Category: Business

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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 Business IBM Management

Making It Up As We Went Along

There was a building along Route 270 in Gaithersburg, Maryland where people kept secrets for a living. Not the cloak and dagger kind. The corporate kind, which in its own way requires just as much discipline. The IBM Washington Systems Center occupied a two-story modern building that looked, from the outside, like any other outpost of late twentieth century American business. Inside it was something else. It was where IBM sent its hardest problems, and where the largest IBM customers in the world — the ones whose names you would recognize immediately — sent their most urgent ones back.

I worked there as a manager. But before I was a manager there, I was a hire. And before I was a hire, I was like every other IBM professional on the outside of a particular line — a line I didn’t fully understand until I crossed it.

At IBM there was a protocol so embedded in the culture it had almost ceased to be a rule and become something closer to a religious observance. New products were not discussed until they were announced. Not hinted at. Not alluded to. Not whispered about with a favored customer over lunch. The announcement came in the form of something called a Blue Letter — a formal communication from senior leadership that functioned as the official moment a product entered the world. Before the Blue Letter, the product did not exist in any conversation you were permitted to have. After it, you could talk about nothing else.

Violation was not a career setback. It was a firing offense. Full stop.

That clarity had a kind of elegance to it. You didn’t have to calibrate how much you could say or navigate gray areas. The line was absolute. And because it was absolute, and because everyone knew the consequence of crossing it, the culture enforced itself. You didn’t need surveillance. You needed people to understand the stakes, and they did.

What I didn’t understand, from the outside, was what that line was doing to my imagination.

When you can’t see the roadmap — when the strategy and the unannounced products and the long arc of where the company is going are all behind a wall you have no access to — you don’t experience that as absence. You experience it as depth. The things you don’t know feel like they must be there for a reason. The gaps in the announced picture feel like the gaps in a great iceberg — what’s visible is impressive, but what’s below the surface must be more impressive still.

I had faith in IBM’s strategic intelligence the way you have faith in things you can’t fully see. And faith, uncontradicted by evidence, tends toward beauty. The hidden roadmap wasn’t just unknown — it was, in my imagination, a thing of coherence and intention and vision. It had to be. The alternative was too unsettling to consider.

Then I got hired into the Washington Systems Center and crossed the line.

There was no single moment of disillusionment. No specific product that shattered the dream, no strategy document that read like a disappointment. It was more like a gradual adjustment of the eyes — the way they adapt when you move from bright sunlight into a room lit quite differently than you expected. The room isn’t dark. It’s just not what you anticipated. And once your eyes adjust you can see perfectly well, but you can never quite recover the image you had of the room before you entered it.

The reality on the inside was messier than the dream on the outside. More improvised. More human. We were, in ways I hadn’t anticipated, almost making it up as we went along. Not carelessly — the people at WSC were extraordinary, the work was serious, the commitment was real. But the beautiful coherent roadmap I had constructed in my imagination from the outside bore only a partial resemblance to the actual thing. Strategy, it turned out, looked different up close. Less like architecture. More like weather.

I absorbed this alone. Nobody sat me down and named what I was experiencing. Nobody had the conversation with me that I would later learn to have with others. I found my way through it by degrees, the way you find your way through most things that don’t come with instructions.

What came out the other side wasn’t cynicism. It was something more useful — a clearer eye, a more grounded relationship to the institution I was part of. The faith hadn’t been wrong exactly. It had just been innocent. And innocence, once lost, can’t be recovered. But what replaces it, if you’re lucky, is something steadier.

Years later I was the manager. And I was hiring IBMers — good ones, experienced ones, people who had spent serious careers on the other side of the blue line. They knew the products cold. They knew the customers. They knew how to work. What they didn’t know, couldn’t know, was what waited for them on the inside of the wall they were about to cross.

I knew it. Because I had been them.

