Tag: Venture Capital

Categories
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 — 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 — 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 — 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 — the transformer — 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 — when the transformer showed up on a website one ordinary day — 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 — 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
Business Storytelling

The Closed Laptop

The conference rooms all look the same after a while. Same long table. Same chairs that cost more than they should. Same window with the same view of the same parking lot baking in the same California sun. You stop seeing them. You develop a kind of practiced receptivity, a professional openness that is also, if you are honest, a professional distance. You have heard the story before. You know where you are in the presentation without looking at the slide number.

Until the day someone sits down across from you and closes their laptop and says: can I just tell you our story?

Fred Wilson, the venture capitalist at Union Square Ventures, has spent forty years learning to tell the difference between founders who can build and founders who can make you believe. The skill he overweights now, he’ll tell you plainly, isn’t technical. It’s selling. Recruiting, fundraising, convincing customers, inspiring teams. “Actually being able to write code,” he said recently, “is probably not a big deal anymore.” What matters is whether you can cross the distance between your vision and someone else’s imagination and deposit something true and alive on the other side.

Most founders never figure this out. They build the deck instead. They pull the projector cable from the drawer — there is always a drawer, there is always a cable — and the room fills with blue light and bullet points and the comfortable geometry of a prepared presentation, and what never happens is the thing that needed to happen.

But there was this one morning.

He came in with his cofounder in the flat gray light that Silicon Valley gets in February, when the rain has stopped but the sky hasn’t decided what it wants to be. They were early. He set his bag down and sat directly across from me — not at the presenter’s angle, not with one eye already calculating the distance to the screen — directly across, the way you sit with someone you already know, or intend to. Neither of them reached for the cable in the drawer.

He looked at me with the particular steadiness of a person who has decided not to manage the moment.

Can I just tell you our story?

I want to be honest about what happened next, which is that I felt something shift before he said another word. Not a decision exactly. More like the precondition for a decision, the ground tilting slightly in a direction I hadn’t chosen. I was, in some way I couldn’t have defended rationally at the time, already with him. And I knew it, and I knew it was not an entirely reasonable response to a man who had been in the room for less than a minute, and I felt it anyway.

The laptop stayed closed for the next twenty minutes. No transitions. No bullet points. No hockey stick arcing toward a number reverse-engineered from a desired outcome. Just his voice and what he believed and the quality of attention you give a person when there is nothing else in the room to look at.

The deck came later. It was beautiful. By then it didn’t need to be anything except true.

Storytelling is not a skill in the way that financial modeling is a skill. It is older than that by such a margin that the comparison almost doesn’t make sense. What we are really talking about is the oldest technology human beings possess — a person in a room, a voice, an image made of nothing but words and the willingness to believe in them. It was doing its work around fires forty thousand years before the first conference room was built, and it has never once required a projector.

What the great storytellers understand, and what the best founders understand in the same unspoken way, is that a story is not a transfer of information. It is a transfer of inner states. When it works — when it really works — something that existed inside one person gets reconstructed inside another, and the listener emerges changed. Not persuaded. Not informed. Changed. These are different experiences, and only one of them makes a person willing to bet their career on something that doesn’t exist yet.

The deck puts glass between the teller and that possibility. The founder stands at the edge of the blue light pointing at things, and the room evaluates the things, and what never happens is the transfer. Everyone files out having formed opinions about the slides rather than beliefs about the person. Opinions and beliefs are not the same.

Wilson understands this even if he wouldn’t use these words. When he says the skill is selling, what he means underneath the selling is: can this person walk into a room and make other people inhabit their vision? Not convince them. Inhabit. The difference is the difference between reading about a place and being there. One of them changes how you act. The other one you forget on the drive home.

The projector cable is still in the drawer. Someone will pull it out next week, and the room will fill with blue light, and another founder will stand at the edge of it pointing at things, hoping that the right font and the right graph will do the work that only a human being, exposed and without props, can actually do.

It won’t. It never does.

The CEO who closed his laptop had been carrying a story he believed in, and he knew the story was the thing, not the packaging around it. He understood that the oldest container is also the most powerful one. His own voice. A room. Someone willing to listen.

