On September 1, the Department of Energy announced an AI project with an almost absurdly ambitious target: let utilities test one billion possible configurations of the electric grid in 24 hours.
Brookhaven National Laboratory leads the $11.5 million GridFM 2.0 project, which aims to increase planning throughput by more than 10,000 times and run some calculations more than 1,000 times faster than conventional methods. A utility trying to connect a new power plant, accommodate a data center or decide where another transmission line would do the most good could spend the morning testing an enormous number of possible grids and, at least in theory, know by afternoon which one works best. Then it might discover that the transformer required to build that grid won't arrive until 2029 or 2030.
DOE says large transformers for substations and generators now carry lead times of three to four years in some cases. Distribution transformers, the smaller units hanging from utility poles or sitting on concrete pads outside buildings, typically took three to six months to procure in 2019; utilities now wait one or two years, sometimes longer, while demand has risen 41%.
GridFM could therefore compress weeks of planning into hours while leaving another clock almost untouched. Somebody still has to turn the answer into copper, steel and insulation, and the factories that do that work already have years of orders ahead of them.
The Part That Weighs Hundreds of Tons
Transformers change voltage; electricity travels long distances more efficiently at very high voltages, then utilities step it down repeatedly before it reaches a factory, a house or a row of server racks at a usable level. More than 90% of the electricity Americans consume passes through a large power transformer somewhere along the way.
Utilities order the largest transformers to fit particular substations and operating requirements, and some weigh hundreds of tons. Moving one from the factory to its eventual home can require specialized railcars or trailers, route studies, bridge reviews and heavy-lift equipment; unlike a server or a generator small enough to move on an ordinary truck, a large transformer can force the transportation system to organize itself around the machine. Utilities can't simply keep a few universal spares in a warehouse and dispatch one when something breaks.
America also doesn't make enough of them. Roughly 82% of the large power transformers that U.S. utilities installed in 2019 came from abroad, according to DOE, while American manufacturers still rely on foreign suppliers for much of the grain-oriented electrical steel that forms transformer cores.
That dependence follows the transformer through almost every stage of production. An American factory may assemble the final machine, but materials may arrive from overseas; the order then enters a factory backlog measured in years before the finished transformer begins a journey planned around its extraordinary weight and dimensions. There aren't many places in that chain where a planner can simply delete six months.
Forty Thousand Ways to Order a Transformer
Distribution transformers present a different version of the same problem because American utilities have spent decades ordering them in an astonishing number of variations.
Utilities specify different bushings, mounts, voltage ratings, connections, accessories and other features. Some differences reflect genuine engineering requirements; others survive because a utility chose a design years ago, wrote it into its standards and kept buying the same thing. Specifications behave a little like old contract language in that respect: once a clause enters the form, it can remain there long after everyone has stopped asking why somebody inserted it in the first place.
DOE says utilities now use almost 40,000 distribution-transformer configurations nationwide. Take only ten common design attributes and the number of possible combinations climbs above 80,000, which turns the familiar gray box hanging from a utility pole into a surprisingly difficult manufacturing problem.
Factories have to accommodate more designs and shorter production runs; utilities hold inventories that don't necessarily fit one another's systems, so a perfectly good transformer sitting in one company's yard may do little for another utility trying to restore power twenty miles away. Variety has value when engineering demands it. When history rather than engineering produces the variety, however, everybody pays to preserve the distinction.
DOE has therefore worked with utilities and manufacturers to narrow specifications, identify common designs and eliminate legacy variations that no longer serve much purpose. Electric systems really do differ, so nobody can standardize the entire country into a handful of boxes; still, 40,000 configurations contain a fair amount of bureaucratic sediment alongside the engineering, and removing even some of it could help manufacturers run longer production batches while allowing utilities to stock and share more interchangeable equipment.
The Factory Has a Lead Time Too
Standardizing a transformer doesn't manufacture one. Producers still need copper, electrical steel, insulation, winding equipment, core assembly, testing facilities and people who know how to put all of it together; when manufacturers decide to increase output, they also need more buildings and specialized machinery, some of which can take a year or two to procure.
