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Unleashing the potential of qubits, one molecule at a time

It all began with a simple origami model.  As an undergrad at Harvard, Danna Freedman went to a professor’s office hours for her general chemistry class and came across an elegant paper model that depicted the fullerene molecule. The intricately folded representation of chemical bonds and atomic arrangements sparked her interest, igniting a profound curiosity about how the structure of molecules influences their function.  She stayed and chatted with the professor after the other students left, and he persuaded her to drop his class so she could instead dive immediately into the study of chemistry at a higher level. Soon she was hooked. After graduating with a chemistry degree, Freedman earned a PhD at the University of California, Berkeley, did a postdoc at MIT, and joined the faculty at Northwestern University. In 2021, she returned to MIT as the Frederick George Keyes Professor of Chemistry. Freedman’s fascination with the relationship between form and function at the molecular level laid the groundwork for a trailblazing career in quantum information science, eventually leading her to be honored with a 2022 MacArthur fellowship—and the accompanying “genius” grant—as one of the leading figures in the field. Today, her eyes light up when she talks about the “beauty” of chemistry, which is how she sees the intricate dance of atoms that dictates a molecule’s behavior. At MIT, Freedman focuses on creating novel molecules with specific properties that could revolutionize the technology of sensing, leading to unprecedented levels of precision.  Designer molecules Early in her graduate studies, Freedman noticed that many chemistry research papers claimed to contribute to the development of quantum computing, which exploits the behavior of matter at extremely small scales to deliver much more computational power than a conventional computer can achieve. While the ambition was clear, Freedman wasn’t convinced. When she read these papers carefully, she found that her skepticism was warranted. “I realized that nobody was trying to design magnetic molecules for the actual goal of quantum computing!” she says. Such molecules would be suited to acting as quantum bits, or qubits, the basic unit of information in quantum systems. But the research she was reading about had little to do with that.  Nevertheless, that realization got Freedman thinking—could molecules be designed to serve as qubits? She decided to find out. Her work made her among the first to use chemistry in a way that demonstrably advanced the field of quantum information science, which she describes as a general term encompassing the use of quantum technology for computation, sensing, measurement, and communication.  Unlike traditional bits, which can only equal 0 or 1, qubits are capable of “superposition”—simultaneously existing in multiple states. This is why quantum computers made from qubits can solve large problems faster than classical computers. Freedman, however, has always been far more interested in tapping into qubits’ potential to serve as exquisitely precise sensors. Qubits store information in quantum properties that can be easily disrupted. While the delicacy of those properties makes qubits hard to control, it also makes them especially sensitive and therefore very useful as sensors. Qubits encode information in quantum properties—such as spin and energy—that can be easily disrupted. While the delicacy of those properties makes qubits hard to control, it also makes them especially sensitive and therefore very useful as sensors. Harnessing the power of qubits is notoriously tricky, though. For example, two of the most common types—superconducting qubits, which are often made of thin aluminum layers, and trapped-ion qubits, which use the energy levels of an ion’s electrons to represent 1s and 0s—must be kept at temperatures approaching absolute zero (–273 °C). Maintaining special refrigerators to keep them cool can be costly and difficult. And while researchers have made significant progress recently, both types of qubits have historically been difficult to connect into larger systems. Eager to explore the potential of molecular qubits, Freedman has pioneered a unique “bottom-up” approach to creating them: She designs novel molecules with specific quantum properties to serve as qubits targeted for individual applications. Instead of focusing on a general goal such as maximizing coherence time (how long a qubit can preserve its quantum state), she begins by asking what kinds of properties are needed for, say, a sensor meant to measure biological phenomena at the molecular level. Then she and her team set out to create molecules that have these properties and are suitable for the environment where they’d be used.  To determine the precise structure of a new molecule, Freedman’s team uses software to analyze and process visualizations (such as those in teal and pink above) of data collected by an x-ray diffractometer. The diagram at right depicts an organometallic Cr(IV) complex made of a central chromium atom and four hydrocarbon ligands.COURTESY OF DANNA FREEDMAN Made of a central metallic atom surrounded by hydrocarbon atoms, molecular qubits store information in their spin. The encoded information is later translated into photons, which are emitted to “read out” the information. These qubits can be tuned with laser precision—imagine adjusting a radio dial—by modifying the strength of the ligands, or bonds, connecting the hydrocarbons to the metal atom. These bonds act like tiny tuning forks; by adjusting their strength, the researchers can precisely control the qubit’s spin and the wavelength of the emitted photons. That emitted light can be used to provide information about atomic-level changes in electrical or magnetic fields.  While many researchers are eager to build reliable, scalable quantum computers, Freedman and her group devote most of their attention to developing custom molecules for quantum sensors. These ultrasensitive sensors contain particles in a state so delicately balanced that extremely small changes in their environments unbalance them, causing them to emit light differently. For example, one qubit designed in Freedman’s lab, made of a chromium atom surrounded by four hydrocarbon molecules, can be customized so that tiny changes in the strength of a nearby magnetic field will change its light emissions in a particular way.   