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An Earthling’s guide to planet hunting

The pendant on Rebecca Jensen-Clem’s necklace is only about an inch wide, composed of 36 silver hexagons entwined in a honeycomb mosaic. At the Keck Observatory, in Hawaii, just as many segments make up a mirror that spans 33 feet, reflecting images of uncharted worlds for her to study.  Jensen-Clem, an astronomer at the University of California, Santa Cruz, works with the Keck Observatory to figure out how to detect new planets without leaving our own. Typically, this pursuit faces an array of obstacles: Wind, fluctuations in atmospheric density and temperature, or even a misaligned telescope mirror can create a glare from a star’s light that obscures the view of what’s around it, rendering any planets orbiting the star effectively invisible. And what light Earth’s atmosphere doesn’t obscure, it absorbs. That’s why researchers who study these distant worlds often work with space telescopes that circumvent Earth’s pesky atmosphere entirely, such as the $10 billion James Webb Space Telescope.  But there’s another way over these hurdles. At her lab among the redwoods, Jensen-Clem and her students experiment with new technologies and software to help Keck’s primary honeycomb mirror and its smaller, “deformable” mirror see more clearly. Using measurements from atmospheric sensors, deformable mirrors are designed to adjust shape rapidly, so they can correct for distortions caused by Earth’s atmosphere on the fly.  This general imaging technique, called adaptive optics, has been common practice since the 1990s. But Jensen-Clem is looking to level up the game with extreme adaptive optics technologies, which are aimed to create the highest image quality over a small field of view. Her group, in particular, does so by tackling issues involving wind or the primary mirror itself. The goal is to focus starlight so precisely that a planet can be visible even if its host star is a million to a billion times brighter. In April, she and her former collaborator Maaike van Kooten were named co-recipients of the Breakthrough Prize Foundation’s New Horizons in Physics Prize. The prize announcement says they earned this early-career research award for their potential “to enable the direct detection of the smallest exo­planets” through a repertoire of methods the two women have spent their careers developing.  In July, Jensen-Clem was also announced as a member of a new committee for the Habitable Worlds Observatory, a concept for a NASA space telescope that would spend its career on the prowl for signs of life in the universe. She’s tasked with defining the mission’s scientific goals by the end of the decade. The Keck Observatory’s 10-meter primary mirror features a honeycomb structure with 36 individual mirror segments.ETHAN TWEEDIE “In adaptive optics, we spend a lot of time on simulations, or in the lab,” Jensen-Clem says. “It’s been a long road to see that I’ve actually made things better at the observatory in the past few years.” Jensen-Clem has long appreciated astronomy for its more mind-bending qualities. In seventh grade, she became fascinated by how time slows down near a black hole when her dad, an aerospace engineer, explained that concept to her. After starting her bachelor’s degree at MIT in 2008, she became taken with how a distant star can seem to disappear—either suddenly winking out or gently fading away, depending on the kind of object that passes in front of it. “It wasn’t quite exoplanet science, but there was a lot of overlap,” she says. “If you just look up at the night sky and see stars twinkling, it’s happening fast. So we have to go fast too.” During this time, Jensen-Clem began sowing the seeds for one of her prize-winning methods after her teaching assistant recommended that she apply for an internship at NASA’s Jet Propulsion Laboratory. There, she worked on a setup that could perfect the orientation of a large mirror. Such mirrors are more difficult to realign than the smaller, deformable ones, whose shape-changing segments cater to Earth’s fluctuating atmosphere. “At the time, we were saying, ‘Oh, wouldn’t it be really cool to install one of these at Keck Observatory?’” Jensen-Clem says. The idea stuck around. She even wrote about it in a fellowship application when she was gearing up to start her graduate work at Caltech. And after years of touch-and-go development, Jensen-Clem succeeded in installing the system—which uses a technology called a Zernike wavefront sensor—on Keck’s primary mirror about a year ago. “My work as a college intern is finally done,” she says.  