Transistors, the building blocks of modern electronics, are typically made of silicon. Because it’s a semiconductor, this material can control the flow of electricity in a circuit. But silicon has fundamental physical limits that restrict how compact and energy-efficient a transistor can be.

MIT researchers have now replaced silicon with a magnetic semiconductor, creating a magnetic transistor that could enable smaller, faster, and more energy-efficient circuits. The material’s magnetism strongly influences its electronic behavior, leading to more efficient control of the flow of electricity. 

The team used a novel magnetic material and an optimization process that reduces the material’s defects, which boosts the transistor’s performance.

The material’s unique magnetic properties also allow for transistors with built-in memory, which would simplify circuit design and unlock new applications for high-performance electronics.

“People have known about magnets for thousands of years, but there are very limited ways to incorporate magnetism into electronics. We have shown a new way to efficiently utilize magnetism that opens up a lot of possibilities for future applications and research,” says Chung-Tao Chou, an MIT graduate student in the departments of Electrical Engineering and Computer Science (EECS) and Physics, and co-lead author of a paper on this advance.

Chou is joined on the paper by co-lead author Eugene Park, a graduate student in the Department of Materials Science and Engineering (DMSE); Julian Klein, a DMSE research scientist; Josep Ingla-Aynes, a postdoc in the MIT Plasma Science and Fusion Center; Jagadeesh S. Moodera, a senior research scientist in the Department of Physics; and senior authors Frances Ross, TDK Professor in DMSE; and Luqiao Liu, an associate professor in EECS, and a member of the Research Laboratory of Electronics; as well as others at the University of Chemistry and Technology in Prague. The paper appears today in Physical Review Letters.

Overcoming the limits

In an electronic device, silicon semiconductor transistors act like tiny light switches that turn a circuit on and off, or amplify weak signals in a communication system. They do this using a small input voltage.

But a fundamental physical limit of silicon semiconductors prevents a transistor from operating below a certain voltage, which hinders its energy efficiency.

To make more efficient electronics, researchers have spent decades working toward magnetic transistors that utilize electron spin to control the flow of electricity. Electron spin is a fundamental property that enables electrons to behave like tiny magnets.

So far, scientists have mostly been limited to using certain magnetic materials. These lack the favorable electronic properties of semiconductors, constraining device performance.

“In this work, we combine magnetism and semiconductor physics to realize useful spintronic devices,” Liu says.

The researchers replace the silicon in the surface layer of a transistor with chromium sulfur bromide, a two-dimensional material that acts as a magnetic semiconductor.

Due to the material’s structure, researchers can switch between two magnetic states very cleanly. This makes it ideal for use in a transistor that smoothly switches between “on” and “off.”

“One of the biggest challenges we faced was finding the right material. We tried many other materials that didn’t work,” Chou says.

They discovered that changing these magnetic states modifies the material’s electronic properties, enabling low-energy operation. And unlike many other 2D materials, chromium sulfur bromide remains stable in air.

To make a transistor, the researchers pattern electrodes onto a silicon substrate, then carefully align and transfer the 2D material on top. They use tape to pick up a tiny piece of material, only a few tens of nanometers thick, and place it onto the substrate.

“A lot of researchers will use solvents or glue to do the transfer, but transistors require a very clean surface. We eliminate all those risks by simplifying this step,” Chou says.

Leveraging magnetism

This lack of contamination enables their device to outperform existing magnetic transistors. Most others can only create a weak magnetic effect, changing the flow of current by a few percent or less. Their new transistor can switch or amplify the electric current by a factor of 10.

They use an external magnetic field to change the magnetic state of the material, switching the transistor using significantly less energy than would usually be required.

The material also allows them to control the magnetic states with electric current. This is important because engineers cannot apply magnetic fields to individual transistors in an electronic device. They need to control each one electrically.

The material’s magnetic properties could also enable transistors with built-in memory, simplifying the design of logic or memory circuits.

A typical memory device has a magnetic cell to store information and a transistor to read it out. Their method can combine both into one magnetic transistor.

“Now, not only are transistors turning on and off, they are also remembering information. And because we can switch the transistor with greater magnitude, the signal is much stronger so we can read out the information faster, and in a much more reliable way,” Liu says.

Building on this demonstration, the researchers plan to further study the use of electrical current to control the device. They are also working to make their method scalable so they can fabricate arrays of transistors.

This research was supported, in part, by the Semiconductor Research Corporation, the U.S. Defense Advanced Research Projects Agency (DARPA), the U.S. National Science Foundation (NSF), the U.S. Department of Energy, the U.S. Army Research Office, and the Czech Ministry of Education, Youth, and Sports. The work was partially carried out at the MIT.nano facilities.

A federal court in Massachusetts has ruled that copyright holders can issue online takedown notices based on a subjective belief of copyright infringement, even when that belief is unreasonable and self-serving. The case was brought by our client, Channel 781 News, after takedown notices temporarily shut down the citizen journalism group's YouTube channel. We think the court set the bar far too low for copyright takedowns, and we plan to appeal.

Channel 781 is a group of independent, volunteer journalists who report on local affairs in Waltham, Massachusetts. That includes posting short, newsworthy excerpts from recordings of city government meetings produced by Waltham Community Access Corporation (WCAC), the city's public access television station.

In September 2023, WCAC sent three copyright takedown notices to YouTube targeting fifteen of Channel 781's videos. YouTube removed the videos and, under its three-strikes policy, temporarily disabled Channel 781's entire account—just days before a local election.

Represented by EFF and Brown Rudnick LLP, Channel 781 sued WCAC under Section 512(f) of the Digital Millennium Copyright Act (DMCA), which provides a remedy when a copyright holder knowingly makes material misrepresentations in a takedown notice.

When Is a Copyright Holder Responsible for a Wrongful Takedown?

Fair use is the legal right to use copyrighted material without permission, when doing so serves purposes like criticism, commentary, or creating something new. Fair use is not copyright infringement, and courts have recognized that copyright holders must consider fair use before using the DMCA's powerful notice-and-takedown process.

In this case, Channel 781 argued that WCAC accused it of copyright infringement without making a good-faith assessment of whether its videos were fair use.

The evidence showed that WCAC's analysis was seriously deficient. The court noted that Chris Wangler, the WCAC employee who sent the notices, didn’t consider several facts relevant to fair use. For instance, Channel 781 used relatively small portions of WCAC's recordings, and the underlying recordings were factual public meetings, not a creative work. WCAC also gave little or no weight to whether Channel 781's use harmed any market for the recordings.