There is a particular expression that crosses a person’s face when the actual roadmap becomes visible for the first time. It isn’t dramatic. It doesn’t announce itself. It’s more like a subtle recalibration — a slight stillness, a momentary adjustment behind the eyes. The person in front of you is doing quiet interior work, reconciling what they imagined with what they’re now seeing. The gap between those two things is doing something to them, and they’re not sure yet what to do with it.

I learned to watch for that expression. And when I saw it I knew what was coming if I didn’t get ahead of it.

The danger wasn’t disappointment. Disappointment is temporary, and smart people move through it. The danger was what disappointment hardens into when it isn’t named and worked through — a corrosive cynicism that poisons not just the person carrying it but everyone around them. A talented IBMer who had invested a career in faith, discovered the faith was misplaced, and decided the whole enterprise was therefore hollow — that person could do real damage to a team. I had seen it happen, or the early stages of it, which was enough.

So I developed what I came to think of as the god is dead conversation.

The name came from Nietzsche, though the application was strictly practical. What Nietzsche meant — or one of the things he meant — was that when the organizing faith of a civilization collapses, the collapse doesn’t leave nothing. It leaves a vacancy that has to be filled with something else, something built rather than inherited. The god is dead conversation was about helping someone through that vacancy quickly, before they filled it with the wrong thing.

It wasn’t a long conversation. It didn’t need to be. What it needed to be was honest, and direct, and delivered before the cynicism had time to set.

I would tell them what I saw happening. I would tell them it was normal, expected, that everyone who crossed this particular line felt some version of it. I would tell them the dream they’d carried on the outside wasn’t foolish — it was a reasonable response to incomplete information, and the information had been incomplete by design, and the design had served real purposes. None of that made them naive. It made them human.

And then I would tell them what I’d learned on my own, without anyone to guide me through it. That the messiness on the inside wasn’t a failure of IBM’s intelligence or intention. It was just what strategy actually looks like when you’re close enough to see the seams. Every institution looks more coherent from the outside than it does from the inside. That’s not a scandal. That’s organizational life.

The conversations were tricky. There was real care required. You were asking someone to grieve something — the beautiful imagined roadmap, the faith in a hidden coherence — without tipping them into bitterness about what replaced it. You were trying to accelerate a process that, left alone, might drag on for months and quietly corrode their effectiveness. And you were doing it while also being their manager, which meant you needed them functional and engaged on the other side of the conversation, not just unburdened.

What I had going for me was credibility. I wasn’t delivering a message from outside the experience. I had made the same crossing. I knew the specific texture of what they were feeling because I had felt it myself — the diffuse quality of it, the absence of a single dramatic moment, the gradual adjustment of the eyes. When I told them I understood what was happening to them, I actually did. I think they could tell.

Trial and error had taught me the shape of it. What didn’t work I had found out the hard way, at some cost, early on. What I arrived at had been load tested by real people in real situations. It wasn’t a framework from a leadership seminar. It was something I owned completely, which meant I could adapt it in the moment rather than execute a script.

Most of them came through it well. Better than well, actually.

What I hadn’t fully anticipated — though in retrospect it makes complete sense — was what replaced the faith once it was gone. It wasn’t the steadier, clearer-eyed pragmatism I had found my way to alone. It was something more potent than that. Something that surprised me the first time I saw it and then became one of the things I quietly counted on.

They came out the other side feeling superior.

Not arrogant. Not dismissive of colleagues still on the outside. But quietly, privately elevated — because they were now keepers of the secrets they had once only believed in. The blue line that had shaped their entire professional identity, that had defined the boundary of what they could know and say and imagine, was now behind them. They were on the inside. They had access. They had been trusted with the actual roadmap, the real strategy, the unannounced products that the rest of the world was still constructing faith-based pictures of.

The believer had become the keeper. And keeping, it turned out, was a more powerful identity than believing. The believer is passive — sustained by what they imagine. The keeper is active, responsible, trusted. They carry something real rather than something projected.

