Invest with AI – Details, episodes & analysis

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Invest with AI

Fundamental Edge

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Technology

Frequency: 1 episode/8d. Total Eps: 10

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.

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From Answer Engines to Work Mechanisms: The AI Inflection on Wall Street

Season 1 · Episode 10

Friday, August 21, 2026 • Duration 48:20

It was never worth an associate's time to strip every KPI out of every CIM that crosses the desk. At $20 of tokens, it is. No guest this week. Brett Caughran and Khe Hy on the work that just became economic, and why the hard part is no longer the technology.

The Mac Minis people bought to run OpenClaw mostly run Codex now, and the reason is reach: sessions on your machine are reachable from your phone. No MCP for DealCloud? The agent drives your logged-in Chrome instead. Which moves the constraint. Both of them train investors on this, and both say the question changed two months ago, from how do I use these tools to what is worth building.

Brett is unsentimental about where that goes wrong. An agent turned loose for 24 hours gives you a long chain of mediocre work and a large token bill. The back half gets into what does work: the five layers, the 20/60/20 primer rebuild, why an out-of-the-box primer hands you Zacks and Motley Fool, and the headless RMS that goes and gets the research sitting in OneNote, Slack, Bloomberg IB, and Outlook.

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Timestamps:

[00:00] Intro
[01:13] — Grokbot, Cursor, and a Three Horse Race Again
[02:56] — Are the Mac Minis Mothballed?
[04:05] — Why the Power Users Moved to Codex
[06:22] — The Always On Machine You Run From Your Phone
[07:27] — No MCP? Let the Agent Drive Your Browser
[09:44] — What This Means for Wall Street's Claude Fluency
[10:49] — Training Shifts From "How" to the Art of the Possible
[13:05] — Brett on the 24 Hour Agent Slop Chain
[14:08] — The Faster Horse Era of AI
[15:00] — $20 of Tokens vs. a $100K Associate
[16:14] — Why AI Makes Lazy Research Easier
[17:24] — Five Layers, and Why Decision Support Is the One
[18:27] — From Answer Engines to Work Mechanisms
[20:38] — The Notion Problem: Staring at a Blinking Cursor
[22:21] — Every Investor Wants Something Different
[23:55] — Rebuilding the Primer Skill: The 20/60/20
[26:36] — Excel Fluent Models and the Boat Pricing Tracker
[28:02] — State of Play: MCP for Investment Firms
[29:57] — Why Out of the Box AI Hands You Zacks and Motley Fool
[31:42] — BlueMatrix, Third Bridge, and AlphaSense's Walled Garden
[32:46] — The Headless RMS and Where Research Actually Lives
[33:50] — The 16 Column Limit Nobody Documented
[37:12] — What a Real Research Dashboard Looks Like
[38:34] — Markdown Extractors as a DIY Knowledge Graph
[41:59] — The Always On Earnings Preview
[42:46] — The Codex Moment for Public Equity AI
[45:53] — Three Pillars of the Midsummer Inflection

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

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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Intelligent Alpha CEO: Letting AI Run the Portfolio

Season 1 · Episode 9

Friday, August 14, 2026 • Duration 55:36

Doug Clinton started with one question in late 2022: can ChatGPT beat the S&P 500? The early results were promising enough that he built a company around it. Today he runs Intelligent Alpha, where frontier models do the investment analysis and the portfolio management and he's still a partner at Deepwater, making the same calls as a human.

He, Brett, and Khe get into where the models are already good enough, why he grades them a B+ analyst and not an A, and the contrarian call on which model is actually best for stock work.

A conversation on AI for hedge funds and the buy-side: knowledge graphs, ontologies, model routing, obsolescence risk, and what it takes to build an AI-native investment process.

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Timestamps:

[00:00] Intro
[02:39] — Can ChatGPT Beat the S&P 500?
[04:11] — Why LLMs Keep Handing You $NVDA
[05:05] — Prompt Engineering to Agentic Workflows
[06:45] — Organizational Context: Every Portfolio You've Ever Built
[09:07] — Public Markets Alpha Is a Power Law Game
[10:09] — Where Human Intuition Still Beats the Model
[11:14] — The Hybrid: Part Quant Book, Part Fundamental Book
[12:30] — The Three Buckets of Data
[13:58] — Can Agents Do Channel Checks?
[14:49] — Is MCP Institutional Grade Yet?
[16:11] — Grading the Models: A B+ Analyst
[16:55] — Knowledge Graphs Are the Frontier Right Now
[18:41] — Schemas, Ontologies, and the Gray Matter
[19:21] — Taste: Getting AI From B+ to A
[20:26] — What Building an EBITDA Ontology Actually Looks Like
[24:04] — The Dev Shop Wants to Interview Your PM
[25:37] — Trade Perfection for Speed
[26:36] — We've Built a Lot of Things We No Longer Use
[28:25] — The Vendor Cycle and Controlling Your Own Destiny
[30:23] — Model Routing and the IA 500 Benchmark
[32:36] — The Contrarian Take: GPT Over Claude for Stock Work
[33:53] — Codex vs. Co-work and a Branding Problem
[37:32] — Why There's No Harvey or Rogo for Investing Yet[40:45] — Do Funds Want the iPhone or the Android?[43:13] — You Need Someone in Leadership Who's AI-Pilled[45:19] — Fable Built a 10-Stock Portfolio. It's All AI.[48:28] — Brett Updates His Skepticism[49:09] — Advice for Building an Asset Manager With AI[51:33] — Getting Comfortable With Uncertainty[53:03] — Go Get Lost in the Models-----------------------------------------------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-acceleratorWatch the full podcast series on our site: https://www.fundamentedge.com/invest-with-aiFollow Invest with AI on:Spotify: https://open.spotify.com/show/033xcEEovVViS7hIYwNuGZApple Podcasts: https://podcasts.apple.com/us/podcast/invest-with-ai/id1896918892

Daloopa CEO Thomas Li: The Magic Isn’t in the Model

Season 1 · Episode 8

Friday, August 7, 2026 • Duration 01:08:12

Former Point72 TMT analyst Thomas Li left the buyside to build Daloopa in 2019 with the goal of capturing market share from the data oligopoly of Bloomberg, FactSet and Cap IQ. Last year data consumption on the platform grew over 100x, and it wasn't the models that did it.

He, Brett, and Khe get into what separates a reliable MCP from a brittle one, why Claude in Excel still can't handle a real buyside model, and the one layer of the analyst job he thinks is already collapsing.

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Timestamps:

[00:00] Intro
[01:29] — Founding Daloopa in 2019 to Take On the Data Oligopoly
[02:29] — Accuracy, Latency, Trust: Why Human Data Collection Breaks
[03:54] — Coach-Built vs. Production Line: The Data Factory Analogy
[05:45] — From 5% of the Database Used to Almost All of It
[09:22] — The Real Unlock: Chatbot to Connected MCP
[10:12] — Not All MCPs Are Created Equal
[14:25] — Where MCP Reliability Is Actually Improving
[16:16] — The Cannibalization Debate: Should All Your Data Go in the MCP?
[17:54] — Building Your Stack: Which Pipe Do You Turn On?
[21:02] — One Pipe or Nine? Where Multi-Vendor Stacks Hallucinate
[24:31] — The Moat Is the Factory, Not the Tool
[26:59] — Going Deeper, and the 13-Week Problem
[31:30] — How Funds Are Building Their Orchestration Layer
[33:29] — Knowledge Graphs: Folders, Markdown, and Pre-Done Analysis
[35:44] — Open Weights vs. Frontier: Where Post-Training Wins
[40:08] — Post-Training Is an Objective Function Problem
[42:49] — If Knowledge Work Isn't Verifiable, Is There a Ceiling?
[45:28] — Can AI Pass Judgment? The Factor Investing Case
[51:40] — The Race to the Middle: Quants and Fundamentals Converge
[53:18] — Why Claude in Excel Still Can't Handle Operating Leverage
[58:47] — Eval-First Product Development
[1:01:17] — Spring 2027: Hiring, Training, and the Collapsing Middle
[1:04:45] — Coding Got Solved. Engineering Didn't.

-----------------------------------------------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-acceleratorWatch the full podcast series on our site: https://www.fundamentedge.com/invest-with-aiFollow Invest with AI on:Spotify: https://open.spotify.com/show/033xcEEovVViS7hIYwNuGZApple Podcasts: https://podcasts.apple.com/us/podcast/invest-with-ai/id1896918892

Hudson Labs CEO Kris Bennatti: Financial AI Still Gets the Numbers Wrong

Season 1 · Episode 7

Friday, July 24, 2026 • Duration 56:06

Kris Bennatti and Hudson Labs are now giving a no-hallucination guarantee, something she says no other financial AI offers. The reason it's rare is the uncomfortable part: her team found state-of-the-art models return the wrong financial number about 30% of the time once you go past a few reporting periods.

Kris, Brett and Khe get into why precision is still a difficult problem, how a forensic risk score turns fraud signals into a hard number, and why she thinks 2026 is "integrate or die."

