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.

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.
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

Automakers enter the energy space with vehicles offering backup power

Power Boost

Winter Storm Uri, the multiday freeze that slammed Texas in February 2021 and pummeled the state's power grid, has been on Kenneth Kovar's mind ever since. Though the resident of New Braunfels didn't lose power at the time, he wasn't able to run his septic tank — it is independent from local systems. He had to fill his toilets with water from his backyard pool.

So when Kovar, 64, bought a Ford F-150 last fall, his hope was to be better prepared for any new crisis.

“I was interested in trying to find some sort of power backup situation,” he said.

Now, Kovar has a setup from Ford that allows drivers of certain F-150 models to plug their vehicle directly into their electric meter to power parts of their home during an outage.

It is the latest example of automakers broadly adapting their electrification technologies to the home energy business, especially as demand on the grid increases and sales of electric vehicles slow. Car companies are looking to leverage their multibillion dollar EV investments to tap into a promising market in vehicle, home and grid technology, both for backup power and for supporting electrical grid resiliency.

“They’re trying to look for other businesses that they might sell into,” said Parth Vaishnav, assistant professor of sustainable systems at the University of Michigan.

Ford's latest connects F-150 drivers to their meter

Drivers of the F-150 PowerBoost hybrid and F-150 Lightning electric pickup trucks can now plug in a one-foot long adapter to a 240-volt outlet onboard. That adapter — which Ford made with company Global Power Products — makes the vehicle compatible to plug into a longer, separate cable. That cable connects to a transfer switch installed directly on one's electric meter.

Through the adapter and cable series, homeowners can connect their vehicle essentially right to their home’s breaker box. The homeowner simply turns on and off which breaker switches they want for which devices they want powered.

“The way we think about it, especially for customers who already have a compatible vehicle is, you already own the power source, it’s in your driveway,” said Amanda Roraff, Ford's grid and energy services business acceleration lead.

The Lightning might provide power for two to three days, depending on what home devices are being used, and the PowerBoost Hybrid, up to five days on a single tank of gas.

The automaker says its solution is a less expensive way to supply backup power. Conventional, diesel-powered portable generators and full-home standby setups require expensive installation, costing several thousands of dollars. This solution, which also requires professional installation at the meter, starts around $1,100.

The setup only applies to about 200,000 vehicles so far, and it is also exclusively for outages. Ford also offers its Home Integration System for bidirectionality, sending power both from the vehicle to the home and from the home to the vehicle, while also being able to feed the grid.

Other automakers are boosting their energy solutions

Over 630,000 U.S. vehicles already have this functionality, estimates say, and automakers are rapidly expanding their available options with the goal of full vehicle-to-grid support in the long run.

South Korean auto brand Kia and Wallbox, an EV charging company, have teamed up so that drivers of eligible compatible vehicles can have home power backup during outages or during periods of high demand, to cut their utility use. They can send power back to the grid.

Tesla’s technology is similar — allowing drivers of equipped vehicles to connect to their home using additional Tesla hardware. The Cybertruck provides full vehicle-to-home capability, where other Tesla models can only connect to and power specific devices or appliances.

General Motors is also in the energy space.

A recent partnership with WeaveGrid, for instance, allows homeowners who drive certain GM EVs — and have the automaker’s home system and a proper grid interconnection — to enroll in some grid reliability utility programs. Once an outage is detected, GM’s vehicle-to-home tech has the capability to disconnect one's home from the grid and start supplying power from their GM EV.

“If you can imagine the future as we go forward, it's having the ability — now that we have this single platform — that allows our customer to experience our system,” said Wade Sheffer, vice president of GM energy, “but also can have the full control of the energy.”

The capability is an important lifeline amid EV sales slowdown

Not only is this business critical amid growing grid demand and increasing power outages, experts say automakers need to pivot with the EV market less active under current U.S. federal policy. Pure EV sales in the U.S. year-over-year are down 23.8%, according to a July Cox Automotive report on the first half of 2026. This demonstrates what an asset that EV and hybrid ownership can be.

The tech is not without challenges.

On the industry side, these systems have to undergo third-party testing to ensure they meet safety standards, and the vehicle and the charger need to be programmed to communicate. It also requires the approval of the utility where the capability is being used. It could take years to get an interconnect agreement.

On the customer side, homeowners need to understand their vehicles' abilities and how to self-manage their system. It also just brings another generator of power into the home mix.

Still, experts see opportunity, especially with interest in EV sales high outside of the U.S.

“We already know during an outage, its impact, providing electricity to the home,” said Scott Samuelsen, engineering professor emeritus at the University of California, Irvine. “This is going to become very, very popular.”

Rice, UH join major quantum, nuclear energy initiatives

energy impact

Rice University and the University of Houston will be playing a part in the future of energy in Texas and beyond, as Rice has joined the U.S. Department of Energy Quantum Science Center and UH has been added to the Texas Nuclear Alliance.

Rice’s role with the DOE Quantum Science Center will expand the university’s work in helping to develop “fault-tolerant quantum computers capable of solving scientific problems,” according to Rice. Tirthak Patel, an assistant professor of computer science, will develop and evaluate quantum error-correction decoding methods on high-performance computing platforms. Patel’s team will receive $900,000 over 5 years from a DOE-funded center at Oak Ridge National Laboratory.

