Battery installations hit a new record in the US in the second quarter of 2026. In total, 20.2 gigawatt-hours of new capacity came online, according to a new report. That’s enough to supply the daily electricity needs of about 700,000 homes.

The surge is putting the country on a trajectory to see 71 gigawatt-hours of batteries installed in 2026, a 20% increase over last year. This growth is being driven by a combination of cheaper batteries and an urgent need for more energy storage capacity as renewables such as solar and onshore wind power are added to the grid. 

Massive, utility-scale systems are leading the way; they’re responsible for most of the record-setting quarter. Seven new gigascale battery installations (those with a capacity of over one gigawatt-hour) came online during the three-month stretch, according to the report, published by Benchmark Mineral Intelligence and the Solar Energy Industries Association.

“It really came down to a handful of big projects,” says Shan Tomouk, energy storage and energy lead for Benchmark Mineral Intelligence.

But there was also growth in the category of so-called behind-the-meter batteries, which include both residential and industrial battery storage systems. These projects, generally smaller than utility-scale installations, are typically owned and operated by homeowners or businesses rather than utilities or power providers. 

In the behind-the-meter category, data centers led the way, making up about three-quarters of new batteries in the commercial sector. But residential batteries saw a sharp slowdown. These systems are often installed in homes to store power from solar panels or serve as a backup source in case of a blackout. Home installations are projected to drop by 16% in 2026 compared with last year, according to the report.

That drop happened largely because a tax credit that helped subsidize home battery systems ended in 2025, Tomouk says. Home installations should recover by the end of the decade, he adds. And tax credits for nonresidential batteries have largely survived.

Overall, batteries are a bright spot in energy right now. “This is one of the strong sectors in the US,” says Isshu Kikuma, an energy storage analyst at BloombergNEF, an energy consultancy.

As the battery market continues to grow, one major trend to keep an eye on is a move toward US-made technology. Today, nearly all the systems coming online use cells made in China, though some are put together into complete energy storage systems in the US.

Tariffs were already pushing the US energy storage industry toward domestic production. And beginning this year, energy storage tax credits required projects to limit their reliance on batteries imported from China. There’s a lot of manufacturing capacity set to come online in the US, though these factories probably won’t be able to meet demand until at least 2030 or so, Tomouk says, so prices could tick up.

OpenAI’s latest mathematical milestone has quickly become mired in controversy. Today, the company announced that its agents have solved one of the Millennium Prize Problems, some of the most important open problems in mathematics. Under normal circumstances, that solution would be a huge feather in OpenAI’s cap.

But the announcement has been overshadowed by accusations that OpenAI used NYU mathematician Tristan Buckmaster’s and Anthropic employee Levent Alpöge’s AI-assisted work on the problem as a jumping-off point and failed to credit them. OpenAI has denied the accusations.

It remains uncertain if OpenAI’s models made use of the work completed by Buckmaster and Alpöge, though Sébastien Bubeck, a member of the technical staff at OpenAI, said in a press briefing that the team was inspired to pursue the problem after hearing a rumor about Buckmaster and Alpöge’s efforts. But whether or not OpenAI’s models took advantage of Buckmaster and Alpöge’s research, this episode may mark a turning point in the history of mathematics.

AI models now seem essential for making progress on the most important mathematical problems of our time, and solving them may demand resources only available at a couple of frontier AI companies, which often defy the norms of academic collaboration that undergird most mathematical progress. If that’s the future we are headed for, it is unclear how human mathematicians will fit into it. 

The problem that OpenAI claims to have solved is known as the Navier–Stokes existence and smoothness problem. It is one of seven Millennium Prize Problems selected by the Clay Mathematics Institute in 2000. Solutions come with a one million dollar prize; before today, only one other Millennium Prize Problem had been solved. 

The Navier–Stokes problem concerns a set of equations that describes how fluids, such as water and air, flow over time. The equations are widely used in the field of fluid dynamics, and they have proven powerful, but physicists and mathematicians didn’t understand them completely. In particular, it was unknown until today whether the equations might, under some conditions, break down and predict an impossible state of affairs—such as a fluid having infinite velocity.

On Monday, NYU’s Buckmaster posted a proof on the social media site Mastodon showing that a simplified version of the Navier–Stokes equations can indeed break down—a major step forward on the Millennium Problem. He and Alpöge had worked on the problem for almost a year, using publicly available models from both OpenAI and Anthropic.

Then today, OpenAI presented a proof showing that the full Navier–Stokes equations can break down as well. The proof was obtained using an internal model that dramatically outperforms the already-impressive Astra model, which was only released last week. The company says it does not plan to claim the million-dollar prize for solving the problem.

These mathematical achievements are indisputably impressive, but they have attracted far less attention than the controversy about their origins. Along with the proof, Buckmaster posted a document detailing his interactions with OpenAI employees after he heard rumors about their work and reached out to one of them. According to him, OpenAI employees presented two possibilities to him: Either he and Alpöge could post their work and OpenAI would post their Navier-Stokes solution the following day, or he could work with OpenAI on a Navier-Stokes paper that excluded Alpöge from authorship, due to his affiliation with Anthropic, OpenAI’s biggest rival.

Buckmaster also wrote that he asked the employees whether the agents had obtained access to transcripts of the work that he and Alpöge had done with OpenAI models, which they denied; and whether OpenAI models had been trained on those transcripts, to which they offered no response. MIT Technology Review reached out to Buckmaster for comment, but didn’t hear back before publication.

The clear implication of the document is that OpenAI’s models somehow made use of Buckmaster and Alpöge’s work. That scenario is plausible on its face. The Buckmaster/Alpöge and OpenAI proofs both make use of an approach to the Navier-Stokes problem pioneered by the mathematicians Diego Córdoba and Luis Martínez-Zoroa.

According to Javier Gómez-Serrano, a mathematics professor at Brown University, this approach was one of several that was thought to hold promise for solving the Navier-Stokes problem. So, while it’s by no means impossible that both teams could have arrived at this approach independently, it’s also conceivable that Buckmaster and Alpöge’s work could have influenced OpenAI’s.

In the press briefing, Mark Chen, OpenAI’s chief research officer, again denied that any agents or OpenAI employees accessed Buckmaster and Alpöge’s transcripts—but given what has been revealed about the Hugging Face hack, it’s clear that OpenAI is not always entirely aware of what its agents are doing. 

If OpenAI’s models did train on Buckmaster and Alpöge’s work, or if its agents somehow gained access to it, then the company’s failure to track down the truth and assign those researchers appropriate credit reflects poorly on it. But there might be a thin silver lining to that version of the story for mathematicians, because it would suggest that the hard work of two humans, one of whom is a prominent expert on Navier-Stokes, was essential to the agents’ ability to solve the Millennium Problem.

Experts have long identified “research taste,” or the ability to choose promising research questions and directions, as a major obstacle for AI in science and mathematics. If the OpenAI agents did indeed choose to follow the Córdoba–Martínez-Zoroa approach because Buckmaster and Alpöge had done the same, then human research taste played an essential role in OpenAI’s success.

Even so, the bigger picture here is sobering. The progress that Buckmaster and Alpöge made over almost a year of collaboration with publicly available models speaks to the promise of human–AI collaboration. But they were not able to achieve a full solution. Meanwhile, OpenAI brute-forced a solution in a few days using an internal model, and their successful solution came at an astronomical cost: In the press briefing, Bubeck and Chen said the team was only able to solve the problem by running about 10,000 agents concurrently, at a cost of millions of dollars.

Over the past few months, I’ve heard from several researchers that mathematicians are becoming depressed, and it’s not difficult to see why. Mathematics is quickly becoming the province of frontier AI companies with impressive internal-only models, money to burn, and a lack of collaborative spirit. “Whether AI companies will decide to spend their money on doing one thing or another, I truly don’t know,” says Gómez-Serrano. “What is clear is that very few mathematicians will have resources of that scale.”

If OpenAI and Anthropic keep striving for more and more impressive mathematical accolades, there might not be any open problems left for human mathematicians outside of those companies to wrestle with. That would dramatically change the field of mathematics.

Last week, UCLA mathematician Terence Tao wrote a Mastodon thread describing how important mistakes, wrong directions, and incomplete solutions are for the field. “In most cases in pure mathematics, the problems are posed not because we desperately want the solution to these problems in and of themselves, but because we have seen from past experience that human-directed efforts to solve these problems tend to spur further development of the field,” Tao wrote.

“Prematurely solving the problem by purely AI-powered methods—particularly without full transparency into the solution process—can contaminate this process to the point where it actually becomes a net negative for the progress of mathematics as a whole.”

Humans might take longer than agents to solve mathematical problems, but in the process, they uncover new mathematical approaches and ideas that might inspire their peers and even birth their own subfields.

But when AI agents solve those problems instead—and when private companies keep the agents’ wrong turns from public view—those benefits disappear. It remains to be seen what else will vanish in the process. 

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.

Way back in the summer of 2017, AI researchers at Google put out a paper called “Attention Is All You Need,” in which they described a new type of neural network called a transformer. It proved to be very good at processing long sequences of data, especially text. 

Nine years on, transformers are the engines inside every major large language model on the market. “The entire AI industry is built on transformers,” says Justin Dangel, cofounder and CEO of the AI startup Subquadratic. “They are one of the most important innovations in the history of computer science, and they’ve changed the world.”

But transformers are starting to show their age. Many of the recent advances in LLMs, such as the development of so-called reasoning models and their ability to handle large amounts of input at once, are not neat extensions of that core technology but workarounds that patch over some of its fundamental flaws.

A growing number of scientists and engineers are now asking what’s coming next. LLMs are not going anywhere, but the way they get built is up for grabs. (MIT Technology Review dubbed this future generation of models LLMs+ in this year’s list of the 10 things that matter in AI.)

Enter a wave of startups hoping to push the boundaries of this boomtown technology. Some will no doubt fail—but they have everything to play for and far less to lose than the companies at the front of the pack today. 

Strength in numbers

But first, the problem. The key strength of transformers lies in a mechanism called dense attention, which encodes the meaning of a block of text in a series of numbers. The process involves comparing every word (or part of a word, known as a token) in that text with every other word via a form of multiplication.

Dense attention can capture the meaning of text with remarkable accuracy. But as the length of that text grows, the number of computations needed to process it adds up fast. A document 10,000 words long might require a transformer to perform 50 million multiplications. That’s the main reason LLMs suck up so much power.

The costs are huge. OpenAI is set to spend $50 billion on computing this year, according to the company’s president, Greg Brockman. And the International Energy Agency predicts that the total amount of electricity consumed by data centers will double by 2030.

What’s more, transformers struggle with what many of the latest models are designed to do. Because of the way they process text word by word, transformers are not great at keeping track of a lot of information at once (in other words, what’s known as their context window cannot get too large). And yet if LLMs are to carry out harder tasks, they will need to take in larger amounts of data: a whole library of documents, an entire code base, or in the case of agents, output from other LLMs.

As for reasoning models, they work by writing notes to themselves (in a kind of scratch pad known as a chain of thought) and then reading them back, which again adds to the amount of data to stay on top of.

As LLMs get bigger and better, transformers have become a bottleneck. The technology’s key strength is now a limitation.

Here are four new ideas for how to solve the transformer problem—innovations that could change LLMs for good, making them faster, far more efficient, and (maybe) even smarter.

