I am extremely excited to officially launch The Fundamental Edge Credit Academy. Four years ago, the equity-focused Fundamental Edge Analyst Academy launched Cohort 1 with a simple insight: across firms, institutional-grade equity investment analysis is more similar than different, but practitioners are mostly too busy to carefully dissect then teach that process with the patience & rigor required to effectively learn. This leads to a suboptimal situation where new investors are left to "learn through osmosis" which mostly translates into "hang around the desk and figure it out on your own". The Analyst Academy was designed as a style-agnostic "salad bar" of investment process intended to be a driver's education for investment process at long short hedge funds & long only asset managers. To my delight, the dogs liked the dog food, and four years later, Fundamental Edge has graduated nearly 2,000 students across our Analyst Academy, Factor Academy & Applied Value Investing programs (in partnership with Wall Street Prep & Wharton), and we have partnered with over 3 dozen institutional firms who use our programs to augment & accelerate new hire training. Along the way, many credit-focused investors have come to the Analyst Academy, despite it's equity focus. It became clear to us that there was demand for a similar program in credit. So, we built it. Most importantly, training is a people business. And I am very pleased to welcome Alex Goston to Fundamental Edge, who will lead our Credit Training practice, both in Cohort & Direct Enterprise level. Alex brings institutional experience at KKR Special Situations and RBC Global Asset Management, but critically, has the soul & patience of a teacher, and a knack for decomposing & explaining an often complicated craft. We couldn't be more happy to have him on board. To learn more about the Credit Academy, please join us for an information session tomorrow at 6pm ET. Replay will be posted to our YouTube channel.

Bingo. 100% AI types don’t understand this. They want to push investors out of Excel because they don’t fundamentally understand investors. Eventually that may happen. But, for now, a quantitative surface is necessary. Reliable Excel read/write changes everything, and by my evals Opus 5.5 first to hit threshold

Two slides from today's AI Accelerator session digging into the AI Excel threshold moment and why I think it's such a big deal for public equity investors: it unlocks a fully embedded research system, for the first time.

With new modeling fluency, I can also update my $NKE model seamlessly and that updated model can then flow through my dividend durability skill to get an updated assessment of NKE's dividend (now a >5% yield, for now). Fwiw, despite management emphatically standing behind the dividend on the call, the output of this process puts the risk of a dividend cut >70% and most likely in the June-November 2027 window. If, for example, you are a running an equity income fund, the ability to scale this analysis across a subset of dividend playing equities (both risk score and catalyst for cuts) feels very valuable to me. (don't take investment advice from a random guy pushing buttons in Claude on the internet...but just saying that if you want to bottom fish NKE, it would be wise to have a view on whether the dividend will be cut)

One of the biggest unlocks in AI for investors over the last year has been how seamless it can be to feed agents the right context to generate alpha signals. The game is no longer LLM parameter count or the size of your context window. The tools will get better, but we have crossed threshold. The real question is how much high quality context you can feed your agents. Shoutout Canary - in my earnings risk feed, the Canary MCP identified some key items on $NKE that were red flags ahead of this quarter, namely new CFO, chief accounting office departure (pattern recognition to set low bar) and rising discounting at DKS. As the orchestration layer for investing workflows moves from the harness back into Skills (bitter lesson strikes again...), two things will matter, 1) how well you build Skills (it again appears there will be real alpha here as Opus 5.5 unlocked scary good orchestration capabilities and is *shockingly* incisive) and 2) the quality of differentiated context you feed the system.

I agree with this. Classically, the Tiger cubs liked moving great analysts around to various sectors in a deep sequential immersion, and when became senior risk takers, the ability to know 3-4 sectors deeply became a huge advantage in comparing the opportunity set cross sector. The reality is each sector will have a time where it is alpha rich (feels easy) and alpha poor (feels impossible). As a healthcare investor, I know this feel SUPER well… I logic through how the next 3-5 years may play out and i think today’s multi-manager discretionary alpha will see increased competitive challenges. Day by day, the typical quarter in the life of a typical multi-manager analyst is being systematized. Quants who are much better at position sizing and execution will figure out a way to scale this, just as they have scaled alternative data signal. And the trade will have already happened before the human can press “buy”. The “super pod” idea of an elite team of perhaps 3-5 decision makers all supported by maybe 1-2 analyst per sector feels like a strategic shift that could elongate multi-manager alpha streams. Allowing slightly longer duration (which requires a bit of a mindset shift on drawdowns / idio constraints) may be necessary. Navigating crowding in this model would be key, and a nontrivial challenge.

