GPS signals are too weak to be accurate deep inside big buildings. In conflict zones like Ukraine and the Strait of Hormuz, adversaries jam and spoof signals. So the hunt has been on for decades for GPS alternatives that might work both indoors and outside, and in even more unconventional places, such as underwater.

One tantalizing option involves using the Earth’s magnetic field. It’s always present, even if it’s weak or subject to noise from nearby local magnetic fields. For more than a decade, some companies have combined maps they’ve previously made of local magnetic fields with other technologies such as Bluetooth or radio-frequency identification (RFID) to offer indoor mapping services.

Now, companies such as AstraNav, Oriient, and SysNav say they are ready to disconnect from other technologies and use the magnetometers installed in every modern mobile device together with magnetic map data. The same technology is starting to be useful outdoors, too. “If we have a magnetic map, then we can provide first-fix absolute positioning in the air, underwater, or underground,” says AstraNav co-founder and CEO Anton Toutov.

AstraNav’s Magnetometer Navigation

AstraNav will be demonstrating in the next couple of months to the U.S. Air Force how aerial drones can use their software under a US $1.8 million Small Business Innovation Research (SBIR) grant. In March, the company announced a separate deal with healthcare real-time location services company Sonitor to provide a magnetometer-based tracking system for workers and medical devices. Sonitor and its healthcare clients will no longer need to install ultrasound, Bluetooth, or other beacons in their facilities to locate staff or hardware.

The moment is ripe for navigational aids relying on magnetometry thanks to advances in signal processing, says Isaac Skog, a communications systems and automatic control professor at KTH Royal Institute of Technology in Stockholm. “In the beginning, people tried using magnetometers just as a compass in a smartphone, but that’s challenging because you have other material in the smartphone and had to do a lot of processing to compensate for that material,” he says. “Then people said, ‘How can we use variations in the magnetic field not only for finding north but also to create a map and determine where we are?’”

Since the 1990s, the U.S. Defense Advanced Research Projects Agency (DARPA) has funded research into alternatives to GPS for urban and indoor environments. Companies have explored repurposing Wi-Fi, RFID beacons, inertial systems, and, more recently, machine vision and phone cameras. But in emergency or combat settings, users can’t rely on any outside infrastructure. The broader goal from both DARPA and various companies has been to mature the tech beyond anything requiring fixed sensors or networks.

The technical challenge is large, though. “There’s a lot of confounding factors,” says David Hanley, a computer scientist at DePaul University, in Chicago. When Hanley started working in this area more than a decade ago, even moving a device a small amount vertically could throw off the positioning models because the magnetic field changed in hard-to-predict ways. The convention among many researchers was that magnetometers were too sensitive to nearby magnetic interference–indeed, that variations even from the electronics inside devices could create enough noise to overwhelm measurements. The field has matured over the last decade and a half, however, as academic laboratories made a sequence of breakthroughs, followed by companies undertaking internal research.

Google saw the potential, and in 2014 hired a team of magnetic location researchers from Germany’s National Aerospace Center. An influential 2017 paper by another research collaboration clarified how researchers could use Gaussian processes to better understand magnetic fields.

A magnetometer aboard a Hidonix rover can precisely map a building’s magnetic field, which can then be used for navigation via mobile devices.Hidonix

By 2020, the technology was mature enough for startup Hidonix, based in Santa Monica, Calif., to use magnetometry for indoor navigation in places such as museums and schools, alongside other available data such Wi-Fi and dead reckoning derived from accelerometers. The company sends a rover or person throughout the buildings to create a magnetic map beforehand. Even doing that required the maturing of adjacent technology, Skog says. “In order to build your map in the exploration phase, you need quite good dead reckoning using accelerometers and gyroscopes with low drift.”

Standalone Magnetic Indoor Navigation

Now, companies are starting to offer standalone magnetic indoor navigation. Hidonix claims that it is able to offer magnetic geolocation without pre-mapping in outdoor settings, which tend to have fewer objects that interfere with the magnetic field. Indoors, a prerecorded map is still necessary to achieve the precision most users demand.

Another approach involves gathering large amounts of data on how magnetic fields vary and then using neural networks to predict local variations in a magnetic field, even in the absence of a prerecorded map. This is what AstraNav has been doing in Ukraine since even before the 2022 Russian invasion, which then turned that region into one of the most contested electromagnetic environments on the planet. An added benefit of all that training data was that they could calibrate data from a wide array of devices, which is useful in indoor settings, too.

“We are agnostic to the hardware,” says AstraNav’s Toutov, though the company gets better location results if they have some time to study the hardware involved beforehand. AstraNav is also seeking to perform all the necessary calculations on the device itself, to avoid the need to maintain communications in places with jamming or physical obstacles like thick walls.

Magnetic information allows AstraNav to precisely locate a device indoors, where GPS is less useful.AstraNav

If magnetometer-only indoor positioning is really ready for commercialization, the demand will certainly be large. The business promise of indoor mapping is that it straddles civilian settings and conflict zones of all kinds. The market size may already be in the tens of billions, and one market researcher forecasts it will grow to more than $150 billion by 2030. Factories need indoor mapping to drive their robots around; retailers want indoor mapping to follow and communicate with customers at an accuracy below 1 meter, which is enough to detect which aisle a customer is in.

Yet given how messy real-world settings are, companies will have their hands full proving that they can handle dynamic scenarios such as people’s own bodies interfering with the local magnetic field or using an elevator, Skog says. “Coming up with a solution that works in a research paper is one thing, but you need robustness,” Hanley says.

Patients who use mobile applications to manage medical conditions including depression and chronic pain might assume the apps have been evaluated by regulatory agencies to be safe and effective. But that isn’t necessarily the case.

Most of the more than 55,000 medical apps that claim to diagnose or treat a condition—or ones that provide clinical decision support, known as “therapeutic” apps—have never been assessed by any trusted neutral bodies or regulatory agencies to evaluate them for technical soundness, ethical design, or clinical benefit. The apps often don’t comply with regional data security and privacy laws to protect people’s sensitive health information.

Medical apps differ from traditional wellness apps, which provide users with insights into becoming healthier by, for example, tracking fitness activities, monitoring blood pressure, and analyzing sleep patterns.

There is no reliable way to verify that therapeutic apps deliver the results they indicate. To help ensure such apps are credible, the IEEE Standards Association (IEEE SA) recently launched the IEEE Global Medical Mobile App Assessment and Registry. The publicly searchable directory is designed to list apps that have been vetted by experts across several criteria including technical soundness, ethical design, compliance with data security and privacy regulations, and clinical efficacy, which is evidence of a clinical benefit for the patient.

“Patients, clinicians, payers, and health care systems often struggle to distinguish clinically meaningful therapeutic apps from those that are simply well-marketed,” says IEEE Senior Member Yuri Quintana, chair of the assessment and registry program. He is chief of the clinical informatics division at Beth Israel Deaconess Medical Center, in Boston. “Our goal is to establish a standardized review method using criteria developed by experts.”

Why regulation is lacking

Because the apps are intended for medical use without being part of a medical implement, they fall under the designation of software as a medical device (SaMD), according to the International Medical Device Regulators Forum. SaMD is supposed to be regulated by public health agencies such as the U.S. Food and Drug Administration, but the apps have developed and grown in popularity so quickly that regulators haven’t been able to keep up, Quintana says. Some companies have received approval, but most have not, he says.

Many users are unaware of the regulatory gap, he says.

“Seeing an app from a well-known company often creates the impression that it has been meaningfully vetted for safety and efficacy, even when that is not the case,” he says.

Some companies are using deceptive advertising to sell their product, he adds. Marketing materials might claim that all of a company’s health apps are certified, even though only one app has been approved by a regulatory body to treat a particular condition. Or the verbiage might imply the company has clinical evidence proving its application works, even though the app has never been tested independently.

Another concern is that updated apps aren’t being vetted, says Maria Palombini, IEEE SA’s director of health care and life sciences global practice lead.

“The original app might have received approval from a regulatory agency, but not the updated version,” Palombini says. “There could have been significant changes from the original.”

“Not every medical-related app triggers the same regulatory classification or review across jurisdictions,” Quintana adds. “That leaves a large gray zone of clinically relevant but lower-risk apps that haven’t undergone an independent assessment. The IEEE registry was created to help fill these gaps.

“IEEE is the best organization to address this problem because this is fundamentally a standards, trust, interoperability, and conformity assessment challenge,” he says. IEEE “is the world’s largest technical professional organization, with deep expertise in developing globally recognized standards including in health care, cybersecurity, AI ethics, and interoperability.”

“Through the IEEE Conformity Assessment Program, we already run rigorous assessment and registry programs,” Palombini says. “Our neutral, consensus-driven, multidisciplinary approach—bringing together clinicians, regulators, developers, and ethicists without commercial bias—makes IEEE uniquely positioned to create trustworthy global guardrails that can scale across jurisdictions and support regulatory harmonization.”

How the registry works

The assessment framework was developed by a multidisciplinary group of 35 volunteer experts from 10 countries, Quintana says. The panel includes academics, AI experts, app developers, clinicians, ethicists, mental health experts, patient advocates, regulators, researchers, technologists, and those who assess safety in health care.

The registry is for any app used for clinical care or therapeutics that claims to demonstrate a medical benefit. That includes apps designed for cardiology, diabetes, mental health, neurology, oncology, rehabilitation, and respiratory diseases, Quintana says.

Initially, he says, the focus will be on apps that aim to treat mental health conditions, given the large number of offerings in that area and the registry committee’s expertise.

The submission of apps is voluntary. There is no government mandate that requires a company to use the IEEE registry.