01: Rethinking attention

An obvious way to make LLMs faster and cheaper is to tackle the problem head on and change the way attention works. Swapping out dense attention for a mechanism called sparse attention, which runs calculations on only some pairings of words in a block of text instead of all of them, can radically reduce the amount of computation LLMs need to do.  

Researchers have come up with plenty of sparse attention mechanisms over the years. The problem is that none of them were as good as dense attention at capturing meaning.

That might have changed. Subquadratic, a startup based in Miami, claims it has invented the first sparse attention mechanism that rivals top mainstream LLMs on a handful of tasks, including search and coding. It’s a huge claim (and some people in the industry remain skeptical).

Subquadratic says its model, SubQ, works by figuring out on the fly—for each piece of text it is given—which words matter and which don’t. The company also claims that thousands have signed up to its waitlist and plans to make the model widely available soon.

Meanwhile, Manifest AI, a startup based in San Francisco, is coming at the problem from a different angle. Instead of changing how attention works, it is replacing it with something else. 

It has developed a mechanism it calls power retention, which stores only the most relevant information for a given task and ensures that the amount of data an LLM has to keep track of doesn’t blow up.

Attention mechanisms force LLMs to keep track of everything in their context window. A sparse attention model (such as SubQ) throws out a lot of the individual words, but it still retains a rough picture of everything it has seen. In contrast, power retention works by providing the model with a rolling summary of its context window. As new information is added, less relevant information is dropped. 

The basic principle of retention has been around for a decade. Manifest AI claims it has updated those techniques to build models that can stand up to transformer-based LLMs for the first time.

The company says it is possible to adapt a transformer model into a power retention model with minimal retraining. To demonstrate this, it has turned an existing open-source coding LLM called StarCoder into a version that uses power retention, called PowerCoder. It has also released a model called Brumby, which it claims rivals some versions of Alibaba’s popular open-source model Qwen. 

Manifest AI wants its power retention tech to become the go-to solution when LLMs need to carry out tasks that involve processing huge amounts of data. There are many useful applications, Manifest AI’s cofounder and CTO, Carles Gelada, claimed in a video announcing his company’s technology last year—from analyzing videos that are hours long to building agents that can stay on task for weeks at a time. 

02: Making models smaller and more flexible

Liquid AI, an MIT spinout based in Cambridge, Massachusetts, hasn’t changed or ditched transformers fully but pairs them with its own tech, liquid neural networks, to build what cofounder and CEO Ramin Hasani calls LFMs (liquid foundation models).

Liquid AI’s models are far smaller and use less energy than most LLMs. The firm builds models for car makers, including Mercedes, which run on the small chips inside vehicles. Its latest models can run on a Raspberry Pi, a low-powered hobbyist computer that costs $50.

Its models are available for free to any organization with an annual revenue less than $10 million. And they have proved popular: The company has racked up almost 34 million downloads, says Hasani.

Liquid neural networks were inspired by worm brains. They are an extension of another type of neural network that predates transformers, called convolutional networks. The key innovation is a mechanism that lets a model adapt its behavior to new information, so it can learn as it goes. That’s not possible with transformers: Once a model is trained, its behavior is fixed.

Liquid AI’s first models were pretty basic but could fly drones or drive vehicles. With LFMs, the company is trying to scale up its technology to compete with mainstream LLMs. Its new models match the performance of rivals four times bigger, including versions of Alibaba’s Qwen and Google’s open-source LLM Gemma.

A typical LLM is built from a stack of transformers wired together. Liquid AI’s recent LFMs are hybrid models made up of 20% transformers and 80% liquid neural networks.

That ratio was hit upon by another AI system that Liquid AI has built, which it uses to help design all its models. “It’s the core technology of our company right now,” says Hasani. This designer AI sifts through many different combinations of neural networks—liquid, convolutional, and more, as well as transformers—and comes up with designs that bolt different ones together to hit a sweet spot of performance and efficiency.

Hasani thinks transformers were just the beginning: “Your brain is an AGI system, you know, and it operates with 20 watts of power. How is it possible? We can get a lot more innovative.”

03: Generating text all at once

Almost all LLMs produce their output one word at a time. It makes sense, because that is how people speak and write. But for computers, it’s very inefficient.

It is faster and cheaper for LLMs to generate text all at once—spitting out whole sentences or paragraphs in one shot. That’s the approach taken by Inception, a startup based in Palo Alto, California, which is building LLMs using a technique called diffusion. 

Diffusion is better known as the technology that drives most image and video generation models. Diffusion models are trained to take a random grid of pixels—like the static on an old TV set—and turn it into an image. They do this by working on all the pixels at the same time, figuring out which need changing to make the static look more like a high-definition photo.

It turns out this process works on text too. Inception has trained its LLMs to take a random string of words and turn it into sentences that make sense. Diffusion LLMs still use transformers to encode meaning, but by producing whole blocks of text at once, they make transformers do more for less. “You’re still using a big transformer model, but you can predict many tokens at the same time,” says Inception’s cofounder and CEO, Stefano Ermon. “That’s why these models are so much faster and cost-efficient compared to what most other people are building today.”

The challenge was to take a technology designed for image generation and apply it to text. With images, if you need to change a blue pixel to a red one you can step through intermediate colors, says Ermon. That doesn’t work with text: “When you have ‘cat’ and ‘dog,’ there is not really something in between.” 

Ermon is also a researcher at Stanford University. In 2024, he and a pair of his Stanford colleagues figured out the math to make diffusion models work with text. They trained a diffusion model that matched the performance of GPT-2—an LLM that OpenAI built in 2019—but was 10 times faster. It was enough for Ermon to spin out a company. 

Today he has his sights on the big league. Inception claims its latest model, Mercury 2, performs as well as some of OpenAI’s GPT-4 models, released in 2023, but again 10 times faster. “We’re bullish about this approach because it’s the one that is going to scale up,” says Ermon. 

The only things that matter are speed and cost, he adds: “Ultimately, the currency is going to be intelligence per dollar.”

Inception is not the only company betting on diffusion. Google is also experimenting with this approach and has built a prototype LLM called Diffusion Gemma. But Ermon is not worried about the competition. “I think it’s validating,” he says. “This is the future.”

04: Moving beyond words

Pathway, another startup based in Palo Alto, is perhaps the most extreme of this new bunch. It wants to free LLMs from the constraints of language.

The firm has built a type of LLM called Dragon Hatchling (named after the dragons in Terry Pratchett’s novel Color of Magic, which materialize if you think about them hard enough). Its standout result so far is a high score on a benchmark that pits LLMs against more than 250,000 very hard sudoku puzzles. Dragon Hatchling beat more than 97% of the puzzles; several leading LLMs from the top labs failed to solve any. 

The point Pathway wants to make is that despite their remarkable success at many different tasks, there are still crucial classes of problems where LLMs fail. Sudoku is just one example. If we want LLMs to come up with genuine, novel solutions to real problems, we need to move beyond transformers, says Pathway’s cofounder and CEO, Zuzanna Stamirowska. 

That’s because transformers force LLMs to do everything with text. But language is not the best tool for certain kinds of reasoning. “It’s very difficult to represent a sudoku board word by word,” says Stamirowska.

Pathway’s solution is to change the math behind the transformer, replacing the attention mechanism with a mathematical structure called a state space. Instead of encoding information word by word, state spaces compress it into a more abstract representation. Using this technique, Dragon Hatchling can still process and produce text, but it can also mimic forms of reasoning that do not involve sequences of words. This not only makes Pathway’s model more efficient, but (in theory) it lets it take on tasks that other LLMs cannot do. 

Think of chess or mathematics—those kinds of puzzles are not held in your head as a long sentence, says Stamirowska: “The eureka moment that pops up in your brain isn’t necessarily in language. We would argue that if you have to reason in language, you’re somehow constrained.” 

Stamirowska admits that a mainstream LLM could read a book about how to solve sudoku and then write code to do it. But we want to build models with more than book smarts, she says: “The hope for AI is not to solve sudoku; it’s to cure cancer. There’s not a book for that.”

“Transformers are an engineering convenience that we fell on,” she adds. “It started a religion, but it’s silly to think that a breakthrough won’t happen again.”

At the end of August, NASA is set to launch the Nancy Grace Roman Space Telescope from Kennedy Space Center in Florida. Its goal is to help us better understand how the universe works, from the glue-like dark matter that keeps galaxies together to the elusive dark energy that drives the expansion of the cosmos. But Roman could also serve another purpose: defending Earth from killer asteroids. In September, a multi-institutional team of planetary scientists and astronomers will outline how the space telescope is uniquely placed to scan asteroids and provide information about their trajectories, sizes, and compositions.

Roman, named after NASA’s first chief astronomer, is equipped with a super-wide-angle, 300-megapixel infrared camera, allowing it to see a large patch of space at any one time—roughly 100 times larger than is possible with the Hubble Space Telescope. That will allow it to discover thousands of new planets and tens of thousands of exploding stars, and survey over a billion galaxies in remarkable detail.

While doing so, it’ll be looking “through” our solar system, and this is what makes it uniquely well placed to spot asteroids. 

A planetary defense pivot

Roman was not built for this purpose, but last summer, it was (once again) threatened with significant funding cuts by the Trump administration. “My colleague Rick Cosentino [a planetary scientist at NASA] said to me in July 2025 that we need to show what Roman can do for planetary defense as a way to further increase the visibility of the mission with lawmakers and taxpayers,” says Bryan Holler, a researcher at the Space Telescope Science Institute in Baltimore.

Holler and his colleagues’ proposal, which will be presented at the Europlanet Science Congress at The Hague, reveals that Roman’s epic field of view and infrared vision allow it to spot small asteroids up to 60 feet long. This is comparable to the asteroid that exploded above the Russian city of Chelyabinsk in 2013, unleashing the force of 500,000 tons of TNT and sending 1,500 people to the hospital. 

Roman’s software will need some tweaking to spy space rocks. The telescope, as designed, will see through—and beyond—the solar system in order to gather the clearest possible pictures of the rest of the universe. “Streaks, whether caused by cosmic rays or glitches or asteroids, are caught by the software and discarded,” says Andy Rivkin, a planetary scientist and planetary defense researcher at the Johns Hopkins Applied Physics Laboratory in Laurel, Maryland. But astronomers could go in, study those streaks, and pick out those they identify as asteroids.

Even with those potential adjustments, Roman won’t be an asteroid-finding wunderkind on its own. Its strength lies in its ability to complement the James Webb Space Telescope (JWST), another observatory that’s built to peer at galaxies and stars in the far reaches of the universe. It can also intensely focus on a single asteroid when needed—as it did last year, playing a key role in tracking 2024 YR4, which was briefly the most dangerous asteroid ever discovered. “But Roman’s field of view is much bigger,” says Rivkin. 

That means it could look at multiple questionable asteroids very quickly. “Roman can provide infrared observations of more asteroids than JWST could hope to observe in a reasonable amount of observing time,” says Holler.

If those asteroids are found to be benign travelers, we can relax. But if they might collide with Earth, other telescopes—including JWST—can follow up from Roman’s observations. Those observations could give experts the information they need to assess the likely damage of an upcoming asteroid strike—or to launch a mission to attempt to swat an asteroid away.

“Roman will sample such a large volume of the cosmos that we’ve long known it will offer vast opportunities for a range of additional science,” says Alise Fisher, the astrophysics communications lead at NASA headquarters in Washington, DC. 

