ADVERTISEABOUT USSUBMIT A STORYSUBSCRIBETERMS OF USEPRIVACY POLICY
Visit our family of Brands

Energy AI startup chooses Houston for first U.S. office after $20M raise

welcome to houston

The company is known for its Orbital AI platform, which is tailored for the energy sector. Photo via solutions.kbr.com

London-based AI firm Applied Computing has announced a $20 million Series A round and a new office in Houston.

The new Bayou City office is Applied Computing’s first in the United States and part of its North American expansion. The company is known for its Orbital AI platform, which is tailored for energy operations.

The funding round was led by Houston-based KBR Inc., with participation from San Francisco-based Databricks Ventures. KBR’s investment was first announced in March.

KBR and Applied Computing have also entered into a multi-year agreement to deliver exclusive AI products for the energy sector. KBR already has integrated Orbital into its INSITE 3.0 platform for energy projects, and is also using the product for ammonia production.

Applied Computing’s Orbital platform combines physics-grounded intelligence with models across chemical engineering, time-series forecasting and language, according to the company. The system analyzes sensor readings and can recognize a facility’s equipment constraints and operator activity. The platform can also allow technicians to run simulations of how a change to a facility could affect the rest of its operations.

According to TechCrunch, Applied Computing will use the $20 million to further explore projects and deployments with the energy sector, hire engineering and research positions, and continue to expand internationally, potentially into the Middle East.

The company is also working on deals with a major U.S. stream operator, TechCrunch reports. And Applied Computing shared on LinkedIn that it plans to announce its first partnership with a major European oil company in the coming weeks.

“Yesterday we showed Orbital live in deployments at our demo day at the Energy Institute in London,” Callum Adamson, CEO and co-founder of Applied Computing, posted on LinkedIn on July 16. “Today, we're announcing the capital to scale it globally as well as the launch of our new offices in Houston and Bangalore. In the weeks following, there will be more announcements on our progress, partnerships and deployments.”

The company opened its Bangalore offices in December.

----

This article originally appeared on our sister site, InnovationMap.com.

From Your Site Articles
Scotty Nyquist discuss the growth in AI data centers and the strain on the system. Photo via HARC report

Houston energy expert asks: Who pays when AI outruns the power grid?

Guets Column

For most of the past 20 years, U.S. electricity policy relied on predictable trends in demand. Electricity use, in most regions, increased gradually, forecasts were stable, and utilities adjusted the system in small steps. Power plants, transmission lines, and substations were generally added to reflect shifts in load, rather than growth, and costs were recovered through modest adjustments to customer bills.

Growth in AI data centers has disrupted this model. A single facility can add as much electricity demand as a small town. That demand comes all at once, runs continuously, and has little tolerance for outages. If electricity service drops even briefly, computation stops, and services shut down. Ironically, data centers need reliable service, a point that their emergence is driving concern around for the rest of the grid.

What the numbers say

The International Energy Agency projects global electricity consumption from data centers to double by 2030, reaching roughly 945 TWh, nearly 3 percent of global electricity demand, with consumption growing about 15 percent per year this decade. McKinsey projects that U.S. data center demand alone could grow 20–25 percent per year, with global capacity demand more than tripling by 2030.

After years of roughly 0.5 percent annual demand growth, many forecasts now place total U.S. electricity demand growth closer to 2–3 percent per year through the mid-2030s, with much higher growth in specific regions. In Texas, some forecasters are saying electricity demand could double over the next five years, a staggering 10 percent per year growth rate. What sounds incremental on paper translates into a major challenge on the ground. Meeting this pace of growth is estimated to require $250–$300 billion per year in grid investment, about double what the system has been absorbing.

Where the system starts to strain

The strain appears first in the interconnection queue. It shows up as long waits, backlogs, and delays for connecting new loads and new generation.

Before new generators or large load customers can be connected, a study is required to assess their impact on the grid, whether it can physically handle the added load, and whether upgrades are required. With AI-driven data centers, utilities face far more connection requests than they can realistically support. In ERCOT, large-load interconnection requests exceed 200 gigawatts, most tied to data centers. That amount exceeds historical norms, and it is several times larger than what can be practically studied or built in the near term.

To be clear, public utility commissions are required to study these requests because they must manage system capabilities to ensure minimal disruption. This means engineers spend time evaluating projects that may never be built, while other more commercially viable projects may wait longer for approvals. This extends timelines and makes infrastructure planning less reliable.

