2026-03-06 03:13:38
Spexi’s crowdsourced drone fleet has mapped over 5 million acres in 200 cities around Canada and the US.
Gaspard-Félix Tournachon, popularly known as “Nadar,” took the first known aerial photographs using a camera attached to a hot-air balloon just outside Paris in 1858. Ever since, technologists have been developing increasingly sophisticated ways to capture high-altitude images of Earth.
In the First World War, military intelligence pushed the technology from artistic novelty to real-world use. Today, everything from urban planning and insurance underwriting to disaster response relies on detailed, high-resolution, and often 3D images of our planet. For emerging fields like autonomous robotics and augmented reality, making a digital copy of the physical world is one of the century’s most consequential infrastructure projects.
While more traditional aerial imagery relies on airplanes, satellites, and the occasional pigeon, today’s industry is also turning to low-cost drones.
Bill Lakeland, CEO and cofounder of Canadian drone imaging company Spexi, says improvement in consumer drones over the last decade is reshaping aerial imagery. In an interview with Joseph Raczynski, Lakeland details how low-cost drones are disrupting older methods involving airplanes and satellites.
“We’re getting better data out of micro-drones than what we get out of a $2 million mapping camera. The time has arrived,” he says.
According to Spexi, because off-the-shelf drones fly low, they can produce imagery at a resolution 30 times higher than satellites. Drones are also more cost-efficient and less time-consuming than airplanes. This means they’re quickly achieving workhorse status.
What’s notable about Spexi is that instead of operating their own fleet of vehicles, they work with a decentralized network of hobbyists. Anyone with a drone can download the company’s software to autonomously fly a pre-determined flight path and capture the necessary images on demand. According to Lakeland, each flight covers roughly 25 acres in about seven minutes. A pilot can expect to earn around $10 per flight, with some earning hundreds of dollars a day. To date, Spexi’s network of over 8,000 drone pilots has mapped more than 5 million acres across more than 200 cities in Canada and the United States.
With this data, Spexi aims to build a sort of Google Street View from the sky. But consider that Google’s rumored investment building Street View was over a billion dollars as they gathered data with car-mounted cameras. While it was a different type of information, Google’s acquisition of Waze in 2013 gave them access to crowdsourced map data Waze collected for free from 40 million users. While Spexi’s approach isn’t free, it appears to be skipping the relatively more expensive in-house phase for something closer to Waze’s approach.
The impact of having up-to-date maps of Earth from above is sure to be significant.
In a Bloomberg profile, Lauren Rosenthal writes that forestry professionals are already leveraging drone data to help prevent wildfires. They’re using images from Spexi to train AI models that can alert forest managers to areas of high fire risk. Similarly, insurance companies are turning to Spexi for risk assessment, underwriting, and claims processing.
In augmented reality and robotics, drone data can also produce 3D maps for visual positioning systems. Author and Wired cofounder, Kevin Kelly, calls this digital twinning project the “mirrorworld.” Some observers suggest it’s one of the most significant technology projects of the age. Using this type of 3D training data, companies are also building generative AI world models, which help AI understand the physical world.
The rise of drone imaging doesn’t yet signal the end of other approaches, and it’s not clear how much of the industry will be serviced by drones versus other means. The race to corner the satellite imaging market is also heating up. In one sense, Tournachon’s 19th century art project was no different than today’s image gathering; attach a camera to a flying object and take pictures of Earth. The main distinction, however, is that these images have evolved from mere curiosity to a digital asset powering the modern world.
The post Thousands of Everyday Drone Pilots Are Making a Google Street View From Above appeared first on SingularityHub.
2026-03-04 08:35:15
The technology, which uses genetically engineered T cells, could target nearly two dozen different solid cancers with one treatment.
Few cancer treatments are as ferocious as CAR T cell therapy.
Often derived from a patient’s own immune cells, CAR T cells are genetically modified to hunt down and destroy cancer cells. The FDA has approved treatments for deadly blood cancers, and treatments tackling autoimmune diseases and preventing tissue scarring in the heart and kidneys have shown promise.
Yet CAR T has struggled against solid tumors. Over 85 percent of cancers fall into this category. Solid tumors have an arsenal of sneaky tactics to evade or deactivate CAR T cells, eventually undermining the treatment.
This month, a Columbia University team broke through one of the barriers with an upgraded design. They engineered a new, ultra-sensitive protein “hook” that seeks out CD70, a protein that dots the surfaces of multiple types of solid cancer cells—but at vastly different levels.
