Categories
AI

The Layers Donโ€™t Hold

Stewart Brand drew the diagram in 1999, in The Clock of the Long Now, though heโ€™d been developing the idea for years before that. Six concentric rings, each representing a layer of civilization, each moving at a different speed. Fashion at the outside, changing season to season. Commerce beneath it, slower. Infrastructure below that โ€” roads, power grids, buildings. Then governance. Then culture. At the center, moving so slowly it seems not to move at all: nature.

The diagram is elegant, but Brandโ€™s real insight is about the relationship between layers, not the layers themselves. He called the framework pace layers. The fast layers innovate. The slow layers stabilize. Fashion gets to be experimental and throwaway precisely because infrastructure doesnโ€™t. Governance can afford to be deliberate because culture provides continuity underneath it. The whole system depends on this differential. Each layer absorbs shock from the one above it and passes only the most durable changes downward. Itโ€™s not inefficiency โ€” itโ€™s architecture.

Brand also had a name for what happens when the differential breaks down. He called it โ€œlayers crashing.โ€ When a fast layer accelerates past the capacity of the layer beneath it to absorb and adapt, the system loses its self-correcting character. The fast layer doesnโ€™t just move quickly anymore โ€” it damages the slow layerโ€™s ability to function. Infrastructure overwhelmed by commerce becomes fragile. Governance overwhelmed by technology becomes irrelevant. The stability that the slow layers provide isnโ€™t guaranteed. It has to be continuously earned.

We are in a layers-crashing moment. The technology layer is moving faster than it has in any of our lifetimes, possibly faster than it ever has. And the layers below it โ€” infrastructure, governance, culture โ€” are discovering that the shock-absorption mechanisms theyโ€™ve refined over centuries werenโ€™t designed for this.


Dario Amodei published a long policy essay recently. He opens with Treebeard โ€” the ancient, slow-speaking tree from Lord of the Rings whom the Hobbits must somehow persuade to act quickly enough to matter. Itโ€™s the same intuition as Brandโ€™s pace layers, arrived at from a different direction. The problem isnโ€™t that governance is broken. The problem is that it was built for a different tempo, and the tempo has changed.

Whatโ€™s new in Amodeiโ€™s essay โ€” and it feels genuinely new โ€” is the shift in register. For several years, Anthropicโ€™s public posture on regulation has been: transparency first, binding rules later, once we understand the shape of the risks well enough to target them precisely. That posture made sense when the risks were theoretical. It makes less sense now. The pivot in the essay is Amodeiโ€™s own most advanced model, Claude Mythos Preview, which he describes as having โ€œscrambled the global cybersecurity landscape.โ€ He is using his own product as the evidence that the moment for incrementalism has passed.

The five policy areas he covers โ€” regulation, macroeconomics, scientific innovation, civil liberties, geopolitics โ€” each map onto a different pace-layer collision. The cybersecurity risk to financial infrastructure is commerce meeting governance too fast. The job displacement problem is commerce and culture in conflict, with governance lagging both. The civil liberties section is perhaps the most unsettling: the worry that AI hands governments tools of surveillance and coercion that the legal architecture of democracy โ€” built for a slower world โ€” simply cannot constrain.

The regulatory framework he proposes is modeled on the FAA: mandatory third-party testing of frontier models, government power to block deployment, four specific risk categories as scope limiters. It is more concrete than anything Anthropic has proposed publicly before. The FAA analogy is meant to reassure โ€” we have regulated powerful technologies before, we know roughly how this works โ€” and it largely does reassure. Though itโ€™s worth holding alongside it a genuine open question: whether regulatory bodies can develop the expertise and independence to govern a technology this fast-moving before the technology moves again. The history of industry regulation suggests this is hard. It doesnโ€™t suggest itโ€™s impossible.

Brandโ€™s diagram has one more feature worth noting. The arrows donโ€™t only point downward, from fast layers shaping slow ones. They also point upward: the slow layers constrain what the fast layers can become. Culture shapes what commerce builds. Governance shapes what infrastructure gets funded. Nature sets limits that no other layer can override. The relationship is bidirectional, and the bidirectionality is the point. What Amodei is calling for โ€” urgently โ€” is for the slow layers to begin exerting upward pressure again, before the differential becomes so extreme that they lose the capacity to do so.

Whether they can move quickly enough is the question Brandโ€™s diagram canโ€™t answer. Treebeard wakes up, eventually. The forest burns faster than he walks.

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 Oโ€™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 doesnโ€™t have the time to watch or listen to them himself so good summaries are really helpful.

Turns out Iโ€™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 โ€œbattlefield AIโ€ 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. Youโ€™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!


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.