“The future is already here - it’s just not evenly distributed.”
— William Gibson

We are not watching the rise of Artificial Intelligence.
We are awatching the formation of a new aristrocracy.

Not of land.
Not of capital.

But of compute.

For almost two decades, we lived in the comforting lie that the problem of scarcity had been solved by software.

Code was free, distribution was free, and some kid in a dingy hostel could out-code some incumbent with nothing but time, internet, and stubbornness. The internet was flattening everything, and it felt as if gravity no longer existed.

But that era has ended.

Let’s stop the TED talk for a second, shall we? You are not witnessing the dawn of universal intelligence. You are witnessing the land grab.

The people shouting about AGI on podcasts are not the futurists that they claim to be; they are the trespassers on someone else’s land, mistaking access for ownership.

Compute is the new land, and the land has already been occupied.

AI has not simply added another layer to the technology stack; it has, in effect, introduced physics into the game. The frontier is not run on cleverness; it is run on fabs, supply chains, air conditioning, and power consumption curves that are indistinguishable from heavy industry itself. The look and feel of the whole operation are silicon, the logic is oil, and the constraint is no longer your idea; the constraint is whether you can procure 20,000 GPUs before someone else does, and whether you can keep them powered.

TSMC makes the chips that are the foundation of almost every serious AI operation on the planet, and if someone were to disrupt that, that is not a supply chain problem; that is the world stalling on its axis. NVIDIA does not sell hardware; they sell possibility, and the H100 is not so much a product as it is the border crossing; if you cannot cross that, you are not at the frontier; you are downstream, watching the summaries of the decisions that were made elsewhere.

Scarcity is once again physical, and once again not equally negotiable.

A single modern data center can draw as much power as a mid-sized city. In various regions of the United States, the utilities are secretly restructuring their grids to accommodate AI clusters while domestic projects languish. In Ireland, data centers have surpassed entire industries in power consumption. The conversation is now about whether homes or hyperscalers get priority on the grid. In Arizona, they siphon water to cool machines that produce words while drought warnings blanket the state.

This is not hyperbole. This is allocation.

The average founder will not train a frontier model. The average nation will not either. Training is an industrial process now. It is capital-intensive and infrastructure-constrained. It is geopolitically complex. Most will simply lease inference. They will pay by token, by call, by tier. Intelligence will be a utility, indistinguishable from electricity except for the fact that it fundamentally changes how we think instead of lighting our rooms.

Ownership is reduced to access.

And from this, a hierarchy starts to form.

At the top are those who own compute: hyperscalers, chip makers, infrastructure nations. Amazon and their AWS platform, Google and their TPUs and vertically integrated data centers, Microsoft and their Azure platform and their close relationship with OpenAI. They do not compete on features. They compete on megawatts, on access to fabs, and on geopolitical strength. They decide who grows and who waits.

Under them, the renters of intelligence. Startups, developers, companies whose product is built on top of those APIs they cannot control. They call it leverage. It works like a dependency. The price change ripples through their business overnight. The model update changes the behavior they never agreed to. The innovation they bring into the world is real. It is just not limitless. Not by imagination, but by rate limits.

And at the bottom, growing quietly, are the automation victims. They are not present at the negotiating table when the GPU contracts are signed. They are not on the blueprints of the infrastructure. They just feel it. The workflows are faster. The headcount is lower. The expectations outpace the retraining schedules. The explanation is always the same. Always delivered with a smile, but a smile that seems practiced. PROGRESS.

No one talks about displacement. The word has been refactored.

This is not conspiracy. This is industrial logic speaking through a new language.

Every product of AI you touch today passes through the same narrow corridors. The silicon was designed here. The silicon was manufactured elsewhere. It was assembled. It was shipped. It was installed in clusters accessible only to a handful of entities. Each one of them taking a margin. By the time it reaches you, it has paid rent several times over.

The system doesn’t conceal this fact. It normalizes it.

We speak of openness, but open weights do not construct data centers. You may download a model, but you may not download the circumstances that made it relevant. Without compute at scale, openness is a performance: engaging, well-produced, structurally irrelevant.

Meanwhile, the map reconfigures itself.

Export controls are strengthened, and research infrastructures grind to a halt. A package of advanced computing chips is denied, and a nation’s aspirations for AI development quietly readjust downwards. Data centers are negotiated like treaties. Energy agreements start to resemble strategic partnerships. The dialogue changes from “what may we create?” to “what may we execute?”

Compute does not apportion itself equally. It accumulates. It solidifies around capital, geography, and power.

And in this accumulation, the form becomes familiar.

You will not own the model that makes you efficient. You will subscribe to it.

I’m not sure that feeling survives close inspection.

Character doesn’t arrive fully formed. It takes shape early, through family, culture, reward, repetition. Long before decisions show up, preferences are already being trained. By the time a choice presents itself, a lot of the work has already been done.