It solved my practical problem neatly, though that wasn’t why it moved me. What moved me was watching people find their footing on the other side of a genuine loss and discover that the ground there was solid — different from what they’d imagined, but solid. They hadn’t just survived the crossing. They’d been changed by it in a way that made them more valuable, more grounded, more fully present to the actual work.

Which was, I suppose, what the god is dead conversation had been for all along.

I think about that blue line often these days.

We are living through a moment when artificial intelligence is advancing faster than most people can track, and the organizations building it — the labs, the research teams, the companies placing enormous bets on where this technology is going — have their own version of the wall. Not identical to IBM’s. The competitive and legal architecture is different. The culture is different. But the basic structure is the same: there is what has been announced, and there is everything else, and most people are working entirely from the announced side.

Which means most people are doing what I did before I crossed the line at WSC. They are filling the gaps with faith. And faith, uncontradicted by evidence, tends toward beauty.

The unrevealed AI roadmap looks, from the outside, like a thing of coherence and intention. The capabilities that haven’t been announced yet must be more impressive than the ones that have. The strategy must be more considered than what’s visible. The gaps in the public picture feel like depth rather than uncertainty — like the part of the iceberg below the surface, which must be vast because the part above is already remarkable.

I am not saying this faith is wrong. I held the same faith about IBM and it wasn’t wrong exactly — it was innocent. The people constructing faith-based pictures of where AI is going are doing a reasonable thing with incomplete information. The information is incomplete partly by design, for reasons that make competitive and strategic sense, just as IBM’s secrecy made sense. None of that makes the faith naive.

But I’ve been inside enough walls to know what the inside tends to look like. And I think it’s worth saying, clearly and without cynicism, that the reality is probably messier than the dream. More improvised. More uncertain. More human. The people building these systems are extraordinary — the work is serious, the commitment is real — but they are also, in ways that might surprise you, almost making it up as they go along. Not carelessly. But without the complete map that the outside imagines must exist somewhere, fully drawn, waiting to be revealed.

Strategy, up close, looks less like architecture and more like weather.

This isn’t a counsel of despair. It’s almost the opposite.

The IBMers who crossed the line and survived the god is dead conversation didn’t end up with less than they started with. They ended up with more — a clearer eye, a more grounded relationship to the institution, a more useful kind of engagement with the actual work. The faith they lost was the innocent kind. What replaced it was steadier and more durable.

I suspect something similar is available to anyone willing to look at the AI moment with clear eyes. Not the disappointed cynicism of someone who expected a beautiful coherent roadmap and found a human institution instead. Not the breathless faith of someone still on the outside of the wall, filling gaps with generous assumptions. Something in between — harder to sustain, more honest, ultimately more useful.

The technology is real. The progress is real. The stakes are real. None of that requires the roadmap to be a thing of beauty. It just requires it to be worked on seriously by people who understand what they don’t yet know — which, from everything I can observe, it is.

What I couldn’t give those IBMers, and what nobody can give you, is the experience of crossing the line yourself. The god is dead conversation only works because the crossing has already happened — because the person sitting across from you has already seen the actual roadmap and is already processing the gap between what they imagined and what they found. You can’t have the conversation in advance. The disillusionment has to be real before it can be worked through.

Most of us will never cross the line into the AI labs. We’ll stay on the outside of the wall, working from the announced picture, filling the gaps as best we can. That’s not a failure — it’s just the condition most of us are in, the same condition those IBMers were in for their entire careers before I hired them.

But knowing the wall exists, and knowing what walls do to imagination, seems like it ought to change something about how we hold our faith. Not abandon it. Just hold it a little more lightly. Stay curious about the seams. Remain open to the possibility that the most important thing about the unrevealed roadmap isn’t what’s in it — but what we’ve projected onto it.

The blue line is still there. Most of us are still on the outside of it.

And the hidden roadmap still looks, from here, like a thing of beauty.