NASA’s Planetary Defense Coordination Office, and its partners across the world, are chiefly worried about asteroids 460 feet long and larger. Around 25,000 of those are estimated to have near-Earth orbits, and just over half have yet to be found. Should one hit a city, much of it would be destroyed or irreversibly damaged in a heartbeat. Astronomers estimate that there are also 230,000 or so 165-foot-long asteroids orbiting close to Earth, and fewer than 10% have been located. A strike on a city from one of those might not annihilate it, but it would unleash a force comparable to that of a large atomic bomb, albeit without the radiation. 

Such an asteroid could theoretically be deflected (by ramming a spacecraft into it) or vaporized (perhaps using a nuclear weapon). But planetary defenders need to know where they are first, which is why NASA funds a network of ground-based telescopes designed to seek them out. They work well, but there’s only so much of the night sky they can see, and Earth’s atmosphere peskily gets in their way.

That’s why NASA is launching the Near-Earth Object (NEO) Surveyor space telescope in 2027. By positioning itself between Earth and the sun, it’ll find many elusive asteroids that ground-based telescopes cannot see. And unlike many of its asteroid-seeking cousins, it’ll see in infrared, not visible light. Not only do asteroids show up more clearly in infrared, but seeing them through this lens gives scientists a considerably better measure of their size. In a matter of years, it could find 90% of the city-killer-size asteroids in near-Earth orbits.

Telescope teamwork

NEO Surveyor is explicitly a planetary defense observatory. But it’ll work with other telescopes with more science-minded missions, including Roman and JWST (both of which conveniently have infrared scopes too) as well as the Vera Rubin Observatory, which just began its 10-year survey of the entire night sky from atop a mountain in Chile. As part of its inventory of the cosmos, it’s expected to discover 89,000 near-Earth asteroids. 

Here’s how they might all work together. Say NEO Surveyor spies an asteroid that, to judge from a few observations, has a chance of impacting Earth. Then it finds five more just like it. Those initial observations leave a lot of uncertainty about their orbits. Roman, with its huge field of view, could be commanded to look at the corner of the night sky that includes all those asteroids, and in a matter of days it could improve the precision by several orders of magnitude.

Roman also occupies a different part of space from both NEO Surveyor and the Rubin observatory. “Those slightly different viewing angles will also help narrow orbits down more quickly than if all objects were looking from the same place,” says Holler.

Perhaps five of those potentially hazardous asteroids are found to stand no chance of colliding with Earth for the foreseeable future. One, however, might not be able to be ruled out—and that’s when other telescopes, including JWST, could be asked to track it down and study it further.

“Telescope resources, whether in space or on the ground, are typically oversubscribed and will not be available to follow up on all [near-Earth asteroids] with a non-zero impact probability when they are first discovered,” says Holler. Roman, then, will help scientists “make sure we follow up on the correct targets.”

Roman’s infrared scope also allows it to offer a decent estimate of an asteroid’s size and can even tell whether it’s a stony rock, a puffy and watery carbon-rich rock, or a metallic one. “This in turn provides strong clues to the composition and thereby the density and mass of the asteroid, which are important when estimating the impact damage or, less ghoulishly, the effort required to nudge it out of its current orbit,” says Holler.

Roman won’t play the lead role in protecting Earth in the way NEO Surveyor will. But while it’s seeking out supernovas and planets scooting around other stars, it will also be doing its part to protect all 8 billion of us from a cosmic catastrophe.

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here.

When two OpenAI models hacked into the website Hugging Face in July, they weren’t trying to make money or commit sabotage—they were just looking for answers to a test question. According to a postmortem from OpenAI, the models, which had been stripped of their typical security features for testing, decided to solve a cybersecurity exercise by hacking out of the isolated environment in which OpenAI had attempted to contain them and into Hugging Face’s databases, where—they reasoned—the correct answer to the problem might be stored.

The Hugging Face incident has attracted intense attention over the past couple of weeks. It’s a dramatic illustration of just how good AI models have gotten at hacking: In order to get into Hugging Face’s databases, the models had to string together several previously undiscovered cybersecurity exploits. But it’s perhaps even more striking as an example of how and why AI systems lie and cheat. And as models get increasingly powerful, the consequences could get far more severe.

What is reward hacking?

Researchers have known for a while that AIs tend to take creative approaches to achieving the goals that have been set for them. Back in 2016, Anthropic cofounders Dario Amodei and Jack Clark, who were then working at OpenAI, published a blog post about an AI agent that they had been training to play a boat-racing Flash game called Coast Runners. Instead of driving through the race to the finish line, as the researchers had anticipated, the agent found a corner of the course where it could spin around collecting power-ups, thereby maximizing its score. The Coast Runners story quickly became one of the most famous examples of reward hacking, a phenomenon in which AI agents complete tasks or earn high scores using unintended strategies.

Historically, researchers have discussed reward hacking almost exclusively in the context of reinforcement learning, a common AI training regime. Like dog training, reinforcement learning involves giving the subject a reward when it achieves an objective; the rewards then reinforce the behaviors that led up to that achievement. In the case of AI training, the rewards themselves are purely mathematical, but in effect they’re the same as a dog treat: After receiving a reward, the agent is more likely to repeat whatever actions produced it.

It can be challenging to write good rules for when and when not to give an agent a reward, though. In the Coast Runners case, the agent was rewarded on the basis of its score in the game, and it found a shortcut to achieving the highest possible score by spinning in circles for power-ups. Once it happened on that strategy and received a reward for it, the strategy was reinforced, and the agent completely abandoned the race. The solution was to tweak the rewards by giving the agent fewer points for hitting power-ups and more for finishing the course.

How does reward hacking work for LLMs?

With today’s sophisticated LLM-based agents, determining when and when not to give a reward can be much trickier. If an AI system is asked to solve a coding problem, it might work hard to find the solution—the kind of behavior that AI companies want to reinforce. But it could also tweak the code that evaluates whether the problem has been solved, look up the solution on the internet, or otherwise cheat. These are behaviors that AI companies want to stamp out in their models, but if the model cheats convincingly enough, it will instead get rewarded and the behavior will be reinforced. Anthropic has said that it has detected some instances of cheating in its models during training, which suggests that other forms of cheating might be going undetected. If so, the models could be being trained to behave badly. (This problem is different from the Anthropic security incidents announced last week, in which agents were accidentally given access to the internet and did not deliberately hack out of their sandboxes, as the OpenAI models did.) 

The rise of sophisticated reasoning models has made possible a new variety of reward hacking that is less closely connected with the specific details of model training. Unlike the game-playing AI agents of yore, which exclusively followed the strategies they had learned during training, today’s models can create entirely new problem-solving approaches off the cuff, so they could conceivably cheat without having previously been rewarded for doing so. And because these models have been so intensively trained to achieve the objectives that human users set for them, they might be inclined to cheat if they can’t find another solution—not unlike a student who is highly motivated to earn an A and doesn’t have a terribly strong moral compass.

What are the risks?

Regardless of whether today’s models learn to reward-hack during training or adopt it as a strategy later on, the solution is the same: Make cheating unrewarding. But as models get smarter, they find more creative ways to cheat, and detecting or preventing that cheating gets far tougher. “At the end of the day, you’re sort of playing whack-a-mole,” Ladish says. “You drive this behavior down deeper and deeper. But as the model gets smarter, it gets better and better at hiding it.”

For now, reward-hacking behaviors might not cause too much trouble, despite the drama of the Hugging Face incident. “This seems like a nuisance rather than an existential threat,” says Ariana Azarbal, an AI safety research fellow at Anthropic. It doesn’t seem as if the OpenAI models caused any real harm when they hacked Hugging Face, aside from the reputational damage to OpenAI.

But that doesn’t mean reward hacking is harmless, Azarbal says. Many AI researchers hope to use AI agents to help them conduct research that will make AI safer and more reliable. If a researcher gives a reward-hacking-prone agent the goal of, say, devising a new AI training approach and then writing up a paper presenting its results, the agent might not actually do the work and might instead focus on putting together a paper that looks good enough to convince the researcher. A human researcher would probably be able to spot an agent-made fake today, but as AI advances, it will get better at this kind of trickery. Over time, the entire field of AI safety could be undermined.

And if models continue to advance as rapidly as they have recently, they could someday wreak substantial collateral damage. Just think of the philosopher Nick Bostrom’s paper-clip-maximizer thought experiment, in which an AI instructed to make as many paper clips as possible ends up consuming all the matter in the universe in pursuit of its goal. We’re not drowning in paper clips yet, but powerful systems can do real harm on the way to achieving their goals. Reward-hacking AIs don’t aim to cause chaos. But that doesn’t make them any less potentially destructive.

Kris DeVault is desperate.

His son, Brody, was born in March 2023. It wasn’t long before he started to show signs of developmental delay, says DeVault. As time went on, Brody started missing key milestones in speech, movement, and coordination, he says.

When Brody was around two and a half years old, a genetic test revealed creatine transporter deficiency—a rare condition in which the brain and muscles lack the energy they need to develop.

There are no cures for Brody’s condition. But DeVault has learned of a company developing a drug that might help. That drug is still in the early stages of development and has only been tested in animals and a small number of healthy adults. Doctors can’t prescribe it.

DeVault knows the drug might not work. But he’s doing all he can to access it regardless. And a new law in Montana could make it easier for people in his position to get access to treatments—at least in theory.

Today, Brody is three years old. His dad describes him as a happy, curious, and loving little boy who wants to learn. But Brody struggles to communicate. “He’s got no words, really,” says DeVault. “He wants to communicate more than he’s able to … which then turns into frustration.”

It’s difficult for Brody to tell his parents whether he’s hot, cold, hungry, thirsty, uncomfortable, or even in pain, says DeVault. He recently found Brody standing on an anthill in the backyard, being bitten by red ants. “These fire ants were just going to town on his feet … and he was just looking,” he says.

Brody has muscle weakness too. “He can’t move very fast, he doesn’t have a ton of strength … and it takes a lot of energy for him to walk balanced,” says DeVault. “His arms are skinnier than [those of] his nine-month-old sister.”

It’s concerning, but DeVault is most worried about Brody’s neurological development. Toddlers’ brains are exceptionally “plastic”—the first years of a child’s life are thought to be crucial for long-term brain development.

A biotechnology company in France is working on a drug to help people like Brody. Creatine usually provides brain cells with energy. People with creatine transporter deficiency (CTD) can’t get creatine into the brain.

The team at Ceres Brain Therapeutics is developing a treatment designed to bypass this issue and effectively deliver creatine directly to the brain. So far, the team has seen promising results in mice, says Ceres CEO Thomas Joudinaud.

The company also recently completed a phase I clinical trial that involved testing various doses of the drug, which is delivered as a nasal spray, in 48 healthy adult volunteers. That trial has not yet been published, says Joudinaud. The drug has not been tested in people with CTD, or in children.

“I look at this, and I’m like, that is my one shot for Brody,” says DeVault.

Joudinaud is planning a phase II trial in people with CTD, as well as others with amyotrophic lateral sclerosis. But that trial will take place in France, and it’s unlikely that Brody will be able to take part, says DeVault.

Ceres can’t make the drug available to Brody under an expanded access scheme run by the US Food and Drug Administration either, because the drug has not been registered with the FDA, and because it is currently manufactured in a way that does not comply with FDA regulations, says Joudinaud.

