Canary CEO & Ex-Tiger Global PM: How Bad AI Is Leaving Alpha on the Table
In this episode of Invest with AI, Brett Caughran and Khe Hy sit down with Joe O'Donnell, founder and CEO of Canary and a former portfolio manager who ran the short book at Tiger Global for almost a decade. Canary is an AI intelligence platform for professional public markets investors, now used by some of the largest hedge funds and mutual funds in the world.
Joe's central claim: because so many investors are misusing AI, there is more alpha available today than there has been in a long time. He argues that AI is only as good as the people who build and train it, that domain expertise is a necessary ingredient for institutional-grade tools, and that AI judgment for investing is still largely an unsolved problem, even as the information-gathering use case has become extraordinarily strong.
We get into:
Why there is more alpha available now, not less, and how the misuse of AI creates it
Why AI is only as good as the investors who build and train it, and the role of domain expertise
Canary's "layer cake": proprietary data sets, an intelligence layer, then tools and agents on top
An AI forensic accountant that unpacks accounting manipulations across roughly 10,000 companies
Super Analyst, an autonomous junior analyst that covers close to 4,000 companies and refreshes daily
AI idea generation agents that deliver 20+ page investment reports backed by primary research and expert calls
Why summarizing an earnings call with AI is "lossy," and how it can inflate conviction you have not earned
Where off-the-shelf AI shines (information gathering, a far better vertical search) versus where it fails (judgment)
What fine-tuning a model really takes: thousands of hours of manual analyst work, not a plug-in
Why "AI for financial services" is a red flag, and whether a fund can rebuild Canary itself
How the investment firm of the future organizes around judgment, and why more small funds may sprout up
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 buy-side and you're trying to make sense of AI, this is a good place to start.
Chapters (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
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