I was ready to work with him before he said another word.

Categories
AI California San Francisco/California

Distant Billboards

Greg Isenberg came back from San Francisco with seventeen observations. The billboards advertising either B2B inference infrastructure or vertical agent companies, the seed rounds, the forward-deployed engineers, the founders showing each other their Obsidian vaults like athletes comparing gym routines.

He noted an important thing in observation fifteen, almost as an aside.

Walking around the Mission I noticed something: the street-level businesses, the taquerias, the barbershops, the laundromats — none of them use any AI at all.

Everett Rogers formalized the technology diffusion model in 1962. He was studying hybrid seed corn in Iowa. He noticed that the farmers who adopted early weren’t just better informed — they had different social networks, different relationships to risk, different orientations toward outside knowledge. The late adopters weren’t slower. They were operating from a different set of facts about what was safe to try.

Those AI billboards in SoMa are not visible in the Mission. That’s not metaphor. That’s just geography.

What strikes me about the taqueria is not that it’s behind. It’s that the conversation happening a mile away — about MCP endpoints and agent fleets — is not legible to it. The vocabulary doesn’t exist there yet. Nobody has sat across from the woman making carnitas for twenty years and said: here is what this could do for your ordering, your scheduling, your response to a customer who asks on Yelp at 11pm whether you’re open on Monday. One day her daughter or son might.

The builder class optimizes for the builder class. You build what you understand, for people whose problems you can see. The founders in SoMa understand each other’s problems with extraordinary precision.

The woman making carnitas has different problems — thinner margins, less access to capital, relationships built over decades that don’t easily transfer to a new system. Nobody is at the Series A meeting making the case that her problems are the interesting ones.

The historian of technology David Nye wrote about the “technological sublime” — the awe Americans felt in the nineteenth century standing before a great bridge or a locomotive or the first electrified city. The feeling was real. But the sublime is a view from a particular angle. The workers who built the bridge experienced something quite different. The families displaced by the railroad’s right-of-way experienced something different still.

The question isn’t whether the technology will eventually reach her. It will. The diffusion curve is patient. It likely will surprise.

The question is whether anyone is doing the translation work. The act of standing in a specific kind of life and asking: what would this actually change here? In the actual kitchen, on the actual Tuesday.

Isenberg noted that the coworking spaces in SF are half empty but the coffee shops are packed. People want to be around people.

The taqueria is also a place where people want to be around people. It has been that for a long time.

She’ll adapt. She’s been adapting for twenty years.

But that’s a very different story than the one being told in San Francisco on those billboards.

Categories
Etiquette Founders

How to Be in a Room

I happened to notice this in a post on X: Slow Ventures ran a 4-hour etiquette class for founders. This is actually a brilliant idea. A non-trivial amount of founders (and VCs) could use some help in this department.

So I asked AI: what would a course syllabus for something like this look like? Below is the response (which I quite like!):

This is a genuinely good idea — etiquette as a business skill is perennially under-taught, especially for technical founders who may have gone deep on product/engineering but shallow on navigating rooms. Here’s how I’d structure a day-long or modular course built around the Slow Ventures premise.

Founder Etiquette — A Field Guide

4-hour intensive · 5 modules · lecture + role-play format

Morning Block — First Impressions

Module 1 · 45 min — The room before the meeting

Arrivals, introductions, and presence under observation

  • Timing: the cost of early vs. late
  • Handshakes, eye contact, the graceful exit from a name you missed
  • Business card and phone etiquette
  • How to work a room without appearing to work a room
Module 2 · 45 min — Table stakes: dining fluency

The business meal as performance and ritual

  • Place settings, utensils, and the BMW rule
  • Who orders first, who orders what
  • Wine: fluency without performance
  • Dietary restrictions — yours and theirs
  • The check: the ritual, the timing, the dodge
  • When and how to talk business at the table