Someone raised in an environment where academic success is valued will tend to move in that direction. Exposure reinforces inclination. Over time, the desire itself starts to feel natural, even necessary. What looks like choice may just be the meeting point of conditioning and opportunity.

The more I think about it, the harder it becomes to see decisions as separate from the systems that produced them.

I still find myself revisiting past choices, wondering how things might have gone if I’d done something else. Everyone does this. It’s comforting to believe that things could have unfolded differently. But that comfort clashes with responsibility. If I wasn’t really free, what exactly am I responsible for?

Take something ordinary. Two job offers. One promises change, the other stability. It feels like a straightforward exercise of agency. But both options pull on values that were formed long before the choice existed. The tension isn’t freedom versus determinism. It’s one inheritance against another.

There’s a strong impulse to force a clean answer here. Either free will exists or it doesn’t. But reality doesn’t seem interested in clean edges. Choices happen, but never in isolation. Influence is always present. Certainty never is.

Maybe free will isn’t an on–off switch. Maybe it’s a gradient. Enough agency to justify responsibility. Not enough to claim full authorship.

That may be as close as I can get to an honest position.

I turn to leave, pressing my thumb.
The mark is erased. Nothing else helps.
There are tall skyscrapers. They never talk.
They make promises. They leave.

We stand in line to work and to eat.
Paper moves slowly. Hunger comes fast.
The flag goes up every year.
Our pay does not.

We are considered. Not heard.

That’s the point isn’t it?.

e/acc is not an ideology. There are no people occupying highways in its name. No moral preaching. No moral accounting. It’s mostly a prop to highlight the uncomfortable fact: acceleration is a victory that’s already been won, and most people are emotionally unprepared for this fact.

What passes for “progressive politics” in the world today is actually conservation masquerading as morality. Limit growth. Stagnate diversity. Prevent anyone from venturing too far above the median. This is not progressivism; this is risk management with a pretty face. History has not been kind to people whose first instinct is to hit the brakes.

The interesting part is not acceleration but administration. Large organizations no longer optimize for outcomes; they optimize for process. The bridge is less important than the checklist that verifies the bridge was built correctly. Process is the end because ends are messy they imply accountability.

This is why the professional managerial class persists. Process is safeguarded by role. Goals are threats to them.

The interesting thing about individuals such as Elon Musk is not the talk, but the modus operandi. Endless disruption will destabilize process before it has a chance to solidify. In such a scenario, all one can do is look at results. Chaos, as a tactic, will shatter the bureaucracy.

This is not something that can be replicated on a human scale. Dictatorship does not parallelize well.

But if the judge, and not the workers, is replaced by something that is non-human, the dynamic changes. A system that is not ruled by a single result, but by an evaluation function that is too complex to be video game-ified. Beyond Goodhart. Beyond checkbox culture.

The open question isn’t whether acceleration continues.
It’s whether our institutions learn to survive it.

“A bad system will beat a good person every time.” — W. Edwards Deming

At a mid-sized tech company, a senior engineer notices a pattern. It starts small. During code reviews, the most thoughtful comments slowly disappear. Not because the team has improved, but because certain comments change the temperature of meetings. Questions about long-term design delay releases. Concerns about architectural debt trigger debates no one has time for. So the feedback becomes safer. Shorter.

At first, it feels like professionalism.

The engineer still sees the problems. They just stop naming them.

Over time, this becomes instinct. Before typing a comment, they imagine the sprint timeline, the product manager’s reaction, the meeting that might follow. They imagine the explanation they would have to give, the momentum they would be accused of slowing, the tension that would linger after the call ends. The code works. The tests pass. The system will hold for now. Intelligence learns where friction lives and quietly steps around it.

This is not incompetence. It is adaptation.

Marcus Aurelius warned that the mind takes the shape of its surroundings. “The soul becomes dyed with the color of its thoughts,” he wrote but just as often, it is dyed with the color of its environment. A mind surrounded by urgency learns urgency. A mind surrounded by approval learns caution. Intelligence that goes unexamined does not aim itself toward truth; it aligns itself with what is rewarded.

In this environment, intelligence does not disappear. It becomes economical. It chooses silence where speech would cost too much. It learns to distinguish between what is correct and what is permissible, and slowly treats the second as more practical. Speed is praised. Certainty is trusted. Alignment is remembered. Hesitation lingers. Yet judgment is easiest to surrender quietly. No one asks the engineer to stop thinking. No one forbids questions. The environment does not punish intelligence it simply teaches it where not to go.

The engineer still thinks deeply. They still notice what is missing, what is deferred, what is quietly accumulating beneath the surface. But the thinking no longer travels far. It remains contained, disciplined by context, trimmed to fit the space it is allowed to occupy.

Nothing breaks. The product scales. The team grows.

And the absence of certain questions leaves no trace, no error, no alert, no record that they were ever there.