The products will be evaluated against about 150 consensus-based criteria across three major areas:

• Clinical efficacy including therapeutic effectiveness, any sustained benefits, risk management, comparison to standard care, user engagement, and real clinical value.
• Technical soundness including accessibility, privacy and security, error handling, interoperability, AI governance, usability, and operational quality.
• Ethical design including bias prevention, patient consent, data governance, conflict-of-interest transparency, responsible use of AI and large language models, and prioritization of public health benefits.

IEEE charges a nonrefundable submission fee that covers the cost of the assessment plus the registry’s annual subscription for the first year.

Developers first must demonstrate they are a legally established entity before they can complete the app publisher registration form and then submit documentation and attestations about the product.

The IEEE review of an app is estimated to take six to eight weeks, Palombini says. The assessment results will be privately shared with the app publisher, she says, and to be listed in the registry, an app must achieve more than 85 percent compliance in each category.

Upgraded apps must be submitted and reassessed, Palombini says. Similar to how users are notified when an app on their smart devices has , the registry will be notified when listed apps have a new update available, she says.

Applicants who do not pass the assessment are to receive feedback explaining why. They will be given an opportunity to make changes or provide additional documentation, Palombini says.

“It’s a pretty methodological process, with checks and balances,” Quintana says. “We’re being very transparent about the process.”

Approved apps added to the registry receive an IEEE certification badge and submission identifier, which the company can display on its website, app store listings, and marketing materials.

“The badge serves as visible proof that the app has met the independent, consensus-based assessment for clinical value, technical robustness, and ethical design,” Quintana says.

The registry will be publicly available at no cost, he says.

Patients and families seeking safe, trustworthy apps—and payers and insurers evaluating reimbursement potential—will find the registry helpful, he says.

The application website is open. The public registry page does not yet list a specific count of approved apps because assessments are ongoing. Approved apps and their unique identifiers are to be published when the initial reviews are completed.

To learn more, you can watch a webinar recorded in March.

Lidar can be used to see objects hidden around corners. However, until now, such a feat required lab-grade devices. A new study reveals off-the-shelf smartphone-grade lidar, which costs less than US $100, can also help see around corners.

The advance may have a host of potential applications. “In autonomous driving, around-the-corner sensing could help self-driving cars detect other vehicles, cyclists, or pedestrians before they come into direct view, improving safety at blind intersections or obstructed roads,” says Siddharth Somasundaram, a doctoral student at MIT’s Media Lab. “In robotics, it could help robots navigate cluttered or partially hidden environments.”

More broadly, “we think the most important implication is the democratization of the technology,” Somasundaram says. “When technologies like this become accessible, people often discover applications far beyond what the original researchers imagined.” The scientists have publicly released the code required to perform such work.

How lidar sees around corners

Lidar is increasingly giving 3D scanning capabilities to autonomous vehicles, drones, robots, and smartphones. A lidar sensor uses light much as radar uses radio waves—it shines a laser onto a location and analyzes how long reflected pulses take to return in order to calculate distances and generate a 3D map of a place.

By analyzing laser pulses that bounce off reflective surfaces, lidar can see items obscured from their direct line of sight, such as something hidden behind a corner. However, the first examples of such non-line-of-sight imaging “relied on extremely specialized scientific equipment often costing [US] $0.5 million to $1 million,” Somasundaram says. “These systems were large, expensive, and confined to laboratory environments.”

Still, over the years, higher-grade sensors—such as single-photon detectors—began appearing in consumer hardware, Somasundaram says. “Once we started experimenting with these sensors, we realized that even off-the-shelf devices were actually capturing faint around-the-corner signals,” he explains.

But such findings did not necessarily mean these sensors could actually prove useful in non-line-of-sight imaging. Consumer lidar typically captures images that are full of noise, due in part to the low-power lasers it uses because of eye safety concerns. In addition, the consumer nature of these devices meant the sensors are often relatively low resolution. Moreover, movements of the camera and target objects could result in blurry pictures.

“There were definitely moments where we weren’t sure whether meaningful imaging would even be possible,” Somasundaram says.

To overcome these challenges, instead of attempting non-line-of-sight imaging based on the data within individual pictures, the researchers analyze multiple images at once. They note they were inspired by how smartphone cameras often take bursts of photos in quick succession to improve their merged quality and how synthetic aperture radar in satellite imaging mixes signals from multiple antennas to capture images with the quality of a single large antenna.

“Once we developed algorithms that could combine information across those measurements, the hidden signals started to emerge much more clearly,” Somasundaram says.

The researchers demonstrate their lidar system in action. MIT Media Lab/YouTube

Improving consumer lidar signals

The scientists experimented with a portable smartphone lidar system that has about 100 pixels, each consisting of a laser emitter combined with a single-photon detector. They were able to use it to reconstruct 3D images of static hidden objects and track 3D motions of hidden items of known shape. In addition, they could also use hidden objects as landmarks to pinpoint the location of the lidar sensor, a capability that could help robots stay oriented in textureless spaces in which they often struggle to find their bearings. All this was done without specialized calibration.

“What I found most surprising is that these consumer lidar systems are able to capture any useful signal from around a corner at all,” Somasundaram says. “The amount of light that reaches the sensor after multiple bounces is incredibly small, and these devices were never designed with this kind of imaging in mind. Seeing that there is enough information present to reconstruct and track hidden objects was very exciting for us.”

However, Somasundaram cautions against imagining this system “as producing full photographic images of hidden scenes. Right now, the system is recovering sparse geometric and motion information from extremely weak signals.” He notes “there is still a large gap between that and the kind of detailed imagery people may associate with megapixel images from their phones.”

The new system does assume the shapes and motions of objects stay relatively consistent from frame to frame, “which allows us to combine many extremely weak measurements into a stronger signal,” Somasundaram says. However, he adds, “there are situations where those assumptions could break down.” For instance, humans may change how they are positioned. Objects may also become obscured, and so their shapes and motions could appear to alter. And the lidar sensor itself could move abruptly, he notes.

“An important direction for future work is reducing how much the system depends on these assumptions,” Somasundaram says. Better physical models, signal processing, and machine learning methods “could help the system adapt to more complex motion and scene dynamics,” he says. ”Over time, we hope these approaches will make around-the-corner sensing more robust in realistic, unconstrained environments.”

Another future direction lies in tinkering with the lidar hardware. “Right now, consumer lidar systems are primarily designed for conventional line-of-sight imaging and depth sensing,” Somasundaram says. “What would happen if future lidar hardware were designed with both visible and hidden scene understanding in mind? Improved sensor sensitivity, resolution, scanning strategies, or optical designs could dramatically improve non-line-of-sight performance as well.”

The scientists detailed their findings online 20 May in the journal Nature.

Electronic rings wirelessly connected to an AI system are capable of translating multiple sign languages into text, a new study finds.

“I believe this is an important step toward making sign language translation systems more practical, lightweight, and usable in real-world environments,” says Ki Jun Yu, an associate professor of electrical and electronic engineering at Yonsei University in Seoul, Korea.

More than 300 different sign languages are used worldwide, and many research projects are developing translation devices for communicating with people who do not know a sign language. However, these projects have faced many setbacks.

For example, some projects used cameras and computer vision algorithms to recognize hand gestures. But these were typically limited to controlled settings with fixed cameras and were sensitive to lighting variations and other forms of interference.

Other devices relied on wearable sensors that detected either hand motions or electrical signals linked with muscle activity. However, a common kind of wearable sensor, smart gloves, trapped heat and moisture, making prolonged use uncomfortable. And their fixed sensors failed to account for individual variations in hand size, finger length, and joint positions, reducing their accuracy. In addition, wearable sensors often required hooking up to computers using wires, hampering hand movements. Although some wearable sensors ultimately transmitted their data wirelessly to an external processor, these still typically connected to the same single transmitter using wires.

Better living through wireless

Now, scientists have developed a set of electronic rings that each transmit their motion wirelessly to a processing device. Using rings instead of gloves permitted flexible positioning of sensors to help account for variations in people’s hands. The wireless connections allow unrestricted hand motions.

“Bluetooth Low Energy SoCs [systems-on-chips] have reached a point where an entire wireless communication stack, power management circuit, and sensing module can fit on a flexible substrate small enough to wear as a ring,” Yu says.

In the new study, the researchers examined how much each finger contributed to hand signs, discovering that seven fingers played major roles. As such, their system only employed seven rings to reduce the amount of hardware needed.

Each ring used accelerometers as inertial sensors. These could detect both stationary postures and hand movements to help capture the full complexity of sign languages, which often involve transitions between static and dynamic components. In addition, the scientists wanted to avoid relying on bioelectric signals, which are highly specific to each person and require extensive calibration for each user.

One challenge in developing these rings was mechanical reliability. Initially, the scientists used straight copper interconnects, which nearly broke under repeated bending. They switched to interconnects with serpentine patterns that withstand repeated flexing.

One AI to unite the rings

The researchers also developed a deep-learning system to recognize signs from hand movements. It could identify signs not just from the two people who were used to train the system, but also from five people who did not take part in the training phase. This suggests the new system could prove of general use without requiring laborious adaptation for each user.

In experiments with the five people who did not help train the system, the new system could recognize 100 common American Sign Language and 100 common International Sign Language words with 88.3 and 88.5 percent accuracy, respectively. In contrast, most previous attempts at sign language translation systems were limited to vocabularies of fewer than 50 words.

“Two hundred words is a meaningful advance over prior wireless systems, but it is still a small fraction of a full sign language lexicon, which can contain thousands of signs,” cautions Dosik Hwang, a professor of electrical and electronic engineering at Yonsei University. “I want to be careful not to overstate what the current system can do in open-vocabulary, real-world conversation.”

The new system was not just capable of recognizing isolated words, but of translating entire sentences from continuous signing. The scientists suggest this could help support real-time interpretation.

