Categories
Aging AI Business Living

The Being Phase

There is a metric making the rounds in technology investing circles that is, on its face, about market share and revenue concentration. Alex Sacerdote of Whale Rock Capital calls it the New Rule of 40 for AI. The formula is simple: take the percentage of a companyโ€™s sales derived from AI, add its percentage market share in that AI category, and if the sum reaches 40, you have a winner. Celestica, a company most people have never heard of, scores extraordinarily well. It owns somewhere between half and sixty percent of the cloud Ethernet white-box switch market. NVIDIA doesnโ€™t need a formula. It simply is what it is.

Sacerdote designed the metric to cut through a specific kind of noise โ€” the companies claiming AI exposure they donโ€™t actually have, the giants whose AI revenue hovers at one or two percent of their base while their press releases suggest otherwise. The framework is a detector. It finds the companies that have stopped becoming AI infrastructure and started simply being it.

I found myself less interested in the companies than in that distinction.


I spent years at Visa watching a network that had long since crossed that threshold. By the time I arrived, Visa wasnโ€™t becoming the global payments infrastructure. It was the global payments infrastructure. The work was real โ€” fraud detection, modeling, the daily labor of keeping something enormous running โ€” but the existential question had been settled before I got there. The network existed. Merchants accepted it because cardholders carried it. Cardholders carried it because merchants accepted it. That loop had been closing for decades. We were custodians of a fait accompli.

Thereโ€™s a particular feeling to working inside something that has already won. Itโ€™s not complacency exactly. The problems are genuine and the stakes are high. But the uncertainty has a different quality โ€” itโ€™s operational uncertainty, not existential uncertainty. Youโ€™re not asking whether the thing will survive. Youโ€™re asking how to run it well.

I didnโ€™t have language for that distinction then. Sacerdoteโ€™s metric gives me some. The companies that score highest on his New Rule of 40 have resolved their existential question. Theyโ€™re not fighting for position. Theyโ€™re administering a position already held.


The question that has followed me out of that career, and out of several decades of watching technology cycles turn, is simpler and more personal than any investment framework.

When did I cross that line myself?


I have been writing at sjl.us since 2001. Thatโ€™s not a boast โ€” itโ€™s a data point. Twenty-five years of thinking out loud, of ideas arriving rather than being argued, of the specific memory as structural anchor. The blog is not becoming anything. It is what it is: a record of a mind moving through time, accumulated into something that has its own weight and shape.

The book on payments systems exists. The career at Visa exists. The photographs exist. The train journeys exist. The years in Dayton exist, and the years on the Peninsula, and the particular way the light falls on the California coast at Pescadero in the late afternoon โ€” when the fog is still offshore and the hills are improbably green and everything goes briefly, completely quiet, as if the world is deciding whether to continue.

These are not things I am building toward. They are things I am.

Sacerdote would say I have high market share in a specific category. The category is small โ€” one person, one particular configuration of experience and attention and accumulated knowing โ€” but the share is essentially total. There is no competitor for the position of having lived this particular life. The moat is absolute. The switching costs are infinite.

I used to find that thought melancholy. The narrowing as loss. The aperture closing on what remains.

Iโ€™m not sure I find it melancholy anymore.


The L-Curve, Sacerdote says, is a long flatline followed by a vertical explosion. The tinkering phase, then the moment of lift. He means it as a description of demand curves for technology infrastructure. But I recognize the shape from somewhere closer. The long middle of a life, building and becoming, and then the morning you wake up and realize the building is substantially done. What remains is the being.

Thatโ€™s not an ending. Itโ€™s a different kind of beginning.


Sacerdoteโ€™s metric will eventually stop working. All frameworks do. The AI infrastructure cycle will mature, the L-Curves will flatten, and some new measure will emerge to find the next thing that is just beginning to become what it will be. Thatโ€™s the nature of markets. The detector has to change as the signal changes.

But thereโ€™s a complication worth naming. Analysts at Citadel Securities published a note recently observing that even the most powerful technologies must pass through the prosaic discipline of cost curves, capacity constraints, and marginal returns. Token bills are arriving unexpectedly. Compute is scarce. The vision of AI as ubiquitous, frictionless, and immediate is colliding with physical reality. Their conclusion: asset prices will periodically be forced to reconcile ambition with physical constraint.

Thatโ€™s not a refutation of Sacerdote. Itโ€™s a reminder that feeling like youโ€™ve arrived and having actually arrived are different things. The being phase has to be load-tested. The position has to hold under pressure.

I think about the fiber optics Corning is laying into the massive data center clusters โ€” ultra-thin, bendable, carrying more light than anything that came before. The cable doesnโ€™t know itโ€™s infrastructure. It just carries what itโ€™s given, at the speed itโ€™s capable of, across whatever distance is required. It doesnโ€™t matter what the cable believes about itself. What matters is whether the light actually moves.

