundamentedge.com/ai-accelerator

Watch the full podcast series on our site:
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In this episode, Brett Caughran and Khe Hy sit down with Raj Shah, co-founder of Stoic Point and a former partner at Light Street, to get into how a two-person fund can run resourced like a firm ten times its size. Raj makes the case that AI lets a lean, concentrated fund compete with much larger teams and argues he's more worried about AI replacing him than the junior analysts everyone else is fretting over.

We get into:

• How a lean fund recreates the institutional resource stack without the institutional headcount
• The three buckets where AI fits the process: screening, research, and monitoring
• The "meta-screen" that surfaces ideas across 50 filters at once
• The black-box quirk where the same prompt run twice returns two different stock lists
• Separating the deterministic screen from the non-deterministic one — and why he starts in Bloomberg
• How automated monitoring caught a read on Lux Experience from an unexpected place
• Why AI makes a strong junior analyst more valuable, not less
• Turning your own process into an intern training guide — and a sparring partner that rips apart a pitch
• Why Excel with AI was the biggest positive surprise of everything he tested
• Single managers vs. platforms, and what an AI-native fund means for raising capital

We're not coming at this as "experts" with all the answers. We're in it every day, testing, breaking things, and trying to understand where this is going. The goal of the podcast is simple: bring you along as we learn, and give you a clearer view of how AI is actually being used in investing. If you work in equity research, at a hedge fund, or on the buyside and you're trying to make sense of AI, this is a good place to start.

***DISCLAIMER: Everything you hear on this podcast is for informational and educational purposes only and should not be considered investment advice. Any companies, securities, or strategies mentioned by our guests or hosts are discussed for illustration and shouldn't be taken advice to buy or sell. Markets carry risk and individual situations differ, so please do your own research or consult a licensed financial advisor before making any investment decisions. The views expressed are those of the individual speakers and don't necessarily reflect those of Fundamental Edge or its affiliates.

Chapters (Timestamps)

Timestamps:

[00:00] Intro
[01:21] — Greenhill to Highline to Light Street: Building Stoic Point
[04:00] — Recreating the $5B Resource Stack at a Lean Fund
[06:30] — The Three Buckets: Screening, Research, Monitoring
[12:00] — Same Prompt, Two Different Stock Lists
[14:39] — Deterministic vs. Non-Deterministic Screening
[15:31] — The UI Problem and the "Meta-Screen"
[17:54] — When Computer Use Got Good Enough to Click Through Bloomberg
[18:49] — Can Codex Run Your Screens Today?
[20:00] — Automated Monitoring: How AlphaSense Caught the Lux Experience Read
[22:30] — The Narrative Violation: Why Juniors Get More Valuable
[25:16] — Turn Your Process Into an Intern Training Guide
[26:55] — Building a Sparring Partner That Rips Apart a Pitch
[27:36] — Excel + AI: The Biggest Positive Surprise
[32:35] — If Big Firms Automate Juniors, Does the Pipeline Break?
[34:41] — What Happens When LLMs Develop Judgment
[35:59] — Measuring ROI in P&L, Not Hours
[38:25] — Single Managers vs. Platforms in an AI World
[43:42] — The Magnetar Read: Build the Product Around the LLM
[47:25] — Flip It: Human on Idea Gen, AI on Risk
[50:42] — Advice for the AI-Native Analyst
[54:34] — Using AI to Deepen an Experience, Not Skip It

Want to actually build these workflows yourself?
The AI Accelerator is Fundamental Edge's 6-month cohort for investors who want repeatable AI workflows. Learn More below:
https://www.fundamentedge.com/ai-accelerator

Watch the full podcast series on our site: https://www.fundamentedge.com/invest-with-ai

Follow Invest with AI on:

Spotify: https://open.spotify.com/show/033xcEEovVViS7hIYwNuGZ

Apple Podcasts: https://podcasts.apple.com/us/podcast/invest-with-ai/id1896918892

Ying Hua left a PM seat at Balyasny to build Implied on a contrarian bet: the big AI labs won't win finance (but it's not for the reason you'd think).

She, Brett, and Khe get into where that leaves the analyst, why Claude Code won't replace your data team, and the one part of the job she's convinced stays human.

Timestamps:

[00:00] Intro
[00:45] — She Left a Balyasny PM Seat to Build This
[01:52] — Why Bet on AI Investing in 2023?
[03:45] — Will the Foundation Labs Eat Every Vertical?
[08:30] — Is Pattern Matching Its Own Kind of Intelligence?
[11:07] — The Data Problem Nobody Talks About
[16:02] — The Alt-Data Nobody Else Will Ever Build
[20:51] — Can a Non-Coder Really Build Scrapers with Claude Code?
[25:21] — Why the Static Dashboard Is Already Dead
[32:32] — Synthesis vs. Judgment: Where the Human Stays
[37:00] — Why AI Still Can't Tell What Actually Matters
[39:24] — Solving Excel: The AI-Native Model in the Cloud
[45:32] — From Glorified Search to Cloning the Analyst

Watch & listen to every episode of Invest with AI:
https://www.fundamentedge.com/invest-with-ai

Want to actually build these workflows yourself? The AI Accelerator is Fundamental Edge's 6-month cohort for investors who want repeatable AI workflow. Learn More below:
https://www.fundamentedge.com/ai-accelerator

Follow Invest with AI
Spotify
Apple Podcasts
YouTube