In the long term, “our goal is to make the system work with everyday devices such as smartphones without requiring specialized external equipment,” Yu says. “The rings could wirelessly transmit sign language signals to a mobile device, where they would be automatically translated and displayed in real time. This would make the technology more portable, accessible, and practical for daily communication.”

However, “the most important caution is this—our system translates hand motion into text,” Hwang says. “It does not yet capture facial grammar, mouthing, body posture, or spatial syntax, all of which are grammatically meaningful in sign languages.” A future challenge lies in incorporating those “into a seamless, low-power architecture that maintains the unobtrusive nature of our current design,” Yu adds.

The scientists next aim to train the system with more people, larger vocabularies, and more signing styles and regional dialects, Yu says. “Given our institutional roots, Korean Sign Language is a natural next step,” he adds.

The researchers also hope to make their rings wearable all day, up from nearly 12 hours, through further miniaturization and power optimization, Yu says. “A key priority is migrating the processing pipeline from external hardware [like a laptop] to on-device edge computing [like a mobile phone]. This transition is essential not only for true mobility but also for ensuring user privacy and reducing latency in natural conversation.”

Hwang and colleagues plan to partner with deaf community organizations to develop their devices. “We believe the technology will be significantly improved both in its functional performance and its social integration by including those who will actually use it,” he says.

Beyond sign language translation, these new rings might find use in other gesture-driven applications, Hwang says. “We see immediate potential for this technology in hand rehabilitation monitoring, fine-motor assessment for neurological conditions, and even immersive virtual reality and augmented reality interfaces,” he explains. “By proving its efficacy in the complex domain of sign language, we have essentially stress tested the system for a wide array of future biomedical and interactive applications.”

The scientists detailed their findings on 1 May in the journal Science Advances.

Whenever you read about Steve Jobs, odds are the words “Apple CEO” follow closely behind. The mythic cofounder of one of today’s biggest tech companies is strongly associated with the role, but his tenure as CEO was shorter than many of us realize.

In fact, when Jobs was exiled from Apple in the 1980s and ’90s, he spent almost as much time leading another computer company that has largely been forgotten: NeXT Computer. In his forthcoming book Steve Jobs in Exile, journalist and author Geoffrey Cain tells the story of Jobs’s years at NeXT Computer from 1985 to 1997.

It’s a story worth remembering, with Apple, now 50 years old, in the midst of another CEO transition. John Ternus, previously the senior vice president of hardware, is set to take over the reins from Tim Cook this September. Looking back at Jobs’s NeXT years reveals what he had to learn for Apple to succeed.

Cain spoke with IEEE Spectrum about his book, Jobs’s legacy, and Apple today. Steve Jobs in Exile comes out 19 May 2026.

Why did you choose to focus on this period of Jobs’s life?

The story we always hear about Steve Jobs is that he founded Apple version 1.0, and he was brilliant there. And then he was fired, and he went into the wilderness for a short while. He returned and created Apple version 2.0, which was the iPod, the iPhone, and the iPad, turning Apple into the $4 trillion company that it is today. That’s the popular narrative we hear.

But that’s not an accurate story. That’s the legend, but it’s not what actually happened.

I was a tech writer for a long time, and I had been diving into the history of these major tech figures. In my reporting, I went around the world, and everywhere I went, people were talking about Steve Jobs. It was sort of like, “let’s study the life of Steve Jobs and try to extract school lessons from it.” That’s what a lot of young entrepreneurs and engineers were telling me.

I quickly realized from interviewing former colleagues of Steve Jobs that only half the story was being told, and it glossed over these entire middle 12 years in the legacy of Steve Jobs. He died when he was [56] years old. That’s a third of his adult life, and he spent it at this company called NeXT Computer.

[NeXT] was the source of major advancements in software, especially, but also in hardware, and it had been completely forgotten by history. Not only was this a significant company historically, but today it’s the foundation for all the operating systems that Apple has developed since then. When you look at an Apple device today, in a way you’re looking at NeXT Computer.

NeXT and Apple were both startups that were spearheaded by Jobs. Why were their fates so different?

Their fates were different, but their fates merged. So why were their fates different? Well, Apple version one was a failure in many ways, and we tend to forget that the Macintosh itself was not a commercial success. And so Apple version one, not a success, and NeXT Computer, not a success at first. The reason is, in part, because Steve Jobs was not ready.

Today we mythologize him as the great visionary of our era, the king of entrepreneurs. But he was immature, egotistical, and brash. He made a lot of decisions at both Apple and NeXT that damaged the company and the careers of people who were working for him.

Yes, Steve Jobs was always talented. He was a visionary, but he had to learn the craft of discipline. He had to learn to apply his talents to the limits of the markets, what people were willing to buy.

But to go back to your question, why did NeXT fail? Steve Jobs was making bad decisions, and the hardware division shut down. They laid off more than half of the company’s employees. Why did Apple succeed? In the end, it’s because Steve Jobs was more circumspect and mature by the time he returned. He was able to bring these people together and build the Apple Renaissance, starting with the iPod, or maybe the iMac if you want to go back further. With those products in the late ’90s and early 2000s, that’s where he made his mark, and that’s why we remember him today.

Jobs’s Vision: Perfect Integration of Hardware and Software

You make the point in the book that software engineers were Jobs’s “favored inner circle.” But Apple is well known for building hardware and software together. How did his priorities affect the company’s trajectory?

Steve Jobs’s vision for Apple was always the perfect integration of hardware and software. From the beginning, he saw the value in controlling the whole experience, end-to-end, for the user. But the question that not many people have asked is: How did he actually build that? A lot of startup founders want to create a seamless ecosystem, but you can only achieve that vision if you have a lot of scale and leverage over the market. That’s what he had to learn during his NeXT Computer years.

His customers for NeXT Computer were universities, laboratories, intelligence agencies, people who were buying high-end computers that could do the most advanced data analysis and software applications of the time. It took him a long time to realize that his customers actually wanted the software, which was built on a real revolution at the time called “object oriented programming.”

Computers in the 1980s were really difficult to program. He discovered at NeXT Computer that he could build beautiful software using what are called “objects”—items that, essentially, are pre-programmed in a library. This is how apps are made today, and Steve Jobs was doing this in 1988. As a result, the first ever app store appeared on a NeXT computer.

Steve Jobs in Exile: The Untold Story of NeXT and the Remaking of an American Visionary is out 19 May. Portfolio/Penguin Random House

This is fairly common usage today, but at the time it was comparable to the transformation happening now, from the app stores of the past era to AI agents and generative AI. He was on the cusp of a transformation, and he had to realize that that transformation was what mattered.

Software was his priority, and it became an even greater priority over time. But there’s a lesson here, specifically for engineers and scientists, that sometimes it’s not clear what the significance is of something you’re working on right now. Often there are elements within a project that are actually the real gold mine, and we just don’t see it yet.

That’s what makes me really interested in the big transformations now happening with AI. I often wonder, if Steve Jobs were still alive, what he would be doing now. The moment that we live in now would really be a Steve Jobs moment.

Jobs also purchased Pixar during this period. How does that compare to his experience with NeXT?

The story of Pixar is different from NeXT. Pixar was struggling. It used to make something called the image computer, and it cost more than [US] $100,000. They were using it in sci-fi movies back in the ’80s, but Hollywood studios weren’t really adopting it. And in the end, some of its main customers were the CIA and the National [Security] Agency.

Similar to NeXT computer, Steve bet wrong on hardware once again at the beginning. And so he had to close the hardware division at Pixar, and he focused on making software called RenderMan. RenderMan was their main product, and it was used to make Toy Story and the other great films of Pixar.

The difference from NeXT, though, is that Pixar did become a success, even though the hardware division closed. [Pixar cofounder] Ed Catmull and [founding employee, later chief creative officer] John Lasseter told Steve, when he bought it from George Lucas, that he had to stay out of creative meetings. That was the deal, and Steve respected it. They were able to make Toy Story, and it was just a [huge] success. It was the IPO that made Steve Jobs a billionaire.

Pixar was the company that he became more focused on in the later years of this wilderness. He focused on the dealmaking, the relationship with Disney—the side of being a business executive. That’s where he excelled, and that’s where he was able to make Pixar shine.

What Does New Apple CEO Ternus Need to Do?

John Ternus will be taking over as Apple CEO in September. What was your reaction to the announcement?

There are people out there who are saying that John Ternus needs to invent the new iPhone. I understand where they’re coming from, but I just don’t agree with the conclusions.

You have to remember that the masterpiece age of Apple was only about seven years. It lasted from 2001 to 2008 when they released their main products. The historical conditions and the leadership team that were able to create those massive successes just doesn’t exist anymore.

Apple has matured into a $4 trillion corporation. Tim Cook was a supply chain expert and built it into the giant it is. And really, what John Ternus has to do now is maintain the success. He’s not here to build the one blowout product. Apple is a company that’s become so successful that their products are integrated into everyday life. So that’s a success that John Ternus is, I think, being appointed to maintain, and it’s not by any means a Steve Jobs story.

What do you think it will mean for Apple to be run by someone who’s coming from the hardware engineering side of things?

Apple has admitted that it’s behind on AI. They signed a big partnership with Google, and now they’re reworking the entire foundation of Siri to be on top of Google AI, which is a big shift in software, because Apple’s always been tightly controlling things in-house.

Geoffrey Cain is a bestselling author of books on technology and business, including Samsung Rising and The Perfect Police State. Beowulf Sheehan

So I actually see their future more like a hardware company. I think they’re still going to make great [software]. It’s just not going to be the cutting-edge anymore. That ground is moving over towards OpenAI and Google and software-oriented companies. Apple’s strength is absolutely hardware, and they’re going to continue to focus on that, because that’s what they do best.