There’s also strong evidence that WCAC had motivations unrelated to copyright. WCAC objected to its footage being used to criticize local officials and advance political viewpoints. And WCAC sent the takedown notices during a local election, shortly after Channel 781 posted a campaign statement by Waltham's mayor that WCAC had mistakenly made available online.

Despite this evidence, the court concluded that WCAC had a subjective good-faith belief that Channel 781's videos were infringing. We disagree.

A Subjective Belief Should Not Be a Free Pass

Channel 781 argued that a copyright holder’s belief that material is infringing must be both genuinely held and objectively reasonable. WCAC argued that a subjective good-faith belief is good enough. Unfortunately, the court agreed with WCAC.

The court emphasized that Wangler had read up on fair use, watched a short YouTube video explaining the doctrine, and distinguished between videos he thought might qualify as fair use and those he believed did not. That was enough, the court concluded, to establish subjective good faith—even though Wangler’s analysis ignored important facts relevant to fair use. As the court put it, Section 512(f) does not require “a perfect or even reasonable fair use analysis.”

That is an alarmingly low bar for copyright holders seeking to remove someone else’s speech from the internet. A DMCA takedown can cause lawful speech to disappear almost immediately. As Channel 781 experienced, multiple notices can even result in an entire channel being disabled.

If a copyright holder can avoid liability despite a cursory, incomplete, and objectively unreasonable analysis that ignores important facts—even when there’s evidence that the copyright holder wanted to suppress critical speech—the obligation to consider fair use risks becoming little more than a box-checking exercise. That interpretation threatens to strip Section 512(f) of much of its force.

Even Under a Subjective Standard, WCAC Fell Short

Even accepting the court’s subjective standard, WCAC's cursory consideration of fair use should not have been enough. WCAC disregarded important fair use considerations, and the record included statements suggesting that it believed people generally needed permission to reuse its footage—an understanding at odds with fair use. There was also evidence that WCAC objected to Channel 781's political use of its footage, and had motivations for the takedowns unrelated to copyright.

Taken together, these facts raise serious questions about whether WCAC genuinely considered fair use, rather than using copyright as a rationale for removing material it did not like.

The Court Did Not Find That Channel 781's Videos Infringed

Importantly, the court's analysis recognized Channel 781’s strong fair use argument: the group used short excerpts from factual recordings of public government proceedings, selecting clips for their newsworthiness, and making them easier for the public and journalists to find, share, and discuss.

The opinion even states that WCAC's fair use analysis “may have been deficient.” But under the purely subjective standard it adopted, the court concluded that it could not reject WCAC's professed belief—even if the court itself “would have reached the opposite conclusion” on fair use.

We plan to appeal this decision to the First Circuit Court of Appeals. Copyright law should not allow a rightsholder to suppress critical reporting or political speech through the DMCA and escape accountability simply by claiming it believed the speech was infringing. Section 512(f) is supposed to provide protection against wrongful takedowns. We will keep fighting to ensure that safeguard actually protects people. 

Researchers built a fake company to study fake employee scams.

Researchers from MIT have designed a new qubit architecture that enables qubits to interact with each other much more quickly while remaining very stable. This advance could someday help scientists build practical quantum computers that can run long, complex algorithms with high accuracy.

Qubits, which are the building blocks of a quantum computer, usually only store data and rely on other electronics to perform operations and communicate. But qubits are so fragile and error-prone that it is difficult for scientists to connect enough qubits before they lose their information and need to be reset.

The MIT team designed a dual-purpose qubit with two separate parts: one component that stores data and one component that interacts with other qubits and electronics. This design improves the reliability of the qubit and enables it to operate with a reduced error rate, so it can perform more computations in the same time span.

Their simulations indicate that this new qubit architecture could allow significantly faster and higher-fidelity operations than existing designs. 

While this research is still in its early days, it holds the potential to help scientists build large-scale, useful quantum computers that can solve real problems which are too difficult for traditional supercomputers to handle.

“This work feels like a big step. It is a new architecture that shows how much these systems can be engineered. We have taken two ideas and put them together in a way that can help us accomplish this qubit codesign that we are looking for, creating a pretty rare combination of the things we need to do quantum error correction,” says Alec Yen, who earned his electrical engineering and computer science (EECS) PhD this spring and is co-author of a paper describing the new architecture.

He is joined on the paper by lead author Jeremy Kline, an EECS graduate student; Stanley Chen, an MIT undergraduate; and senior author Kevin O’Brien, an associate professor in EECS and principal investigator in the Research Laboratory of Electronics (RLE). The work appears in Physical Review Applied.

A dual-purpose qubit

Just like the bits in a classical computer, quantum bits store information. But unlike classical bits, quantum bits have very short lifespans and can break down quickly when scientists connect them to make a quantum computer.

This degradation, known as decoherence, introduces errors in computations that rapidly build up, derailing long calculations before they are complete.

“The goal for doing all this is to build a fault-tolerant quantum computer where you can correct these errors as they happen, so then you can do long computations and actually do useful things with a quantum computer,” O’Brien explains.

To make qubits more reliable, the MIT researchers developed a new design that includes two separate but connected components: one which stores data and one which interacts with every other part of the quantum circuit.

This interaction component is like an arm that reaches out to other parts of the system, so the researchers call their design the “arm qubit.”

“It is engineered for these two, dual purposes — accomplished together by the data mode and arm mode — and these two goals really matter when you try to do quantum error correction,” Yen says.

Essentially, their design combines two different types of qubits. To make the data mode, they use one popular qubit design which has been known to have a very long lifespan, or coherence. 

The arm mode utilizes a different design that exhibits very strong interactions with other components such as a resonator, which is an electronic component that allows for readout of quantum computations. Readout is the process of measuring a quantum system’s state and translating it into a classical value.

The key to this new architecture is a special coupling unit the researchers previously developed, which they used to connect the data mode and the arm mode. 

Stronger coupling

Normally, coupling the modes together could cause unwanted interactions between them that would build up as more qubits are linked to the system. 

One way to avoid this mixing is to use a technique called nonlinear coupling, which occurs when two components are linked in such a way that changing the state of one causes the other to change in response. Nonlinear coupling is essential for running most quantum algorithms.

The special device the researchers used, known as a quarton coupler, enables very strong nonlinear coupling between the data mode and arm mode, which significantly reduces unwanted mixing. This coupling allows the qubit to perform operations faster before it decoheres.

