Why Data Centers Are Starting to Look Like Crypto Farms
As AI changes the physical and economic constraints of infrastructure, compute is converging toward the same industrial attractor state — driven by power, heat, density, speed, and cost per unit of compute.
In evolutionary biology, carcinisation describes a strange but powerful phenomenon: unrelated species repeatedly evolve into crab-like forms. They do not start from the same origin. They do not follow the same path. But under similar environmental pressures, they converge toward the same physical structure.
The reason is simple: the crab works. When nature faces the same constraints again and again, it often arrives at the same answer.
For decades, data centers were built under the assumptions of the internet era. They were designed around latency, urban proximity, Tier III / Tier IV redundancy, HVAC air cooling, raised floors, heavy UPS systems, and human-friendly maintenance.
Urban Proximity
Latency to end users
Tier III / IV
Maximum redundancy
Air Cooling
HVAC and raised floors
That model made sense for enterprise software, financial systems, web applications, and consumer internet workloads. But AI changes the environment.
AI training clusters do not primarily optimize for proximity to users. They care less about being 50 milliseconds closer to a city. They care much more about whether thousands or tens of thousands of GPUs can stay powered, cooled, and economically productive at scale.
When the constraints change, the morphology changes.
Crypto mining facilities evolved under one brutal variable: maximum compute per kilowatt-hour. To survive, they stripped away everything non-essential.
Cheap Power
Close to stranded energy
Simple Structures
Industrial wrappers
Fast Deployment
Low CapEx, high output
Crypto farms were not solving the same problem — they were solving a more industrial one: how to convert electricity into computation as efficiently as possible.
AI training is now pushing hyperscale data centers toward the same industrial logic. Data centers are not literally becoming Bitcoin mines. But they are increasingly responding to the same physical and economic pressures.
The same pressures produce the same morphology
Power
Heat
Density
Speed
Cost / Compute
As those pressures intensify, the old one-size-fits-all framework for data center design begins to break.
The traditional model assumed all serious data centers should maximize uptime, redundancy, permanence, and urban proximity. AI forces a more segmented view. Training, inference, storage, and enterprise workloads have different engineering requirements.
Training
Density and power first
Inference
Latency-segmented
Storage
Capacity-optimized
Enterprise
Uptime and proximity
A single universal data center model no longer makes sense. The industry is moving toward ruthless practicality.
Meta's Hyperion project is one of the clearest signals of this shift. It represents engineering radicalization at the infrastructure level — compromises that were previously deemed extreme are now becoming rational.
The facility is no longer designed around perfection. It is designed around throughput.
At small scale, these choices look like engineering details. At gigawatt scale, they become financial strategy.
Traditional Hyperscale
$8M–$10M
per MW
Stripped-Down AI Compute
$4.5M–$5.5M
per MW
At 5GW Scale
Every dollar saved from overbuilt redundancy and excessive building complexity can be redirected toward the assets that actually produce value: silicon, power, cooling, substations, transmission, and network fabric.
The old data center was built to serve humans and applications. The AI factory is built to feed machines. That difference changes everything.
Nature builds crabs because crabs work. Economics builds crypto farms because industrial compute has its own attractor state.
The carcinisation of compute has already started.
Explore how we capture power-first infrastructure for AI compute.