Category: Investing

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AI Business Investing Technology

The Scarcity Portfolio: Navigating Sovereign Debt, Wafer Bottlenecks, and Orbital Compute

Today I was watching the interview of Gavin Baker by Patrick O’Shaughnessy on his Invest Like the Best podcast. Like prior conversations this was another fascinating excursion into the mind of a sophisticated and very successful tech venture investor.

During the conversation, Patrick asked Gavin what agents he was using that were especially helpful and he mentioned one which summarizes YouTube podcasts and videos for him. Like most of us Baker just doesn’t have the time to watch or listen to them himself so good summaries are really helpful.

Turns out I’ve been working on a Google Gemini Gem that does this for me. When Baker mentioned his I fired up the new Gemini 3.5 Flash model and asked it to summarize the Baker interview.

Later in the conversation Baker used the term “battlefield AI” which caused me to go back to Gemini again to learn more about that. The results were so interesting that I asked Gemini to create a syllabus for a semester class on these subjects. After that I asked it to convert our whole conversation into a Markdown file so I could share it. You’ll find it below.

I found this whole experience pretty stunning. I came away very impressed with Gemini 3.5 Flash both for the quality of the responses but also the sheer speed. Wow!

Anyway I hope you enjoy the following!

Continue reading “The Scarcity Portfolio: Navigating Sovereign Debt, Wafer Bottlenecks, and Orbital Compute”
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Investing Living

The Lonely Quadrant: Why the Crowd Never Outperforms

There is a profound comfort in the consensus. When we agree with the crowd, we are protected by a shared canopy of logic. If we are wrong, we are wrong together. The sting of failure is diluted by the sheer number of people who made the exact same miscalculation. We can shrug our shoulders, look at our peers, and say, “Who could have known?”

But this comfort comes at a steep price: mediocrity.

Years ago, the legendary investor Howard Marks crystallized a framework that has haunted my thinking ever since. He mapped out the relationship between predictions and outcomes, arriving at a blunt, inescapable truth about generating extraordinary results. To make really good money—or to achieve outsized success in almost any competitive endeavor—you cannot simply be right. You have to be right when everyone else is wrong.

“You can’t do the same things others do and expect to outperform.”

Marks’ logic is beautifully ruthless. If your prediction aligns with the consensus and you are right, the rewards are merely average. The market, or the world, has already anticipated and priced in that outcome. There is no edge in seeing what everyone else sees. If your consensus prediction is wrong, you lose, but you lose alongside the herd.

The danger, and the opportunity, lies in the contrarian view.

If you are non-consensus and wrong, you look like a fool. You bear the entirety of the failure alone, stripped of the insulation of the crowd. This is the quadrant of public mockery, isolated defeat, and bruised egos. It is the fear of this quadrant that keeps most people safely tucked inside the consensus.

But the magic—the life-changing returns, the paradigm-shifting innovations, the profound personal breakthroughs—lives exclusively in the final quadrant: being non-consensus and right.

This isn’t just an investing principle; it’s a philosophy for navigating life. We are biologically wired to seek the safety of the herd. To step outside of it requires not just immense intellectual conviction, but a formidable emotional threshold. You have to be willing to sit with the discomfort of being misunderstood, sometimes for years. You have to endure the sympathetic smiles of peers who think you’ve lost the plot.

Creating truly great art, building a lasting company, or making an exceptional investment demands a willingness to be lonely in your convictions. It requires looking at the exact same data as everyone else and seeing a completely different narrative.

However, a vital caveat remains: being different isn’t enough. There are plenty of contrarians who are simply wrong, confusing blind rebellion with profound insight. The goal isn’t to be a contrarian for the sake of being difficult or edgy. The goal is to perceive a truth the crowd has missed.

It is a quiet, solitary bet against the world’s prevailing wisdom. And when the world finally catches up to where you have been standing all along, the reward is entirely yours.

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Business Investing

Achilles and the Algorithm

There’s something almost poetic in the connection between Jim Simons and Zeno’s paradox — two minds separated by millennia, both obsessed with the hidden structure beneath apparent motion.

Zeno’s paradox, in its most famous form, claims Achilles can never catch the tortoise. Before he closes the gap, he must first close half of it. Before that, half of that. An infinite series of steps… and yet somehow motion happens. The paradox isn’t really about motion at all — it’s about whether an infinite process can have a finite sum. The resolution, as we now know, is that it can: 1/2 + 1/4 + 1/8 + … = 1. Infinity folded neatly into something whole.