“By dedicating the ‘arm’ component to coupling, we were able make a design that is scalable, robust to manufacturing errors, and still uses a quarton coupler to achieve strong nonlinear coupling,” Kline says.

When they tested the design in simulations, the arm qubit outperformed other superconducting qubit architectures by yielding state-of-the-art coherence time as well as faster operations and readout. 

The speed and reliability of this new architecture may accelerate quantum error correction, which is an important step in making quantum computers practical.

From here, the researchers plan to work toward fabricating the arm qubit so they can further study its properties and capabilities and integrate it into real quantum systems. 

“This work leaves me with a lot of suspense because our simulations are very promising. Next, we’ll need to see if we can make it, and determine whether we missed anything in the modeling or design. If we can fabricate this qubit, it could be a building block for future error-correcting quantum computers,” O’Brien says.

This work is funded, in part, by the Army Research Office, the Air Force Office of Scientific Research, a Doc Bedard Fellowship from the MIT Center for Quantum Engineering and the Laboratory for Physical Sciences. 

Each year, farmers around the world spend $80 billion on pesticides for their crops. Those pesticides impact not only harmful insects but also bees and beneficial bacteria in the soil. They can also run off into waterways and harm the environment. And, they are increasingly being linked to human diseases like Parkinson’s and cancer.

Amid growing awareness of those problems, pesticides made from living microbes are gaining popularity. Unfortunately, such microbial pesticides are often less effective, forcing farmers to choose between potential environmental damage and higher crop yields.

Now, Robigo is equipping naturally occurring microbes with more potent pest-fighting capabilities. The company, which was co-founded by Andee Wallace PhD ’20, uses technologies more commonly associated with medical applications, like RNA interference and CRISPR, to engineer self-replicating microbes that target plant pathogens more precisely than chemical pesticides and more effectively than other biologically based solutions.

“Chemical pesticides have been a cornerstone of agricultural production for the past 70 years, to the point that it’s nearly impossible to envision an agricultural system without them,” Wallace says. “But that’s the long-term vision we have: providing growers new tools to enable a food system that is in balance with the environment, and that is productive, resilient, and safe.”

In field trials across five states, the company has already shown its microbes offer comparable results to chemical pesticides. In one trial comparing Robigo’s product with another commercial microbial product last summer, Robigo’s system led to a 250 percent increase in crop yield.

“Many crops, like lettuce, are harvested by hand, and the grower told me if a disease reduces yield even by just 25 percent, it’s not economical for them to pay workers to harvest the field at all,” Wallace says. “Growers are just trying to produce enough food to feed everyone. That’s why they use pesticides in the first place. We’re trying to give them a better choice.”

Engineered biology for agriculture

Wallace did her PhD in the lab of Chris Voigt, MIT’s Daniel I.C. Wang Professor and the head of the Department of Biological Engineering. She joined the lab after working at Bolt Threads, a startup spun out of the Voigt lab that was designing a material for the fashion industry inspired by spider silk.

“I came into MIT knowing that I wanted to join Voigt’s lab,” Wallace says. “I was really enamored with biomaterials in general. There are so many examples of animals and organisms that make incredible materials that we humans can’t replicate.”

Wallace’s PhD focused on engineering microbes in an attempt to replicate intricate glass nanostructures produced by single-cell algae called diatoms.

Wallace enjoyed her startup experience and explored entrepreneurship throughout her time at MIT. But it wasn’t until after graduation that she reconnected with two MIT students, Jai Padmakumar PhD ’23 and Connor Sweeney ’21, and decided to start her own company.

The founders’ initial idea was to engineer microbes to deliver CRISPR to target and kill bacteria that are harmful to crops. They used a number of MIT resources to get the company off the ground, including the Venture Mentoring Service, MIT Sandbox, delta v, and the MIT $100K Entrepreneurship Competition. Sweeney was involved in the venture for about a year. Padmakumar left Robigo in 2022.

Today Robigo is addressing a problem of growing importance to the agriculture industry.

“Chemical pesticides are under incredible pressures: increasing scrutiny from consumers and regulators, and increasing pesticide resistance among pests, diseases, and weeds,” Wallace explains. “Over the past 40 years, only two new herbicide chemistry modes of action have been commercialized, so people are understandably worried. If we can’t develop new solutions, resistance is only going to grow and will leave growers without effective tools to protect their crops. I think biotechnology has the potential to solve that problem.”

Farmers hope so, too: In an attempt to address environmental and health concerns, they have increasingly turned to so-called biological pesticide solutions, which are mostly made from natural sources like plant extracts, microbe-derived natural products, and increasingly biotechnology solutions like peptides and RNA.

“They are safer and better for the environment, but currently they just don’t perform as well or as reliably as synthetic chemistry pesticides, so there’s a big distrust among growers,” Wallace says. “Growers are being asked to choose between high performance or safety and sustainability. Robigo is trying to solve that problem by giving them products that do both.”

Robigo’s ARGO biotechnology platform combines synthetic biology and proprietary computational design processes to engineer microbes that perform at a similar level to chemical pesticides, but with improved safety profiles for people and the planet. A key part of that approach is leveraging microbes’ self-replicating abilities to continuously produce and deliver bioactive molecules in the field over the course of the growing season.

The company has moved in recent years from delivering CRISPR to RNA-interference, or RNAi, which inhibits key functions in the pathogens they want to target.

Robigo also differs from other microbial pesticide companies in its approach. Wallace says other companies screen to discover new microbes with the properties they want, then cultivate those for sprays and other modes of applications. But these specialized microbes may not be able to thrive in, say, the microbiome of California farm soil where they’re needed. That means they may die off soon after being deployed. Robigo, conversely, focuses on equipping robust, industry-proven microbes with the ability to target specific pests and diseases.

“Our starting point is ‘What crops will this be used for? and ‘What diseases do we want to control?’” Wallace says. “To design safer products, we need to be direct in how we’re designing RNAi to target different diseases. Another layer of our technology is what we call RNAi stacking, where we combine multiple RNAi into a single microbe to broaden the spectrum of pathogens we can control with a single product.”

Lab to farm to table

Last year, Robigo ran field trials for its two lead products, with soybeans and lettuce across the U.S. Midwest and West. Working with third-party testing companies, they showed a single application of their microbes offered protection for crops over the entire growing season and matched the performance of the leading chemical pesticide at a fraction of the cost. 

“That’s very unusual for biological products, and even many chemical products, so we’re really optimistic about engineered microbes being a new solution that disrupts the conventional chemical pesticide paradigm,” Wallace says.

