INTRODUCING CREDIT ACADEMY, LAUNCHING SEPTEMBER 28th We have partnered with Alex Goston (fmr KKR/RBC) to build Credit Academy, a structured training program for credit investors. The program includes 30+ hours of core pre-recorded content, plus live office hours, AI labs, and supplemental guest speaker sessions. Credit Academy is divided into 3 pillars that cover everything you need to know to thrive in buyside credit: Credit Foundations - Fundamental credit analysis - Credit modeling - Credit document analysis - Research process fundamentals Advanced Credit Strategies - Distressed credit - Event-driven and opportunistic credit - Performing high yield - Direct lending Credit AI - Agentic AI fundamentals - Credit specific AI workflows - Shipped skills.md files - Live build sessions The goal of this program is to teach a desk-ready credit investment process, founded on experiences and lessons learned from decades in the seat. Our last few years have been spent training students from all walks of the buyside (multi-managers, Tiger-style funds, long-only firms, family offices) and we are excited to bring the same rigorous, institutional-grade training in a credit-focused program. This is an intensive curriculum, not an investing-for-beginners course. If you want to learn more, we would like to invite you to our upcoming info session this Thursday the 10th at 6 PM ET. Alex and Brett will be there live to answer questions (registration link is in the comments). To enroll in the Credit Academy, see our website at www(dot)fundamentedge(dot)com. You can find Credit Academy under the "Training" dropdown at the top of the page. If this resonates with you in the current stage of your buyside credit journey, we would be thrilled to have you in our inaugural cohort of Credit Academy. We hope to see you there!

Head Modeling Instructor — Fundamental Edge Fundamental Edge is seeking an experienced buyside investor and educator to help build and lead Modeling Academy, a practical financial modeling program for early-career equity analysts and professionals transitioning from MBA programs, sell-side research, and investment banking. The program will teach analysts to build, update, and use institutional-grade financial models quickly and effectively. Students will learn the underlying modeling logic, develop sound forecasting judgment, and build repeatable workflows they can apply across sectors. Instruction will combine hands-on model building, real company case studies, and practical applications of AI modeling tools. The role You will help shape the curriculum and serve as a lead instructor, with responsibilities including: - Developing and recording lessons, model demonstrations, and practical exercises. - Teaching core modeling principles, sector-specific considerations, and the connection between operating assumptions, financial forecasts, and investment decisions. - Integrating AI tools into modeling workflows while teaching students to evaluate and validate the output. - Helping source, interview, and prepare guest speakers. - Hosting monthly office hours to answer questions and work through modeling challenges. - Collaborating with the Fundamental Edge team on program production and selected marketing activities. Required experience - Institutional experience building and updating public-equity financial models at a fundamental buyside investment firm. - Experience teaching, training, or coaching students or investment professionals. - Practical experience applying AI tools to financial modeling. - The ability to explain complex concepts clearly and translate investing experience into structured, actionable instruction. - Capacity to develop and record a rigorous curriculum on a defined production schedule. Preferred experience - Modeling public equities across multiple sectors and business models. - Creating courses, internal training programs, or other educational materials. - Helping analysts transition from academic, sell-side, or investment banking models to models designed for buyside investment decisions. - Flexible on location Compensation and structure This is a contract role with revenue-share compensation. - Revenue share from all open-enrollment participants in Modeling Academy. - Potential for additional earnings through custom enterprise training engagements. Timeline Fundamental Edge is targeting a Q4 2026 launch. DM us with your interest and we will provide next steps.

You can’t automate what you can’t measure. This means that evals are one of the gates to diffusion of AI in the enterprise. We can test our deterministic processes through software, but most enterprises have no useful way of understanding how their non-deterministic processes are working today. Specifically the work that agents are doing for them. Evals are mission critical for enterprises adopting AI because you have no other way of knowing what’s working, what’s broken, what changed, what improved, what you can do more of, etc. if you don’t have a good sense of how agents work in your environment today. All changes, upgrades, and deployments are downstream from good evals. Not only are we going to get vastly more domain specific evals over time for the labs and across the industry, but every enterprise will also need a clear sense of how agents are performing in their environment as well. Huge opportunity.

The @BostonCollege Investment Committee (an LP and my beloved alma mater) asked for a few thoughts on what's happening in AI. I recorded a test run yesterday morning and then shared it with my partners, who encouraged me to share it more broadly... so here you go! This is not a sales pitch, it's just a reflection on what we're seeing. And it wasn't intended to be shared, so please pardon the rough edges. loom.com/share/c016702964a04…

