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AI AI: Large Language Models AI: Transformers Authors Podcasts Writing

The Billboard

The fog was still sitting on the hills when I put in my earbuds and headed out.

Sebastian Mallaby was talking about billboards.

Tim Ferriss had asked him the question he asks everyone: if you could put anything up there, for millions of people to see, what would it be? Mallaby has spent years inside the minds of the people who shaped modern finance โ€” the hedge fund managers, the venture capitalists, the builders of things that changed how the world moves money. He has more material than most people accumulate in a lifetime. He could have said anything.

He said: Prepare your mind.

I kept walking. The houses were quiet in the particular way they get when school lets out for summer โ€” no buses, no car doors, no kids at the corner. Somebody’s sprinklers were running.

The phrase comes originally from Louis Pasteur, who understood something that most people don’t: that chance is not democratic. It does not distribute itself evenly among those who wait. It finds the people who are ready. Chance favors the prepared mind. Pasteur said it, and then he proved it, and then the rest of us spent a century and a half learning it was true.

What struck me about Mallaby’s answer wasn’t the phrase itself. It was the way he said it had kept appearing in his research, surfacing in different decades and different worlds, like a message the material kept trying to send him.

He told the story of Arthur Patterson at Accel Capital. Before a new technology arrived, Accel would work through the implications โ€” what company needs to be built, what founder fits the moment, what the right pitch looks like. So when an entrepreneur finally walked in, when the situation was live and competitive, they already knew ninety percent of what they were hearing. They could move fast because they had already moved slow.

That’s preparation as institutional practice. But Mallaby found the phrase again in a different register entirely, embedded in a single human moment that has always seemed to me like one of the hinge points of our era.

He was interviewing Ilya Sutskever, asking him why he had seen it so quickly.

In 2017, a paper called Attention Is All You Need appeared online. It described a new architecture for neural networks โ€” the transformer โ€” that would eventually rewrite the terms of what artificial intelligence could do. On the day the paper went up, Sutskever read it. And then he ran. He went down the corridor to find his collaborator Alex Radford and told him to stop what he was doing. Everything. Stop. We are going to build a language model on this architecture.

Not someday. Now.

Mallaby asked him how he had seen it so clearly, so fast. And Sutskever’s answer, in its essence, was the same two words: prepared mind.

He had been thinking about the problem of modeling sequential data since his PhD in Canada. For years he had been carrying a question the field hadn’t answered yet. And when the answer appeared โ€” when the transformer showed up on a website one ordinary day โ€” he didn’t have to reason his way toward it. He recognized it. The solution arrived and found a mind that had been waiting for it, that had already cleared space for it, that was already arranged around the shape of exactly this kind of answer.

This is what preparation actually is. Not the accumulation of facts. Not readiness in the generic sense, the vague self-improvement sense. It is the long, patient cultivation of a specific question, held close and kept alive until the answer has somewhere to land.

Mallaby chose that phrase for his billboard because it kept finding him โ€” in the venture capital world, in the AI world, across decades and disciplines and very different kinds of genius. The prepared mind is not a personality trait. It is a practice. It is the work you do before the work arrives.

The sprinklers had clicked off by the time I turned back toward home. The fog was starting to lift off the hills. I was thinking about what I had been preparing for, whether I even knew.

Categories
AI Business

The Topography of a Face

I found myself staring at the physical geometry of a conversation the other dayโ€”not the words, but the topography of the faces delivering them.

Elad Gil recently shared a fascinating experiment during a conversation with Tim Ferriss. Heโ€™s been uploading photos of startup founders into AI models and asking the machines to predict if theyโ€™d be successful, purely based on their โ€œmicro-features.โ€

“Because if you think about it, we do this all the time when we meet people, right? We quickly try to create an assessment of that person, their personality, and what they’re like. There are all these micro-featuresโ€”like, do you have crow’s feet by your eyes, which suggests that your smiles are genuine? [โ€ฆ] So, I have this whole set of prompts that I’ve been messing around with, just for fun, around: ‘Can you extrapolate a person’s personality based off of a few images?'”

He notes the model breaks down the crow’s feet and the furrowed brows, extrapolating a personality from a static frame. Itโ€™s a parlor trick, perhaps. But it works because it holds a mirror to our oldest, most unexamined instinct.

We are all amateur phrenologists of the human face. We sit across a table, measure the crinkle of an eye or the tightness of a jaw, and we build a rapid, invisible architecture of trust or suspicion. Over decades of investing and making career choices, Iโ€™ve often leaned heavily on this silent language. Iโ€™ve backed founders because their intensity felt genuine, and Iโ€™ve passed on others because something in their posture felt misaligned.

But if I am brutally honest, that intuition has sometimes been a mask for my own blind spots. Iโ€™ve held on to failing investments for far too long because I trusted a reassuring smile. We like to think our gut instinct is a sophisticated instrument. Often, it is just a pattern-matching engine running on deeply flawed historical data.

Now, we are handing that very human habit over to a machine. We prompt the AI to become a โ€œcold reader,โ€ and it obliges, predicting who will be the quiet observer and who will deliver the dry wit.