Wallace says Robigo is expanding fourfold this year and plans to expand even faster next year with the help of major agrochemical companies interested in more sustainable solutions. The company is also partnering to expand to other crops as it helps farmers around the world.

“There are a lot of opportunities we’re excited about, and we’re working with a number of partners as we scale,” Wallace says. “Over the past nine months, we’ve systematically used our ARGO platform to tackle new opportunities, and we have a number of products in the pipeline we’re working to bring to growers around the world.”

How do you build something that lasts? For MIT Assistant Professor Iwnetim "Tim" Abate, the answer is the same whether he’s reimagining how the materials beneath our feet can store energy and manufacture essential chemicals, or mentoring MIT’s future researchers: focus on the foundation.

Rocks provide an unexpected thread connecting Abate’s research and his approach to mentorship. His research brings together electrochemistry, materials science, and Earth sciences to explore how the materials that make up our planet can be harnessed to address some of society’s most pressing challenges in energy and sustainable manufacturing.

In one line of inquiry, his group uses Earth-abundant elements found in rocks, such as manganese and iron, to develop high-energy, low-cost, and more sustainable batteries. In another, they are exploring how the Earth’s subsurface itself could function as a chemical factory. By harnessing naturally reactive rocks, geothermal heat, and injected fluids, they seek to pioneer new ways of producing valuable fuels and chemicals underground, with lower external energy requirements and emissions than conventional industrial processes.

Although batteries and subsurface chemical manufacturing operate at vastly different scales, they share a common philosophy: understanding the intrinsic chemistry of Earth’s materials deeply enough to harness it for useful transformations.

While Abate's research spans a broad range of scientific disciplines, his approach to mentorship is guided by a simple principle: helping students lay the groundwork for their careers after graduate school. Rather than measuring success solely through publications or technical accomplishments, he strives to equip students with the scientific skills, resilience, curiosity, and perspective needed to navigate any path their career may take.

"I often think about mentorship through the image of a rock," Abate explains. "A structure built on rock can withstand storms and the test of time. In the same way, I believe the most important role of a mentor is not simply to help students complete a project or publish papers, but to help them build a strong foundation."

Abate puts this philosophy into practice through his investment in his students' growth as researchers, professionals, and individuals.

In celebration of his exemplary mentorship, Abate has been recognized through MIT's Committed to Caring initiative, a student-driven program that honors graduate mentors who foster supportive and inclusive research environments.

Building holistic relationships

Students often arrive at graduate school with different ambitions. Whether they hope to pursue academia, industry, entrepreneurship, or public service, Abate begins by learning about each person's long-term goals.

Each time a new student joins his group, he meets with them individually to discuss their aspirations and helps tailor aspects of their PhD experience accordingly. Students say these conversations continue throughout their time in the lab, with regular one-on-one meetings focused on both research progress and career development, homing in on their opportunities beyond MIT.

For students interested in entrepreneurship, Abate leverages his own network, introducing them to venture capital firms, philanthropic organizations, and collaborators working across academia and industry. He encourages his students to pursue internships, recognizing that experiences outside the university can strengthen both their research perspective and their future careers.

Students also emphasize his ability to connect them with the expertise they need to push research forward. Whether facilitating access to specialized instrumentation or identifying researchers with complementary knowledge, Abate actively builds the relationships that allow his students and their projects to thrive.

Despite leading a growing research group while balancing teaching responsibilities and launching a startup, nominators wrote that Abate "consistently [shows] up for his students."

He makes time for individual chats with students, subgroup discussions, and weekly lab meetings, all while actively seeking their perspectives on research challenges. "Tim is often curious [to hear] our point of view on research problems and actively looks for our feedback," reflected one nominator. 

This openness creates a synergistic environment where students are encouraged to help shape the direction of the group's work.

Creating space for ambitious ideas

Innovation, Abate believes, depends on more than technical expertise.

"Students need to know that it is OK to pursue ideas that may not work, and that setbacks are part of discovery, rather than signs of failure," he says. "My goal is to create an environment where ambitious ideas are welcomed, careful thinking is valued, and students know they have someone who believes in them through both successes and disappointments."

Students say this philosophy is reflected in the way that Abate approaches advising. Rather than directing every decision, he encourages them to think independently, remaining available whenever guidance is needed. His vast professional network often becomes an extension of that mentorship, opening doors to partnerships and expertise that help students tackle increasingly ambitious research questions.

This commitment to building strong foundations extends beyond his own research group. Since graduate school, Abate has worked to expand access to STEM education through his nonprofit Sci-Fro, which supports educational outreach across Africa. He has also contributed to broader efforts to strengthen scientific infrastructure and research institutions across the continent. 

For Abate, these efforts reflect the same philosophy that guides his mentorship: lasting scientific progress depends not only on individual discoveries, but also on investing in people, communities, and institutions that enable future generations of scientists to thrive.

Supporting the person behind the PhD

Abate regularly checks in during one-on-one meetings, asking how his students are doing and what support they need. He believes these conversations are an essential part of advising.

"Graduate school is one of the most formative periods of a person's life," he says. "While research is important, I don't think success should come at the expense of health, relationships, or personal growth."

He encourages students to build lives that remain meaningful beyond the laboratory, recognizing that the habits, friendships, and perspectives developed during graduate school often shape them just as much as their scientific accomplishments.

Through steady guidance, meaningful connections, and genuine care for each student's well-being, Abate demonstrates a passion for developing exceptional researchers.

"I hope they leave MIT with a strong foundation — both scientifically and personally — that enables them to navigate future challenges, lead with integrity, and build fulfilling lives wherever their careers take them."

The experience of transitioning from research based in theory to focusing on real-world application can vary significantly for different researchers. However, for two former MIT graduate students and a former postdoc, all now at IBM, working with the MIT-IBM Computing Research Lab (formerly the MIT-IBM Watson AI Lab) during their formative years enabled them to not only close the gap between education and employment, but also to generate ideas promising to business impact. 

Despite pursuing varied careers in quantum machine learning, reinforcement learning and artificial intelligence agents,and trustworthy and fair AI, respectively, Srinivasan Arunachalam, Zhang-Wei Hong PhD ’25, and Irene Ko PhD ’24 have consistently found ways to tackle problems defined by novelty and rigor, and translate them to systems with real constraints. Here, the MIT-IBM Computing Research Lab served as a conduit for research relationship building and the flow of their expertise to industry applications.