The system, which is currently used for occasional recalibrations rather than continuous adjustments, includes a special kind of glass plate that bends the light rays from the mirror to reveal a specific pattern. The detector can pick up a hairbreadth misalignment in that picture: If one hexagon is pushed too far back or forward, its brightness changes. Even the tiniest misalignment is important to correct, because “when you’re studying a faint object, suddenly you’re much more susceptible to little mistakes,” Jensen-Clem says. She has also been working to perfect the craft of molding Keck’s deformable mirror. This instrument, which reflects light that’s been rerouted from the primary mirror, is much smaller—only six inches wide—and is designed to reposition as often as 2,000 times a second to combat atmospheric turbulence and create the clearest picture possible. “If you just look up at the night sky and see stars twinkling, it’s happening fast. So we have to go fast too,” Jensen-Clem says.  Even at this rapid rate of readjustment, there’s still a lag. The deformable mirror is usually about one millisecond behind the actual outdoor conditions at any given time. “When the [adaptive optics] system can’t keep up, then you aren’t going to get the best resolution,” says van Kooten, Jensen-Clem’s former collaborator, who is now at the National Research Council Canada. This lag has proved especially troublesome on windy nights.  Jensen-Clem thought it was an unsolvable problem. “The reason we have that delay is because we need to run computations and then move the deformable mirror,” she says. “You’re never going to do those things instantaneously.” But while she was still a postdoc at UC Berkeley, she came across a paper that posited a solution. Its authors proposed that using previous measurements and simple algebra to predict how the atmosphere will change, rather than trying to keep up with it in real time, would yield better results. She wasn’t able to test the idea at the time, but coming to UCSC and working with Keck presented the perfect opportunity.  Around this time, Jensen-Clem invited van Kooten to join her team at UCSC as a postdoc because of their shared interest in the predictive software. “I didn’t have a place to live at first, so she put me up in her guest room,” van Kooten says. “She’s just so supportive at every level.” After creating experimental software to try out at Keck, the team compared the predictive version with the more standard adaptive optics, examining how well each imaged an exoplanet without its drowning in starlight. They found that the predictive software could image even faint exoplanets two to three times more clearly. The results, which Jensen-Clem published in 2022, were part of what earned her the New Horizons in Physics Prize.  Thayne Currie, an astronomer at the University of Texas, San Antonio, says that these new techniques will become especially vital as researchers build bigger and bigger ground-based facilities to capture images of exoplanets—including upcoming projects such as the Extremely Large Telescope at the European Southern Observatory and the Giant Magellan Telescope in Chile. “There’s an incredible amount that we’re learning about the universe, and it is really driven by technology advances that are very, very new,” Currie says. “Dr. Jensen-Clem’s work is an example of that kind of innovation.” In May, one of Jensen-Clem’s graduate students went back to Hawaii to reinstall the predictive software at Keck. This time, the program isn’t just a trial run; it’s there to stay. The new software has shown it can refocus artificial starlight. Next, it will have to prove it can handle the real thing.  And in about a year, Jensen-Clem and her students and colleagues will brace themselves for a flood of observations from the European Space Agency’s Gaia mission, which recently finished measuring the motion, temperature, and composition of billions of stars over more than a decade.  When the project releases its next set of data—slated for December 2026—Jensen-Clem’s team aims to hunt for new exoplanetary systems using clues like the wobbles in a star’s motion caused by the gravitational tugs of planets orbiting around it. Once a system has been identified, exoplanet photographers will then be able to shoot the hidden planets using a new instrument at Keck that can reveal more about their atmospheres and temperatures.  There will be a mountain of data to sort through, and an even steeper supply of starlight to refocus. Thankfully, Jensen-Clem has spent more than a decade refining just the techniques she’ll need: “This time next year,” she says, “we’ll be racing to throw all our adaptive optics tricks at these systems and detect as many of these objects as possible.” Jenna Ahart is a science journalist specializing in the physical sciences. 