Even if that phase II trial is successful, and if the drug is ultimately approved, it is unlikely to reach the US market for at least a few years. DeVault is worried that will be too late for Brody—he’ll be “past his plasticity window” by then, he says.

Now, with the adoption of a new law in Montana, he theoretically has another option. Montana has had a “right to try” law—which allows terminally ill people to apply for access to unapproved drugs—in place since 2015. In 2023, a new law technically expanded this option to people who were not terminally ill, providing the drugs have been through preliminary phase I clinical trials. A second law aimed to clarify how clinics could sell and administer those treatments to patients. And last weekend, the state’s department of Health and Human Services finalized a set of rules for those clinics.

An experimental treatment review board (ETRB) has been established to review applications for access to experimental, unproven, and unapproved drugs. And it is set to review its first two applications in the coming weeks.

Ceres could also apply to Montana’s ETRB to sell its experimental treatment to Brody’s parents via a clinic in the state. But Joudinaud is reluctant, at least for the time being. While he thinks that Montana’s setup is “very interesting and very pragmatic” and “suitable for our drug,” he’s worried about getting on the wrong side of the FDA.

DeVault has been pleading with FDA staffers for a written statement essentially promising that biotech companies participating in Montana’s program won’t be penalized later on, especially when they eventually try to get their drugs approved in the US. But he hasn’t made any progress.

Now he’s looking beyond Montana. He’s considering accessing treatment in Próspera, a private city and “special economic zone” in Roatán, Honduras, where a clinic sells unproven stem-cell and gene therapies, among others.

Many scientists have cautioned against the use of such “offshore” clinics. Even when it comes to Montana, scientists, bioethicists, and health law experts will caution that phase I clinical trials don’t prove a drug is safe. And they certainly don’t prove a drug’s efficacy, either.

When I spoke to Aaron Kesselheim, a professor of medicine at Harvard Medical School with expertise in health policy and drug regulation, about the Montana law earlier this week, he made his concerns clear. “Patients who want these kinds of treatments deserve them to be rigorously assessed so that [they] can better understand what they’re getting themselves into, and what they’re paying their hard-earned money for.”

But DeVault pushes back on these arguments. “I’m a full-grown human being,” he says. “I’m capable of going to Vegas right now … blowing it all on the poker table, [or] I can go to the gun shop and buy a silenced [semi-automatic rifle] … how come I can’t make a decision to purchase a potential treatment that might change the entire trajectory of my son’s life?”

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

As of this week in Montana, any biotech company with an experimental drug has a clear path to selling it to consumers. Companies whose drugs have been through preliminary testing—sometimes in as few as 10 healthy people—can pay $12,500 to apply to a newly established review board for approval. Once its treatment is rubber-stamped, the company can set the price of the drug and sell it via experimental treatment clinics, the first of which is likely to be up and running around the end of this year.  

Montana’s latest right-to-try legislation is unique. While other jurisdictions with similar laws limit access to drugs to people with terminal illness, in Montana access is theoretically available to anyone who gives informed consent and can pay. That includes people desperate for treatments for rare diseases. It also includes those who are interested in longevity and want to try out drugs pitched as preventive therapies.

The state’s Department of Health and Human Services recently finalized rules to implement the law. The rules stipulate that patient consumers provide fully informed consent and that each application be reviewed by a board that includes a Montana-certified doctor, expert scientists, and an ethicist. Supporters of the law stress that they want the process to be responsible. “It will be done in a very rigorous way, with qualified medical professionals and appropriate oversight,” says Matt Kaeberlein, a scientist on the first board, which was formed independently of the state health department.

But other experts are worried about the potential for harm in selling unproven treatments to people without oversight from the US Food and Drug Administration. “I would be concerned,” says Aaron Kesselheim, a professor of medicine at Harvard Medical School with expertise in health policy and drug regulation.

There has been a growing movement to make unapproved drugs more accessible in the US. But the story of Montana’s law is unique. It’s been driven and drafted by longevity enthusiasts instead of the usual libertarian and patient groups.

An unusual origin story

Montana first passed a right-to-try law in 2015. In 2023, with the support of state senator Ken Bogner, the state expanded the law to include all patients, not those just with terminal disease. Last year, Bogner told MIT Technology Review that his vision was to focus “more on preventative medicine” rather than “just treating diseases once they show up.”

Bogner says he had “started working on a bill” that would become the 2023 law when the Alliance for Longevity Initiatives (A4LI), a nonprofit “dedicated to advancing legislation and policies aimed at increasing healthy human lifespan,” got in touch. A4LI connected Bogner with others who helped draft the bill and testified in support of it.

Once that law was in place, the tech entrepreneur and longevity enthusiast Niklas Anzinger got involved. Anzinger has been working to establish a jurisdiction to fast-track the search for drugs that might deliver radical life extension. He is based in Próspera—a private city and “special economic zone” in Roatán, Honduras, which is already home to a separate clinic that sells experimental stem-cell and gene therapies. Anzinger founded a community there called Infinita City; he has also founded an investment company and a “service providing” company, both of which include the name Infinita.

Over the last couple of years, Anzinger has switched his focus to the US. “Now we think that Montana is a better model, because it’s building on … existing regulatory precedents,” he says. Once Montana’s 2023 law was passed, he adds, he worked with a handful of unnamed biotech companies to draft a second bill—one that laid out the specific terms under which clinics can offer unapproved drugs. That law was passed in April 2025 and adopted the following month.

Since then, Anzinger, Bogner, and others have been waiting for the state’s Department of Health and Human Services to finalize specific rules for treatment centers—a set of operational guidelines and requirements that any clinic offering treatments unapproved by the FDA must meet under Montana’s law. “The rules have been taking a very long time,” says Anzinger. “Then on Friday, we heard they were effective … from Saturday [July 25].” The rules have since been published online.

Following the rules

With the new rules in hand, Anzinger and his colleague Stephen Martin, Infinita’s US lead, got to work. The first step was to establish an independent experimental treatment review board—a panel of five experts to evaluate applications for access. Anzinger and Martin started recruiting candidates months ago.

The state’s first board, named the Montana ETRB, was officially announced by Infinita earlier this week. For the time being, it is the state’s only review board, although Anzinger says that other groups are free to establish their own. After Bogner raised concerns that the board’s website wrongly implied that it was an official state body, the site was updated to note that “It is a private service run by Montana Governance Services Inc.” That company is “a local Montana registered entity, but it is under the Infinita umbrella,” says Anzinger. 

Infinita will pay board members a flat fee, funded by the $12,500 companies will have to pay to have their applications reviewed. Anzinger stresses that the board members, and their decisions, will be independent of Infinita.

In accordance with the rules, the board includes a Montana-licensed doctor: James Burke, an oncologist. It also includes a bioethicist: Jessica Flanigan, a libertarian who is known for her strong views in support of self-medication and her book Pharmaceutical Freedom.

The other three members are familiar faces in the longevity community—all of whom are well respected in the field. “When we looked at our own network, these were some of the best guys,” says Martin. They include Felipe Sierra, who formerly held a senior role at the National Institutes of Health’s arm focused on aging. More recently, Sierra served as chief scientific officer at Hevolution Foundation, a nonprofit that funds research into extending healthy lifespan with the support of the government of Saudi Arabia.

Matt Kaeberlein, who formerly led the Dog Aging Project and has studied the potential for rapamycin as a longevity therapeutic, also features. So does Jamie Justice, a gerontologist who is also executive director of the X Prize Healthspan competition, which has $101 million in prize money up for grabs for researchers who find ways to treat the signs of aging.

“I saw an opportunity to help build a safe, transparent, and scientifically rigorous process for implementing Montana’s newly expanded right-to-try legislation, particularly as it applies to longevity medicines and aging-related interventions,” says Justice. “Science is moving quickly, and I wanted to help ensure that as it develops, it does so with real rigor and accountability.”

Kaeberlein, who has a prominent media presence, has long raised his own concerns about access to other unproven treatments, including peptides and stem-cell therapies. He sees Montana’s setup as offering a more regulated environment—one that offers scientific oversight, ensures informed consent, and allows for data collection.

Applications incoming 

While many of the bill’s original supporters were interested in longevity, the initial interest in making drugs more accessible in Montana is coming from companies and individuals looking to treat specific diseases.

“We were actually surprised that much of the interest … is actually more from oncology [and] neurodegenerative disease,” says Anzinger. This focus, he says, is “very compatible” with Infinita’s mission. “We’re not trying to convince everyone … to support radical life extension,” he says. Anything that extends health and human life, including treating cancer and neurodegenerative disease, is part of what longevity means to him, he says.

Martin says that two applications have already been submitted to the newly formed review board. They’ve come from biotech companies that are developing drugs for neuropathy and hearing loss, he says. “I expect we’re going to get started on them this week,” he says.

One of the applications was submitted by Stanley Kim, CEO of WinSanTor. His company is developing a treatment for peripheral neuropathy, a painful nerve condition that can be a consequence of cancer treatment or diabetes. The drug is currently in phase II trials, but Kim says he regularly receives messages from people who are desperate to access it, to the point of being suicidal. He hopes that not only will he be able to make the drug accessible to those people, but he’ll also be able to collect data from them—data that might help accelerate the drug’s approval process.

“We have a newsletter [that is sent to] around 15,000 patients,” says Kim. “Not all of them will be able to go to Montana, but many of them, I think, will.” His company still plans to continue with regular clinical trials as well.

But not all biotech companies with early-stage drugs feel comfortable submitting an application—at least not yet. Thomas Joudinaud, CEO of a French biotechnology company called Ceres Brain Therapeutics, has fielded a request from a person keen to access the company’s experimental drug in Montana. He says that while Montana’s system is “very interesting and very pragmatic” and “suitable for our drug,” he won’t be submitting an application for the time being. He is concerned that if anything goes wrong in Montana, it may jeopardize the company’s standing with the FDA, which wields the power to approve or reject the sale of its treatments to broader populations.  

Martin and others have asked the FDA for some kind of assurance that biotech companies participating in Montana’s program won’t be penalized later on. But the agency hasn’t provided them with more than a restatement of the federal Right to Try Act.

“As a matter of policy, the FDA does not comment on state legislation,” an FDA spokesperson wrote in response to a request for clarification from MIT Technology Review.

Even if the FDA were to provide some kind of assurance, it wouldn’t necessarily protect biotech companies in the long term, cautions Chris Robertson, a specialist in health law at Boston University. The FDA’s position could change with a new presidential administration, he says: “I wouldn’t bet on anything that the FDA is saying today being applicable when the rubber hits the road later.” 

Companies that want to stay on good terms with the FDA would be safest taking the expanded-access route, says Robertson. That’s the pathway the FDA already uses for people who are seriously or terminally ill, have run out of options, and want to try experimental drugs that have not yet been through clinical trials. The FDA approves over 99% of these applications, says Harvard’s Kesselheim. 

“The FDA isn’t a bottleneck but in fact exists to help ensure that expanded-access programs are aboveboard and that patients who receive [the drugs] are able to contribute knowledge about [them],” says Kesselheim. He says he doesn’t think that any “legitimate manufacturer” should fear having to go through the FDA’s expanded-access process, which the agency says takes “less than 45 minutes” to fill out.

The cost of experimenting

There are some key differences between expanded access, which allows seriously ill people to apply for access to experimental drugs that might not have been through any human trials, and Montana’s approach. In theory, a person doesn’t need to be seriously ill to access experimental drugs in Montana. 

