Fundamental Edge

LAUNCHING ANALYST ACADEMY, OCT 19th COHORT. Hello! We are very excited to be launching our next cohort of Analyst Academy. What is Analyst Academy? A six month guided cohort of the 60+ hours of institutional training curriculum. The basic tools needed to survive & thrive on the buy-side: - How to build your investment process - How to conduct a deep dive research process - How to figure out what will move the stock - How to generate investment ideas - How to prepare for a management meeting - How to manage the deluge of information - How to develop & communicate a thesis - How to be a good buy-side analyst - How to leverage AI into your investment process (NEW, including 10+ hours of AI curriculum) - And much more In the Analyst Academy, we attempt to teach the "hedge fund equity research process" and endeavor to try to present what we think are best practices for the buy-side equity research process. We have hosted over 1,500 students from all walks of the buyside - analysts from large multi-managers, tiger style funds, long only firms, and family offices. Increasingly, Fundamental Edge is building internal training programs in partnership with Top 100 Asset Managers. Roughly 2/3 of students are already on the buyside, with the remainder working to break into the buyside. This is a rigorous, intensive curriculum - not an investing for beginners program. If you would like to learn more, you can watch our most recent info session in the comments below or visit our site fundamentedge . com to learn more

Importance of Primary Diligence in the Age of AI As AI continues to automate more analyst workflows and informational edge compresses, the value of investment research will increasingly shift towards gathering and analyzing information that the models don’t have. The importance of boots-on-the ground (off-the-desk) primary diligence in the age of AI will become increasingly more important. Young credit investors often ask for concrete examples of how primary diligence can shape a credit investor’s understanding of an industry or business. A good example that comes to mind is the field research I did when analyzing distressed grocer credits prior to the pandemic. At the time, grocers were under pressure from Amazon’s entry into the space via the acquisition of Whole Foods and the expansion of European discount grocers Aldi and Lidl into the US market. Primary diligence was key to developing my understanding of competitive dynamics in the grocery industry at the time. AI can comb through a bunch of materials and tell you that Aldi's competitive advantage is efficiency. But it's hard to truly understand what drives this, and whether efficiency is, in fact, a durable competitive advantage. There are some things that you can only learn if you tour a bunch of Aldi stores in New Jersey and talk to some of the people that work there. Walking through an Aldi store with a diligence hat on, you learn that they have a ruthless focus on saving every last penny, and it shows up everywhere if you look closely. Shelf design, shelf stocking process, warehouse/store layout, and even shopping cart design. Examples of this approach: 1.) Nearly all packaged product comes into the store in boxes that are designed to fit directly on the shelf and open in the front (so no unpacking boxes and manual stocking of shelves is necessary). 2.) Each store's warehouse is connected to the back of refrigerated shelves - so refrigerated items come off the truck and are stocked right away from the back (never wheeled into the actual store and stocked from the front). 3.) Coin operated shopping carts that need to be returned by the customer to get their quarter back (so less labor needed to gather carts), etc., etc. All of this adds up, and in the uber to the city back from New Jersey you better understand that Aldi builds out its stores and in-store workflows with an approach that is hard for large existing grocers to copy. For peers, it’s difficult to shift to this intense efficiency-first approach when you already have established processes and store designs across a giant footprint. Primary diligence can give analysts a level of understanding that goes far beyond what is possible with desk research. From the Desk of @Alex_Goston Fundamental Edge Instructor

AI agents are only as good as the real-time data they reason on, and that shift is about to change how alternative data is bought, priced, and used on Wall Street. @FundamentEdge Brett Caughran sits down with @CarbonArcAI founder @MckeownKirk, who spent two decades building the primary research and data operations at Glenview and Point72 before going out on his own. Kirk walks through how credit card panels went from real alpha to expensive table stakes ("paying alpha prices for smart beta"), why he built CarbonArc around consumption pricing and MCPs that plug straight into AI agents, and how markets could trade as agents get plugged into live data. Enjoy! Timestamps: 16:34 Where Alpha Goes When Data Goes Mainstream 30:09 Why He Started CarbonArc 39:43 AI Is Only as Good as Its Inputs 40:41 Will AI Change How Markets Trade? 44:09 Can Claude Be Trained to Pick Stocks? youtube.com/watch?v=g12Z7HS5…