Mid-Morning Block — Communication

Module 3 · 45 min — Communication that doesn’t leak desperation

Written and verbal signals investors actually read

  • Email: subject lines, length, tone, and the follow-up cadence
  • Reading the room on Slack/text vs. formal channels
  • The thank-you note — when, how, why it still matters
  • Saying no, disagreeing, and delivering bad news
  • How to receive feedback without deflecting or over-agreeing
  • The exit from a conversation: graceful, not abrupt

Afternoon Block — High-Stakes Scenarios

Module 4 · 45 min — Investor relations: the long game

Board rooms, pitch meetings, and the unwritten VC code

  • Board meeting protocols — who speaks, in what order
  • Managing up to your board without managing them
  • The warm intro — how to ask, how to use, how to close the loop
  • Confidence vs. bravado: what the room hears
  • What to do when the meeting is going badly
  • Conference behavior: panels, hallways, and the after-party
Module 5 · 60 min — The edges: cultural fluency and hard situations

Where etiquette meets judgment

  • Cross-cultural norms: Japan, Middle East, Europe, India — the high-risk differentials
  • Power dynamics: how to behave when you have more (or less)
  • Navigating someone else’s social gaffe gracefully
  • Gift-giving: what to give, what never to give
  • The fine line between casual and sloppy in casual settings
  • Live role-play: difficult scenarios, coached debrief

A few things I’d stress if actually running this:

Module 3 is where the real behavioral work happens for founders specifically. The desperation signal leaks through email more than anywhere else — the too-fast reply, the over-explanation, the follow-up that arrives four hours after the first message. That’s the stuff that’s hard to see in yourself.

One thing Slow Ventures probably got right: four hours is about the right ceiling. Any longer and you start to feel like you’re at finishing school, which kills the energy.

After reviewing this, I thought it might be useful to add two role playing scenarios to the course. Here’s what the AI recommended:

First, add this role play in after Module 2:

Role-play 1 · 20 min — The wrong bread plate

Recovering from a gaffe without losing the room

A founder is taken to lunch by a partner at a firm they’ve been trying to get in front of for six months. It’s going well — good rapport, the partner seems genuinely interested. Then the founder accidentally takes the partner’s bread roll, realizes it mid-bite, and has to navigate the next ten minutes without letting the gaffe collapse the energy in the room. Secondary layer: the check arrives and it’s ambiguous who’s hosting.

This tests everything from Modules 1 and 2 simultaneously — table fluency, recovery under embarrassment, reading whether to acknowledge the mistake or absorb it silently, and the check ritual. Low-stakes enough to be funny, high-stakes enough to feel real.

Next, add this role play after Modules 3, 4, and 5:

Role-play 2 · 30 min — The meeting that’s going sideways

Holding the room when the numbers missed

A founder is in a board meeting presenting quarterly numbers that missed. Midway through, one board member interrupts with a pointed question the founder doesn’t have a clean answer to. A second board member piles on. The founder has to hold the room — not deflect, not over-apologize, not go defensive — while also reading which of the two is genuinely concerned versus performing concern for the room. Cultural wrinkle: the more aggressive questioner comes from a context where directness reads as respect, not attack.

This pulls from Modules 3, 4, and 5 simultaneously. It’s the hardest scenario in the course and should feel that way. The coached debrief afterward is where most of the learning actually happens — ending here means it lands with weight, which is the right note to close on.

More commentary from the AI:

The dining module is the most load-bearing. A remarkable amount of professional signaling happens at the table and most people don’t realize they’re broadcasting anything. The BMW rule alone (bread-meal-water, left to right, so you don’t eat someone else’s roll) is the kind of thing nobody tells you until you’ve already grabbed the wrong bread plate in front of a Series B lead. The live role-play after Module 2 brings this to life.

The live role-play in Module 5 is what separates a good version of this class from a mediocre one. You can lecture about graceful exits from bad meetings all day. Watching someone flail through one in real time, then getting coached on the spot, is what actually moves the needle.

Categories
Business History IBM Infrastructure Nvidia Programming Semiconductors

The Half-Life of Moats

Prompted by an article on X by @magicsilicon on the CUDA moat. Research and drafting assistance from my AI intern assistant Clark.

