2026-06-25 21:00:01
In the mid-noughties, when music by the Killers and Franz Ferdinand blared out of every pub and nightclub I passed, I spent my days and nights struggling through a Ph.D. in applied mathematics. My research focused on simulating how special light waves interact in liquid crystals and using simple equations to approximate and understand those interactions. When I look back at my thesis now, liquid crystal technology is old hat, and I imagine my work could be completed with AI assistance in a matter of days—maybe hours.
But the same cannot be said for the work of the pure mathematics Ph.D. students with whom I shared a cramped office at the University of Edinburgh. At the time, I felt sorry for these colleagues, who day after day sat at their desks, seemingly tearing their hair out and making no progress. (Though I was struggling too, I was at least always making some headway.) When we finished and went our separate ways, some hadn’t even published a paper.
Now, in hindsight, I finally understand why they toiled for years on abstract mathematical problems that only a handful of people in the world care about. It wasn’t arrogance, as I thought at the time; they weren’t trying to prove their superior intelligence by being the first to solve a seemingly intractable mathematical problem. It wasn’t even a form of masochism (which was my second guess)—penance for some imagined inadequacy. I realized they derived joy, satisfaction, and meaning from the long journey toward understanding.
“Sometimes, understanding just strikes you as being very beautiful.” —Jeremy Avigad, Carnegie Mellon University
“Sometimes, understanding just strikes you as being very beautiful. Sometimes it’s a feeling of accomplishment, like completing a marathon,” muses Carnegie Mellon University mathematician Jeremy Avigad. “But it’s not quite either of those: It’s just a wonderful feeling when you’ve been thinking long and hard about something complex, difficult, and then—all of a sudden—it just comes together.”
This feeling has driven mathematicians throughout history. Likewise, the way mathematicians pursue that feeling has changed little over the centuries. They notice or imagine links, patterns, or properties in numbers, shapes, or logical structures. From this, they write conjectures—unproven statements of their speculation. They or other mathematicians then use logical reasoning and the tools of mathematics in often creative ways to prove or disprove those conjectures. Finally, yet other mathematicians verify (or challenge) the proofs.
Invariably, this process requires a whole heap of thinking time. “I went to a pure maths camp with classes where we would sit with hard maths problems for half an hour and no one would say anything—everyone was just thinking,” says Krystal Maughan, a mathematician and computer scientist about to get her Ph.D. at the University of Vermont. “But then we would work together and kind of tease out the problem.”
This is the age-old joy of math in action. But today’s AI systems are starting to make inroads into bypassing this slow, deliberative process. Taking this trend to its logical conclusion, what happens if AI makes the mathematician’s struggle completely unnecessary? Might AI even sideline humanity completely?
For decades, computation has accelerated mathematical progress. This began 50 years ago, when mathematicians used a computer to prove the four-color theorem, which asks whether any map can be colored using no more than four colors, with no adjacent regions sharing the same color. The answer is yes, and the computer proved it, controversially, by checking 1,936 cases in a way no human could realistically verify.
Yet throughout this computational era, even in proofs relying on massive computational resources, the role of the human mathematician has remained central. Humans propose conjectures, guided by intuition. They devise strategies to prove them, guided by creativity and experience. And humans verify whether those proofs are correct.
Now AI is challenging the status quo. In just a few years, large language models (LLMs) have evolved from “stochastic parrots,” capable of little more than regurgitating basic mathematics scraped from the internet, into advanced mathematical reasoning machines.
Last summer, systems from Google DeepMind and OpenAI reached a level equivalent to the world’s most mathematically gifted high school students, achieving gold-medal status at the International Mathematical Olympiad. In this annual competition, contestants must solve six notoriously difficult problems from various areas of mathematics.
Earlier this year, Google DeepMind’s experimental AI system Aletheia achieved an even more significant milestone when it autonomously produced publishable Ph.D.-level research results. While the work itself is obscure mathematically—calculating structure constants in arithmetic geometry—the significance lies in the complex reasoning it displayed in tackling an unsolved mathematical problem. And more recently, a new general-purpose AI system from OpenAI disproved an important conjecture in combinatorial geometry. This result would have been worthy of publication in a major mathematics journal if humans had been the authors, and top mathematicians hailed the feat as a milestone for AI in mathematics, demonstrating independent, original, and sophisticated thinking.
Another shift has come from combining LLMs with mathematical tools known as proof assistants, which have been around for more than a decade. These systems—such as Isabelle, Lean, and Rocq—are specialized programming languages that check mathematical proofs step-by-step, verifying their logical correctness. Traditionally, mathematicians have had to translate their theorems and proofs into this machine-readable format by hand, a laborious process known as formalization. Now, LLMs are starting to remove this bottleneck, automating the translation of informal proofs into formal code that proof assistants can verify.
Euclid’s famous proof that there are infinitely many prime numbers appears very different when formalized in Lean, a proof assistant. Human mathematicians routinely skip steps and rely on shared understanding; formalization makes every assumption and inference explicit so a computer can verify the proof.
Here is the fully updated HTML with the “1” in p ∣ 1 on line 3 colored blue as well!HTMLProof Comparison
We want to show that for every natural number n, there’s a prime p that is at least n.
Consider the smallest prime factor of n! + 1. Call it p. It is obviously prime.
To show p is at least n, assume, for contradiction, that it is not.
p then clearly divides n!, so it also divides (n! + 1) − n! = 1.
But this is impossible: p is prime, and 1 has no prime divisors.
So p is at least n.
/- Euclid’s theorem on the **infinitude of primes**.
Here given in the form: for every `n`, there exists a prime number `p ≥ n`. -/
theorem exists_infinite_primes (n : ℕ) : ∃ p, n ≤ p ∧ Prime p :=
1let p := minFac (n ! + 1)
have f1 : n ! + 1 ≠ 1 := ne_of_gt 2have pp : Prime p := minFac_prime f1
have np : n ≤ p :=
le_of_not_ge fun h =>
have h1 : p ∣ n ! := dvd_factorial (minFac_pos _) h
3have h2 : p ∣ 1 := (Nat.dvd_add_iff_right h1).2 (minFac_dvd _)
pp.not_dvd_one h2
⟨p, np, pp⟩
❶ Definitions must be explicit. The proof formally defines p as the smallest prime factor of n! + 1 before it can use that quantity.
❷ Formal proofs build on earlier formal proofs. Here Lean invokes a previously verified theorem showing that p is prime.
❸ Hidden logical steps become explicit. A human mathematician can write that p “clearly” divides 1. Lean requires the proof to invoke a formal theorem about divisibility and show exactly why that conclusion follows.
Versions of such systems, sometimes called reasoning agents, are becoming highly sophisticated. In February, for example, the AI company Math, Inc. used its aspirationally named reasoning agent Gauss to formalize a proof that had earned the mathematician Maryna Viazovska, of EPFL, in Switzerland, a Fields Medal in 2022. Gauss first helped human mathematicians complete the formalization of Viazovska’s solution to the 8-dimensional sphere-packing problem in a matter of days, and then autonomously formalized the more complicated 24-dimensional case in just two weeks.
Such achievements suggest that AI is already capable of handling some mathematical tasks long considered uniquely human. As the technology advances, more of the day-to-day work of human mathematicians is likely to become fair game for AI.