Why policymakers are rethinking the rules

Utilities and their regulators must decide how much generation, transmission, and substation capacity to build years before it comes online. Those decisions are based on expected demand at the time projects are approved. When it comes to data centers, by the time infrastructure is completed, they may end up deploying newer, more efficient chips that use less power than originally assumed. This can result in grid infrastructure built for a higher load than what actually materializes, leaving excess capacity that still must be paid for through system-wide rates.

That’s the central dilemma. If utilities build too little capacity, the system operates with less reserve margin. During periods of grid stress, operators have fewer options, increasing the likelihood of curtailments or outages. However, if utilities build too much, customers may be asked to pay for infrastructure that is not fully used.

In response, policymakers are adjusting the rules. In some regions, regulators are moving toward bring-your-own-power approaches that require large data centers to supply or fund part of the capacity needed to serve them or reduce demand during system stress. At the federal level, permitting reforms tied to datacenter infrastructure increasingly treat electricity as a strategic economic input.

As Ken Medlock, senior director at the Baker Institute Center for Energy Studies (CES), explains:

“Many of the planned data centers are now also adding behind-the-meter options to their development plans because they do not anticipate being able to manage their needs solely from the grid, and they certainly cannot do so with only intermittent power sources.”

Behind-the-meter (BTM) refers to power that a consumer controls on its side of the utility meter, such as on-site gas generation or a dedicated power plant. These resources allow data centers to keep operating during grid-related service. Most facilities remain connected to the grid, but the backup BTM generation serves as insurance for operating their core business.

This shifts responsibility. Utilities traditionally manage reliability across all customers by maintaining an operating reserve margin, or spare capacity. Increasingly, large-load customers manage part of their own electricity reliability needs, which changes how infrastructure is planned and how risk is distributed.

Bottom line

AI-driven load growth is arriving faster and in more concentrated places than the power system was built to accommodate. Utilities and regulators are being forced to make decisions sooner than planned about where to build, how fast to build, and which customers get priority when capacity is limited. The effects extend beyond data centers, showing up in system costs, reliability margins, competition for grid access, and pressure on communities and industries that depend on affordable and dependable power. The issue is not whether electricity can be generated, but how the costs and risks of rapid demand growth are distributed as the system tries to keep up. How regulators balance these decisions will determine who pays as AI demand outruns the power grid.

-----------

Scott Nyquist is a senior advisor at McKinsey & Company and vice chairman, Houston Energy Transition Initiative of the Greater Houston Partnership. The views expressed herein are Nyquist's own and not those of McKinsey & Company or of the Greater Houston Partnership. This article originally appeared on LinkedIn.

Collide has rolled out RIGGS, a large language model for energy professionals. Photo via Getty Images

Houston AI startup rolls out platform to reshape oil and gas workflows

AI for energy

Houston-based Collide is looking to solve AI issues in the energy industry from within.

Co-founded by former oil roughneck Collin McLelland, the company has developed AI software for operators and field teams, shaped by firsthand oilfield experience. Its AI-native platform “retrieves and synthesizes data from authoritative sources to deliver accurate, cited, and energy-focused insights to oil and gas professionals,” according to the company.

“Oil and gas has a graveyard full of technology that was technically impressive and operationally useless,” McLelland tells Energy Capital. “The reason is almost always the same: the people who built it didn't understand what they were actually solving for. When you're an outsider, you see workflows and try to automate them. When you're an insider, you understand why those workflows exist—the regulatory constraints, the physical realities, the liability concerns, the trust dynamics between operators and service companies.”

Collide’s large language model, known as RIGGS, performed well in recent benchmarking results when taking a standardized petroleum engineering (SPE) exam, the company reports. The exam assesses understanding from conceptual terminology to complex mathematical problem-solving.

According to Collide, RIGGS achieved a score of 67.5 percent on a 40-question subset of the SPE petroleum engineering exam, outperforming other large language models like Grok 4 (62.5 percent), Claude Sonnet 4.5 (52.5 percent) and GPT 5.1 (4 percent).

RIGGS completed the test in 15 minutes, while Grok took two hours. Collide hopes over the next few months, RIGGS will receive a score between 75 percent to 80 percent accuracy.

The software could potentially help oil and gas companies produce accurate outputs and automate trivial workflows, which can open up valuable time for engineers and teams to work on other pressing matters, according to McLelland.

“Collide exists because we sat in those seats — we were the engineers, the operators, the field guys,” he says. ”RIGGS scoring higher on the PE exam versus the frontier labs isn't a party trick. It's evidence that the model understands petroleum engineering the way a petroleum engineer does, because it was built by people who do.”

