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Invest with AI

Invest with AI

著者: Fundamental Edge
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Brett Caughran and Khe Hy lead a deep dive into AI for investing, joined by guests at the cutting edge of the field. Our goal is to be your Sherpa through a rapidly changing landscape by distilling what's working, what isn't working, and how you can leverage AI in your own process. Follow along as we tackle AI's biggest challenges and opportunities, one episode at a time.

© 2026 Invest with AI
個人ファイナンス 経済学
エピソード
  • Canary CEO/Ex-Tiger Global PM: How Bad AI Is Leaving Alpha on the Table
    2026/07/10

    Joe O'Donnell ran the short book at Tiger Global for almost a decade before leaving to build Canary, an AI intelligence platform now used by some of the largest hedge funds and mutual funds in the world.

    His take: because so many investors are misusing AI, there’s more alpha available today than there has been in a long time. He, Brett, and Khe get into the 20+ page investment reports his agents write on their own, why summarizing an earnings call is "lossy" in ways that quietly inflate your conviction, and the one line he uses to spot a finance-AI company that's already lost.

    If you're deploying capital with AI in the loop, this episode could help you avoid expensive mistakes.

    Timestamps:

    [00:00] Intro
    [00:38] — Running Tiger Global's Short Book to Founding Canary
    [01:15] — Why 2023 Was Too Early for Institutional AI
    [03:28] — AI Is Only as Good as the People Who Build It
    [04:57] — Buffett & Druckenmiller vs. 1,000 Junior Analysts
    [06:25] — The Layer Cake: How Canary Is Actually Built
    [10:40] — What a Model Upgrade Actually Changes
    [16:21] — Is AI Judgment Real Yet?
    [17:16] — The 20-Page Investment Report an Agent Writes Alone
    [19:22] — Why AI Summaries Are "Lossy" in Dangerous Ways
    [24:46] — Can You Just Build Canary With Claude Code?
    [29:28] — Super Analyst: A Junior Analyst Across 4,000 Names
    [32:32] — What Fine-Tuning a Model Actually Takes
    [36:07] — Why "AI for Financial Services" Already Lost
    [39:08] — Untraining AI: The Excel Problem
    [46:53] — Your Proprietary Data, Headless Canary, and MCP
    [51:07] — Advice for Funds Starting From Zero
    [54:52] — Why There's More Alpha Available Than Ever

    -----------------------------------------------

    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

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    57 分
  • Deploying AI on the Buyside: A 20-Year Engineer's Playbook
    2026/07/03

    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

    -----------------------------------------------

    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
    Apple Podcasts
    YouTube


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    56 分
  • Stoic Point Co-Founder: AI is Bringing Back the Lean Hedge Fund
    2026/06/26

    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

    続きを読む 一部表示
    56 分
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