Simons, the mathematician-turned-trader who built Renaissance Technologies and the Medallion Fund, was doing something structurally similar. Markets look like noise — chaotic, memoryless, efficiently random. The conventional wisdom was essentially a financial version of Zeno: you can never beat the market, because any edge you think you’ve found will be arbitraged away before you fully exploit it. An infinite regress of efficient corrections.

But Simons, trained as a geometer, suspected that beneath the apparent randomness there were patterns — small, fleeting, but real. Not the crude patterns that chartists chased, but subtle statistical regularities, the kind that only reveal themselves when you treat financial data the way a mathematician treats a noisy signal from a distant star. He wasn’t looking for a story about why a price would move. He was looking for the mathematical signature that it would.

The deeper parallel is this: Zeno’s mistake wasn’t his logic, it was his intuition that infinite subdivision must mean infinite duration. Simons’ insight was similarly counterintuitive — that markets being mostly efficient doesn’t mean they’re entirely efficient, and that the residual inefficiency, compounded relentlessly with the right models and leverage, can generate extraordinary returns. A small, persistent edge across billions of trades is its own kind of convergent infinite series.

There’s also something Zenonian about Simons’ secrecy. You can approach an understanding of what Medallion does, but you can never quite arrive. Each step closer — the hiring of physicists and cryptographers, the signals in weather patterns and earnings releases, the hidden Markov models — reveals another half-distance still to close. The full picture perpetually recedes.

Zeno would have appreciated that.

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AI AI: Large Language Models Investing

The Ledger of Curiosity

We often romanticize the “back of the napkin” idea. It is the symbol of spontaneous genius—the startup mapped out in a coffee shop, the ticker symbol hurriedly scribbled during a dinner party. But we rarely talk about what happens to the napkin afterwards.

Usually, it gets thrown away. Or lost. Or stuffed into a drawer, becoming just another artifact of a fleeting thought that had momentum but no direction.

In the first two parts of this experiment, I used Gemini 3 Pro to solve the friction of entry (transcribing my messy handwriting) and the friction of analysis (stress-testing the ideas against 10-K realities). But there was one final gap: Permanence.

An analysis that lives and dies in a chat window is barely better than one that lives and dies in a notebook. It is still ephemeral. To truly build a “Second Brain” for investing, the data needs to leave the conversation and enter a system.

“The goal of technology should be to stop us from losing the work we’ve already done.”

I tweaked my workflow one last time. I asked the AI to not just judge the stocks, but to format its judgment into a raw CSV block.

With a simple copy-paste, my handwritten scribble wasn’t just digitized; it was database-ready. It went from a piece of paper to a row in Google Sheets with columns for “Market Cap,” “P/E Ratio,” and “Primary Risk.”

Suddenly, I wasn’t just looking at a list; I was building a ledger. I can now track these ideas over months. I can see if the “Red Flag” the AI identified actually played out. I can measure my own batting average.

The goal of technology shouldn’t just be to make us faster at doing work. It should be to stop us from losing the work we’ve already done. By turning ink into data, we stop treating our ideas as disposable. We give them the respect of memory.

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AI AI: Large Language Models Investing

The Digital Devil’s Advocate

There is a seduction in the handwritten note. When I scribble down a company name in a notebook, it is purely additive. It represents potential upside, a future win, a brilliant insight caught in ink. The notebook is a safe harbor for optimism because it lacks a “Reply” button. It doesn’t argue back.

But optimism is an expensive luxury in investing.

After my initial experiment—using Gemini 3 Pro to transcribe my messy list into tickers—I felt a surge of productivity. But productivity is not the same as discernment or understanding. I had a list of stocks, but I didn’t have a thesis. I just had digitized hope.

So, I took the next step. I didn’t ask the AI for validation; I asked for a fight. I fed the tickers back into the model with a specific directive: “Act as a contrarian hedge fund analyst. Find the red flags. Kill my enthusiasm.”

“I didn’t ask the AI for validation; I asked for a fight.”

The results were immediate and sobering. The “promising tech play” I had noted? The AI highlighted a massive deceleration in user growth hidden in the footnotes of their latest 10-Q. The “stable dividend payer”? It flagged a payout ratio that was mathematically unsustainable.

