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NVIDIA and Emerald AI Join Leading Energy Companies to Pioneer Flexible AI Factories as Grid Assets

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NVIDIA (NVDA) and Emerald AI announced a collaboration with AES, Constellation, Invenergy, NextEra Energy, Nscale Energy & Power and Vistra to design power‑flexible AI factories that accelerate interconnection, generate AI tokens, and operate as grid assets.

The plan uses the Vera Rubin DSX reference design and DSX Flex software; Emerald Conductor will orchestrate compute, onsite generation and storage. The companies say this approach can unlock up to 100 GW of flexible capacity and deploy DSX Flex at scale later this year at NVIDIA’s Virginia research center.

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Positive

  • Partnership with six major energy firms to accelerate AI factory deployment
  • DSX Flex deployment planned at NVIDIA AI Factory Research Center later in 2026
  • Up to 100 GW of potential flexible capacity across U.S. power system
  • Nscale onsite capacity scaling from 2 GW to 8 GW

Negative

  • Reliance on co‑located generation can leave assets underutilized and raise long‑term cost per AI token
  • Conventional grid interconnection timelines remain a constraint for rapid AI capacity build‑out

News Market Reaction – NVDA

+1.70%
1 alert
+1.70% News Effect

On the day this news was published, NVDA gained 1.70%, reflecting a mild positive market reaction.

Data tracked by StockTitan Argus on the day of publication.

Key Figures

Potential unlocked capacity: up to 100 gigawatts Onsite generation (initial): 2 gigawatts Onsite generation (target): 8 gigawatts +1 more
4 metrics
Potential unlocked capacity up to 100 gigawatts U.S. power system capacity from power-flexible AI factories
Onsite generation (initial) 2 gigawatts Nscale Monarch campus onsite generation capacity
Onsite generation (target) 8 gigawatts Nscale Monarch campus scaling plan
Data center trials 5 commercial data centers AI power flexibility demonstrations over last year

Market Reality Check

Price: $175.75 Vol: Volume 216,935,910 vs 20-...
normal vol
$175.75 Last Close
Volume Volume 216,935,910 vs 20-day average 200,360,298 suggests only modestly elevated trading ahead of this AI-grid collaboration headline. normal
Technical Shares at $172.93 are trading below the 200-day MA of $178.44, about 18.5% under the $212.19 52-week high and up 99.64% from the $86.62 52-week low.

Peers on Argus

While NVDA was down 3.28%, key peers also showed weakness: AVGO -3%, TSM -2.21%,...
1 Down

While NVDA was down 3.28%, key peers also showed weakness: AVGO -3%, TSM -2.21%, AMD -1.74%, MU -4.74%, NXPI -0.53%. Scanner momentum only flagged MU, so this looks more stock-specific than a confirmed, coordinated sector-momentum event.

Previous AI Reports

5 past events · Latest: Mar 16 (Positive)
Same Type Pattern 5 events
Date Event Sentiment Move Catalyst
Mar 16 AI ecommerce launch Positive -0.7% Partner launched NVIDIA-powered generative AI sizing and virtual try-on for fashion.
Mar 16 Robotics AI update Positive -0.7% New robotics software and models to scale physical AI across industrial fleets.
Mar 16 AI-RAN pilots Positive -0.7% Pilots with T-Mobile to deploy vision and reasoning AI on AI‑RAN edge infra.
Mar 16 Industrial AI deals Positive -0.7% Partnerships with major industrial software firms to enable GPU-accelerated workflows.
Mar 16 Data factory blueprint Positive -0.7% Launch of open Physical AI Data Factory Blueprint for large-scale training data.
Pattern Detected

Recent AI-tag announcements have been followed by modest negative moves, with an average -0.7% reaction despite generally expansionary AI initiatives.

Recent Company History

Over the past weeks, NVIDIA’s AI-tagged news has centered on broadening its AI ecosystem. On Mar 16, 2026, it highlighted robotics (“physical AI”), AI‑RAN pilots with T‑Mobile, industrial software partnerships, and an open Physical AI Data Factory Blueprint, plus a partner fashion-sizing launch. Each event saw a -0.7% 24‑hour move, suggesting a pattern where expansive AI announcements coincided with modest share pullbacks rather than positive knee‑jerk reactions.

Historical Comparison

-0.7% avg move · Recent AI-tagged NVIDIA news, including robotics and data factory blueprints, averaged a -0.7% next-...
AI
-0.7%
Average Historical Move AI

Recent AI-tagged NVIDIA news, including robotics and data factory blueprints, averaged a -0.7% next-day move, so AI ecosystem expansions have not recently produced strong positive price reactions.

