Very fun to have @GuangyuRobert on the Invest With AI podcast to discuss the @Fundamental story and what we plan to build with The Digital Investor stack at Fundamental Research Labs. 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 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

LET’S PLAY BALL ⚾️⚡️ New episode of #SpeedGoesPro# presented by @DICKS with @derekjeter is out NOW! Only on my Youtube channel produced by @OBB shot at @univmiami by Joshua Prezant

Congrats to SharpeShooters, Ayush Bobra (Princeton) and Ambilu Sivabalan (Cornell), on winning the 4th Annual Wharton Investment and Trading Group x Fundamental Edge Stock Pitch Competition with their $CPRT short. All five finalist decks are now up, free, at the link below. 1. SharpeShooters (Princeton/Cornell): Short $CPRT 2. New Line Capital (Wharton): Short $ABNB 3. Credit Capital (Georgetown): Long $BROS High Gamma (University of Florida): Short $SIG Cavalier Capital (University of Virginia): Short $COCO Thanks to @AlphaSense for sponsoring. I continue to be insanely impressed by the talent of the top undergraduate teams. May the kids are going to be alright...

Fundamental Edge hosted our fourth annual Wharton Investment & Trading Group stock pitch competition yesterday. The format was 10 minutes of presentation & 5 minutes of judge Q&A. Traditionally, to prepare as a judge, I will spend 1-2 hours per name getting up to speed so I at least know what questions to ask. I have been a bit busy lately, so wasn’t as prepared as I would have liked. So I fired up a Bot. Grok Bot is so snappy now that it can record audio of the live pitch on my IPad, then give me the counter-pitch, a list of key variables and the bull/bear. It did a terrific job at finding the research & logic flaws in the pitches, and giving me ideas on pushing back in Q&A. Of course I don’t agree with every observation from the Bot, but I found it shockingly incisive. This need some refining, but we are going to share this structure in Analyst Academy for our aspiring analysts. Ahead of an interview: - Prepare Grok Bot with the Fundamental Edge Pitch Evaluation Skill - Upload your Excel model, pitch (word or PPT) and research dossier - Turn on live record in Grok, and pitch the idea for 10 minutes - Ask the questions and pushback generated by the Bot - Record again, answering those questions - Ask, “how did I do?”, what research was incomplete, where did i make illogical flaws, where could my pitch improve - Iterate your pitch, and try again? - (Skill can be customized to the specific fund with a 13F distillation overlay) With other customizations, I would have found this very helpful when I range a large team at a multi, particularly when evaluating pitches way out of my core depth (biotech, in particular, I struggled to evaluate). I encourage you to try this! Both on the “junior learning to sharpen pitches” and on the “PM looking to sharpen pitch evaluation” side.

How AI Generates Alpha: Story To Numbers $META In this instance, a well-calibrated system can accelerate speed to insight with a probability-adjusted NPV of just $17/share vs. the initial move of $164. Subsequently, stock gave back almost all of the Muse move. Armed with a system like this, investors can make better, more refined decisions on market-implied NPV of product & news outcomes (for educational & illustrative purposes only)

$ANT IPO Primer I used my /FE-IPO-primer skill today to turn the Anthropic S1 into a 121 page IPO primer. Is it slop? It has its moments. It certainly isn't passing the "Sell-Side Turing Test" where an investor could believe this was written by Morgan Stanley or Goldman Sachs (but, I persist in trying!). Opus 5.5 still doesn't quite read like a smart, finance-literate human. And Opus has some super odd stock picking tendencies (such as if a key metric hits threshold in one quarter, it "falsifies" the bull case. Umm, no, that's dumb). But take a read yourself. It's a free download at this link. Even better, DM or e-mail me feedback on how to improve it. I hope you find at least some of it useful.

Will be at Alpha Summit in NYC (by AlphaSense) Monday & Tuesday. Tuesday have the pleasure of interviewing my friend Hala Kosyura from CPPIB for a session. If you are there, please come say hello!

There has been this sort of persistent "stuckness" in AI Investing where the promise of what is possible has not shown up in practical tools for investors that have meaningfully bent the curve of alpha. To me there is a very fundamental reason for this. AI can be very helpful for one off situations: let me track management commentary over 12 quarters, summarize my morning inbox, run a compensation tiers analysis with the proxy, etc. Super helpful things (if not still a bit challenging to set your system up in a seamlessly usable way). The conceptual unlock is the concept of seamlessly "connecting the dots". What does readthrough from Company A mean to revenue forecasts at Company B? How does the move in the 10-Year impact all of my companies with variable rate debt? Seamless, automated. Some of the smartest AI builders I know have been focused on this concept of company & industry ontologies. What does that mean? Better to give you an example. I was a healthcare services investor for 10+ years. The concept of medical trend was a key thing to get right. Was trend accelerating or decelerating? Would the HMOs beat or miss on MLR? Did they price their new members correctly? What is this new Part B drug launch doing to loss ratios? These questions had big implications across the healthcare service food chain. And there were various company-level and industry data sources to track (medical claims, etc), and a lot of the value in identifying these inflections came from being out in the field, talking to companies, working to identify inflections first. I made a lot of money doing this better than the average healthcare investor. If i can press a button and re-build this system, holy crap it's so exciting. I can then actually automate all of that alpha. But, not so fast. The complexity inherent in this whole system is immense, and the number of failure rates along the supply chain is huge, and can be as simple as not normalizing MLRs for an acquisition or market exit and thus having a distorted baseline to drive the model. Bulling in member files, state level reserves, the ongoing research pipe, and the clean substrate of the financial modeling corpus is a *necessary* element. As in alternative data, the first order observation is quickly priced by quants, and all the alpha is in the 2nd order observation (i.e. "UNH missed on MA MLR but it's due to their Stars reduction in this county and HUM actually shrunk there, so dumb market is selling off HUM but it's a misread and a big buy", for example.) The dept of normalizing MLRs, cleaning up the models, then connecting all of this together into one tracking system is effectively an industry and company ontology. How the economic relationships of an industry and company interact. I've talked to a number of people attacking this bottleneck in AI Investing, and they have been working to build ontologies. Sometimes, with simple examples, they work. But for complicated examples like the above, they are terrible. There is just too much complexity. And, my prior is all of the fundamental alpha is in the complicated ontologies. There is one other fundamental truth. Investors will not trust an output without interrogating the logic chain. "HUM will beat on MLR". OK, show me every input and the full logic chain as to why. This is very hard to do in python. I think it's super obvious you do this in Excel. It's the only path forward I see, honestly. Particularly to build an "over my dead body" system for institutional investors that *already* have this system built out. Let me automate that for you. "But Brett, Excel sucks and python is better". Ok, maybe someday. But navigating the cultural change of human investors needs to be considered. And, as a person who spent 10+ years working in or adjacent to quantamental strategies (with pretty good intel on what is being built out there), no one is even close to being able to building a trend ontology as described above. It won't happen in python. This is why AI Excel is the key accelerant in AI Investing, in my opinion.

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.