A key benefit of using such molecules for sensing is that they are small enough—just a nanometer or so wide—to get extremely close to the thing they are sensing. That can offer an unprecedented level of precision when measuring something like the surface magnetism of two-­dimensional materials, since the strength of a magnetic field decays with distance. A molecular quantum sensor “might not be more inherently accurate than a competing quantum sensor,” says Freedman, “but if you can lose an order of magnitude of distance, that can give us a lot of information.” Quantum sensors’ ability to detect electric or magnetic changes at the atomic level and make extraordinarily precise measurements could be useful in many fields, such as environmental monitoring, medical diagnostics, geolocation, and more. When designing molecules to serve as quantum sensors, Freedman’s group also factors in the way they can be expected to act in a specific sensing environment. Creating a sensor for water, for example, requires a water-compatible molecule, and a sensor for use at very low temperatures requires molecules that are optimized to perform well in the cold. By custom-­engineering molecules for different uses, the Freedman lab aims to make quantum technology more versatile and widely adaptable. Embracing interdisciplinarity As Freedman and her group focus on the highly specific work of designing custom molecules, she is keenly aware that tapping into the power of quantum science depends on the collective efforts of scientists from different fields. “Quantum is a broad and heterogeneous field,” she says. She believes that attempts to define it narrowly hurt collective research—and that scientists must welcome collaboration when the research leads them beyond their own field. Even in the seemingly straightforward scenario of using a quantum computer to solve a chemistry problem, you would need a physicist to write a quantum algorithm, engineers and materials scientists to build the computer, and chemists to define the problem and identify how the quantum computer might solve it.  MIT’s collaborative environment has helped Freedman connect with researchers in different disciplines, which she says has been instrumental in advancing her research. She’s recently spoken with neurobiologists who proposed problems that quantum sensing could potentially solve and provided helpful context for building the sensors. Looking ahead, she’s excited about the potential applications of quantum science in many scientific fields. “MIT is such a great place to nucleate a lot of these connections,” she says. “As quantum expands, there are so many of these threads which are inherently interdisciplinary,” she says. Inside the lab Freedman’s lab in Building 6 is a beehive of creativity and collaboration. Against a backdrop of colorful flasks and beakers, researchers work together to synthesize molecules, analyze their structures, and unlock the secrets hidden within their intricate atomic arrangements. “We are making new molecules and putting them together atom by atom to discover whether they have the properties we want,” says Christian Oswood, a postdoctoral fellow.  Some sensitive molecules can only be made in the lab’s glove box, a nitrogen-filled transparent container that protects chemicals from oxygen and water in the ambient air. An example is an organometallic solution synthesized by one of Freedman’s graduate students, David Ullery, which takes the form of a vial of purple liquid. (“A lot of molecules have really pretty colors,” he says.) Freedman is a passionate educator, dedicated to demystifying the complexities of chemistry for her students. Aware that many of them find the subject daunting, she strives to go beyond textbook equations. Once synthesized, the molecules are taken to a single-crystal x-ray diffractometer a few floors below the Freedman lab. There, x-rays are directed at crystallized samples, and from the diffraction pattern, researchers can deduce their molecular structure—how the atoms connect. Studying the precise geometry of these synthesized molecules reveals how the structure affects their quantum properties, Oswood explains. Researchers and students at the lab say Freedman’s cross-disciplinary outlook played a big role in drawing them to it. With a chemistry background and a special interest in physics, for example, Ullery joined because he was excited by the way Freedman’s research bridges those two fields.  Crystals of an organometallic Cr(IV) complex. Freedman’s lab designed a series of molecules like this one to detect changes in a magnetic field.COURTESY OF DANNA FREEDMAN Others echo this sentiment. “The opportunity to be in a field that’s both new and expanding like quantum science, and attacking it from this specific angle, was exciting to me both intellectually and professionally,” says Oswood. Another graduate student, Cindy Serena Ngompe Massado, says she enjoys being part of the lab because she gets to collaborate with scientists in other fields. “It allows you to really approach scientific challenges in a more holistic and productive way,” she says. Though the researchers spend most of their time synthesizing and analyzing molecules, fun infuses the lab too. Freedman checks in with everyone frequently, and conversations often drift beyond just science. She’s just as comfortable chatting about Taylor Swift and Travis Kelce as she is discussing research. “Danna is very personable and very herself with us,” Ullery says. “It adds a bit of levity to being in an otherwise stressful grad school environment.” Bringing textbook chemistry to life In the classroom, Freedman is a passionate educator, dedicated to demystifying the complexities of chemistry for her students. Aware that many of them find the subject daunting, she strives to go beyond textbook equations. For each lecture in her advanced inorganic chemistry classes, she introduces the “molecule of the day,” which is always connected to the lesson plan. When teaching about bimetallic molecules, for example, she showcased the potassium rubidium molecule, citing active research at Harvard aimed at entangling its nuclear spins. For a lecture on superconductors, she brought a sample of the superconducting material yttrium barium copper oxide that students could handle.  Chemistry students often think “This is painful” or “Why are we learning this?” Freedman says. Making the subject matter more tangible and showing its connection to ongoing research spark students’ interest and underscore the material’s relevance. Freedman sees frustrating research as an opportunity to discover new things. “I like students to work on at least one ‘safer’ project along with something more ambitious,” she says.M. SCOTT BRAUER/MIT NEWS OFFICE Freedman believes this is an exceptionally exciting time for budding chemists. She emphasizes the importance of curiosity and encourages them to ask questions. “There is a joy to being able to walk into any room and ask any question and extract all the knowledge that you can,” she says.  In her own research, she embodies this passion for the pursuit of knowledge, framing challenges as stepping stones to discovery. When she was a postdoc, her research on electron spins in synthetic materials hit what seemed to be a dead end that ultimately led to the discovery of a new class of magnetic material. So she tells her students that even the most difficult aspects of research are rewarding because they often lead to interesting findings.  That’s exactly what happened to Ullery. When he designed a molecule meant to be stable in air and water and emit light, he was surprised that it didn’t—and that threw a wrench into his plan to develop the molecule into a sensor that would emit light only under particular circumstances. So he worked with theoreticians in Giulia Galli’s group at the University of Chicago, developing new insights on what drives emission, and that led to the design of a new molecule that did emit light.  “Frustrating research is almost fun to deal with,” says Freedman, “even if it doesn’t always feel that way.” 