AI-tag history shows NVIDIA moving from data factory blueprints and robotics to AI‑RAN and industrial AI, with this article extending the theme into energy infrastructure and grid-integrated AI factories.

Market Pulse Summary

This announcement highlights NVIDIA’s push to make AI factories function as flexible grid assets, us...
Analysis

This announcement highlights NVIDIA’s push to make AI factories function as flexible grid assets, using the Vera Rubin DSX reference design and Emerald AI’s orchestration to unlock up to 100 gigawatts of system capacity. Historically, AI-tagged news around data factories, robotics and AI‑RAN averaged a modest -0.7% move, suggesting tempered reactions. Investors may track the first commercial DSX Flex deployments, partner energy campus builds, and how quickly these projects reach large-scale grid interconnection.

Key Terms

ai factories, behind-the-meter, demand response, interconnection timelines, +1 more
5 terms
ai factories technical
"NVIDIA and Emerald AI today announced that they are working ... to power and advance a new class of AI factories"
AI factories are organized platforms and processes that turn raw data and computing power into finished AI products and services at scale — think of them as automated assembly lines for machine intelligence. For investors, they matter because they concentrate the tools, data and infrastructure that speed up development, lower unit costs and make it easier to roll out new AI features, which can translate into faster revenue growth or cost savings for companies that operate them.
behind-the-meter technical
"onsite generation, batteries and other behind-the-meter resources to deliver precise, grid-responsive power"
Equipment or systems located on a customer’s side of the electricity meter—such as rooftop solar panels, battery storage, electric vehicle chargers, or energy controls—that generate, store, or manage power for use on-site rather than being supplied through the utility’s grid. Investors care because behind-the-meter assets change how much power a customer buys, can create new revenue or savings streams, affect demand patterns, and shift regulatory or business models in the energy market, much like a homeowner installing their own water tank reduces municipal supply needs.
demand response technical
"They can also address the need for additional capacity through demand response."
Demand response is a program or market mechanism where electricity users are paid or incentivized to reduce or shift their power use when the grid is stressed or prices are high, similar to turning down nonessential appliances during a heat wave to ease a traffic jam. It matters to investors because it can lower peak energy costs, affect utility revenues and market prices, and create opportunities for companies that provide the software, equipment, or services that enable those load changes.
interconnection timelines technical
"Many gigawatt-scale AI projects are turning to co-located generation and storage because conventional interconnection timelines can be too slow"
Interconnection timelines are the planned schedule for when a new asset, such as a power plant, telecom node, or industrial facility, will be physically and legally linked to the public network it needs to operate. For investors, these dates matter because connection delays can postpone revenue, increase costs and change project valuation, much like a storefront that can’t open to customers until utilities are hooked up and inspected.
grid-connected technical
"support flexible AI factories that are grid-connected from the outset"
Grid-connected describes a power system, such as a solar array, battery or generator, that is physically linked to the public electricity network so it can send electricity to and draw electricity from that shared grid. For investors, grid-connected projects matter because they can sell excess power, buy backup power, and qualify for incentives or utility contracts, making revenue streams and operating risks more predictable—like a small business hooked into a city’s water and sewer system instead of relying on a private well.

AI-generated analysis. Not financial advice.

Collaboration Combines AI Factory Design, Energy Resources and Flexibility to Speed Time to Power and Support Grid Reliability

HOUSTON, March 23, 2026 (GLOBE NEWSWIRE) -- CERAWeek 2026 -- NVIDIA and Emerald AI today announced that they are working with AES, Constellation, Invenergy, NextEra Energy, Nscale Energy & Power and Vistra to power and advance a new class of AI factories that connect to the grid faster, generate valuable AI tokens and intelligence, and operate as flexible energy assets that can support the grid.

By bringing together technology, energy and infrastructure leaders, the collaboration demonstrates how companies across industries can convene to support AI innovation in the United States, while building a more reliable power system for Americans.

These next-generation AI factories will harness the new NVIDIA Vera Rubin DSX AI Factory reference design, which includes the DSX Flex software library for connecting AI factories to power-grid services.

For accelerated deployment, the factories can use co-located energy generation and storage as bridge power for hybrid AI factories, then later harness these resources to flexibly supply the grid, accelerate AI factory interconnection and support the broader power system. This approach helps bring AI capacity online faster while creating broader value for customers and communities.

The DSX reference architecture can also support flexible AI factories without co-located energy resources to achieve larger and faster power grid connections.

Emerald AI’s Conductor platform will orchestrate computational flexibility alongside onsite generation, batteries and other behind-the-meter resources to deliver precise, grid-responsive power flexibility while ensuring quality of service for AI compute tenants. This coordination helps operators meet power targets, protect priority workloads, shorten time on bridge power, and support larger and faster interconnections. It can also help reduce the need for infrastructure to be sized around peaks, easing pressure on future system costs.