"I believe we're the only people on the market that are willing to do a no-hallucination guarantee, which does give you a bit of a sense of how much risk there is in the average financial AI software." — Kris Bennatti, CEO of Hudson Labs

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Timestamps:

[00:00] Intro
[00:40] Meet Kris Bennatti, CEO of Hudson Labs
[01:22] From Pre-LLM Filings to Founding Hudson Labs
[04:16] Why Precision Is Finance AI's Hardest Problem
[05:35] The Test: Wrong Numbers 30% of the Time
[06:29] Launching the No-Hallucination Guarantee
[10:20] Why NotebookLM Feels Better (It's the Search)
[12:01] Vector Databases vs. Just Using Claude
[13:36] Screening for the "Most Stressed-Out CEOs"
[14:16] Have MCP and Connectors Fixed Accuracy?
[19:50] From Prompts to Skills
[20:50] MCP vs. CLI
[23:04] Integrate or Die: The 2026 Business Model
[26:52] Where Generalist Models Catch Up (and Where They Won't)
[33:36] Inside the Forensic Risk Score
[38:47] The Track Record: Score 70+, a 1-in-3 Chance of SEC Action
[44:17] The Cost Problem: $100 for Hudson, $13K on Opus
[46:26] Fable, Co-work, and the New Token Economics
[51:20] The Guidance LLMs Still Miss (It's the Verb Tense)
[54:05] Where to Find Kris & Hudson Labs

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

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-acceleratorWatch the full podcast series on our site: https://www.fundamentedge.com/invest-with-aiFollow Invest with AI on:Spotify: https://open.spotify.com/show/033xcEEovVViS7hIYwNuGZApple Podcasts: https://podcasts.apple.com/us/podcast/invest-with-ai/id1896918892

Fable Is Here, But Is It Actually Better? | Invest with AI Vibe Check

Season 1 · Episode 6

Friday, July 17, 2026 • Duration 38:55

Fable just shipped with the promise of being Anthropic’s most capable release yet. However, daily power users Brett Caughran (Fundamental Edge) and Khe Hy (Rad Reads) still can't conclude whether models have gotten meaningfully better in the last six months.

We run a round-the-horn vibe check: the eval where Fable read 10,000 minutes of Khe's calls and surfaced the one thing his business was avoiding, why complex Excel modeling still breaks, the Karpathy "knowledge wiki" for a 500-document deal folder, and the quiet case for Cowork over Claude Code.

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

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

Timestamps:

[00:00] Intro
[00:57] — First Impressions of Fable (and Why the Ban Was a Relief)
[03:05] — The Eval That Found the Thing He Was Avoiding
[05:14] — Why Evals Are Hard, and the One-Shot Short Signal
[06:56] — The 100-Company Earnings Preview Test
[07:46] — The Bridgewater Judgment Layer (High-40s → Mid-70s)
[10:48] — Braintrust and Scoring MCP Vendors
[12:30] — The Karpathy Knowledge Wiki for 500-Doc Folders
[18:20] — Making Your File System Legible to Agents
[21:11] — Turning a 120-Page Manual into Skills
[22:04] — Thin vs. Thick Skills and Composable Subagents
[25:36] — Why Complex Excel Still Breaks (and the Subagent Fix)
[27:24] — Claude Code vs. Cowork: The Line Is Collapsing
[29:57] — The Infosec Case for Cowork (VM vs. Root Access)
[33:50] — Vibe Check: Skepticism Is Up, and "Bot-Sitting"
[36:26] — Digital Twins and Where the Buy-Side Actually Is

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

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/033xcEEovVViS7hIYwNuGZApple Podcasts: https://podcasts.apple.com/us/podcast/invest-with-ai/id1896918892

Canary CEO/Ex-Tiger Global PM: How Bad AI Is Leaving Alpha on the Table

Season 1 · Episode 5

Friday, July 10, 2026 • Duration 57:28

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-aiFollow Invest with AI on:Spotify: https://open.spotify.com/show/033xcEEovVViS7hIYwNuGZApple Podcasts: https://podcasts.apple.com/us/podcast/invest-with-ai/id1896918892

Deploying AI on the Buyside: A 20-Year Engineer's Playbook

Season 1 · Episode 4

Friday, July 3, 2026 • Duration 55:52

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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Stoic Point Co-Founder: AI is Bringing Back the Lean Hedge Fund

Season 1 · Episode 3

Friday, June 26, 2026 • Duration 55:56

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.

Implied CEO on the Limits and Capabilities of AI for Investing

Season 1 · Episode 2

Friday, June 19, 2026 • Duration 50:54

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
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Investing with AI: From Chatbots to Agents (What Changed for Investors)

Season 1 · Episode 1

Friday, June 12, 2026 • Duration 38:06

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

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Invest with AI – Details, episodes & analysis

Podcast details

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