Private AI on power that already exists
Gigawatt campuses are built to rent AI to the whole market. Many organizations need the same computing power on a smaller scale—and can’t send their files out.
UNICANN turns a powered indoor cannabis grow into a private AI node of up to 5 MW.
Same building. Same power. New job.
Looping animation: an indoor grow room with lights and plants goes dark, AI server racks move into the same building on the same electrical service, and files stay inside the building instead of going to a public cloud. Then it starts again.
Indoor grows were built to run megawatts of lights and cooling. The lights come out. The heavy power service stays. You don't have to fight for a new 100 MW substation.
With closed-loop cooling, onsite water use can drop sharply too.
See the water comparisonRun open-weight AI models, fine-tuned on your own files, on hardware you control. A public AI service may cost less per use, but contracts or the law rule it out for some files.
If you only rent an AI app, you don't need a site. If you can run the model yourself, power is the question.
When faster, more efficient chips arrive, they plug into the same power service. You don't need a new campus, and keeping your data private doesn't leave you a generation behind.
One example (illustrative)
What makes them good is know-how: the toolpaths, the fixtures they already own, and the scrap history in one programmer’s head. None of it can be sent to a public AI service. Sending controlled drawings out can create an export-control problem.
Ten shops like this can share the power without sharing their files.
Clinics, counties, and factories with a secret recipe have the same problem.
Every site still needs utility, engineering, and permit sign-off. See how we check
For owners and infrastructure buyers
A new data center starts from scratch. A large indoor grow may already have the building and the power. We check whether it can become a 1.5–5 MW AI node.
Every step starts at zero
Can start farther along
Reuse only saves time if the utility, permits, building, cooling, and fiber all check out. That is what we verify.
Choose your path
We match powered buildings with people whose AI work has to stay private. Pick your side to see if you fit.
Companies with private AI work
Indoor grow owners
1.5–5 MW is the size range we screen for. It is not a promise of usable power or value at any site.
Who actually needs the power
One simple question decides it: can you run the AI model yourself?
01 · Closed, vendor-only model
The provider keeps the model weights and runs the GPUs. You use an app or API; you do not need to power the model yourself.
02 · Deployable model
The weights are available and licensed for your use—often called open-weight, open source, or privately licensed. You choose where the model runs.
When power becomes strategic
<20 kW
Useful private inference. No megawatt-scale site needed.
20 kW–<1.5 MW
Several systems with their own networking, storage, cooling, and operators—still short of a dedicated node.
1.5–5 MW
Sustained multi-rack capacity for an AI company or regulated enterprise.
A dedicated node is worth considering only when the model is deployable, the workload is sustained, and control matters enough to justify it instead of cloud or ready-to-use colocation.
“Deployable” is used here as plain language. Model licenses vary; open-weight does not always mean open source, and deployment rights must be reviewed for the intended use.
Meta deployable-model examplesNVIDIA DGX B300 powerNIST AI Risk Management FrameworkAWS rented GPU capacityPower 2026 — electricity pricing in the age of AIWhy dedicated nodes
A campus is built for buyers at gigawatt scale. A node is built for organizations whose work can’t leave their control.
Node versus campus
Deployment unit
Dedicated node
A bounded 1.5–5 MW capacity block at one qualified site.
Gigawatt campus
A multi-building, multi-phase platform measured in hundreds of MW or gigawatts.
Primary fit
Dedicated node
Defined production workloads where dedicated control, data locality, or staged capacity matters.
Gigawatt campus
Fleet-scale cloud operations and the largest tightly coupled training programs.
Power path
Dedicated node
May enter through local distribution in a suitable location, subject to utility study and upgrades.
Gigawatt campus
More routinely requires dedicated substations, upstream transmission, and regional large-load planning.
Commercial logic
Dedicated node
Release one customer-defined phase after site, power, technical, and contract gates are met.