In seconds, the warm glow of my handwritten discovery was doused with the cold water of 10-K realities. And it was fantastic.

We often view AI as a tool for creation—generating text, images, and code. But its highest leverage application might actually be destruction. By using it to stress-test our assumptions, we outsource the emotional labor of being the “bad cop.” It allows us to kill bad ideas quickly, cheapy, and privately, before we pay the market tuition for them.

My notebook is still where the dreams live. But the digital realm is now where they go to survive the interrogation.

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AI AI: Large Language Models Investing

From Ink to Insight

There is a distinct friction that exists between the analog world and the digital one. For years, analog notebooks have been the graveyard of good intentions—lists of books to read, article ideas to write, and companies to investigate, all trapped in the amber of my barely legible handwriting.

I recently found myself looking at one of these lists: a scrawl of company names I had jotted down while reading an article discussing possible companies for investment in 2026. Usually, this is where the work begins—taking my handwritten notes, typing them out one by one, searching for tickers, opening tabs, etc. It is low-value administrative work that often kills any spark of curiosity before it can turn into useful analysis.

“The barrier to entry for deep research drops to the time it takes to snap a photo.”

On a whim, I snapped a photo and uploaded it to Gemini 3 Pro. “Transcribe this,” I asked. “Give me the tickers.”

I expected errors. My handwriting is, to put it mildly, not easy to read (even for me!).

Instead, the AI didn’t just perform Optical Character Recognition (OCR); it performed contextual recognition. It understood that the scribble resembling “Apl” in a list of businesses was likely Apple, and returned $AAPL. It deciphered the intent behind the ink.

But the real shift happened when I asked Gemini to pivot immediately into research. Within seconds, I went from a static piece of paper to a dynamic analysis of P/E ratios, recent news, and market sentiment. The friction was gone.

This experience wasn’t just about productivity; it was about the fluidity of thought. We are moving toward a reality where the interface between the physical world and digital intelligence is becoming permeable. When the barrier to entry for deep research drops to the time it takes to snap a photo, our curiosity is no longer limited by our patience for data entry. We are free to simply think.

Categories
Financial Planning Investing

The Mistake of Balance

We are culturally conditioned to hedge. We are taught the virtues of a balanced portfolio, a balanced diet, and a balanced life. We spread our chips across the table—a little bit of energy here, a little bit of time there—hoping that if we just cover enough bases, the aggregate sum of our efforts will amount to a meaningful existence. We find comfort in the average because it protects us from the zero.

But nature, and certainly the mechanics of outsized success, rarely operates on a bell curve. It operates on a Power Law.

Sam Altman, reflecting on the errors of intuition in investing, noted that his second biggest mistake was failing to internalize this mathematical reality. He said:

“The power law means that your single best investment will be worth more to you in return than the rest of your investments put together. Your second best will be better than three through infinity put together. This is like a deeply true thing that most investors find, and this is so counterintuitive that it means almost everyone invests the wrong way.”

The math is brutal in its clarity. It suggests that the drop-off from our primary point of leverage to everything else is not a gentle slope; it is a cliff.

When we apply this to capital, it makes sense. One Google or one Stripe returns the fund. But this is a “deeply true thing” that transcends venture capital. It applies to our attention, our relationships, and our creative output.

Consider the “investments” of your daily energy. Most of us spend our days in the “three through infinity” zone. We answer emails, we manage low-leverage maintenance tasks, we entertain lukewarm acquaintanceships. We busy ourselves with the long tail of distribution because the long tail is where safety lives. It feels productive to check fifty small boxes.

However, if Altman’s observation holds true for life as it does for equity, then that single, terrifyingly important project—the one you are likely procrastinating on because it feels too big—is worth more than the rest of your to-do list combined.

The “counterintuitive” pain point Altman mentions is that to align with the Power Law, you have to be willing to look irresponsible to the outside observer. You have to neglect the “three through infinity.” You have to let small fires burn so that you can pour all your fuel onto the one flame that actually matters.

We invest the wrong way because we are afraid of the volatility of focus. We dilute our potential because we are terrified that if we bet on the “single best,” and it fails, we are left with nothing. But the inverse is the quiet tragedy of the modern age: we succeed at a thousand things that don’t matter, missing the one thing that would have outweighed them all.