My architecture for models has always been: a core MOD tab that includes 10-20+ years of annuals and 5-10 years of quarters, then a string of tabs that build out deep analytical work around all of the core questions of the business (KPI correlations, EPS scenarios/sensitivities, balance sheet stress test, normalized profitability, historical guidance evolution, divestiture scenarios, etc - whatever the unique investment question demands). This is where the real thesis in a company emerges "oh this competitive threat on this device will lead to a 7% revenue miss and given it's high GM structure a 12% EBITDA miss, and the company may also have to pull their LT guidance at investor day in Fall". This doesn't show up in the P&L on first cut, but in the Excel analytics around the model, informed by multi-dimensional due diligence (talking to mgmt, peers, reading industry trade rags, reading sell-side research, studying past category priors, talking to KOLs, etc), that then feeds back into generally a singular P&L assumption (incredibly depth behind the simplicity of one assumption). And in pitching ideas (I've always been a fan of 100 page decks with the 15 key slides then 85 appendix slides), these tabular analytics become copy and pasted into PPT for team conversation. The opposite of vibes based investing... But the historical PL/BS/CF baseline is critical to start asking questions around KPI correlations or historical behaviors of the business & management team (i.e. "when revenue was weak, how did the CFO pull cost levers"). This quantitative history lesson is a critical step in building deep comprehension around companies & industries. And is one (of the many reasons) I have never let my teams use sell-side or other modeling templates, they just weren't flexible enough to serve as the right backbone. But is there alpha directly in just updating 20 years of annuals and 10 years of quarters? Of course not. And how much of my life has been wasted doing so in a clean, accurate way. A LOT, lol. A lot of my 20s to be honest, sadly, was spent as an Excel jockey late into the evenings ordering delivery from Tao...generally orange chicken & banana pudding, which is why I tipped the scales at 280+ at one point.... But the alpha-generating insights sit on top of this important analytical infrastructure. Building a clean historical model is necessary but not sufficient, and, at the top funds, no vendor/service really solved this (until roughly 2015 when I started using Insync for historical baselines). So it is so nice that the updating & spreading of historical numbers has become a "push-button exercise". This frees up my time to go deeper on the key questions that matter, in TABS around the core MOD structure. I'll give you one example, with Claude in Excel, I am curious how much of the $GOOG story is contingent on YouTube. Not a SOTP / spin-off story, but more of a value derivation question today, but more importantly in thinking about the forward 3-7 year growth algorithm at Google in a world where search may mature, how much can YouTube carry the baton to sustain high growth in revenue/EBITDA/EPS? Companies don't make this easy for us as investors with obfuscated disclosures that require layers of assumptions, generally for competitive reasons. Calls with IR, formers and sell-side can often refine these assumptions. Once you have the right PL/BS/CF baseline, these sorts of analyses are becoming incredibly easy to run, and off of each company model there could be a dozen+ of these sorts of questions, quantified.

Opus 5.5 is pretty insane for Excel read/write I have a few dozen models that are 4-8 quarters out of date. Prior LLMs had gotten better at simple, single quarter updates, but were still sketchy with multi-quarter refreshes (and failed on from scratch builds, to a typical hedge fund standard). Opus is really the first model that can one-shot my Refresh Modeling Skill Pack in a way that passes my Turing test (i.e. I couldn't tell if this was updated by a human or an agent) and passes final validation in a way that I find reliable Exciting progress.

I loved this interview with @PitchThePM and Chandler Bocklage (former PM at Point72 that sat directly next to Steve for a decade and current Head of Business Development at Point72 in charge of hiring analysts and PMs)... “Usually, you have to be narrower to try to get deeper. But [Ai] might actually allow people to be as deep but broaden out, which means that you can have a wider universe of potential alphas. It’s pretty exciting.” “Everything can’t be a two-year idea based on compounded earnings growth…Those are great ideas and the thing you can anchor your book on, but you have to have other things that might work in the intervening two years. Very few places are only looking at the P&L over a two-year block.” “You can’t have the same type of ideas…you have to have different themes, different durations, different catalysts…” “To be a long-term successful PM, you can’t be a one-trick pony.” "Alpha decay is compressing. There are more and more people chasing the same alphas.” “Can you teach what you do and what makes you special to somebody else and are you willing to teach what makes you special to somebody else? There are a lot of people that aren’t.” “When a war breaks out, things change, and the PM might have to think about that a little more holistically than the individual analyst.” Thoughts @pmje73, @DanielSLoeb1, @davidein? 🙏💙 (Not investment advice).