In April, the White House invoked the Defense Production Act for transformers, substations, high-voltage circuit breakers, electrical core steel and other grid equipment. The presidential determination described America's capacity to design, manufacture and deploy major grid infrastructure as “dangerously limited,” citing long procurement times, inadequate domestic production and dependence on foreign suppliers.
Manufacturers have started spending accordingly. Hitachi Energy broke ground in June on a $457 million expansion in South Boston, Virginia, which the company says will become the country's largest large-power-transformer factory; Siemens Energy is directing part of a $1 billion U.S. manufacturing investment toward additional transformer production in North Carolina and a new high-voltage switchgear plant in Mississippi. Prolec, meanwhile, is spending $140 million in Goldsboro, North Carolina, to increase annual medium-power-transformer production from roughly 220 units to 420, and the company expects the expansion to take about two and a half years.
Hitachi added another project on September 15, announcing a $528 million transformer factory in Gallman, Mississippi. The company plans to build a plant more than twice the size of its existing Crystal Springs operation and more than double its production capacity there; with the new project, Hitachi puts its broader American manufacturing expansion at roughly $1.5 billion. Construction should begin late this year, while Hitachi expects the factory to start producing transformers in 2029.
That date matters because industrial capacity has its own supply chain. Utilities can't solve a three-year transformer backlog merely by demanding more transformers; manufacturers have to order machinery, expand plants, recruit and train workers, qualify production lines and test the equipment before another unit reaches the market. The obvious answer to a shortage is more production, but the machinery that creates more production belongs to the same stubborn physical economy as the product itself.
McKinsey put some scale around the problem three days after Hitachi's announcement. Its analysis estimated that Europe and North America had 914 gigavolt-amperes of transformer demand against 566 GVA of domestic supply in 2025, a gap of roughly 38%. Manufacturers could add about 186 GVA of announced capacity by 2030 under McKinsey's model; demand keeps growing in the meantime, however, leaving an estimated annual shortfall of roughly 312 GVA even after those expansions come online.
The analysis becomes more interesting when McKinsey strips away some of the demand that usually dominates the discussion. In one downside scenario, it removed data-center demand entirely and sharply reduced renewable construction; the model still produced a transformer shortfall of roughly 180 GVA in 2030. Data centers plainly make the problem harder, particularly because a handful of very large projects can alter a utility's load forecast much faster than anyone can expand a factory, but the manufacturing constraint runs deeper than the AI buildout.
Utilities also have aging equipment to replace, new generation to connect, industrial loads to serve and ordinary grid investment that they deferred for years.
So manufacturers are spending billions to relieve a shortage whose solution arrives on roughly the same schedule as the shortage itself. Hitachi's newest factory will begin producing equipment in 2029; a utility placing an order for certain large transformers today may also expect delivery around 2029 or 2030. Capacity is coming, certainly, but nobody gets to teleport through the years required to build it.
172 Tons at a Time
Ukraine offers a harsher demonstration of what those physical constraints mean once equipment becomes urgently necessary. Russia has repeatedly attacked the country's power system, destroying generating capacity and grid equipment while Ukraine and its allies race to replace it; through its Civil Protection Mechanism, the European Union has sent more than 7,200 transformers since Russia launched its full-scale invasion.
One of the more remarkable efforts began with an entire thermal power plant in Lithuania. Crews dismantled the plant and spent eleven months moving it to Ukraine in 149 separate shipments containing 2,399 tons of equipment; forty loads exceeded ordinary dimensions, and some of the transformers and stators weighed about 172 tons each.
At that scale, infrastructure stops behaving like the diagrams used to describe it. The replacement equipment first has to exist somewhere; crews then have to disconnect it, prepare it for transport and load it onto a vehicle capable of carrying it. Engineers have to find roads and bridges that can bear the weight, officials have to clear border crossings and permits, and cranes and specialized crews have to meet the cargo at the other end before anyone can install it and connect it to a live electrical system.