RIGGS was trained on Collide’s Spindletop hardware and is supported by a vast library of information, as well as a reasoning engine and validation layer that uses logic to solve problems.

“Longer term, we see RIGGS as the intelligence layer that sits underneath every operator's workflow — not a chatbot you open in a browser, but something embedded in the tools engineers already use,” McLelland says. “The goal is to give every engineer the knowledge and pattern recognition of a 30-year veteran, on demand."

According to McLelland, Collide is already building toward reservoir analysis and production optimization, automated regulatory compliance (Railroad Commission filings, W-10s, G-10s), workover report generation, and engineering decision support in the field for near-term use cases. In March, Collide and Texas-based oil and gas operator Winn Resources announced a collaboration to automate the time-intensive process of filing monthly W-10 and G-10 forms with the Texas Railroad Commission, completing what’s normally a multi-hour task in under 30 minutes. Collide reports that Winn’s infrastructure now automates regulatory filings and provides real-time visibility into data gaps, which has reduced processing time by over 95 percent.

“Before Collide, I'd spend hours manually keying in filings,” Buck Crum, director of operations, said in a news release. “(In March), we had 50 wells to file and I was done in 20 minutes. It does the majority of the heavy lifting while keeping me in control. That human-in-the-loop approach saves meaningful time and gives us greater confidence in our compliance and reporting.”

Collide was originally launched by Houston media organization Digital Wildcatters as “a professional network and digital community for technical discussions and knowledge sharing.” After raising $5 million in seed funding led by Houston’s Mercury Fund last year, the company said it would shift its focus to rolling out its enterprise-level, AI-enabled solution.
This are heating up for Fervo. Photo via Fervo Energy

Fervo taps new geothermal site and more energy transition news to know

Trending Topics

Editor's note: We're looking back at the biggest energy transition headlines from the second half of February, including Fervo's hottest well to date and the future of data centers in Texas. Below are the five most-read EnergyCapital stories from February 16-28, 2026.

1. Fervo taps into its hottest-ever geothermal reservoir

Fervo Energy has unlocked multigigawatt potential from a new geothermal energy site. Photo courtesy Fervo Energy.

Things are heating up at Houston-based geothermal power company Fervo Energy. Fervo recently drilled its hottest well so far at a new geothermal site in western Utah. Fewer than 11 days of drilling more than 11,000 feet deep at Project Blanford showed temperatures above 555 degrees Fahrenheit, which exceeds requirements for commercial viability. Fervo used proprietary AI-driven analytics for the test. Continue reading.

2. Texas could topple Virginia as biggest data-center market by 2030, JLL report says

A new report shows the role Texas could play as the data-center sector enters "hyperdrive." Photo via JLL.com.

Everything’s bigger in Texas, they say—and that phrase now applies to the state’s growing data-center presence. A new report from commercial real estate services provider JLL says Texas could overtake Northern Virginia as the world’s largest data-center market by 2030. Northern Virginia is a longtime holder of that title. Continue reading.

3. Texas claims No. 1 spot on new energy resilience report

Texas outpaced the rest of the country in a new energy resilience report, despite grid challenges and rising AI demand. Photo courtesy of ERCOT

A new report by mineral group Texas Royalty Brokers ranks Texas as the No. 1 most energy-resilient state. The study focused on four main sources of electricity in hydroelectric dams, natural gas plants, nuclear reactors and petroleum facilities. Each state was given an Energy Resilience Score based on size and diversity of its power infrastructure, energy production and affordability for residents. Continue reading.

4. Texas' oil and gas foundation could boost its geothermal future, UH says

A new UH white paper says that Texas is poised to lead advanced geothermal due to its oil and gas skills and capacity. Photo via UH.edu.

Equipped with the proper policies and investments, Texas could capitalize on its oil and gas infrastructure and expertise to lead the U.S. in development of advanced geothermal power, a new University of Houston white paper says. Continue reading.

5. HETI leader reflects on four years focused on the energy transition

Jane Stricker reflects on four years at HETI. Courtesy photo

It’s hard to believe only four years have passed since I joined the Greater Houston Partnership to lead the Houston Energy Transition Initiative. We talk about COVID years feeling simultaneously like just yesterday and a lifetime ago, and my time at HETI feels quite similar. The energy and climate landscape has evolved dramatically over the last few years, but one constant has endured…Houston remains at the heart of the energy industry. Continue reading.