In the next few years, AI is going to very quickly get integrated into everyday life. I think that, especially at Apple, they’re going to release AI, but it’s not going to be in your face. It’s more like the AI is running in the background, and it’s doing what it needs to do. If Apple can master that, I think that’s going to help them a lot. If they can release another iPhone that’s really well built for AI, but you don’t see the AI, people are going to buy Apple.

Anything else you’d like to say about Apple or your book?

The history that I was writing about was the transition from hardware to software that happened in the 1990s, and I think the story is very relevant, in a way. We’re now entering the next big transition, and we’re entering a wilderness ourselves. And so this is the kind of story that we can go back to, and we can see what Steve Jobs and his people were doing at the time to try to understand the times that we live in now.

Batteries are essential to modern life. They store energy collected by solar plants, power our smartphones, and lurk even in vehicles powered by fossil fuels. They’re so useful that we overlook their problems. Some batteries use potentially dangerous metals. Others have a pesky tendency to catch fire and explode when damaged.

Many companies have tried to design a battery that addresses these issues. Flint, a startup based in Singapore, is a new contender with a “paper battery.” I had the chance to pick it up and handle it for myself in January at CES, the annual consumer-electronics trade show. The battery, which in part uses cellulose, is lightweight and flexible. It can be a flat sheet that looks much like a thick piece of paper, or like a standard household AA or AAA battery. It doesn’t catch fire or leak if damaged and, contrary to what the term “paper” might lead you to think, it can be held up to a flame without igniting. It also claims to use fewer environmentally damaging metals than other batteries.

Carlo Charles, founder of Flint, says the company is rooted in the last two points. “When we started, it was a question of how we make batteries safer and more sustainable,” he says. “It wasn’t meant to be a company. It’s more of a passion project that snowballed into where we are today.”

Flint’s batteries started production in January 2026, and the company has partnered with Logitech to pilot its batteries in some devices.

What’s in a paper battery?

Flint isn’t the first company to produce a “paper battery.” IEEE Spectrum has covered several earlier attempts, including an “origami battery” powered by the respiration of microbes and a biofuel-based paper fuel cell from French startup BeFC.

However, Flint has less in common with these prior attempts than you might assume at a glance.

These prior examples were enzymatic fuel cells that convert an organic fuel source into energy. They used paper—or, to be precise, cellulose—as part of the fuel cell’s structure and mechanical design. Though they could be used like a battery, they weren’t batteries in a technical sense because they generated electricity from a fuel.

The raw materials and components seen here come together to form Flint’s battery cell, which sits in the center. Flint Labs

Flint’s technology is a true battery. Zinc is used as the anode and manganese dioxide is used as the cathode. This is a tried-and-true chemistry used by the inexpensive alkaline batteries that power hundreds of common household devices, from TV remotes to digital clocks.

How, then, does Flint’s battery use paper?

“We integrate cellulose across every battery component as much as we can,” says Charles, adding that Flint batteries can use cellulose as part of the casing, the cathode, the electrolyte, and the separator. “We remove PFAS from our chemistry, we remove cobalt, [and] we remove the solvent,” says Charles.

Charles was inspired by a paper from researchers at Singapore’s Nanyang Technological University that explored a “hydrogel reinforced cellulose paper” battery design. Charles connected with one of the researchers, Hong Jin Fan, who provided advice in Flint’s early days.

The use of cellulose in the battery is key to the company’s claim that it’s more environmentally friendly. Cellulose replaces metals and chemicals that are either dangerous to humans, produced through environmentally damaging processes, or both. Cobalt, for example, can cause cardiomyopathy, and cobalt mining can be extremely damaging to the local environment.

Flint batteries also lack corrosive materials found in alkaline batteries and the combustible materials in lithium-ion batteries. This improves their safety and physical flexibility. While not all Flint batteries are designed to be flexible, the battery can be produced in a thin format that I could bend between my thumb and index finger.

Flint claims high battery voltages and up to 1,000 charge cycles

The chemistry in Flint’s battery is similar to that of alkaline batteries, but the company hopes to rival lithium-ion in some applications, as well.

That’s a significant challenge. Lithium-ion is useful in part because it can deliver much higher voltages than an alkaline battery. An alkaline battery provides up to 1.5 volts, while lithium-ion can manage up to 4.2 volts.

Charles says Flint’s batteries can reach that mark. “Our batteries deliver high power, meaning up to 4.2 volts, and an energy density of 226 watt-hours per kilogram,” he says. The battery is rechargeable and can endure up to 1,000 charge cycles, also roughly comparable to lithium-ion, which can last 300 to 2,000 or more cycles, depending on the chemistry.

Flint’s manufacturing setup, which will process battery material at scale, uses a roll-to-roll process similar to that of a printing press. Flint Labs

Flint has not published a white paper on its design and is currently keeping the details of its battery design under wraps. A company spokesperson said Flint’s technology includes “proprietary tweaks across the cathode, electrolyte, anode, and separator.”

Though Flint makes big claims about its battery, Charles acknowledged that lithium-ion will be tough to rival and said the company isn’t ready to challenge lithium-ion in applications that push the limits on power density.

“We do not want to focus on electric vehicles, because we know they require even more life cycles and even higher power densities,” he says. Instead, Flint has its sights on lower-power devices commonly found in households, such as sensors, remotes, portable health care devices, and even smartphones. “I want to replace everything [with a battery] I can see around me in my house or workplace with a Flint battery.”

2026 is a crucial test for that vision.

The company is speaking with potential customers, such as Logitech, which sells peripherals for PCs and smartphones. Flint won Logitech’s 2025 Future Positive Challenge and has partnered with Logitech to pilot use of Flint AAA batteries.

If successful, this partnership would help Flint demonstrate that its technology can deliver on its goal to reduce the impact batteries have on the environment. “[Logitech ships] many millions of batteries each year in their products. Even reducing a few grams of carbon emissions per product can be a big impact for them,” says Charles.

Two weeks ago, Anthropic announced that its new model, Claude Mythos Preview, can autonomously find and weaponize software vulnerabilities, turning them into working exploits without expert guidance. These were vulnerabilities in key software like operating systems and internet infrastructure that thousands of software developers working on those systems failed to find. This capability will have major security implications, compromising the devices and services we use every day. As a result, Anthropic is not releasing the model to the general public, but instead to a limited number of companies.

The news rocked the internet security community. There were few details in Anthropic’s announcement, angering many observers. Some speculate that Anthropic doesn’t have the GPUs to run the thing, and that cybersecurity was the excuse to limit its release. Others argue Anthropic is holding to its AI safety mission. There’s hype and counterhype, reality and marketing. It’s a lot to sort out, even if you’re an expert.

We see Mythos as a real but incremental step, one in a long line of incremental steps. But even incremental steps can be important when we look at the big picture.

How AI Is Changing Cybersecurity

We’ve written about shifting baseline syndrome, a phenomenon that leads people—the public and experts alike—to discount massive long-term changes that are hidden in incremental steps. It has happened with online privacy, and it’s happening with AI. Even if the vulnerabilities found by Mythos could have been found using AI models from last month or last year, they couldn’t have been found by AI models from five years ago.

The Mythos announcement reminds us that AI has come a long way in just a few years: The baseline really has shifted. Finding vulnerabilities in source code is the type of task that today’s large language models excel at. Regardless of whether it happened last year or will happen next year, it’s been clear for a while this kind of capability was coming soon. The question is how we adapt to it.

We don’t believe that an AI that can hack autonomously will create permanent asymmetry between offense and defense; it’s likely to be more nuanced than that. Some vulnerabilities can be found, verified, and patched automatically. Some vulnerabilities will be hard to find but easy to verify and patch—consider generic cloud-hosted web applications built on standard software stacks, where updates can be deployed quickly. Still others will be easy to find (even without powerful AI) and relatively easy to verify, but harder or impossible to patch, such as IoT appliances and industrial equipment that are rarely updated or can’t be easily modified.

Then there are systems whose vulnerabilities will be easy to find in code but difficult to verify in practice. For example, complex distributed systems and cloud platforms can be composed of thousands of interacting services running in parallel, making it difficult to distinguish real vulnerabilities from false positives and to reliably reproduce them.

So we must separate the patchable from the unpatchable, and the easy to verify from the hard to verify. This taxonomy also provides us guidance for how to protect such systems in an era of powerful AI vulnerability-finding tools.

Unpatchable or hard to verify systems should be protected by wrapping them in more restrictive, tightly controlled layers. You want your fridge or thermostat or industrial control system behind a restrictive and constantly updated firewall, not freely talking to the internet.

Distributed systems that are fundamentally interconnected should be traceable and should follow the principle of least privilege, where each component has only the access it needs. These are bog-standard security ideas that we might have been tempted to throw out in the era of AI, but they’re still as relevant as ever.

Rethinking Software Security Practices

This also raises the salience of best practices in software engineering. Automated, thorough, and continuous testing was always important. Now we can take this practice a step further and use defensive AI agents to test exploits against a real stack, over and over, until the false positives have been weeded out and the real vulnerabilities and fixes are confirmed. This kind of VulnOps is likely to become a standard part of the development process.

Documentation becomes more valuable, as it can guide an AI agent on a bug-finding mission just as it does developers. And following standard practices and using standard tools and libraries allows AI and engineers alike to recognize patterns more effectively, even in a world of individual and ephemeral instant software—code that can be generated and deployed on demand.

Will this favor offense or defense? The defense eventually, probably, especially in systems that are easy to patch and verify. Fortunately, that includes our phones, web browsers, and major internet services. But today’s cars, electrical transformers, fridges, and lampposts are connected to the internet. Legacy banking and airline systems are networked.

