Myriam Heiman, the John and Dorothy Wilson Professor of Neuroscience at MIT, will become the director of MIT’s Picower Institute for Learning and Memory, effective July 1. She succeeds Picower Professor Li-Huei Tsai, who is stepping down after leading the institute for 16 years.

Heiman, a molecular neurobiologist and geneticist, studies the neurodegenerative diseases of the brain’s basal ganglia, including Huntington’s disease and Parkinson’s disease. Using cutting-edge techniques, including single-cell genomics and a powerful transcriptomic technique she helped invent, called translating ribosome affinity purification, she aims to understand the molecular changes that eventually lead to cell death in these diseases. 

“Myriam is an extraordinary scientist, a proven leader within MIT, and a deeply caring and generous mentor. Her research to determine why specific brain cell types are particularly vulnerable to diseases such as Huntington’s has produced studies that are both deep in their insight and sweeping in their scope,” says Nergis Mavalvala, dean of the MIT School of Science and the Curtis and Kathleen Marble Professor of Astrophysics. “I firmly believe that Myriam will be an excellent leader during the Picower Institute’s next chapter.”

“I am honored to take on this role to support the institute’s exceptional scientists and trainees as they pursue discoveries that deepen our understanding of the brain and improve human health,” says Heiman, a professor in MIT’s Department of Brain and Cognitive Sciences (BCS). 

The Picower Institute is a community of 16 neuroscience labs dedicated to understanding the mechanisms that drive learning and memory and related functions such as cognition, emotion, perception, behavior, and consciousness. Institute neuroscientists explore the brain and nervous system at multiple scales, from genes and molecules to cells and synapses to circuits and systems, producing novel insights into how disruptions in these mechanisms can lead to developmental, psychiatric, or neurodegenerative disease. 

Picower Professor Susumu Tonegawa founded the institute as a center in 1994 before a transformative gift from Barbara and Jeffry Picower enabled it to become an institute in 2002. Li-Huei Tsai has served as director since 2009, but announced in March that she would step down after more than 16 years to focus on her research.

Heiman joined the Picower Institute, BCS, and the Broad Institute of Harvard and MIT in 2011, after completing her postdoctoral training at The Rockefeller University. She holds a PhD from Johns Hopkins University and a BA from Princeton University. 

“Ever since joining the institute, Heiman’s research has been guided by the principle that fundamental understanding can lead to breakthroughs in addressing disease,” Tsai says. “Myriam has made it her mission to address these kinds of urgent questions in neuroscience.”

Heiman employs sophisticated DNA and RNA analysis technologies to gain detailed insights into how brain cell states change amid disease, revealing molecular pathways that contribute to the particular vulnerability of different cell types. In 2020, Heiman published the results of an innovative in vivo screening of every mouse gene’s impact on the survival of neurons in the brain, identifying hundreds necessary for sustaining neurons and highlighting a specific gene that promoted their resilience in the context of Huntington’s disease. 

Other studies, both in mice and in postmortem human brain samples, have revealed errant immune responses in neurons and in the brain’s blood vessels that contribute to the disease’s progression. The latter finding arose in a 2022 paper, published with MIT Computer Science and Artificial Intelligence Laboratory colleague Manolis Kellis, that also provided the field one of the first cellular atlases of the brain’s vasculature.  

Her research has also produced insights into other neurodegenerative and psychiatric disorders, including ALS and frontotemporal dementia. In 2024, together with Kellis, Heiman published a paper in Cell showing the diseases have remarkable overlaps at the cellular and molecular levels, revealing potential targets that could yield therapies applicable to both disorders. Heiman’s latest research is also producing new insights into substance use disorders and schizophrenia.

Her research program has garnered many awards. In 2021, Heiman became co-recipient of a National Institutes of Health Transformative Research Award, which “promotes cross-cutting, interdisciplinary approaches that could potentially create or challenge existing paradigms” as part of the NIH’s High-Risk, High-Reward Research program. The next year she also received a prestigious NIH R35 grant to find early triggers of disease progression.

