The AI buildout has spent the past two years being described mostly as a problem for the electric grid: too much load, arriving too fast, landing in places the wires were never sized to serve. That framing is accurate as far as it goes. But at TechCrunch Disrupt, three people who work directly on the problem argued that the pressure is also doing something the industry has struggled to do on its own, which is create a reason to modernize.
Apoorv Bhargava, founder and CEO of WeaveGrid, Tim Heidel, CEO of Veir, and Kristin Eberhard, VP of policy at Rewiring America, all acknowledged the strain. They also pointed out that the scale of investment in AI infrastructure is pulling technologies off the shelf and into deployment at a pace that ordinary utility planning cycles rarely allow. That is the upside worth taking seriously: not that data centers are harmless, but that scarcity has become a forcing function.
The concern is not invented. Data center connections have become a real source of tension for grid operators, with warnings about higher bills in the near term and reliability risk further out. What the Disrupt panel added was a second half to that sentence. "The scale of the investment in AI infrastructure is driving a tremendous amount of innovation," Heidel said. "There's a ton of new technologies that are coming off the shelves and starting to get to scale that, pulled together, can actually really make sure that data centers are the best community neighbors and citizens they can possibly be."
Why AI load stresses the grid differently than ordinary demand growth
Electricity demand in the United States was roughly flat for years, which let utilities plan around slow, predictable growth. Data centers change the shape of the problem in three ways.
First, the requests are large and arrive in clusters. A single campus can ask for hundreds of megawatts, and multiple campuses often converge on the same corridor because they are chasing fiber routes, land, tax incentives and each other. Second, the load is spiky. Training workloads and inference traffic do not draw power in a smooth band; they swing. Third, and most importantly for the engineering, the binding constraint is frequently local. A region can have plenty of generation and still have a transformer, feeder or substation that cannot take another megawatt without an upgrade.
That last point is what makes the current wave different from previous load growth. Traditional planning tools and demand management programs were not built for bottom-up, asset-driven risk. Utilities often have no clear visibility into localized stress until assets are already overloaded.
"It's a very different electric grid, right?" Bhargava said. "We're dealing with a ton of new variables, including renewable energy. We're dealing with a ton of new load in the form of electric vehicles and data centers and heat pumps, and the ability to manage and orchestrate is fundamentally what utilities are now grappling with."
The bottleneck is often not generation
The mismatch that creates room for innovation is one of timelines. Grid infrastructure moves slowly. Transmission projects take years to site, permit and build, and distribution upgrades are gated by equipment lead times, crews and capital budgets. AI infrastructure moves on a different clock. When a hyperscaler wants a campus energized in a fraction of the time a new line takes, the practical answer stops being "build more" and starts being "use what is already there more intelligently."
That is the fork in the road the industry is now standing at. One path is to expand and densify the physical grid. The other is to orchestrate demand so existing assets are not overwhelmed. The interesting thing about the current moment is that both paths are being funded at once, by different companies with different technical bets.
Two paths, two kinds of innovation
WeaveGrid and Veir sit on opposite sides of that fork. One sells software that changes when and where power is drawn. The other sells hardware that changes how much power a given piece of real estate can carry.
| Dimension | Load orchestration (WeaveGrid) | Power delivery hardware (Veir) |
|---|---|---|
| Primary lever | Timing and location of flexible demand | Physics of the conductor |
| Unit of action | Device-level signals to EVs, batteries, thermostats | Superconducting cable and cryogenic cooling systems |
| What it avoids | New distribution capacity for peak hours | Copper-intensive, space-hungry power runs |
| Time to value | Software deployment on existing assets | Manufacturing scale-up and commercial projects |
| Main limits | Enrollment, consent, measurement, interoperability | Cryogenic complexity, cost, early-stage supply chain |
Neither approach solves the capacity problem alone. But together they illustrate why the "data centers versus the grid" framing is too simple. The load is the reason both markets exist.