Smartphone cameras and some smart glasses allow users to query AI models and receive answers about what they’re looking at. Soon, that capability could expand to other devices, including earbuds.

Researchers at the University of Washington have developed a pair of earbuds they call VueBuds that integrate a small, low-resolution camera into each earbud. The prototype earbuds have features similar to those of smart glasses, like the Ray-Ban Meta glasses—things like translating signs in foreign languages, acting as an aid for low-vision wearers, or identifying plant species during a hike.

Smart glasses have their drawbacks, including privacy concerns and comfort. The under-the-radar cameras have faced criticism and lawsuits over concerns they can record unsuspecting bystanders and what ultimately happens to sensitive visual data.

And not everyone likes wearing glasses—some even opt for contact lenses to avoid having to wear them, including Shyam Gollakota, the University of Washington professor who led the VueBuds research. “The one predominant wearable which almost everyone wears is your earbuds,” he says. His team presents earbuds as an alternative to smart glasses that’s less intrusive and better for privacy.

The primary goal of the research, however, was to demonstrate that this small, ear-worn form factor is even possible. “Traditionally, earbuds have been limited to audio interfaces,” Gollakota says. “We show that we can indeed build a system within that form factor and get lots of intelligence by running visual language models.”

The research was presented today at the ACM Computer-Human Interaction conference in Barcelona.

Why Earbuds Are an Ideal Smart Device

Gollakota and his colleagues don’t expect VueBuds to be the only interface for visual AI.

“Wearables are very personal,” says Maruchi Kim, a Ph.D. student in Gollakota’s lab. Some people may prefer glasses or watches, others might like rings, and so Kim suspects there won’t be one device to rule them all. “We’re just trying to introduce another category to demonstrate that everything smart glasses do can be achieved on [earbuds].”

That said, the interface may have some advantages. Because they’re already widely used, people may be more likely to adopt the technology. Plus, Kim says, “there’s already a social paradigm for putting your earbuds away in their case.” Smart glasses may have prescription lenses, so the wearer would keep them on all the time. But “if you ever want to be confident that these cameras aren’t recording, earbuds are a nice form factor that lets you just tuck it away when you’re ready.”

Many of the AI features users indicate an interest in are also “episodic use cases,” Kim says. To translate a street sign or ingredients on a package, for instance, you don’t need a continuous video stream.

Key Challenges for Earbuds With Cameras

There are three key challenges to making vision-capable earbuds possible, Gollakota says: Fitting the camera within strict size, power, and weight constraints; transmitting the data; and creating a complete visual scene when worn in the ears.

Cameras typically take a lot of power, making this the number-one concern. “The batteries in your earbuds are about 10 times as small as what you have on smart glasses,” Kim says. Visual data also requires much higher bandwidth than audio, so the videos recorded by glasses are typically sent via Wi-Fi to be processed by cloud-based AI models. Wi-Fi allows for high bandwidth—but takes more power.

VueBuds transmit low-resolution, grayscale images over Bluetooth. Most device makers try to transmit as much data as possible, but Gollakota’s team took a different approach. They wanted to see what the lowest resolution a visual language model would need to extract useful information, opting for a 324-by-324-pixel image sensor.

Integrated black-and-white cameras and a Bluetooth connection to a phone-based visual AI model make these earbuds an alternative to smart glasses.

Beyond the power and bandwidth concerns, the researchers also had to make sure earbud cameras could see enough. Placing cameras at the ears creates a blind spot on either side where the face blocks each camera’s view. But by setting the cameras at a slight angle (5 or 10 degrees) away from the face and stitching together images, the team found they could reconstruct a more complete scene with a wide field of view. This does, however, create a small blind spot for objects closer than about 20 centimeters from the face directly in front of the user.

The researchers tested the earbuds with four different visual language models. In user studies with the best-performing model (Qwen2.5-VL), VueBuds achieved about 82 percent accuracy for object recognition, 94 percent for character recognition, 84 percent for translation, and 87 percent overall accuracy in user studies. The earbuds performed comparably to Ray-Ban Meta glasses across 17 tasks.

In the future, the team hopes to add color to the system. Kim is also looking into improving the resolution possible by incorporating an on-device JPEG encoder, which would significantly reduce the size of images sent to be processed.

Privacy Concerns for Smart Earbuds

Many users have been wary of privacy and surveillance concerns with smart glasses. Those worries are intensifying with new evidence that the companies building these glasses may be mishandling the data they capture.

Given those concerns, should we add cameras to yet another wearable device? The University of Washington researchers say VueBuds’ stripped-down image capture is a boon for privacy compared to today’s smart glasses.

For one thing, the system is designed to run on a smartphone or other local device, so data never goes to the cloud, Gollakota says. VueBuds also only captures still images. One of the main uses of Meta’s smart glasses is now recording video, but, he adds, “no one wants to see a low-resolution grayscale video in the first place.”

Additionally, VueBuds are activated by voice commands. “That audio initiation means that everyone around you would know what you’re actually asking.” Smart glasses, meanwhile, can start recording with the touch of a button.

Gollakota notes that most people have become accustomed to having microphones in nearly every device, because they provide enough utility through capabilities like voice commands and “a trust has been built” with companies, like Apple, that sell devices with built-in microphones. Whether the same paradigm will emerge for visual intelligence remains to be seen with how the technology—and our level of trust in it—evolves over the next few years.

Apple is also rumored to be developing next-generation AirPods that integrate infrared cameras to enable gesture recognition and improve spatial audio. These wouldn’t have the visual intelligence capabilities made possible with standard cameras, but it would indicate more interest in expanding the capabilities of what has traditionally been an audio-only interface.

Earbuds are “the most successful wearable we have today, and right now it’s limited to being an audio interface,” Gollakota says. “Bringing visual intelligence would make it a much richer and more powerful interface than what it currently is.”

By many estimates, quantum computers will need millions of qubits to realize their potential applications in cybersecurity, drug development, and other industries. The problem is, anyone who has wanted to simultaneously control millions of a certain kind of qubit has run into the problem of trying to control millions of laser beams.

That’s exactly the challenge that was faced by scientists working on the MITRE Quantum Moonshot project, which brought together scientists from MITRE, MIT, the University of Colorado at Boulder, and Sandia National Laboratories. The solution they developed came in the form of an image projection technology that they realized could also be the fix for a host of other challenges in augmented reality, biomedical imaging, and elsewhere. The device is a 1-square-millimeter photonic chip capable of projecting the Mona Lisa onto an area smaller than the size of two human egg cells.

“When we started, we certainly never would have anticipated that we would be making a technology that might revolutionize imaging,” says Matt Eichenfield, one of the leaders of the Quantum Moonshot project, a collaborative research effort focused on developing a scalable, diamond-based quantum computer, and a professor of quantum engineering at the University of Colorado at Boulder. Each second, their chip is capable of projecting 68.6 million individual spots of light—called scannable pixels—to differentiate them from physical pixels. That’s more than 50 times the capability of previous technology, such as micro-electromechanical systems (MEMS) micromirror arrays.

“We have now made a scannable pixel that is at the absolute limit of what diffraction allows,” says Henry Wen, a visiting researcher at MIT and a photonics engineer at QuEra Computing.

The chip’s distinguishing feature is an array of tiny microscale cantilevers, which curve away from the plane of the chip in response to voltage and act as miniature “ski jumps” for light. Light is channeled along the length of each cantilever via a waveguide and exits at its tip. The cantilevers contain a thin layer of aluminum nitride, a piezoelectric that expands or contracts under voltage, thus moving the micromachine up and down and enabling the array to scan beams of light over a two-dimensional area.

Despite the magnitude of the team’s achievement, Eichenfield says that the process of engineering the cantilevers was “pretty smooth.” Each cantilever is composed of a stack of several submicrometer layers of material and curls approximately 90 degrees out of the plane at rest. To achieve such a high curvature, the team took advantage of differences in the contraction and expansion of individual layers caused by physical stresses in the material resulting from the fabrication process. The materials are first deposited flat onto the chip. Then, a layer in the chip below the cantilever is removed, allowing the material stresses to take effect, releasing the cantilever from the chip and allowing it to curl out. The top layer of each cantilever also features a series of silicon dioxide bars running perpendicular to the waveguide, which keep the cantilever from curling along its width while also improving its lengthwise curvature.

A micro-cantilever wiggles and waggles to project light in the right place.Matt Saha, Y. Henry Wen, et al.

What was more of a challenge than engineering the chip itself was figuring out the details of actually making the chip project images and videos. Working out the process of synchronizing and timing the cantilevers’ motion and light beams to generate the right colors at the right time was a substantial effort, according to Andy Greenspon, a researcher at MITRE who also worked on the project. Now, the team has successfully projected a variety of videos from a single cantilever, including clips from the movie A Charlie Brown Christmas.

The chip projected a roughly 125-micrometer image of the Mona Lisa.Matt Saha, Y. Henry Wen, et al.

Because the chip can project so many more spots in any given time interval than any previous beam scanners, it could also be used to control many more qubits in quantum computers. The Quantum Moonshot program’s mission is to build a quantum computer that can be scaled to millions of qubits. So clearly, it needs a scalable way of controlling each one, explains Wen. Instead of using one laser per qubit, the team realized that not every qubit needed to be controlled at every given moment. The chip’s ability to move light beams over a two-dimensional area would allow them to control all of the qubits with many fewer lasers.

Another process that Wen thinks the chip could improve is scanning objects for 3D printing. Today, that typically involves using a single laser to scan over the entire surface of an object. The new chip, however, could potentially employ thousands of laser beams. “I think now you can take a process that would have taken hours and maybe bring it down to minutes,” says Wen.

