While the AI energy bottleneck has been linked solely to energy companies and AI data center operators, today it affects businesses of all sizes and sectors that use AI and advanced compute.

Whether you are seeing your cloud costs escalate, trying to connect new projects to the grid and facing delays, worried about the AI-energy cost and relationship, or exploring alternative ways to get your project online, in this report, executives and leaders Hut8, Critical Loop, and Runware answer three simple questions.

How does the AI energy bottleneck affect you, which industries are the most impacted, and how businesses of all sizes are bypassing grid delays and energy queues.

The AI Energy Bottleneck Affects All Business Using AI

In the U.S., as well as in other regions, energy is holding back AI development. As AI companies scramble to build new data centers for the next generation of AI and compute, energy grids are struggling to keep up. New energy projects must go through a lengthy process to get grid connection approval, and the queues and waiting times are doubling.

The Queued Up: 2026 Edition of Berkeley Lab’s Lawrence Berkeley National Laboratory found that new energy projects that amount to roughly 2,061 gigawatts (GW) of new capacity in the U.S. are actively seeking interconnection. On average, energy projects built in 2025 took 61 months to go from interconnection requests to commercial operations. Waiting times for new energy to come online are increasing; in 2015 it took operators almost half that time to get online (36 months). Numerous energy projects end up withdrawing entirely from the process.

“Completion rates are generally low; wait times remain long,” the Berkeley report said.

The AI energy problem is no longer solely a problem for AI companies building data centers and energy operators trying to meet demands. It impacts all businesses that use AI in some way.

“Companies that don’t run their own data centers think they’ve outsourced the energy problem,” Bill Tai, member of ATLAS and founding chairman of Hut8 (NASDAQ: HUT), the energy infrastructure and compute platform, told me.

“They’ve only moved it upstream where they can’t see it,” said Tai.

While every cloud provider and AI vendor competes to access energy capacity in the same slow-moving, monopoly-controlled grid, when a provider can’t get new generation interconnected for years, that cost surfaces later as higher compute pricing, rationed access, or delayed product rollout, Tai explained,

“That company never touches a power line, but it inherits the consequences of a system with no competitive pressure to move faster,” said Tai.

Ioana Hreninciuc, co-founder at Runware , a company that recently announced Sonic Inference Pod, a shipping-container-sized data center that requires no grid interconnection, agreed.

“They (businesses) pay too much for it, in two ways,” Hreninciuc told me.

First, they pay for the amortization of large data centers designed for traditional servers, which are inefficient for AI due to legacy GPU and cooling systems, and second, they pay a premium because data center capacity has become scarce.

The Sectors Impacted the Most by the AI Energy Bottleneck

While the AI energy bottleneck affects all companies, some sectors are more impacted by it than others.

“The reality is that the power grid crisis impacts any capital-intensive sector where growth depends on immediate access to heavy electricity,” Bala Ramamurthy, CEO of Critical Loop , a California-headquartered energy technology company that provides rapid, behind-the-meter (BTM) power solutions, on-site energy storage, and flexible microgrid deployment, told me.

However, some sectors feel it is worse. These include data centers, advanced manufacturing plants, EV fleet operators, critical transportation infrastructure such as airports or seaports, and commercial real estate developers who are leasing large industrial properties, said Ramamurthy.

On the other hand, Hreninciuc from Runware told me that SaaS, services, and other knowledge-based sectors are the most exposed.

Companies that integrate AI into a product for millions of users, for example to generate text or videos, are going to pay a premium on every user, said Hreninciuc, adding that any business using AI online is affected, including small businesses.

How Businesses Are Bypassing the AI Energy Bottleneck and Costly Queues?

Frustrated by bureaucratic gridlock, well-funded enterprises are going around the public grid and building their own supply, businesses are defecting from the energy system altogether, Tai from Hut8 told me.

“If you generate power on site, behind the meter, you never file an interconnection application, and you never enter the queue,” said Tai.

Companies that are providing off-grid, behind-the-meter capacity, like Caterpillar, whose gas turbines are going to facilities that can’t wait for public transmission lines, are taking big wins.

“Individual corporations can build their own silos, but we cannot route the entire nation’s energy needs around the public wires,” said Tai. Under the status quo, the centralized grid remains a physical chokepoint that must be fundamentally upgraded, not just temporarily bypassed, he added.

Another option for smaller operations is modular data centers. Hreninciuc from Runware said that companies bypassing the AI energy bottleneck are either spending more on fixing infrastructure or choosing to work with new alternative energy providers that offer alternative energy infrastructure such as modular data centers.

Balancing the AI-Energy Equation Cost-Efficiently while Meeting Safety and Compliance Standards

Getting alternative energy up and running is only part of the equation. Optimizing that energy with a layer of intelligent energy optimization software and meeting compliance and safety standards are a must.

“Businesses are combining existing grid capacity with behind-the-meter battery storage, smart generation, and real-time software controls,” Ramamurthy from Critical Loop said.

This enables facilities to pull maximum available grid power during unconstrained hours and dynamically switch to on-site reserves during peak times, bringing critical operations online in months rather than years, said Ramamurthy.

At the software layer, executives need automated tools to dynamically align compute-heavy workloads with fluctuating hourly power rates, said Ramamurthy.

For example, using behind-the-meter battery storage or local generation during peak windows actively shaves expensive demand charges without sacrificing performance. At the same time, bridging assets should be modular and flexible, Ramamurthy said.

“Companies can get the full power they need without procuring and operating a permanent power plant for what may be an 18-month problem, then cleanly exit those systems when grid upgrades arrive,” said Ramamurthy

Alternatively, companies can choose to keep those assets in place and use them for ongoing peak shaving and grid flexibility. This approach helps companies avoid amortizing redundant permanent infrastructure while preserving optionality as their energy needs evolve.

“From a compliance standpoint, these systems meet strict grid safety and interoperability standards, such as UL 3141 for power control, and undergo rigorous testing to ensure software enforces hourly grid capacity limits in lock step with the utilities,” said Ramamurthy.

AI Energy Bottleneck: Final Thoughts

As the AI energy bottleneck spills beyond energy operators and big tech, and impacts today in some form all types of businesses using AI, new alternative energy services and infrastructure providers are cutting down waiting times and closing energy gaps.

Balancing the energy-AI equation using an energy optimization intelligent software layer is a fundamental part of the pipeline.

“Decision-makers must treat energy as a dynamic operational variable rather than a static utility bill,” said Ramamurthy.

“Cost-efficiently balancing the AI-energy equation requires moving beyond basic energy procurement to real-time, automated energy orchestration.”