The NVIDIA H100 looks, in retrospect, like an inevitability. It wasn’t.

What Jensen Huang built is more accurately understood as a sixteen-year accumulation of optionality — a platform investment made in 2006 for a market that wouldn’t fully materialize until 2022. NVIDIA intros the G80 architecture in November 2006, laying the groundwork for CUDA’s release a few months later. The stated ambition was to let scientists write C++ that ran on GPU cores without needing to understand 3D graphics pipelines. The unstated bet was that parallel computation would eventually matter for something bigger than rendering shadows in video games.

For sixteen years, it mostly didn’t. Not at scale. Not commercially. CUDA lived in research labs and HPC clusters. It attracted a small, devoted, and economically marginal user base — the kind that papers cite but investors ignore. NVIDIA kept investing in it anyway: cuDNN for deep learning operations, cuBLAS for linear algebra, a layered ecosystem of libraries that made CUDA not just accessible but nearly irreplaceable for anyone doing serious numerical computation. When TensorFlow and PyTorch emerged as the standard frameworks for neural network research, they didn’t adopt CUDA because it was the only option. They adopted it because CUDA was where the optimized kernels already lived.

AlexNet won the ImageNet competition in 2012 and did it on two NVIDIA GPUs. The deep learning community noticed immediately. The financial community largely did not.

Then ChatGPT launched in November 2022, and suddenly everyone needed H100s they couldn’t get.

The parallel to Intel is instructive and also undersells how strange this kind of story looks while you’re living through it. Intel was founded in 1968 as a memory company. DRAM. The founders — Noyce, Moore, Grove — were materials scientists and engineers who believed the future was in silicon memory chips. They were right, briefly: in the early 1970s Intel dominated the DRAM market. By 1984, that share had collapsed to 1.3%, ceded almost entirely to Japanese manufacturers who had commoditized the product.

What saved Intel wasn’t a pivot so much as a realization that a stopgap had become a foundation. The 8086, conceived in 1976 as an internal hedge and launched in 1978 was never supposed to matter. It was a 16-bit processor designed to hold off Zilog while Intel finished its ambitious 32-bit iAPX 432 architecture. The 8086 was assigned to a single engineer. “If management had any inkling that this architecture would live on through many generations,” its designer Stephen Morse later recalled, “they never would have trusted this task to a single person.”

IBM chose the 8088 — a cost-reduced variant — for the original IBM PC in 1981. That decision wasn’t destiny, it was simply a procurement. And yet from that accident of selection, Intel’s x86 line became the backbone of personal computing for four decades. The Pentium in 1993 was Intel’s Wintel moment — the flag bearer the @magicsilicon tweet gestures at — but the flag had been quietly sewn since 1978.

What these histories share is not just a pattern of “slow build, explosive payoff.” The structural similarity is subtler: in both cases, the moat was a software abstraction layer built on top of hardware. Intel’s real lock-in wasn’t transistor count or clock speed. It was backward compatibility — the commitment, formalized with the 80386 in 1985, that every future Intel chip would run software written for older ones. That promise created a flywheel that trapped developers and buyers in a virtuous (for Intel) dependency loop for decades.

CUDA is the same architecture at a different layer. The lock-in isn’t the H100’s 80 gigabytes of HBM3. It’s that switching to an AMD MI300X or Google TPU means potentially rewriting training pipelines that have been optimized against CUDA kernels for years. AMD’s ROCm platform exists. It is, by most accounts, maturing. Engineers who have tried the migration report that it costs months and hundreds of thousands of dollars. The moat isn’t a wall. It’s accumulated friction — the switching cost of a decade of engineering decisions baked into codebases that no one wants to touch.

But to find the actual origin of this pattern, you have to go back further than Intel. To 1964, and to a decision IBM made that Fred Brooks — its project manager — called a bet-the-business move.

The IBM System/360 was announced on April 7, 1964, after five years of turbulent internal development. What it introduced wasn’t just a new computer. It was a new concept: the separation of architecture from implementation. Before the 360, IBM ran five incompatible product lines simultaneously. A customer who outgrew their machine had to scrap all existing software and start over. The 360 replaced all five lines with a single unified architecture — six models covering a fiftyfold performance range, all running the same operating system, all sharing the same instruction set. The name itself encoded the ambition: 360 degrees, all directions, all users.