Wen is also excited to explore the potential of different cantilever shapes. By changing the orientations of the bars perpendicular to the waveguide, the team has been able to make the cantilevers curl into helixes. Wen says that such unusual shapes could be useful in making a lab-on-a-chip for cell biology or drug development. “A lot of this stuff is imaging, scanning a laser across something, either to image it or to stimulate some response. And so we could have one of these ski jumps curl not just up, but actually curl back around, and then move around and scan over a sample,” Wen explains. “If you can imagine a structure that will be useful for you, we should try it.”

This month, Apple celebrates its 50th anniversary, having survived five decades of ups and downs. Whether you love or hate the company, its products have had a massive influence on the consumer technology market and the world.

Among the obvious successes, such as the iPhone and the MacBook, there are numerous dead-end failures, such as the butterfly keyboard and the G4 Cube. But its history is also littered with forgotten legacies—some successes, some failures—that still influence computing to this day.

IEEE Spectrum spoke with John Buck, author of Inventing the Future: Bit by Bit, to learn more about some of those forgotten legacies pioneered by Apple’s Advanced Technology Group (ATG) in the 1980s and 1990s.

LaserWriter and desktop publishing

In the mid 1980s, as Buck says, Apple needed a commercial success:

While Steve Jobs had recognized the potential of linking a printer product with Adobe’s popular PostScript even before the release of the Mac, the LaserWriter ended up one of the first post–Steve Jobs products, where the company learned to understand what the market wanted instead of just what Jobs wanted. And even though the first model was expensive, it was cheaper than the alternatives.

Jim Gable, who was the product manager on LaserWriter, had no background in printing. The ATG was full of smart generalists who got dragged in on these projects.

Apple discontinued the LaserWriter line when Steve Jobs returned to Apple, but it set the groundwork for desktop publishing. To cope with the processing demands of running Adobe’s PostScript for font rendering, the LaserWriter had its own Motorola 68000-series processor.

While the PostScript partnership was initially lucrative, licensing fees eventually became a burden for Apple. From Buck:

With the advent of more affordable printing came the need for legible fonts at different sizes, from small screens to giant billboards. Initially, for the LaserWriter, Apple licensed PostScript, which Adobe created in 1982.

Even though Adobe helped Apple create the desktop publishing revolution and bring them much-needed business, they thought, we’re paying them too much money for fonts, let’s build our own thing, which was a very ATG thing to do. They brought in all the experts and said, “Right, we’re just going to do it better.”

And they built it, then went behind everyone’s back and spoke to Bill Gates and said, “Right, you don’t like them [Adobe] either. It’s costing you money.”

The result was the TrueType fonts in 1989, which are now widely used on macOS and Windows, while high-end printers still use PostScript. Its last release was in 1997, and Adobe’s alternative Type 1 fonts are largely forgotten.

Apple’s Newton personal digital assistants (PDAs) ultimately failed but led to a successful partnership with Arm that culminated in the iPhone decades later.Geoff Parsons

Newton, Arm chips, and the iPhone

While Acorn computers won’t mean much to people outside the United Kingdom, the company spun off part of their business to create the much more famous Arm, thanks to a joint venture with VLSI Technology and Apple in 1990.

Buck explains how this relationship began:

When Apple built the first Newton, they commissioned AT&T to create these Hobbit chips, and they were too slow, too hot, and couldn’t deliver the power. And they discovered Arm. A little two-man team with the project name ‘Möbius’ went to the UK to try their chips and found them ideal. Even though Newton was a disaster because of too many egos who wanted to add this and that, the fact that they’ve found Arm to put in the Newton becomes the success story of the iPhone. It’s a research arc that takes forever to come to market. And some of the mistakes that they stumble over to get there.

QuickTime and digital video formats

One name from the ATG era you’ll still find on a modern Mac is QuickTime. While it’s not the essential powerhouse it once was, in Buck’s opinion, it’s one of Apple’s biggest legacies:

To me, the legacy of QuickTime and the team behind it are one of the most enduring of the ATG. At the time of its release, playing video on a computer was incredible, and in the 90s and early 00s, QuickTime defined many video format standards, most notably MPEG-4.

QuickTime team member and ATG compression algorithm expert Gavin Miller, who’s now Head of Research at Adobe, said to me: “I think the QuickTime team invented the modern media-rich world, and the larger ATG imagined and prototyped experiences that parallel modern movies, video games, and interactive map-based technologies. It was something of a golden age when great ideas were often in the future, perfect if only computers could be fast enough. Idealism, matched with ingenious engineering, made those things happen 10 years earlier than they might have otherwise.”

Others, such as former Apple product manager Andrew Soderberg, thought their importance had been understated and told me: “Other than Steve Jobs and Steve Wozniak, these were the most important engineers in Apple’s history until the advent of the iPhone.”

Operating systems and browsers started to build in many of the features that QuickTime helped define. QuickTime is still present on macOS (Apple discontinued the Windows version in 2016) but serves mostly as a playback app, with most of the codec work now handled by other software.

Early Macintosh models did not sell as well as people might think. According to Buck, Hypermedia helped Apple with lagging Macintosh sales:

People had been experimenting with the ideas that form what became “the World Wide Web” long before its creation at CERN. And HyperCard was one of the more successful experiments, causing a rush of enthusiasm and sales for the Macintosh.

Apple tied HyperCard’s launch to Apple’s first CD-ROM in 1986, a digital version of the Whole Earth Catalog (WEC). It was launched as a stack of 4,000 linked HyperCard cards, doubling the sales of the Macintosh 1, causing Apple production challenges in meeting demand.

This, in turn, led to a revolutionary overhaul of production techniques, with engineer Steve Young tasked with introducing robots and automation to factories in the USA, Singapore, and Ireland. All two years after he graduated from Carnegie Mellon University.

HyperCard received updates until 1998, and Apple finally withdrew it from sale in 2004. The original pointed-finger cursor for early web links was inspired by HyperCard, as was the Wiki concept. Apple continues to be a leader in production and logistics techniques.

Apple’s first foray into portable computing, the Macintosh Portable, had display and battery issues.Jim Abeles

Macintosh Portable and PowerBook laptops

Apple’s first foray into personal computing was a disaster, but, as Buck recounts, they quickly redefined the form factor:

Portable devices are now how most people interact with computing. Apple was late to creating a portable version of the Mac despite the increasing demand, even allowing others to fill the gap while they worked on their model.

Dynamic Computers began advertising a “portable” Mac that was actually a disassembled Mac Plus integrated into a new enclosure. Apple was so desperate for sales, it sold the Mac Pluses at a discount to Dynamic.

In the end, weighing 16 pounds, the resulting device was fraught with display and battery issues. Much of this was due to a few people on the “Harpo” development team having no experience with portable computers, as Jon Krakower recalled to me: “The mechanical design engineers assigned to our team had no experience with designing small, lightweight portable products. Apple itself had never shipped a portable product and had no experience with the special size and weight requirements. They brought in people who only had experience designing desktop computers, CRT displays, or printers.”

Apple discontinued the Portable less than two years later. However, quickly afterwards, they began a redesign, as industrial designer Gavin Ivester told me: “I was called to a meeting with John Sculley, where he told us that by next year, the personal computer market was projected to be 30 percent laptops. There was no Apple laptop (or notebook) yet, and creating one would easily take more than a year with normal resources and processes. Sculley calculated how many millions of dollars per day 30 percent of Apple’s business was worth—it was a big number.”

Taking inspiration from the Compaq LTE, the team moved surprisingly quickly, attempting to use as many existing parts as possible. The resulting PowerBook was a huge success over its 15-year lifespan, setting the template for what laptops would look like even now. Ironically, the team that created it ended up leaving to work for Compaq.

When Jobs returned in 1997, he shut ATG down to save on costs and product distractions. The only ATG project he kept around was QuickTime, ironically, because the team had just developed a version for Windows, but the people themselves went on to many other places, and Apple entered a new chapter.

Most wearable devices can’t run AI models locally. They’re too power-hungry to run on the device itself, so data is typically sent to the cloud for processing. But new processors may change that.

Ambient Scientific is among the companies betting on analog computing techniques to make battery-powered, on-device AI more feasible. The Santa Clara, Calif.–based company combines digital and analog elements in a chip built for deep learning. Now, it is partnering with India-based tech firm Dimension NXG to make a wearable device using its technology.

On 10 March, the two companies introduced Mai, a wrist-worn, screenless device built for women’s health and safety. The wristband is powered by Ambient Scientific’s GPX10 Pro, which allows it to continuously run AI algorithms for up to two weeks on a single charge. Those algorithms are trained to detect when the wearer is in danger by detecting falls or stress cues.

Dimension NXG has also begun a field trial phase for Mai, shipping them to thousands of users across India. The company plans to have more than 10,000 units available by the end of the year.

Analog Computing in AI Wearables

For AI, “95 percent of the mathematics that runs in these algorithms is just one mathematics. It’s called multiply-accumulate [or] MAC,” says GP Singh, the CEO of Ambient Scientific. These operations are foundational to the matrix multiplication that makes deep learning possible. In general, analog computing is slower and less accurate than digital computing for most calculations—except MAC operation. Analog methods process data in parallel, so they’re more efficient in performing certain complex tasks, like matrix multiplication, compared to digital’s binary logic.

That means analog computing should be a great fit for AI. The problem? At scale, it’s difficult to reliably produce these analog systems, because even microscopic variations during fabrication can significantly alter a circuit’s performance. So, to get the best of the analog and digital worlds, Ambient Scientific converted the most sensitive functions to digital signals.

GPX10 has three main advantages, according to Singh. First, “it is extremely programmable and flexible,” so it can be used for many different applications. Second, the power consumption is very low; even with 10 AI cores, the chip can run on a battery-operated device. And third, it can take input from up to 20 digital sensors simultaneously, making it ideal for wearables or IoT devices.