Heiman is also a dedicated teacher and mentor. In 2017, she earned the Department of BCS award for excellence in graduate mentoring; and in 2020, she received the department’s award for excellence in undergraduate teaching. In 2024, she was named one of 23 faculty across MIT who are “committed to caring” — an award given out by MIT’s Office of Graduate Education to faculty members who have served as exceptional mentors to graduate students.

Beyond MIT, Heiman serves on editorial boards and the scientific advisory board of the nonprofit Huntington’s Disease Foundation, an organization that supports research aimed at finding treatments and a cure for Huntington’s and related disorders.. 

Heiman says she is looking forward to her new role in service to MIT by leading the Picower Institute.

“I approach this role with humility and enormous enthusiasm,” Heiman says. “The Picower Institute has an extraordinary legacy, and I’m eager to do everything I can to help support the next generation of transformative research.”

A new kernel, or core program within an operating system, gives researchers a cleaner view of what’s happening inside a processor. Called Fractal and developed at MIT, the kernel has already surfaced previously unknown behavior in Apple’s M1.

When security researchers want to understand what a modern processor is really doing with the kind of detail that determines whether attacks like Spectre and Meltdown are possible, they usually run their experiments on top of an operating system that was never built for the job. They open up macOS or Linux, patch the kernel by hand, and hope the modifications hold. The approach is unstable, hard to reproduce, and on Apple’s platforms, slated for deprecation.

A team at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) decided to build something different. Fractal, an operating system kernel written from the ground up, treats the hardware itself as the object of study. Its first major use, a deep look at branch predictors — a CPU’s way of guessing what code to run next, before it knows for certain, so it doesn’t have to waste time waiting to find out — inside Apple’s M1 processor, has already turned up findings that prior work missed, including the first evidence that a class of speculative attack known as “Phantom” affects Apple Silicon.

“We’re using hardware in ways it wasn’t designed for,” says Joseph Ravichandran, the MIT PhD student in electrical engineering and computer science (EECS) who led the project. “It’s not even obvious that this is a possible thing you could do with the hardware. But we found a way to pull all these different primitives off. It’s like a microscope. If you’ve got a hand magnifying glass, you can see a little bit. But if you had an electron microscope, now we’re really talking. That’s what Fractal is. The electron microscope of operating systems.”

A clean room for chip research

The core problem Fractal solves is one that researchers have worked around for years. Modern processors keep state in many internal structures: branch predictors, caches, translation lookaside buffers, and more. To study how those structures behave across the boundary between user code and kernel code, two domains the chip is supposed to keep isolated, researchers need to run nearly identical experiments on each side of that boundary. On a general-purpose operating system, that is very difficult. The system itself manages privilege levels, address spaces, and scheduling, and it injects its own activity into every measurement.

Fractal inverts the model. It boots directly on bare metal, with no other software running, and exposes primitives that let a single experiment switch privilege levels at runtime while executing the same instructions in the same address space. The team calls the underlying technique multi-privilege concurrency, and it relies on a new construct they introduced: the outer kernel thread, which sits inside a user process’s memory but executes with kernel privileges.

The result is an experimental setup with almost no background noise. Where measurements taken under macOS or Linux are blurred by interrupts, scheduler activity, and address-space management, Fractal produces flat baselines and clean signals.

What Fractal found on the M1

Apple’s M1 implements an ARM specification called CSV2, which is supposed to prevent code running in one privilege level from steering speculation in another. Using Fractal, the MIT team confirmed that the protection works for the execute stage of indirect branch prediction: a user-mode program cannot make the kernel speculatively execute a chosen target through the indirect branch predictor.

But the team also found something the chip’s designers may not have intended. The CPU still fetches the target into the instruction cache before the protection kicks in. That fetch is observable through a side channel, which means user code can still influence what the kernel pulls into its caches across the privilege boundary. The same pattern appeared between processes assigned different address space identifiers.

