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Partner Content This year, the global build-out of datacenters has become impossible to ignore, with the debate spilling into national media, local newspapers, and community council meetings alike. From Arkansas to Southern California, Nevada, Pennsylvania, West Virginia, and most recently Box Elder, Utah, communities are weighing the economic promise of datacenter expansion against mounting concerns over energy, infrastructure, and residential impact. The same dynamic is playing out in the UK, where OpenAI's "Stargate UK" project has been partly shelved amid energy consumption concerns and regulatory pressure.
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A typical new hyperscale datacenter can face grid-connection bottlenecks of up to seven years in certain markets, well before the necessary transmission, substations, generation capacity, and transformers are in place. McKinsey, meanwhile, estimates that global datacenter spending could reach $7 trillion by 2030 - a figure comparable to the size of a top-12 global economy.
AI intelligence at scale now dominates enterprise strategy and global politics because the promise of the technology is matched only by the infrastructure required to deliver it. Energy consumption is unavoidable in this new world, and the bet enterprise leaders are making is that the value AI creates will outstrip the cost of the power feeding it. That trade-off has produced a new equation for executives: intelligence per watt.
Is your agentic ambition constrained by energy?
AI-driven datacenters already account for roughly 1.5 percent of global electricity consumption, and the IEA expects that demand to more than double by 2030, approaching three percent of global electricity use. That's more than many major industrial sectors, including agriculture.
The pressure will compound over the next three years, with IDC projecting onebillion agents running 217 billion daily actions by 2029. From Seattle to Barnsley in the UK, the race to build more datacenters close to energy sources is now a daily occurrence.
If the right datacenter, grid, and power infrastructure for the first billion agents takes up to seven years to build, supporting two, three, or even eight billion agents implies timelines the industry has yet to cost. The mismatch between enterprise intent and energy capacity is widening.
For enterprise leaders, this is a defining moment of decision. With 95 percent of global enterprises intending to become their own AI and data platforms in less than 780 days, AI, data, and energy can no longer be treated as separate priorities; they are now interconnected parts of a single platform strategy. The harder question is how executives can pursue those AI ambitions while managing energy efficiently at agentic scale.
BFSI might be showing us the way forward
Banking, financial services, and insurance (BFSI) enterprises have traditionally invested more heavily in technology than any other major sector. McKinsey estimates banking IT spending typically runs at between six and 12 percent of revenue, compared with 3.75 percent to five percent for the next-highest sector.
The pressure to deliver new technology value, particularly through AI and agentic systems, is creating an operating language shared by CIOs, CTOs, and business leaders alike. AI and data are increasingly framed as the new competitive moat, yet the energy costs associated with maintaining that moat introduce a fresh dynamic into technology decision-making.
The 13 percent of global enterprises winning with AI and agentic systems are more likely to build their data strategies around control, efficiency, and sustainability. The common pattern is repatriation: pulling AI and data out of single-hyperscaler silos and into their own control planes, where they can govern and manage information across clouds, on-premises environments, and systems they own.
The pattern recurs among agentic AI leaders across EMEA, North America, Singapore, and Japan. The principle is straightforward: bring AI to the data, because the two must work together across the front lines and back offices of the business rather than operating as separate concerns.
That logic explains why BFSI leaders such as Wells Fargo, Mastercard, HSBC, JPMorgan Chase, Bank of America, Citigroup, Goldman Sachs, BNP Paribas, ING, Crédit Agricole, UBS, and NatWest have made public carbon-neutrality commitments alongside ambitious plans to become their own sovereign AI and data platforms.
AI and data sovereignty in Postgres wins on OpEx, environment, and ROI
Agents operate at the data layer, which means energy must be managed at the same layer, since this is where much of the work happens. The alternative is the equivalent of turning on the heat while leaving every window open in the middle of winter. Only by controlling the data layer, agents, and broader data estate can enterprises build the foundation for managing energy consumption.
Energy efficiency has to begin where enterprise operations already run, which is why PostgreSQL®, the world's most widely used database among developers, is well suited to the challenge. EDB Postgres AI is built specifically to address the energy-intensive nature of modern datacenters by improving database and AI efficiency at the point where workloads execute.
By shrinking core usage requirements and tightening data-intensive agentic operations such as search, retrieval, and vector indexing, EDB Postgres AI can cut datacenter energy consumption by up to 81 percent and reduce emissions by as much as 87 percent.
The ambition to become an AI and data platform carries one foundational requirement: AI and data sovereignty. Organizations that adopt this model not only achieve 5x ROI and deploy 2x more AI and agentic AI systems; they also gain more control, greater efficiency, and a smarter way to design and operate datacenters for the agentic era.
The formula for success is sovereignty in Postgres — the most practical path to achieving more intelligence per watt.
Contributed by EDB.
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