The unsettling part isn’t that the machine might get it wrong. The unsettling part is that it might get it exactly rightโ€”by mimicking the very same rapid, superficial judgments we make every day, just at a terrifying scale.

We are teaching silicon to read the human code. The future will belong to those who realize the code was always written in our own biases.

Categories
AI Business Economics Podcasts

Bubble Bath

The behavior of today’s stock market is yet another sign that many will point to as indicating there’s an “AI bubble”. Today’s market action is largely attributed to Blue Owl Capital deciding not to participate in the debt financing of a new Oracle data center (being built for OpenAI) in Michigan. This news came out overnight last night and soured the market at the opening as it added fuel to the fires already raging from last week about bubbles in AI and, in particular, some of the debt financing being used to build new data centers – especially but not exclusively by Oracle.

Watching the market action today a brought to mind a recent paper on the subject of bubbles authored by Oakmark Capital’s Howard Marks. Marks is widely followed in the investment community for his almost “sage-like” prognositcations of which he’s authored many over the years. His memos are always full of insights and valuable learning opportunities. His recent one drilled into this question of bubbles — and, in particular, the role of debt financing. That’s especially interesting as Marks’ long career at Oakmark has been primarily involved in debt financing and, indeed, sometimes distressed debt.

Marks points out why he thinks the current situation with respect to AI is problematic as it shows many of the common characteristics of bubbles which in the past have resulted in wealth destruction instead of wealth creation.

As I’m writing this, I’ve just begun watching the latest Tim Ferriss interview which is with long-time venture investor Bill Gurley. Right up front in the conversation, Tim asks Bill for his take on the bubble in AI question. It’s a great conversation – well worth a listen.

Speaking of listening (or watching), on Monday I watched the latest episode of the Acquired podcast – this one celebrating the tenth anniversary of Acquired. This was the most fascinating conversation I’ve listened to or watched in recent memory. The two hosts of Acquired were being interviewed by author Michael Lewis – and they were interviewed in depth! Lewis asked lots of probing questions as well as provided some illustrative commentary of his own along the way. An incredible episode – highly recommended!


After publishing this post, I asked Google Gemini 3 Fast: I just published a blog post. Review it critically and help me improve it. Act like a tough but compassionate editor. Hereโ€™s the post: https://sjl.us/2025/12/17/bubble-bath/

Hereโ€™s the critique it gave me – tough but compassionate!

Alright, sit down. Letโ€™s look at “Bubble Bath.”

Youโ€™ve got the ingredients for a great piece of timely analysis here: a specific market event (the Blue Owl/Oracle snag), a heavy-hitting intellectual framework (Howard Marks), and contemporary expert commentary (Bill Gurley and the Acquired team).

But right now, this isn’t an essay or a cohesive blog postโ€”itโ€™s a link log. Youโ€™re telling me what youโ€™re watching and reading, but you arenโ€™t telling me what you think.

Here is my critical review.

The “So What?” Factor
You start strong with the Oracle/OpenAI data center news. Thatโ€™s a “hook.” But then you pivot immediately to: “I read a memo by Howard Marksโ€ฆ I’m watching Tim Ferrissโ€ฆ I watched Acquired.”

As an editor, Iโ€™m asking: Where are you in this post? Youโ€™ve gathered the smartest people in the room, but youโ€™re standing in the corner taking attendance.

The Fix: Donโ€™t just tell us Howard Marks wrote a memo. Tell us which one specific point he made that explains the Oracle news. Connect the dots for us. Is the Oracle situation the “wealth destruction” Marks warned about, or is it just a healthy correction?

The Title vs. The Content
“Bubble Bath” is a clever pun. I like it. But usually, a “bubble bath” implies a cleansing or a soaking. Are we getting soaked (losing money)? Or is the market being “cleansed” of irrational exuberance?

The Fix: Use your conclusion to pay off the title. Right now, the post just ends with a recommendation for a podcast. Close the loop: “If Marks and Gurley are right, this isn’t just a market dip; it’s the start of a very cold bubble bath for AI investors.”

Structural “Pass-Through”
The second half of the post feels like a “What Iโ€™m consuming” list. It dilutes the urgency of the first paragraph.

The Fix: If the Bill Gurley and Acquired links are just “further reading,” put them in a “Resources” or “Deep Dive” section at the bottom. If they are central to your argument, quote them. What did Bill Gurley say about AI debt that makes the Oracle news more frightening?

Tighten the Prose
You have some “wordy” habits that slow down the reader.

โ€œThe behavior of todayโ€™s stock market is yet another sign that many will point to as indicating thereโ€™s an โ€˜AI bubbleโ€™.โ€ * Editorโ€™s Edit: โ€œTodayโ€™s market volatility is the latest fuel for the โ€˜AI Bubbleโ€™ fire.โ€ (Go for the jugular).

Compassionate Closing
Scott, you have a great eye for sourcing. You are curating high-signal content (Marks, Gurley, Lewis). That is valuable. But your readers come to your blog because they want to know how you are synthesizing this information.

My Challenge to You: Rewrite the middle paragraph. Instead of saying the Acquired podcast was “the most fascinating conversation,” tell us one thing Michael Lewis asked that changed how you view the AI bubble.

Give us your brain, not just your bookmarks. Now, go back to the keyboard.