Can Poland Power Its AI Ambitions? The Infrastructure Costs Beyond the Model
An AI subscription price is not an infrastructure strategy. For Polish businesses moving from pilots to production, the budget question extends beyond model access to the electricity, cooling and hardware needed to deliver reliable services. PARP’s October 23, 2026 briefing puts these dependencies at the centre of digitalisation planning, connecting resource availability with deployment costs and the pace of expansion.
The practical implication is not that every AI service will become more expensive. It is that companies should assess total deployment economics before committing to a platform, hosting model or growth forecast. A successful pilot should demonstrate more than useful outputs. It should establish whether the service can scale within an acceptable cost, capacity and operational envelope.
Poland’s AI Opportunity Depends on Infrastructure Readiness
Poland’s infrastructure challenge is about coordinating computing investment with the systems that support it. The Institute of Telecommunications’ October 2026 briefing identifies data, energy, infrastructure, security and skills as interconnected foundations for successful AI adoption. It also cites a PSE forecast under which Polish data centres could consume 17-18 TWh annually by 2035, with electricity availability potentially influencing investment locations. That figure is a forecast reported by the institute, not current consumption.
The wider trend reinforces the planning challenge. Global data-centre electricity demand grew 17% in 2025, compared with 3% for electricity demand overall. At the same time, energy consumption per AI task is declining. Greater adoption and more demanding applications are increasing aggregate demand despite those efficiency gains. Expansion also faces constraints involving transformers, advanced chips, planning approvals and grid connections.
For business planning, these findings support a distinction: model efficiency and infrastructure availability are separate questions. A more efficient application still needs dependable access to computing capacity.
Procurement teams should therefore request evidence about the capacity available to their specific deployment, rather than treating a provider’s announced expansion as sufficient assurance.
Separate Infrastructure Exposure From Cloud Pricing
PARP’s warning reaches beyond data-centre operators. Its briefing identifies energy availability, transmission constraints, component prices and hardware replacement as factors that can influence AI deployment economics, including for companies using cloud services.
However, that does not establish a direct relationship between Polish electricity prices and a particular provider’s API charges. Treat an automatic price increase as an unsupported budgeting assumption.
Instead, use different evaluation questions for each proposed deployment:
Managed AI services: What throughput, quotas and regional availability can the provider commit to? What happens when demand exceeds the agreed limits?
Dedicated cloud infrastructure: Which GPU configurations are available, how quickly can capacity expand, and what commitments are required?
On-premises or colocated systems: Has the proposal included power, cooling, equipment replacement and operating responsibility?
Self-hosting can be economical at scale, but it adds ongoing security, monitoring, scaling and incident-response work. Managed services can offer better total economics when engineering capacity is constrained. The comparison should include operating effort, not just the advertised compute rate.
A useful procurement requirement is to ask each bidder to price the same workload under normal demand, peak demand and reduced-capacity conditions. This makes the trade-offs explicit without pretending that one hosting model is universally cheaper.
Treat Cooling and Water as Location-Specific Questions
Water deserves attention, but not a universal consumption estimate attached to every AI request. PARP identifies water use across electricity generation, data-centre cooling and component manufacturing, and notes that local availability can affect infrastructure development.
Cooling choices also involve trade-offs. Google’s published operating approach balances energy supply, local water availability and alternatives to freshwater when selecting cooling systems. Water cooling can improve energy efficiency, but its suitability depends on the location and design. These mechanisms do not, by themselves, establish a water shortage at a Polish facility.
For a material infrastructure commitment, turn that uncertainty into a due-diligence checklist:
Request the cooling design for the proposed facility, not only a company-wide sustainability statement.
Ask how heat is ultimately rejected from the building.
Identify the water source and any seasonal operating constraints.
Request the expected electricity and water requirements during hot weather.
Ask whether the disclosed figures describe measured operations or projected performance.
Review the continuity plan for restricted cooling or power availability.
The objective is not to select the lowest headline water figure. It is to understand the facility’s operating resilience and the assumptions behind its environmental claims.
Budget Around a Successful Business Outcome
Tokens and GPU-hours help explain consumption, but they do not establish business value. A stronger unit is cost per successful outcome, such as an accepted document extraction or a resolved support case. Include the supporting data, retrieval, evaluation and operating costs, not inference alone.
For a production approval, define an internal measurement framework:
Outcome: What completed business task counts as success?
Quality: What accuracy or acceptance threshold must the result meet?
Service: What response time and availability does the workflow require?
Cost: What expenditure belongs to that outcome, including review and recovery?
Ownership: Who can change the design when economics deteriorate?
Test smaller models against task-specific quality requirements. Evaluate batching for asynchronous work, caching for repeated inputs, and limits on agent iterations and retries. These are workload-dependent controls, not guaranteed savings.
Keep the financial and environmental scorecards separate. A lower invoice is not, on its own, a measurement of lower electricity or water consumption.
Make Capacity and Exit Planning Part of Production Approval
The institute’s briefing frames technological sovereignty as control over data, computing infrastructure, cloud arrangements and supplier jurisdiction, rather than simply ownership of a model. That provides a useful basis for evaluating deployment independence.
Before approving production, require a short architecture and capacity review covering:
Workload fit: Separate interactive services from work that can wait in a queue.
Capacity evidence: Confirm the usable resources and contractual limits for the chosen deployment.
Fallback behaviour: Define what the application should do when its preferred model or region is unavailable.
Exit readiness: Document how data, prompts, evaluations and integrations would move to an alternative.
Review triggers: Set thresholds for cost, quality and service performance that require a new decision.
Poland’s infrastructure squeeze should encourage more disciplined AI investment, not a retreat from adoption. The strongest plans will connect useful software with credible capacity, transparent operating assumptions and measurable business outcomes. Model selection remains important, but durable value depends on the system around it.