Gigawatt campus
Capture scale advantages in standardization, procurement, shared operations, and fleet management.
Power architecture
“Behind the meter” means generating power on site, alongside the building’s existing utility service, so the project needs less new capacity from the utility. Pick a setup to see how power flows.
Selected architecture
Onsite generation, storage, or both operate in parallel with the grid and may reduce measured grid imports under defined conditions.
What it can be designed to do
Support an engineered import-management, resilience, or non-export strategy while retaining utility service.
What must be proven
Boundary: Behind the meter does not mean utility-free, permit-free, off-grid, or exempt from interconnection and tariff requirements.
Interactive one-line concept
Select a component to see its role and current state. Animated lines show which paths are energized in the selected setup; control signals are shown separately from power flow.
Swipe horizontally to inspect the full one-line.
Selected component
The electrical and commercial boundary where metering, protection, import/export settings, tariff terms, and operating agreements are defined.
Closed for synchronized parallel operation
Power-flow reading
Generation and storage may reduce net import. Directional animation indicates the conceptual energized route, not power quality, capacity, or protection logic.
Engineering boundary
Non-export controls, interconnection, protection, metering, standby service, fuel, emissions, and permits remain project-specific.
Production inference, applied fine-tuning, private AI, and data-local workflows can fit when the model, data, network, and service objective match the node.
Frontier pre-training is not the primary use for this segment.
An already-developed industrial shell can be a redevelopment option worth testing against the real computing design.
A cultivation license or historic electrical service does not establish AI readiness.
An initial capacity block can be defined and commissioned, then expanded only after the next power, design, approval, and demand gates are met.
Phasing limits the amount committed at one time; it does not eliminate risk.
Claims about water, cooling, onsite generation, noise, land disturbance, permits, taxes, and workforce should rest on project-specific evidence.
Smaller scale does not automatically mean lower impact.
The investment discipline
Build only the phases that are verified and under contract. A smaller first capacity block can limit the capital exposed before later phases, but it does not remove customer concentration, utility, technology, operating, or residual-capacity risk.
This is an educational infrastructure framework—not an offer, an investment recommendation, a utility commitment, an engineering design, a permit determination, an environmental claim, a reliability guarantee, or a projection of cost, schedule, value, or return.
Explore a workload-qualified deploymentWorkload, distribution, reuse, and power-architecture statements are bounded by the cited official and first-party sources. None evaluates a specific cultivation property or establishes site capacity, economics, environmental performance, or approval status.
Risk, sized down
When credit tightens, the size and structure of an AI project matter as much as the chips inside it. A 1.5–5 MW node changes which risks you carry. It does not remove them.
Oct. 14, 2026 · Markets
Shares and bonds of several AI-infrastructure companies fell sharply as investors grew concerned about how heavily the build-out is being financed with debt. A fixed-income strategist told CNBC that investors are now demanding more compensation for risk.
Reported by CNBC, Oct. 14, 2026(opens in a new tab)“More than $3.5 trillion of AI infrastructure spending could be exposed to impairment by the end of 2030, if AI providers continue their current pace of capital spending”
The same release says: “Failure to achieve sustainable ROIC on rapidly depreciating assets would almost certainly necessitate write-downs.”
The mismatch at the center of AI finance
When short-lived hardware and long-lived real estate sit in one debt-financed bundle, a faster chip cycle can strain the whole project. Keeping the layers apart is one way to limit that.
Power infrastructure
25+ years
Building shell
~20 years
AI accelerators
4–6 years
One building can outlast 3–5 generations of AI chips.
Typical accounting lives, not a forecast for any asset. Each chip bar represents five years.
Goldman Sachs Global Institute, May 1, 2026(opens in a new tab)Four structural levers
A node is sized for one organization’s defined AI work and released in phases only after power, design, approval, and contract gates are met. Less capital is committed before a customer signs.
Texas ordered an audit of every data center in its grid queue, and grid operators have struggled with load forecasts inflated by speculative projects.