The team also produced the first evidence that Apple Silicon exhibits Phantom speculation, a class of misprediction previously demonstrated only on AMD and Intel processors. In Phantom, ordinary instructions, including a no-op, can be misinterpreted by the CPU as branches, triggering speculative behavior the program never asked for. On the M1, Fractal showed that Phantom fetches succeed across both privilege levels and address spaces, though the execute phase remains blocked.

A separate Fractal experiment overturned a finding from earlier work on the M1’s conditional branch predictor, which had reported that cross-privilege training worked on Apple’s performance cores, but not its efficiency cores. The Fractal team showed that the conditional branch predictor has no privilege isolation at all, on either core type, and that the earlier result was likely an artifact of macOS quietly migrating threads between cores during system calls.

“For us, it is a true independent variable,” Ravichandran says. “You change the privilege level, nothing else changes. The only thing that could explain whether the attack succeeds or not is the privilege level.”

A tool, not a one-off

Fractal supports x86_64, ARM64, and RISC-V, and consists of more than 31,000 lines of code. The team designed it as infrastructure rather than as a single experiment, with familiar POSIX system calls, a C library, and ports of standard tools like vim, GCC, and the dash shell, so that researchers can move existing experiment code over with minimal friction.

The MIT team disclosed its M1 findings to Apple’s product security team. In an unusual reversal, Apple’s engineers also examined Fractal. 

The longer-term ambition is bigger than any single result. Ravichandran wants Fractal to become to microarchitecture research what tools like QEMU and FFmpeg are to their fields: shared infrastructure that the whole community builds on. 

“My hope is that our results as a community get significantly more reliable, significantly more accurate,” says Ravichadran. “With this reduced noise, this clarity, and this guarantee that you’re running on the right core, on the right system.”

“Fractal is a strong architecture contribution because it turns an often ad hoc microarchitectural reverse-engineering workflow into reusable research infrastructure,” says University of Southern California assistant professor Mengyuan Li, who wasn’t involved in the paper. “By reducing software noise and giving researchers tighter control across privilege boundaries, it makes difficult hardware experiments much easier to interpret.”

Ravichandran worked with Mengjia Yan, an MIT associate professor of EECS and CSAIL principal investigator, on the paper. Their work was supported, in part, by the National Science Foundation, the U.S. Air Force Office of Scientific Research, and ACE, which is part of a program sponsored by the U.S. Defense Advanced Research Projects Agency. They presented their work at the IEEE Symposium on Security and Privacy in San Francisco, California.

Enshittification isn't just a sweary word to describe the accelerating decay of the online platforms, apps, and services that we rely on.  

It's a framework for understanding the structural incentives that make tech companies enemies of their own users over time—the surveillance business model, the erosion of privacy, the monopoly power that eliminates alternatives, the regulatory capture that prevents accountability.  

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These are some of EFF's core fights and have been for over 35 years. EFF sues. EFF advocates. EFF codes. And EFF wins. EFF is the most profound and powerful disenshittifying force on the planet Earth, and I’ve been proud to fight alongside them for nearly 25 of those years.  

One of the lessons you learn in battles with very long timelines against very powerful actors is that these battles are deeply serious, and because of that they must also be fun. “Enshittification” took off as a shorthand in part because of the minor license to vulgarity it confers. It's slightly crass for a reason: getting people to engage with the abstract issues of tech policy can be hard at the best of times. No one knows this better than my colleagues at EFF, who consistently surprise me with their ability to make complex, technical concepts concrete, memorable, and sometimes even joyful. 

Words matter, but so do visuals. For the cover of the U.S. edition of my book, Enshittification, designer Devin Washburn of No Ideas studio created an iconic variation of the "pile of poo" emoji, with angry eyebrows and a grawlix-scrawled censor bar over its mouth. It instantly became the symbol of enshittification I’d been looking for. 

I liked it so much I ordered a couple hundred enamel pins and a couple thousand vinyl stickers and handed them out to people I met on my 33-city book tour. Even when giving them away, I was inundated with requests to buy more of them.  

