America is pouring
concrete for compute.
There are eighty-two million houses already metered.
Data centers are landing in residential neighbourhoods because the compute has to go somewhere. This is the arithmetic for putting it in the houses instead — rebuilt five times in exact arithmetic, with two passes that said no.
The compute has nowhere to plug in.
US data centers drew 176 TWh in 2023. Berkeley Lab now projects 521–843 TWh in 2030, with a 649 TWh reference case — a power-delivery problem as much as a construction problem.
The bottleneck is not just silicon or capital. It is a grid connection. ERCOT reported 410 GW of prospective large loads in April 2026, about 87% associated with data centers. That queue is not a forecast of what will be built; it is evidence of how much demand is converging on the same constrained process.
Meanwhile a megawatt of idle accelerators loses $520,833 every month it spends waiting.
Don’t pour the concrete.
Light the windows.
A 5 kW node behind an existing house meter may avoid a campus-scale grid connection. Site screening, panel capacity, permits and utility approval still decide whether it can connect.
We tried to kill this five times.
Every figure here comes from exact rational arithmetic — no spreadsheet, no rounded intermediate. That discipline is why the answer flipped twice, and why we trust the one that survived.
Most decks show you only the pass that worked. Here is every pass, including the two where our own model said no.
Pass 1 compared buildings, and a house is genuinely 17.8× cheaper per megawatt than a datacenter shell. True — and the wrong denominator. Pass 2 put the accelerators back and the verdict inverted: they cost $0.9567 per IT-kWh and dominate everything else, so the contest is decided by how many kilowatt-hours each venue spreads the same silicon over, not by what the venue costs.
Pass 3 pulled real GPU quotes and broke the premise that both venues run the same parts. Pass 4 batched the workload the way anyone actually serves traffic, and 192 GB of memory beat 32 GB decisively. Pass 5 asked what changes with model size — and found the answer.
It is a small-model fleet. That is the whole specification.
A consumer card runs out of memory where a datacenter card runs out of compute. Shrink the model and that asymmetry reverses completely.
At 8B the home fleet costs half what a Blackwell datacenter costs per million tokens — with no credit for the interconnection queue at all. At 32B the memory ceiling bites and we would need a twelve-month queue to catch up. At 70B the weights alone exceed 32 GB, so that case is excluded by this single-GPU model; multi-GPU serving was not modeled.
Small models are not a niche. Routing, classification, extraction, drafting, speculative decoding and every cheap tier all live there.
Not a bigger computer. A wider one.
Four million houses cannot be pooled to run one enormous model — the physics forbids it. What they can do is hold every small model in the world warm at once, which is precisely what a datacenter is worst at.
Matching 1,024 PB of warm weights takes 76,417 GB200 racks and a 9.17 GW interconnection. The memory is distributed across operator-purchased nodes rather than free. A million-model catalogue would get 200 warm replicas of every model.
At 16.71 weight swaps an hour, a datacenter’s cost per token climbs to meet ours on cold starts alone — before any of the capital argument. Serve a long tail of fine-tunes and it swaps constantly. A stable allocation can avoid many of those swaps.
One rule makes it work: no durable session affinity. The serving node still holds an in-flight request’s KV cache; if that house disappears, the generation is lost and must restart elsewhere. New requests can route around it at an assumed 1.031× spare-capacity cost.
What a family actually gets.
The proposed contract asks for no household purchase, loan or roof work. A dedicated 240-volt circuit and recurring payment are design assumptions still awaiting field validation.
The heat is not a side effect. It is part of the offer.
Every watt of compute becomes a watt of heat. In a cold-climate, electric-resistance home that heat displaces electricity bought at full retail — worth $1,963 a year more than the same node in a gas-heated house.
The payment is a share, not a promise.
Households take 15% of what their own node earns, with a floor beneath it. A fixed payment would convert a market risk into a solvency risk for everyone; a share keeps the household and the operator on the same side of the trade.
It moves with the house.
The model assumes the agreement conveys to the buyer. That clause lifts average tenure from 8.3 to 15.4 years, and is worth $169B across a full fleet.
Three ways to live with five kilowatts.
Wall, tower and duct-integrated form studies make the household appliance concrete enough to inspect — and to criticize.
These are appearance and installation concepts, not engineered products. Dimensions, acoustics, thermal performance, safety and utility approval remain validation work.
Explore the form factorsSame tokens. A quarter of the capital.
Sized to produce identical output, a distributed fleet costs a fraction of a purpose-built campus — because the buildings, the land, the cooling and the grid site and meter already exist, while upgrades and utility work remain deployment costs.
That last pair is the counterintuitive one. Everyone assumes a four-million-home consumer programme lives or dies on marketing. Acquisition is 5.7% of the capital. This is a procurement business wearing a consumer business’s clothes.
220,519 homes, behind four gates.
Each gate buys the answer to exactly one question. If an answer comes back wrong, the programme stops there and the capital behind it was never committed.
The cash curve
Peak funding is $1.51B in month 59 — not the $4.44B of gross capital, because earlier cohorts fund later stages. Free cash turns positive in month 60 and the programme is cash-whole in month 74.
Eighteen percent.
That is all the headroom between the price we assume and the price at which this stops working. Everything else in the programme has slack. This does not — so it is the first thing we test, not the third.
Named, priced and sequenced.
A pitch that hides its failure modes is just a slower way of finding them. Here are ours, in the order they should be tested.
The last one is a flaw we found in our own plan. Staged by what is natural to build, $822M gets committed before the question most likely to kill the programme is answered — and that question is the cheapest of all to test. It should be first, and it can be: resell rented datacenter capacity at the target price before a single node is installed.
Prove the price. Then light the windows.
Nothing here needs four million homes to be true. It needs 250 homes, one signed offtake contract and one silicon supply agreement — in that order.
The buildings are already there. The wiring is already there. The grid connection — the thing everyone else is waiting years for — is already there, behind eighty-two million meters.
We are not asking to build a data center. We are asking to turn the lights on.
Frequently asked questions.
The ten questions this study gets asked most, answered from the model rather than from intuition.