Human mathematicians could become “priests to oracles.” —Yang-Hui He, London Institute for Mathematical Sciences
In September 2025, I attended the 12th Heidelberg Laureate Forum—an annual conference that brings hundreds of young mathematicians and computer scientists together with their intellectual idols. AI dominated the conversation and, from the get-go, tension was in the air.
Speakers described a future in which superhuman AI mathematicians transcend human knowledge and capabilities: forming conjectures, searching solution spaces, proving conjectures, and finally verifying the proofs and generalizing the results, all without human involvement. If this future comes to pass, Yang-Hui He of the London Institute for Mathematical Sciences memorably declared, human mathematicians could become “priests to oracles.”
While such startling predictions were being voiced on stage, my gaze was drawn to the audience. Frowning, fidgeting, and exchanging furtive glances—the crowd’s unease was palpable. Trill White, a student at Australia’s Deakin University, later recalled sitting in that hall and thinking: “ ‘That’s devastating. What will people have to contribute to mathematics? Will it become something that no one understands?’ I did get a sense that this is going to change everything.”
“We certainly started realizing AI has the potential to replace us.” —Jessica Randall, Google Developer Groups
Jessica Randall, a South African mathematician for Google Developer Groups, says she sensed a collective existential dread rising among the young mathematicians. “I could feel everyone was worried, because they hadn’t thought that far ahead,” she says. “It was like a big bombshell that hit us, and we certainly started realizing AI has the potential to replace us.”
Some established mathematicians, including He, seem comfortable with AI taking on tasks that are currently the preserve of human mathematicians. That’s because they just want to know the answers to the biggest questions in mathematics—such as the six remaining Millennium Prize Problems—even if AI does it all. “A lot of mathematicians are pragmatic and just want to understand. They would sell their soul for the solution to a problem,” jokes Avigad. “Whatever it takes, right?”
But this “just want to know” camp is by no means the only faction: Most mathematicians do not hope or expect AI to replace them entirely. Instead, two broad alternatives are emerging. The first is a human-centric aspiration that prioritizes human understanding of mathematics and treats AI as a tool, much like a calculator. The second is a collaborative “teamwork makes the dream work” vision, where humans and AI work together to tackle problems neither could solve alone.
Numbers are “a way of bringing us to agreement.” —Akshay Venkatesh, Princeton University
Fields Medalist and Princeton mathematician Akshay Venkatesh has been thinking about this topic from the human-centric viewpoint for years. In 2022, he used his Fields Medal Symposium to implore the mathematics community to deeply consider what AI might mean for the practice of mathematics. At the time, the idea that AI could replace mathematicians seemed far-fetched. Now, he says, “we’re reaching the point where, for at least some tasks with abstract mathematical reasoning, computers are becoming competitive with humans.”
For Venkatesh, the question is not just what computers can do, but what mathematics is for. “Sometimes I think when we use numbers, it’s not so much that we are describing phenomena that are intrinsically numerical, but that we can all agree exactly what the numbers mean,” he says. “It’s a way of bringing us to agreement.”
Mathematician and machine learning expert Maia Fraser, of the University of Ottawa, shares this sentiment. She says the joy she derives from mathematics is something distinctly human that integrates the subconscious and conscious mind. She describes starting with an intuitive sense that a certain thing should be true and gradually bringing out something that she can express in a rigorous proof. Communicating and sharing these deep-born thoughts is “a form of collective intelligence that is something beautiful about the human spirit,” she says.
By these arguments, an AI proof of a mathematical conjecture that has stubbornly resisted human efforts would be useful only if comprehensible to humans. “That the statement can be proved by AI is already useful information,” concedes Fraser. “But then it’s still an open problem to come up with an elegant, beautiful human proof.” Even if no such proof exists, she says, searching for it “is still a valuable endeavor.”
A more collaborative approach to AI in mathematics comes from Terence Tao, who first competed in the math Olympiad at the age of 10. In 1986, 1987, and 1988, he won bronze, silver, and gold medals, respectively, making him the youngest winner of each of the three medals in Olympiad history. Now a Fields Medalist and professor at the University of California, Los Angeles, he has earned a reputation as one of the most gifted mathematicians alive.
Unlike some of his peers, Tao is neither dismissive of AI nor fearful. Instead, he sees it as the catalyst for a fundamental shift in the discipline—a transition toward what he calls “big mathematics.” He envisions a future of large-scale, decentralized collaborations between humans and machines, where complex mathematical tasks can be diced and sliced, with humans claiming the creative parts and AI doing the lion’s share of the technical grunt work.
| AI as a tool | AI as a partner | AI as an oracle | |
|---|---|---|---|
| Role of AI | Assistant | Collaborator | Autonomous researcher |
| What matters most? | Human understanding | Shared discovery | Answers |
Already, Tao is experimenting with this concept, working on problems alongside scores of online collaborators, some using AI tools. “A hundred years ago, almost every mathematics paper was single author,” he says. “But now I collaborate with people I’ve never met—and maybe in the future, I won’t even know if they are AI or real people.”
The key to Tao’s vision is uniquely mathematical: formalization. When a proof is translated into code and checked step-by-step by proof assistants, it removes any chance of human error or dishonesty. This approach changes how collaboration works, because trust is established through verification rather than reputation or rapport. An idea from an unknown researcher or even an amateur can be taken seriously if it has a formal proof.
“If it wasn’t for this formal verification layer, opening projects up without any safeguards would just be a disaster,” adds Tao. “But in math, we can completely check and verify outputs, and this really filters out a lot of the rubbish.”
From the young researchers at the Heidelberg Laureate Forum to some of the biggest names in the field, mathematicians all seem to agree on one point: AI has the potential to transform their discipline. But there’s far less consensus on what that transformation will mean in practice.
Some worry about the accessibility of AI tools. Traditionally, mathematicians have required little more than intuition, training, and a pen and paper to advance their field. If this slow, deliberative process is no longer valued by society, and particularly by research funders, then mathematics could become an elitist activity, only practiced by select organizations that can afford to work with proprietary AI models.
Another concern is motivation. As AI systems take on more of the work, the incentive to engage deeply with difficult problems may weaken. Princeton’s Venkatesh says that the long human process of formulating and understanding a proof may be hard to justify, not just to funders, but even to mathematicians themselves. “There have been times where I’ve spent years thinking about something, and I’ve slowly struggled to understand it,” he says. “If your computer can do large chunks of that for you, will you have the motivation to spend that time?”
That concern extends to the next generation. If students can use AI to jump straight to answers, they most likely will. But every time they skip the struggle, they miss an opportunity to build the foundations of their own unique intuition. Over time, some worry, the next generation of mathematicians may suffer from a form of intellectual atrophy, unable to think outside the AI box that trained them.
In response to such fears, the mathematics community is taking action. Individuals are writing essays, organizing workshops, and debating in journals, while institutions and community groups are developing guidelines for how AI should be used in research and publication. Indeed, mathematicians are applying the same rigor and curiosity that they use every day to reckon with the challenges of AI. Taken together, these efforts reflect a broad effort to try to retain control over the direction of mathematics in the era of AI.