I've since bought out Devin's rights to the image and released it under a Creative Commons Attribution 4.0 license—free for anyone to use, remix, or build on, including commercially, with attribution. The high-resolution files are on Wikimedia Commons, Flickr, and the Internet Archive (including a PSD with an ink-density adjustment layer). It belongs to the commons now. 

But I made sure EFF had first crack at the design for their “official merch,” and they've done right by it. There are two items available now in the EFF shop, and all proceeds go directly to EFF's work defending digital rights. I’ve spent years admiring EFF’s merch and consistent, creative visual identity, so it fills me with pride to see this more-than-a-mere-poop-emoji in their shop.  

A recognizable visual shorthand is a genuine organizing tool. When someone sees the enshittification emoji, they know what the conversation is about. When you wear the pin or slap the sticker on your laptop, you're signaling that you understand what's happening to the internet, and that you know we can do better.  

You can get a $5 sticker:

Or a $10 pin:

 

 Because the design is CC-licensed, you don't have to buy one. You can make your own merch, your own swag, your own illustrations. I made a lawn flag for my front garden.

 

But if you do want to buy a sticker or pin, you can do so while supporting the most profound and powerful disenshittifying force on the planet Earth—the Electronic Frontier Foundation.

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Across the country, surveillance companies have spun a vast web of tens of thousands of license plate cameras. The people selling this tech want you to believe that it's for your safety, but how are authorities really using automated license plate readers (ALPR)? In this week's EFFector newsletter, we're looking at how these powerful surveillance networks have become universal people-trackers used for noise complaints and other low-level investigations.

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For over 35 years, EFFector has been your guide to understanding the intersection of technology, civil liberties, and the law. This week's issue covers a victory for facial privacy, EFF's testimony to Congress about AI and surveillance, and troubling new examples of ALPR mission creep.

Prefer to listen in? EFFector is now available on all major podcast platforms. This week, we're chatting with EFF Associate Director of State Affairs Rindala Alajaji about what she uncovered about police use of ALPR. And don't miss the EFFector news quiz. You can find the episode and subscribe on your podcast platform of choice:

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MIT professor of physics Pablo Jarillo-Herrero is among 10 researchers worldwide to receive this year’s prestigious Kavli Prize. 

Jarillo-Herrero is co-recipient of the 2026 Kavli Prize in Nanoscience “for foundational work that established the field of twistronics.” His co-recipients are professors Eva Y. Andrei at Rutgers University and Allan MacDonald from the University of Texas at Austin.

These three physicists are being honored for the theoretical foundation and experimental validation of a new field of “twistronics,” where superconductivity, magnetism, and other properties can be obtained by rotating two-dimensional materials such as graphene to a “magic angle.”

A partnership among the Norwegian Academy of Science and Letters, the Norwegian Ministry of Education and Research, and the Kavli Foundation, the Kavli Prizes are awarded every two years to “honor scientists for breakthroughs in astrophysics, nanoscience and neuroscience that transform our understanding of the big, the small and the complex.” The laureates in each field will share $1 million.

“Pablo’s groundbreaking research has once again been given well-deserved recognition,” says Nergis Mavalvala, dean of the MIT School of Science and the Curtis and Kathleen Marble Professor of Astrophysics. “Pablo and his co-recipients have pioneered twistronics, very fundamental scientific research that has opened up a new field with myriad possibilities for novel quantum materials.”

In 2009, using scanning tunneling microscopy and spectroscopy on graphene, most commonly found as a single layer of carbon atoms arranged in hexagons resembling a honeycomb structure, Andrei and her research group demonstrated that small variations in twist angle profoundly modified the electronic structure. This demonstration — that geometric control, rather than chemical composition, could modify a material’s electronic structure — represented a fundamental advance in materials design and arguably launched the field now known as “twistronics.”

In 2011, MacDonald quantitatively explained the emergence of this electronic structure by geometries at discrete magic angles. This framework has since become the theoretical foundation of what are known as moiré materials, and has guided subsequent experimental and theoretical developments across a wide range of twisted and layered systems. 

