News

• AI expansion is overwhelming transformer factories across global electricity markets today
• Power transformer delivery times have stretched from months into several years
• Aging electricity grids are driving an urgent wave of transformer replacements

Electricity grids across Europe and North America are facing a serious equipment shortage that could delay new electricity connections for years, experts have warned.

Power transformers, the large devices that regulate voltage before electricity reaches homes and factories, now take considerably longer to manufacture.

Orders that once took 6 to 12 months before 2020 can now take between 24 and 48 months to complete.

The rise of electric vehicles and the shift toward industrial electrification are placing significant new demands on local power grids.

Utility companies increasingly compete directly with private developers for the same limited factory capacity, extending wait times for nearly everyone.

Much of the substation infrastructure built 30 to 50 years ago across the United States and Western Europe now requires urgent replacement.

Wind farms and solar installations also require specialized step-up transformers to convert the power they generate before long-distance transmission occurs.

Battery storage projects add further complexity, since each installation requires its own dedicated transformer connected directly to the wider power grid.

The most significant new demand originates from data centers built to support artificial intelligence and AI tools, which consume electricity at an extraordinary rate.

A single facility of this kind can draw several hundred megawatts, comparable to the electricity consumption of a mid-sized city.

Large technology firms can pay in advance to reserve years of factory output, leaving smaller buyers to wait even longer.

The situation is so critical that the largest transformers, rated above 100 MVA and 230 kV, once shipped within 12 to 18 months, can now require well more than 36 months for delivery.

The principal constraint lies at the material level, since transformer cores depend on grain-oriented electrical steel that remains in short supply.

Alternative steel grades cannot satisfy strict efficiency standards set by the European Union and the United States Department of Energy.

Copper prices for internal winding materials have remained high, adding considerable cost pressure to already constrained manufacturing budgets across the industry.

Skilled labour shortages further complicate matters across the industry, since transformer assembly still depends heavily on precise, hands-on manual craftsmanship.

Factory testing facilities, where each unit undergoes impulse voltage and short-circuit evaluation, can only process a limited volume weekly, further limiting overall output.

As a result, equipment prices have climbed 50% to 80% above pre-2020 levels, driven largely by rising material and labour costs.

While industrial transformers take more time, smaller units used in residential and commercial settings ship faster, usually within 12 to 20 months.

Industry analysts regard these pressures as structural rather than temporary, suggesting sustained capacity investment will be required before conditions ease

Buyers who plan early, secure factory slots in advance, and standardize technical specifications appear better equipped to manage prolonged delays.

Diversifying supplier relationships beyond congested manufacturers may offer flexibility as global demand continues to outpace available production capacity worldwide.

Via Evernew Electrical (originally in Swedish)

• Xiaomi reveals its first extended-range electric vehicle
• SkyNomad will sit separately from its pure EV Xiaomi Auto company
• The 1.5-liter engine is manufactured by Changan's subsidiary Harbin Dongang

Xiaomi is set to enter the hotly contested luxury SUV sector with an all-new business that it has dubbed SkyNomad.

Fresh off the success of both the SU7 and YU7, the former of which has outsold the Tesla Model 3 in the Chinese market, smartphone-maker Xiaomi sees a gap in the market for its first extended-range electric vehicle (EREV), which sees a gasoline engine act as a generator to charge battery packs on the move.

While the powertrain is still in its infancy in Europe, with just the Leapmotor C10 REEV and Mazda MX-30 R-EV currently on sale in many markets, it has experienced sales success in much of China.

Li Auto is the current market leader, with six models offering a mix of combustion engines and battery packs, while AITO, Deepal, Avatar and Leapmotor also offer similar solutions.

Unlike traditional plug-in hybrids, which use a gas engine to drive the wheels or charge the batteries, EREVs rely solely on a fully electric powertrain for propulsion, with the combustion engine serving as a generator to charge the batteries.

According to Car News China, Xiaomi's SkyNomad brand will offer the N70 and N90, the latter coming as a full-size, three-row SUV with rotating front seats, a full leather premium interior, and an N90 Max Camping Edition that adds a pop-up roof and a built-in side awning for upmarket camping trips.

SkyNomad is also selling the idea of modularity, stating in its promotional material that the cabin can transform into a studio for one, a cafe for two, a meeting room for three, or a play area for the whole family.

Under the skin, a 1.5-liter gasoline engine from Changan's subsidiary, Harbin Dongang, sends power to a 76 kWh ternary NMC battery pack in the N90, while a pair of electric motors team up to deliver 310 kW (416 hp) of power.

Xiaomi's decision to launch an EREV-focused brand, SkyNomad, is a clear shot at market leader Li Auto, which is experiencing a 74% year-over-year sales decline in the first four months of 2026, according to Electrek.

The introduction of EREVs to Xiaomi's stable will undoubtedly help it boost sales in China, but it's difficult to get away from the fact that the N90 is absolutely enormous. It measures over five meters in length and weighs 3,361 kg, which makes the 416 hp feel slightly underpowered.

