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.

Pretty fantastic video from Japan of a giant squid eating another squid.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

In a dramatic middle-of-the-night stand off, a bipartisan set of lawmakers pushing for true reform and privacy protections for Americans bought us some more time to fight! They are holding out for, at a minimum, the requirement of an actual probable cause warrant for FBI access to information collected under the mass spying program known as 702.

A reauthorization with virtually no changes was defeated because a core group of lawmakers held strong; they know that people are hungry for real reform that protects the privacy of our communications. We now have a 10-day extension to continue to push Congress to pass a real reform bill. 

The Lawmakers rallied late Thursday night to reject a proposed amendment that made gestures at privacy protections, but it would not have improved on the status quo and would have reauthorized Section 702 for five more years to boot. 

Take action

TELL congress: 702 Needs Reform

Section 702 is rife with problems, loopholes, and compliance issues that need fixing. The National Security Agency collects full conversations being conducted by and with targets overseas – including by and with Americans in the U.S. –  and stores them in massive databases. The NSA then allows other agencies, including the Federal Bureau of Investigation, to access untold amounts of that information. In turn, the FBI takes a “finders keepers” approach to this data: they reason that since it's already collected under one law, it’s OK for them to see it. 

Under current practice, the FBI can query and even read the U.S. side of that communication without a warrant. What’s more, victims of this surveillance  won’t even know and have very few ways of finding out that their communications have been surveilled. EFF and other civil liberties advocates have been trying for years to know when data collected through Section 702 is used as evidence against them.  

Reforming Section 702 is even more urgent because of revelations hinted at by Senator Ron Wyden’s public statements concerning a “secret interpretation” of the law that enables surveillance of Americans, and a public  “Dear Colleague” letter he sent to fellow Senators about FBI abuse of Section 702. 

That’s right—the way the government conducts mass surveillance is so secret and unaccountable even the way they interpret the law is classified. 

 “In many cases these will be law-abiding Americans having perfectly legitimate, often sensitive, conversations,” Wyden wrote. “These Americans could include journalists, foreign aid workers, people with family members overseas - even women trying to get abortion medication from an overseas provider. Congress has an obligation to protect our country from foreign threats and protect the rights of these and other Americans.” 

We have 10 days to make it clear to Congress: 702 needs real reforms. Not a blanket  reauthorization. Not lip service to change. Real reform.

Take action

TELL congress: 702 Needs Reform

Last week, Anthropic pulled back the curtain on Claude Mythos Preview, an AI model so capable at finding and exploiting software vulnerabilities that the company decided it was too dangerous to release to the public. Instead, access has been restricted to roughly 50 organizations—Microsoft, Apple, Amazon Web Services, CrowdStrike and other vendors of critical infrastructure—under an initiative called Project Glasswing.

The announcement was accompanied by a barrage of hair-raising anecdotes: thousands of vulnerabilities uncovered across every major...

Nations split over doubling down on fossil fuels or accelerating renewables.

Bipartisan legislation would bring summer cooling standards closer to the state's existing rules for winter heating.

In a letter, CenterPoint Energy Indiana labeled the plant "unreliable" and said it would need "substantial investment."

The Democratic senator is asking EPA to take enforcement actions against Elon Musk’s company.

The measure would let the state and insurers sue fossil fuel companies for damages tied to climate-driven disasters.

Federal courts have now rebuffed both administration attempts to stop states from suing Big Oil to pay for the effects of climate change.

Steel is one of Ukraine’s top sources of export revenue but is being hurt by carbon tariffs imposed by Europe, manufacturers claim.

Political tension could further delay legislation, which is aimed at securing the country's electricity supply with gas-fired plants when output from wind and solar sources run low.

In a “reasonable worst-case scenario," officials don’t foresee aisles of empty shelves but expect a lack of product variety if the Strait of Hormuz has not reopened by June.

The Midwest, which in recent days has been hit by storms, large hail and tornadoes, may face a more significant storm outbreak Friday.

Nature Climate Change, Published online: 17 April 2026; doi:10.1038/s41558-026-02618-9

The sea–land breeze acts to counter urban heat in many coastal cities. Here the authors simulate how this circulation changes with warming ocean water, showing that it decreases in most of them, adding heat stress to urban areas.

Artificial intelligence is already proving it can accelerate drug development and improve our understanding of disease. But to turn AI into novel treatments we need to get the latest, most powerful models into the hands of scientists.