In 2018, Jarillo-Herrero’s group observed correlated insulating phases and superconductivity in magic-angle twisted bilayer graphene devices. The resulting platform, “combining atomic-scale structural simplicity with electronic tunability, has enabled systematic investigations has had broad and lasting impact across nanoscience and quantum material research,” according to the Kavli Prize citation.

“It was a big surprise, because the technique we used, though conceptually straightforward, was hard to pull off in the lab,” said Jarillo-Herrero recently. He is also the Cecil and Ida Green Professor of Physics at MIT and a member of the Research Laboratory of Electronics. 

“I’m humbled and incredibly honored to be sharing this award with [Andrei and MacDonald],” Jarillo-Herrero noted in an essay describing his journey to the Kavli Prize. “I want to also emphasize that this award honors fundamental physics research in nanoscience. It is incredibly important for society to continue to support fundamental research: Although it often doesn’t have a direct near-term application, in the long run it happens to be the most transformative and impactful in society.”

“Pablo’s research has helped spark a revolution in condensed matter physics and nanoscience, inspiring physicists worldwide to explore superconductivity and other emergent phenomena in engineered quantum materials. This work could potentially lead to the creation of superconductors at room temperature, which would would have an enormous technological impact,” says Deepto Chakrabarty, physics department head and William A. M. Burden Professor in Astrophysics.

Jarillo-Herrero's win brings the number of all-time MIT faculty recipients of the Kavli Prize to nine. Prior winners include Nancy Kanwisher in neuroscience (2024), Bob Langer in nanoscience (2024), Sara Seager in astrophysics (2024), Rainer Weiss in astrophysics (2016), Alan Guth in astrophysics (2014), Mildred Dresselhaus in nanoscience (2012), Ann Graybiel in neuroscience (2012), and Jane Luu in astrophysics (2012).

WhatsApp has caught the NSO Group phishing its users, in violation of a court order.

Inside the demise of a 15-year-old modeling scenario built off the worst possibilities scientists could imagine.

A three-year drought — and rising electricity demand — has complicated plans to power New York and Massachusetts with Canadian hydropower.

The leading vote-getter in the race for California insurance commissioner wants a state program to insure all wildfire damage to homes.

The forecast from the government's energy statistical bureau comes even as Energy Secretary Chris Wright said shipping traffic through the Strait of Hormuz is “rising very meaningfully.”

Brussels hopes that EU funding for solar panels in the Sahara Desert and wind turbines along the Mediterranean will lure private money to co-invest.

A faster switch from fossil fuels to electricity will be a “defining” priority of the Antalya conference.

Factory managers reported that severe temperatures are causing sweat stains on fabric, dust contamination, stitching errors and forced production halts.

A tropical storm warning was in effect from Puerto Sandino to the Guatemala/El Salvador border.

Interpreting medical ultrasound images is a difficult task, requiring a technician to look at 2D images and mentally arrange them into a 3D representation of what the tissue looks like. 

To make that job easier, MIT researchers developed a new approach to ultrasound imaging that allows the user to visualize a 3D augmented-reality image of the object being scanned. Using a virtual-reality headset, they can see a precise 3D digital representation of what the object actually looks like, making it easier to identify and analyze.

This technique could help speed up the training process for ultrasound technicians and other health care providers who use ultrasound. It could also be deployed for use in hospitals, for tasks such as using ultrasound to place a needle in the right location for a biopsy.

“For training, this could make ultrasound more intuitive and more understandable. On the clinical side, it could be less time-consuming, more accurate, and also give health care providers more peace of mind. They wouldn’t have to wonder if they missed anything,” says Canan Dagdeviren, an associate professor of media arts and sciences at MIT and the senior author of the study.

MIT graduate students Jason Hou and Shrihari Viswanath are the lead authors of the paper, which appears today in Nature Communications Engineering. Other authors of the paper include Bowen Wu ’24 and two MIT Summer Research Program students, Cinay Dilibal, a senior at Dartmouth College, and Tanisha Shende, a senior at Oberlin College.

