my take
the demand for compute is real. compute needs power, power has side effects, and people push back. maybe the future is data centers far from anyone.
what i'm wondering: is the answer remote? off-grid turbines, the ocean, orbit. places with power and no neighbors.
either way, people shouldn't have to absorb this locally. that's what policy is for: protect the town, and make the users of the compute pay for it.
and i'm not sure the backlash is really about data centers yet. bills have barely moved. it reads as preventative, fear of ai aimed at the one piece you can vote against.
how much power
us data centers, electricity per year (twh)
twh = energy used across a whole year. measured to 2024 (4.7% of all us power, ≈ 18m homes). then lbnl's 2030 forecast: likely ~649, about 3x every home's ac.
no official number for 2025 or 2026 yet. data centers aren't metered separately, so counts lag 1 to 2 years. the hollow dot is one outside 2025 estimate. all us homes use ~1,500 twh. comparisons from eia, rough.
one data center, by size (power draw → energy per year, log scale)
the bar is the draw at any moment (a rate, like speed). the label is what that adds up to over a year of running nonstop, the same twh as the chart above. a home averages ~1.2 kw. a gigawatt campus draws like 800,000 homes, a big city, around the clock.
what that power buys: gpus
| scale | draw | ~gpus (b200-class) | example |
|---|---|---|---|
| one 8-gpu server | ~15 kw | 8 | about 12 homes |
| a university lab | ~0.2 mw | ~100 | a few racks |
| one clippership ship | 3 mw | ~1,650 | their number |
| a mid-size ai startup | ~5–9 mw | ~3,000–5,000 | trains its own models, mixed gpu generations. about 2–3 ships |
| hyperscale building | 100 mw | ~55,000 | one big cloud building |
| first ai cluster | ~300 mw | ~200,000 | xai colossus 1 (h100/h200 mix) |
| gigawatt campus | 1 gw | ~400,000–550,000 | stargate abilene, up to ~400k chips |
| planned max | 5 gw | ~2.5 million | meta hyperion, full build |
rule of thumb: ~1.8 kw per gpu all-in (the chip ~1 kw, plus the server, networking, cooling). one b200 does ~2.25 petaflops (bf16). so a gigawatt is ~1,000+ exaflops. a mid-size ai startup is ~1/150 of one campus. the frontier labs are each building several gigawatts.
what it takes to power one 1 gw campus, and what it costs
| power source | typical size | delivers 24/7 | plants needed | cost per mwh | $0 → $260 | |
|---|---|---|---|---|---|---|
| nuclear reactor | 1,100 mw | ~1,000 mw | 1 | $141–220 | ||
| gas combined cycle | 700 mw | ~630 mw | 2 | $48–129 | ||
| coal plant | 700 mw | ~600 mw | 2 | $71–177 | ||
| wind farm | 200 mw | ~70 mw avg | ~14 + batteries | $37–99* | ||
| solar farm | 250 mw | ~60 mw avg | ~17 + batteries | $40–98* | ||
| single gas turbine | 50 mw | ~45 mw | ~22 | $149–251 | ||
| clippership (claimed) | 3 mw ship | ~2.6 mw | ~380 | too many to draw | $28–35 |
data centers run flat out, day and night. firm sources deliver near their size. *wind and solar are cheapest per mwh but only when it blows or shines; firming them 24/7 with batteries costs more. for scale, the average us home pays ~$170/mwh (17¢/kwh) retail. cost = lcoe, the all-in price to build and run a new plant. rough numbers.
how they get the power
| move | how | example | catch |
|---|---|---|---|
| plug into the grid | wait in the utility's queue | most data centers | up to 7 years for 100+ mw in virginia |
| sit next to a plant | buy an existing plant's output directly | amazon at a pennsylvania nuclear plant, microsoft restarting three mile island | takes power the grid was already using |
| build your own | gas turbines on site, off the grid | xai in memphis, stargate's backup turbines | turbine backlog into the 2030s, local air |
| buy clean on paper | contract wind and solar elsewhere, still draw from the grid | most big tech climate pledges | the electrons at the plug are still the grid's |
| leave land | go to sea or orbit | clippership, space proposals | unproven |
the real bottleneck isn't power plants, it's time. new generation takes 5+ years from request to running. whoever gets power fastest wins.
who's building the power
clippership →
- what
- autonomous 70 m catamarans. wing sails pull the ship, turbines under the hull turn the motion into power, seawater cools the chips.
- one ship
- 3 mw, ~1,650 gpus, ~$19m to build. same cost per mw as a land data center shell.
- the trick
- the ship moves to chase wind. ~88% capacity factor in simulation, vs ~50% for offshore wind.
- cost
- ~$28–35/mwh claimed. cheaper than anything in the table above.
- scale
- 1 gw ≈ 330–380 ships. 300 mw/yr of shipyard capacity lined up.
- roadmap
- 10 kw nodes now → 0.25 mw in 2027 → 3 mw prototype in 2028 → production 2029.
- hard parts
- bandwidth over starlink (50–100 ms, so inference, not training). salt (sealed nitrogen halls). servicing means sailing to port.
my read: it dodges both bottlenecks on this page at once, the grid queue and the neighbors. the bet is that enough ai work doesn't care about latency.
parallax →
- what
- a ~10 mw gas turbine designed from scratch for data centers. small enough to ship in a container.
- why
- the big turbine makers are backlogged into the 2030s, and grid hookups take years.
- how
- cheaper materials and simpler manufacturing, built to come off a line fast.
- scale
- 1 gw campus ≈ 100 units.
- money
- $117m seed + series a, sep 2026. greylock, general catalyst, founders fund, lux.
- founder
- carl schoeller. before: theseus, tesla, apple.
- hard parts
- unproven at scale. still burns gas.
my read: trades cost and carbon for speed. small gas turbines are among the priciest power in the table, but when time is the bottleneck, speed is worth paying for.
pushback
29+local bans passed, nov 2026 midterms
7 in 10oppose one near them
145+projects delayed or canceled
90%+of 10k public comments negative
water#1 concern. only 21% mention ai
it's about the building, not the tech. neighbors pay the cost, someone else gets the benefit.
sources: wapo · r/technology