So, is AI sucking the soul out of math? In one way, it is doing the opposite. It is forcing mathematicians to confront deep questions about what mathematics is, why they have devoted their lives to it, and the purpose math serves in society. At the same time, though, it is reshaping the practice of mathematics in a way that may be difficult to reverse.
“Mathematics makes me a better problem solver at normal problems, because it frames my mind to think in a very logical, rational way,” says Randall, who noted the existential dread at the Heidelberg Forum. “It helps with every aspect of my life.” As AI transforms mathematics, many researchers wonder whether future mathematicians will be able to say the same.
2026-06-25 02:00:01
When considering the 1960s sitcoms Bewitched and I Dream of Jeannie, both of which featured women with supernatural powers navigating life with mortals, most people wouldn’t connect them with pursuing an engineering career. But Karen Panetta did. The sitcoms’ main characters—Samantha Stevens, a witch; and Jeannie, a genie—were “strong, empowered female leads using magic,” Panetta says, and they inspired her to become an engineer, as it was like sorcery to her.
Panetta, an IEEE Fellow, is dean of graduate education at the Tufts University engineering school, in Medford, Mass., outside of Boston.
Employer
Tufts University, in Medford, Mass.
Title
Dean of the engineering school’s graduate education
Member grade
IEEE Fellow
Alma maters
Boston University and Northeastern University in Boston
Like Samantha and Jeannie, Panetta has made magic happen, such as when she helped to invent the first CPU digital-twin simulator. Digital twins are computer simulation programs that track and adjust the operations of a physical device in detail. Her simulator has been adapted for several industrial uses, including by NASA to help design spacecraft.
Panetta also mentors young women to encourage them to pursue a STEM career through the Nerd Girls program she launched at Tufts in 2000. Engineering undergraduate students work on technology for socially conscious projects such as environmental cleanup, renewable energy, and the development of assistive devices to improve mobility for people with disabilities.
Panetta received this year’s IEEE Mildred Dresselhaus Medal for “contributions to computer vision and simulation algorithms, and for leadership in developing programs to promote STEM careers.” The award, sponsored by Google, was presented at the IEEE Honors Ceremony on 24 April in New York City.
Receiving the medal is particularly special to Panetta, she says, because she knew its namesake: Mildred Dresselhaus, an IEEE Life Fellow who pioneered the study of carbon nanostructures at a time when researching physical and material properties of commonplace atoms was unpopular. She was a MIT professor of physics and electrical engineering, and died in 2017.
Panetta nominated Dresselhaus for the IEEE Medal of Honor, which she received in 2015.
“Millie was a rock star,” Panetta says. “I can’t think of another medal that really encapsulates her spirit and what I’ve dedicated my life to.”
As a child growing up in Boston, Panetta built trapdoors and other features in her treehouse, she says.
“I also explored fashion and sewed my own clothes,” she adds. “I wasn’t very successful, but I was very creative.”
She was a top performer in math and science classes in high school, so her father encouraged her to pursue civil engineering.
“I didn’t know what an engineer was, and my father, who was a mechanic working on heavy construction equipment, only knew about civil engineers,” Panetta says. “I started taking computer programming classes at school, but knowing how to type on a keyboard and make a software program wasn’t good enough for me. I wanted to know what was inside the box.”
Her thirst for knowledge inspired her to pursue a bachelor’s degree in computer engineering at Boston University.
“My father was very disappointed that I didn’t pick civil engineering,” she says, laughing.
She commuted to school, and she struggled to find study groups for her classes, so she joined IEEE to connect with peers.
She became active in the university’s student branch, organizing events including the IEEE Student Professional Awareness Conference, which helps students learn practical career skills including résumé building, interviewing, and networking. She organized a SPAC for her branch, and IEEE Life Senior Member Jim Watson volunteered to speak at the event. It changed her life, she says.
Watson was the director of commercial and industrial marketing at Ohio Edison in Akron, where he worked for 36 years.
“He flew to Boston to speak at our event, but fewer than 20 students attended. I was embarrassed,” Panetta says. But Watson told her the important lesson was that she showed up and organized the event.
“He said I would be successful because of that,” she says. “He didn’t care about the attendees’ grade point averages, only that we were professional enough to organize the talk.
“That encouragement was the first time anyone outside of my family ever told me that I would succeed, so it was reaffirming. To this day, I still use some of the techniques that I learned in his presentation in my own classroom to teach students.”
Panetta graduated in 1986. Her IEEE membership helped her get hired for her first dream job: a diagnostic engineer at Digital Equipment Corp.
While attending the IEEE Computer Society’s annual symposium on very large-scale integration in Boston, she handed her résumé to a DEC representative, who hired her to work in Hudson, Mass.
While working full time, Panetta attended Northeastern University, in Boston, as a part-time graduate student. She earned a master’s degree in electrical engineering in 1988.
In the early 1990s, Panetta was assigned to work with Ernst Ulrich, one of DEC’s most respected consulting engineers, she says. He was developing a new CPU using millions of CMOS transistors.
“I thought, ‘Wow, what a great opportunity,’” she says, “not realizing they assigned it to me because no one else wanted to work with him, as he set rigorous standards, expecting those who worked with him to think outside of the box and hold their own to bullet-proof new concepts.”
Panetta and Ulrich wanted the ability to test the CPU while still designing the hardware and software. That way, both would be ready to use at the same time. Typically, the hardware was developed before the software was written.
“We decided that we were going to simulate the machine to see how it was going to run—which was unheard of,” she says.
During a meeting with the company’s top engineers, Panetta shared her idea for an algorithm that could accomplish the team’s goal. She was met with silence.
“It’s going to be the engineers who better society because we know how to work together. We’ve proven that IEEE members know how to work across geographic boundaries, ethnic boundaries, and gender boundaries. And that’s a good model for the world.”
“I thought to myself, ‘Did I just say something stupid?’” she says. “But then, the top engineer looked at me and said, ‘I have been doing this for 50 years, and you, a kid just out of school, comes up with this [solution] like it’s obvious.’”
Her idea became the basis for the digital twin simulator. It used behavioral models to run software on a CPU simulation. The software passes information through the system, she says, just like it would pass information through wires or interconnects.
“We did successfully have a complete model of millions of transistors,” Panetta says. “I efficiently simulated hundreds of thousands of experiments and ran the software on this simulated model so that we knew exactly how it was going to perform on the real machine. That had never been done before.”
Her groundbreaking work led to a promotion: from computer analyst to principal software engineer.
When she began managing a team and hiring staff members, Panetta noticed the younger employees knew the theory but didn’t have the technical skills to hit the ground running, she says.
“It took the company two years to train somebody before they could really contribute technically to a team,” she says. She decided she wanted to help prepare students for jobs in industry.
In 1995 she was accepted into DEC’s Engineers and Education program, in which full-time employees who wanted to teach could take a leave of absence to complete a degree while still being paid. Participants were then placed in academic institutions for two-year stints to help students bridge the gap between classroom theory and real-world problem-solving.
After earning a Ph.D. in electrical engineering from Northeastern in 1994, Panetta began her teaching assignment at Tufts. After one year, she left her job at DEC to join the university as its first female electrical engineering professor. At the time, the department had only one female undergraduate EE student.