Merab Momen, founder of AI CTO Services. Courtesy Photo

How this Houston expert helps startups turn AI hype into real impact

now streaming

Artificial intelligence is now everywhere. It is mentioned in every startup pitch deck, and every corporate roadmap claims to use it. However, many early-stage businesses struggle with the simple question, “What does AI actually mean for my business?”

In a recent podcast episode of EnergyTech Startups, Merab Momen, founder of AI CTO Services and a long time AI practitioner, explains why most founders misunderstand AI, how startups can practically apply it and why Houston is quietly becoming a serious hub for AI-driven innovation.

Filling the AI Leadership Gap

Merab’s career has spanned decades of technology transitions. He worked on neutral networks in the 1990s, constructed computer vision systems long before they were common, and helped install AI solutions inside huge industrial companies. However, he noticed a huge problem when generative AI started to explode into the mainstream-The requirement of a real partner by the founders for AI integration but inability to rely on a full-time CTO and project-based consultants.

“I really needed something which is much more engaging where I can give that partner-level advice to the founders,” he said. By giving firms on-demand access to high-level AI knowledge and expertise, his methodology enables them to analyse tools, steer clear of cost blunders and eventually transition to a permanent technology leader when the time is right.

AI is Older than Most People Think

Despite its recent rise in popularity, AI is nothing new. AI actually began in the 1950s. Merab in his conversation explained how he worked on his first AI project back in the year 1996 that worked perfectly, but the processing power wasn’t just there to make it practical. He continued how he utilized the swarm intelligence models to optimize supply chains, now referred to as MLPOs and data engineering.

From Language Models to Physical World

Much of the public conversation about AI revolves around chatbots and text generation. But Merab sees far greater potential in AI’s interaction with the physical world, especially in industrial settings. He emphasized edge computing and vision language models (VLMs) as significant advances in manufacturing and energy. This physical shift is opening doors for new opportunities for robotics, automated inspections, and industrial safety applications. Merab added that Houston is uniquely positioned for this transition.

Why Houston has an AI Advantage

Silicon Valley may dominate the AI headlines, but Merab believes Houston’s advantage lies beneath the surface. The city doesn’t lag in AI utilization; it just operates in industries where results show differently.

Machine learning isn’t new to Houston’s core industries. Energy companies, manufacturers, logistics providers, and healthcare systems have been using advanced analytics for decades. The difference lies in them innovating in industrial sectors rather than consumer technology.

What’s Next

With the AI CTO Services growing, Merab is working with startups across industries to deploy AI in practical, business-first ways.

He is more interested in assisting founders in finding answers to critical issues than following new trends.

For Houston’s energy and climate tech community, it needs to transform AI enthusiasm into real-world impact.

Listen to the full conversation with Mehrab Momin on the Energy Tech Startups Podcast to learn more.

---

Energy Tech Startups Podcast is hosted by Jason Ethier and Nada Ahmed. It delves into Houston's pivotal role in the energy transition, spotlighting entrepreneurs and industry leaders shaping a low-carbon future.


A new report shows the role Texas could play as the data-center sector enters "hyperdrive." Photo via JLL.com.

Texas could topple Virginia as biggest data-center market by 2030, JLL report says

data analysis

Everything’s bigger in Texas, they say—and that phrase now applies to the state’s growing data-center presence.

A new report from commercial real estate services provider JLL says Texas could overtake Northern Virginia as the world’s largest data-center market by 2030. Northern Virginia is a longtime holder of that title.

What’s driving Texas’ increasingly larger role in the data-center market? The key factor is artificial intelligence.

Companies like Google and Microsoft need more energy-hungry data centers to power AI innovations. In a 2023 article, Forbes explained that AI models consume a lot of energy because of the massive amount of data used to train them, as well as the complexity of those models and the rising volume of tasks assigned to AI.

“The data-center sector has officially entered hyperdrive,” Andy Cvengros, executive managing director at JLL and co-leader of its U.S. data-center business, said in the report. “Record-low vacancy sustained over two consecutive years provides compelling evidence against bubble concerns, especially when nearly all our massive construction pipeline is already pre-committed by investment-grade tenants.”

Dallas-Fort Worth has long dominated the Texas data-center market. But in recent years, West Texas has emerged as a popular territory for building data-center campuses, thanks in large part to an abundance of land and energy. Nearly two-thirds of data-center construction underway now is happening in “frontier markets” like West Texas, Ohio, Tennessee and Wisconsin, the JLL report says.