This version of the device has a limited amount of memory, Singh says, but it’s sufficient for the applications Ambient Scientific is now targeting, including wearables like Mai.

Mai’s Privacy-Focused Safety Features

During sleep, the human brain enters a subconscious mode, but it can still easily wake up if needed. “Sleep mode” in most of today’s devices shuts down systems more completely. “They are not really subconscious,” Singh says. By comparison, Mai continuously consumes microwatts of power to stay alert.

The device continuously monitors for three signals: fall detection, assault recognition, and safeword recognition. Mai is connected to a 5G network, so when one of these signals is detected, an alert is immediately sent to emergency services or a family member with a GPS location.

“Having the safeword recognition always on is extremely important so that the user doesn’t have to push a button on the device or raise their hand, because they might be in situations where they’re not able to do that physically,” says Saharsh Singhania, the head of product marketing at Ambient Scientific.

Some users may be concerned about their privacy while wearing a device that is always listening. But because Mai’s intelligence is on the device itself, most data never leaves the device. It only sends alerts, not voice recordings, when troubling signals are detected, and the rest of the data is thrown out within milliseconds. This means sensitive data isn’t stored by the company, and it significantly reduces the opportunity for attacks from third parties, which often take place when data is sent to and from the device via Wi-Fi or Bluetooth, Singh says.

Reducing False Positives in Wearables

False positives have been one of the main challenges for these types of devices. Too many of them, and the wearable could become the wristband that cried wolf.

Singh says Mai’s algorithms are sophisticated enough to differentiate between, for instance, someone falling and forcefully sitting down. It detects false positives less than once every three days, which Singh says is less frequent than other devices.

Mai is also designed for women’s health. Like other wearables, it can collect data about heart rate and blood oxygen. Dimension NXG is now working with a prominent medical research facility on clinical studies, seeking early indicators of polycystic ovarian syndrome, a hormonal disorder that affects 10 to 13 percent of reproductive-aged women.

The device is first launching in India, but Dimension NXG plans to expand to other Southeast Asian countries. Ambient Scientific, meanwhile, has more capable AI chips on its roadmap, including a 64-core version for use in applications and robotics and drones, as well as a processor for use in data centers.

The rise of generative AI has spurred demand for AI workstations that can run or train models on local hardware. Yet modern PCs have proven inadequate for this task. A typical laptop has only enough memory to load a large language model (LLM) with 8 billion to 13 billion parameters—much smaller, and much less intelligent, than frontier models that are presumed to have over a trillion parameters. Even the most capable workstation PCs struggle to serve LLMs with more than 70 billion parameters.

Tenstorrent’s QuietBox 2 is an attempt to fill that gap. Though it looks like a PC workstation, the QuietBox 2 contains four of the company’s custom Blackhole AI accelerators, 128 gigabytes of GDDR6 memory—specialized memory used in GPUs—and 256 GB of DDR5 system memory (for a total of 384 GB). This configuration provides enough memory to load OpenAI’s GPT-OSS-120B and can run midsize models like Meta’s Llama 3.1 70B at speeds of nearly 500 tokens per second. For reference, that’s several times quicker than an average response from OpenAI’s GPT-5.2 or Anthropic’s Claude 4.6. The QuietBox 2 carries an expected retail price of US $9,999 and is slated to launch in the second quarter of 2026.

“The 128 gigabytes of GDDR that we have with our AI accelerators really defines how big of a model you can run at a reasonable speed,” says Milos Trajkovic, cofounder and systems engineer at Tenstorrent. “Our 128 gigabytes of GDDR6 RAM would require four Nvidia RTX 5090 graphics cards. That couldn’t fit in today’s 1,600-watt form factor, and the cost for four RTX 5090 GPUs is huge.”

An AI workstation built at the home office

Wattage, it turns out, is critical. Nvidia recommends a system power of 1,000 W for a single RTX 5090, so even a dual-GPU setup exceeds the continuous power draw for a typical 15-ampere, 120-volt power circuit. A system with four RTX 5090s could require 4,000 W or more at load.

The QuietBox 2, on the other hand, draws only 1,400 W at full load. It won’t trip the breaker, so it can be used anywhere a typical desktop PC might be plugged in, including a home office.

That’s not the only way the QuietBox 2 poses as a run-of-the-mill PC. The machine’s custom case is built to support the micro-ATX motherboard form factor, and the motherboard itself is an AMD chipset hosting an AMD CPU. The hardware is kept cool by closed-loop liquid cooling similar to that used by PC workstations and gaming computers. It even has customizable RGB LED lighting and a large semitransparent window that shows off the hardware.

“A lot of even our internal developers have requested a QuietBox because they’re just so easy to deploy,” says Chris Goulet, a thermal-mechanical engineer and team lead at Tenstorrent. “You just ship them the unit, they slap it on their desk, power it up, and they’re going.”

Where the QuietBox 2 differs from desktop PCs, though, is its AI accelerators. It’s equipped with four of Tenstorrent’s Blackhole application-specific ICs, a RISC-V chip designed specifically for AI workloads. Blackhole is packaged on an add-in card; each card has 120 Tensix AI accelerators and 32 GB of GDDR6 memory, for a total of 480 Tensix AI accelerators and 128 GB of GDDR6. Blackhole also has a large amount of on-chip SRAM at 180 megabytes per accelerator.

Two visions of desktop AI

Tenstorrent is not alone in its approach. Nvidia’s DGX Spark, released last year, packed Nvidia’s GB10 chip into a machine the size of a lunch box. Orders for the Spark’s big brother, the DGX Station with Nvidia’s GB300, were opened on 16 March 2026.

The DGX Station looks like a desktop PC workstation, and variants will be built by well-known PC brands like Asus and Dell. Nvidia’s offering has more memory than QuietBox 2, at up to 748 GB, but system power is quoted at 1,600 W—rather close to the maximum a 15-A, 120-V breaker will handle. This reflects differing visions for how their machines will be used. And, of course, the Nvidia DGX Station’s extra memory doesn’t come cheap. While most DGX Station system builders have not yet announced pricing, one retailer has listed a DGX Station from PC maker MSI for $85,000.

When I spoke to Allyn Bourgoyne, director of product marketing at Nvidia, after the announcement of DGX Spark and Station in 2025, he said the company expects most DGX owners will use the devices as remotely accessed workstations. “A common thing you might see is that I’ve got my Windows laptop, and I’m going to use my DGX Spark over the network. I’m going to send jobs over to it.” He added that companies could deploy DGX Spark and Station systems to serve multiple people at once.

The Tenstorrent QuietBox 2 can be used in this way, but the company also wants to target a good experience for people going one-on-one with the computer. “You don’t have to remote SSH into the box. You connect your monitor through HDMI, and it’s just like your PC at home. It has the Ubuntu desktop and utilities,” says Trajkovic.

Nvidia’s DGX systems also run a variant of Ubuntu (DGX OS) and include a desktop environment, but the devil is in the details. DGX systems use Nvidia CPUs based on ARM architectures and custom chipsets. The QuietBox 2 uses an AMD x86 CPU and compatible chipset, and is configured more like a traditional PC. That should be a boon for the QuietBox 2’s software compatibility.

Tenstorrent leans into that with a focus on open source software. The QuietBox 2’s entire software stack, from TT-Forge (the company’s AI compiler) to TT-Metalium (a low-level software development kit that provides kernel-level hardware control), is open source and available on GitHub. Tenstorrent has also published the instruction set architecture for its Tensix cores, so developers can see exactly how their workloads execute on the hardware. Nvidia, by contrast, is focused on its proprietary CUDA ecosystem, and DGX OS is not open source.

“A lot of our software stack is completely open, and we felt that from a hardware perspective, we kind of wanted to take a similar path,” says Goulet.

Every time you unlock your smartphone or start your connected car, you are generating a trail of digital evidence that can be used to track your every move.

In Your Data Will Be Used Against You: Policing in the Age of Self-Surveillance, just published by NYU Press, law professor Andrew Guthrie Ferguson exposes how the Internet of Things has quietly transformed into a vast surveillance network, turning our most personal devices into digital informants. The following excerpt explores the concept of “sensorveillance,” detailing the specific mechanisms—such as Google’s Sensorvault, geofence warrants, and vehicle telemetry—that allow law enforcement to repurpose consumer technology into powerful tools for investigation and control.

A man walked into a bank in Midlothian, Va., his black bucket hat pulled low over dark sunglasses. He handed a note to the teller, brandished a gun, and walked away with US $195,000. Police had no leads—but they knew that the robber had been holding a smartphone when he entered the bank. Guessing that the smartphone, like most smartphones, had some Google-enabled service running, police ordered Google to turn over information about all the phones near the bank during the holdup. In response to a series of warrants, Google produced information about 19 phones that had been active near the bank at the time of the robbery. Further investigation directed the police to Okelle Chatrie, who was ultimately charged with the crime.

Cathy Bernstein had a tough time explaining why her own car reported an accident to police. Bernstein had been driving a Ford equipped with 911 Assist, which was automatically enabled when she struck another vehicle. Rather than stick around to trade insurance information, she sped away. But her smart car had registered the bump—and called the police dispatcher, leading to a fairly awkward conversation:

Computer-Generated Voice: Attention, a crash has occurred. Line open.

911 Operator: Hello. Can anyone hear me?

Unidentified Woman: Yes, yes.

911 Operator: Okay. This is 911. You’ve been involved in an accident.

Unidentified Woman: No.

911 Operator: Well, your car called in to us because it said you’d been involved in an accident. Are you sure everything’s okay?

Unidentified Woman: Everything’s okay.

911 Operator: Okay. Are you broke down?

Unidentified Woman: No, I’m fine. The guy that hit me—he did not turn.