“Some molecules have been identified that are found in 25%, 50%, or 75% of tumor cells,” said study author Michel Sadelain in a press release. “Though a therapy directed at those targets might be successful…you can’t cure somebody if you just eliminate a small fraction or even 90% of their tumor.”
In tests, the supercharged cancer-killers, dubbed HIT cells, detected and wiped out cancer cells with extremely low levels of CD70—so low that the protein was undetectable using traditional methods. In kidney, ovarian, and pancreatic cancer grown from patients’ cells in petri dishes and in mouse models, HIT completely eliminated all signs of these tumors.
Like CAR T, the new approach is plug-and-play. The protein hook can be redesigned to target other faint cancer protein markers that have previously escaped detection.
“We hope our CD70-directed HIT cells help us find a way to eradicate the entire tumor,”
said study author Sophie Hanina.
Our immune system naturally fights off cancer. T cells, for example, roam the body looking for threats. When they identify cancerous cells, they signal other immune cells to launch a coordinated effort to wipe out the cancer before it expands.
The identification process relies on antigens, proteins that dot the surfaces of cancer cells like beacons. But tumors are highly versatile and rapidly evolve their antigen signature, essentially cloaking themselves from immune attacks.
CAR T cells override the defense. Here, T cells are extracted from a patient’s body and genetically engineered with custom-designed protein hooks to grab onto cancer antigens.
Multiple blood cancers have a heavy coat of a single shared protein on their surfaces, making them a perfect target for CAR T therapy. Solid tumors, however, are different. Tumors are dotted with a wide range of antigens, many of which are present in normal tissues. This increases the chances CAR T might attack healthy cells and reduces its effectiveness.
Even for the same antigen, some cells in solid tumors express high levels, others very low. The latter escape CAR T detection and linger as a reservoir that can regrow the tumor.
For a persistent solid cancer cure, “you have to get down to the very last cell,” said Sadelain.
An ideal target antigen needs to check two boxes: It’s expressed across multiple tumor cell types, and at the same time, it’s absent in normal cells.
The antigen in the new study, CD70, fits the bill. It occurs in a variety of solid cancers, making it a valuable target beacon. But previous attempts targeting CD70 struggled to control cancer in clinical trials. This is partly because cancer cells within a single tumor have different levels of the antigen, and some seemingly lack the marker altogether, allowing them to escape detection.
But are these cancer cells truly devoid of the antigen, or is it just that scientists, and the CAR T cells they’ve engineered, can’t find them using current methods?
Researchers can see most proteins under the microscope but only if they’re at high enough levels. Rather than relying on conventional imaging, the team looked for CD70 gene expression in donated cancer patient samples. These lab models mimic the complexity of solid tumors.
CD70 antigens dotted each cell in multiple tumors, although at different levels of intensity. “We found that apparent CD70-negative tumor cells do in fact express low levels of CD70, though not at a level high enough to be eliminated by conventional CAR T cells,” wrote the team.
Taking aim at cancer cells with faint CD70 levels, the team tapped into their previous work genetically engineering cells to detect low-level antigens. The hooks on these HIT cells mimic those from a population of highly sensitive T cells naturally found in our bodies.
The team redesigned HIT cells to specifically target CD70. Because normal cells don’t use this molecular pathway, HIT cells largely ignored them, lowering the risk of collateral damage.
“HIT cells are the next generation of CAR T cells. They can be programmed like a CAR T cell, but they have the sensitivity of a natural T cell and can detect cancer cells that have only a vanishingly small number of target molecules,” said Hanina.
Ovarian and pancreatic cancer cells have mixed levels of CD70. Several tests in highly aggressive models for these cancers found that HIT cells completely eradicated the tumors in petri dishes. The treatment also cleared cancer cells in different types of solid tumors in mice, even ones with low CD70 levels. Conventional CAR T only eliminated a fraction of the cancer.
A recent CAR T clinical trial targeting CD70 found CAR T cells could infiltrate and linger near kidney tumors, but their effectiveness was based on detection, which varied depending on the number of CD70 beacons. Because HIT cells are more sensitive, they could hunt down and kneecap more cancer cells.
But HIT cells may have side effects. Although CD70 isn’t expressed in most healthy tissues, its level skyrockets in immune cells during infections, which could trigger friendly fire. The team plans to investigate the treatment’s safety and efficacy in patients with ovarian cancer at the Columbia University Irving Medical Center.