3D representations

Ultrasound imaging works by bouncing high-frequency sound waves off tissues in the body, which are then reflected back to an ultrasound transducer. The transducer converts these sound waves to electrical signals, which are used to create a 2D image of the tissue. Ultrasound technicians are trained to convert these images into a 3D mental representation of the tissue.

“It's a difficult skill to master, and there are long learning curves,” says Hou. “The hardest thing is this mental tomography bottleneck where you’re trained to reconstruct the 2D slices in your 3D mental space. That is a cognitive burden that can lead to inaccuracies in scanning.”

To reduce that cognitive load, the MIT team thought it could be helpful to combine two technologies: 3D ultrasound imaging and augmented reality (AR). 

Three-dimensional ultrasound imaging is occasionally used in fields such as fetal imaging and echocardiography, which is used to image the heart, but most 3D ultrasound imaging systems are expensive and not widely available. For this study, the MIT team used a real-time 3D system they developed recently for use in breast-cancer detection.

Their new system includes an ultrasound probe, slightly smaller than a deck of cards, that transmits information using a chirped data acquisition system (cDAQ). The probe contains an ultrasound array arranged in the shape of an empty square, a configuration that allows the array to take 3D images of the tissue below.

Because this system has fewer ultrasound elements than a typical 3D ultrasound system, it requires less power and is less expensive to build.

The data collected by the ultrasound probe can then be compressed and streamed into a 3D computer graphics engine called Unreal Engine, which converts the voxel data from the ultrasound image into a direct 3D representation of the object, with no loss of information. Wearing an AR/VR headset, the user can see this 3D rendering representing the internal structure, superimposed over the object’s actual location — like X-ray vision. By tilting their head or approaching from a different direction, the user can see different views of the object, making it easier to identify.

Easier to use

The researchers tested their new technology, which they call AR-VIU (augmented real-time volumetric imaging in ultrasound), with a group of 18 participants. Nine of the subjects were experts in ultrasound technology (including sonographers and physicians), and nine had never used ultrasound before.

Each user performed identification tasks using four different ultrasound technologies. In one condition, they viewed 2D images on a regular screen, which is the way that most ultrasounds are now performed. They also viewed 3D images on a regular screen, as well as two augmented reality conditions: one 2D and one 3D (AR-VIU).

In one round of experiments, users were asked to identify an object embedded in gelatin — such as a spring, a ball, or a screw — inside an opaque container that was scanned with ultrasound. In a second set, they were asked to use a pen to mark the location of “tissue phantom” — a gel-like material engineered to mimic human tissue. This simulates the task of locating the right spot for a needle during a biopsy.

The researchers found that the AR-VIU system significantly improved all users’ ability to identify and locate objects. The effect was especially strong for novices, who performed nearly as well as experts when using AR-VIU. When using the traditional 2D imaging system, experts performed much better than novices.

“Overlaying images with the anatomy and providing 3D visual context makes ultrasound significantly easier for novices to understand,” Viswanath says.

In interviews after the experiments, most of the novices reported that they preferred the AR-VIU approach, with many saying that it made the tasks easier.

“The 3D system imposes less brain drain, it’s more intuitive, and it’s easier to understand what is happening in the targeted region,” Dagdeviren says.

Many of the experts said they preferred the traditional 2D imaging because that is what they were accustomed to and had been trained to use. However, those experts also said they could see the benefits of the AR-VIU system in some situations, such as placing a needle for a biopsy or visualizing the movement of the heart wall during echocardiography.

The researchers are now working on further improving the resolution of the imaging and doing additional tests to demonstrate the accuracy of the AR-VIU technology.

The research was funded by the MIT Media Lab Consortium, the National Science Foundation, an MIT HEALS graduate fellowship, and an MIT-Tata graduate fellowship.

Nature Climate Change, Published online: 10 June 2026; doi:10.1038/s41558-026-02659-0

Sea-level rise in conjunction with storm surge and tidal variations leads to extreme sea levels that threaten coastal systems. Here the authors use tide-gauge data and models to quantify how anthropogenic climate change has increased the risk of these extreme sea-level events since 1900.