It all began with a simple origami model. 

As an undergrad at Harvard, Danna Freedman went to a professor’s office hours for her general chemistry class and came across an elegant paper model that depicted the fullerene molecule. The intricately folded representation of chemical bonds and atomic arrangements sparked her interest, igniting a profound curiosity about how the structure of molecules influences their function. 

She stayed and chatted with the professor after the other students left, and he persuaded her to drop his class so she could instead dive immediately into the study of chemistry at a higher level. Soon she was hooked. After graduating with a chemistry degree, Freedman earned a PhD at the University of California, Berkeley, did a postdoc at MIT, and joined the faculty at Northwestern University. In 2021, she returned to MIT as the Frederick George Keyes Professor of Chemistry.

Freedman’s fascination with the relationship between form and function at the molecular level laid the groundwork for a trailblazing career in quantum information science, eventually leading her to be honored with a 2022 MacArthur fellowship—and the accompanying “genius” grant—as one of the leading figures in the field.

Today, her eyes light up when she talks about the “beauty” of chemistry, which is how she sees the intricate dance of atoms that dictates a molecule’s behavior. At MIT, Freedman focuses on creating novel molecules with specific properties that could revolutionize the technology of sensing, leading to unprecedented levels of precision. 

Designer molecules

Early in her graduate studies, Freedman noticed that many chemistry research papers claimed to contribute to the development of quantum computing, which exploits the behavior of matter at extremely small scales to deliver much more computational power than a conventional computer can achieve. While the ambition was clear, Freedman wasn’t convinced. When she read these papers carefully, she found that her skepticism was warranted.

“I realized that nobody was trying to design magnetic molecules for the actual goal of quantum computing!” she says. Such molecules would be suited to acting as quantum bits, or qubits, the basic unit of information in quantum systems. But the research she was reading about had little to do with that. 