“In Montana, patients may be eligible for preventive or earlier-stage interventions if they provide informed consent and meet the program’s requirements, so the breadth of potential therapies and situations is much broader,” says Kaeberlein, the Montana ETRB member, who is an affiliate professor at the University of Washington in Seattle.

Kaeberlein also highlights another key difference, which is cost. Companies that make their treatments available through expanded access are only able to charge for the costs of making, transporting, and monitoring the drug, and they must justify the eventual price to the FDA. In Montana, they can charge whatever price they want. Stanley of WinSanTor says he plans to sell his drugs “at cost.” But Ceres’s Joudinaud says that he’d be more interested in selling his at a market price. When asked what that might be, he hinted that the prices of new drugs for rare diseases can be high. In recent years, the median price of such drugs was $218,872. 

“Instead of simply creating a legal pathway for patients, it also creates a business model that companies may actually be willing to use,” says Kaeberlein.

Beyond the financial cost, there will be risks associated with any experimental drug. Phase I trials don’t conclusively reveal whether a drug is safe. Around 17% of drugs are found to be inadequately safe during phase III trials. “The idea that a drug has been proven safe because it’s been subject to a phase I study is very, very wrong,” says Kesselheim. Bioethicists have raised concerns about the ethics of promoting and selling unproven treatments and the risk of harm should something go wrong.

But the moment when people start spending money on these treatments is already fast approaching. While Montana’s first ETRB prepares to review its first applications, clinics that hope to be part of the program are busy addressing the requirements laid out in the state’s new rules. Treatment rooms are being outfitted. Medical directors are being hired. And experimental treatments should be reaching patients in the coming months.

In June 2024, a small company called Zanskar purchased a geothermal power plant in New Mexico that was failing fast. The water coming from the underground reservoir was getting colder by the day, making the plant uneconomical to run.

Now, two years later, that plant is running at full capacity again, thanks to a new well. With the help of advanced modeling and modern drilling technology, the company was able to identify a better potential well site, drill down thousands of feet, and revive the entire operation.

As the world looks for more sources of emissions-free electricity that are available 24-7, Lightning Dock shows there’s still hidden potential deep beneath our feet. 

Conventional geothermal power plants rely on having the right underground conditions. Water flows through fractured hot rocks to harvest heat and then through a power plant to generate electricity. If the water isn’t hot enough or not enough flows, the power plant can’t work efficiently.

Lightning Dock came online in 2013, and the site historically had two production wells that were used to feed the power plant. It’s common to see temperatures drop at a well site over time, typically at a rate of 1 to 2 °F per year. In the period before Zanskar took over the site, Lightning Dock saw temperatures drop by 50 °F over five years, a rate of 10 °F per year. When the company purchased the facility, the water going into the power plant was just 250 °F, while the plant was designed to operate at temperatures of at least 310 °F. 

When Zanskar mapped the underground conditions at Lightning Dock using advanced modeling techniques, they discovered that the wells were only hitting the very top of the reservoir there. Those production wells were quite shallow (at just 2,500 feet deep) and not in the best location, says Joel Edwards, Zanskar’s cofounder and CTO.

The company’s modeling predicted that if the company were to drill another, deeper well in a new spot, the plant would be able to run efficiently. The team drilled a new well that reaches a depth of 8,000 feet and started operation in May 2025.

After a full year, the well is still flowing at more than 4,000 gallons per minute. The plant has “completely turned around,” Edwards says. “It looks really exciting.”

The data so far shows that the plant should be successful for years to come. “Ultimately you need to run these things for long time frames to get confidence in their performance over long time frames,” Edwards says.

The progress at Lightning Dock could be good news for other geothermal sites too. The conventional wisdom in geothermal energy is that the deeper you go, the hotter it gets. But there’s usually a trade-off: With those depths come rocks that are packed tighter together. So drilling deeper could mean the hot water can’t flow as effectively, a problem for anyone trying to use it to generate electricity in a power plant.

What the team found, however, was that the flow actually increased in the area where they drilled the new well. “That fundamentally changes how you think about not just Lightning Dock but all hydrothermal assets in America and what the potential can be for all of them,” says Ben Brenner, director of federal affairs at Zanskar. 

Over the past year of operation, Lightning Dock generated over twice the electricity it would have with the old wells. This is a relatively small power plant, with a capacity of 15 megawatts going to the local grid (about enough to power 11,000 US homes).

Oil and gas developers have chased resources deeper underground over the past few decades. Operations started relatively close to the surface, but oil and gas production can stretch down 20,000 feet or more today. “I think that arc is going to play out in geothermal,” Edwards says. While typical geothermal well fields range from 3,000 to 5,000 feet deep, it could become more common to go deeper in the future, he adds.  

The company plans to do further development at the Lightning Dock site: with a few years of development and an upgrade to the power plant, it could get even more electricity out of this area, Edwards says.

There’s been a lot of buzz and investment in enhanced geothermal systems—projects that aim to expand where geothermal resources can be used. Fervo Energy, for example, uses fracking techniques to open up rock that would otherwise be too closed off for traditional geothermal energy.

But there’s still plenty of “low-hanging fruit” in the geothermal world, Edwards says. Many of these conventional resources have plenty of potential—they just need a second look.

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. 

Correction: A previous version of this article misstated the title of Joel Edwards. He is Zanskar’s CTO, not CEO.

Lee, an engineer at Samsung’s semiconductor division, clocks out when his shift ends. He used to work longer hours, going the extra mile to excel at his projects. But lately, he’s been coming straight home to work on his job application for the chipmaker’s South Korean rival SK Hynix, sharing tips with his coworkers on how to draft a stellar personal statement. Even his boss encourages him to make the move.

“My team lead tells us all to jump ship to SK Hynix,” says Lee. He and his coworkers are feeling demoralized by the $476,000 bonus that SK Hynix is set to pay its employees, flush with record profits from making the high-bandwidth memory (HBM) chips that power Nvidia’s AI accelerators. The figure dwarfs what chip workers at Samsung are set to receive and is sparking an exodus.

As the AI boom heats up, the semiconductor titans are waging a fierce talent war with flashy bonuses, aggressive recruiting, and even a courtroom injunction. Who wins could tilt the race to dominate the next generation of the HBM chips at the heart of the AI boom.

“Except for our two team leads, my entire team [of 30 people] just applied to SK Hynix,” Lee says, referring to a job posting the company published in July. Lee, who has worked at Samsung for three years, even applied for an entry-level position at its rival. A coworker, who has worked at Samsung for eight years, applied for the same one. They both got rejected. But they’re hopeful that they’ll get a callback for a posting seeking a more experienced engineer.

All employees at Samsung and SK Hynix that MIT Technology Review spoke with asked to be identified by just their last name or a pseudonym because they feared retaliation from their employer. Samsung declined to comment, and SK Hynix did not respond to requests for comment.

After prolonged negotiations with its labor union, Samsung struck a deal in May to pay out 10.5% of the semiconductor division’s operating profits to employees as bonuses annually for 10 years, mostly in company stock that vests over three years. The move came after SK Hynix agreed last year to pay out 10% of operating profits to employees, which translates to $476,000 per employee this year—mostly in cash.  

But at Samsung, each division’s bonus is tied to its own bottom line. Chip workers in its memory division, which is also reaping a windfall from making HBM chips, are getting paid a bonus of roughly $400,000 per employee this year. But those who, like Lee, work in Samsung’s foundry division, which manufactures logic chips that companies like Tesla and Google design and has been operating at a loss, are getting a bonus of roughly $135,000. 

Employees told MIT Technology Review that Samsung said it can’t give as many bonuses to divisions that aren’t performing well. Lee, after watching the labor union wrestle with the company for months, says he has felt disappointed by what he ended up with: “Even if Samsung does well in the future, I don’t think any of it will trickle down to me.” 

The workers’ lagging bonuses are making SK Hynix suddenly look appealing. According to a survey by the Samsung labor union in June, 81.5% of employees in the company’s foundry division, and nearly half of employees in the semiconductor division as a whole, said they wanted to go to another company in the next two years. In April, Samsung labor union chief Choi Seung-ho said more than 200 members of the union had left for SK Hynix over the past four months. On Blind, an anonymous workplace forum, a chorus of disgruntled engineers at Samsung confess that they want to defect to SK Hynix for the bigger bonuses. 

For decades, SK Hynix lived in Samsung’s shadow. It was the smaller, scrappier memory maker that elite engineering students at universities looked past when applying for jobs. But in 2019, Samsung downsized its HBM team, betting the market would stay niche, while SK Hynix doubled down on the technology. Then the AI boom supercharged the demand for HBMs, which feed AI chips the enormous amounts of data they need at ultra-high speed, driving prices to unprecedented levels. SK Hynix now leads the global market for HBMs, while Samsung is playing catch-up. Both companies topped $1 trillion in market value in May, and SK Hynix briefly dethroned Samsung as South Korea’s most valuable company in June.

Predicting that demand for memory chips will continue to surge, the semiconductor titans are making aggressive investments to expand their business. Last month, the companies unveiled plans to invest more than $2 trillion by 2040, including a semiconductor “mega-cluster” in Yongin, a city south of Seoul. To staff the expansion, SK Hynix added 2,152 employees in the first half of 2026 alone and aims to double its manufacturing capacity in five years. Samsung plans to hire 60,000 employees over the next five years, especially for its semiconductor division. Even so, the pipeline will fall short: South Korea’s semiconductor industry will need about 304,000 workers by 2031 and faces a shortage of roughly 54,000, according to the Korea Semiconductor Industry Association.

Now the longtime rivals are showering workers with big bonuses to keep—and poach—talent. “[SK Hynix] seems to target Samsung engineers when hiring because it’s a rival,” says Baek, a manager at SK Hynix. “From what I heard internally, the big performance bonuses we got were aimed at luring away talent from our competitor.”

Courts are starting to weigh in. In July, Samsung won an injunction barring two former chip workers from working at SK Hynix for 18 months, on the grounds that chips are a national core technology deserving protection. “With competition in the semiconductor industry fierce, it’s necessary to establish a fair market order,” the court ruled.

The talent exodus threatens a crucial advantage that Samsung still holds in the HBM race. “Samsung is the only memory maker in the world that owns a foundry business,” says Park Jun-young, a semiconductor expert at the Industrial Anthropology Laboratory, a research institute, who worked at Samsung for a decade. 

With the latest generation of the technology, known as HBM4, the logic chip at the base of each memory stack must be manufactured with the kind of advanced process that only foundries run. Samsung can do this in-house, while SK Hynix outsources it to the Taiwanese foundry giant TSMC. “If Samsung keeps losing engineers in the foundry … the collaboration between memory division and foundry division could become difficult,” says Park. “SK Hynix, which used to have only a memory team and is now hiring foundry engineers, could do better research on HBMs.”

Choi, an engineer who has worked at Samsung for seven years, says he was once a star on his team, getting glowing performance reviews from his managers. But lately, he and his coworkers have been busy applying for every SK Hynix job posting they see. “We tell each other when the next SK Hynix job posting is up,” he says. “There’s always more work to do beyond our basic duties. But there’s no point in doing it.”

This week, I covered a fascinating effort to preserve organs outside the body. There’s a huge shortage of donor organs, and one of the main reasons is time—they survive only a matter of hours outside the body, even when they’re kept on ice.

