AI Data Center Costs: +66% for gas power plants

The context: when AI demand meets physical energy limits

Generative artificial intelligence has transformed the very nature of global energy consumption in just a few years. Large language models, real-time inference systems, and training pipelines require unprecedented amounts of electricity. However, the power grid and generation infrastructure are not updating at the speed of software.

According to reports by TechCrunch, the construction costs of natural gas power plants have increased by 66%%over two years. Furthermore, construction times have lengthened by 23 days%. These figures do not concern a single geographical area: the phenomenon is systemic, with its epicenter in the United States but with cascading effects on a global scale.

So, we're facing a physical bottleneck. Electricity generation capacity can't keep up with the growing demand from data centers. This structural imbalance has direct consequences on the operating costs of the entire digital supply chain.

The numbers that matter: +66%, 23%, and the weight of kilowatts on AI

The 66%% increase in construction costs is significant in itself. However, it becomes even more relevant when read together with the 23%% extension of construction times. Together, these two indicators describe a market under pressure on both critical dimensions: cost and speed.

According to the analysis of International Energy Agency, the electric demand of data centers could double by 2026 compared to 2022 levels. Therefore, the problem is not expected to subside in the short term. On the contrary, pressure on installed capacity is expected to increase further with the proliferation of next-generation AI models.

Furthermore, the financial component must be considered. The capital required to build new generation capacity has grown disproportionately. As a result, large data center operators—Microsoft, Google, Amazon, Meta—are internalizing much higher energy costs than three years ago. Sooner or later, these costs will be passed down the value chain.

In particular, McKinsey He estimated that investments in energy infrastructure for data centers could exceed $500 billion by 2030. Therefore, the scope of the phenomenon is systemic, not episodic.

From the gas power plant to the cloud bill: the cost transmission chain

To understand the practical implications, it’s useful to trace the path connecting a gas power plant to an Italian company using cloud services or AI tools. The mechanism is linear, although not always visible to those purchasing software licenses.

First of all, the large hyperscalers build or rent data centers. These data centers consume enormous amounts of electricity. Therefore, when energy costs rise — due to more expensive power plants to build and operate — the hyperscalers' operating margins are squeezed.

Operators then have two options: absorb costs or pass them on to customers. In recent quarters, there has been a growing trend towards the latter. In fact, Microsoft Azure, AWS, and Google Cloud have revised the prices of several services, particularly those related to AI and machine learning processing. Therefore, SMEs using artificial intelligence APIs or cloud environments for data analysis are already facing evolving price lists.

In addition to this, the effect on the market of second-tier SaaS providers must be considered. Many marketing automation platforms, advanced CRMs, and predictive analytics tools rely on third-party cloud infrastructure. Consequently, these vendors might also adjust prices in future contract renewal cycles.

Strategic Reading: What it Means for Italian B2B and Retail SMEs

Italian SMEs find themselves in a unique position. On one hand, they are accelerating the adoption of advanced digital tools, driven by the need to compete in increasingly automated markets. On the other hand, they operate with limited budgets and a high sensitivity to cost fluctuations.

Therefore, the energy dynamics described in this article are not an abstract issue. It is a variable that concretely influences digital investment planning. Specifically, those considering the adoption of AI solutions for customer service, content generation, or predictive sales analysis must incorporate this variable into their cost models.

However, this doesn't mean giving up on innovation. On the contrary, it means taking a more selective and conscious approach. We at SHM Studio We work daily with companies that need to balance digital ambition and economic sustainability. In fact, choosing the right tools—in terms of computational efficiency and pricing model—becomes a concrete competitive advantage.

Similarly, retail companies investing in AI-driven customer experience personalization must consider that inference costs may increase. Therefore, it is preferable to favor solutions with predictable pricing and efficient consumption models over platforms that charge exclusively based on variable consumption.

The construction site still open: the unknowns that no one knows how to quantify

There are several variables that make precise forecasting of cost evolution difficult. First and foremost is the speed of renewable energy development. If the construction of new solar and wind capacity were to accelerate significantly, pressure on natural gas could ease.

However, renewables present intermittency problems that make them, in their current state, insufficient to cover the base load of data centers. Therefore, natural gas remains a structural component of the energy mix for the coming years. Despite this, several hyperscalers are investing in long-term renewable energy purchase agreements, seeking to stabilize their energy costs.

Furthermore, the efficiency of AI chips is rapidly improving. New processors from NVIDIA, AMD, and the internal teams at Google and Amazon consume less energy per unit of computation compared to previous generations. Consequently, the energy demand for equivalent workloads may stabilize in the medium term, even though absolute demand will continue to grow.

In summary, the landscape is characterized by structural uncertainty. SMEs would be wise not to assume cloud cost stability in their multi-year plans.

Operational implications: how to navigate digital investments

Faced with this scenario, some operational guidelines can help SMEs navigate the context with greater awareness. These are not universal recipes, but common-sense principles applied to a changing market.

• Cloud contract review: It is advisable to review the price adjustment clauses in contracts with cloud service providers. Furthermore, it is useful to negotiate multi-year commitments when possible to lock in current rates.
• Computational efficiency prioritize AI solutions that optimize computational resource consumption. In fact, a smaller, specialized model can offer equivalent performance at a lower cost compared to a large, generalist model.
• Supplier diversification: avoid dependence on a single hyperscaler. Therefore, evaluate multi-cloud architectures or on-premise solutions for the most intensive workloads.
• Real-time cost monitoring Implement FinOps tools to keep cloud spending under control. Consequently, it is possible to intervene quickly in case of anomalies or unexpected increases.
• AI ROI Assessment Every investment in AI tools should be accompanied by rigorous return measurement. Therefore, it is essential to define clear KPIs before adoption, not after.

For SMEs wishing for structured support during this phase, the services of digital marketing e AI at SHM Studio include a preliminary cost-benefit analysis phase. Furthermore, the team SHM Studio can support the choice of the most suitable platforms for the company's risk profile and budget.

Outlook: A Market That Is Readjusting, Not Stopping

It is important not to interpret this data in a catastrophic way. The increase in energy costs will not stop the adoption of AI. However, it will introduce a natural selection among the solutions available on the market. Therefore, platforms that can offer real value at a sustainable cost will survive and thrive.

For Italian SMEs, this is actually an opportune moment to adopt a more mature approach to AI. Instead of chasing every technological novelty, it's preferable to focus on specific use cases with measurable impact. For example, customer service automation, campaign personalization LinkedIn o Google Ads, or improving content quality through tools assisted copywriting.

Finally, the SEO and the web presence there remain investments with a stable cost-benefit ratio, less exposed to global energy cost fluctuations. Therefore, diversifying the digital investment mix is more than ever a prudent strategic choice. To delve deeper into these topics, the SHM Studio Blog offers updated analyses tailored to the needs of Italian businesses. For personalized consulting, you can contact the team through the page contacts.