If successful, the technology could benefit roughly 20 other types of solid cancer that express CD70, including deadly brain cancers such as glioblastoma.
“Curing solid tumors is not easy, but this work solves one piece of the puzzle,” said Sadelain.
The post These Supercharged Immune Cells Completely Eliminated Solid Tumors in Mice appeared first on SingularityHub.
2026-03-03 07:47:26
The flower-shaped device wraps around brain organoids and maps nearly all electrical activity.
Pea-sized brain blobs are a chatty bunch. Packed with neurons that spark with electrical activity, brain organoids—or “mini brains”—are a now popular way to study the human brain.
Some organoids model the brain’s wiring during early development. Others, made from patients’ skin cells, retain DNA mutations that could lead to schizophrenia or autism. Scientists studying mini brains hope to find patterns associated with diseases and test ways to fix them.
But it’s harder to record activity in organoids than neurons lying flat in a dish. Traditional electrode arrays are too large and stiff. More recent soft electronics are a better fit, but they can only record a fraction of each organoid at a time. This makes it difficult to figure out how all the neurons work together and could miss key aspects of the way the brain functions as a whole.
Recently, a team at Northwestern University and collaborators offered up a unique solution: A pop-up electrode mesh that envelopes entire mini brains. Each device starts out as a flower-like lattice before transforming into a breathable 3D net that gently wraps around brain organoids. The device’s 240 microelectrodes simultaneously capture electrical activity from nearly the entirety of an organoid’s surface, providing a birds-eye view of overall function.
Using the device, the team detected brain-wave-like electrical oscillations rippling across the organoids. Thanks to its porous design, the mesh allows nutrients—and drugs—to flow through. In several tests using drugs known to spike or lower neural activity, the device readily picked up changes across the mini brain, hinting at its potential as a drug testing platform.
“This advance is really about building the right tools…we can now record from and stimulate hundreds of locations across [an organoid’s] surface at once. This allows us to study neural activity at the level of whole networks rather than isolated signals,” said study author Colin Franz in a press release.
Technologies that tap into and alter brain activity have exploded in variety and efficiency. Some sit on top of the brain, under the skull, to monitor large areas. Others directly record a vast number of single neurons as they fire away. Trusty non-invasive methods like electroencephalograms (EEG) have mapped whole-brain activity for over a century.
Recording from mini brains is different. Recording devices meant for the human brain are far too bulky. Those designed for cells in petri dishes break if they’re bent.
“Integrated circuits in consumer electronics are perfectly planar, sitting on wafer-based substrates,” said study author John Rogers. “That conventional layout represents a very significant geometrical mismatch relative to the spherical shapes of these organoids.”
Scientists have recently turned to mesh-like electrode systems that are more flexible, such as a basket-like design inspired by Japanese paper folding. Another, shaped like a flower, turns into an electrode-studded claw to grasp organoids. Most of these can record single cells as the organoids develop without changing their shape or cellular and genetic makeup.
Researchers already use these designs to eavesdrop on a variety organoids. But the devices struggle to capture whole-brain dynamics. Some have just a few dozen electrodes; others cover only a small region. To truly decipher mini brain activity, scientists need full-coverage hardware.
The new device is like a draw-string coin purse. It starts out completely flat like a flower and then gradually cinches itself into a soft, flexible mesh that envelops the mini brain.
Matching the mesh to the organoid’s shape without damage is one challenge. Another is to ensure all 240 electrodes are distributed across a mini brain’s entire surface.
The team turned to a design that allows electrodes to move in predictable ways as the device changes shape. When flat, the device is smaller than a US quarter. Once converted into a 3D purse, its electrodes are evenly spread out. Each one is only 10 microns across, roughly the size of a single cell. The shape-shifting mechanism works like a pop-up book. Each petal of the flower is carefully tailored to bend into differently sized purses and shapes to minimize gaps between the electrodes and the organoid surface.
The device’s mesh allows nutrients to flow into the organoid but prevents cells from spreading past the pores and beyond the reach of the electrodes.
“The device’s structure needs to support these metabolic processes to sustain the viability of the tissue,” said Rogers. “Basically, the organoid needs to breathe. The hardware must not significantly constrain or suffocate it.”