That seems right to me. You become what you are over a long time, largely without noticing. And then one day someone builds a metric that accidentally describes your life, and you recognize yourself in it, and you think: yes. Thatโ€™s the shape of it. High concentration. High share. A moat that deepened while you were looking elsewhere.

But the moat still has to hold.

The being phase, it turns out, is not the end of something. Itโ€™s the proof that something was built. And the daily question โ€” for companies, for infrastructure, for a person in his late seventies still writing, still paying attention โ€” is whether what was built is actually load-bearing.

You donโ€™t get to stop finding out.

Categories
AI Software

The Thermodynamics of Thought

For the last two decades, we have lived in the era of zero marginal cost. The defining characteristic of the internet age was that once software was written, distributing it to the billionth user cost virtually the same as distributing it to the first. We grew accustomed to the economics of abundanceโ€”infinite copies, infinite reach, lightweight infrastructure.

But the recent commentary regarding the true nature of Artificial Intelligence forces a jarring mental correction:

“AI is not software riding on old infrastructure. It is a new industrial system that converts energy into intelligence – requiring a capital stack measured in trillions, not billions.”

This distinction is not merely semantic; it is physical.

When we view AI through the lens of traditional SaaS (Software as a Service), we miss the magnitude of the shift. We are looking for an app; what is being built is a refinery. We are witnessing a return to heavy industry, but the commodity being refined isn’t crude oilโ€”it is information, and the byproduct is reasoning.

This requires us to think less in terms of code and more in terms of thermodynamics. In this new industrial system, intelligence is an energy-intensive output. Every token generated, every inference drawn, requires a specific, measurable conversion of electricity into heat and computation. Unlike the static code of a website, an AI model is a furnace. It must be fueled constantly.

This explains the capital stack. We are seeing numbers that seem irrational in the context of venture capitalโ€”trillions, not billions. But if you view a data center not as a server farm, but as a power plant that generates intelligence, the numbers align with historical precedents. We are not funding startups; we are funding the modern equivalent of the electric grid, the transcontinental railroad, or the petrochemical complex.

We are pouring concrete, smelting copper, and manufacturing silicon on a planetary scale. The “cloud” was always a misleading metaphorโ€”it sounded fluffy and ethereal. The reality of the AI transition is heavy, hot, and incredibly expensive.

We are moving from an era where we organized the world’s information (low energy) to an era where we synthesize new reasoning (high energy). We are building a machine that eats electricity and excretes intelligence. That isn’t a software update; that is a new industrial revolution.

Categories
AI Web/Tech

Why the AI PC is the New 3D TV

A close-up of a laptop showing an 'AI READY' sticker on its surface, alongside a pair of glasses, a coffee mug, and a notepad on a wooden desk.

I was reading the coverage coming out of CES 2026 this week, and the silence was deafening. Just a year ago, the industry was shouting about the “AI PC” as the inevitable successor to the computing throne. Every laptop lid, keyboard deck, and press release was plastered with the promise of Neural Processing Units (NPUs) and local intelligence.

But looking at the tepid market reactionโ€”and Dell explicitly dialing back the “AI sermon” this yearโ€”I canโ€™t help but feel a sense of dรฉjร  vu. It reminds me of the “3D Ready” stickers that adorned every television set circa 2011.

There is a distinct pattern in consumer technology where the hardware cart gets placed miles ahead of the software horse. We saw it with 3D televisions, a technology that demanded we wear goofy glasses to watch a limited library of content, offering a friction-heavy solution to a problem nobody really had. We saw it, more tragically, with Appleโ€™s Vision Pro. Despite being a marvel of engineering, it stalled because it asked too much of us (financial and physical weight) for too little return in our daily lives.

The “AI PC” seems to be falling into a similar, albeit subtler, trap.

The issue isnโ€™t that AI is a fadโ€”far from it. Agentic AI and local models are transforming how we work. The issue is the marketing category. Consumers are realizing that an “AI PC” is just… a PC. The magic of AI isn’t in the hardware badge or a dedicated Copilot key; it’s in the software that runs anywhere. We are realizing that we don’t buy “Internet PCs” anymore, we just buy computers. The utility is ubiquitous, not proprietary to a specific chassis.

When technology truly succeeds, it disappears. It becomes boring. The “flop” of the AI PC isn’t a failure of technology, but a failure of hype. It is the market collectively shrugging and saying, “Show me the value, not the specs.” Until the software experiences are so undeniable that we can’t live without that local NPU, the “AI PC” will remain a marketing sticker, destined to peel off and fade away, much like 3D glasses or Vision Pros gathering dust for those few who bought them.