The problem is that most scientists aren’t machine-learning experts. Now the company OpenProtein.AI is helping scientists stay on the cutting edge of AI with a no-code platform that gives them access to powerful foundation models and a suite of tools for designing proteins, predicting protein structure and function, and training models.

The company, founded by Tristan Bepler PhD ’20 and former MIT associate professor Tim Lu PhD ’07, is already equipping researchers in pharmaceutical and biotech companies of all sizes with its tools, including internally developed foundation models for protein engineering. OpenProtein.AI also offers its platform to scientists in academia for free.

“It’s a really exciting time right now because these models can not only make protein engineering more efficient — which shortens development cycles for therapeutics and industrial uses — they can also enhance our ability to design new proteins with specific traits,” Bepler says. “We’re also thinking about applying these approaches to non-protein modalities. The big picture is we’re creating a language for describing biological systems.”

Advancing biology with AI

Bepler came to MIT in 2014 as part of the Computational and Systems Biology PhD Program, studying under Bonnie Berger, MIT’s Simons Professor of Applied Mathematics. It was there that he realized how little we understand about the molecules that make up the building blocks of biology.

“We hadn’t characterized biomolecules and proteins well enough to create good predictive models of what, say, a whole genome circuit will do, or how a protein interaction network will behave,” Bepler recalls. “It got me interested in understanding proteins at a more fine-grained level.”

Bepler began exploring ways to predict the chains of amino acids that make up proteins by analyzing evolutionary data. This was before Google released AlphaFold, a powerful prediction model for protein structure. The work led to one of the first generative AI models for understanding and designing proteins — what the team calls a protein language model.

“I was really excited about the classical framework of proteins and the relationships between their sequence, structure, and function. We don’t understand those links well,” Bepler says. “So how could we use these foundation models to skip the ‘structure’ component and go straight from sequence to function?”

After earning his PhD in 2020, Bepler entered Lu’s lab in MIT’s Department of Biological Engineering as a postdoc.

“This was around the time when the idea of integrating AI with biology was starting to pick up,” Lu recalls. “Tristan helped us build better computational models for biologic design. We also realized there’s a disconnect between the most cutting-edge tools available and the biologists, who would love to use these things but don’t know how to code. OpenProtein came from the idea of broadening access to these tools.”

Bepler had worked at the forefront of AI as part of his PhD. He knew the technology could help scientists accelerate their work.

“We started with the idea to build a general-purpose platform for doing machine learning-in-the-loop protein engineering,” Bepler says. “We wanted to build something that was user friendly because machine-learning ideas are kind of esoteric. They require implementation, GPUs, fine-tuning, designing libraries of sequences. Especially at that time, it was a lot for biologists to learn.”

OpenProtein’s platform, in contrast, features an intuitive web interface for biologists to upload data and conduct protein engineering work with machine learning. It features a range of open-source models, including PoET, OpenProtein’s flagship protein language model.

PoET, short for Protein Evolutionary Transformer, was trained on protein groups to generate sets of related proteins. Bepler and his collaborators showed it could generalize about evolutionary constraints on proteins and incorporate new information on protein sequences without retraining, allowing other researchers to add experimental data to improve the model.

“Researchers can use their own data to train models and optimize protein sequences, and then they can use our other tools to analyze those proteins,” Bepler says. “People are generating libraries of protein sequences in silico [on computers] and then running them through predictive models to get validation and structural predictors. It’s basically a no-code front-end, but we also have APIs for people who want to access it with code.”

The models help researchers design proteins faster, then decide which ones are promising enough for further lab testing. Researchers can also input proteins of interest, and the models can generate new ones with similar properties.

Since its founding, OpenProtein’s team has continued to add tools to its platform for researchers regardless of their lab size or resources.

“We’ve tried really hard to make the platform an open-ended toolbox,” Bepler says. “It has specific workflows, but it’s not tied specifically to one protein function or class of proteins. One of the great things about these models is they are very good at understanding proteins broadly. They learn about the whole space of possible proteins.”

Enabling the next generation of therapies

The large pharmaceutical company Boehringer Ingelheim began using OpenProtein’s platform in early 2025. Recently, the companies announced an expanded collaboration that will see OpenProtein’s platform and models embedded into Boehringer Ingelheim’s work as it engineers proteins to treat diseases like cancer and autoimmune or inflammatory conditions.

Last year, OpenProtein also released a new version of its protein language model, PoET-2, that outperforms much larger models while using a small fraction of the computing resources and experimental data.