“I showed up to work dressed in an all-pink suit,” she says, laughing. “Other professors looked at me like I didn’t belong there because I looked different.”
She didn’t let that stand in the way of reaching her goals: preparing the next generation of students for jobs and mentoring young women who were interested in becoming engineers but who felt they wouldn’t be accepted and therefore couldn’t pursue a career in the field.
When Panetta began teaching, she noticed that students weren’t getting any hands-on engineering experience, so in 1996 she created an internship program. It was the precursor to Nerd Girls.
At the time, she was consulting for NASA’s data visualization and animation lab in Langley, Va., translating complex information into a user-friendly animated form. The programs visualized Earth’s atmosphere and identified pollutants, their origins, and their effects on people and the environment.
Panetta needed a larger team to help conduct the research, so she asked her undergraduate students if they wanted to participate.
“Female students flocked to me because they could relate to the work I was doing, loved how their skills could benefit humanity, and didn’t see me as the classic nerd professor with no life,” Panetta said in a 2008 interview with The Institute about the program. “Eventually, the girls outnumbered the boys.”
“The research project ended up winning awards,” she added. “Tufts couldn’t believe that undergrads had a hand in it. That’s when things really turned around.”
Nerd Girls officially launched at Tufts in 2000 as a class where students work closely with industry on engineering projects. Examples have included building a solar-powered car, developing a battery for the last functioning twin lighthouse in the United States, and creating devices to help people train service animals.
“Everyone who has participated in the program graduated with a bachelor’s degree,” Panetta says. “I’m also very proud that 98 percent of participants pursue a graduate degree within three years of earning their bachelor’s.”
The program is open to all students, regardless of gender.
Panetta became an active IEEE volunteer in 2004 after meeting Arthur Winston, the IEEE president at the time. Winston, an IEEE Life Fellow, was an electrical engineering professor at Tufts. He helped found the Gordon Institute, a leadership-focused engineering school at the university.
“I sat next to him on a bus, and he invited me to attend the IEEE Boston Section meetings,” she says.
Panetta eventually was elected by the section as a member-at-large—which allowed her to attend conferences and other events.
To help spread the word about the Nerd Girls program throughout IEEE, Winston connected Panetta to Mary Ellen Randall, who was chair of IEEE Women in Engineering at the time. Randall is the current IEEE president and CEO. Panetta joined IEEE WIE and was elected as its 2007–2009 chair.
In that position, she worked with Randall and Leah Jamieson, the 2007 IEEE president, to hire more staff to support the program and launch its magazine.
“At that time, we didn’t have any way to connect to members or tell the stories of women in technology,” Panetta says. “I wanted people to read the stories of women from around the globe and how they overcame adversity. So I launched the IEEE Women in Engineering Magazine in 2007.”
Panetta serves as the award-winning publication’s editor in chief, and she is a member of several other IEEE societies and committees.
IEEE is helping to change the world for the better, she says.
“It’s going to be the engineers who better society,” she says, “because we know how to work together.
“We’ve proven that IEEE members know how to work across geographic boundaries, ethnic boundaries, and gender boundaries. And that’s a good model for the world.”
2026-06-24 22:00:01
What could you do if you could make a circuit trace by just bending a piece of paper? How about bridging modern technologies and traditional handicrafts while providing opportunities for learning skills in both.
As part of our interdisciplinary research into digital craftsmanship at the MEI Lab at the School of Creative Media, City University of Hong Kong, we came across research that demonstrated how to impregnate paperlike material (technically a “nonwoven textile”) with the kind of liquid metal used to make conductive ink. Initially, the impregnated material is nonconductive because an insulating oxide layer forms that encapsulates microscopic droplets of the liquid metal. However, applying pressure via shaped molds will crack open the insulating layer, allowing neighboring particles to merge, and thus creating conducting regions in the shape of the mold.
Both of us were introduced as children to origami and kirigami (similar to origami, except that cutting is allowed in addition to folding). We, along with our colleagues, decided to see if those traditional techniques could be used on the new material to eliminate the need for molds. Our goal was to allow crafters to make hybrid papercraft creations that contained easily integrated elements such as LEDs and motors.
In particular, we were interested in the possibility of combining the separate stages of creating a papercraft object and adding electrical conductors. Previous approaches to creating electrified papercraft objects relied on adding a separate flexible conductor—such as adhesive copper tape—to the paper. This increases the effort required and runs the risk of creating open circuits as the conductive material conforms to the object’s shape.
Isopropanol and a gallium-indium liquid material are used to impregnate a paperlike material that is 55 percent polyester and 45 percent cellulose. Electronic components such as LEDs and motors are held in place with masking tape. James Provost
Our first step was to see if the pressures involved in bending and cutting alone would be sufficient to create conductive traces. We became frequent visitors to our university’s materials science and engineering department to fabricate samples and then to borrow equipment to characterize their behavior.
We soon confirmed that the pressures involved in folding and cutting—ranging from 2.5 to 100 megapascals—were enough to create conductive traces. We also confirmed that normal handling of the paper didn’t accidentally create conductive paths.
We made a number of changes to the original method for creating the impregnated paper. For example, instead of immersing the paper in a mixture of isopropanol and liquid metal, we used an airbrush to spray the mixture onto the paper. That allowed us to vary how much was deposited on the paper and to use cardboard stencils to mask some areas from being impregnated, allowing folding and cutting in those regions without creating unwanted conductive traces. We also experimented with the ratios of isopropanol and liquid metal.
We became frequent visitors to our university’s materials science and engineering department.
After optimizing the mixing ratios and amount applied via airbrush, we were left with a material that reliably conducts with a resistance of 23.18 ohms per centimeter for cut edges and 4.4 Ω/cm for folded edges. The folded edges retain their conductivity even if later flattened out, and the conductivity is the same on either side of the paper. We estimate the combined cost of the paper and liquid metal (available from many online vendors) is about US $1.80 to make a 10- by 10-cm piece.
The next step was attaching electronic components to the traces. To make the connections more flexible, we cut down the rigid leads of LEDs and attached conductive thread to the stumps. We then held the threads in place using masking tape. Similarly, we connected conductive thread to the terminals of a power supply.
As our goal was to use this material educationally, we now needed to make it easy for a beginner—whether in papercraft or electronics—to try it out. We created a toolkit, dubbed LiqMetCraft. This consists of all the required materials, plus a browser-based software tool that lets the user select or create designs and then gives guidance on physical construction.
We created three versions of LiqMetCraft. The first is based on Chinese papercraft in which a piece of paper is folded into a fanlike segment and then cut to create a radially symmetric design. We provided circles of paper with a doughnot-shape impregnated region, with an untreated region that created a gap in the donut. We attached positive and negative terminals to either side of the gap. The user could specify in the software how many times they wanted to fold the disk and then draw potential cuts, receiving immediate feedback on what the unfolded disk would look like, as well as guidance on how to place LEDs.
To make our paper sample, isopropanol and liquid metal are mixed in specific ratios while being cooled by an ice bath. Sonic waves are used to ensure the liquid metal breaks up into microscopic droplets. The mixture is then applied via airbrush, while stencils prevent some areas being covered for different papercraft templates. James Provost
The second version of LiqMetCraft was based on origami. We supplied rectangular pieces of paper with two conductive regions separated by a border down the middle. The software tool provided templates for 12 origami designs, with step-by-step instructions for folding them. Once the project was completed, the user could add components, such as a motor, by taping them to the folds.