Software can help with nearly every decision surrounding that process. An engineer can identify the missing section of a network, calculate how to route power around it and determine what equipment should replace it; better models can perform those calculations much faster, while AI may eventually test thousands or millions of alternatives during the same period. None of those gains, however, change the logistical fact that somebody still has to move a machine weighing 172 tons across actual roads.
Military logistics has always imposed the same discipline. Sophisticated systems attract attention because they embody technological advantage, while sustained operations depend on ammunition, spare parts, maintenance, transportation and the industrial capacity to replace whatever breaks. Electrical infrastructure obeys much the same rule, although its supply chain usually attracts considerably less attention until the lights go out.
Every Server Rack Reaches the Grid
Commentators usually describe the AI race in terms of chips, GPUs, models, compute and talent, all of which matter enormously. Eventually, however, every server rack reaches an electrical system assembled from much less glamorous machinery.
A data center needs electrical distribution equipment inside and around the building; it also needs a connection to a substation full of transformers, breakers and switchgear. That substation connects to transmission lines and generation, which bring their own layers of equipment, while semiconductor fabs and advanced manufacturing plants require much of the same infrastructure. Even the factories that manufacturers are building to produce more transformers, breakers and switchgear need additional electrical equipment of their own.
Demand therefore begins to compound in slightly strange ways. The industries driving new electricity consumption need more grid equipment; the factories expanding grid-equipment production consume power and require grid connections themselves, while the mines, mills and component suppliers feeding those factories add another layer. The feedback loop doesn't continue forever, obviously, but it helps explain why adding industrial capacity involves more than announcing a factory and pouring concrete.
GridFM 2.0 could still matter enormously precisely because it may help utilities avoid some of those physical requirements. If a billion simulations reveal a safe way to reroute power around a constraint, show that existing equipment can carry more load or identify a different transmission upgrade that eliminates the need for another transformer, the avoided order has real value; removing one major piece of long-lead equipment can move a project's completion date by months or even years.
Faster planning also gives utilities something the manufacturing system badly needs: warning. A company that can see several years ahead to the point where its network runs out of capacity can reserve manufacturing slots, order equipment and begin building substations before the load actually arrives. GridFM can't shorten every factory backlog, but it may allow a utility to encounter that backlog earlier, when time remains to do something about it.
Eventually, though, the simulation produces an answer that has to leave the computer.
Then Procurement Begins
Suppose GridFM works exactly as DOE hopes. A utility feeds it the network, demand forecasts, generation plans and proposed projects; the system tests a billion possibilities and identifies the best answer in a day. At that point the intellectual problem may have collapsed from weeks into hours, while the industrial problem has barely begun.
Steel mills have to roll electrical steel and manufacturers have to wind copper, assemble transformers and test them. Hitachi has to finish factories in Virginia and Mississippi; Prolec has to install more machinery, while manufacturers everywhere have to find space in order books already crowded with other utilities' projects. Carriers need permits and route plans for oversized loads, and substations need land, concrete, electricians and lineworkers before anybody energizes the equipment.
That doesn't diminish the value of faster planning. Knowing what to build in a day beats spending weeks or months finding the same answer, especially when a poor planning decision can tie up scarce equipment and capital for years; GridFM may also help utilities avoid some orders entirely, or at least place the unavoidable ones before everybody else discovers that they need the same equipment.
Rather, the improvement changes where the delay lives. Once software strips time out of planning, procurement, manufacturing, transportation and construction consume more of the schedule, and the constraints that once hid behind engineering studies become much harder to ignore. DOE puts some large-transformer lead times at three to four years; McKinsey says portions of the market can stretch toward five.
GridFM is trying to let utilities reason about an enormous physical system at something approaching machine speed; the factories, mills, railcars, trailers, cranes and crews that turn those answers into an electrical system still operate according to the stubborn tempos of materials, labor and construction. If a utility places an order today, 2029 isn't merely a date on a planning chart. It may be the year the transformer arrives, and, in Hitachi's case, the year one of the new factories meant to relieve the shortage finally begins making them.