Nature Climate Change, Published online: 10 June 2026; doi:10.1038/s41558-026-02689-8

Author Correction: Rising temperatures increase added sugar intake disproportionately in disadvantaged groups in the USA

Nature Climate Change, Published online: 10 June 2026; doi:10.1038/s41558-026-02633-w

Southern California is rarely affected by tropical cyclones at present. Here the authors show that this could change with warming, leading to an increase in landslide risk that is expected to disproportionately affect low-income households.

The rise of artificial intelligence is riding on the back of an enormous data center expansion. Data centers are projected to account for anywhere from 9 to 17 percent of total electricity usage in the U.S. by the end of the decade. Today, around a third of data center electricity is devoted to cooling the chips that run AI models.

That’s the process Ferveret is working to make more efficient. The startup, founded by Reza Azizian, a former MIT postdoc in nuclear engineering, and Matteo Bucci, MIT’s Esther and Harold E. Edgerton Associate Professor in the Department of Nuclear Science and Engineering, is adapting an approach from nuclear reactors to cool chips using no water and significantly less electricity.

The company’s cooling system submerges computer servers in a specialized liquid that absorbs heat much more efficiently than air from a fan. What makes the solution different from other liquid cooling systems are the bubbles: Ferveret’s Adaptive Phase Cooling (APC) solution produces much smaller bubbles at the surface of the server, which detach more frequently, accelerating the heat transfer process.

Ferveret is already testing its solutions with companies including CleanSpark, the data center developer and operator, as well as FuriosaAI, an AI accelerator company, and Switch, one of the largest data center operators in the U.S.

In a recent study in collaboration with the Samueli Computer Science Department at the University of California at Los Angeles, Ferveret found its APC solution led to a 15 percent improvement in computational power efficiency compared to state-of-the-art liquid cooling solutions. By combining those savings with Ferveret’s power control system to optimize operating conditions, the company says it allows data centers to get 35 percent more tokens — small pieces of text or data — from their AI models with the same amount of power.

“Our goal is to make data centers as sustainable as possible and help them use every single watt of power to generate tokens, which are the most useful outputs,” Azizian says. “Our system enables the operation of more powerful chips, it helps data centers waste a lot less energy, and it accomplishes all that with zero water consumption.”

From nuclear reactors to AI

Azizian was a postdoc at MIT in 2013 when he met Bucci, who was then a research scientist. They worked on heat transfer in nuclear reactors before Azizian went into industry, where he shifted his focus to cooling chips. Azizian first worked on Microsoft’s HoloLens augmented reality headset and then joined Nvidia, which produces the graphical processing units companies use to train and run the latest AI models. Meanwhile, Bucci continued conducting research at MIT, becoming an assistant professor in 2016.

Azizian walked into his first data center in 2017, where he was struck by the massive, noisy fans that filled the building as they cooled.

“I thought, ‘Holy crap, this is not how you cool facilities,’” Azizian recalls, noting air cooling can still take up 40 percent of the power going into a data center. “It was not an efficient way of doing things, but since it wasn’t hurting the performance, no one cared that the cooling technology was 50 years old.”

Azizian began talking with Bucci about applying their knowledge around optimizing heat transfer in nuclear reactors to data centers. Scientists have spent decades finding better ways to move heat in nuclear reactors.

“Heat transfer determines how much energy you can extract from the reactor core, which translates directly to revenue,” Azizian explains.

The founders started Ferveret in 2021. A lot has changed since Azizian walked into his first data center. Chip companies have packed more and more components onto their chips as the explosion in artificial intelligence has put a premium on squeezing as much computing capacity as possible out of limited power supplies.

That has driven data center operators to use liquid to cool chips — often through a technique known as immersion cooling that submerges chips in liquid. The most effective form of immersion cooling brings the liquid to a boil.

“Liquid is a better heat transfer medium than air. That’s why when you stick your hand into room temperature water it still feels cold,” Bucci explains. “When liquid is boiling, it becomes even better at removing heat because the phase change requires a lot of energy, which is the energy you remove from the chip. That lets you transfer large quantities of heat with minimal temperature differences between the chips and the liquid.”