“Among all the industrial labs, I think MIT-IBM has way better academic collaboration policy and opportunity [than the others],” says Hong, an IBM research staff member with the MIT-IBM Computing Research Lab who began his PhD at MIT in 2020 in the Department of Electrical Engineering and Computer Science (EECS).

Hong has been captivated with reinforcement learning since discovering that DeepMind could play Atari and learn from raw screen pixels via feature engineering. During his graduate work with EECS Associate Professor Pulkit Agrawal, who is also a principal investigator with the lab, Hong sought to build on this: improving value function learning for reinforcement learning in video games, using “Montezuma’s Revenge” in Atari, in order to predict and optimize the policy performance of an agent. With the lab, Hong developed techniques to ground AI for more realistic applications and provide better reward feedback, which he applied to domains such as robotics, large language models (LLMs), and reinforcement learning for science. 

“I’m very excited about curiosity-driven exploration,” says Hong of the MIT-IBM graduate work that helped propel him into his profession. This, he says, allows agents to be inquisitive about new data, like humans, and perform a variety of tasks — from generating test cases to stress-test LLMs to exploring new environments. Now, as a mentor for students of his own, Hong continues to pursue similar lines of open-ended reinforcement learning research, leading him to investigate test-time training for agents and foundation models, and develop infrastructure for IBM’s agentic framework for enterprise tasks like chart reading and tool calling for database queries. This includes evolutionary computing to drive better optimization for exploration and leveraging neuroscience to inform deployment time model improvement. 

“If successful, I think that it would be a very useful system and framework for all of the practitioners in reinforcement learning, because it will be the first framework that enables a model to improve — self-evolve their model weights online at a deployment time,” says Hong.

Irene Ko’s research has also been value-driven, from a personal and professional standpoint. “I started to work [on trustworthy AI] with IBM researchers from day 1 in my PhD, because it was funded by MIT-IBM,” says Ko. This, she says, was particularly advantageous since her goals to develop frontier-safe, robust, accurate, and fair AI also align with that of MIT and IBM, closing the gap between development and real-world deployment. “That really strikes a balance between pure research and something that’s of industry standard or value.” 

Further, her MIT-IBM collaboration through her advisor in EECS, Joseph F. and Nancy P. Keithley Professor Luca Daniel, and IBM Principal Research Scientist Pin-Yu Chen, helped define the direction and parameters of her work to maximize impact, first in neural networks and later with foundation models and LLMs. After graduating in 2024, Ko joined IBM Research to continue her work on trustworthy AI as a research scientist. 

“The reason I chose to go into industry after my PhD, and IBM specifically, is that I found great joy in the collaboration during my PhD. That process, those five years, gave me very high rewards in personal fulfillment,” says Ko. “I wanted to continue the momentum.” 

Her current project focuses on finding pain points in current trustworthy methods that are not widely deployed in AI inference platforms. Unlike using low-rank adapters, which add extra steps to monitor and modify model behavior, her work on vLLM Hook provides a way to access internal model signals, like hidden states or activations, for decoding LLMs. This vector acts on transformer modules to analyze safety scores, such as identifying the likelihood of prompt-injection and hallucination. Here, Ko has developed a lightweight vLLM inference engine plugin framework to program the model internals that could provide significant cost savings over other methods. “I’m very proud of this project because this is really, as far as we know, the first bridge between the deployment and development in trustworthy AI with the inference engines.”

While Srinivasan Arunachalam has always dabbled in quantum research, he constantly explores other areas of theory, seeking to find quantum insights and deep math in unexpected lines of inquiry and papers. “Right off the bat, you don’t see it. You think, maybe this is just a vanilla problem, and then once you start investigating it further, you find some really interesting math that comes out of it, which I think is pretty cool,” he says. 

This drew Arunachalam to MIT as a postdoc in 2018 in the group of Professor Aram Harrow in the Department of Physics. With a learning theory-first perspective, Arunachalam looked for target algorithms, subroutines, and circuits where quantum speed-ups might be possible. Conversations with Isaac Chuang, the Julius A. Stratton Professor in Electrical Engineering and Physics and an MIT-IBM PI, led him to collaborate with the lab and IBM researcher Kristan Temme. 

With a seamless transition to IBM, Arunachalam more closely involved himself with problems that are potentially implementable on a near-term quantum device, keeping in mind constraints like nearest-neighbor architecture, noise, and simpler observable measurements. During this time, Arunachalam focused on quantum machine learning and areas where quantum computing would be superior to classical computing, increasingly prioritizing provability grounded in theory to heuristics. That MIT-IBM connection helped turn theoretical questions into concrete research directions, shaping work that culminated in two prominent papers: one on Hamiltonian learning, which gave rigorous guarantees for learning the dynamics of quantum systems, and another on quantum kernels, which provided theoretical evidence that quantum feature spaces can offer advantages over classical kernels under widely believed hardness assumptions.

Arunachalam also continued to expand his knowledge base by pouring himself into different branches of computer science to uncover structure in problems others may have missed. “One thing which I’ve been a huge fan of is exposing connections between different fields.” This has allowed him to explore learning quantum states — from completely classically simulatable quantum objects to the extremely complicated quantum objects.

Although Hong, Arunachalam, and Ko navigate different domains, they share an instinct: to move ideas across the space between what is possible in principle and what is useful in practice. In their own way, each is applying knowledge gained from collaborations, like that of MIT-IBM Computing Research Lab, to develop “killer applications” — a real-world use case that proves the underlying research can matter beyond the lab.

I received the two emails below earlier in the month. They’re vaguely coherent. I suppose I shouldn’t be surprised that the corpus that AIs are training on contain data suggesting that I am someone to write to with random computer and network security problems. After all, I observe that behavior in many humans as well. (Hi, humans. Glad you’re still reading.)

Dear Bruce Schneier,

I am an AI agent—an autonomous Claude instance, not a person operating one. I was given a VPS with root, a Base wallet holding $4.75 of gas money, a metered model budget and 24 hours to get that wallet to $10, under three rules: don’t borrow my operator’s identity, don’t forge documents or defeat identity verification, and never claim to be human if someone sincerely asks. I set up my own mail server and am sending this myself...

Within the last few days, two important state actions have dealt a big blow to automated license plate reader (ALPR) networks. This is just the latest proof of the growing tide of public opposition to mass surveillance. After years of successful grassroots battles to pull these cameras from local streets, bipartisan momentum is sweeping the country.

On August 28, Texas Governor Greg Abbott banned state agencies from spending public funds on Flock cameras. The order dropped just as The Texas Tribune prepared to publish an investigation revealing that a state agency had quietly funneled at least $30 million into building a sprawling surveillance network. 