“AI factories are the engines of the intelligence era, and like any great engine, every system must be designed together — energy, compute, networking and cooling as one architecture,” said Jensen Huang, founder and CEO of NVIDIA. “NVIDIA and Emerald AI are working together to enable a future for AI where performance, efficiency and grid responsiveness can be tapped into immediately.”

“AI factories are too valuable to be treated as either passive loads or permanent islands,” said Varun Sivaram, founder and CEO of Emerald AI. “They produce tremendously valuable AI tokens and knowledge, and with DSX Flex, they can also provide measurable relief back to the grid. Emerald Conductor orchestrates compute flexibility alongside onsite energy resources to support the grid, so projects can connect sooner, preserve quality of service for AI tenants and ultimately strengthen the power system around them.”

Building AI Factories That Strengthen the Grid
Today’s electric systems are built to serve peak demand but are underutilized during most hours of the day. Power-flexible AI factories can help unlock up to 100 gigawatts of capacity across the U.S. power system by combining optimized infrastructure design with efficient use of existing assets and, where needed, new-build generation, while flexing during limited periods of grid stress to reduce the need for broader grid expansion to support reliability.

AI factories convert electricity into AI tokens, models and intelligence — among the highest-value outputs modern infrastructure can produce. Meeting that opportunity will require innovation in computing as well as in how companies plan, build and operate energy infrastructure.

Many gigawatt-scale AI projects are turning to co-located generation and storage because conventional interconnection timelines can be too slow for the pace of AI investment. However, permanently isolating generation and storage from the grid has drawbacks. It can leave assets underutilized, raise long-term cost per AI token and prevent energy resources from supporting grid reliability.

AES, Constellation, Invenergy, NextEra Energy, Nscale Energy & Power and Vistra are committed to building the energy generation capabilities necessary to ensure supply meets surging demand.

The companies will collaborate to evaluate optimized generation applications designed to power the AI factories built with the architecture developed by NVIDIA and Emerald AI, including through hybrid projects that use co-located power, to speed time to power and create value for the broader grid. By pairing large AI loads with flexible operations, new energy generation capabilities and intelligent controls, this approach can help boost grid reliability.

The companies can also support flexible AI factories that are grid-connected from the outset, using co-located energy resources if available.

“Grid flexibility will be key to addressing AI’s unprecedented demand while supporting system reliability,” said Andrés Gluski, CEO of AES. “At AES, we are enabling next-generation AI infrastructure to accelerate our clients’ time to power. DSX Flex embeds flexibility from the outset, allowing AI infrastructure to operate as a grid asset that supports faster, more efficient growth.”

“As the largest producer of clean energy in the U.S., we know data centers have enormous potential to unlock energy infrastructure investment, job creation and benefits for our communities,” said Joe Dominguez, president and CEO of Constellation. “They can also address the need for additional capacity through demand response. We don’t have a supply problem — we have a peak problem. By effectively using what we already have, including power-flexible AI factories that also enable AI-powered demand response, we can accommodate new load growth more efficiently.”

“AI is changing how we’re thinking about energy, and our customers need power fast, with the ability to scale over time,” said Michael Polsky, founder and CEO of Invenergy. “Combining near-term generation solutions with a path to full grid connection and flexible operations is an innovative and efficient way to help our customers meet their energy needs faster while keeping the system reliable.”

“To meet unprecedented new electricity demand while maintaining a reliable and resilient grid, now more than ever, we need to add generation resources,” said John Ketchum, chairman, president and CEO of NextEra Energy. “We also need technologies that allow new demand and related generation to integrate into the grid quickly and at the lowest possible cost. NextEra looks forward to working with NVIDIA and Emerald AI to help design efficient energy campuses and flexible AI factories that economically support rising demand while further strengthening America’s energy infrastructure.”

“We are committed to stabilizing the grid and helping West Virginia families and businesses have ready access to the power they need,” said Daniel Shapiro, chief power and energy officer of Nscale Energy & Power. “When we’re interconnected, we’ll be there on the grid’s highest-demand days to supply electricity back — that’s what 2 gigawatts scaling to 8 gigawatts of onsite generation means. Nscale’s Monarch campus is a power asset for West Virginia, not a load on it.”

“U.S. grids are designed to handle the highest-peak demand scenarios, which make up very few hours during the year,” said Jim Burke, president and CEO of Vistra. “AI factories that have the flexibility to adjust their power use with grid conditions are a faster solution, especially with co-located generation, for better utilization of the current grid infrastructure. This helps boost speed while we continue to build out more infrastructure for the long term.”