Northern Virginia, the current data-center champ in the U.S., boasted a data-center market with 6,315 megawatts of capacity at the end of 2025, the report says. That compares with 2,423 megawatts in Dallas-Fort Worth, 1,700 megawatts in the Austin-San Antonio corridor, 200 megawatts in West Texas, and 164 megawatts in Houston.

Ad Placement 300x100
Ad Placement 300x600

CultureMap Emails are Awesome

Property Showcase

NASA and Houston researcher tackle climate-driven water quality risks

water watch

Climate change means far more to public health than living with hotter days. Transformations in our weather are contributing to challenges in accessing safe drinking water in some communities.

One of the most dire situations is along the US–Mexico border. The National Aeronautics and Space Administration (NASA) is seeking to address that issue with its Water Quality Applications program. An 11-researcher project led by a UTHealth Houston School of Public Health faculty member has been selected to participate.

“Drinking water is one of the most fundamental public health protections, but producing safe drinking water involves a delicate balance,” Yun Hang, assistant professor of environmental and occupational health sciences, said in a news release. Her team’s proposal was one of 93 that were submitted for funding through NASA’s Research Opportunities in Space and Earth Sciences (ROSES)-2025 program.

This is the first time that NASA has worked with a team devoted to water quality applications. The group, which includes researchers from across the nation, will use satellite observations of Earth, as well as hydrologic modeling, to potentially anticipate and act on water quality conditions as they change. Challenges addressed over the course of the three-year program, which kicked off in June, might include problems with water quality due to climate variability and increased pressure on water resources.

Hang’s team will focus on a pair of borderlands: Paso del Norte and the Rio Grande Valley.

“Working closely with El Paso Water ensures that our research addresses real operational needs while helping utilities better prepare for climate-related water quality changes and continue providing safe drinking water to communities across the Texas border region,” Hang added in the release.

She and the team will use data gathered by NASA on both past and future Earth-observing missions, which will allow them to track environmental changes that may affect source water quality. Combined with past water treatment records and hydrologic models, the team will also utilize artificial intelligence to develop predictive tools that aim to stop issues before they become larger hurdles to water safety.

Another one of the project’s goals is to create visualization tools and source water summaries that can be utilized by those without scientific expertise. The tools will be produced in English and Spanish to further broaden their accessibility.

The hope is that the materials made by the team will also go far beyond the border, with protocols that can be adapted or adopted by other areas dealing with water quality issues.

---

This article originally appeared on our sister site, InnovationMap.com.

New research reveals what really drives data center location decisions

Guest Column

Recent power outages and the surge in AI-driven computing have made data center siting decisions more consequential than ever, especially as energy and water constraints tighten. Communities invest public dollars on the promise of jobs and growth, while firms weigh long-term commitments to land, power and connectivity.

Against that evolving backdrop, a critical question comes into focus: Where do data centers get built — and what actually drives those decisions?

A new study by Tommy Pan Fang (Rice Business) and Shane Greenstein (Harvard Business School) provides the first large-scale statistical analysis of data center location strategies across the United States. It offers policymakers and firms a clearer starting point for understanding how different types of data centers respond to economic and strategic incentives.

Published in the journal Strategy Science, the study examines two major types of infrastructure: third-party colocation centers that lease server space to multiple firms, and hyperscale cloud centers owned by providers like Amazon, Google and Microsoft.

Key takeaways:

  • Third-party colocation centers are physical facilities in close proximity to firms that use them, while cloud providers operate large data centers from a distance and sell access to virtualized computing resources as on‑demand services over the internet.
  • Hospitals and financial firms often require urban third-party centers for low latency and regulatory compliance, while batch processing and many AI workloads can operate more efficiently from lower-cost cloud hubs.
  • For policymakers trying to attract data centers, access to reliable power, water and high-capacity internet matter more than tax incentives.

What are the two main data center location strategies?

The study draws on pre-pandemic data from 2018 and 2019, a period of relative geographic stability in supply and demand. This window gives researchers a clean baseline before remote work, AI demand and new infrastructure pressures began reshaping internet traffic patterns.

The findings show that data centers follow a bifurcated geography:

  • Third-party centers cluster in dense urban markets, where buyers prioritize proximity to customers despite higher land and operating costs.
  • Cloud providers, by contrast, concentrate massive sites in a small number of lower-density regions, where electricity, land and construction are cheaper and economies of scale are easier to achieve.

Third-party data centers, in other words, follow demand. They locate in urban markets where firms in finance, healthcare and IT value low latency, secure storage, and compliance with regulatory standards.