The pendant on Rebecca Jensen-Clem’s necklace is only about an inch wide, composed of 36 silver hexagons entwined in a honeycomb mosaic. At the Keck Observatory, in Hawaii, just as many segments make up a mirror that spans 33 feet, reflecting images of uncharted worlds for her to study. 

Jensen-Clem, an astronomer at the University of California, Santa Cruz, works with the Keck Observatory to figure out how to detect new planets without leaving our own. Typically, this pursuit faces an array of obstacles: Wind, fluctuations in atmospheric density and temperature, or even a misaligned telescope mirror can create a glare from a star’s light that obscures the view of what’s around it, rendering any planets orbiting the star effectively invisible. And what light Earth’s atmosphere doesn’t obscure, it absorbs. That’s why researchers who study these distant worlds often work with space telescopes that circumvent Earth’s pesky atmosphere entirely, such as the $10 billion James Webb Space Telescope. 

But there’s another way over these hurdles. At her lab among the redwoods, Jensen-Clem and her students experiment with new technologies and software to help Keck’s primary honeycomb mirror and its smaller, “deformable” mirror see more clearly. Using measurements from atmospheric sensors, deformable mirrors are designed to adjust shape rapidly, so they can correct for distortions caused by Earth’s atmosphere on the fly. 

This general imaging technique, called adaptive optics, has been common practice since the 1990s. But Jensen-Clem is looking to level up the game with extreme adaptive optics technologies, which are aimed to create the highest image quality over a small field of view. Her group, in particular, does so by tackling issues involving wind or the primary mirror itself. The goal is to focus starlight so precisely that a planet can be visible even if its host star is a million to a billion times brighter.

In April, she and her former collaborator Maaike van Kooten were named co-recipients of the Breakthrough Prize Foundation’s New Horizons in Physics Prize. The prize announcement says they earned this early-career research award for their potential “to enable the direct detection of the smallest exo­planets” through a repertoire of methods the two women have spent their careers developing. 

In July, Jensen-Clem was also announced as a member of a new committee for the Habitable Worlds Observatory, a concept for a NASA space telescope that would spend its career on the prowl for signs of life in the universe. She’s tasked with defining the mission’s scientific goals by the end of the decade.

The Keck Observatory’s 10-meter primary mirror features a honeycomb structure with 36 individual mirror segments.
The Keck Observatory’s 10-meter primary mirror features a honeycomb structure with 36 individual mirror segments.
ETHAN TWEEDIE

“In adaptive optics, we spend a lot of time on simulations, or in the lab,” Jensen-Clem says. “It’s been a long road to see that I’ve actually made things better at the observatory in the past few years.”

Jensen-Clem has long appreciated astronomy for its more mind-bending qualities. In seventh grade, she became fascinated by how time slows down near a black hole when her dad, an aerospace engineer, explained that concept to her. After starting her bachelor’s degree at MIT in 2008, she became taken with how a distant star can seem to disappear—either suddenly winking out or gently fading away, depending on the kind of object that passes in front of it. “It wasn’t quite exoplanet science, but there was a lot of overlap,” she says.

“If you just look up at the night sky and see stars twinkling, it’s happening fast. So we have to go fast too.”

During this time, Jensen-Clem began sowing the seeds for one of her prize-winning methods after her teaching assistant recommended that she apply for an internship at NASA’s Jet Propulsion Laboratory. There, she worked on a setup that could perfect the orientation of a large mirror. Such mirrors are more difficult to realign than the smaller, deformable ones, whose shape-changing segments cater to Earth’s fluctuating atmosphere.

“At the time, we were saying, ‘Oh, wouldn’t it be really cool to install one of these at Keck Observatory?’” Jensen-Clem says. The idea stuck around. She even wrote about it in a fellowship application when she was gearing up to start her graduate work at Caltech. And after years of touch-and-go development, Jensen-Clem succeeded in installing the system—which uses a technology called a Zernike wavefront sensor—on Keck’s primary mirror about a year ago. “My work as a college intern is finally done,” she says. 

The system, which is currently used for occasional recalibrations rather than continuous adjustments, includes a special kind of glass plate that bends the light rays from the mirror to reveal a specific pattern. The detector can pick up a hairbreadth misalignment in that picture: If one hexagon is pushed too far back or forward, its brightness changes. Even the tiniest misalignment is important to correct, because “when you’re studying a faint object, suddenly you’re much more susceptible to little mistakes,” Jensen-Clem says.

She has also been working to perfect the craft of molding Keck’s deformable mirror. This instrument, which reflects light that’s been rerouted from the primary mirror, is much smaller—only six inches wide—and is designed to reposition as often as 2,000 times a second to combat atmospheric turbulence and create the clearest picture possible. “If you just look up at the night sky and see stars twinkling, it’s happening fast. So we have to go fast too,” Jensen-Clem says. 

Even at this rapid rate of readjustment, there’s still a lag. The deformable mirror is usually about one millisecond behind the actual outdoor conditions at any given time. “When the [adaptive optics] system can’t keep up, then you aren’t going to get the best resolution,” says van Kooten, Jensen-Clem’s former collaborator, who is now at the National Research Council Canada. This lag has proved especially troublesome on windy nights. 

Jensen-Clem thought it was an unsolvable problem. “The reason we have that delay is because we need to run computations and then move the deformable mirror,” she says. “You’re never going to do those things instantaneously.”

But while she was still a postdoc at UC Berkeley, she came across a paper that posited a solution. Its authors proposed that using previous measurements and simple algebra to predict how the atmosphere will change, rather than trying to keep up with it in real time, would yield better results. She wasn’t able to test the idea at the time, but coming to UCSC and working with Keck presented the perfect opportunity. 

Around this time, Jensen-Clem invited van Kooten to join her team at UCSC as a postdoc because of their shared interest in the predictive software. “I didn’t have a place to live at first, so she put me up in her guest room,” van Kooten says. “She’s just so supportive at every level.”