The device covered roughly 91 percent of the surface of an average-sized mini brain after 60 days in culture. Because the team knew each electrode’s location, they were able to reconstruct the neural oscillations to create a 3D widescreen view of the organoid’s activity.
In one case, triggering activity in a small region led to highly synchronized, wave-like activity across most surface neurons. The activity patterns, previously undetectable in brain organoids, mimicked those seen in developing human brains.
These synchronized waves broke down dramatically after a dose of botulinum toxin, which is known to dampen brain activity by inhibiting chemical connections between neurons. This suggests their wiring is similar to that of our brains.
The device also captured brain-wide activity changes due to a neurochemical imbalance. Watching these kinds of changes could help scientists study a wide range of diseases such as Parkinson’s, multiple sclerosis, and amyotrophic lateral sclerosis (ALS).
The system also worked with other established observation methods. For example, it reliably recorded electrical signals in tandem with a technology that tracks brain chemicals. The system also responded to light beams activating groups of neurons (an approach called optogenetics).
Mixing and matching different strategies, including wholly new approaches, like the one in this study, could yield a more in-depth understanding of mini brains—and in turn, our own.
The post New Device Detects Brain Waves in Mini Brains Mimicking Early Human Development appeared first on SingularityHub.
2026-02-28 23:00:00
Breaking Encryption With a Quantum Computer Just Got 10 Times EasierKarmela Padavic-Callaghan | New Scientist ($)
“In 2019, Craig Gidney at Google Quantum AI co-authored a paper that reduced [the requirement to break RSA encryption] from 170 million to 20 million quantum bits, or qubits. And in 2025, Gidney devised a way to slash that number to less than a million qubits. Now, Paul Webster at Iceberg Quantum in Australia and his colleagues have managed to decrease the number even further to about 100,000 qubits.”
Tech Has Never Caused a Job Apocalypse. Don’t Bet on It Now.Greg Ip | The Wall Street Journal ($)
“No one should dismiss any scenario, even the most dystopian, with high conviction. Certainly not journalists, whose way of life is in AI’s crosshairs. But I keep stumbling over one small problem with the doomsday vision: It requires a breakdown in how the market economy functions. Nothing like it has happened in the US before, and there is no evidence it is happening now.”
A Recent 3D Printing Breakthrough Brings Us One Step Closer to You Downloading a CarJustin Caffier | Gizmodo
“A team at [MIT] has recently developed a printer with four different extruders that outputs five different materials to produce a fully functioning linear motor in about three hours. …The team explained how by retrofitting a printer with enough extruders to handle the various materials needed to make a working motor, they decimated the usual production time for such a device and brought the material costs down to around $0.50.”
Human Brain Cells on a Chip Learned to Play Doom in a WeekAlex Wilkins | New Scientist ($)
“A clump of human brain cells can play the classic computer game Doom. While its performance is not up to par with humans, experts say it brings biological computers a step closer to useful real-world applications, like controlling robot arms.”
Waymo Robotaxis Are Now Operating in 10 US CitiesKirsten Korosec | TechCrunch
“‘Waymo is serving more riders than ever, as we are on track to serve over one million rides per week by the end of this year,’ Mawakana said in a blog post Tuesday, adding that the company is laying the groundwork for robotaxi service in more than 20 cities.”
AI Will Never Be ConsciousMichael Pollan | Wired ($)
“By the time I finished digesting the Butlin report, the Copernican moment I’d worried about seemed more distant than the report’s bold conclusion had led me to believe. After reviewing the half‑dozen or so theories of consciousness covered by the report, it seemed clear that all of them stacked the deck by taking for granted that consciousness could be reduced to some kind of algorithm.”
Andrew Ng Says AGI Is Decades Away—and the Real AI Bubble Risk Is in the Training LayerVictor Dey | Fast Company ($)
“Maybe a year ago, AGI felt 50 years away. Over the past year, perhaps we’ve made a solid 2% of progress, with another 49 years to go. These numbers are metaphorical, so don’t take them too seriously. [Laughs] But we are closer than before, yet many decades away from an AI that matches human intelligence. If you stick with the original definition—aligned with what people genuinely imagine AGI to be—we remain very, very far away.”
The Hidden Cost of Letting AI Make Your Life EasierShai Tubali | Big Think
“‘[AI] is designed to take over tasks that are effortful for us,’ Nyholm says. …The difficulty, he adds, is that many effortful tasks are precisely the ones that carry meaning. Deep relationships require patience, friction, and vulnerability. Skills demand time, frustration, and persistence. …When effort, creativity, and skill fall away, meaningfulness no longer seems the right category.”