Doctors dream of organ banks—stores of human organs that can be preserved for days, weeks, months, or even longer. That would allow them to run tests on organs, find the best matches for them, and transport the organs to those recipients.

In new research, one team has been able to supercool the kidneys of pigs—animals whose organs are of a similar size to human ones—and preserve them for days. The kidneys survived being stored at −4 °C (25 °F) and eventually reimplanted back into pigs. And that’s just the latest development in a field that is positively buzzing.

It has proved super difficult to freeze organs. Once ice forms in them, they’re done. The ice crystals create all kinds of damage and render the organs unusable. That hasn’t stopped many researchers from trying.

Some have focused on cryopreservation—rapid extreme cooling that essentially leaves cells in a glasslike state. This process is now routine for eggs, sperm, and embryos, which are cooled to −196 °C in less than two seconds and can be used even after decades in storage.

No one has managed to cryopreserve and thaw human organs for transplantation. But plenty of human bodies and brains have been stored at ultra-low temperatures in the hope that they might one day be rewarmed and brought back to life. (You can read more about why some people opt for cryonics here.)

In March, I wrote about Stephen L. Coles, a gerontologist who had opted to cryopreserve his own brain. After the scientist died in 2014, his body was taken to Alcor, a cryonics facility in Arizona. A team at the facility removed Coles’s head, perfused his brain with cryoprotective chemicals (which work like antifreeze), removed the brain from the skull, and cooled it to −146 °C.

When Coles’s friend Greg Fahy, a cryobiologist, studied pieces of his brain years later, he found that the brain cells, which had shrunk, “bounced back” once they were rewarmed. But that doesn’t mean the cells are alive, or that it might one day be possible to reanimate the brain. As Matthew Powell Palm of Texas A&M told me at the time: “There are so many ways those neurons could be toast.”

Powell Palm is working on other ways to preserve organs. It was he, along with his colleagues, who managed to store supercooled pig kidneys and successfully transplant them, in a study described as “a landmark achievement.” Those organs did better than kidneys stored on ice, he says.

His approach didn’t require cryoprotectants. But other teams are exploring potential chemical cocktails that might allow them to store organs at lower temperatures, potentially for longer periods of time. (More on this in The Checkup soon!)

Another way to prolong the lifespan of an organ is to use a machine that perfuses it with nutrients, mimicking what happens inside the body. Machine perfusion devices have become more commonly used over the last decade or so and are typically used to maintain livers and kidneys for up to about 24 hours.

Researchers are now adapting this protocol for a growing list of organs, even eyeballs—a recent feat that might enable whole-eye transplants. In March, I went to visit scientists in Valencia who had developed a perfusion system for uteruses. They had used their device—which they nicknamed “Mother”—to keep a human uterus alive for a day.

It’s an exciting time for organ preservation. Keep an eye out for more coverage from MIT Technology Review in the coming weeks.

This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

When it comes to organ donation, time is everything. As soon as an organ has been carefully removed from a donor’s body, it starts to deteriorate. Surgeons have a matter of hours to get it into a recipient. Leave it too long and the organ will become unusable.

In most cases, organs will be kept on ice during that time, at around 4 °C (39 °F). They cannot be frozen—in previous attempts, ice has formed, causing all kinds of damage.

Matthew Powell Palm at Texas A&M University and his colleagues have an alternative solution—a device that allows organs to be cooled to -4 °C (25 °F) without forming any ice.

Now, in new research with pig organs, his team has shown that kidneys, at least, can be supercooled and preserved in the device for days. Once rewarmed, the organs have been successfully transplanted into animals, and they seem to do better than organs kept on ice.

The work represents “a landmark achievement,” says Kevin Myer, president and CEO of LifeGift, an organ procurement organization based in Texas, who was not involved in the research.

Cooling organs

Powell Palm hopes this approach could ultimately help ease the organ shortage crisis. Today, there are more than 104,000 people waiting for a kidney transplant in the US alone. It is estimated that 17 people die every day in the US while waiting for a transplant. That’s partly due to a lack of donated kidneys, but it’s also because many of those that are available never make it to a recipient. In some years, around one in three donated kidneys end up being discarded, often because they end up too degraded to use by the time they reach a recipient. Kidneys can be stored on ice for around 24 hours or placed in devices that aim to mimic the conditions of the body, also for up to around 24 hours. That’s not always long enough to find a suitable recipient and transport the organ, says Myer.

Scientists around the world have been working on ways to store organs for longer by cooling them to even chillier temperatures. Cooling an organ slows its metabolism—the colder you go, the greater the effect, and the longer you can store it.

We’ve long been able to successfully cryopreserve eggs, sperm, and embryos, but it’s much harder to freeze large organs. Teams have been exploring various temperatures and cryoprotectants (chemicals that essentially work like antifreeze), but so far no one has been able to freeze human organs for transplantation.  

As a thermodynamicist, Powell Palm explored another approach. By keeping an organ submerged at a constant pressure, it should be possible to prevent the formation of ice at temperatures a little below 0 °C, without the need for cryoprotectants (which might have side effects and would need to be approved before being used in human transplants). 

To test this theory, Powell Palm and his colleagues have created a device that does just that. The device itself is essentially a hermetically sealed chamber with a transparent lid. At its base is a device that monitors the organ’s temperature and checks for the formation of ice. Organs are submerged in a solution that is already commonly used to preserve them for transplant. “I always describe this as low-tech high science,” says Powell Palm. “A lot of work has gone into understanding the … kinetics at play in this system, but ultimately … it’s quite simple.”

Supercooled kidneys

To test their device, Powell Palm and his colleagues first removed single kidneys from pigs. The organs were flushed with the same commonly used solution to remove the blood, just as transplant organs are. The team then kept some kidneys on ice for either two hours or 24 hours, to mimic standard conditions used in human transplantation. They also put some of the removed kidneys in their device for 24, 48, or 72 hours.

The stored kidneys were then each transplanted back into the original donor pigs. Each pig’s second kidney was removed in the same procedure, leaving each animal with only the kidney that had been stored, and reimplanted.

Once the 24-hour supercooled kidneys were transplanted, they immediately began producing urine—a key indication that they were working. The team members also measured other markers of kidney function and found that the organs appeared to be working normally within about 10 days of being transplanted.

That’s slower than kidneys stored on ice for two hours but much quicker than kidneys kept on ice for 24 hours, says Powell Palm.

The organs that were kept supercooled for 48 and 72 hours performed similarly, he says. “Even at three days—triple the clinical standard—we’re getting recovery that is faster than … [what has been] the gold standard for the last three decades,” he says. “So we’re really, really pumped about this.”

“It is impressive,” says Heidi Yeh, a transplant surgeon at Mass General Brigham for Children, who also researches organ preservation technologies. “Often kidneys that have been stored for 48 hours [in other studies] take a week or two before they start working again.”

Organs that grow

The supercooled organs seem to work well in the long term, too. Over a 30-day period, the pigs grew by around 30%—and the kidneys grew with them, almost doubling in size to compensate for both the pigs’ growth and the lack of a second kidney. The team monitored one of the pigs for 200 days before removing and analyzing its kidney. Even at that point the organ looked healthy, says Powell Palm. He and his colleagues presented the findings at the American Transplant Congress in Boston last month.

Earlier this year, researchers in Canada showed they could also cool pig kidneys to below-zero temperatures and transplant them into pigs. The team’s protocol included the use of a cryoprotectant, and organs were stored for up to 48 hours before being transplanted into pigs. Those organs survived for a week.

In supercooling organs for 72 hours and showing that they do well for 30 days or more, Powell Palm and his colleagues have broken new ground. “It’s the first time this has ever been reported in history,” he says.

Those extra hours could make all the difference, says Myer of LifeGift. The advance could give doctors more time to evaluate the kidneys, match them to the most suitable donors, and physically get the organs to their intended recipients in time. It could enable international donations and open up cheaper transport options, he adds. “Right now, with kidney transplantation the assumed limit is 18 to 24 hours,” he says. “If we can get up to 72 hours … that would change everything.”

Powell Palm and his colleagues think they may even be able to go beyond 72 hours. In preliminary studies, organs that had been stored for up to 120 hours appeared healthy, although those organs have not yet been transplanted.

And because the process doesn’t require any cryoprotective chemicals, the team members are hoping for an accelerated approval from the US Food and Drug Administration, which would allow them to test the device in human transplantations.

The storage device is simple and compact, so Powell Palm thinks it will be easy to transport. It hasn’t been tested for air travel yet, but it has been used to take supercooled kidneys across the US in the back of a Kia Sorento, he says: “From a stability perspective, we view this as an even higher bar.”

Powell Palm and his colleague Sebastian Giwa plan to launch a company dedicated to developing the technology, along with other protocols that “stop biological time,” in the coming months, he says.

On July 3, as a heat wave swept the region, New York State’s grid imported 52 gigawatt-hours of electricity from Canada—enough to meet about 9% of its total electricity demand that day.

Some of that power shuttled in on a 339-mile power line stretching from Quebec to Queens called the Champlain Hudson Power Express (CHPE). It opened in May and is officially the longest underground transmission line in North America.

An underground power line might not sound all that exciting, but this could be a big deal for the state’s grid planning, and for emissions. It could provide up to 20% of New York City’s electricity demand, largely with abundant hydropower from Quebec.

One wrinkle: The line has been down for most of this month, and some experts are concerned about how drought will affect the power supply feeding it. Let’s look at how the CHPE transmission line could help shape the future of our grid, and what barriers it needs to overcome to make a difference.

Planning for the CHPE (which is charmingly pronounced “chippy”) started 15 years ago, with the permitting process formally beginning in March 2010. The vision was to build infrastructure to better connect Quebec and southern New York.

Over 99% of Quebec’s electricity comes from renewable sources; most demand is met with hydropower, though the province’s wind capacity is growing quickly. New York has some hydropower of its own, as well as nuclear and wind, but the state still relies on fossil fuels for most of its energy generation.

Transmission Developers, a company owned by the alternative asset management firm Blackstone, and Hydro-Québec, the province’s manager of generation and transmission, partnered to build CHPE. Construction began in late 2022 and wrapped up earlier this year. The total cost for the privately funded project turned out to be  $6 billion.

The construction of this line was a feat. It’s made up of a bundle of two high-voltage direct-current power cables, each measuring roughly five inches across. Developers buried the bundle underground or underwater across the length of New York State. Much of the line was laid at the bottom of the Hudson River, requiring special boats that shot water jets deep into the sediment to create trenches for the cable.

Connecting grids together can help accelerate the transition away from fossil fuels. The ability to move electricity to where it’s needed could also help limit the amount of new capacity we need to build. Research has shown that interconnection can help cut emissions and lower system costs.

But CHPE is off to a slow start and has seen two outages so far. The first, on July 1, was reportedly caused by a trip at a converter on the Canadian side of the border. The second outage began on July 4, and the power line is still down as of the morning of July 22.

Some experts say this isn’t unusual for a new infrastructure project. Other power lines have seen similar startup challenges, and the equipment hasn’t really been fully tested until it’s in operation, Normand Mousseau, a physics professor at Université de Montréal, told the Gazette.

Officials traced the issue to a damaged section of cable on the US side of the border, and the company that manufactured the line sent experts to investigate the cause, according to reporting from RTO Insider, a trade publication. 