Categories
AI Business Consulting

The Toll Bridge and the Terrain

For fifteen years of my life, I lived inside the fortress of information asymmetry. I was part of a payments consulting business, and our model was exactly what Andrew Feldman described on a recent Moonshots episode when he pointed a sharp finger at traditional professional services.

His observation was simple, cutting, and entirely true:

“Their role today is to stand between ordinary people and obscure knowledge. And the application of that obscure knowledge to everyday problems.”

When I heard him say that, it landed with a quiet thud of recognition.

For a decade and a half, my colleagues and I were the ones standing in that gap. The payments industry—with its labyrinth of interchange fees, compliance structures, clearing networks, and legacy tech stacks—is a monument to obscure knowledge. Clients didn’t come to us because we possessed some divine, unreplicable wisdom. They came to us because the map was locked in our heads, and navigating the terrain without us was a recipe for an expensive disaster.

We charged for our time, and we earned it. We untangled complexity and solved real, everyday business problems for people who just wanted to move money safely from point A to point B.

But looking back now, I can see the architectural flaw disguised as a premium service. The economic foundation of that entire era relied on friction. It relied on the fact that it took an immense amount of human energy to retrieve a piece of obscure data and map it onto a specific business dilemma. You weren’t just paying for strategic guidance; you were paying a premium on artificial scarcity.

We are living through a moment where the marginal cost of intelligence is rapidly trending toward zero. When the barrier of “obscure knowledge” evaporates, the traditional toll bridges begin to look absurd.

For anyone starting a consulting business today, the playbook would have to be entirely different. When an LLM can parse thousands of pages of network operating rules, interchange tables, and regulatory compliance frameworks in a handful of seconds, the gatekeeper’s standing ground liquefies.

If your value proposition is merely standing between a client and a hidden database, your business model isn’t just flawed—it’s obsolete.

Yet, this collapses into a fascinating paradox. You might assume that when you democratize expertise, you eliminate the need for the expert. But as Dan Shipper recently observed, the reality of AI is completely counterintuitive.

Shipper points out that AI effectively packages up “yesterday’s competence” and makes it cheap and ubiquitous.

Suddenly, anyone can generate a complex contract, a software pull request, or a payments flow strategy with the click of a button. But when cheap competence skyrockets, adoption explodes, resulting in an unprecedented glut of generic output—what the internet has collectively taken to calling “slop”. It’s the default, lazy answer that lacks soul, context, and nuance.

When everything begins to look and smell the same, a strange thing happens: the market’s demand for genuine difference sky-rockets.

The shift we are facing across all professional services—whether legal, financial, or consulting—isn’t about eliminating the expert. It is about changing the expert’s job from data-retriever to orchestrator and judge. The floor has been raised. Yesterday’s ceiling is today’s baseline.

What remains is the ability to read a room. To watch a client’s shoulders tighten when you present an option that’s technically correct but organizationally impossible. To notice the glance exchanged across the table before anyone speaks. No LLM parses that. The map is universal now; the guide still has to be in the room.

We don’t need fewer guides; we need fewer toll booths. The future of consulting doesn’t belong to those who hoard the map. It belongs to those who use a universally available map to help people actually walk the terrain.

Categories
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”
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
AI Business Work

The Tipping Point Was Last November

Matthew Prince, Cloudflare’s CEO, said something on today’s earnings call that I keep turning over. He didn’t bury it or soften it. He named a date.

“Internally, the tipping point was last November.”

That’s a specific thing to say. Not “we’ve been on a journey” or “AI has been transforming our industry.” A month. A moment. The thing changed, and he knows when.

What changed, by his account, is that Cloudflare’s teams began seeing productivity gains so dramatic they were hard to describe — people who were two times more productive, ten times, in some cases a hundred times. “It was like going from a manual to an electric screwdriver.” Usage of AI tools internally is up more than 600% in just the last three months. Every line of production code is now reviewed by an autonomous AI agent.

