Alberto Echevarría | The Investing Monk

After 5 years of all nighters and weekends, MonkStreet is live. One score. 149 signals. 25 years of data. t-stat of 9.20. No opinions. No noise. Just math.

My car has a full self-drive mode that works really well. When I drive to Penn from home, the car drives itself about 95% of the time. The self-drive mode isn’t perfect. The car sometimes refuses to take a side street, a few yards after leaving home, that could shorten the commute by a few minutes. Just before I arrive, I override the system, turn left onto my desired street, and then engage self-driving mode again. Once I reach Penn, the car can't handle the parking lot, so I park it myself. Overall, though, the self-drive mode makes the commute much more pleasant and less tiring. For instance, I can concentrate much more on the audiobook I am listening to. I love it. This little anecdote highlights an important point about AI that is often overlooked. You should view AI as a complement to your skills, not as a replacement. The goal isn’t to ask Claude for a literature review and accept whatever it provides. Instead, you ask for a review, then verify the papers yourself, identify what’s missing, push back, and keep working until you’re confident you understand the relevant work. Similarly, you might get a first version of Python code from an LLM, but then you test, modify, and refine it yourself. Most horror stories about LLMs come from people who blindly trusted the model, not from those who used it as a highly capable assistant. In economic terms, AI is complementary capital to your human capital, not a substitute. Your goal should be to reorganize your workflow to maximize the elasticity of complementarity between AI and your skills, and to accumulate further skills that are complementary to AI. My life has been revolutionized over the last three years. I drive to school with AI, I research for papers with AI, I learn new material with AI, I prepare my lectures with AI, I handle email with AI, I shop for groceries with AI, I pick movies to watch with AI, I search for new books to read with AI, I plan my travels with AI, and yes, I write on X with AI. Without AI, I would not be active on X because I refuse to spend three hours in the morning agonizing over how to make each sentence flow correctly or to generate a nice figure illustrating my point. I started writing this post at 9.15 am, and I am about to post it at 9.38 am: this was only possible due to AI. Being against AI is like being against electricity because a moron electrocuted himself using a hairdryer in the shower.

Un empresario suele tener 3 o 4 métricas clave del negocio que vigila y sabe que son relevantes, representativas, y normalmente ninguna de ellas es un descuento de flujos de caja hipercomplejo y académico.

AI has created an abundance problem. Sure, you could build a website yourself. But now you're overwhelmed with tools, plaforms and infinite data to plug together in infinite combinations. You need a domain expert to find the right path. Nothing has changed.

Let's be clear: Spain: enacts rent control -> no more homes Argentina: ends rent control -> tons of new homes Rent control is a better way to destroy cities than dropping bombs on them.

Vibes are very pro-Claude right now, but it does seem that OpenAI has all the pieces to build an incredibly good “Cowork”style agentic work platform: > GPT 5.4 Pro (very good at Excel) > The best Deep Research capability > Codex (a better one shot coding agent) Investment Process Deep Research running 24/7 365 on 3 Key Drivers, 2 key Risk Factors on every name in coverage with Excel ingress/egress with exoskeleton research support where needed (often with the human orchestrated by the LLM). Ideas for Investing AI that felt conceptual only 3 months ago feel like they could be very close to an operational reality. It’s a strange time to be alive. What vendor wins? I don’t know, but the prior thus far has been leapfrogging models, fast cycle feature parity (I.e laggard copies the winner), and the continuous abstraction of harness engineering, which I think should be considered carefully before making large scale institutional decisions that are annoying/costly to reverse.

Marc Andreessen says raw intelligence might be the worst qualification for leadership — and it changes everything about how we should think about AI. "If the leader is more than one standard deviation of IQ away from the followers, it's a real problem." Andreessen points to the US military, one of the earliest and most rigorous adopters of IQ testing, as the source of this insight. They slot people into specialties and leadership roles based on IQ scores. And over the years, they kept seeing the same pattern. A leader who is significantly less intelligent than their people struggles to model how those people think. That part is intuitive. But the reverse turns out to be equally true. "It's actually very hard for very smart people to model the internal thought processes of even moderately smart people." A leader who is two standard deviations above the norm of the organisation they're running also loses theory of mind, that ability to hold an accurate model of what's happening inside someone else's head. The gap is too wide in both directions. Andreessen then takes this to its logical conclusion: "If you had a person or a machine that had a thousand IQ or something like it, its understanding of reality would be so alien to the people or the things that it was managing that it wouldn't even be able to connect in any sort of realistic way." An AI that vastly outthinks every human in the room isn't positioned to lead those humans. It's positioned to be completely incomprehensible to them. Leadership has never really been an intelligence problem. It's a connection problem. And no amount of raw intelligence closes that gap — past a certain point, it only widens it. The world will not be run by the smartest thing in the room for a long time. Maybe ever.