Parent company economics

Parent-company model: royalties, site ownership, systems integration and platform growth.

The parent company plan frames Cloud Wash Laundry as a platform business that combines royalties on gross compute revenue, selected lease income, systems packages and related services.

Twenty-year parent-company growth model

Four trajectories. One scalable platform thesis.

Parent Company 20-Year Cumulative Profits chart — **20-year cumulative after-tax profits**Current parent plan: approximately $1.2B Bear, $5.4B Conservative, $12.4B Baseline and $28.9B Bull at Year 20, excluding Artificial Inventor.

These are business-model projections, not guaranteed returns. The chart reflects modeled hardware-price compression and a four-year reset cycle.

10,000

Baseline modeled network size by Year 20 in the current parent plan.

$12.4B

Baseline cumulative after-tax profit by Year 20 in the current four-scenario parent model, excluding Artificial Inventor.

17,500

Bull-case modeled sites by Year 20; conservative and bear cases reach 6,000 and 3,500 respectively.

Scenario pages from the parent plan

Bear scenario parent pro forma — **Bear scenario pro forma**Long-range parent-company table for the downside scenario.

Conservative scenario parent pro forma — **Conservative scenario pro forma**Parent-company conservative rollout scenario.

Baseline scenario parent pro forma — **Baseline scenario pro forma**Parent-company baseline rollout scenario.

Bull scenario parent pro forma — **Bull scenario pro forma**Parent-company high-growth rollout scenario.

Parent unit economics scenarios — **Unit economics scenarios**Comparison of franchisee-site cases.

Enterprise value growth chart — **Enterprise-value growth**Scenario growth and enterprise-value analysis.

How the parent company fits

Royalty structure

The parent entity can scale by collecting a fraction of gross compute revenue rather than operating every workload directly.

Site ownership & lease strategy

In some scenarios the parent company owns sites and receives lease income while franchisees operate the businesses and local compute infrastructure.

Standardized deployment packages

Repeatable integration packages reduce engineering repetition and help create a recognizable network standard.

Artificial Inventor demand support

The broader platform can help create baseline workload demand for early locations without exposing internal implementation details on the public website.

Hybrid solar and heat-reuse efficiencies

Utility savings reinforce the economics rather than being treated as a separate side benefit.

Expansion path

Because the platform uses many smaller sites, growth can follow opportunity rather than requiring one giant all-or-nothing buildout. The parent-company thesis goes further: connect those sites by dedicated dark fibre and NVIDIA Spectrum-XGS scale-across networking, validate workload performance experimentally, and show that mega data centres can be displaced by a geographically distributed compute fabric.

Open science downloads

Current papers, methods and model limits.

First page of the current heat-sink-constrained distributed AI infrastructure manuscript

Heat-sink-constrained siting of distributed AI infrastructure: energy, carbon and water impacts in Los Angeles

The current deterministic hourly study places 6,160 B300-equivalent accelerators across 120 representative car-wash and laundromat hosts and tests useful heat, carbon, water, climate and metropolitan-network constraints. Its network programme uses NVIDIA Spectrum-XGS as the scale-across layer over dedicated optical fibre and specifies fibre-spool and multi-site experiments to test whether distributed sites can match co-located clusters — the key experiment behind the project’s objective of proving that mega data centres can become obsolete as the default architecture for many workloads.

Current paper PDF Model + files

First page of the current mechanism-resolved fanless photovoltaic-thermal heat-rejection manuscript

Mechanism-resolved modeling and adaptive control of a fanless photovoltaic-thermal heat-rejection array

The current 170 m² roof-array study resolves passive and wetted mechanisms separately, quantifies the water-capacity trade-off, rejects array-wide ionic wind on energetic grounds and tests roof-area matching from micro facilities to hyperscale.

Current paper PDF DOCX Model + files

The goal: prove the mega data centre is no longer necessary.

Cloud Wash Laundry is being designed around a direct technical challenge to the conventional mega-campus model: place compute where useful heat already exists, then connect those heat-sink-sited AI factories with dedicated dark fibre and NVIDIA Spectrum-XGS scale-across networking so the distributed sites can operate as one metropolitan compute fabric.

The dark fibre is the dedicated optical transport; Spectrum-XGS is NVIDIA’s scale-across networking layer. The project’s proposed fibre-spool testbed and multi-site field pilot are intended to determine whether distributed sites can match co-located clusters on workload-level measures such as time-to-result and energy per token.

Measured next. Modeled now. Heat-sink constrained.

Current design-stage results combine the 120-host Los Angeles heat-sink-siting model with the updated fanless heat-rejection screening figure. They are screening outputs, not field-certified performance. In parallel, the network programme tests whether NVIDIA Spectrum-XGS scale-across networking over dedicated dark fibre can make geographically separated AI factories behave as one compute system. The mega-data-centre conclusion remains an experimental objective, not an already validated result.