And then he said goodbye to 1,100 people — about 20% of the company.

Today wasn’t just Cloudflare. Earnings season has become something like a drumbeat. Meta is cutting 8,000 employees this month. Amazon cut 16,000 in Q1. Oracle eliminated roughly 30,000 to fund AI infrastructure. Block cut almost half its workforce. PayPal is reportedly planning to cut 20% of its staff over the next few years. Coinbase cut 14%. Snap cut 16%. As of this week, more than 92,000 tech workers have been laid off in 2026 alone.

The scale is striking. But what strikes me more is the framing — the specific language being used to describe what’s happening. These aren’t being announced as cost-cutting moves or post-pandemic corrections, the way they might have been in 2022. They’re being announced as architectural decisions. Structural adaptations. Evolution.

Prince was careful to be explicit: “This isn’t a cost-cutting exercise or an assessment of individuals’ performance. It’s about defining how a world-class, high-growth company operates and creates value in the agentic AI era.” That’s not empty corporate language, or at least not only empty corporate language. The distinction he’s drawing — between trimming fat and reimagining how a company is built — maps to something real about what AI agents can now actually do.

There’s a legitimate version of this argument and a convenient one, and they’re being delivered in the same sentence by the same people, which makes them hard to separate. Some analysts suspect companies are using AI as cover for cuts they wanted to make for other reasons — rightsizing from pandemic-era overhiring, funding massive infrastructure buildouts, chasing margin. Oxford Economics flagged this: maybe some firms are “dressing up layoffs as a good news story.” The cynicism is warranted.

But then there’s the Cloudflare number: 600% increase in AI usage in three months. That’s not a narrative. That’s a measurement.

What’s different about this moment — what makes Prince’s “tipping point” language feel accurate rather than convenient — is that the people making these decisions are themselves users of the tools. They’ve seen the productivity numbers internally before anyone else has. They’re not theorizing about what AI might do to their workforce; they’re describing what it already did.

That’s the thing that changed. For years, AI’s labor impact was a future tense conversation. Economists studied it, think pieces warned about it, conferences debated the timeline. Then, somewhere around last November apparently, a cohort of technology companies crossed from hypothetical to empirical. The future tense became past.

Whether you read that as tragedy, as transformation, or as both depends on where you’re standing. 1,100 people at Cloudflare today are standing somewhere very specific. Prince acknowledged this with what felt like genuine difficulty: “A number of friends will no longer be colleagues.” Whether that difficulty changes anything material for the people leaving is a fair question.

But the acceleration itself — the thing he named — is real. The tipping point was last November. And if it was last November for Cloudflare, it was some nearby month for Amazon, for Meta, for Block, for all of them. Whatever these companies learned that changed everything, they all seem to have learned it around the same time.

That’s what I find myself sitting with today: not just the scale of the disruption, but the synchrony of it. The realization arrived, and then the decisions followed. Quietly at first, then all at once.

Categories
Business Creativity Space SpaceX

Test like you fly!

There’s a phrase in the SpaceX documentary that keeps coming back to me: “Test like you fly.” It sounds like a slogan. The kind of thing that gets painted on a factory wall and eventually stops meaning anything. But the more I sit with it, the more I think it’s actually a philosophy that reaches well beyond rocket engineering.

The video — a 25-minute documentary SpaceX released last week — is ostensibly about Starship Version 3. New ship, new booster, new engines, new pad, new test site. Everything rebuilt. And they’re not shy about framing it as a reset, not an upgrade. One description I read called it “a quiet violence in progress.” That phrase stopped me cold, because it’s exactly right. Progress that looks violent from the outside — all that fire and metal — but is somehow quiet in its inevitability.