Nevertheless, that realization got Freedman thinking—could molecules be designed to serve as qubits? She decided to find out. Her work made her among the first to use chemistry in a way that demonstrably advanced the field of quantum information science, which she describes as a general term encompassing the use of quantum technology for computation, sensing, measurement, and communication. 

Unlike traditional bits, which can only equal 0 or 1, qubits are capable of “superposition”—simultaneously existing in multiple states. This is why quantum computers made from qubits can solve large problems faster than classical computers. Freedman, however, has always been far more interested in tapping into qubits’ potential to serve as exquisitely precise sensors.

Qubits store information in quantum properties that can be easily disrupted. While the delicacy of those properties makes qubits hard to control, it also makes them especially sensitive and therefore very useful as sensors.

Qubits encode information in quantum properties—such as spin and energy—that can be easily disrupted. While the delicacy of those properties makes qubits hard to control, it also makes them especially sensitive and therefore very useful as sensors.

Harnessing the power of qubits is notoriously tricky, though. For example, two of the most common types—superconducting qubits, which are often made of thin aluminum layers, and trapped-ion qubits, which use the energy levels of an ion’s electrons to represent 1s and 0s—must be kept at temperatures approaching absolute zero (–273 °C). Maintaining special refrigerators to keep them cool can be costly and difficult. And while researchers have made significant progress recently, both types of qubits have historically been difficult to connect into larger systems.

Eager to explore the potential of molecular qubits, Freedman has pioneered a unique “bottom-up” approach to creating them: She designs novel molecules with specific quantum properties to serve as qubits targeted for individual applications. Instead of focusing on a general goal such as maximizing coherence time (how long a qubit can preserve its quantum state), she begins by asking what kinds of properties are needed for, say, a sensor meant to measure biological phenomena at the molecular level. Then she and her team set out to create molecules that have these properties and are suitable for the environment where they’d be used. 

To determine the precise structure of a new molecule, Freedman’s team uses software to analyze and process visualizations (such as those in teal and pink above) of data collected by an x-ray diffractometer. The diagram at right depicts an organometallic Cr(IV) complex made of a central chromium atom and four hydrocarbon ligands.
COURTESY OF DANNA FREEDMAN

Made of a central metallic atom surrounded by hydrocarbon atoms, molecular qubits store information in their spin. The encoded information is later translated into photons, which are emitted to “read out” the information. These qubits can be tuned with laser precision—imagine adjusting a radio dial—by modifying the strength of the ligands, or bonds, connecting the hydrocarbons to the metal atom. These bonds act like tiny tuning forks; by adjusting their strength, the researchers can precisely control the qubit’s spin and the wavelength of the emitted photons. That emitted light can be used to provide information about atomic-level changes in electrical or magnetic fields. 

While many researchers are eager to build reliable, scalable quantum computers, Freedman and her group devote most of their attention to developing custom molecules for quantum sensors. These ultrasensitive sensors contain particles in a state so delicately balanced that extremely small changes in their environments unbalance them, causing them to emit light differently. For example, one qubit designed in Freedman’s lab, made of a chromium atom surrounded by four hydrocarbon molecules, can be customized so that tiny changes in the strength of a nearby magnetic field will change its light emissions in a particular way.  

A key benefit of using such molecules for sensing is that they are small enough—just a nanometer or so wide—to get extremely close to the thing they are sensing. That can offer an unprecedented level of precision when measuring something like the surface magnetism of two-­dimensional materials, since the strength of a magnetic field decays with distance. A molecular quantum sensor “might not be more inherently accurate than a competing quantum sensor,” says Freedman, “but if you can lose an order of magnitude of distance, that can give us a lot of information.” Quantum sensors’ ability to detect electric or magnetic changes at the atomic level and make extraordinarily precise measurements could be useful in many fields, such as environmental monitoring, medical diagnostics, geolocation, and more.

When designing molecules to serve as quantum sensors, Freedman’s group also factors in the way they can be expected to act in a specific sensing environment. Creating a sensor for water, for example, requires a water-compatible molecule, and a sensor for use at very low temperatures requires molecules that are optimized to perform well in the cold. By custom-­engineering molecules for different uses, the Freedman lab aims to make quantum technology more versatile and widely adaptable.

Embracing interdisciplinarity

As Freedman and her group focus on the highly specific work of designing custom molecules, she is keenly aware that tapping into the power of quantum science depends on the collective efforts of scientists from different fields.