Software: seeing and shaping load at the asset level
WeaveGrid started in EV managed charging, which turned out to be a useful training ground. Electric vehicles are large, mobile, schedulable loads attached to cloud-connected devices, and orchestrating them at scale teaches you how flexibility actually behaves on a distribution system. In January 2026 the company extended that model beyond EVs with an expansion of DISCO, short for Distribution Integrated System Capacity Orchestration, to cover a broader set of flexible loads and distributed energy resources.
The mechanics matter. DISCO pulls in vehicle telematics and data from cloud-connected devices, then sends individualized charging and dispatch signals so that load shifts away from moments when a transformer, feeder or bulk system constraint would otherwise be breached. The utility gets continuous, hyper-local visibility into how much flexible capacity is available, and a single interface to forecast, manage and verify load shaping across hundreds of megawatts.
"DISCO creates a new orchestration layer for utilities, a single pane of glass to see and manage all flexible assets, unlocking more capacity from the grid infrastructure they already have," Bhargava said when the expansion was announced.
The commercial pitch is straightforward: serve more load on existing infrastructure, protect reliability, and defer or avoid costly distribution upgrades, which in turn helps keep bills down. That affordability argument is the one that resonates with regulators, because the alternative is a rate case for capacity that may only be needed a few hours a year.
The approach depends heavily on partnerships. WeaveGrid runs managed charging and time-of-use programs with utilities including Pacific Gas and Electric, Dominion Energy, DTE, Xcel Energy, Baltimore Gas and Electric, Alabama Power, Ameren Illinois and Portland General Electric, and integrates directly with automakers and device makers such as Toyota, Lexus, Rivian, Hyundai, Kia, ChargePoint, Wallbox, Emporia, SolarEdge, FranklinWH and ecobee. Enrollment friction is the quiet killer of demand flexibility programs, and direct integrations are how that friction gets removed.
The company also carries a standards and security story that utilities need before they will let software touch customer devices. It holds SunSpec CSIP certification for an IEEE 2030.5 Aggregator Client, and SOC 2 Type II across all five trust service criteria. Program expansions have moved through regulators, including a Maryland Public Service Commission decision extending BGE's Smart Charge Management offering.
The limits are worth stating plainly. DISCO is strongest as behind-the-meter load shaping; it is not a replacement for a distribution management system, a power-flow study suite or a network model of record. Its public developer surface is thin, with a partner API for aggregators but no public specification. Some drivers report friction with charger control and question savings that can overlap with self-managed time-of-use scheduling. And security evidence tends to be shared privately rather than published.
Hardware: superconductors and the limits of copper
Veir takes the other path. The Woburn, Massachusetts company, founded in 2019 and led by Tim Heidel, builds high-temperature superconducting power delivery systems. The architecture combines superconducting cables with a cryogenic cooling system, controls, termination units and monitoring. Superconductors carry large amounts of current with very low resistance when cooled to low temperatures, which means more power through a smaller footprint than copper-based systems allow.
The company has said its cables can carry power five times farther than traditional cables while requiring 20 times less space. In October 2026 it closed an oversubscribed $110 million Series C co-led by Matter Venture Partners and Tyche Partners, with participation from LG Technology Ventures, Gates Frontier, Sabanci Climate Ventures and Hui Capital, plus existing investors including Engine Ventures, Galvanize Climate Solutions, Piva Capital, Congruent Ventures and VXI Capital. Total capital raised reached $225 million.
The milestone behind that round was a demonstrated 3-megawatt superconducting power delivery system. Veir is now working with customers and industry partners on reference designs for AI campuses and preparing for initial commercial deployments, while building out manufacturing and supply chain capacity.
"AI infrastructure is advancing at a pace that requires us to rethink how power is delivered, not simply optimize the systems that exist today," Heidel said. "This financing gives us the resources to move from demonstrating the potential of superconducting power delivery to manufacturing and deploying systems at commercial scale."
The strategic detail is where Veir is starting. Its initial commercial focus is behind the meter at AI data centers, meaning power distribution inside a customer's own facility rather than across the wider utility grid. That is a deliberate sequencing choice. Behind-the-meter projects involve one sophisticated customer, a defined engineering scope and fewer of the siting and permitting hurdles that transmission projects face. It is also where the pain is most acute: as campuses get denser, the physical space and labor required to run copper becomes a real constraint on how fast compute can be energized.