A few thoughts: 1) if public data is instantly priced, the premium on proprietary, unescapable insights increases. Alpha generation moves entirely to the edges: nuanced management body language, channel checks outside of the Tegus corpus and deep industry relationships. That has always been true but becomes more true. I think it is obvious public equity investors will spend less time in front of a Bloomberg screen and more time in the field, researching key drivers. 2). Even more fundamental, capital trust. Being a good fiduciary & steward of capital. Why does one CIO raise $5 billion and another raises zero? Accountability, direct communication, adaptability, judgment during dislocations that ultimately shows up in track record. It is exceedingly hard to completely rewire the investment process of a large investment organization, made harder by the compliance & infosec requirements in place to earn said LP trust (to wit, many large investment organizations are still left with Microsoft CoPilot). It will take years not months to fully shift approach, and very few firms live at the intersection of both leading AI innovation & adaptable infrastructure & talent. You can be fully native and raise no money, and you will have no impact on market price discovery. There will be a window of time where adaptability & urgency as well as the ability to earn trust of capital partners will show up in alpha, in my opinion. 3) the psychological endurance & judgment required to know when to fade a hyper-efficient consensus. Any consumer PM who has lived through the rise of alternative data will know this dynamic well. When do you ride the machine-generated consensus vs. fade that consensus based on a nuanced variant perception? As in alt data, the game will shift to 2nd & 3rd order thinking. As certain "now-casting" alpha pools compress, certainly other distortions will expand due to positioning, capital flows & narrative over-reactions. These are definitionally alpha opportunities vs. the conceptual intrinsic value baseline (if not difficult, jagged & frustrating to monetize). Call it behavioral resilience and deep intuition around market participants and market price discovery (I am actually quite bullish on vol-adjusted dollar edge, don't think this will be the problem...markets aren't going to wake up in 3 months and all of a sudden be hyper-efficient in price discovery).

Where do you see edge moving?

I'm building out "AI-native" coverage on my old healthcare coverage (153 healthcare stocks ex-therapeutics that I covered institutionally for ~10 years). I am testing how close I can get to institutional-grade coverage while doing as little as possible manually: ramping research, building/updating models & building active coverage systems all in an agentic work platform. TO BE CLEAR, the goal isn't to deploy capital and I have zero belief that I can build an alpha generating portfolio without deep focus & rigorous primary research. However, I'm coming up with all sorts of ideas to both speed and deepen rigor on this process. It's been fun, at times frustrating, and at times mind blowing. But day by day I'm convinced that if I was back in the seat ramping coverage for real, high-stakes capital deployment, I would adopt many of these workflows. One I rather liked as a really nice triage & focus tool is a Skill I called "The #1 Thing", which is the most important fundamental metric or debate on a stock which is the key hinge point between the stock working or not. The idea is in re-embracing coverage, what is the #1 bet I would need to make on all 153 stocks to put them in the bucket of "potential long" or "potential short". This will help inform the custom agentic research process for each name. I built this for DHR below, and I'm building this into a 153 page report (1 per name under coverage). This will help me understand, across my coverage, the active research I will need to deploy to build active, deep dive agentic research & the proper tracking systems for the #1 thing across my coverage. Effectively, it will help my systems "climb the right research mountains". If you are interested, I will share a bit more on how I built this on our next open webinar next Thursday (will drop registration link in bio).

LIVE WEBINAR — BUILDING A CREDIT AI WORKFLOW Thursday, September 24th, 6 PM ET Every FE program is built on two distinct but related sets of workflows: the traditional workflows that form a foundational basis for investing, and AI augmented workflows that use agents to improve and enhance existing processes. Credit Academy is no different. Alex Goston, Head Credit Instructor, is drawing from his years of sitting in the seat learning how a credit investor actually thinks and works, to build a masterclass for credit investors. On Thursday, he and Brett take a piece of the Credit Academy curriculum and show you how a manual credit workflow can be rebuilt as an AI-augmented one using custom skills files. This session teaches one practical workflow, start to finish, live. Our view on AI hasn't changed. It can do real work in your day-to-day but it can be useless and counter-productive if you don't already know what good looks like. A skills file is only as good as the process you build underneath it, which is why the fundamentals come first and the workflow comes second. Join us live Thursday for a real look at how we take a manual credit workflow and rebuild it as an AI-augmented one, start to finish. Click the link in the comments to register

My conversation with Gabe Stengel (@GabeStengel), founder and CEO of Rogo. For years, Gabe and I have talked about how much of an investor's job AI will eventually do and how he is building Rogo toward that future. Today, Rogo helps some of the world's largest financial institutions research companies, run diligence and execute M&A. But Gabe's ambition is much bigger. He is building toward investing superintelligence, where Rogo does much of the work inside investment banks and firms and becomes the venue where they do their deals. It's a fascinating business and has been so fun watching Gabe build it. We discuss: - 10,000 agents searching for one great investment idea - Which investing skills will still matter - Why Anthropic/OpenAI won't win finance - "Chewing glass" - Why the harness around the models matters so much - Getting rejected by 40+ investors - Building an AI native Bloomberg - Becoming a black hole for talent Enjoy! TIMESTAMPS: 0:00 Intro 2:38 Building Rogo 6:12 10,000 AI Agents 12:02 Skills That Still Matter 17:31 Beating OpenAI and Anthropic 28:35 Bloomberg of the AI Era 37:37 Rogo’s Company Brain 44:19 Chewing Glass 53:34 AI-Native Finance 59:21 What Humans Still Do Better

Am I the only one who nuked their sellside research consumption by like -90%? Between ASKB, X, groupchats, substack and podcasts the marginal utility for me has gone to near zero. Still skim spec sales for positioning but even that is down by a lot since I start with TMTB.