After creating experimental software to try out at Keck, the team compared the predictive version with the more standard adaptive optics, examining how well each imaged an exoplanet without its drowning in starlight. They found that the predictive software could image even faint exoplanets two to three times more clearly. The results, which Jensen-Clem published in 2022, were part of what earned her the New Horizons in Physics Prize. 

Thayne Currie, an astronomer at the University of Texas, San Antonio, says that these new techniques will become especially vital as researchers build bigger and bigger ground-based facilities to capture images of exoplanets—including upcoming projects such as the Extremely Large Telescope at the European Southern Observatory and the Giant Magellan Telescope in Chile. “There’s an incredible amount that we’re learning about the universe, and it is really driven by technology advances that are very, very new,” Currie says. “Dr. Jensen-Clem’s work is an example of that kind of innovation.”

In May, one of Jensen-Clem’s graduate students went back to Hawaii to reinstall the predictive software at Keck. This time, the program isn’t just a trial run; it’s there to stay. The new software has shown it can refocus artificial starlight. Next, it will have to prove it can handle the real thing. 

And in about a year, Jensen-Clem and her students and colleagues will brace themselves for a flood of observations from the European Space Agency’s Gaia mission, which recently finished measuring the motion, temperature, and composition of billions of stars over more than a decade. 

When the project releases its next set of data—slated for December 2026—Jensen-Clem’s team aims to hunt for new exoplanetary systems using clues like the wobbles in a star’s motion caused by the gravitational tugs of planets orbiting around it. Once a system has been identified, exoplanet photographers will then be able to shoot the hidden planets using a new instrument at Keck that can reveal more about their atmospheres and temperatures. 

There will be a mountain of data to sort through, and an even steeper supply of starlight to refocus. Thankfully, Jensen-Clem has spent more than a decade refining just the techniques she’ll need: “This time next year,” she says, “we’ll be racing to throw all our adaptive optics tricks at these systems and detect as many of these objects as possible.”

Jenna Ahart is a science journalist specializing in the physical sciences. 

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DCF Global: Five Markets Redrawing the AI Infrastructure Map

The Global Constraint Is Execution These five markets demonstrate that the worldwide AI infrastructure buildout is no longer organized around a single development model. Spain is using permitting reform to attract investment. China is concentrating construction around energy resources. India is translating enormous capital commitments into an expanding development pipeline. Latin America is combining hyperscale growth with national industrial objectives. And Southeast Asia is creating interconnected infrastructure markets whose boundaries increasingly extend beyond individual cities and countries. Not every market is competing for the same business. Sovereign cloud services, enterprise applications, AI training clusters, and large-scale inference platforms carry different requirements involving power, connectivity, latency, data residency, and operating economics. But the infrastructure questions remain remarkably consistent. Can a project obtain enough electricity? Can transmission and supporting networks be delivered on schedule? Will the supply chain provide the required equipment? Can the facility be constructed and commissioned efficiently? And will the development retain the regulatory certainty and community acceptance required to operate for decades? The gap between announced development and actual capacity is becoming a defining measurement of the industry. Cushman & Wakefield estimates that Asia-Pacific alone had approximately 26.5 GW of data center capacity in its development pipeline during H1 2026. Of that total, roughly 4.8 GW was under construction, with another 21.7 GW in planning. Those figures reveal both the extraordinary scale of the opportunity and the considerable infrastructure work still required to realize it. What the Global Buildout Means for North America For U.S. data center operators, developers, engineering firms, equipment suppliers, and investors, international expansion is not a separate story. It is increasingly part of the same global competition for capital, equipment, power infrastructure, and specialized expertise. The transformers, switchgear, generators, advanced cooling systems, fiber networks, and electrical distribution equipment required by a major AI campus

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Bidding war over key component could eliminate third hard disk maker

What if the three remaining hard disk manufacturers became two? Seagate Technology and Western Digital control 90% of the market, according to market researcher TrendForce, with Toshiba picking up the crumbs. Seagate and WD both make their own magnetic drive heads, but Toshiba relies on a third party, TDK, which also sells to Seagate and WD when they need extra capacity. Seeking to guarantee supplies of this critical component, Toshiba made a bid for TDK’s drive head business earlier this year, according to Bloomberg. Concerned that this could affect its ability to meet the growing demand for hard disks from AI data centers, Seagate made a counteroffer, Bloomberg reported. TDK is staying mum.

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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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Gemini 4 Argon for Robotics

Gemini 4 Argon marks a step change in physical understanding, code generation, and visual-semantic reasoning—unlocking new workflows across the robotics stack and accelerating progress toward

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