Gene Amdahl, the 360’s chief architect, had a precise formulation for what this meant: the architecture was “an interface for which software is written, independent of any implementation.” The Principles of Operation manual described what the machine did; separate Functional Characteristics documents described how each model did it. This distinction — separating the contract from the execution — was genuinely new. It’s the conceptual root of everything that came after.

The 360 generated over $100 billion in revenue for IBM and established the first platform business model in computing. Jim Collins would later rank it alongside the Model T and the Boeing 707 as one of the three greatest business achievements of the twentieth century. But its deepest legacy was architectural: the insight that if you make your abstraction layer the standard, the hardware underneath becomes fungible. Customers didn’t buy specific IBM machines. They bought into OS/360. The machines were an implementation detail.

Intel understood this by the 1980s, even if implicitly. The 80386’s backward compatibility commitment in 1985 was IBM’s 360 insight applied to microprocessors — the architecture is the product, the silicon is the vehicle. CUDA is the same insight applied to GPU compute. What NVIDIA sold researchers in 2006 wasn’t the G80 card. It was the abstraction: write parallel code in C++, run it on any NVIDIA hardware, trust that the next generation will be faster and compatible.

The pattern is now sixty years old. It has reproduced in every major platform transition. And it keeps working for the same reason it worked in 1964: when you own the layer that developers write to, your customers’ switching costs compound every year they stay.

There’s something worth sitting with here. Neither Jensen Huang in 2006 nor Gordon Moore in 1968 could have specified exactly what the payoff would look like. What they shared was a willingness to build infrastructure for a demand they could sense but not yet see — and the discipline to keep investing in it through the long years when it looked like a research project rather than a business.

The question that doesn’t resolve cleanly is whether that kind of patience is a strategy or a personality. And whether, in an industry that now moves faster than the cycles it’s lived through, sixteen-year moats are still the kind that get built.

Which raises the uncomfortable corollary: the same AI tools that CUDA enabled may be what ultimately erodes it.

The attack on CUDA’s moat is now structurally different from anything AMD or Intel could mount before. OpenAI’s Triton compiler lets developers write GPU kernels in Python without touching CUDA at all, and generates optimized machine code that often matches hand-tuned CUDA performance. MLIR — Multi-Level Intermediate Representation, originally from Google — provides a compiler infrastructure that can target any hardware backend from a single codebase. AMD’s ROCm has historically been dismissed as immature; ROCm 7, released this year, delivers meaningfully better inference performance than its predecessors. And perhaps most directly: Claude Code reportedly ported a CUDA codebase to AMD’s ROCm in thirty minutes — work that previously took months of engineering time.

The irony is almost too neat. CUDA’s moat was built on accumulated switching costs: the friction of rewriting code, the library dependencies, the tribal knowledge encoded in a decade of kernel optimizations. AI coding tools are specifically good at exactly that kind of mechanical, high-context translation. The weapon is attacking the wall it was built behind.

That said, it’s worth being careful about the speed of this. Abstraction layers that “should” erode moats often take far longer than expected, because the moat isn’t just the code — it’s the ecosystem of tooling, documentation, community knowledge, and hardware-software co-optimization that took eighteen years to compound. Triton and MLIR are real. They’re also early. The question isn’t whether the moat is vulnerable; it’s whether it erodes before NVIDIA’s next generation of chips makes it irrelevant to argue about.

As for what comes next — which company is building the IBM 360 of this decade — the honest answer is that it’s too early to call with confidence. But there’s a candidate worth watching.

Anthropic’s Model Context Protocol, launched in late 2024, has the structural fingerprint of a platform play. MCP is a standard for how AI agents connect to external tools and data sources — a common interface layer, hardware-agnostic (or rather, model-agnostic), that any system can implement. By late 2025 it had been donated to the Linux Foundation, adopted by OpenAI and Google, and was tracking 97 million monthly SDK downloads. There are now over 10,000 MCP servers. It is becoming the way agents talk to the world.