Unfortunately, boiling liquid adds complexity to the system because it forces operators to capture and reliquefy the bubbles while controlling for pressure, temperature, and fluid inventory.

Ferveret’s system is adapted from a process in nuclear reactors called subcooled boiling. It uses a liquid with a low boiling point and none of the toxic PFAS “forever chemicals” that other approaches rely on. At the surface of the chip, Ferveret’s liquid produces smaller bubbles than other immersion cooling approaches. Those bubbles detach more frequently and quickly recondense in the surrounding liquid, accelerating the bubble-rewetting cycle at the surface of the chip to hasten heat transfer.

Ferveret delivers its APC system in small boxes, each of which houses one server. The founders say their modular systems make it easier to deploy the system and simplify maintenance.

“The physics enable us to get to form factors that weren’t possible in the past,” Azizian says. “Most immersion cooling solutions are large tanks that people submerge the servers in. We have a smaller, modular rack-mounted solution that makes it adaptable to the current infrastructure, so it’s easier for people to deploy our technology.”

Ferveret also offers control software that adjusts the power going to each server in real-time to further improve efficiency.

“We deliver full-stack systems that include the cooling box, the rack, the cooling distribution units, and sensors that measure the temperature and pressure,” Bucci says. “Our software monitors those sensors and optimizes the operating condition inside each box to ensure that energy consumption is minimized in the system.”

AI with fewer resources

In addition to helping data centers to run more efficiently, Ferveret is also improving sustainability by making it easier to operate data centers in remote regions with more renewable energy.

“The sun shines in places where you don’t have much water, so the advantage of us being water-free is we allow you to build data centers where you have solar energy but nothing to cool the data center down,” Bucci says. “This technology can help deploy data centers in regions where normally you wouldn’t have the resources to do so, including Africa, the Middle East, and of course parts of America. It’s a huge unlock.”

Ferveret is in talks with the large cloud computing companies known as hyperscalers, and is currently part of Nvidia’s Inception program for startups. The company plans to announce expanded partnerships later this year. From there, the founders plan to quickly scale their technology to help the AI industry continue to grow without further straining the planet.

“The computing industry is facing a huge challenge in the form of access to power, and they have a problem with access to water in many regions,” Azizian says. “That will only become more limiting as the industry grows. The main goal for these data center operators would be to get more tokens from the power they have. We’ve shown we can do that.”

The Senate Judiciary Committee is set to consider and vote on the Nurture Originals, Foster Art, and Keep Entertainment Safe Act (NO FAKES). Instead of targeting the real privacy harms posed by AI-generated replicas, this law would create another layer of internet censorship on top of the already existing legal and voluntary takedown systems. Congress should reject NO FAKES.

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Tell Congress to Say No to NO FAKES

As currently written, NO FAKES proposes to tackle the problems of misleading AI-generated replicas by creating a broad property right in someone's look, voice, and general style. However, there are all kinds of First Amendment-protected expression that would be swept under the NO FAKES regime—think about parody, news, criticism.

NO FAKES also does a laughable job of protecting artists from use of their image in misleading ways. It doesn’t create a privacy right, but rather a property right that can easily be signed away—as major studios and record labels are almost certain to require in their contracts with artists. As a result, NO FAKES actually creates a new avenue for the exploitation of artists by companies instead of protection from misleading replicas. 

The bill also makes it trivially easy for protected speech to be censored. It is a supercharged version of the already flawed copyright takedown regime. It would essentially require platforms to institute filters that don't just look for exact matches of copyrighted material, as current filters do, but anything that might be a digital replica. Even though the latest version of this bill adds some forms of redress for bad faith takedowns, those provisions lack the teeth required to deter a malicious actor. 

NO FAKES targets speech, tools, and innovation instead of focusing on the real concern posed by these replicas: privacy. This bill was a bad idea when it was introduced, and got even worse when it was amended last year. Tell Congress to just say no to NO FAKES.

Take action

Tell Congress to Say No to NO FAKES