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

Welcome to episode one of Investing with AI Podcast for Financial Analysts.

We’ve spent years in investing, and over the past couple years, we’ve been deep in the weeds with AI. Testing tools. Working with firms. Trying to understand what matters versus what’s just noise.

For a while, most of it didn’t feel that useful but that’s starting to change rapidly.

In this episode, we talk through what’s shifted, from basic chatbots to more agent-based workflows, and why that’s starting to matter for investors, analysts, and buy-side teams.

We get into:

• The difference between AI chat tools and agent workflows
• Why AI felt overhyped before and what’s different now
• Where AI is actually useful in investment research today
• The limitations that still exist (and there are a lot)
• How investors should start thinking about using AI in their process

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 this 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’re working in equity research, hedge funds, or the buy side and trying to make sense of AI, this is a good place to start.

Chapters (Timestamps)

00:00 – Intro: Why We Started Investing with AI
00:21 – Khe’s Background (BlackRock → AI Consulting for Hedge Funds)
02:06 – Brett’s Background (Hedge Funds → Fundamental Edge)
03:30 – The Real Shift: From Chatbots to AI Agents
04:17 – When AI Actually Started Working (2025 Inflection Point)
06:11 – “AI-Pill” Moments: What Changed Our View on AI
07:43 – What Are Agent Workflows in Investing?
10:04 – Why AI Tools Failed Before (and What’s Better Now)
11:43 – How Much of an Investor’s Workflow Can AI Handle?
12:20 – Defining “Agentic” AI (Simple Explanation)
14:42 – Data Accuracy, MCP, and Why This Matters for Finance
17:19 – The Biggest Unlock: Using AI for Validation
19:57 – Common Problems Firms Have with AI Adoption
22:02 – Why Most Investment Workflows Are “Vibes”
24:00 – Turning Intuition Into Process (Hardest Part of AI)
26:44 – Expectation vs Reality: What AI Can’t Do Yet
28:39 – How to Start Using AI in Your Investment Process
30:10 – How We Stay Ahead in AI (Learning, Tools, Research)
33:20 – Translating AI Into Real Investing Workflows
35:14 – Why There Is No “Final State” of AI
36:09 – What AI Means for the Future of Investing Careers
37:30 – Outro: What to Expect From This Podcast