The parallel to OS/360 is imprecise but instructive. What IBM built in 1964 was a standard interface between software and hardware that decoupled what you wrote from what you ran it on. MCP is attempting something similar one abstraction layer higher: decoupling what an agent does from the specific models, tools, and data sources it does it with. If it becomes the standard — the layer that developers write to — then whoever owns or most deeply shapes that standard controls the integration tax of an industry whose applications we can’t fully specify yet.

The counterargument is that open standards, once donated to foundations and broadly adopted, don’t generate the same lock-in as proprietary platforms. OS/360 was IBM’s. CUDA is NVIDIA’s. MCP is now the Linux Foundation’s, with OpenAI and Google as co-stewards. The historical pattern suggests the moat accrues to whoever owns the layer, not whoever invented it.

Which may mean the next great platform play is still being assembled in a room we haven’t seen yet — the way IBM’s System/360 was being architected in a Connecticut motor lodge in 1961, three years before anyone else knew what was coming.

Categories
Authors Business Living

The Terror of the Empty Chair

It is comforting to believe that when the world breaks—when housing markets collapse, when “unicorn” startups vaporize, or when seasoned scouts overlook generational talent—it is because of a miscalculation. We want to believe the math was wrong, the data was bad, or the algorithm was flawed. We want to believe it was a glitch in the intellect.

I heard a commentator recently mention that Michael Lewis, the chronicler of our most expensive delusions in his best selling books, has suggested something far more unsettling. In looking at the connective tissue between The Big Short, Moneyball, and Going Infinite, he identifies a different culprit. He notes that the “glue” holding these irrational systems together isn’t incompetence. It is FOMO: The Fear Of Missing Out.

“They are more afraid of being left behind than they are of being wrong.”

This observation completely reframes the narrative of catastrophic failure. It explains why high-IQ individuals—people paid millions to be rational—consistently make decisions that look insane in retrospect. The banker, the VC, and the scout aren’t necessarily blinded by greed, though greed is certainly a passenger in the car. They are blinded by the terror of the empty chair.

Lewis points out that for the social animal, the pain of being left behind is acute and immediate, whereas the pain of being wrong is often abstract and distant. If you sit out a bubble and the bubble keeps inflating, you look like a fool today. You are isolated. You are the cynic at the party who refuses to dance. If you join the bubble and it bursts, well, you have company. As the old financial adage goes, “It is better to fail conventionally than to succeed unconventionally.”

There is a profound, empathetic tragedy in this. It suggests that our systems don’t fail because we aren’t smart enough; they fail because we are too human. We are wired for the herd. The biological imperative to stay with the group—originally a survival mechanism against predators—has been warped into a financial suicide pact.

When we look at the irrational exuberance of a market, we aren’t seeing a mathematical error. We are seeing a materialized anxiety. We are seeing a collective hallucination held together not by logic, but by the sticky, desperate glue of not wanting to be the only one who didn’t buy the ticket.

The antidote, then, isn’t just better data or faster computers. It is the emotional discipline to be lonely. It is the willingness to stand apart from the warmth of the herd and accept the short-term social cost of being “out” for the long-term reward of being right.

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.

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. Here’s the post: https://sjl.us/2025/12/17/bubble-bath/

Here’s the critique it gave me – tough but compassionate!

Alright, sit down. Let’s look at “Bubble Bath.”

You’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 post—it’s a link log. You’re telling me what you’re watching and reading, but you aren’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. That’s a “hook.” But then you pivot immediately to: “I read a memo by Howard Marks… I’m watching Tim Ferriss… I watched Acquired.”

As an editor, I’m asking: Where are you in this post? You’ve gathered the smartest people in the room, but you’re standing in the corner taking attendance.

The Fix: Don’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 I’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.

“The behavior of today’s stock market is yet another sign that many will point to as indicating there’s an ‘AI bubble’.” * Editor’s Edit: “Today’s market volatility is the latest fuel for the ‘AI Bubble’ fire.” (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.