Car News China says the N90 can manage around 230 miles before the batteries are depleted, by which point the 1.5-liter engine is called upon. Overall range is in excess of 1,500 km — or around 930 miles.

It's also interesting that Xiaomi, a company that found great success with pure EVs, is pivoting back to fossil fuels.

All of the PR coming out of China suggests that its public EV charging network is both faster and more widespread than most other markets, which raises the question of why the market needs big, heavy range extenders like this in the first place.

Somewhere right now, a customer is repeating themselves. They are explaining their problem for the third time, to the third person, because the organization on the other side has no shared memory of the previous two conversations. It is an infrastructure problem that AI is making harder to ignore.

It is also becoming impossible for policymakers to ignore. Just in April, the Mayor of London launched a new AI and Jobs Taskforce to examine how AI is changing work across the capital, signaling that the conversation has moved well beyond investment announcements and into the harder question of what AI does inside organizations.

It is also shining a spotlight on a memory crisis inside modern business.

As UK organizations rush to deploy AI in the workplace, many are layering it onto fragmented systems that were never designed to preserve institutional memory in the first place.

According to research published in Harvard Business Review, knowledge workers toggle between applications and tools roughly 1,200 times per day, a pattern known as "toggling tax". That figure alone tells the story: we aren’t short of tools, but there is no coherence among them.

The result is a new kind of productivity paradox. Work is moving faster, but clarity is not improving.

This is where much of the current enterprise AI conversation unravels. A surprising amount of what is marketed as AI today still relies on humans to do the synthesis work themselves. The system retrieves documents. It summarizes conversations. It surfaces links. But employees still carry the burden of reconstructing meaning, and so do the customers and end-users waiting on the other side of those decisions.

Notably, when these types of AI tools do the retrieval, but humans skip the synthesis, the output feels hollow. That creates a trust and credibility problem - not just for the individual, but for AI as a category. People start associating "AI-assisted" with "low-effort".

When context is lost internally, the effects aren't invisible. They surface as slow responses, repeated requests for information that customers already provided, support experiences that feel fragmented, and sales teams reconstructing account history manually before every renewal, escalation or executive review.

The AI models themselves are becoming more capable, but the organizational foundation beneath them remains fragmented.

Most AI systems today are fundamentally stateless. They generate outputs based on temporary context windows rather than durable organizational memory. Every interaction requires the system to repeatedly reconstruct understanding from fragments.

Consider how databases work. We do not recompute everything from scratch every time a query arrives. We cache and index, then preserve relationships between entities, because continuously recomputing context is computationally irrational.

Yet much of enterprise AI is still being deployed exactly this way and the industry has started mistaking activity for intelligence.

What I believe organizations should focus on is whether they have structured, durable memory that lets AI and humans reason from the same shared context. Without that foundation, AI outputs remain generic.

Most collaboration systems multiply this problem in two ways. First, they encode knowledge into naming conventions and tribal memory – the kind that lives in channel names nobody can decode and folder structures only three people understand. New employees are not learning the business, they are learning the conventions.

Second, even when information exists, it remains inaccessible. The same decision appears as "PostgreSQL migration", "database move Q3", and "backend infrastructure change" across three different channels. They are semantically identical but textually invisible to any system trying to surface it.

This problem becomes even more acute in distributed organizations. I don’t believe you can build modern global companies on a "you had to be there" culture. Yet many businesses still operate as though important context naturally transfers through proximity and synchronous communication.

Search was designed to discover information, whereas modern enterprise work requires systems that understand the relationships in data.

A customer escalation is not just a support ticket. It is connected to product decisions, engineering discussions, account history, contractual obligations, and revenue impact. A sales opportunity is tied to customer sentiment, historical support patterns, product usage, and internal stakeholder alignment.

Traditional collaboration systems flatten these relationships into disconnected channels and documents, whereas AI knowledge graphs preserve them.

Researchers call this a transactive memory system: the collective understanding of who knows what, how decisions were made, and how work is coordinated across teams. The same logic now extends to AI. Intelligent systems can participate in that process too by encoding context, surfacing relevant history, and routing knowledge to the right people at the right time.

The Office for National Statistics has consistently flagged weak productivity growth as one of the UK's most persistent economic challenges. Since 2010, UK productivity has grown at 6.2%, compared with roughly 10% across the euro area and nearly 15% in the United States over the same period. AI is increasingly being positioned as a mechanism to help close that gap.

But productivity does not improve because your business has added more AI agents to the workflow. If every important decision still requires humans to manually reconstruct fragmented context, organizations just accelerate confusion.

What UK businesses need are systems capable of preserving context, maintaining institutional memory, and grounding AI systems in trusted organizational knowledge. Better AI infrastructure starts with a simple question: Does your organization remember anything? For most, the honest answer is no.

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