The final version supported 3D paper model making. In this case, the initial paper supplied was a rectangle with an untreated rectangular central area. By cutting this paper in half and then further cutting the halves into patterns separated by a spacer, the user could make various self-standing models. The software allowed the user to draw a pattern on screen, and then have a cutting machine produce a template for cutting the impregnated paper.
We had 42 participants, evenly divided into three groups, try out the different versions. All found it easy to use, and we were pleasantly surprised that some participants moved beyond the supplied designs to their own creations.
For full details of the current process, see our open access LiqMetCraft research paper published in CHI ‘26: Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. In the future, we plan to try different substrates for the impregnating solution, as well as explore further types of papercraft, such as pop-up books. We’re also interested in developing ways to use the material to support inputs as well as outputs by constructing switches and potentiometers directly out of the material. Imagine traditional papercraft creations becoming interactive devices!
2026-06-24 21:00:01
Take a moment and try to imagine your life without the wireless advances of the past three decades.
Have you lost your luggage? What a shame AirTags have not been invented. The airline representative has promised to call with updates, so settle in for a long wait by the kitchen telephone, because there are no affordable cellphones. You’ll be stuck listening to whatever is on the radio while you wait, because there are no streaming services. That’s not even to speak of all the movie plots that would have been ruined.
This is just a tiny sliver of how wireless technology makes itself felt in your day-to-day existence. The effects it has had on supply chains, infrastructure, and how the economy runs have been world-altering.
None of it would be possible without the radio-frequency integrated circuits that allow all our devices to unobtrusively send and receive information.
Now imagine what the further evolution of this technology will bring: Wide-spread autonomous vehicles, quantum communications, 6G mobile service and satellite communications. Continued momentum will depend on newer and more advanced versions of today’s RF chips.
But there’s the rub. Whereas the design of most of the world’s computing chips has been standardized into its own science, RF design has remained stubbornly in the realm of art. A dark art, even, that is mastered only through years of experience. As any sorcerer will tell you, the dark arts keep their own schedule. And that schedule is impeding progress not just in RF chip design but in every other technology that depends on it.
About seven years ago, in the wake of AlphaGo’s victory over world Go champion Lee Sedol, my students at Princeton and I began to wonder: Could AI be taught this art as well? Recent successes suggest that, to a large extent, it can. Over the last few years, our group and other leaders in the field have started to develop machine-learning-driven algorithmic methods for designing RFICs. Some of the resulting chips look more like modern art than circuit layouts. Yet in many cases, the physical prototypes bested state-of-the art circuits in terms of performance. The real achievement, however, is that it took the AI orders of magnitude less time to conceive a working design than it would a human designer.
This is not about one or two RF chips. AI-enabled design could be the future of all RF design, and maybe much more.
So why do these chips all have to be crafted by hand? Why aren’t RFICs designed with an algorithmic synthesis process, much as CPUs and GPUs are?
The design of RFICs is an exercise in engineering across multiple physical domains. Maxwell’s equations, operating across different spatial and temporal scales, govern how electromagnetic fields interact with active and passive devices that must be carefully codesigned for the chip to function. Alongside these are the laws of thermodynamics, which determine how heat is generated and removed during operation, as well as the mechanics of thermal expansion and contraction that dictate how reliably the chip and its packaging survive temperature changes.
The design of a radio-frequency integrated circuit requires human intuition and multiple, often-repeated optimization steps. The hope is that through an understanding of Maxwell’s Equations, an AI can be taught to short-circuit this process and quickly produce a design.
Simultaneously accounting for all the physical constraints these impose makes the design space almost impossibly large. Every decision involves complex priorities that often compete with one another, preventing the optimization of any of them.
To better understand the issue, let’s walk through the steps involved, after which you’ll better understand why a single new chip design takes years and tens to hundreds of millions of dollars.
Most of the area of radio-frequency integrated circuits is dominated by complex electromagnetic structures. Human-designed RFICs, like this broadband power amplifier [1], start with templates and follow a symmetric, understandable pattern. But freed from the constraints of human-designed templates and the need for humans to even understand the rationale of electromagnetic structures, power amplifier ICs [2–5] and low-noise amplifiers [6] can take on truly wild-looking yet efficient designs. SENGUPTA LAB
Let’s say you’re an engineer assigned to design a new 28-gigahertz power amplifier for a 5G-millimeter-wave handset. (This is the type of RFIC that boosts the 5G signals on your phone and transmits them to the antenna where they can be picked up by a distant base station). Where do you start?
RFIC design has some features in common with house building. Just as the blueprint for a house dictates the number of bedrooms and bathrooms to be built and the hallways connecting them, the blueprint for an RFIC—called the architecture—establishes the kinds of elements the RFIC needs to fulfill its intended function. Instead of rooms, the architecture includes, for example, the number of stages of amplification your power amplifier needs. Instead of hallways, it shows the paths that signals must take to get through those stages.
The blueprint for RFICs is actually mostly hallway; passive elements, like inductors and transmission lines, take up far more real estate than active elements like transistors.
Here’s why. As you have probably experienced yourself, a typical CPU’s transistors overheat when faced with operating frequencies of just a few gigahertz. The frequencies RFICs can operate at are higher by an order of magnitude—28 and 39 GHz for 5G signals, 26.5 to 40 GHz and even higher for satellite communications, and 77 GHz for automotive radar. Under this onslaught, a CPU’s transistors would fail.
RFIC transistors avoid this fate because these chips cleverly manage the signal’s energy with careful electromagnetic design. This takes the form of byzantine networks of metal elements that dominate the chip’s real estate. These structures are geometrically regular, often symmetrical, and so intricately constructed they sometimes resemble lacelike filigree. But while they may look decorative, they are essential to the chip’s functioning.
Electrically speaking, these “hallways” work more like the chip’s plumbing. Like plumbing, this extensive labyrinth of passives confines electromagnetic energy only to the places it should be traveling around the chip.
The major challenge in RFIC design is putting all these elements together to ensure they work, just as constructing a house from its blueprints demands exact specs for load-bearing beams, pipes, and external walls. On an RFIC, the architecture needs to be realized with physically fabricable transistors and passive components that are connected just so, to permit the signal to travel through the chip and be processed. The way these devices are connected locally is what we call the circuit’s topology.
To make that power amplifier, then, your first step is to identify a candidate circuit template: The combination of structures that will meet the goals of a particular architecture with a specific circuit topology. Over the years, researchers have eased your burden by developing reusable design templates for specific functions. For example, templates suggest how many amplification stages a circuit needs (because sometimes, combining the output of two smaller amplifiers will result in better bandwidth and efficiency than you would get from a single larger one). And they suggest what the general configuration of the passive structures should be. Today there is an extensive library of such templates.
However, these can’t simply be used off-the-shelf, because each comes with trade-offs. Some have better gain at the expense of stability; some better bandwidth at the expense of efficiency; still others are more energy efficient at the expense of output power, and so on. There is rarely a clear best choice.