“We really want to solve the question of how we describe proteins,” Bepler says. “What’s the meaningful, domain-specific language of protein constraints we use as we generate them? How can we bring in more evolutionary constraints? How can we describe an enzymatic reaction a protein carries out such that a model can generate sequences to do that reaction?”

Moving forward, the founders are hoping to make models that factor in the changing, interconnected nature of protein function.

“The area I am excited about is going beyond protein binding events to use these models to predict and design dynamic features, where the protein has to engage two, three, or four biological mechanisms at the same time, or change its function after binding,” says Lu, who currently serves in an advisory role for the company.

As progress in AI races forward, OpenProtein continues to see its mission as giving scientists the best tools to develop new treatments faster.

“As work gets more complex, with approaches incorporating things like protein logic and dynamic therapies, the existing experimental toolsets become limiting,” Lu says. “It’s really important to create open ecosystems around AI and biology. There’s a risk that AI resources could get so concentrated that the average researcher can’t use them. Open access is super important for the scientific field to make progress.”

Animal behavior reflects a complex interplay between an animal’s brain and its sensory surroundings. Only rarely have scientists been able to discern how actions emerge from this interaction. A new open-access study in Nature Neuroscience by researchers in The Picower Institute for Learning and Memory at MIT offers one example by revealing how circuits of neurons within C. elegans nematode worms respond to odors and generate movement as they pursue of smells they like and evade ones they don’t.

“Across the animal kingdom, there are just so many remarkable behaviors,” says study senior author Steven Flavell, associate professor in the Picower Institute and MIT’s Department of Brain and Cognitive Sciences and an investigator of the Howard Hughes Medical Institute. “With modern neuroscience tools, we are finally gaining the ability to map their mechanistic underpinnings.”

By the end of the study, which former graduate student Talya Kramer PhD ’25 led as her doctoral thesis research, the team was able to show exactly which neurons in the worm’s brain did which of the jobs needed to sense where smells were coming from, plan turns toward or away from them, shift to reverse (like old-fashioned radio-controlled cars, C. elegans worms turn in reverse), execute the turns, and then go back to moving forward. Not only did the study reveal the sequence and each neuron’s role in it, but it also demonstrated that worms are more skillful and intentional in these actions than perhaps they’ve received credit for. And finally, the study demonstrated that it’s all coordinated by the neuromodulatory chemical tyramine.

“One thing that really excited us about this study is that we were able to see what a sensorimotor arc looks like at the scale of a whole nervous system: all the bits and pieces, from responses to the sensory cue until the behavioral response is implemented,” Flavell says.

Seeing the sequence

To do the research, Kramer put worms in dishes with spots of odors they’d either want to navigate toward or slither away from. With the lab’s custom microscopes and software, she and her co-authors could track how the worms navigated and all the electrical activity of more than 100 neurons in their brains during those behaviors (the worms only have 302 neurons total).

The surveillance enabled Kramer, Flavell, and their colleagues to observe that the worms weren’t just ambling randomly until they happened to get where they’d want to be. Instead, the worms would execute turns with advantageous timing and at well-chosen angles. The worms seemed to know what they were doing as they navigated along the gradients of the odors.

Inside their heads, patterns of electrical activity among a cohort of 10 neurons (indicated by flashing green light tied to the flux of calcium ions in the cells), revealed the sequence of neural activation that enabled the worms to execute these sensible sensory-guided motions: forward, then into reverse, then into the turn, and then back to forward. Particular neurons guided each of these steps, including detecting the odors, planning the turn, switching into reverse, and then executing the turns.

A couple of neurons stood out as key gears in the sequence. A neuron called SAA proved pivotal for integrating odor detection with planning movement, as its activity predicted the direction of the eventual turn. Several neurons were flexible enough to show different activity patterns depending on factors such as where the odors were and whether the worm was moving forward or in reverse.

And if the neurons are indeed turning and shifting gears, then the neuromodulator tyramine (the worm analog of norepinephrine) was the signal essential to switch their gears. After the worms started moving in reverse, tyramine from the neuron RIM enabled other neurons in the sequence to change their activity appropriately to execute the turns. In several experiments the scientists knocked out RIM tyramine and saw that the navigation behaviors and the sequence of neural activity largely fell apart.

“The neuromodulator tyramine plays a central role in organizing these sequential brain activity patterns,” Flavell says.

In addition to Flavell and Kramer, the paper’s other authors are Flossie Wan, Sara Pugliese, Adam Atanas, Sreeparna Pradhan, Alex Hiser, Lillie Godinez, Jinyue Luo, Eric Bueno, and Thomas Felt.