“Quantum is a broad and heterogeneous field,” she says. She believes that attempts to define it narrowly hurt collective research—and that scientists must welcome collaboration when the research leads them beyond their own field. Even in the seemingly straightforward scenario of using a quantum computer to solve a chemistry problem, you would need a physicist to write a quantum algorithm, engineers and materials scientists to build the computer, and chemists to define the problem and identify how the quantum computer might solve it. 

MIT’s collaborative environment has helped Freedman connect with researchers in different disciplines, which she says has been instrumental in advancing her research. She’s recently spoken with neurobiologists who proposed problems that quantum sensing could potentially solve and provided helpful context for building the sensors. Looking ahead, she’s excited about the potential applications of quantum science in many scientific fields. “MIT is such a great place to nucleate a lot of these connections,” she says.

“As quantum expands, there are so many of these threads which are inherently interdisciplinary,” she says.

Inside the lab

Freedman’s lab in Building 6 is a beehive of creativity and collaboration. Against a backdrop of colorful flasks and beakers, researchers work together to synthesize molecules, analyze their structures, and unlock the secrets hidden within their intricate atomic arrangements.

“We are making new molecules and putting them together atom by atom to discover whether they have the properties we want,” says Christian Oswood, a postdoctoral fellow. 

Some sensitive molecules can only be made in the lab’s glove box, a nitrogen-filled transparent container that protects chemicals from oxygen and water in the ambient air. An example is an organometallic solution synthesized by one of Freedman’s graduate students, David Ullery, which takes the form of a vial of purple liquid. (“A lot of molecules have really pretty colors,” he says.)

Freedman is a passionate educator, dedicated to demystifying the complexities of chemistry for her students. Aware that many of them find the subject daunting, she strives to go beyond textbook equations.

Once synthesized, the molecules are taken to a single-crystal x-ray diffractometer a few floors below the Freedman lab. There, x-rays are directed at crystallized samples, and from the diffraction pattern, researchers can deduce their molecular structure—how the atoms connect. Studying the precise geometry of these synthesized molecules reveals how the structure affects their quantum properties, Oswood explains.

Researchers and students at the lab say Freedman’s cross-disciplinary outlook played a big role in drawing them to it. With a chemistry background and a special interest in physics, for example, Ullery joined because he was excited by the way Freedman’s research bridges those two fields. 

Crystals of an organometallic Cr(IV) complex. Freedman’s lab designed a series of molecules like this one to detect changes in a magnetic field.
COURTESY OF DANNA FREEDMAN

Others echo this sentiment. “The opportunity to be in a field that’s both new and expanding like quantum science, and attacking it from this specific angle, was exciting to me both intellectually and professionally,” says Oswood.

Another graduate student, Cindy Serena Ngompe Massado, says she enjoys being part of the lab because she gets to collaborate with scientists in other fields. “It allows you to really approach scientific challenges in a more holistic and productive way,” she says.

Though the researchers spend most of their time synthesizing and analyzing molecules, fun infuses the lab too. Freedman checks in with everyone frequently, and conversations often drift beyond just science. She’s just as comfortable chatting about Taylor Swift and Travis Kelce as she is discussing research.

“Danna is very personable and very herself with us,” Ullery says. “It adds a bit of levity to being in an otherwise stressful grad school environment.”

Bringing textbook chemistry to life

In the classroom, Freedman is a passionate educator, dedicated to demystifying the complexities of chemistry for her students. Aware that many of them find the subject daunting, she strives to go beyond textbook equations.

For each lecture in her advanced inorganic chemistry classes, she introduces the “molecule of the day,” which is always connected to the lesson plan. When teaching about bimetallic molecules, for example, she showcased the potassium rubidium molecule, citing active research at Harvard aimed at entangling its nuclear spins. For a lecture on superconductors, she brought a sample of the superconducting material yttrium barium copper oxide that students could handle. 

Chemistry students often think “This is painful” or “Why are we learning this?” Freedman says. Making the subject matter more tangible and showing its connection to ongoing research spark students’ interest and underscore the material’s relevance.

Danna Freedman
Freedman sees frustrating research as an opportunity to discover new things. “I like students to work on at least one ‘safer’ project along with something more ambitious,” she says.
M. SCOTT BRAUER/MIT NEWS OFFICE

Freedman believes this is an exceptionally exciting time for budding chemists. She emphasizes the importance of curiosity and encourages them to ask questions. “There is a joy to being able to walk into any room and ask any question and extract all the knowledge that you can,” she says. 