Utility Dive, Aug. 2026(opens in a new tab)Doesn’t fix: Relying on one customer concentrates risk. Contracts can end or not be renewed.
The building and power service last decades; AI chips turn over in years. When the organization running the workload owns and refreshes its own hardware, the site’s shell and power can be valued and financed separately from the chips.
AI accelerators are typically given four to six years of useful life, buildings about 20, and power infrastructure 25 or more.
Goldman Sachs Global Institute, May 1, 2026(opens in a new tab)Doesn’t fix: A contract can shift obsolescence risk to one party, but it cannot eliminate that risk.
An existing industrial building with heavy power service may face a different local approval path than a raw-land rezoning would. In one 2026 tracker, most canceled projects were stopped at the local-permit stage.
At least $260 billion of data-center investment was canceled in 2026 after sustained local opposition.
Heatmap Pro, Oct. 6, 2026(opens in a new tab)Doesn’t fix: A change of use still needs local approval, and opposition can form around any project.
Texas now studies new loads of 75 MW or more together in one batch; in June, ERCOT said it expected the final transmission plan in fall 2027. A 1.5–5 MW node is a small fraction of that threshold and may be served through local distribution.
ERCOT groups large loads of 75 MW or greater into a single Batch Zero study.
ERCOT, June 18, 2026(opens in a new tab)Doesn’t fix: Smaller loads still need a utility study, upgrades, and tariff approval. Feeder capacity can be limited.
What research outside AI suggests
“granularity is associated with faster diffusion, lower investment risk, faster learning”
The authors add that these advantages are “contingent on access to infrastructure, substitutability, and standardisation.”
Wilson et al., Science 368 (6486), 2020(opens in a new tab)
“Repeatable projects are cheaper, faster, and scale at lower risk of failure.”
Based on a comparison of 203 space missions that used either bespoke or repeatable-platform strategies.
Ansar & Flyvbjerg, Oxford Review of Economic Policy 38 (2), 2022(opens in a new tab)
These studies cover energy and space programs, not AI data centers. They describe a pattern worth testing, not a proven result for this asset class.
Educational commentary only—not an offer, a recommendation, or investment advice. UNICANN does not comment on or recommend any company’s securities or creditworthiness. News and forecasts are quoted with attribution as of their publication dates; forecasts are their authors’ opinions, not facts, and no source quoted here endorses UNICANN. A smaller node still carries real risk, including the loss of capital committed to it.
Size a node to a workloadMarket events are described at the sector level as reported. Quotations are reproduced verbatim from the cited publications. GARTNER is a trademark of Gartner, Inc. and/or its affiliates. Gartner does not endorse UNICANN.
The conversion band
Large indoor grows may offer a useful starting envelope: industrial shells, high-capacity service, and heat-rejection infrastructure. None of that automatically becomes AI-ready.
The useful question is whether documented post-shutdown power, site control, cooling design, fiber, fire protection, and local approvals can support a 1.5–5 MW dedicated node.
UNICANN screening band
1.5–5 MW
Dedicated AI nodes—not single servers, not gigawatt campuses.
Entry node
1.5 MW
A dedicated multi-rack starting point when workload and site evidence align.
Core target
3 MW
The center of the screening band for scaled, controlled compute.
Expansion edge
5 MW
More deployment headroom when utility and conversion conditions support it.
What must be proven before hardware
Usable utility power
What the utility will support for the new AI use—load, rate class, ramp, and date.
Complete change of use
Cultivation ceases; ownership, shutdown, and local approval paths are documented.
AI-duty conversion
Cooling, fiber, fire protection, controls, and electrical distribution are redesigned for continuous IT load.
A better alternative
The site must beat cloud, colocation, or another powered location on the buyer’s actual requirements.