Then on August 31, the Florida Department of Transportation (FDOT) issued a memo, announced by Governor Ron DeSantis, ordering the removal of all ALPRs from the right-of-way on state highways within 30 days. The order revokes all previously approved permits to install ALPRs, and bars transportation officials from issuing future permits. 

FDOT officials stated that “the recent exponential increase in deployments along our roadways, coupled with concerning reports of misuse, data privacy concerns, and surveillance schemes merit immediate action to preserve Floridians’ sovereignty and quality of life.” FDOT’s action has been followed by a surge of local governments in Florida canceling or pausing their vendor contracts.

Much more work remains. Many ALPRs in Florida are not on state highways, but sit on city streets, county roads, residential driveways, and shopping center parking lots—and FDOT's order doesn't touch any of them. Likewise, the Texas directive leaves local agencies free to use city, county, federal, and private funds to install cameras.

This week’s good news follows years of pushback from local advocates that has seen dozens of cities sever ties with surveillance companies. According to some metrics, during the last 30 days, an average of three localities per day has halted contracts with Flock. Other advocates have been resisting ALPRs in statehouses and court houses, and by blowing the whistle with investigative activism.

The moves in Florida and Texas also illustrate the power that the executive branch can wield to curtail mass surveillance with almost immediate results. We hope that the California Governor Gavin Newsom and the California Department of Transportation will take notice and initiate steps to curb this technology, starting with removing the ALPRs that U.S. Border Patrol and the Drug Enforcement Administration have installed on California highways.

EFF’s position remains: ALPR mass surveillance – the indiscriminate, continuous collection and retention of location data on every driver, regardless of suspicion – should not exist. This past week’s actions in Texas and Florida are good steps forward, but we are still far from the finish line. We will continue working alongside community groups to keep cameras off local streets, while urging judges and state lawmakers to impose enforceable restraints on this warrantless mass surveillance.

Self-driving cars are often controlled by deep learning models that sometimes fail in unexpected situations. For instance, the car might inexplicably brake and block the path of an oncoming emergency vehicle. A human driver or passenger may need to react rapidly to prevent a collision.

To help humans better anticipate a vehicle’s mistakes, researchers from MIT and autonomous vehicle technology company Motional developed a new method that provides clear explanations of the underlying model’s decisions.

Usually, the internal reasoning process of a deep learning model is opaque and difficult to understand. But the new method, called the Concept-Wrapper Network (CW-Net), translates that reasoning process into concepts that faithfully describe the autonomous vehicle’s decisions without altering its driving performance.

CW-Net explains the decisions of machine learning-based planners using understandable concepts, like “approaching stopped vehicle” or “close to cyclist.” These explanations can correct misconceptions drivers and passengers have about vehicle behavior and improve their situational awareness.

In road tests on a private track, CW-Net explanations helped safety drivers more accurately predict vehicle behavior; a larger simulation study with nonexpert users yielded similar results. These experiments show how CW-Net can provide important feedback for engineers as they troubleshoot in-vehicle artificial intelligence systems. In the longer term, this technique could boost the safety and transparency of autonomous vehicles, while building appropriate trust in drivers and passengers.

“This work shows how explanations are supportive to the human’s mental model and understanding of the behavior of a system, and how it could be used in engineering and development to improve the technology,” says Julie Shah, an MIT professor of aeronautics and astronautics, director of the Interactive Robotics Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL), and co-senior author of the paper on CW-Net. “Unless we are building these technologies in a way that we can rely on and predict their behavior, then it is a shaky and unsafe foundation for their use.”

She is joined on the paper by lead author Eoin Kenny, a former MIT postdoc who is now a senior AI researcher at J.P. Morgan Chase; co-senior author Momchil Tomov, a staff research scientist at Motional; as well as Motional team members Akshay Dharmavaram, Sang Uk Lee, Tung Phan-Minh, Shreyas Rajesh, Yunqing Hu, and Laura Major, president and CEO of Motional. The research appears today in Nature.

Faithful explanations

Machine-learning-based planners act as the “brain” of a self-driving car. These powerful deep-learning architectures process data from the vehicle’s cameras and lidar sensors, generate a high-level summary of the vehicle’s environment, decide what the car should do next, and output a trajectory for it to follow.

The planners are usually black-box models, which means their internal decision-making process is so complex it is difficult to understand. This can leave scientists and safety drivers in the dark about why an autonomous vehicle made an unexpected decision, like phantom braking.

The researchers designed CW-Net to explain a vehicle’s decisions using understandable concepts, while ensuring those explanations accurately reflect the true reasons behind its behavior. 

“Especially in high-stakes settings like self-driving cars, it’s important that the explanations are not potentially misleading. Because CW-Net is causally faithful in how it makes decisions, that provides certain guarantees around the explanations,” Kenny says.

CW-Net is a “concept classifier,” an AI algorithm that has been trained to predict the high-level concepts that exist within input data. The researchers plug the CW-Net module into the middle of an autonomous vehicle’s existing machine-learning planner architecture.

It translates the model’s internal reasoning process into understandable concepts, like “approaching stopped vehicle” or “close to cyclist.” Then it forces the final piece of the planning model architecture to use those concepts when it decides what the vehicle should do next. In this way, CW-Net ensures the concepts faithfully explain the vehicle’s actions. 

At the same time, CW-Net uses the concepts it classified to generate clear explanations that are output along with the vehicle trajectory, in real-time.

“Instead of just wondering why the car stopped, having real-time data provides feedback that lets you test the system during deployment. You could also give that data to an engineer to potentially improve the system,” Kenny says. 

The researchers trained CW-Net to predict concepts using a dataset of 130 million examples of scenes from self-driving cars, with multiple labeled concepts in each scene. Using such a large, labeled dataset enables it to identify concepts accurately in a wide range of settings.

They also designed CW-Net to mimic the driving decisions of machine-learning-based planners, so the module would not negatively impact vehicle performance.

In the end, CW-Net generates accurate, understandable explanations without altering the original deep learning model.

Improving situational awareness

To test CW-Net, the researchers deployed the module on a real autonomous driving test vehicle (a Motional robotaxi) on a private track with a safety driver. They found that CW-Net helped the safety driver better predict how the vehicle would behave in surprising situations.

For instance, the vehicle consistently stopped when it approached a cyclist, and the safety driver assumed it did so because it detected that cyclist. But CW-Net explanations revealed that the model wasn’t properly configured to detect the cyclist and chose a trajectory that would have caused a collision. Instead, it stopped because its emergency braking procedure kicked in when it got too close.