In her own research, she embodies this passion for the pursuit of knowledge, framing challenges as stepping stones to discovery. When she was a postdoc, her research on electron spins in synthetic materials hit what seemed to be a dead end that ultimately led to the discovery of a new class of magnetic material. So she tells her students that even the most difficult aspects of research are rewarding because they often lead to interesting findings. 

That’s exactly what happened to Ullery. When he designed a molecule meant to be stable in air and water and emit light, he was surprised that it didn’t—and that threw a wrench into his plan to develop the molecule into a sensor that would emit light only under particular circumstances. So he worked with theoreticians in Giulia Galli’s group at the University of Chicago, developing new insights on what drives emission, and that led to the design of a new molecule that did emit light. 

“Frustrating research is almost fun to deal with,” says Freedman, “even if it doesn’t always feel that way.” 

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Norway Monthly Gas Output Falls: Preliminary Data

Preliminary official figures released Wednesday showed Norway produced 351.3 million standard cubic meters a day (MMscmd) of natural gas in March, dropping month-on-month and year-on-year but beating the official forecast by 0.4 percent. The Nordic country, which has displaced Russia as the European Union’s top source of pipeline gas in the aftermath of the war in Ukraine, sold 10.9 billion standard cubic meters (Bscm) in March. That is up 100 MMscm from February, according to figures published online by the Norwegian Offshore Directorate. According to the latest quarterly gas market report of the European Commission, Norway accounted for 50 percent of gas imported into the EU by pipeline in the fourth quarter of 2024. In March Equinor ASA said it has put the Halten East field in the Norwegian Sea onstream, unlocking new gas for Europe. “We are starting up Halten East at a time where piped gas from Norway is in high demand and important for energy security”, Geir Tungesvik, executive vice president for projects, drilling and procurement at Equinor, said in a company statement March 17. Halten East, a tie-in to be developed in two phases, holds about 100 million barrels of oil equivalent recoverable reserves, according to the Norwegian majority state-owned energy company. “The second phase is planned in 2029”, Equinor said. Meanwhile Norway’s oil production in March 2025 averaged 1.76 million barrels per day (MMbd), up sequentially but down year-over-year. In late March Equinor achieved first oil at the Johan Castberg field in the Barents Sea, growing Norway’s production capacity by 220,000 barrels per day (bpd) at peak. “The Johan Castberg field will contribute crucial energy, value creation, ripple effects and jobs for at least 30 years to come”, Tungesvik said. Recoverable volumes are estimated to be 450-650 million barrels, Equinor said. “Johan Castberg opens a

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Energy Security is National Security, OEUK Says

Energy security is national security, industry body Offshore Energies UK (OEUK) stated in a release sent to Rigzone by the OEUK team on Tuesday, which highlighted that OEUK is holding a Security and Resilience Conference in Aberdeen at the end of the month. In the release, OEUK noted that “specialists from industry, defense policy, the security services, and academia” will address the “high-level” conference, which is being held  in the Union Kirk in Scotland on April 30. OEUK said in the release that the conference will hear from leading defense specialists at the cutting edge of technological solutions, including the use of artificial intelligence and machine learning for the enhancement of maritime security. It added that delegates will also be given an overview of approaches to defense used by other countries in protecting offshore energy supply chains. “The event will underline why energy security is national security and will give offshore energy operators and supply chain companies an overview of the collaborative approach being taken to possible risks and help them develop strategies to protect energy distribution installations from potential threats,” OEUK stated in the release. “The offshore energy industry is highly regulated with close lines of communication to the various agencies of national defense, but sharing information to protect vital UK offshore energy producers from deliberate damage to their operations is of paramount importance,” it added. In the release, Mark Wilson, OEUK’s director of health, safety, environment, and operations, who is also a former army officer, said, “this is the first event of its kind to be held by OEUK”. “Given the current geopolitical instability and the potential risks to subsea infrastructure, we believe it is important for the offshore energy industry to take seriously the need for optimum security,” he added. “We have an unbeatable line-up of leading

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TSMC targets AI acceleration with A14 process and ‘System on Wafer-X’