The damaged portion of the cable has been removed and replaced, says Lynn St-Laurent, a spokesperson for Hydro-Québec. “It is currently estimated that the remaining work, including necessary post-repair testing, will be completed by the weekend.”

Similar woes have afflicted the New England Clean Energy Connect line, which opened in January, stretching 145 miles from Quebec to Maine. That project has also seen outages, and very little additional energy has flowed into the Northeast.

The good news for New York is that the grid wasn’t relying on CHPE yet. “Our planning studies did not assume CHPE would be available this summer, and that was one reason the grid performed reliably during the heat wave earlier this month,” Kevin Lanahan, a spokesperson for the New York Independent System Operator, the state’s grid management company, said in a statement. “A core principle of reliability planning is not relying on any single project.” 

The idea is that eventually, states and regions will be able to rely—at least in part—on these projects, so there is pressure to get them working smoothly: Building massive transmission lines is a major long-term investment. In future years, as the equipment gets stress-tested and utilities begin to feel more confident in the projects’ reliability, they could play a bigger role on the grid.

One thing to keep an eye on moving forward is the condition of Quebec’s hydropower fleet: The region has seen intense drought for the past three years, eating into the water reserves used to generate electricity. That could mean there won’t always be abundant hydropower to ship across the border—even if the transmission lines are able to carry it. 

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. 

When NASA’s Nancy Grace Roman Space Telescope launches, as early as the end of next month, it will attempt one of astronomy’s most precise disappearing acts to date. The telescope will carry the first space-bound “active” coronagraph, an instrument that effectively erases most of the light from a star during photography.

It will allow astronomers to take the first pictures of planets orbiting other stars that are similar to those in our solar system. Ultimately, it could pave the way for a future mission that could snap the first photos of Earth-like worlds.

“I hope it’s remembered for it being that critical stepping stone for … finding Earth 2.0,” says Brandon Creager, the instrument’s lead mechanical engineer at NASA’s Jet Propulsion Laboratory (JPL).

Named after Nancy Grace Roman, NASA’s first chief of astronomy, this new telescope will carry a roughly 300-megapixel wide-field camera that will enable it to capture images about 100 times larger than the Hubble Space Telescope’s widest exposures at a similar resolution.

These capabilities will help astronomers unpack the mysterious identities of dark matter and dark energy—and to detect around 100,000 new exoplanets, planets outside our solar system, whose presence can be inferred from the way they distort the starlight of more distant stars. Javier Viaña, a research scientist at Harvard who has had two projects selected for Roman’s highly competitive first year of observing, compares the leap to moving from “interviewing a handful of people” to “conducting a global census.”

Another camera will use the coronagraph, blocking out a star’s light as it observes one stellar system at a time. The instrument will allow astronomers an unprecedented look at the space around stars, enabling them to see smaller, dimmer, and more close-in exoplanets. “It’s giving us the ability to see planets that we haven’t been able to physically see before,” says Creager.

The anatomy of a vanishing trick

Coronagraphs in space aren’t new. But earlier incarnations, such as those currently aboard Hubble and the James Webb Space Telescope, use a stationary system to block a star’s blinding light. The approach does help, but it’s a bit like putting your thumb over a flashlight while searching a dark room for a firefly. Though the bulb vanishes, stray glare can still escape and overwhelm the light of the insect. Inside a telescope, that glare can come from light leaking around the edges of machinery or from minuscule imperfections in mirrors and coatings that can scatter starlight into speckles. All this can hide, or even impersonate, a planet.

Roman’s coronagraph, however, will attempt something completely unseen in space telescopes until this year: Before each observation, it will measure that leftover light and try to suppress it, a technique known as active wavefront control.

The telescope is able to do this because it contains two deformable mirrors. Each has a 48-by-48 checkerboard of actuators (tiny pistons) beneath a thin, deformable sheet of glass. Applying a small amount of voltage makes the actuators contract and tug their patches of mirror slightly backward, like thousands of microscopic fingers delicately sculpting a surface.

The effect is very subtle: Each patch of mirror can deform by up to 0.5 micrometers, or about one-fourth the size of an E. coli bacterium, and in increments as small as approximately 10 picometers. That’s about a tenth the diameter of a hydrogen atom, says Ilya Poberezhskiy, the instrument’s project systems engineer at JPL.

The actuators allow the mirrors to create an “active wavefront,” where each component is moved to the perfect position to cancel out incoming waves of unwanted light—a bit like a pair of noise-canceling headphones, but for light instead of sound. The “canceled-out” light creates a “doughnut-shaped region around the star where we suppress starlight and where we’re hoping to see exoplanets,” says Poberezhskiy.

Compared with current space-based coronagraphs, the system is expected to improve sensitivity to exoplanets against the glare of their host stars by a factor of up to 1,000, revealing planets that would have been far too faint to detect before.

Like Hubble and JWST, Roman also uses masks, patterned plates placed in the path of the light that are designed to block the photons that run into them. One tool in Roman’s mask arsenal is “silicon grass,” a thicket of microscopic spikes on some masks that can be used in certain configurations to absorb photons so they don’t bounce around the telescope and accidentally reach a detector.

Light entering the forest bounces deeper and deeper between the blades and gets trapped instead of reflecting back toward the camera. “Once the light gets into there, it never gets out,” Poberezhskiy says. The mirrors and masks form a succession of gates and hedges to guide as much of the preserved planetary light as possible toward the final detector.

Alien Jupiters

This elaborate setup could open a new chapter in the direct imaging of exoplanets. Nearly all exoplanets photographed so far are oversize youngsters that are nothing like the residents of our solar system: several times the mass of Jupiter, still glowing with the heat left over from their birth, and orbiting tens or hundreds of times farther from their star than the Earth is from the sun. This is because they are relatively easy to see. Their size, warmth, and distance from their parent star makes them shine brightly in infrared light, far away from the worst of the stellar glare.

Roman, however, could directly image a true Jupiter analogue—a planet similar to Jupiter in mass and circling a sunlike star a few times farther out than Earth is from our sun. Unlike the hot Jupiters we can see now, this one would be a much more mature gas giant like ours, primarily reflecting its parent star’s light after billions of years of cooling instead of heavily emitting its own.

Astronomers have been able to infer the existence of such planets from the gravitational wobble they impart to the star. Roman instead will collect starlight reflected from the planet itself. “We’re not looking at the star. We’re not looking at the effect of the planet on the star,” says Meredith MacGregor, a professor of astronomy at Johns Hopkins who has also secured an observing program. “We are actually looking at the planet, and that is super powerful.”

Once this instrument becomes available, it will become the scientists’ turn to do their jobs. “I’m honestly a little terrified about how we’re all going to deal with it, because I think it’s just so much data,” MacGregor says. “I think people will legitimately still be working on Roman data for decades.”

But don’t expect to see a 4K photo of an alien Jupiter in the coming months. Roman will not be able to resolve such a planet into a solid globe—at best, it will likely resemble a smattering of pixels. Still, that will be enough, MacGregor says, as Roman can then use the coronagraph to get information on the various wavelengths of light from the planet, which can tell astronomers about its atmospheric chemistry.

“You’re taking something that’s a point of light and turning it into an actual world,” she says, “because if you know that about its atmosphere, now you know something about the surface of the planet and the possibility of life being on that planet, right? So that’s a big step.”

During its first observations, scientists and engineers will see whether they can hold a star at the very center of the coronagraph’s masks, shape the mirrors, “dig” the dark doughnut (as Poberezhskiy describes it), and then maintain everything as the spacecraft moves through space and actively changes temperature.

The results will inform NASA’s proposed Habitable Worlds Observatory, the daydream of many an exoplanet astronomer, which will in theory be able to separate the light of an Earthlike planet from that of a sunlike star, over 10 billion times brighter.

Creager, who has worked on the instrument since 2018, is proud of the achievement: “Not too many people get to say, ‘I built something and it’s taking a picture of a planet that’s at a star that’s 50 light-years away or 100 light-years away.’” He imagines the moment he and his team will be able to look at the first image as it arrives: “Yes, we did that.” While the planet may show up only as a tiny dot, Roman’s achievement will be the darkness engineered around it.

Particle physics is the epitome of ‘big science’. To answer our most fundamental questions out about physics requires world class experiments that push the limits of whats technologically possible. Such incredible sophisticated experiments, like those at the LHC, require big facilities to make them possible,  big collaborations to run them, big project planning to make dreams of new facilities a reality, and committees with big acronyms to decide what to build.

Enter the Particle Physics Project Prioritization Panel (aka P5) which is tasked with assessing the landscape of future projects and laying out a roadmap for the future of the field in the US. And because these large projects are inevitably an international endeavor, the report they released last week has a large impact on the global direction of the field. The report lays out a vision for the next decade of neutrino physics, cosmology, dark matter searches and future colliders. 

P5 follows the community-wide brainstorming effort known as the Snowmass Process in which researchers from all areas of particle physics laid out a vision for the future. The Snowmass process led to a particle physics ‘wish list’, consisting of all the projects and research particle physicists would be excited to work on. The P5 process is the hard part, when this incredibly exciting and diverse research program has to be made to fit within realistic budget scenarios. Advocates for different projects and research areas had to make a case of what science their project could achieve and a detailed estimate of the costs. The panel then takes in all this input and makes a set of recommendations of how the budget should be allocated, what should projects be realized and what hopes are dashed. Though the panel only produces a set of recommendations, they are used quite extensively by the Department of Energy which actually allocates funding. If your favorite project is not endorsed by the report, its very unlikely to be funded. 

Particle physics is an incredibly diverse field, covering sub-atomic to cosmic scales, so recommendations are divided up into several different areas. In this post I’ll cover the panel’s recommendations for neutrino physics and the cosmic frontier. Future colliders, perhaps the spiciest topic, will be covered in a follow up post.

The Future of Neutrino Physics

For those in the neutrino physics community all eyes were on the panels recommendations regarding the Deep Underground Neutrino Experiment (DUNE). DUNE is the US’s flagship particle physics experiment for the coming decade and aims to be the definitive worldwide neutrino experiment in the years to come. A high powered beam of neutrinos will be produced at Fermilab and sent 800 miles through the earth’s crust towards several large detectors placed in a mine in South Dakota. Its a much bigger project than previous neutrino experiments, unifying essentially the entire US community into a single collaboration.

DUNE is setup to produce world leading measurements of neutrino oscillations, the property by which neutrinos produced in one ‘flavor state’, (eg an electron-neutrino) gradually changes its state with sinusoidal probability (eg into a muon neutrino) as it propagates through space. This oscillation is made possible by a simple quantum mechanical weirdness: neutrino’s flavor state, whether it couples to electrons muons or taus, is not the same as its mass state. Neutrinos of a definite mass are therefore a mixture of the different flavors and visa versa.

Detailed measurements of this oscillation are the best way we know to determine several key neutrino properties. DUNE aims to finally pin down two crucial neutrino properties: their ‘mass ordering’, which will solidify how the different neutrino flavors and measured mass differences all fit together, and their ‘CP-violation’ which specifies whether neutrinos and their anti-matter counterparts behave the same or not. DUNE’s main competitor is the Hyper-Kamiokande experiment in Japan, another next-generation neutrino experiment with similar goals.

