Why Isn't Data Underwater?
This is what the hyperscalers [Google, Microsoft] disclose in their own filings; let's see if it's actually true
Goodmorning to you guys on D-Day. Enjoy your day today.
With all the propaganda being spewed about AI, data centers, energy prices, etc., I couldn’t help but wonder: why the world’s data is not just underwater by now. If it were the case that we could achieve better results underwater, without building more on land, then that should be ‘Plan A’.
Once I saw 30,000 acre plots being used to essentially spin GPU’s (Wyoming), I wanted to study what the org’s actual documentation argued for, both from Project Natick (Microsoft) & Project Hamina (Google), to then see what their own signals were to build more data centers onshore.
This letter is the fruit of that study…
First principles here are physics, depreciation schedules, TCO structure and what these orgs actually disclose in filings.
Here are the REAL reasons:
The Submarine Hull Couples a 25 Year Asset into a 3 Year Asset
This is a pure accounting killer, not engineering.
In a data center on land, the shell normally lasts about 25 years while the hardware inside refreshes on a 3-6 year life cycle. (Amazon recently just shortened their own server useful life from 6 years to 5 years because AI churns hardware faster.)
A sealed pressure vessel welds those two depreciation curves together: whatever you seal in is what you have until a ship retrieves it.
Sealing ~$300-$500M of accelerators (kind of a modest AI deployment) into hulls means accepting that the asset is at ~30% of book value when you can next touch it, with zero mid life densification. Kind of obvious why CFO’s wouldn’t want to sign that.
The land megacampus is, financially, a vehicle for decoupling long term capital vs. short term capital; that is why it wins.
Cooling was Never the Cost Driver
An actual TCO stack of an AI datacenter will look something like this: Hardware costing ~60%-70% of total cost of ownership; the GPUs in one NVL72 rack cost costing ~$3M, several times the fully built, shell and power cost of the floor space holding them.
Cooling overhead at hyperscale is the gap between Hamina’s PUE ~1.10 and Natick’s ~1.07. So, the ocean’s entire value proposition attacks only ~3% of ~30% of the cost stack, under 1% of TCO, while degrading the 70% (i.e., the hardware).
Hyperscalers solved their own problem on land before Natick could matter. The experiment’s premise was arbitraged away by its own sponsors during the experiment.
Rack Power density Outran the Hull’s Physics
Natick Phase 2 was 12 racks at ~240 kW, passively conducting head through the hull to seawater.
Current AI racks are 130 kW and NVIDIA’s announced Rubin Ultra generation targets ~600 kW/rack.
You cannot move 600 kW/rack through a hull wall by convection; you need direct to chip liquid loops and large engineered heat exchangers inside the vessel (which, turns out to be the same cooling plant as on land). The ocean as a passive heatsink only works at power densities the industry abandoned.
This is why Microsoft’s takeaway was “immersion cooling onshore”: once you are doing the liquid loop engineering anyway, the seawater does not add anything. But, for Project Hamina, where Google uses the Baltic Sea to cool data centers on land, this approach is more promising under this specific point of hull physics.
Training Models is the Most Centralizing Workload in Computing History
A training cluster needs every GPU inside a single low latency, multi terabit, grid fabric; this is why the industry builds single 100 MW+ halls with hundreds of thousands of short run optical links.
Underwater pods are the opposite, replacing one 300 MW hall requires ~1,000 Natick class vessels, each its own network island connected by individual subsea cables. The fabric you’d need between them does not exist at any price, and the speed of light through that much fiber breaks synchronous training regardless.
Natick was designed for the 2015 workload: stateless, latency tolerant cloud services, etc. Hamina is still running with better results from adding wind at a point in the bottleneck.
The world pivoted to the one workload that structurally cannot be sharded across pods (AI/Agents). Which is why those same wins in 2015, that spurred the underwater experiment to begin with, is not longer even the meta. The seabed didn’t lose to land; it lost to the reduction focused operation.
There is No Marine Industrial Base for $400B/yr
Hyperscaler data center apex is running at hundreds of billions a year. That money is more absorbable on land because the supply chain already exists.
The global marine heavy construction industry: vessel days, dynamic positioning ships, subsea welding capacity, is sized for oil, gas, offshore wind and could potentially absorb single digit billions of data center work per year. My prediction is that more companies will want to go underwater now that we can run way larger param models on much smaller hardware.
Even at max enthusiasm, underwater could not have been where the money went; the deployment pipe is two orders of magnitude too narrow. Capital flows to whatever can absorb it at the required velocity, and the #1 binding constraint since 2023 is the time needed to compute, not efficiency.
Every term in the equation moved against the ocean between 2018 and 2023.
The one bet that could still flip an era is wind colocated pods (attacking power, the real constraint, rather than cooling) and that is precisely the variant being attempted as we speak…
What do you think?
God-Willing, see you at the next letter
GRACE & PEACE