Nvidia’s flagship GPUs currently integrate two chips, while its forthcoming Rubin Ultra platform will connect four. “The SoW-X delivers wafer-scale compute performance and significantly boosts speed by integrating multiple advanced compute SoC dies, stacked HBM memory, and optical interconnects into a single package,” said Neil Shah, partner and co-founder at Counterpoint Research. “This approach reduces latency, improves power efficiency, and enhances scalability compared to traditional multi-chip setups — giving enterprises and hyperscalers AI servers capable of handling future workloads faster, more efficiently, and in a smaller footprint.” This not only boosts capex savings in the long run but also opex savings in terms of energy and space. “Wafer-X technology isn’t just about bigger chips — it’s a signal that the future of AI infrastructure is being redesigned at the silicon level,” said Abhivyakti Sengar, practice director at Everest Group. “By tightly integrating compute, memory, and optical interconnects within a single wafer-scale package, TSMC targets the core constraints of AI: bandwidth and energy. For hyperscale data centers and frontier model training, this could be a game-changer.” Priorities for enterprise customers For enterprises investing in custom AI silicon, choosing the right foundry partner goes beyond performance benchmarks. It’s about finding a balance between cutting-edge capabilities, flexibility, and cost. “First, enterprise buyers need to assess manufacturing process technologies (such as TSMC’s 3nm, 2nm, or Intel’s 18A) to determine if they meet AI chip performance and power requirements, along with customization capabilities,” said Galen Zeng, senior research manager for semiconductor research at IDC Asia Pacific. “Second, buyers should evaluate advanced packaging abilities; TSMC leads in 3D packaging and customized packaging solutions, suitable for highly integrated AI chips, while Intel has advantages in x86 architecture. Finally, buyers should assess pricing structures.”

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Cloudbrink pushes SASE boundaries with 300 Gbps data center throughput

Those core components are functionally table stakes and don’t really serve to differentiate Cloudbrink against its myriad competitors in the SASE market. Where Cloudbrink looks to differentiate is at a technical level through a series of innovations including: Distributed edge architecture: The company has decoupled software from hardware, allowing their platform to run across 800 data centers by leveraging public clouds, telco networks and edge computing infrastructure. This approach reduces network latency from 300 milliseconds to between 7 and 20 milliseconds, the company says. This density dramatically improves TCP performance and responsiveness. Protocol optimization: Cloudbrink developed its own algorithms for SD-WAN optimization that bring enterprise-grade reliability to last mile links. These algorithms significantly improve efficiency on consumer broadband connections, enabling enterprise-grade performance over standard internet links. Integrated security stack: “We’ve been able to produce secure speeds at line rate on our platform by bringing security to the networking stack itself,” Mana noted. Rather than treating security as a separate overlay that degrades performance, Cloudbrink integrates security functions directly into the networking stack. The solution consists of three core components: client software for user devices, a cloud management plane, and optional data center connectors for accessing internal applications. The client intelligently connects to multiple edge nodes simultaneously, providing redundancy and application-specific routing optimization. Cloudbrink expands global reach Beyond its efforts to increase throughput, Cloudbrink is also growing its global footprint. Cloudbrink today announced a global expansion through new channel agreements and the opening of a Brazil office to serve emerging markets in Latin America, Korea and Africa. The expansion includes exclusive partnerships with WITHX in Korea, BAMM Technologies for Latin America distribution and OneTic for African markets. The company’s software-defined FAST (Flexible, Autonomous, Smart and Temporary) Edges technology enables rapid deployment of points of presence by leveraging existing infrastructure from multiple

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CIOs could improve sustainability with data center purchasing decisions — but don’t

CIOs can drive change Even though it’s difficult to calculate an organization’s carbon footprint, CIOs and IT purchasing leaders trying to reduce their environmental impact can influence data center operators, experts say. “Customers have a very large voice,” Seagate’s Feist says. “Don’t underestimate how powerful that CIO feedback loop is. The large cloud accounts are customer-obsessed organizations, so they listen, and they react.” While DataBank began using renewable energy years ago, customer demand can push more data center operators to follow suit, Gerson says. “For sure, if there is a requirement to purchase renewable power, we are going to purchase renewable power,” she adds.

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Copper-to-optics technology eyed for next-gen AI networking gear