I’d love to chat with people doing interesting things (or with interesting ideas) on the deployment layer for Investing AI Very much agree with @levie

Every investment decision I've ever made has been informed by a Excel spreadsheet. A financial model that serves as a business simulation and a translation layer between research & insight. The model ultimately is a place to quantify my variant perception and quantify the downside if I am wrong, the two critical pillars of any good public market investment thesis. One of my PM friends says his analysts spend all day with "their noses in models", identifying and researching the hinge variables in a business. This is the game of alpha generation, simple not easy. A cornerstone of the hedge fund interview process is the modeling test, and hedge funds routinely hire ex bankers & private equity for this skill. (A certain hedge fund founder is rumored to have fired analysts who tried to repurpose sell-side models as their own.) When you peel back the onion on Tier 1 fundamental research process, four years since the Chat GPT 3.5 moment, AI adoption is slow & uneven with the median investment process far from transformed (despite an exceptional amount of tokens burned...). Why is that? To me, it's simple. The "bar to impress" a public equity investor with AI has to start with the financial model architecture as the nerve center of the investment process. Almost every AI workflow for investors I've seen is a "hey this is sort of interesting", but not a "you will take this from me over my dead body" workflow. An agent that can summarize your morning inbox well is convenient but won't bend the curve of alpha. An automated, auditable, full-stack research system built around an architecture of Excel models that can help me do the rigor and depth of investment research in 3 days that used to take me 3 weeks? That changes everything. Financial modeling as the foundational layer in that workflow, the critical unlock to truly enact a full end to end research & risk system. Yes, it is productivity, but ultimately it is more about speed to insight and surfacing the right insight to the decision maker at the right time (this is what great analysts do). "But judgment will still sit with the PM, right?" What is investment judgment if not gathering and weighing as many unique insights about a business as possible then continuously comparing that view to market implied expectations? "Brett, but won't modeling move to JSON?". I've heard selective instance of that. Perhaps eventually. But Excel is just this beautiful, auditable, sharable, trusted substrate on Wall Street. It won't die a quick death. The challenge investors have of reinventing their investment process while running risk is materially different than software engineers adopting coding agents. Until September 22nd when Opus 5.5 was released, the technology wasn't there. It's here now. For this simple reason, I believe we are entering a period of accelerated adoption of AI in Finance, particularly in public equity investment process. I am having conversations with firms who are interested in co-developing and enacting this vision for their investment teams. If this resonates with you, please DM.

Credit Academy kick off session today for the inaugural Credit Academy Cohort. Will run 6 months and have Credit AI curriculum blended in, including a baseload Credit Skills Pack . All sessions recorded so mid-stream joiners are very welcome. Enroll at fundamentedge.com

Awesome

Yes! Would add, even on a less technical basis, as Claude's memory and ability to call other Skills in a system has improved, it is hard to disentangle why one Skill works (did it see a reference Model 3 weeks ago and put that into memory, for example?). And thus when I share my Skills, sometimes those users get inconsistent results. That's the bad news. Not to mention as models improve and best practices change (which seems to happen every ~30 days...). It sort of requires a continuous loop of eval, updating, iteration, validation, eval again. Then the human experimentation layer. And, in my experience, the whole Skill Architecture increasingly needs so stick together more than it did in the past (to produce coherent, Analyst grade outputs). The good news is that the ability to create a system (DeployCo/FDE) has become much, much easier. And to make this curated to an individuals fund's process & preferences is also becoming much easier.

I find this really cool too, the ability of Opus 5.5 to do and do all the little modeling hygiene nits it takes to develop a clean & accurate forecast. This is for $SOLV (the 3M Healthcare Spin) which on first blush reported a terrific Q2 (+9.5% organic rev, +33% operating income, $10 of run-rate EPS on a $90 stock). But a hygiene skill can go into the documents/filings/transcripts and pull out non-recurring, non-baseline items. At SOLV, an advance ERP order and IEEPA tariff refund flattered the quarter, and the underlying business momentum is not as compelling as it looks initially. These sort of complex tasks would choke prior models, in my experience. And the manual ticking and tying, parsing commentary and calling IR to get all this stuff tight in our models. It's endless (and often mindless) but critical for investors working to build the most accurate forecasts possible. The ability to run a "forecast-hygiene" skill capturing all of these little nits, then reflect that back in my Excel forecast. The analyst can then spend so much more time focusing singularly on the operating assumption that drives numbers up or down, not all the little modeling nits. Pretty cool. (Particularly for multi-manager analysts who are graded on estimate accuracy...)

The most common push-back I got to this is some version of "does this drive alpha?". Of course not. The financial model is an instrument for a decision. It is not the decision itself. I have deployed, and been on teams that have deployed, billions of dollars in public equity investments. Every single investment was supported by analytical conclusions embedded in an Excel model. Excel is an auditable, reliable place to quantify assumptions, see sensitivities, and trace how a research conclusion is likely to translate into a fundamental forecast/revision and, subsequently, a stock move. Also, when I went from one multi-manager to another, my two associates spent three painful months rebuilding 300+ healthcare models. And many, many hours updating and revising them as companies restated, re-segmented, divested or acquired. There was no alpha in this motion either, but it ate up a lot of hours, and was a *critical* foundation for the next step of decision making. What I don't think people see yet is how critical Opus 5.5's level of Excel fluency is for finance. Qualitative chat-bot use cases are sort of helpful for investors. The ability to quantify research autonomously and reliable is a big deal. No prior LLMs could do this. Opus 5.5 finally unlocks the full loop of ideation, quantification, differentiation and the identification of asymmetry and catalysts, all centered around the most context-rich research artifact in the public investor's tool kit: the financial model. I truly think you now can automate probably 50-65% of what my junior associates were doing. Focus shifts to context input & research dossier building, as well as the new skillset for the investor: AI research architect.

Example of using Opus 5.5 to build a model from scratch on $OSCR loom.com/share/2a9973c34b834…

100% Existential question across so many industries - how does junior talent learn things the right way when the temptation to bypass with AI is so acute? No easy answers