What moved me watching it wasn’t the engines. It was the engineers. SpaceX put the people on camera: the ones running cryogenic pressure tests at 80 Kelvin, stress-testing tank structures at 70% proof, explaining their failures and their data with the flat affect of people who have made peace with how long hard things take. There’s something almost monastic about it. You choose a problem that will not yield easily. You accept that the work will outlast any individual sprint of enthusiasm. You go back to it anyway.

I keep thinking about that in the context of what we’re doing with AI — the other enormous, fast-moving project that I spend so much of my mental energy on. The development arc is different: iterative releases, weeks not years between jumps, demos that blur into deployment. But the same principle is buried in there somewhere. The best AI teams I read about aren’t the ones shipping the most polished demos. They’re the ones building infrastructure for failure — evals, red-teaming, structured feedback loops. Test like you fly.

The Raptor 3 engines now produce 280 metric tons of thrust each. Thirty-three of them on a Super Heavy booster means over 17 million pounds of liftoff force. I have no intuitive frame for that number. What I do have a frame for is what those numbers represent: three years of iteration on top of five years before that, on top of a theoretical foundation laid by people who didn’t live to see any of this. There’s a compounding in that which I find genuinely moving. Nobody built the Raptor 3 in isolation. It came from everything that broke before it.

The hardest part of the documentary isn’t the engineering. It’s the implicit acknowledgment of how much remains undone. No Starship has yet achieved full orbital velocity with both stages intact. In-space refueling is still untested. The thermal protection systems need more work. And yet — SpaceX talks about unmanned cargo missions to Mars before the end of this year like it’s on the roadmap, not the wish list. That sentence used to sound like marketing. Watching the footage, it doesn’t anymore.

I’m not sure what to do with that feeling exactly. It’s something between awe and vertigo. We’re living in a moment when the audacious has started to have quarterly milestones. When the impossible keeps showing up on timelines and then — bewilderingly, uncomfortably — meeting them.

Test like you fly. Fail with rigor. Build the thing you actually need, not the thing you could more easily explain.

I keep turning that over. There’s a post in there somewhere about writing, too — about the drafts nobody sees, the structural tests that fail, the versions that taught you the one that worked. But that’s for another day.

For now I’m just sitting with the footage of those 33 engines lighting up, and the quiet weight of how much went wrong before they could do that.

Categories
AI Business

The Topography of a Face

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

Elad Gil recently shared a fascinating experiment during a conversation with Tim Ferriss. He’s been uploading photos of startup founders into AI models and asking the machines to predict if they’d be successful, purely based on their “micro-features.”

“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-features—like, do you have crow’s feet by your eyes, which suggests that your smiles are genuine? […] 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. It’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, I’ve often leaned heavily on this silent language. I’ve backed founders because their intensity felt genuine, and I’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. I’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 “cold reader,” 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 right—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
Business Living Retirement Trading

The Whetstone and the Hammock

We spend the first half of our lives trying to build a fortress of comfort, operating under the assumption that the ultimate reward for a lifetime of labor is the sudden, permanent cessation of it. We dream of the hammock. We dream of the empty calendar. But an empty calendar is really just a blank canvas with no paint.

Patrick O’Shaughnessy recently sat down with Paul Tudor Jones, and their conversation inevitably drifted toward the later chapters of life. Jones shared a story about fulfilling a promise to his wife to move to Palm Beach after their youngest child went to college. Upon arriving, she sent him to a local general practitioner—an 83-year-old doctor still seeing patients. Jones asked the man for the secret to longevity in a town (Palm Beach) he bluntly described as the “land of the walking dead.” The doctor’s response was a swift hammer blow:

“It’s real simple. You retire, you die.”

It’s a jarring diagnosis, but it cuts right to the bone.

We are biological machines designed for friction. Take away the resistance, and the gears don’t just stop; they rust.

Jones took the lesson to heart, noting that if you don’t use it, you lose it. He works out two hours a day and continues to trade, deliberately keeping his mind pressed against the whetstone of the markets.