Broadcom’s demonstration and a follow-up session explored the benefits of further developing CPC, such as reduced signal integrity penalties and extended reach, through channel modeling and simulations, Broadcom wrote in a blog about the DesignCon event. “Experimental results showed successful implementation of CPC, demonstrating its potential to address bandwidth and signal integrity challenges in data centers, which is crucial for AI applications,” Broadcom stated. In addition to the demo, Broadcom and Samtec also authored a white paper on CPC that stated: “Co-packaged connectivity (CPC) provides the opportunity to omit loss and reflection penalties from the [printed circuit board (PCB)] and the package. When high speed I/O is cabled from the top of the package advanced PCB materials are not necessary. Losses from package vertical paths and PCB routing can be transferred to the longer reach of cables,” the authors stated. “As highly complex systems are challenged to scale the number of I/O and their reach, co- packaged connectivity presents opportunity. As we approach 224G-PAM4 [which uses optical techniques to support 224 Gigabits per second data rates per optical lane] and above, system loss and dominating noise sources necessitate the need to re-consider that which has been restricted in the back of the system architect’s mind for years: What if we attached to the package?” At OFC, Samtec demonstrated its Si-FlyHD co-packaged cable assemblies and Samtec FlyoverOctal Small Form-factor Pluggable (OSFP) over the Samtec Eye Speed Hyper Low Skew twinax copper cable. Flyover is Samtec’s proprietary way of addressing signal integrity and reach limitations of routing high-speed signals through traditional printed circuit boards (PCBs). “This evaluation platform incorporates Broadcom’s industry-leading 200G SerDes technology and Samtec’s co-packaged Flyover technology. Si-Fly HD CPC offers the industry’s highest footprint density and robust interconnect which enables 102.4T (512 lanes at 200G) in a 95 x

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The Rise of AI Factories: Transforming Intelligence at Scale

AI Factories Redefine Infrastructure The architecture of AI factories reflects a paradigm shift that mirrors the evolution of the industrial age itself—from manual processes to automation, and now to autonomous intelligence. Nvidia’s framing of these systems as “factories” isn’t just branding; it’s a conceptual leap that positions AI infrastructure as the new production line. GPUs are the engines, data is the raw material, and the output isn’t a physical product, but predictive power at unprecedented scale. In this vision, compute capacity becomes a strategic asset, and the ability to iterate faster on AI models becomes a competitive differentiator, not just a technical milestone. This evolution also introduces a new calculus for data center investment. The cost-per-token of inference—how efficiently a system can produce usable AI output—emerges as a critical KPI, replacing traditional metrics like PUE or rack density as primary indicators of performance. That changes the game for developers, operators, and regulators alike. Just as cloud computing shifted the industry’s center of gravity over the past decade, the rise of AI factories is likely to redraw the map again—favoring locations with not only robust power and cooling, but with access to clean energy, proximity to data-rich ecosystems, and incentives that align with national digital strategies. The Economics of AI: Scaling Laws and Compute Demand At the heart of the AI factory model is a requirement for a deep understanding of the scaling laws that govern AI economics. Initially, the emphasis in AI revolved around pretraining large models, requiring massive amounts of compute, expert labor, and curated data. Over five years, pretraining compute needs have increased by a factor of 50 million. However, once a foundational model is trained, the downstream potential multiplies exponentially, while the compute required to utilize a fully trained model for standard inference is significantly less than

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Google’s AI-Powered Grid Revolution: How Data Centers Are Reshaping the U.S. Power Landscape

Google Unveils Groundbreaking AI Partnership with PJM and Tapestry to Reinvent the U.S. Power Grid In a move that underscores the growing intersection between digital infrastructure and energy resilience, Google has announced a major new initiative to modernize the U.S. electric grid using artificial intelligence. The company is partnering with PJM Interconnection—the largest grid operator in North America—and Tapestry, an Alphabet moonshot backed by Google Cloud and DeepMind, to develop AI tools aimed at transforming how new power sources are brought online. The initiative, detailed in a blog post by Alphabet and Google President Ruth Porat, represents one of Google’s most ambitious energy collaborations to date. It seeks to address mounting challenges facing grid operators, particularly the explosive backlog of energy generation projects that await interconnection in a power system unprepared for 21st-century demands. “This is our biggest step yet to use AI for building a stronger, more resilient electricity system,” Porat wrote. Tapping AI to Tackle an Interconnection Crisis The timing is critical. The U.S. energy grid is facing a historic inflection point. According to the Lawrence Berkeley National Laboratory, more than 2,600 gigawatts (GW) of generation and storage projects were waiting in interconnection queues at the end of 2023—more than double the total installed capacity of the entire U.S. grid. Meanwhile, the Federal Energy Regulatory Commission (FERC) has revised its five-year demand forecast, now projecting U.S. peak load to rise by 128 GW before 2030—more than triple the previous estimate. Grid operators like PJM are straining to process a surge in interconnection requests, which have skyrocketed from a few dozen to thousands annually. This wave of applications has exposed the limits of legacy systems and planning tools. Enter AI. Tapestry’s role is to develop and deploy AI models that can intelligently manage and streamline the complex process of

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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