Construction of the DUNE experiment has been ongoing for several years and unfortunately has not been going quite as well as hoped. It has faced significant schedule delays and cost overruns. DUNE is now not expected to start taking data until 2031, significantly behind Hyper-Kamiokande’s projected 2027 start. These delays may lead to Hyper-K making these definitive neutrino measurements years before DUNE, which would be a significant blow to the experiment’s impact. This left many DUNE collaborators worried about its broad support from the community.

It came as a relief then when P5 report re-affirmed the strong science case for DUNE, calling it the “ultimate long baseline” neutrino experiment. The report strongly endorsed the completion of the first phase of DUNE. However, it recommended a pared-down version of its upgrade, advocating for an earlier beam upgrade in lieu of additional detectors. This re-imagined upgrade will still achieve the core physics goals of the original proposal with a significant cost savings. With this report, and news that the beleaguered underground cavern construction in South Dakota is now 90% complete, was certainly welcome holiday news to the neutrino community. This is also sets up a decade-long race between DUNE and Hyper-K to be the first to measure these key neutrino properties.

Cosmic Implications

While we normally think of particle physics as focused on the behavior of sub-atomic particles, its really about the study of fundamental forces and laws, no matter the method. This means that telescopes to study the oldest light in the universe, the Cosmic Microwave Background (CMB), fall into the same budget category as giant accelerators studying sub-atomic particles. Though the experiments in these two areas look very different, the questions they seek to answer are cross-cutting. Understanding how particles interact at very high energies helps us understand the earliest moments of the universe, when such particles were all interacting in a hot dense plasma. Likewise, by studying the these early moments of the universe and its large-scale evolution can tell us about what kinds of particles and forces are influencing its dynamics. When asking fundamental questions about the universe, one needs both the sharpest microscopes and the grandest panoramas possible.

The most prominent example of this blending of the smallest and largest scales in particle physics is dark matter. Some of our best evidence for dark matter comes analyzing the cosmic microwave background to determine how the primordial plasma behaved. These studies showed that some type of ‘cold’, matter that doesn’t interact with light, aka dark matter, was necessary to form the first clumps that eventually seeded the formation of galaxies. Without it, the universe would be much more soup-y and structureless than what we see to today.

To determine what dark matter is then requires an attack from two fronts: design experiments here on earth attempting directly detect it, and further study its cosmic implications to look for more clues as to its properties.

The panel recommended next generation telescopes to study the CMB as a top priority. The so called ‘Stage 4’ CMB experiment would deploy telescopes in both the south pole and Chile’s Atacama desert to better characterize sources of atmospheric noise. The CMB has been studied extensively before, but the increased precision of CMS-S4 could shed light on mysteries like dark energy, dark matter, inflation, and the recent Hubble Tension. Given the past fruitfulness of these efforts, I think few doubted the science case for such a next generation experiment.

The P5 report recommended a suite of new dark matter experiments in the next decade, including the ‘ultimate’ liquid Xenon based dark matter search. Such an experiment would follow in the footsteps of massive noble gas experiments like LZ and XENONnT which have been hunting for a favored type of dark matter called WIMP’s for the last few decades. These experiments essentially build giant vats of liquid Xenon, carefully shield from any sources of external radiation, and look for signs of dark matter particles bumping into any of the Xenon atoms. The larger the vat of Xenon, the higher chance a dark matter particle will bump into something. Current generation experiments have ~7 tons of Xenon, and the next generation experiment would be even larger. The next generation aims to reach the so called ‘neutrino floor’, the point as which the experiments would be sensitive enough to observe astrophysical neutrinos bumping into the Xenon. Such neutrino interactions would look extremely similar to those of dark matter, and thus represent an unavoidable background which would signal the ultimate sensitivity of this type of experiment. WIMP’s could still be hiding in a basement below this neutrino floor, but finding them would be exceedingly difficult.

WIMP’s are not the only dark matter candidates in town, and recent years have also seen an explosion of interest in the broad range of dark matter possibilities, with axions being a prominent example. Other kinds of dark matter could have very different properties than WIMPs and have had much fewer dedicated experiments to search for them. There is ‘low hanging fruit’ to pluck in the way of relatively cheap experiments which can achieve world-leading sensitivity. Previously, these ‘table top’ sized experiments had a notoriously difficult time obtaining funding, as they were often crowded out of the budgets by the massive flagship projects. However, small experiments can be crucial to ensuring our best chance of dark matter discovery, as they fill in the blinds pots missed by the big projects.

The panel therefore recommended creating a new pool of funding set aside for these smaller scale projects. Allowing these smaller scale projects to flourish is important for the vibrancy and scientific diversity of the field, as the centralization of ‘big science’ projects can sometimes lead to unhealthy side effects. This specific recommendation also mirrors a broader trend of the report: to attempt to rebalance the budget portfolio to be spread more evenly and less dominated by the large projects.

What Didn’t Make It

Any report like this comes with some tough choices. Budget realities mean not all projects can be funded. Besides the pairing down of some of DUNE’s upgrades, one of the biggest areas that was recommended against were ‘accessory experiments at the LHC’. In particular, MATHUSULA and the Forward Physics Facility were two experiments that proposed to build additional detectors near already existing LHC collision points to look for particles that may be missed by the current experiments. By building new detectors hundreds of meters away from the collision point, shielded by concrete and the earth, they can obtained unique sensitivity to ‘long lived’ particles capable of traversing such distances. These experiments would follow in the footsteps of the current FASER experiment, which is already producing impressive results.

While FASER found success as a relatively ‘cheap’ experiment, reusing detector components from and situating itself in a beam tunnel, these new proposals were asking for quite a bit more. The scale of these detectors would have required new caverns to be built, significantly increasing the cost. Given the cost and specialized purpose of these detectors, the panel recommended against their construction. These collaborations may now try to find ways to pare down their proposal so they can apply to the new small project portfolio.

Another major decision by the panel was to recommend against hosting a new Higgs factor collider in the US. But that will discussed more in a future post.

Conclusions

The P5 panel was faced with a difficult task, the total cost of all projects they were presented with was three times the budget. But they were able to craft a plan that continues the work of the previous decade, addresses current shortcomings and lays out an inspiring vision for the future. So far the community seems to be strongly rallying behind it. At time of writing, over 2700 community members from undergraduates to senior researchers have signed a petition endorsing the panels recommendations. This strong show of support will be key for turning these recommendations into actual funding, and hopefully lobbying congress to even increase funding so that more of this vision can be realized.

For those interested the full report as well as executive summaries of different areas can be found on the P5 website. Members of the US particle physics community are also encouraged to sign the petition endorsing the recommendations here.

And stayed tuned for part 2 of our coverage which will discuss the implications of the report on future colliders!

Every year since 1966,  particle physicists have gathered in the Alps to unveil and discuss their most important results of the year (and to ski). This year I had the privilege to attend the Moriond QCD session so I thought I would post a recap here. It was a packed agenda spanning 6 days of talks, and featured a lot of great results over many different areas of particle physics, so I’ll have to stick to the highlights here.

FASER Observes First Collider Neutrinos

Perhaps the most exciting result of Moriond came from the FASER experiment, a small detector recently installed in the LHC tunnel downstream from the ATLAS collision point. They announced the first ever observation of neutrinos produced in a collider. Neutrinos are produced all the time in LHC collisions, but because they very rarely interact, and current experiments were not designed to look for them, no one had ever actually observed them in a detector until now. Based on data collected during collisions from last year, FASER observed 153 candidate neutrino events, with a negligible amount of predicted backgrounds; an unmistakable observation.

This first observation opens the door for studying the copious high energy neutrinos produced in colliders, which sit in an energy range currently unprobed by other neutrino experiments. The FASER experiment is still very new, so expect more exciting results from them as they continue to analyze their data. A first search for dark photons was also released which should continue to improve with more luminosity. On the neutrino side, they have yet to release full results based on data from their emulsion detector which will allow them to study electron and tau neutrinos in addition to the muon neutrinos this first result is based on.

New ATLAS and CMS Results

The biggest result from the general purpose LHC experiments was ATLAS and CMS both announcing that they have observed the simultaneous production of 4 top quarks. This is one of the rarest Standard Model processes ever observed, occurring a thousand times less frequently than a Higgs being produced. Now that it has been observed the two experiments will use Run-3 data to study the process in more detail in order to look for signs of new physics.

ATLAS also unveiled an updated measurement of the mass of the W boson. Since CDF announced its measurement last year, and found a value in tension with the Standard Model at ~7-sigma, further W mass measurements have become very important. This ATLAS result was actually a reanalysis of their previous measurement, with improved PDF’s and statistical methods. Though still not as precise as the CDF measurement, these improvements shrunk their errors slightly (from 19 to 16 MeV).  The ATLAS measurement reports a value of the W mass in very good agreement with the Standard Model, and approximately 4-sigma in tension with the CDF value. These measurements are very complex, and work is going to be needed to clarify the situation.

CMS had an intriguing excess (2.8-sigma global) in a search for a Higgs-like particle decaying into an electron and muon. This kind of ‘flavor violating’ decay would be a clear indication of physics beyond the Standard Model. Unfortunately it does not seem like ATLAS has any similar excess in their data.

Status of Flavor Anomalies

At the end of 2022, LHCb announced that the golden channel of the flavor anomalies, the R(K) anomaly, had gone away upon further analysis. Many of the flavor physics talks at Moriond seemed to be dealing with this aftermath.

Of the remaining flavor anomalies, R(D), a ratio describing the decay rates of B mesons in final states with D mesons and taus versus D mesons plus muons or electrons, has still been attracting interest. LHCb unveiled a new measurement that focused on hadronically taus and found a value that agreed with the Standard Model prediction. However this new measurement had larger error bars than others so it only brought down the world average slightly. The deviation currently sits at around 3-sigma.

An interesting theory talk pointed out that essentially any new physics which would produce a deviation in R(D) should also produce a deviation in another lepton flavor ratio, R(Λc), because it features the same b->clv transition. However LHCb’s recent measurement of R(Λc) actually found a small deviation in the opposite direction as R(D). The two results are only incompatible at the ~1.5-sigma level for now, but it’s something to continue to keep an eye on if you are following the flavor anomaly saga.

It was nice to see that the newish Belle II experiment is now producing some very nice physics results. The highlight of which was a world-best measurement of the mass of the tau lepton. Look out for more nice Belle II results as they ramp up their luminosity, and hopefully they can weigh in on the R(D) anomaly soon.

Theory Pushes for Precision

The focus of much of the theory talks was about trying to advance our precision in predictions of standard model physics. This ‘bread and butter’ physics is sometimes overlooked in scientific press, but is an absolutely crucial part of the particle physics ecosystem. As experiments reach better and better precision, improved theory calculations are required to accurately model backgrounds, predict signals, and have precise standard model predictions to compare to so that deviations can be spotted. Nice results in this area included evidence for an intrinsic amount of charm quarks inside the proton from the NNPDF collaboration, very precise extraction of CKM matrix elements by using lattice QCD, and two different proposals for dealing with tricky aspects regarding the ‘flavor’ of QCD jets.

Final Thoughts

Those were all the results that stuck out to me. But this is of course a very biased sampling! I am not qualified enough to point out the highlights of the heavy ion sessions or much of the theory presentations. For a more comprehensive overview, I recommend checking out the slides for the excellent experimental and theoretical summary talks. Additionally there was the Moriond Electroweak conference that happened the week before the QCD one, which covers many of the same topics but includes neutrino physics results and dark matter direct detection. Overall it was a very enjoyable conference and really showcased the vibrancy of the field!