As the artificial intelligence (AI) revolution accelerates, its AI power demands are rising at an unprecedented pace. The exponential growth of data centers—fueled by AI workloads such as training large language models, running autonomous robotics systems, and processing vast amounts of real-time data—has introduced a new kind of bottleneck: electricity.
A recent Reuters Breakingviews article highlights an emerging concern: that the demand forecasts for AI-related power consumption may be significantly inflated. The article likens these overestimations to AI “hallucinations”—the phenomenon where generative models produce false outputs with high confidence. For investors and AI robotics developers, the real implications lie in understanding whether the infrastructure being built today is adequately scaled—or dangerously speculative.
Data Center Expansion and the AI Energy Surge
Big Tech companies—Microsoft, Amazon, Alphabet, and Meta—are collectively investing over $300 billion into building new data centers, the backbone of AI operations. These data hubs are critical for both training AI models and supporting the real-time processing needed for autonomous robotics and other AI-driven systems.
This explosion in infrastructure spending is already influencing national power consumption. Data centers currently account for around 4% of U.S. electricity usage, a figure expected to triple to 12% by 2030, according to utility projections cited by Reuters.
These figures are backed by reports of long lead times for key electrical components such as gas turbines and transformers. As demand outpaces supply, costs rise and timelines extend. Delays are already being reported across the U.S. grid, especially in states that are seeing AI hubs emerge—Virginia, Georgia, and Texas among them.
In tandem, AI robotics—especially those deployed in healthcare, logistics, and manufacturing—are relying on these data pipelines for real-time computation, coordination, and learning. The burden placed on energy grids is a direct result of this technological shift.
Misjudging AI Energy Needs: Are Forecasts Overblown?
Despite the urgency surrounding energy infrastructure, many analysts now believe that these forecasts may be exaggerated. Some utility companies may be inflating projections to justify large infrastructure expansions, while tech firms may be securing more power than needed to ensure long-term flexibility.
Reuters notes that the North American Electric Reliability Corporation (NERC) is preparing for 140 gigawatts of power demand from AI and cloud infrastructure by 2030. However, other grid experts argue the actual figure might be closer to half that, suggesting a possible overestimation of needs.
Adding to the uncertainty are signs of “double-ordering” in the AI hardware supply chain, reminiscent of the semiconductor overstocking that followed the 2020 pandemic boom. Nvidia and other chipmakers have reported large bulk orders for AI hardware, some of which have not materialized into actual deployments. Microsoft, notably, has also canceled or delayed several data center builds.
In effect, we may be building energy capacity for an AI future that is more speculative than certain. The term “hallucination” is apt—not only because it echoes the quirks of AI language models, but because the assumptions driving today’s infrastructure investment may be rooted in flawed logic or optimistic projections.
Implications for AI Robotics Investors and Infrastructure Strategy
For robotics enthusiasts and AI investors, the takeaway is both cautionary and strategic. The success of AI robotics depends not just on breakthroughs in algorithms or hardware, but on the availability of robust and affordable infrastructure. If energy resources become bottlenecked—or are misallocated due to inaccurate forecasts—innovation could stall.
Startups and research teams building edge-deployed AI robotics systems should consider low-power AI alternatives, such as neuromorphic computing, and energy-efficient chips like those under development by Graphcore and Cerebras Systems. These solutions may reduce dependency on massive cloud computing clusters and thus mitigate risk from energy disruptions.
Meanwhile, investors need to apply critical scrutiny to companies with aggressive infrastructure expansion plans. Are the forecasts driving these builds based on real, scaled deployments? Or are they based on hypothetical AI use cases that have yet to be proven?
The power infrastructure race presents both risk and opportunity. Those who anticipate shifts in energy policy, grid modernization, and AI optimization will have a competitive edge. Conversely, overinvestment in the wrong capacities could create stranded assets and lead to shareholder disappointment.
As the AI robotics sector advances, integrating energy planning into business strategy will be essential. Like AI itself, we must distinguish between what is real and what is imagined.