Over the last year, Emerald AI and NVIDIA trialed AI power flexibility demonstrations at five commercial data centers around the world. DSX Flex is expected to be deployed at commercial scale later this year at the NVIDIA AI Factory Research Center in Virginia, planned as one of the world’s first power-flexible AI factories with NVIDIA Vera Rubin infrastructure.

The companies intend to identify and advance project opportunities built using the Vera Rubin DSX reference design with DSX Flex to accelerate large-scale AI infrastructure deployment, support larger and faster grid interconnections, unlock technology pathways for new generation builds, expand the economic benefits of AI and energy investment for local communities, strengthen U.S. energy leadership and enable broader AI deployment over time.

About Emerald AI
Emerald AI is the pioneer in AI-driven data center flexibility management, transforming energy-intensive data centers into intelligent grid assets.

About NVIDIA
NVIDIA (NASDAQ: NVDA) is the world leader in AI and accelerated computing.

For further information, contact:
Olivia Wright
Corporate Communications
NVIDIA Corporation
press@nvidia.com

Rob Bartnichak
Corporate Communications
Emerald AI, Inc.
press@emeraldai.co

NVIDIA Forward-Looking Statements

Certain statements in this press release including, but not limited to, statements as to: NVIDIA and Emerald AI working together to enable a future for AI where performance, efficiency and grid responsiveness can be tapped into immediately; the benefits, impact, performance, and availability of NVIDIA’s products, services, and technologies; expectations with respect to NVIDIA’s third party arrangements, including with its collaborators and partners; expectations with respect to technology developments; expectations with respect to AI and related industries; and other statements that are not historical facts are forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended, which are subject to the “safe harbor” created by those sections based on management’s beliefs and assumptions and on information currently available to management and are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic and political conditions; NVIDIA’s reliance on third parties to manufacture, assemble, package and test NVIDIA’s products; the impact of technological development and competition; development of new products and technologies or enhancements to NVIDIA’s existing product and technologies; market acceptance of NVIDIA’s products or NVIDIA’s partners’ products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of NVIDIA’s products or technologies when integrated into systems; NVIDIA’s ability to realize the potential benefits of business investments or acquisitions; and changes in applicable laws and regulations, as well as other factors detailed from time to time in the most recent reports NVIDIA files with the Securities and Exchange Commission, or SEC, including, but not limited to, its annual report on Form 10-K and quarterly reports on Form 10-Q. Copies of reports filed with the SEC are posted on the company’s website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances.

Many of the products and features described herein remain in various stages and will be offered on a when-and-if-available basis. The statements above are not intended to be, and should not be interpreted as a commitment, promise, or legal obligation, and the development, release, and timing of any features or functionalities described for our products is subject to change and remains at the sole discretion of NVIDIA. NVIDIA will have no liability for failure to deliver or delay in the delivery of any of the products, features or functions set forth herein.

© 2026 NVIDIA Corporation. All rights reserved. NVIDIA and the NVIDIA logo are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Features, pricing, availability and specifications are subject to change without notice.

A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/82affc8b-5f63-4c89-83e5-113095d62753


FAQ

What did NVIDIA and Emerald AI announce on March 23, 2026 about NVDA AI factories?

They announced a collaboration to build power‑flexible AI factories that speed interconnection and act as grid assets. According to the company, the design uses the Vera Rubin DSX reference architecture and DSX Flex software to coordinate compute with onsite energy.

How much grid capacity could NVDA’s DSX Flex approach unlock in the U.S.?

The companies say it could unlock up to 100 gigawatts of flexible capacity across the U.S. power system. According to the company, this combines optimized design, co‑located generation, storage and flexible operations to increase usable capacity.

When will DSX Flex be deployed at NVIDIA’s research site for NVDA?

DSX Flex is expected to be deployed at commercial scale later in 2026 at NVIDIA’s Virginia AI Factory Research Center. According to the company, the site is planned as one of the first power‑flexible AI factories using Vera Rubin DSX.

Which energy companies are partnering with NVDA and Emerald AI on grid‑responsive AI factories?

AES, Constellation, Invenergy, NextEra Energy, Nscale Energy & Power and Vistra are collaborating on the initiative. According to the company, these partners will evaluate generation and hybrid projects to speed time to power and support reliability.

What role does Emerald Conductor play in NVDA’s AI factory strategy?

Emerald Conductor will orchestrate compute flexibility alongside onsite generation and batteries to deliver grid‑responsive power while protecting AI service quality. According to the company, this helps meet power targets and shorten time on bridge power.

What are the deployment trade‑offs NVDA highlights for co‑located generation?

Co‑located generation can speed time to power but may leave assets underutilized and raise long‑term cost per AI token. According to the company, pairing flexible operations with grid connection aims to preserve value and support reliability.
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