Using county-level data, the researchers modeled how population density, industry mix and operating costs predict where new centers enter. Every U.S. metro with more than 700,000 residents had at least one third-party provider, while many mid-sized cities had none.

Map of data centers

This pattern challenges common assumptions. Third-party facilities are more distributed across urban America than prevailing narratives suggest.

“For industries where speed is everything, being too far from the physical infrastructure can meaningfully affect performance and risk,” Pan Fang says. “Proximity isn’t optional for sectors that can’t absorb delay.”

In critical operations, even slight pauses can have real consequences. For hospital systems, lag can affect performance and risk exposure. And in high-frequency trading, milliseconds can determine whether value is captured or lost in a transaction.

Why does distance matter for cloud data center costs?

For cloud providers, the picture looks very different. Their decisions follow a logic shaped primarily by cost and scale. Because cloud services can be delivered from afar, firms tend to build enormous sites in low-density regions where power is cheap and land is abundant.

These facilities can draw hundreds of megawatts of electricity and operate with far fewer employees than urban centers. “The cloud can serve almost anywhere,” Pan Fang says, “so location is a question of cost before geography.”

The study finds that cloud infrastructure clusters around network backbones and energy economics, not talent pools. Well-known hubs like Ashburn, Virginia — often called “Data Center Alley” — reflect this logic, having benefited from early network infrastructure that made them natural convergence points for digital traffic.

Local governments often try to lure data centers with tax incentives, betting they will create high-tech jobs. But the study suggests other factors matter more to cloud providers, including construction costs, network connectivity and access to reliable, affordable electricity.

When cloud centers need a local presence, distance can sometimes become a constraint. Providers often address this by working alongside third-party operators. “Third-party centers can complement cloud firms when they need a foothold closer to customers,” Pan Fang says.

That hybrid pattern — massive regional hubs complementing strategic colocation — may define the next phase of data center growth.

Looking ahead, shifts in remote work, climate resilience, energy prices and AI-driven computing may reshape where new facilities go. Some workloads may move closer to users, while others may consolidate into large rural hubs. Emerging data-sovereignty rules could also redirect investment beyond the United States.

“The cloud feels weightless,” Pan Fang says, “but it rests on real choices about land, power and proximity.”

---

This article originally appeared on Rice Business Wisdom. Written by Scott Pett. Pan Fang and Greenstein (2025). “Where the Cloud Rests: The Economic Geography of Data Centers,” Strategy Science.

ERock scores Anthropic deal, sees order backlog soar to $1.7B

power deal

Two months after its $400 million IPO, Houston-based ERock (NYSE: EROC) has landed a power-generator deal with AI powerhouse Anthropic, owner of the Claude platform.

In its Q2 earnings report, ERock says it will provide equipment to Anthropic with a 470-megawatt capacity. ERock specializes in utility-grade, onsite microgrid power systems for data centers and other customers. The company previously did business as Enchanted Rock.

The Anthropic deal adds to ERock’s backlog of about $1.7 billion in orders—a figure representing a 1,000 percent year-over-year jump in backorders thanks in large part to the AI boom. ERock expects most of the backlog to convert to revenue by the end of 2027, said Ian Blakely, the company’s chief financial officer.

Bank of America analyst Ross Fowler says the Anthropic contract boosts confidence in ERock’s ability to secure other major deals, according to Traders Union. The Anthropic deal is ERock’s third major data center contract, with separate Anthropic deals for operations and maintenance services expected to follow, Fowler said.

CEO John Carrington says Anthropic’s order “reinforces the momentum” ERock is witnessing across its customer base.

“We are seeing a lot of interest in Texas. It seems to be the easiest place to get a site set up,” company President Corey Amthor said

In its Q2 earnings release—its first as a public company—ERock also reported:

  • Launching generator-assembly operations at its Hyperion equipment factory in Northwest Houston. The plant will expand ERock’s assembly capabilities to 1.2 gigawatts of capacity by the end of 2026.
  • Starting construction of a $473 million, 366-megawatt El Paso Electric natural-gas-powered plant to supply power for Meta Platforms’ $14 billion, 1,000-acre AI data center campus in El Paso. Meta is the parent company of the Facebook and Instagram social media companies.

“Meta gains access to an operational data center years earlier than may otherwise be possible, while El Paso Electric gains a flexible, low-cost, low-emissions grid asset that can support long-term system reliability,” Carrington said.

View All Listings