To arrive at the “sweet spot” where all these different parameters are balanced into optimal harmony, designers will typically lay out several different versions of the circuit, using intuitions and methods they have picked up in their years of training.
The challenge is that the decision around the architecture, circuit topology, or the electromagnetic passives cannot be done separately. One decision influences the others. So, designing an RF circuit can often feel like trying to fit an oversized carpet into too small a room—press down one corner, and another pops up.
At microwave and millimeter-wave frequencies, even the smallest misstep is the difference between a chip that works and one that doesn’t, and any number of things can go wrong. For example, when an electromagnetic wave encounters a transistor—or any other component —the path it travels must be properly “matched” to what comes next. If it isn’t, some of the energy reflects backward instead of flowing forward. Imagine trying to connect a high-pressure fire hose directly to a narrow garden hose. Without the right adapter, water will splash backward at the junction. Very little will make it through. In electronics, this is called the impedance-matching problem.
To prevent those reflections, engineers design special transitions, essentially microscopic adapters, that smooth the handoff between components. On a chip, these adapters can be surprisingly intricate. They don’t just pass the signal along; they can also split it, combine it, or distribute it across multiple paths with carefully controlled timing and strength.
Once you’ve done the architecture, plumbing, and everything in between comes the moment of truth. Have all the choices you have navigated through the enormous design space resulted in an RFIC that meets its specifications? If the specifications are not met, you will have to go back, either redoing the topology or the entire architecture, and repeat the whole process. So get ready for months of time- and resource-heavy simulation and iteration. Perhaps you now see why, for decades, a core belief has persisted in the RFIC community: “RF design is an art.” It was said that only an experienced designer—with an artisanal understanding of how the pieces make up the whole—could master the subtleties of analog and RF design. Unfortunately, this entrenched notion has long held back algorithmic innovations in the field just when we need them most. Traditional, artisanal RFIC design is hitting its limits as the complexity of these systems inexorably grows.
While RFIC designers continued their battle against their “oversized carpet” problem, a series of interesting developments emerged in allied disciplines. Across a range of other previously intractable problems like protein folding and climate modeling, AI has been able to successfully navigate multidimensional complex spaces. This gave us the incentive to look deeper into AI for RF. After all, the combinatorial complexity of protein folding is not that different from the nature of the design space in our domain.
We were not the first to think of using artificial intelligence to speed up parts of RFIC design. Researchers had previously trained machine learning algorithms on circuit templates in the hope of speeding up the normal optimization processes. While this approach was undoubtedly faster than humans at optimizing templates, it still relied fundamentally on libraries of existing designs invented by humans.
A machine learning system learns to do end-to-end RFIC design like other AIs learned to play such games as Go. Essentially, it turns the process into a game, learning from the results of its own efforts.
We didn’t want that. We wanted to break free from the restrictions of prefabricated topologies. Because while a designer’s experience and hard-won heuristics are crucial to building a working design, they also place fundamental limits on it. Furthermore, such an approach would necessarily require simulation steps as part of the optimization cycle, and even the fastest simulations use a lot of computing resources. Worse still, in many advanced cases, such as for broadband designs, there are no existing templates.
But if we didn’t start with templates, where could we start?
The goal here was to allow algorithms to determine—entirely from scratch—every parameter for architecture, constituent circuits, and electromagnetic passives. This approach differs fundamentally from conventional optimization, which is limited to determining the parameters—like transistor dimensions and passive component geometries—that optimize structures originally devised by humans.
In our new approach, the architecture begins essentially from nothing and is progressively assembled through successive iterations. The system explores the design space by generating myriad candidate circuit combinations and mapping the resulting performance trade-offs as it navigates this landscape. Because the process is not biased by prior human design choices, it can produce completely novel circuit topologies that look markedly different from those created by human designers.
In some ways, the approach echoes AI systems such as AlphaGo Zero, which achieved superhuman performance not because it was trained on games played by humans but because it explored the rules by playing against itself. Similarly, our algorithm develops new circuit architectures by exploring and evaluating its own design strategies. In so doing, it learns to understand circuits, electromagnetics, and the close codesign they need to achieve the end-to-end design of RFIC.
To realize this capability, we proceeded in two stages. First, we developed a reinforcement-learning (RL) framework that determines the optimal system architecture, circuit topology, device parameters, and even the properties of the electromagnetic interfaces that connect different circuit elements. In this stage, the algorithm effectively defines how signals should propagate and interact across the system.
The algorithm trains very similarly to how a computer learns to play a game. If you let it play enough times, it can learn to play better by observing the relationship between the actions it took and the score it achieves. In a similar way, the RL agent here learns to design effective circuits by playing with a set of combinations, and over time, it can map the space between the circuit performance to its architecture, topology, and parameters. This training takes a few days to a week, but once trained, the agent can design circuits very quickly
The next step was to determine the physical structure of the IC’s electromagnetics—the plumbing—that can create the desired properties of the passive elements, which are characterized by a set of metrics called scattering parameters. These measure if a signal entering a component actually moves forward—or is reflecting backward, being wasted, as in our previous example with the fire hose and the garden hose.
Deriving the structure from the desired scattering parameters is an example of an approach called inverse design, which appears across many areas of engineering. In structural engineering, for example, one might collaborate with an architect on a physical goal—such as creating large interior spaces with high ceilings—and then determine the arrangement of arches or buttresses that can support it.
But RF integrated crcuits pose a particular challenge for inverse design: The process must account simultaneously for circuit behavior and the electromagnetic responses of the interconnects and passive elements that link them together. But it has to figure that out without doing a lot of artisanal iterating.
So we replaced our RF circuit simulator with an AI-based emulator. This AI model can predict the behavior of electromagnetic fields going through any structure—even totally arbitrary two-dimensional shapes—without having to compute the underlying physics from scratch, as simulation tools do. It would predict the solution of Maxwell’s equations and tell you the scattering parameters for any structure you showed it, without actually doing the math. With such an AI in hand, what a time-consuming electromagnetic solver normally takes minutes or hours to accomplish is reduced to milliseconds.
We chose to build our emulator around a convolutional neural network—a machine learning model that has been remarkably successful for image processing. Such networks can extract spatial features from any structure, and it turns out that the image of a structure contains a lot of spatial information that can accurately predict its electromagnetic performance. Then we trained it on a vast number of random pixelated structures whose scattering parameters had been labeled.
Once we had our inverse-design RL and suitable AI emulator, we essentially had an end-to-end AI designer. So we asked it to design us a power amplifier.
In 2023, we published this proof of concept—a power amplifier targeting the millimeter-wave band, specifically spanning 30 to 100 GHz, which covers most of the relevant 5G and radar frequencies. The final design achieved the best combination of wide bandwidth, output power, and efficiency then reported for a silicon-based power amplifier—meaning it could amplify a large amount of data across a wide swath of frequencies—while maintaining record efficiency.
The structure of the IC’s electromagnetic pathways was unlike anything any human would ever consider. Since the AI is not trained on human designs, the layout that emerged looked more like an arbitrary pattern or perhaps a QR code than the regular symmetrical structures we are used to seeing.