A MathWorks Science Fellowship, the National Institutes of Health, the National Science Foundation, The McKnight Foundation, The Alfred P. Sloan Foundation, the Freedom Together Foundation, and HHMI provided funding to support the work.

Asian immigrants are both the fastest-growing and highest-earning immigrant ethnic group in the United States, facts that have caught the attention of many economists interested in how these groups — whether investors or residents — impact housing prices, K-12 education, and other important aspects of community life.

A new study by economists at MIT and the University of Cincinnati delves into this trend, focusing on the potential mechanisms at work behind the correlation of rising home prices and subsequent improvements in education at the county level. Their findings suggest that home prices rise not simply due to increased demand, but because the new neighbors have a positive influence on the quality of K-12 education, which in turn increases desirability.

The study focuses on 2008 to 2019, a period that saw a relative spike in US immigration from six Asian countries in particular — China, India, Japan, Korea, the Philippines, and Vietnam. Among this group, the economists focused specifically on those who arrived on non-permanent visas for study or work — a cohort that represents a distinct and growing channel of new immigrant inflow, and is often pre-selected by universities and employers.

“We’re looking at a window when the influx of Asian immigrants has a particularly strong preference for education, and who themselves were also highly educated,” says Eunjee Kwon, the West Shell, Jr. Assistant Professor of Real Estate in the Department of Finance at the University of Cincinnati, a co-author on the study published in the May issue of the Journal of Urban Economics. “This period also marks a notable shift in the socioeconomic profile of Asian immigrants to the U.S., with this cohort arriving with higher levels of education and income relative to earlier waves of Asian immigrants and, in many cases, relative to the native-born population.”

While county data is not granulated to the neighborhood or even municipality level, the researchers found that 30 to 40 percent of the rise in home values purchased in areas where Asian immigrant buyers have school-age children correlates with improved quality of education, as indicated by the average rise in standardized test scores of all children in the county.

“Maybe some Asian buyers are pure investors, but many of them become residents who buy homes for themselves and their families, and transform the neighborhoods,” says co-author Siqi Zheng, the Samuel Tak Lee Professor of Urban and Real Estate Sustainability at the MIT Center for Real Estate and the Department of Urban Studies and Planning. “We show that this is not negligible; it is a big component. We can attribute at least one-third of housing price increases to improved education.”

Amanda Ang, a postdoc in the Department of Economics at Aalto University in Helsinki, is the third co-author of the paper. The work is somewhat personal for the scientists, who undertook the study without funding in order to see for themselves what impact this particular group of immigrants had on neighborhoods.

“We wanted to understand what this group contributes to the communities where they settle," Kwon says. “We found that their presence benefits children of all other backgrounds, too."

Ang, Kwon, and Zheng use an econometric approach called an instrumental variable to home in on a causal correlation, and not just an association. To help ensure accuracy, they carefully omitted counties that have long been home to large Asian communities — such as San Francisco, Los Angeles, and New York — in order to capture the impact of recent immigrants on other counties.

“I believe that this will be a highly influential paper because it asks a very important question and uses credible statistical methods to try to disentangle selection effects from treatment effects, using a subtle analysis accounting for displacement,” says Matthew Kahn, the Provost Professor of Economics and Spatial Sciences at the University of Southern California, who was not involved with the research. 

“What really interests me about this paper is that it suggests that there can be a positive spillover effect: that U.S. areas that attract Asian immigrants also gain from improved school quality,” Kahn says. “It’s the first I’ve seen undertaken on this very important hypothesis, which certainly merits additional future research, possibly using school-level and individual-level data.”

Consider the chief difference between living systems and electronics: The first is generally soft and squishy, while the latter is hard and rigid. Now, in work that could impact human-machine interfaces, biocompatible devices, soft robotics, and more, MIT engineers and colleagues have developed a soft, flexible gel that dramatically changes its conductivity upon the application of light.

Enter the growing field of ionotronics, which involves transferring data through ions, or charged molecules. Electronics does the same, with electrons. But while the latter is well established, ionotronics is still being developed, with one huge exception: living systems. The cells in our bodies communicate with a variety of ions, from potassium to sodium.