The Human Work Behind Humanoid Robots Is Being HiddenJames O’Donnell | MIT Technology Review ($)
“The roboticist Aaron Prather told me about recent work with a delivery company that had its workers wear movement-tracking sensors as they moved boxes; the data collected will be used to train robots. The effort to build humanoids will likely require manual laborers to act as data collectors at massive scale. ‘It’s going to be weird,’ Prather says. ‘No doubts about it.'”
The 5 Biggest Obstacles to AI Data Centers in SpaceEthan Siegel | Big Think
“Is this an example of an emerging technology that could provide an off-world solution to the problem of competing demands for limited resources? Or is it, like the hyperloop, an example of grift: where the concept itself isn’t exactly physically impossible, but is rendered so impractical due to the actual physical constraints of the endeavor, that it absolutely cannot materialize as advertised?”
Meta Director of AI Safety Allows AI Agent to Accidentally Delete Her InboxMatthew Gault | 404 Media
“As countless people on X have said in response to her post, seeing the person in charge of making sure powerful AI tools are safe at one of the biggest tech companies in the world trust an AI agent that is known to pose several serious security risks, does not inspire a lot of confidence in what Meta and other big AI companies are doing.”
The post This Week’s Awesome Tech Stories From Around the Web (Through February 28) appeared first on SingularityHub.
2026-02-27 23:00:00
We need to give models knowledge that anchors their behavior to the realities of our world.
Modern AI chatbots can do amazing things, from writing research papers to composing Shakespearian sonnets about your cat. But amid the sparks of genius, there are flashes of idiocy. Time and again, the large language models, or LLMs, behind today’s generative AI tools make basic errors—from failing to solve basic high school math problems to stumbling over the rules of Connect Four.
This instability has been called “jagged intelligence” in tech circles, and it isn’t just a quirk—it’s a critical failing and part of the reason many experts believe we’re in an AI bubble. You wouldn’t hire a doctor or lawyer who, despite giving sound medical or legal advice, sometimes acts like they are clueless about how the world works. Enterprises seem to feel the same way about putting “jagged” AI in charge of supply chains, HR processes, or financial operations.
To solve the jagged intelligence problem, we must give our AI models access to a more powerful, more structured, and ultimately far more human stock of knowledge. Having engineered a range of AI systems over 30 years, I have found such knowledge to be an indispensable component of any reliable system.
This is because the technological innovations that launched the AI era aren’t capable of smoothing out these jagged edges. Current AI models don’t possess clear rules about how the world works; instead, they infer things from vast pools of data. In other words, they don’t know things, so they’re forced to guess—and when they guess wrong, the results range from the comical to the catastrophic.
Think about how humans learn. Born into “blooming, buzzing confusion,” babies spot patterns in the world around them: Faces are fun to look at, mom smells great, the cat scratches if you yank its tail. But pattern recognition is soon supplemented by clearly articulated knowledge: rules we’re taught, rather than things we absorb. From ABCs to arithmetic to how to load a dishwasher or drive a car, we use codified knowledge to learn efficiently—and avoid idiotic or dangerous mistakes along the way.
Current AI models don’t possess clear rules about how the world works; instead, they infer things from vast pools of data.
Frontier AI labs are already dabbling in this approach. Early LLMs struggled with grade-school math, so researchers bolted on actual mathematical knowledge—not hazy inferences, but explicit rules about how math works. The result: Google’s latest models can now reliably solve math Olympiad problems.
Adding more data of different types—for example video data, being advocated by AI luminaries such as Yann LeCun—won’t overcome the fundamental challenge of jagged intelligence. Even with extra data, it’s mathematically certain that the models will keep making mistakes—because that’s how probabilistic, data-driven AI works. Instead, we need to give models knowledge—rigidly described concepts and constraints, rules and relationships—that anchor their behavior to the realities of our world.
To give AI models a human stock of knowledge, we need to rapidly build a public database of formal knowledge spanning a range of disciplines. Of course, the rules of math are clear; the workings of other fields—health care, law, economics, or education, say—are, in some ways, vastly more complex. This challenge is now within our reach, as the growth of companies such as Scale AI, which provides high-quality data for training AI models, points to the emergence of a new profession—one that translates human expertise into machine-readable form and, in doing so, shapes not just what AI can do, but what it comes to treat as true.