One unexpected insight revealed by this prototype, and our research generally, is that there’s no evidence that the templates we’ve historically relied on are even close to optimal for modern design goals. It’s not that a human designer can never come up with a better design. But with the removal of the templates and the time to synthesize cycle upon cycle of optimized circuits, it is now clear that AI-driven synthesis could break traditional design barriers and push the limits of RFIC capabilities.
Our 5G amplifier had only one input port and one output port. Adding more inputs and outputs to a design is not straightforward. Every port electromagnetically couples to every other port, so the scattering parameters quickly add up. Two ports give you four scattering parameters. Four ports, 16 scattering parameters. The math gets ugly fast. Could our model keep up?
We next trained our model on larger classes of electromagnetic structures with many input and output ports. In 2024, we published work showing that multiport integrated circuits are no problem for these AI algorithms either. Where previously multiport electromagnetic simulation required days or weeks of toil, this model evolved new structures in minutes. Since then, a plethora of work in the space by research communities across the globe have demonstrated the power of inverse design in RFIC.
Combining the reinforcement learning framework with the inverse design, we now had the ability to create an RFIC from specifications all the way to a fabrication-ready layout. We’ve so far shown this is true for RFICs ranging from low-noise amplifiers to subterahertz and broadband power amplifiers. The hope is that this will work just as well for other circuits.
Our goal was to make RFIC design better and easier, but we didn’t want to make it beyond human understanding. Chip testing and debugging is a long, arduous process, sometimes even more so than design. Engineers often prefer ICs to have interpretable structures, so that if a problem crops up, they can understand how the chip works well enough to debug it.
To create structures that are more interpretable, we turned to diffusion models, which you may know from their remarkable ability to generate realistic images from text prompts.
AI-driven synthesis could break traditional design barriers and push the limits of RFIC capabilities.
Imagine you go to your favorite image-generation engine and ask it to create a painting of the sky in the style of Picasso, Van Gogh, or Michelangelo. You will get images that capture the essence of their brushstrokes, their use of colors, and their framing. All are pictures of the sky nonetheless, but in different styles.
Electromagnetic design is similar in that multiple structures can have very similar electromagnetic responses. Instead of using text input, we used scattering parameters as our input, and the electromagnetic structure of an RFIC chip as our output. As part of the inputs to the diffusion model, we created a dial that sets the spatial frequency of the final structure. By turning the dial, a designer can direct the model to synthesize structures with low (classical-looking and interpretable), medium (mazelike structures), or high (pixelated or arbitrarily-shaped) spatial frequency.
From prompts to output, the entire process took about 6 minutes. With this diffusion model, algorithms can now both discover novel architectures and accelerate the creation of conventional, so-called classical ones.
All an RFIC designer needs to do is specify virtually any valid set of scattering parameters. As long as they are physically realizable under Maxwell’s equations, the model pops out a corresponding structure as if it were a vending machine.
The results of our investigations have drawn the attention of the RF community. The traditional bottom-up design process is clearly beginning to reverse.
But there are still questions: How generalizable are these methods? Can they consistently deliver truly high performance? Can we get to a place where AI produces designs that maximize every conceivable trade-off, holistically optimizing every parameter to its most ideal physical state? We want to take this strategy beyond RFIC design and invent other kinds of circuits that are different from anything humans have ever done.
These are exciting and ambitious prospects, but we are not there yet. AI can hallucinate a design that creates bad circuits that don’t work. This means verification methods need to remain under human oversight. And, while hallucinations are rare, it would still be good to reduce their occurrence.
History suggests that meeting these dreams of the future will take much more data than we’ve been using. Before the creation of the ImageNet repository—a repository of 14 million varied, human-annotated images—image-recognition models didn’t function well in the real world. The datasets they had been trained on were too tiny to be effective. ImageNet’s massive amounts of training data ushered in a revolution that led to AI that can generalize and recognize images in the wild. The rest was history.
If the goal for RFIC and analog design is a universal foundational model—something that learns the governing laws of electromagnetics and circuit behavior—then we also need data.
The good news is that this data is plentiful. Around the world, countless engineers at companies and academic labs simulate nearly identical RF circuits and passive structures every day. The bad news is that it’s all locked away behind nondisclosure agreements.
Open ecosystems have propelled other areas, and we think the RFIC community should do the same. There had been some movement toward this. Natcast, the operator of the U.S. CHIPS and Science Act’s R&D program, would have bolstered shared infrastructure and innovation for the next generation of wireless, sensing, and defense technologies. Unfortunately, both the organization and the program it ran specifically for machine learning and RFICs have been closed.
But the momentum Natcast’s effort sparked hasn’t died out. Building on our early work, groups across the community have already demonstrated remarkable advances. AI-driven IC design is part of a much broader technological shift. From biology and materials science to automotive and aerospace engineering, AI is reshaping how complex systems are conceived and optimized. Deeper collaboration between AI researchers and chip designers will unlock the field’s full potential. It’s by no means a foregone conclusion, but if we get this right, this genie won’t stay in its bottle.
2026-06-24 18:00:02
5G telecommunications, according to industry hype when 5G first launched in 2019, was going to be all about buzzy applications like mobile augmented reality and autonomous vehicles. But the surprise plot twist came when replacing home cable internet turned into 5G’s most widely adopted new application.
Fixed wireless access (FWA) now serves over 14 million U.S. customers, and contributes 28 percent of worldwide wireless traffic. Fixed wireless access is what the term sounds like: broadband internet delivered over a cellular radio link to a stationary location—no cable, no fiber, no trenching, no satellite broadband antenna pointed at the sky. What makes FWA distinctive is that it repurposes the same towers, spectrum, and 5G infrastructure that was built for mobile devices.
One U.S. Federal Communications Commission commissioner has called FWA 5G’s killer app. And that’s true not just in the United States either. Jio, India’s largest carrier, is also one of the world’s largest FWA providers, with over 9 million customers as of last year.
Carriers discovered they could repurpose surplus 5G capacity, while also exploiting a usage pattern quirk: mobile traffic starts to drop after 8 p.m., just when home internet usage peaks. The result is broadband, delivered via traditional cellphone towers, at a lower cost than fiber deployment. For these reasons, FWA provides real price competition to cable broadband, while reaching underserved rural and suburban communities.
FWA is cheaper to deploy than fiber, and for most homes and small businesses, fiber’s gigabit speeds are overkill anyway. And since FWA uses the same wireless networks built for cellular service, FWA works anywhere that receives a steady cellular signal.
As cellular networks extend into areas with minimal service, FWA’s coverage map expands with them. In these remote locales, the other main viable broadband alternative typically comes from satellite services like Starlink—which are, compared to FWA, more expensive, with higher delays, and lower bandwidth.
While most FWA deployments use currently underused microwave bands, some FWA deployments use electromagnetic spectrum that 5G launched but that mostly failed with mobile users. Millimeter waves operate at frequencies 10 to 40 times higher than 4G’s spectrum, offering high data rates from their wide available bandwidth.
However, there are good reasons 5G mobile users today don’t generally use millimeter-wave spectrum. Millimeter waves can’t penetrate buildings. Plus, they lose signal strength within a kilometer or two of the transmitter. Millimeter-wave antennas are also a real drain on cellphone batteries compared to microwave and radio-wave tech.