Why adaptive reuse is worth testing
A powered industrial shell may shorten one part of the path. It does not prove transferable utility capacity, AI-duty cooling, change-of-use approval, schedule savings, or economics.
Start from zero
Start from evidence
Efficiency is not a single number
Accelerator vendors report material performance-per-watt gains, but each result depends on a specific model, precision, runtime, system boundary, and interactivity target. UNICANN keeps those comparisons separate rather than turning them into one universal score.
Review models, hardware, and cooling assumptionsThe water question
A dry, closed-loop node can use less onsite water than an operating indoor grow, but its larger electric load can shift water use upstream. Our sourced comparison keeps those ledgers separate and shows the Arizona heat tradeoff.
After preliminary fit
UNICANN’s private diligence layer is designed to make uncertainty visible—not to manufacture certainty. Each claim stays linked to its evidence state, each match explains its logic, and each report freezes the assumptions used at that moment.
A field-level ledger of recorded facts, evidence state, confidence, blockers, and independent G0–G7 readiness gates.
Encrypted document storage with scoped review, expiring record-level grants, revocation, and access history.
Deterministic buyer-to-facility screening with visible hard gates, criterion contributions, evidence penalties, and stale-data warnings.
Reproducible scenario snapshots that separate facility input, IT load, dependencies, alternatives, unknowns, and limitations.
Sourced utility, fiber, hazard, permitting, water, and labor factors mapped only at a deliberately coarse level of location detail.
Illustrative workflow
See how a cultivation campus can move from document review through utility, conversion, operations, and phasing gates—without implying a live offer.
Questions, answered plainly
What UNICANN does, who it is for, and what still has to be proven for any one building.
UNICANN connects U.S. indoor cannabis cultivation facilities that already have heavy power service with buyers who need a private, dedicated AI node of about 1.5–5 MW. The grow ends completely, and the building gets a new job. It is not a shared cannabis-and-computing site.
Large indoor grows were built to run megawatts of lights and cooling. When the building and the power service already exist, a project can start farther along than a ground-up build that begins with land, zoning, a new power line, and construction. Whether converting a specific site is actually faster or cheaper depends on its utility documents, building condition, permits, and conversion design.
See the six conversion phasesA private block of AI computing—typically about 1.5 to 5 megawatts for servers, cooling, and networking—run for one organization on hardware it controls, instead of renting capacity on a gigawatt-scale campus built to serve the whole market.
Size a private AI deploymentTwo groups: organizations that can run open-weight models themselves but whose AI work can’t go to a public cloud because of contracts, regulation, or confidentiality; and owners of powered indoor grows who are exploring a complete change of use.
No. Cultivation ends completely and lawfully before conversion work begins. There is no phase where cannabis and computing share the building.
It depends on the cooling design and the climate. With dry or closed-loop cooling, onsite cooling water use can be very low; evaporative or hybrid cooling uses more, especially in hot, dry regions. A conclusion about a specific property needs metered data and an engineering review.
Compare water and power by climateUNICANN screens for sites that may support about 1.5–5 MW of usable power after conversion. The utility’s documents and an engineering review determine what is actually usable—the size of the old grow alone does not.
Screen a facilityNot less risky—differently risky. A 1.5–5 MW node commits less capital at once, can be built for one named workload, and can keep the long-lived building and power infrastructure separate from AI chips that are typically replaced every four to six years. It still carries customer, hardware, financing, utility, and approval risk, and smaller sites give up some economies of scale.
See what a smaller node changes—and what it doesn’tNo. UNICANN provides screening information only. Nothing on this site is an offer, a valuation, a guarantee, or legal, tax, engineering, financial, or investment advice.
Read the disclosuresThe honest test
UNICANN exists for the cases where dedicated infrastructure wins. If cloud or colocation meets the need better, use it.
Best when the model stays with its provider or demand remains uncertain.
Best when you want to own hardware without acquiring and operating a building.
Consider it when demand is sustained, control matters, and evidence supports the location.