This knowledge base could be accessed on demand by developers (or even AI agents) to provide verifiable insights covering everything from loading a dishwasher to the intricacies of the tax code. AI models would make fewer absurd mistakes, because they wouldn’t need to deduce everything from first principles. (Some research also suggests that such models would require far less data and energy, though these claims have yet to be proven.)
Unlike today’s opaque AI models, whose knowledge emerges from pattern recognition and is spread across billions of parameters, a formally distilled body of human knowledge could be directly examined, understood, and controlled. Regulators could verify a model’s knowledge, and users could ensure that tools were mathematically guaranteed not to make idiotic mistakes.
We need to give models knowledge—rigidly described concepts and constraints, rules and relationships—that anchors their behavior to the realities of our world.
The ambition to create such a knowledge resource is nothing new in AI. Even though previous efforts produced inconclusive results, it’s time to make a fresh start. Much as biologists use algorithms to speedrun the once-laborious process of modeling proteins, AI researchers could leverage generative AI to aid knowledge modeling.
It’s clear that current AI models are getting smarter and will get better by using different data. And yet, to overcome the challenge of jagged intelligence—and turn AI models into trusted partners and true drivers of value—we need to redefine the way models relate to and learn about the world. Data-driven algorithms allowed us to start talking to machines. But knowledge, not data, is the key to sustaining the future of AI past the potential bubble.
This article was originally published on Undark. Read the original article.
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2026-02-27 07:28:40
Fossil fuels still dominate the energy mix. But growth in renewables offset nearly 75% of new power demand.
Booming energy demand is driving a scramble to set up new generating capacity, and one technology is proving to be the clear winner. Newly released federal data shows that solar power grew by more than 35 percent year-over-year in 2025, outpacing all other forms of generation.
After decades of relatively flat electricity use, US power demand is rising again. A new report from the US Energy Information Administration shows that consumption jumped 2.8 percent in 2025, thanks to rising industrial activity and the rapid expansion of energy-hungry AI data centers.
While increased fossil-fuel generation met much of that additional demand—a revival in coal, in particular—solar power posted the fastest growth of any major source. According to an analysis by Ars Technica, the Energy Information Administration data shows US solar generation increased by more than 35 percent year-over-year, driven by 27 gigawatts of newly installed capacity.
The surge pushed total solar output above hydroelectric power for the first time in terms of total annual generation. Hydropower output itself was relatively stable compared with the prior year, while solar continued its recent breakneck expansion, with capacity increasing rapidly across multiple regions.
Solar’s surge, which added about 85 terawatt-hours of generation, met about two-thirds of the increased energy demand. This number rose to 73 percent when combined with wind power, which grew by a more modest 2.8 percent.
But the data’s not-so-silver lining is that the remaining demand was met primarily by 13 percent growth in coal power. That bucks the recent trend of coal’s diminishing importance in the US power supply and was driven by a complex confluence of changes in the US energy system.
In previous years, natural gas has been the go-to fossil fuel due to abundant domestic supply and the ability to rapidly ramp power up and down. However, the Trump administration’s tariff policies have made it more difficult and expensive to source the equipment for gas power plants, and rapid expansion in gas exports means domestic utilities are competing with foreign buyers for fuel.
Altogether, this made gas generation a less reliable bet, and generation actually shrank 3.3 percent last year. That made coal a more attractive option, and its position in the US energy mix grew.
Still, the outlook remains bright for renewables, and solar in particular. A recent Energy Information Administration analysis showed roughly 43 gigawatts of utility-scale solar capacity is planned or under construction for 2026, potentially making it an even bigger year for solar than 2025. More than half of this new capacity is in just four states: Texas, Arizona, California, and Michigan.
And wind power’s growth could more than double this year with planned additions of 11.8 gigawatts, 60 percent of which are in New Mexico, Texas, Illinois, and Wyoming.
Growth in renewables will also be supported by another record-breaking year for added battery storage. Last year, the industry added a record 15 gigawatts of capacity to the grid. Planned projects would grow capacity by another 24 gigawatts in 2026, 80 percent of which will be in Texas, California, and Arizona.
It’ll be a while yet before green energy overtakes fossil fuels, which still accounted for 58 percent of total generation in 2025. But the data suggests the energy transition is well underway. If solar and wind continue to meet growing energy demand, the US energy system could soon look very different.
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