"Power delivery to AI server racks is emerging as one of the critical constraints on how efficiently and densely AI compute can scale and cluster," said Wen Hsieh of Matter Venture Partners. Tyche Partners made the same point more bluntly, describing the physics of copper as a fundamental limitation.
The open questions are real. Cryogenic systems add operational complexity and maintenance requirements that conventional cable does not. Cost per meter at commercial scale is not yet publicly established. The company is still building the manufacturing and installation ecosystem needed for repeatable deployments. And because the first market is behind the meter, the near-term benefit to the wider grid is indirect, arriving through faster campus energization rather than through added transmission capacity for everyone else.
The improv era
Eberhard of Rewiring America framed the current period in a way that cuts through the usual either-or debate. Her organization advocates for household electrification, which means she spends her time on another source of new load: heat pumps, induction stoves and home EV charging.
"I feel like this era of AI and load growth is a very improv era," she said. "There's a lot of yes-and-ing going on. So yes, we need more transmission lines. Yes, we need more centralized power plants, and we also need to make sure that we're taking advantage of all this other stuff that we had previously been leaving on the table when we didn't have this urgency of the AI demand."
That is the most useful idea in the whole discussion. The urgency created by AI demand is not replacing the traditional grid agenda; it is adding to it. Transmission, generation, distributed flexibility, managed charging and higher-capacity conductors are complements, not substitutes. What changes under pressure is the willingness to run them in parallel and to accept less familiar technologies in the portfolio.
It also changes the politics. Flexibility programs that once struggled to get budget attention suddenly have a constituency. A utility that can defer a substation upgrade by shaping EV and battery charging has a story for its regulator. A data center operator that can energize racks faster with a smaller power footprint has a story for its board.
What has to go right
Optimism about grid innovation is only credible if the hard parts get solved. Several stand out.
Measurement and verification. Flexibility is only a capacity resource if its contribution can be measured and paid for. Utilities need defensible baselines, auditable reporting and settlement-grade data. WeaveGrid emphasizes reporting and verification as part of DISCO's value, and that is not a marketing detail; it is the difference between a pilot and a program.
Consumer consent and control. Orchestration only works at scale if customers trust it. Override capability, clear communication and predictable savings matter more than elegant optimization. Driver complaints about control friction and questioned savings claims are a warning about what happens when the software gets ahead of the user experience.
Interoperability. Utilities buy standards, not just products. IEEE 2030.5 and CSIP certification provide a common language for aggregator-to-utility links. Gaps remain, notably around other protocol coverage and wholesale market interfaces, and those gaps determine how far a single vendor's platform can reach.
Regulatory throughput. Programs have to be approved, and approvals take time. The Maryland decision extending BGE's managed charging offering shows the path works, but each jurisdiction moves at its own speed.
Community legitimacy. Heidel's phrase about data centers being the best possible community neighbors is the political test. Load growth that arrives without local benefit invites resistance that no amount of software can fix.
Speed of adoption. Heidel's closing argument was about tempo: "I think that we really need to look at ways that we can really adopt all of that as fast as possible to ease this next stage of growth." The technologies exist. The deployment machinery is the constraint.
What to watch next
For utility planners, the practical question is whether flexible capacity gets treated as a real asset in planning models rather than a demand-side afterthought. For data center operators, it is whether flexibility commitments become part of how a campus is designed, not an add-on negotiated at the end. For regulators, it is whether cost recovery rules reward deferring upgrades instead of rewarding steel in the ground.
The honest caveat is that none of this has been proven at the scale the AI buildout demands. Managed charging is well established; multi-resource orchestration across hundreds of megawatts is newer. Superconducting power delivery has a 3-megawatt demonstration and a manufacturing plan, not a track record of repeat installations. The innovation is real, but so is the distance between a promising deployment and a solved grid.
The more durable outcome may be structural. A grid that learns to see constraints at the transformer, to shift load with consent and precision, and to move power through smaller footprints will still be useful when the AI cycle cools. The data centers are the stress test. The modernization is the thing that stays.