Ionotronics, in turn, can provide a bridge between electronics and biological tissues. Potential applications range from soft wearable technology to human-machine interfaces

“We’ve found a mechanism to dynamically control local ion population in a soft material,” says Thomas J. Wallin, the John F. Elliott Career Development Professor in MIT’s Department of Materials Science and Engineering and leader of the work. “That could allow a system that is self-adaptive to environmental stimuli, in this case light.” In other words, the system could automatically change in response to changes in light, which could allow complex signal processing in soft materials.

An open-access paper about the work was published online recently in Nature Communications.

A growing field

Although others have developed ionotronic materials with high conductivities that allow the quick movement of ions, those conductivities cannot be controlled. “What we’re doing is using light to switch a soft material from insulating to something that is 400 times more conductive,” says Xu Liu, first author of the paper and former MIT postdoc in materials science and engineering who is now an incoming assistant professor at King’s College London.

Key to the work is a class of materials known as photo-ion generators (PIGs). These can become some 1,000 times more conductive upon the application of light. The MIT team optimized a way to incorporate a PIG into polyurethane rubber by first dissolving a PIG powder into a solvent, and then using a swelling method to get it into the rubber.

Much potential

In the material reported in the current work, the change in conductivity is irreversible. But Liu is confident that future versions could switch back and forth between insulating and conducting states.

She notes that the current material was developed using only one kind of PIG, polymer (the polyurethane rubber), and solvent, but there are many other kinds of all three. So there is great potential for creating even better light-responsive soft materials.

Liu also notes the potential for developing soft materials that respond to other environmental stimuli, such as heat or magnetism. “We’re inspired to do more work in this field by changing the driving force from light to other forms of environmental stimuli,” she says.

“Our work has the potential to lead to the creation of a subfield that we call soft photo-ionotronics,” Liu continues. “We are also very excited about the opportunities from our work to create new soft machines impacting soft wearable technology, human-machine interfaces, robotics, biomedicine, and other fields.”

Additional authors of the paper are Steven M. Adelmund, Shahriar Safaee, and Wenyang Pan of Reality Labs at Meta. 

New York's proposed 2026-2027 budget currently includes provisions that will require all 3D printers sold in the state to run print-blocking censorware—software that surveils every print for forbidden designs. This policy would also create felony charges for possessing or sharing certain design files. The vote on the state budget could happen as early as next week, so New Yorkers need to act fast and demand that their Assemblymembers and Senators strip this provision from the budget.

Take action

Tell Your Representative to Stand with Creators

State legislators across the US are rushing to regulate 3D-printed firearms under the syllogism “something must be done; there, I've done something.” The most reckless of these proposals is a mandate for manufacturers to implement print blocking on all 3D printers. We, and other experts, have already pointed out that this algorithmic print blocking is simply unfeasible and will only serve to stifle competition, free expression, and privacy. While most detrimental to the creative communities lawfully using these printers, every New Yorker will be impacted by this blow to innovation.

This policy is unfortunately buried in Part C of the New York State’s proposed budget for the 2026-2027 fiscal year (S.9005 / A.10005), which is urgently moving toward a vote after facing extensive delays. It’s also bundled with a policy that would allow felony charges to be brought against researchers and journalists for sharing design files restricted by the state.  The worst of these impacts won’t be known until after it is negotiated behind closed doors, with no safeguards for creative expression or privacy.

Researchers and Journalists Could Face Felony Charges

Part C Subpart A of the budget includes two particularly concerning provisions: §2.10 and 2.11. These threaten Class E felony charges for distributing or possessing 3D-printer files that would produce firearm parts with a 3D printer or CNC machine. 

Under these provisions merely sharing a print file with any of them could result in criminal charges

The first provision, 2.10, makes it a felony to sell or distribute files that can produce major firearm components to someone who is not a federally and NY-licensed gunsmith. Under 2.11, it’s also a felony to possess these files if you intend to illegally print a firearm or share them with someone you believe is not permitted to own or smith a firearm.

A journalist reporting on 3D-printed guns. A researcher studying printable firearms. An artist incorporating parts into a new work commenting on gun culture. Under these provisions merely sharing a print file with any of them could result in criminal charges, even if no one involved intends to assemble a firearm.

Criminalizing information doesn’t work. Someone intent on illegally printing a firearm is already subject to charges for that act. Adding felony liability for simply possessing a file or design piles on additional charges while doing nothing to stop printing. New charges for someone distributing these files won’t make them inaccessible to lawbreakers, but they will have a chilling effect on legitimate and entirely legal work. 

Unsurprisingly, a similar law was proposed and subsequently scrapped in Colorado due to First Amendment concerns. We recommend New York do the same.

