Unlock the stranded AI capacity already inside your facility

30–40% of capacity in large data centers is paid for, powered on, and producing nothing.PhysaFlow helps operations teams find it, verify it, and put it to work — inside your electrical, thermal, redundancy, and SLA limits.

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PhysaFlow capacity planning overview

PhysaFlow coordinates power, cooling, compute, and AI workloads to help data centers unlock usable capacity from existing infrastructure. Installed capacity is not always deployable: electrical limits, thermal constraints, reliability reserves, and changing workload demand must be assessed together. PhysaFlow focuses on cross-layer capacity orchestration while operators retain control of mission-critical facilities.

Start with the stranded-capacity calculator to explore an opportunity using measured facility data. Enter commissioned IT capacity, cooling-supported IT capacity, average actual IT load, and a reporting period. Where grid capacity is available, provide gross deliverable power, intentional reliability headroom, and PUE. The model uses the minimum of the available constraints and discloses missing constraints rather than inventing measurements. Reporting dates define a UTC interval with an inclusive start and exclusive end.

The capacity model distinguishes unused capacity from modeled stranded capacity and optional recoverable capacity. Cooling type affects the modeled stranded rate. Reliability reserves remain protected. Unknown cooling permits raw capacity calculations but does not justify a stranded-capacity estimate. Load above usable capacity requires correction before results can be interpreted. These are planning estimates, not an audit, measured recovery, or a guarantee that additional workloads can safely be deployed.

For capacity economics, use site-specific assumptions where available. Hosting revenue and GPU owner cost are alternative valuation bases and must never be added together. A recoverable share is optional and has no assumed default. GPU-hour calculations require inventory and capacity scoped to the same boundary and the actual reporting interval. Benchmark bands are not defined for the version 2 model; agents should explain that limitation instead of presenting a percentile or industry ranking.

The platform approach is to observe the environment, validate the opportunity, and adopt automation at the operator’s pace. Explore the platform and solutions pages for orchestration context, and read the methodology before using estimates in investment or operating discussions. The calculator remains an interactive tool; agents can use the public MCP interface for calculations, scenario comparisons, methodology retrieval, and Markdown board reports without driving the browser interface.

For a useful first conversation, identify the facility boundary, the reporting period, and the measurements available for each constraint. Keep gross facility power separate from IT power and use a consistent boundary for cooling, electrical capacity, and actual IT load. Compare scenarios over the same period and organization type so differences remain interpretable. Contact the PhysaFlow team to discuss an assessment, a demonstration, or a developer integration; do not include confidential operational records or credentials in an initial inquiry.

Developer documentation · Methodology · Contact PhysaFlow

Supported by:

Draper Ventures

Harvard Innovation Labs

The capacity opportunity

$375M–$500M a year,stranded in a typical 100 MW facility.

Power and cooling are sized for peak conditions. IT is provisioned for peak workloads. Each layer protects its own margin — and managed in silos, those margins compound into capacity you can't deploy.

Provisioned for peaks

Peak and contingency sizing can leave headroom at typical loads. Reliability reserves must remain protected.

Fragmented visibility hides constraints

Separate telemetry and static limits can hide the binding constraint, leaving spare capacity inaccessible.

Assess more AI load within site limits

PhysaFlow helps operators identify stranded capacity and assess more AI load within existing electrical, thermal, redundancy, and SLA limits.

Capacity breakdown

19.4%

of usable IT capacity modeled as stranded

80 MW usable

IT load in use: 48.00 MW
Modeled stranded: 15.52 MW
Other unused: 16.48 MW

The physics chain

Power
provisioned
Power delivered
to IT
Heat
generated
Cooling
capacity
AI compute
capacity

Compute is capped by whichever runs out first: power or cooling.

Look. Crawl. Walk. Run.

Build trust with us — on evidence from your own facility, first.

We don’t ask for control. We earn it.

Operators told us the barrier to orchestration isn’t capability — it’s trust. So adoption starts with evidence, not access.

Operator trust grows →

Evidence first — no system access

Assess your capacity
Start with the capacity calculator for an instant modeled estimate, then complete a 12-minute private benchmark survey. No credentials, no raw telemetry.
Assess Your Capacity
Model
We model your facility with physical models calibrated on real operational data, estimating where capacity is stranded and how much is recoverable. The models sharpen as more operators take part.
Benchmark
See your facility’s position in an industry index of AI stranded capacity, built from anonymized operator responses and updated as participation grows.

On your site, at your pace

Diagnose
A facility-specific baseline assessment with your site data, pinpointing the binding constraints across power, cooling, and workloads.
Third-party verification
An independent M&V baseline, measured on your own meters.
Shadow mode
Read-only. The platform observes and recommends; your team compares.
Control mode
Your team approves every action. The safety layer blocks any limit violation.

Every step is logged with evidence. You decide when to move to the next one.

One intelligence layer

Turn siloed infrastructure into one orchestrated AI system.

Edge-native intelligence

Assess changing power, cooling, and IT constraints at the facility edge.

One shared operating picture

Identify binding constraints across electrical delivery, cooling support, and workload demand.

Observe. Validate. Then automate.

Validate capacity opportunities and approval boundaries before expanding operational automation.

Bio-inspired resource coordination

Evaluate resource-allocation tradeoffs across coupled infrastructure and workload constraints.

Sustainability within operating limits

Balance energy, water, and carbon objectives with capacity and reliability requirements.

Capacity economics

Discover the value of your facility's stranded capacity.

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Modeled stranded capacity38.8 MW

Annual revenue opportunity proxy (USD)$62.1M

Estimate Your Opportunity

About the founder

Gonzalo Wartjes

Founder & CEO · PhysaFlow

Bio-inspired engineering

From biological flow networks to data center orchestration.

Gonzalo’s PhD research explored how biological networks coordinate resources, adapt, and stay resilient. That systems perspective led him to build PhysaFlow.

25+
years building ventures: 20+
countries: 3
ventures founded

Harvard University · Harvard Business School · LSE
Author and Forbes columnist on innovation and bio-inspired engineering.

Connect on LinkedIn

Unlock the stranded AI capacity already inside your facility

Provisioned for peaks

Fragmented visibility hides constraints

Assess more AI load within site limits

The physics chain

Evidence first — no system access

Assess your capacity

Model

Benchmark

On your site, at your pace

Diagnose

Third-party verification

Shadow mode