New on Invest with AI: Brett Caughran (@FundamentEdge) sits down with Robert Yang (@GuangyuRobert ), co-founder and CEO of Fundamental Research Labs (@Fundamental). Robert ran a computational neuroscience lab at MIT before leaving to build AI agents. FRL now builds Shortcut, an Excel agent used by buy-side analysts, and Brett is joining the team as Chief Growth Officer. Inside the episode: - 1,000 AI agents in Minecraft that organized a search party on their own - Why going viral wasn’t enough to make Shortcut work - Why hedge fund analysts, not everyday Excel users, became the core customer - The case for one agent that manages your other 50 - Why today’s models fold the moment you push back - What it takes to build an agent that holds a real investment view Watch the full episode below TIMESTAMPS [00:00] Intro [00:43] Why Robert Wanted to Build AI Before It Was Popular [08:40] Running an MIT Lab and Questioning the Work [14:05] The Agent Research That Pushed Him to Leave [19:20] Project Sid: Building a Society of 1,000 Agents [22:18] The Search Party Nobody Programmed [26:58] From Minecraft to Excel Agents for Wall Street [33:05] Going Viral Wasn’t Enough: Making Shortcut Work [37:28] Why Hedge Fund Analysts Became the Customer [41:51] From an Excel Agent to an Investment Team [45:51] Why Nobody Wants to Manage 50 Agent Threads [50:12] How Close Are We to a Digital Hedge Fund Analyst? [53:52] The Problem with AI That Always Agrees [56:50] Teaching an Agent to Hold an Investment View [1:04:29] When Analysts Start Working More Like PMs [1:06:29] Brett’s Role: Training the Digital Analyst [1:09:07] If AI Frees Up Your Time, Where Does It Go? [1:11:34] What This Means for FE and Invest with AI youtube.com/watch?v=qVPJv5ga…

When people talk about AI’s growing transformation of fundamental investing, what often comes up is how AI interfaces with models. The attached Claude skill screenshot looks like an innocuous clarifying question about a spreadsheet, but I’d argue that the screenshot is itself emblematic of fundamental investing. It is the fundamental investment analyst’s duty to triage, weigh, and decide how to triangulate what comes from the company (i.e. LT guidance), published consensus (which may cluster around the midpoint of company guidance), and individual variant view derived from front-footed research around a company’s key drivers. As the debate evolves on which elements of fundamental equity investing are best transferred to machines and which remain squarely in the purview of the irreducible human, it is fair to say that more judgment is required of junior analysts earlier on in their career than has historically been the case. I speculate that this is because most experienced analysts’ judgment and discernment came from an upbringing comprising some combination of long hours spent in transcripts and models, trial and error vs. the market, and the general osmotic learning that comes with time in a seat. With AI radically shrinking the cycle time of certain types of desk research, some fundamental analyst workflows supported by AI now yield results without sweat, output without toil, and instant black-box outputs that replace apprenticeship-style observation. Today’s analysts at the helm of AI tools risk getting dessert without dinner, the medal without the race. I know this because it is a frequent topic of discussion at our firm and from our clients’ CIOs, DoRs, and PMs. An analyst eager to get a name in the book might now uncritically forward to a PM an AI-generated emphatic and persuasive writeup generated by an LLM with no accountability is a recipe for disaster when analysts are accountable to PMs who are accountable to management who is ultimately accountable to LPs. What we used to say is that Fundamental Edge amounted to “driver’s ed for the buyside,” but the emergence of capable tools wielded responsibly has compelled us to update our priors and reimagine early-career analyst development as flight school. Analysts now sit at the helm of force-multiplier tools where missteps carry larger consequences. We must simulate difficult scenarios and install judgment earlier on to avoid disaster later. Our training must not quaintly prepare analysts for yesteryear, it must prepare them for today. That’s why we are folding AI workflows back into all of our core academies. If you’re interested, please email [email protected] to learn more. FROM THE DESK OF ANDREW CARR Fundamental Edge Instructor