Yet none of these challenges applies to a fixed station with a clear line of sight to a nearby tower. FWA home units (called customer premise equipment or CPEs) outperform 5G handsets by a significant margin. That’s mostly because of hardware. CPEs carry larger, more sensitive antennas than a typical cellphone, paired with more capable transceivers. CPEs also tend to be plugged into wall outlets, making battery concerns a nonissue.
Another 5G technology that did not gain traction in mobile wireless is multi-user multiple-input multiple-output (MU-MIMO). A base station with MU-MIMO uses an array of antennas to serve multiple users on the same frequency simultaneously.
However, maintaining a MU-MIMO signal involves tracking each user individually—a problem that quickly becomes overwhelming with enough mobile users. FWA is different, however. Static CPEs, with their steadier downlink traffic loads, are an ideal match for MU-MIMO technology.
So, FWA internet service not only uses mostly fallow spectrum but also uses 5G spectrum more efficiently than do 5G mobile users—for whom, of course, these 5G technologies were originally designed!
Not long ago, the high-bandwidth use cases for 5G made for an impressive list: millisecond latency for autonomous vehicles, mobile augmented reality headsets with extensive high-speed data needs, and massive machine connectivity for an expanding internet of things (IoT).
These applications have all stalled. Autonomous vehicles pose challenging—and still unsolved—problems unrelated to spectrum allocation. Augmented and virtual reality technologies have yet to create meaningful spikes in bandwidth demand. And the IoT has, to date at least, fragmented across an array of competing standards.
Mobile carriers had built dense 5G networks for mobile customers whose needs rarely saturated the network’s capacity. Home broadband usage peaks in the evening hours, precisely when cellular networks are quietest.
FWA sits at cellular networks’ crossroads of supply and demand.
In December, the telecom standards body, the Third Generation Partnership Project (3GPP), issued its latest 5G specification—Release 20, the final “5G only” update. So, although 6G is still years away (its first specifications are expected in early 2029), engineering decisions that will define 6G are being made today. And FWA is not on the margins of that conversation; FWA is currently considered an established day-one use case.
6G wireless technology promises to expand FWA’s reach—not only via spectrum but also via geometry. Instead of following 4G and 5G’s connectivity model—strong signals near towers and weak signals far away—future 6G networks will let homes connect to multiple towers simultaneously, using a technology called distributed MIMO (multiple-input, multiple-output).
Where 5G’s version of MIMO (a.k.a. massive MIMO) concentrates user communication with dozens of antennas at a single tower, distributed MIMO uses antennas across multiple base stations and coordinates them to deliver signals to your home from multiple directions simultaneously.
The practical result: Because no single tower is responsible for any given connection, the “edge” of a cell network—that outer boundary where signal strength falls off and service degrades—no longer represents a hard limit on who gets well served. A home that would once have been too distant from a tower, or blocked by terrain, could now be within reach of several base stations working together.
6G may eventually adopt distributed MIMO technology for mobile users, when synchronization challenges and other signal engineering hurdles are solved and deployed for real-world cellular networks. The jury, as of 2026, is still out on whether the full distributed MIMO problem will be solved once the 6G standards start to be set in place, within three years.
As demand for FWA grows, carriers will also deploy increasingly capable millimeter-wave infrastructure for fixed customers first—the stationary CPE use case that millimeter wave best suits. The dense millimeter-wave antenna infrastructure that FWA requires is the same infrastructure that future mobile applications will eventually inherit. AR glasses, AI-powered wearables, and other bandwidth-hungry applications originally promised for 5G are not canceled—they are waiting for the infrastructure to arrive.
The pathway to FWA is being prepared at lower frequencies, too. There is growing interest today in the largely unoccupied FR3 band, which spans roughly 7 to 24 gigahertz, situated between crowded low/mid-bands and the much higher millimeter-wave frequencies.
Recent field trials by Nokia have demonstrated FR3’s viability for both cellular and FWA applications. FR3 is emerging as one of the more promising near-term frontiers for extending FWA coverage beyond its current footprint.
None of this was the plan. No carrier executive in 2020 stood on a stage and announced that 5G’s defining achievement would be delivering living room broadband to rural homes and suburban subdivisions underserved by cable.
FWA became 5G’s killer app because the engineering economics made it happen. Surplus wireless capacity met unmet consumer broadband demand, with the physics of a stationary receiver doing the rest.
That is not a criticism of the engineers or the carriers. It is simply how technology sometimes advances—sideways, through gaps nobody was trying to fill.
But FWA’s model of prioritizing unconnected users may in the end prove to be telecom’s on-ramp to everything else. Fix the digital divide first. Tomorrow’s sci-fi future appears set to follow close behind.
2026-06-23 21:00:01
By most accounts, the United States appears poised to fall woefully short of meeting new electricity demand over the next five years as data centers and domestic manufacturing proliferate.
Ian Magruder is the founder of Utilize Coalition and previously served as director of market mobilization at Rewiring America, an affordable electrification advocacy group.
Building new power plants and transmission lines may seem like the obvious solution, but there are other options, says Ian Magruder, founder of Utilize Coalition, a nonprofit based in Washington, D.C. The U.S. uses only about half of its grid capacity, and a lot more power could be tapped by deploying a spate of newly available technologies.
Backed by Google, Tesla, HVAC systems manufacturer Carrier, and several other companies, Utilize Coalition advocates for more thorough use of grid capacity through policy change and new technologies. Magruder spoke with IEEE Spectrum about those efforts.
Why does the United States use only half of its grid?
Ian Magruder: Most studies have found that average utilization rates are between 40 and 55 percent across different geographies. And the reason is that we’ve built our grid to meet peak demand. We have to ensure that on the hottest summer day or the coldest winter morning we have enough power. But in many parts of the country, we really only hit peak a few days a year, and it’s really only a few specific hours within those days.
It didn’t used to be this way. What’s changed?
Magruder: Over the last 20 years we’ve seen the gap between average use and peak use grow wider. There are a variety of reasons for that. Grid operators have become more conservative following major blackouts and reliability events. And with more variable-generation sources such as wind and solar, grid operators are building in more capacity. But this also presents us with an incredible opportunity to get more out of the grid using new technologies.
What technologies are being deployed to address the problem?
Magruder: Pairing battery storage with energy generation is a key part of this, as are other kinds of distributed energy resources, like managed [electric vehicle] charging and smart thermostats. I would also say that transmission technologies that safely maximize the current in power lines, increase conductivity, and optimize power routes all play a critical role here. And then there’s demand flexibility, which is when utility customers adapt their power use to accommodate the grid during peak hours. Some really good work is being done around flexible data centers.
Is grid underutilization also happening elsewhere in the world?
Magruder: It’s a global phenomenon, but it varies widely by country. European grids face similar dynamics as [those in] the U.S., and in some places utilization is even lower. But Australia and the United Kingdom are further ahead in measuring and managing utilization with new technologies.
What’s the downside to overbuilding our grids?
Magruder: Mainly cost. Electricity rates have gone up, and we [at Utilize Coalition] think it’s because utilization has gone down. A report that we released earlier this year shows that a 10 percent increase in grid utilization could save Americans over US $100 billion over the next decade.