I’ve watched this play out in my own circles over the years. I’ve seen brilliant, energetic colleagues hand over their keys, step out of the arena, and within months, seemingly deflate. The sudden absence of daily problems to solve doesn’t bring peace; it brings a creeping atrophy.

I’ve found myself deliberately holding onto certain complex projects and investments not because they are financially necessary, but because they demand my attention. They force me to wake up and solve a puzzle. They provide the necessary gravity to keep my feet on the ground.

But Jones offered a second, perhaps more profound reason for staying in the game. He wants to make “an absolute pot of money” specifically to give it away. He views his daily work not as a grind, but as the pursuit of nobility. He found a way to bridge the gap between the selfish need to keep his own mind sharp and the selfless desire to fuel the causes he cares about. The work becomes an engine for something larger than himself.

The hammock is a trap. The mind requires weight to bear, a horizon to move toward. The goal is not to finally lay down our tools, but to choose precisely what we want to build with them until the very end.

Stay hungry, stay foolish – and stay busy!

Categories
AI Anthropic Business Google

The Weight of the Bill

Technicians working among server racks with real-time performance graphs displayed on overhead screens
Technicians monitor server performance and network data in a modern data center

Jordi Visser has been making the case for months — in his weekly YouTube commentary and on his Substack — that we are living through an exponential transition that most people are measuring with the wrong instruments. I think he’s right. I found two data points this week that suggest why.

I was somewhere in the middle of an Invest Like the Best episode when Dylan Patel said it — almost as an aside, the kind of thing you drop to establish context before moving on to the point you actually came to make. His firm, SemiAnalysis, analyzes the semiconductor and AI industries for a living. And their usage of Claude, he noted, has been growing. The costs have been growing too.

Exponentially.

He moved on. I didn’t.

I think Patel’s API bill might be one of the more honest documents in the current AI moment — more honest than the analyst reports his firm produces, more honest than the earnings calls where every public company performs its AI fluency for shareholders.

Surveys bend. When you ask someone whether they’re using AI in their work, you’re asking them to self-report on a technology that has become a proxy for relevance, for not being left behind. The incentive to say yes is enormous. And even when the yes is genuine, it tells you nothing about depth — whether AI has become load-bearing in how someone actually works, or whether it’s an impressive thing they do occasionally.

Nobody pays exponentially growing API costs for show. Money is the honest witness.

What makes Patel’s situation quietly strange is the recursion in it. SemiAnalysis exists to help sophisticated investors and technologists understand this industry — and they cannot predict their own consumption curve. They are inside the exponential the same way everyone else is. They just happen to be watching their bill.

Then this morning, a different number arrived. Google announced it will invest up to $40 billion in Anthropic — $10 billion committed now, another $30 billion contingent on performance milestones. This follows a separate $5 billion from Amazon, part of a broader arrangement under which Anthropic is expected to spend up to $100 billion on compute over time.

The temptation with numbers like these is to treat them as spectacle. Forty billion dollars is so large it becomes almost aesthetic — a statement about ambition, about the kind of bets that define eras. You feel the weight of the zeros and move on.

But I keep coming back to Patel’s API bill.

Because Google’s $40 billion and SemiAnalysis’s compounding monthly costs are saying the same thing, expressed at scales so different they almost don’t seem related. One is a research firm noticing that their tool usage has quietly escaped prediction. The other is one of the most sophisticated capital allocators on earth making a bet that strains comprehension. But both are pointing at the same reality: that this technology, wherever it takes hold, does not plateau. It compounds.

We have been waiting, I think, for the moment when AI adoption becomes legibly real — some threshold event that separates the signal from the noise, the press release from the actual change. The surveys were supposed to mark that moment. The enterprise announcements. The benchmark numbers.

Patel’s aside suggests we’ve been waiting for the wrong thing. You don’t arrive at the exponential. You just eventually notice you’re already in it — in an aside on a podcast, before moving on to the point you actually came to make.