Nvidia-backed ThinkLabs AI raises $28 million to tackle a growing power grid crunch

ThinkLabs AI, a startup building artificial intelligence models that simulate the behavior of the electric grid, announced today that it has closed a $28 million Series A financing round led by Energy Impact Partners (EIP), one of the largest energy transition investment firms in the world. Nvidia’s venture capital arm NVentures and Edison International, the parent company of Southern California Edison, also participated in the round.

The funding marks a significant escalation in the race to apply AI not just to software and content generation, but to the physical infrastructure that powers modern life. While most AI investment headlines have centered on large language models and generative tools, ThinkLabs is pursuing a different and arguably more consequential application: using physics-informed AI to model the behavior of electrical grids in real time, compressing engineering studies that once took weeks or months into minutes.

"We are dead focused on the grid," ThinkLabs CEO Josh Wong told VentureBeat in an exclusive interview ahead of the announcement. "We do AI models to model the grid, specifically transmission and distribution power flow related modeling. We can calculate things like interconnection of large loads — like data centers or electric vehicle charging — and understand the impact they have on the grid."

The round drew participation from a deep bench of returning investors, including GE Vernova, Powerhouse Ventures, Active Impact Investments, Blackhorn Ventures, and Amplify Capital, along with an unnamed large North American investor-owned utility. The company initially set out to raise less than $28 million, according to Wong, but strong demand from strategic partners pushed the round higher.

"This was way oversubscribed," Wong said. "We attracted the right ecosystem partners and the right capital partners to grow with, and that's how we ended up at $28 million."

Why surging electricity demand is breaking the grid's legacy planning tools

The timing of the raise is no coincidence. U.S. electricity demand is projected to grow 25% by 2030, according to consultancy ICF International, driven largely by AI data centers, electrified transportation, and the broader push toward building and vehicle electrification. That surge is crashing into a grid that was engineered decades ago for a fundamentally different set of demands — and utilities are scrambling to keep up.

The core problem is one of computational capacity. When a utility needs to understand what will happen to its grid if a large data center connects to a particular substation, or if a cluster of EV chargers goes live in a residential neighborhood, engineers must run power flow simulations — complex calculations that model how electricity moves through the network. Those studies have traditionally relied on legacy software tools from companies like Siemens, GE, and Schneider Electric, and they can take weeks or months to complete for a single scenario.

ThinkLabs' approach replaces that bottleneck with physics-informed AI models that learn from the same engineering simulators but can then run orders of magnitude faster. According to the company, its platform can compress a month-long grid study into under three minutes and run 10 million scenarios in 10 minutes, while maintaining greater than 99.7% accuracy on grid power flow calculations.

Wong draws a sharp distinction between what ThinkLabs does and the generative AI models that dominate public discourse. "We're not hallucinating the heck out of things," he said. "We are talking about engineering calculations here. I would really compare this to a computation of fluid dynamics, or like F1 cars, or aerospace, or climate models. We do have a source of truth from existing physics-based engineering models."

That source of truth is crucial. ThinkLabs trains its AI on the outputs of first-principles physics simulators — the same tools utilities already trust — and then validates its models against those simulators. The result, Wong argues, is an AI system that is not only fast but fully explainable and auditable, a critical requirement in an industry where a miscalculation can cause blackouts or damage physical infrastructure.

How ThinkLabs' three-phase power flow analysis differs from every other grid AI startup

The competitive landscape for AI in grid management has grown crowded over the past two years, with startups and incumbents alike racing to apply machine learning to utility workflows. But Wong contends that ThinkLabs occupies a fundamentally different position from most of its competitors.

"As far as we know, we're the only ones actually doing AI-native grid simulation analysis," he said. "Others might be using AI for forecasting, load disaggregation, or local energy management, but fundamentally, they're not calculating a power flow."

What ThinkLabs performs is a full three-phase AC power flow analysis — examining every node and bus on the electric grid to determine real and reactive power levels, line flows, and voltages. This is the same type of analysis that utility engineers perform today using legacy tools, but ThinkLabs can deliver it at a speed and scale that those tools simply cannot match.

The distinction matters because utilities make capital investment decisions — worth billions of dollars — based on exactly these types of studies. If a power flow analysis shows that a proposed data center connection will overload a transmission line, the utility may need to build new infrastructure at enormous cost. But if the analysis can also suggest alternative solutions — battery storage placement, load flexibility scheduling, or topology optimization — the utility can potentially avoid or defer those capital expenditures.

"With many utilities, existing tools will basically show them all the problems, but they can only address solutions by trial and error," Wong explained. "With AI, we can use reinforcement learning to generate more creative solutions, but also very effectively weigh the pros and cons of each of these solutions."

Inside ThinkLabs' strategic relationships with NVIDIA, Edison, and Microsoft

The presence of NVentures in the round — Nvidia’s venture arm does not write many checks — signals a deeper strategic relationship that extends well beyond capital. Wong confirmed that ThinkLabs works extensively within the Nvidia ecosystem on the energy and utility side, leveraging CUDA for GPU-accelerated computation and integrating Nvidia’s Earth-2 climate simulation platform into ThinkLabs' probabilistic forecasting and risk-adjusted analysis pipelines.

"We are what one utility mentioned as the only high-intensity GPU workload for the OT side — the operational technology side — that's planning and operations," Wong said. He added that ThinkLabs is also in discussions with Nvidia’s Omniverse team about additional utility use cases, though those efforts are still early.

Edison International's participation carries a different kind of strategic weight. In January 2026, ThinkLabs publicly announced results from a collaboration with Southern California Edison (SCE), Edison International's utility subsidiary, that demonstrated the real-world capabilities of its platform. As the Los Angeles Times reported at the time, the collaboration showed that ThinkLabs' AI could train in minutes per circuit, process a full year of hourly power-flow data in under three minutes across more than 100 circuits, and produce engineering reports with bridging-solution recommendations in under 90 seconds — work that previously required dedicated engineers an average of 30 to 35 days.

In today's announcement, Edison International's Sergej Mahnovski, Managing Director of Strategy, Technology and Innovation, reinforced that urgency: "We must rapidly transition from legacy planning tools and processes to meet the growing demands on the electric grid — new AI-native solutions are needed to transform our capabilities."