AI continues to reshape the investment landscape in novel and unexpected ways, and some of the industry's leading voices are coming together to discuss what comes next. This coming Wednesday, November 4, Fundamental Edge instructor Paul Johnson hosts Fordham University's 2nd Annual AI and Value Investing Conference in NY at GSB Lincoln Center Campus in the McNally Amphitheater. The all-day event brings together CEOs, investors, and industry pioneers for a conversation on the intersection of AI and value investing. Learn more and register here: eventbrite.com/e/ai-and-valu…

“The only alpha is the stuff that's in people's heads.” — Sagar Kadakia What happens when AI can conduct 50 expert interviews in a week? @FundamentEdge Brett Caughran and @khemaridh sit down with @S_Kadakia , founder of Qualitate, to discuss how AI is changing primary research and what this means for investors. We discuss: - The future of expert networks - Scaling expert interviews with AI - Automating channel checks and primary research - Research quality, compliance, and human judgment - Whether AI will commoditize investment alpha - Where investors will find their next competitive edge As Sagar puts it, “It's ultimately what they do with that information.” Watch the full episode here: youtube.com/watch?v=rc2Io6KN…

Download Alex's CoreWeave Analysis Presentation: fundamental-edge.kit.com/crw…

CoreWeave's unsecured bonds are trading in distressed territory, yet demand for compute is strong and the company has more than $100 billion in contracted revenue backlog. What is going on with CoreWeave? Behind the company’s rapid expansion in AI infrastructure is a complex capital structure with SPV-level GPU financing and billions of structurally subordinated corporate-level debt. Understanding the company's financial position requires looking beyond the headline numbers. In this breakdown, Alex Goston, Lead Instructor of Fundamental Edge's Credit Academy, examines CoreWeave's debt structure, the economics behind its GPU investments, and the assumptions that could make or break the investment thesis. Are traditional financial metrics telling investors the full story? How much cash flow does the backlog generate? How does the credit vs. equity bet differ here? And is there a more attractive way to invest in the AI infrastructure boom? Alex walks through the analysis and the key questions investors should be asking as AI infrastructure spending continues to reshape the market. Timestamps 00:00 Why CoreWeave's Bonds Are Trading in Distressed Territory 01:20 How CoreWeave's GPU Business Model Works 04:13 Project-Level Economics and Financing 07:43 How Pricing and Margins Impact Returns 09:02 Key Risks: Contracts, Customers and GPU Values 14:14 Breaking Down CoreWeave's $44B Capital Structure 18:54 Why Traditional Financial Metrics Fall Short 20:46 Analyzing CoreWeave's $103.7B Backlog 26:41 CoreWeave Bonds vs. Stock: Evaluating the Investment Thesis

Register for the info session here: zoom.us/webinar/register/WN_…

Earnings season doesn't slow down just because you're still learning. Multiple names reporting. Models to update. PMs looking for answers. And rarely enough time to step back and improve your process. For many analysts, learning happens on the job, through trial and error. But as AI continues to reshape investment research, having a strong foundation and knowing how to leverage new tools is more important than ever. That's why we built Analyst Academy. Our approach to modern buyside training is built around three pillars: → Fundamentals: Build a repeatable investment process, from idea generation to pitching with conviction. → Ongoing Support: Learn directly from experienced buyside investors through live office hours and AI Labs. → AI Integration: Learn how to incorporate AI into your workflow, automate repetitive tasks, and spend more time on high-value research. Want to learn more? Join instructor Andrew Carr this Thursday, October 22, at 6 PM ET for a live info session where we'll break down the program and answer your questions. The registration link is in the comments below.