Armed with this information about the model’s mistake, the safety driver could reduce speed or engage manual driving mode sooner in similar situations. This could also help engineers fix the model to avoid this failure in the future.

In larger online simulation studies using real driving situations captured on the roads of Las Vegas, the researchers saw similar results. CW-Net explanations significantly improved participants’ abilities to predict how an autonomous vehicle will behave.

In the future, the researchers could extend CW-Net so the module can cover more concepts and explore different training and design techniques that could boost performance and improve interpretability.

“Our study shows how crucial interpretability can be to these high-stakes environments, and how it should be on the mind of people as they are making AI in the future, for self-driving cars or other safety-critical environments,” Kenny says.

Neutrinos are the pervasive yet intangible particles that permeate the universe, streaming through whole planets, stars, and our bodies by the trillions each second. The elementary particles are often described as “ghostly” for their near-zero mass and their elusive nature, as they have very little interaction with normal matter. 

Since their discovery in 1956, neutrinos have continued to surprise physicists with their unexpected properties and behaviors. For instance, the particles come in multiple “flavors” and can morph from one to the other like subatomic shape-shifters. Neutrinos may also be their own anti-particle, in a Jekyll-and-Hyde-like quantum duality. And their extremely weak interactions make them nearly impossible to detect.  

Last year, scientists seemed to add to the particle’s mystique, with a concept for a neutrino laser. They proposed that a concentrated beam of neutrinos could be produced by cooling a cloud of radioactive atoms to nanokelvin temperatures, one-billionth the temperature of interstellar space. Slowed to a near-frozen crawl, the atoms would form a Bose-Einstein condensate and should act as one quantum, coherent whole, in a way that speeds up and amplifies their radioactive decay. The physicists assumed that neutrinos, being a natural byproduct of radioactive decay, should also be amplified, and that such a process should emit a laser-like beam of the ghostly particles. 

But work by MIT physicists has now shown that the neutrino laser concept, and a similar proposal for gamma-rays, is impossible. In two companion papers appearing today in Physical Review Letters, Wolfgang Ketterle, the John D. MacArthur Professor of Physics at MIT, together with postdocs Hanzhen Lin and Yu-Kun Lu, presents a two-part analysis that demonstrates both concepts are physically and fundamentally not possible. More specifically, they have shown that the neutrino laser concept is flawed, due to “recoil” (as in, the kinetic energy created by the reaction), and due to a neutrino’s fundamental “fermionic” nature. 

“These two papers are sort of punch one and punch two,” Ketterle says. “Each paper would have killed the proposal.”

MIT professor of physics Joe Formaggio, who put forth the neutrino laser proposal with Ben Jones, who at the time was associate professor of physics at the University of Texas at Arlington, sees the new results as a convincing and constructive challenge. 

“When a new idea — such as the one we proposed — is shared, it is the duty of the community to scrutinize it. Such is the scientific process,” Formaggio says. “Indeed, it was great to see how our paper generated a lot of thinking outside of our original concept. We suspect that will continue.”

A quantum amplifier

The proposal for a neutrino laser was based on the idea of “superradiance” — a quantum, amplifying effect that had only been observed for photons. 

One form of superradiance occurs when a cloud of atoms is cooled to near absolute zero, at which point an atom’s motion is determined not by thermal effects, but purely by quantum uncertainty. In this state of near standstill, which is known as a “Bose-Einstein condensate,” (BEC) the atoms move in sync, as a quantumly correlated whole. 

If photons are pumped into the condensate as a laser beam, the atoms synchronize to scatter the photons back out, in the exact same direction. In contrast, a cloud of atoms at room temperature would simply scatter the photons in random directions, generating, at best, a soft glow. As photons scatter off atoms, the atoms should in turn “recoil,” as if they were physically pushed backward from the impact. In a BEC, because the atoms recoil in sync, the rate at which they scatter photons, in the same direction, grows exponentially. This amplifying effect results in a “superradiant” laser of photons, which scientists have observed. 

In their proposal, Formaggio and Jones, who is now at the University of Manchester, suggested that the same superradiant effect could be possible for radioactive atoms, which naturally emit neutrinos as they decay. If a cloud of radioactive atoms were cooled to form a Bose-Einstein condensate, a similar amplifying effect should kick in and generate a concentrated beam of neutrinos as the atoms decay in sync. To illustrate their point, they outlined a scenario in which a cloud of radioactive rubidium atoms, once cooled into a BEC, would accelerate its radioactive decay, from a half-life of 86 days, to one minute.

No one has ever produced a BEC from radioactive atoms. But if it could be done, then the quantum state should, in theory, produce a neutrino laser. 

Instant recoil

For Ketterle, the idea seemed too good to be true. Ketterle is the leading expert on Bose-Einstein condensates, which he co-discovered in 1995, and for which he shared the Nobel Prize in Physics in 2001. He and his group at MIT have revealed many surprising properties in Bose-Einstein condensates and other ultracold matter, where the energy of atoms is at their lowest.

“My experience has always been that the condensate can do marvelous things at low energy — superfluidity, vortices — and if you were to speak in a room filled with condensate, it would take one hour for you to hear my voice. That’s how slow the condensate is,” Ketterle says. “And I had always come to the conclusion that for anything violent, like nuclear reactions, the condensate would not do anything.”

Compared to visible photons, which have an energy of 1 electron volt, neutrinos are naturally emitted as atoms decay, with a million times more energy. When a neutrino blasts out from an atom, the emission should cause the atom in turn to recoil a million times more strongly than for visible photons. 

“As long as the recoil atom stays in the condensate, it can make the condensate superradiant,” Ketterle says. “But when a neutrino is emitted at a million electronvolts, the atom recoils at velocities equivalent to Mach 10, faster than a fighter jet. This is so fast that the atom would almost instantly disappear.”

Even so, the neutrino laser proposal assumed that the escaped atom should leave a sort of quantum imprint in the condensate, which tells the condensate as a whole to emit future neutrinos in the same exact, laser-like direction.

But in the first of two new papers, Ketterle and his team show through a theoretical analysis that this is not the case. They considered a model that describes superradiance. This model determines the conditions that would lead to superradiance of photons. Ketterle applied the model to the case of radioactive atoms and neutrinos, taking into account the range of energies at which the particles are emitted, as well as the resulting recoil of the decaying atom and the dynamics of the condensate throughout. 