The Quantum Science Center was established in 2020 under the National Quantum Initiative Act, and brings together national laboratories, universities and industry partners like IBM, AMD, IQM, Quantinuum and Riverlane, and others to advance quantum information science. The Quantum Science Center is one of the DOE’s five National Quantum Information Science Research Centers, and has planned funding of $125 million over 5 years.

“Reliable error correction is one of the biggest challenges in making quantum computing useful for accelerating scientific discovery,” Patel said in a news release. “Our work is focused on developing methods that can scale to future systems and support practical scientific applications.”

Meanwhile, as power demand continues to rise in Texas and North America, the Texas Nuclear Alliance brings industry, academic, and government leaders together to advance nuclear technologies to meet growing energy demands, support economic efforts, bolster domestic manufacturing, and protect overall energy security.

UH brings expertise to the Texas Nuclear Alliance from UH Energy, the Texas Center for Superconductivity at UH (TcSUH), and the Advanced Manufacturing Institute (AMI). UH says that 11 of its 16 colleges will contribute research to the alliance.

“Texas and the University of Houston have long led the nation in energy innovation and research,” Ramanan Krishnamoorti, vice president of energy and innovation, said in a news release. “As demand for reliable, affordable and secure energy continues to grow, advanced nuclear technologies will become increasingly important. The University of Houston is uniquely positioned to contribute through world-class research and deep industry partnerships that help transform breakthrough discoveries into real-world solutions. We look forward to working with the Texas Nuclear Alliance to accelerate technologies that will shape the future of the energy industry.”

Projects from both Rice and UH were selected this week to participate in the DOE's Genesis Mission. Read more here.

4 Houston researchers awarded projects in DOE's Genesis Mission

federal funding

The U.S. Department of Energy has named the nearly 300 projects selected under the Trump Administration's Genesis Mission, which aims to address some of the largest science and technology challenges in the country. The group features four projects from Houston universities and companies.

The initiative aims to unite government, industry, academia and philanthropy to lead to breakthroughs in energy, scientific discovery and national security, according to a release.

The selected projects feature 87 led by DOE and National Nuclear Security Administration (NNSA) National Laboratories, 168 led by universities, 19 led by companies and 4 led by nonprofit organizations—totalling 342 participating institutions.

“America has no shortage of bold ideas or talented scientists, and the response to the Genesis Mission proves that,” U.S. Secretary of Energy Chris Wright said in a news release. “The 278 projects selected today represent the very best of our nation’s scientific enterprise. The remarkable number of high-quality proposals we received demonstrates that America’s innovation pipeline is strong, and it points to even greater opportunities for future investment and continued expansion of the Genesis Mission portfolio.”

Twelve Texas-based projects were selected among the 278. The Houston projects and their researchers include:

Caroline Ajo-Franklin

Ajo-Franklin recieved a Phase I grant for her project "Predictive AI to Map Point Mutation Effects on Protein Function: Measurement and Biosynthesis of Isoprenoids." Ajo-Franklin is a professor of biosciences at Rice University, a CPRIT Scholar in Cancer Research and member of the Rice Synthetic Biology Institute. The project aims to accelerate the engineering of microbes that can produce isoprenoids, which are natural compounds that could replace petroleum-derived fuels, solvents and materials.

“This project creates a continuous feedback loop in which AI guides experiments and each experiment generates more detailed data to better hone the AI model,” Ajo-Franklin said in a news release. “In addition, it demonstrates the extraordinary star power Rice has recruited in protein engineering and synthetic biology.”

Anastasios Kyrillidis

Kyrillidis recived a Phase I grant for his project "Cracking the VQA Optimization Bottleneck: AI Methods for Quantum Chemistry and Materials." Kyrillidis is the Noah Harding Associate Professor of Computer Science at Rice and a member of the Ken Kennedy Institute. The project aims to develop AI tools to resolve bottlenecks in quantum computing for chemistry and materials research.

“Our goal is to replace fragile, hand-tuned optimization methods with intelligent systems that can learn from quantum computations while still operating within frameworks that provide strong mathematical guarantees,” Kyrillidis added in the release.

Myoungkyu Lee

Lee received a nearly $750,000 Phase I grant for his project "Physics-Informed AI Surrogates for Turbulent Forced Convection in Energy System." Lee is an assistant professor of mechanical aerospace engineering at the University of Houston. Lee will collaborate with researchers from Lawrence Livermore National Laboratory and University of Pennsylvania on the project and develop artificial intelligence to accelerate the design of materials for advanced nuclear fission and fusion reactors.

“The goal is to develop a tool that runs much faster than today’s most detailed simulations while keeping errors small,” Lee said in a news release. “If successful, the approach could support better heat-transfer predictions for molten-salt reactor design and provide a starting point for studying heat removal in fusion blankets. This is one contribution among many toward reliable, carbon-free energy.”

Amit Padhi

Padhi recieved a grant for his project "Probabilistic Inference of Subsurface Fracture Connectivity for Stimulation Control with Physics-Informed AI." Padhi is a scientific advisor for Halliburton.

The DOE first called for applications for the Genesis Mission in March. At the time, the DOE shared that it would grant approximately $293 million to the selected teams via Phase I awards, ranging from $500,000 to $750,000 for nine-month project periods, and Phase II awards, for $6 million to $15 million over a three-year project period.

Since then, however, the initiative has grown with 15 federal agencies now granting research awards and funding opportunities under the Genesis umbrella. The White House announced this week that it had secured more than $5 billion in federal commitments to expand the initiative.