Mandated Surveillance, Less Access

Part C Subpart B would require every 3D printer and CNC machine sold in New York to include algorithms that scan your design files and block prints the system identifies as producing firearm components. Furthermore, all sales and deliveries of these machines must be made face-to-face. 

Unlike other bills we have seen, there are no exceptions to this mandate. These restrictions apply to sales to researchers, commercial manufacturers, and—oddly enough—federally and state-licensed gunsmiths.

Applying these restrictions to CNC machine sellers is particularly absurd. These cousins of 3D printers, which make 3D objects by removing materials, are often tens of thousands of dollars and used by commercial manufacturers. Automotive, aerospace, medical manufacturers, and many others industries will be subject to the in-person sales, surveillance risk, and all the other problems with these print-blocking algorithms introduce.

Industries will be subject to the in-person sales, surveillance risk, and all the other problems

Even limiting the focus to individual buyers—hobbyists and artists who use these machines at home—this restriction to face-to-face sales comes with its own issues. Beyond unnecessarily complicating the use of printers in the state, this barrier to access will hit rural New Yorkers the hardest. People in rural or remote locations can stand to benefit from the saved time and costs of printing useful parts at home. With this restriction, they will need to drive to one of the few retailers who actually sell this equipment and settle for the models they stock. 

That is, if sellers continue to stock these printers despite the risk. Subpart B §§ 2.3 and 2.5 open sellers up to liability, including anyone on the second-hand market, for selling out-of-date printers. Meanwhile, buyers hoping to illegally print firearms can simply build their own printer with widely available equipment.

The Law Won’t Work as Advertised 

Here’s what makes Subpart B of the New York budget particularly reckless: the technology it mandates is not capable of doing what it is supposed to. 

There is very little detail provided about requirements for the mandated algorithms. What the bill does outline boils down to this: the algorithms must evaluate print files to determine whether they would produce a firearm or illegal firearm parts, and if so, block the print. In an attempt to enable this, New York state would also create and maintain a library of forbidden files with tightly restricted access. 

We’ve already gone over why this idea simply won’t work. Design files are trivially easy to modify, split into segments, or otherwise alter to evade pattern detection. Even if printers fully rendered and analyzed the print with cloud-based AI, any number of design or post-print tricks can be used to dodge detection. Meanwhile, such fuzzy AI interpretation will rapidly increase the percentage of lawful prints censored. 

Firearms aren’t a highly specific design like paper currency; these proposed algorithms are futilely attempting to block an infinite number of designs capable of—or that can be made capable of—the few simple mechanical functions that make up a firearm. 

This group has no peer review requirements, so it could easily be loaded with profiteers or incumbent manufacturers

As we’ve said before: the internet always routes around censorship. Anyone determined to print a prohibited object has straightforward workarounds. The people who get surveilled and blocked are the people trying to follow the law.

The bill aims to enforce this impossible mandate by creating a working group to define the actual technical requirements of enforcement—but only after the law passes. This group has no peer review requirements, so it could easily be loaded with profiteers or incumbent manufacturers who are already lining up to participate. These incumbents stand to profit from shutting out new competitors and locking in users to their devices, and sellers into their platform, subjecting both to the type of enshittification seen with Digital Rights Management (DRM) software. There are also no safeguards in the law to prevent the most surveillance-heavy approaches to print scanning, or to stop this censorship infrastructure from being further weaponized against lawful speech.

On the other hand, unbiased experts in open-source manufacturing in the working group can at best pause the clock by showing such algorithms are unfeasible. That is, until a new snake oil company comes along to restart it. 

New York Won't Be the Last Stop 

New York is one of the largest consumer markets in the country. When it mandates a feature in hardware, manufacturers hardly ever build a New York-only version. They build the New York version and sell it globally. A print-blocking mandate adopted in New York will become the national standard in practice.

New Yorkers deserve more than this rush job buried in a budget bill. This is an unfeasible tech solution, built without the consumer protections that would be required of any serious policy proposal, and creates new costs and inconveniences amidst a protracted annual budget process. It also threatens First Amendment protections. This policy will take shape without consumer guardrails, behind closed doors, and risks the worst outcomes for grassroots innovation and creativity enabled by these machines. Worse still, these practices can become the norm across other states and among 3D-printer manufacturers worldwide. 

Your representatives could vote on this ill-conceived measure in the next week.  If you're a New Yorker, email your legislators now, and tell them to strip this measure from the budget today. 

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