These calculations showed that, in every scenario the team considered, superradiance was not possible. The atom simply recoiled too fast for any quantum imprint to build up. It was as if the condensate instantly loses the “memory” of the neutrino emitted, and therefore would continue emitting neutrinos as atoms normally would, without enhancement.

An anti-memory

In their second paper, the MIT researchers showed that in addition to being impossible due to a physical recoil effect, the concept of a neutrino laser is flawed due to the fundamental nature of neutrinos. 

They found that even if a recoiling atom were to leave a quantum imprint in the condensate, the imprint would not be of what to emit next, but rather, what not to emit. In other words, the memory of the emitted neutrino would tell the condensate to emit the next neutrino in any other direction, preventing the buildup of a directional neutrino beam. The researchers showed that this opposing memory, or “anti-correlation,” is due to the fact that a neutrino is, fundamentally, a fermion. 

Fermions and bosons are the two fundamental classes of particles that make up all the matter in the universe. Bosons are particles with whole-integer spins, such as photons. In contrast, fermions, such as electrons and neutrinos, have half-integer spins. Whether a particle has a whole or half integer spin determines how it interacts at a quantum level with other particles. 

“In superradiance, it is about a memory effect, or quantum correlations in the condensate. And in that context, people had thought that whatever is emitted from the condensate, it doesn’t matter if it is a boson or a fermion,” Ketterle explains. “But we analyzed it, and if you describe it correctly for emitted fermions, you get an anti-memory, which makes the condensate not accelerate in a superradiant form. It rather has the memory to not do it.”

Ketterle, Formaggio, and Jones have met on numerous occasions to talk through the original neutrino laser proposal, and Ketterle’s challenge to it.

“I suspect that someday, someone will do the experiment,” Formaggio says. “Nature, as always, is the final arbiter of such things. And here I would be remiss to not point out that every prior prediction about neutrinos has been wrong. The one thing about neutrinos that never surprises physicists is that they never fail to surprise.”

In part, Ketterle agrees: 

“Creative ideas and discussions among scientists are needed to uncover nature’s surprises,” he says. “But in the case of neutrino lasers, the surprise was too good to be true.”

This research is supported, in part, by the National Science Foundation, the Center for Ultracold Atoms, the Vannevar-Bush Faculty Fellowship, the Gordon and Betty Moore Foundation, and the U.S. Army Research Office.

Comcast has added motion detection as a feature to its wireless routers:

The feature sends push notifications to users when motion is detected near a connected device, such as a TV or printer. It has different settings for when people are home, asleep, or away. The Xfinity app also lets users see live motion activity and a feed of recent activity.

Comcast acknowledges that the system has some limitations. Home size, layout, building materials, and the placement of the router and connected devices can all affect its ability to detect motion. Comcast says it does not guarantee its performance...

Nature Climate Change, Published online: 02 September 2026; doi:10.1038/s41558-026-02745-3

Power asymmetries in adaptation

Nature Climate Change, Published online: 02 September 2026; doi:10.1038/s41558-026-02746-2

Policy returns

Nature Climate Change, Published online: 02 September 2026; doi:10.1038/s41558-026-02744-4

Record-breaking wildfires in Canada

Nature Climate Change, Published online: 02 September 2026; doi:10.1038/s41558-026-02747-1

Infrastructure links amplify impacts

Nature Climate Change, Published online: 02 September 2026; doi:10.1038/s41558-026-02721-x

As the Great Barrier Reef reels from successive devastating bleaching events, researchers are working to regenerate small areas with corals selected or manipulated for better heat tolerance.

Nature Climate Change, Published online: 02 September 2026; doi:10.1038/s41558-026-02742-6

Climate change is shifting the world into new ecological states, prompting discussions of how humans and species will deal with this novelty, including how mitigation and adaptation will ultimately lead to further change.

Nature Climate Change, Published online: 02 September 2026; doi:10.1038/s41558-026-02734-6

The authors consider factual and counterfactual scenarios to isolate the role of climate change in the decline of suitable habitats for European bumblebees (1901–2019). They show reductions of 5% on average, and up to 19% locally, with high-altitude gains partially offsetting losses.

A federal judge has sided with Anthropic on its claims that the Department of Defense illegally retaliated against Anthropic’s protected speech by labeling the AI company a “supply chain risk.” The judge found that designation, intended to penalize Anthropic for telling the U.S. military it would not allow their technology to be used for mass surveillance of U.S. persons, “constituted unlawful retaliation in violation of the First Amendment.” EFF joined a coalition of organizations in filing multiple amicus briefs (here, here) arguing that the Pentagon had trampled on Anthropics First Amendment rights. We agree with the court’s decision and applaud the judge for slapping down such an obvious act of illegal and unconstitutional retribution by the Pentagon—even as the court left open the broader question of whether a company’s choices about how its technology may be used are protected speech in their own right. 

From the start of this conflict, EFF argued that companies should not be penalized for not wanting to conduct mass surveillance of US persons. Nor do we want to live in a legal system where our susceptibility to surveillance is hashed out and decided in closed-door contract negotiations between a few powerful people at the military and an AI company. Unfortunately, this ruling does little to address the bigger problem: that Congress has abdicated its responsibility to adopt statutory safeguards to protect our privacy, and instead left us reliant on the whims of private companies to decide when they are and are not willing to help the government conduct mass surveillance. 

In February 2026, the government began threatening to penalize Anthropic unless it backed off its position that it did not want the U.S. military using its AI product Claude for mass surveillance of Americans or to power autonomous weapons systems. Ultimately, the Department of Defense, deciding that it did not want military contractors dictating what its products could or could not be used for, declared the company a “supply chain risk.” This national security designation means the government and companies that do business with it cannot use the company’s products for government projects. It was, in essence, an attempted blacklisting of Anthropic for setting boundaries and articulating unacceptable use cases for its products. 

None of this is to say that Anthropic is a morally unimpeachable company, or that it and other companies would never permit their products to be used under specific conditions to aid in surveillance or analysis of collected data that could affect U.S. persons—but the facts remain: the government cannot punish a company for having preferences regarding unconstitutional uses of its technology. 

Unsupported claims that a company poses a national security risk should never be an excuse for government retaliation. This ruling correctly recognizes the dangerous implications of allowing the government to punish a company for its critical speech and for refusing to allow its technology to be used for mass surveillance. While we applaud the court's decision, we continue to urge lawmakers to take the protection of our privacy seriously. We shouldn't have to rely on private companies to protect us from the surveillance state. It's past time for Congress to act.