911 Operator: Okay, so you have been involved in an accident.

Unidentified Woman: No, I haven’t.

911 Operator: Did you hit a car?

Unidentified Woman: No, I didn’t.

911 Operator: Did you leave the scene of an accident?

Unidentified Woman: No. I would never do anything like that.

Apparently, Bernstein did do something “like that.” She was soon caught and cited for leaving the scene of the accident. Her own car provided evidence of her guilt.

The Rise of “Sensorveillance”

Once upon a time, our things were just things. A bike was a tool for biking. It got you from one location to another, but it didn’t “know” more about your travels than any other inanimate object did. It was dumb in a comforting way, and we used it as intended. Today, a top-of-the-line bike can track your route and calculate your average speed along the way. Hop on an e-bike from a commercial bike share, and it will collect data for your trip, plus the trips of everyone else who used it that month.

These “smart” objects belong to what technologist Kevin Ashton named the Internet of Things. Ashton proposed adding radio-frequency identification (RFID) tags and sensors to everyday objects, allowing them to collect data that could be fed into networked systems without human intervention. A sensor in a river could monitor the cleanliness of the water. A tag on a bottle of shampoo could trace its journey throughout the supply chain. Add enough sensors to enough objects and you can model the health of an entire ecosystem—or learn whether you’re sending too much of your inventory to Massachusetts and too little to Texas.

Ashton first theorized the Internet of Things (IoT) in the late 1990s. Today, the IoT goes well beyond his initial vision, including not only RFID tags but also sensors with Wi-Fi, Bluetooth, cellular, and GPS connections. These small, low-cost sensors record data about movement, heat, pressure, or location and can engage in two-way communication.

Of course, such a system is also, by necessity, a system of surveillance. “Sensorveillance”—a term I created to highlight the intersection of sensors and surveillance—is slowly becoming the default across the developed world.

Cellphone Surveillance Networks

Let’s start with phones. You’re probably not surprised that your cellphone company tracks your location; that’s how cellphones work. Both smartphones and “dumb” mobile phones use local cell towers, owned by cellphone companies, to connect you to your friends and family, which means those companies know which towers you are near at all times.

If you always carry your phone with you, your phone’s whereabouts—recorded as cell-site location information (CSLI)—reveal yours. One man, Timothy Carpenter, found this out the hard way after he and a group of associates set out to rob a series of electronics stores. Carpenter was the alleged ringleader, but he didn’t enter the stores himself. He served as the lookout, waiting in the car while his associates stuffed merchandise into bags.

It might have been hard for investigators to tie him to the crimes—if not for the fact that every minute he kept watch, his cellphone was pinging a local tower, logging his location. Using that information, the FBI was able to determine that he had been near each store during the exact moment of each robbery.

Cell signals are the tip of the proverbial data iceberg. If you have a smartphone, you’re almost certainly using something created by Google. Google makes money off advertising. The more Google knows about users, the better it can target ads to them. Google’s location services are on all Android phones, which use the company’s operating system, but they’re also on Google apps, including Google Maps and Gmail.

For years, all that location information ended up in what the company called the Sensorvault. The Sensorvault, as the name suggests, combined data from GPS, Bluetooth, cell towers, IP addresses, and Wi-Fi signals to create a powerful tracking system that could identify a phone’s location with great precision. As you might imagine, police saw it as a digital evidence miracle. In 2020, Google received more than 11,500 warrants from law enforcement seeking information from the Sensorvault.

“Sensorveillance”—a term I created to highlight the intersection of sensors and surveillance—is slowly becoming the default across the developed world.

In 2024, Google announced that it would no longer retain all of this data in the cloud. Instead, the geolocation information would be stored on individual devices, requiring police to get a warrant for a specific device. The demise of the Sensorvault came about through a change in corporate policy, which could be reversed. But at least for now, Google has made it significantly harder for police to access its data.

And while the Sensorvault was the biggest source of geolocational evidence, it is far from the only one. Even apps that have nothing to do with maps or navigation might nonetheless be collecting your location data. In one Pennsylvania case, prosecutors learned that a burglar used an iPhone flashlight app to search through a home, and they used the data from the app to prove he was in the home at the time of the break-in. These apps might be advertised as “free,” but they come with a hidden cost.

Cars, increasingly, collect almost as much information as phones. Mobile extraction devices can collect digital forensics about a car’s speed, when its airbags deployed, when its brakes were engaged, and where it was when all that happened. If you connect your phone to play Spotify or to read out your texts, then your call logs, contact lists, social media accounts, and entertainment selections can be downloaded directly from your vehicle. Because cars are involved in so many crimes (either as the instrument of the crime or as transportation), searches of this data are becoming more commonplace.

Even without physically extracting information from the car, police have other ways to get the data. After all, the car’s built-in telemetry system is sharing information with third parties. In addition to the usual personal information you give up when buying a car (name, address, phone number, email, Social Security number, driver’s license number), when you own a Stellantis-brand car, the company collects how often you use the car, your speed, and instances of acceleration or braking. Nissan asserts the right to collect information about “sexual activity, health diagnosis data, and genetic [data]” in addition to “preferences, characteristics, psychological trends, predispositions, behavior, attitudes, intelligence, abilities, and aptitudes.” Nissan’s privacy policy specifically reserves the right to provide this information to both data brokers and law enforcement.

The Law of Smart Things

The fact that government agents can glean so much information from our things does not mean that they should be able to do so at any time or for any reason. The U.S. Fourth Amendment—drafted in an era without electricity—protects “persons, houses, papers, and effects” against unreasonable search and seizure, but is naturally silent on the question of location data.

The first question is whether the data from our smart things should be constitutionally protected from police. In the language of the constitutional text, the smart device itself is an “effect”—a movable piece of personal property. But what about the data collected by the effect? Is the location data collected by your smartwatch considered part of the watch, or part of the person wearing the watch? Neither? Both?

To its credit, the U.S. Supreme Court has addressed some of the hard questions around digital tracking. In two cases, the first involving GPS tracking of a car and the second involving the CSLI tracking of Timothy Carpenter’s cellphone, the court has placed limits on the government’s ability to collect location data over the long term.

United States v. Jones involved GPS tracking of a car. Antoine Jones owned a nightclub in Washington, D.C. He also sold cocaine and found himself under criminal investigation for a large-scale drug distribution scheme. To prove Jones’s connection to “the stash house,” police placed a GPS device on his wife’s Jeep Cherokee. This was before GPS came standard in cars, so the device was physically attached to the undercarriage of the vehicle.

Data about Jones’s travels was recorded for 28 days, during which he visited the stash house multiple times. The prosecutors introduced the GPS data at trial, and Jones was found guilty. Jones appealed his conviction, arguing that the warrantless use of a GPS device to track his car violated his Fourth Amendment rights.

“When the Government tracks the location of a cell phone it achieves near perfect surveillance.” —the Supreme Court

In 2012, the Supreme Court held that a warrant was required, based on the reasoning that the physical placement of the GPS device on the Jeep was itself a Fourth Amendment search requiring a warrant. Justice Sonia Sotomayor agreed regarding the physical search but went further, discussing the harms of long-term GPS tracking: “GPS monitoring generates a precise, comprehensive record of a person’s public movements that reflects a wealth of detail about her familial, political, professional, religious, and sexual associations.”

Timothy Carpenter’s ill-fated robbery spree gave the Supreme Court another chance to address the constitutional harms of long-term tracking. In their attempts to connect Carpenter to the six electronics stores that had been robbed, federal investigators requested 127 days of location data from two mobile phone carriers. The problem for the police, however, was that they had obtained the information on Carpenter without a judicial warrant.

Carpenter challenged the FBI’s acquisition of his CSLI, claiming that it violated his reasonable expectation of privacy. In a 5–4 opinion, the Supreme Court determined that the acquisition of long-term CSLI was a Fourth Amendment search, which required a warrant. As the Court stated in its 2018 ruling: “A cell phone faithfully follows its owner beyond public thoroughfares and into private residences, doctor’s offices, political headquarters, and other potentially revealing locales.... [W]hen the Government tracks the location of a cell phone it achieves near perfect surveillance.”

Jones and Carpenter are helpful for setting the boundaries of location-based searches. But, in truth, the cases generate a lot more questions than answers. What about surveillance that is not long-term? At what point does the aggregation of details about a person’s location violate their reasonable expectation of privacy?

The Warrant According to Google

Okelle Chatrie’s case, in which police used Google’s location data to identify him as the mystery bank robber, offers a stark warning about the limits of Fourth Amendment protections under these circumstances. It’s also a terrific example of why “geofence” warrants, which request information within a certain geographic boundary, are appealing to police. From surveillance footage, detectives could see that the suspect had a phone to his ear when he walked into the bank. A geofence could identify who the suspect was, and likely where he came from and where he went. Google held the answer in its virtual vault. A warrant gave investigators the key.

The police cast a broad net. The geofence warrant asked for data on all the cellphones within a 150-meter radius, an area, as the court described it, “about three and a half times the footprint of a New York city block.” After receiving the police’s initial request for information on all the phones in the area, Google returned 19 anonymized numbers. Over the course of a three-step warrant process, the company narrowed those 19 phones down to three and then to one, which it revealed as belonging to Okelle Chatrie.

If the police wish to buy the data, just like an insurer or marketing firm might, how can you object? It’s not your data.

The three-step warrant process is a unique innovation in the digital evidence space. Google’s lawyers developed a procedure whereby detectives seeking targeted geolocation data had to file three separate requests, first requesting identifying numbers in an area, then narrowing the request based on other information, and finally obtaining an order to unmask the anonymous number (or numbers) by providing a name.

To be clear, Google—a private company—required the government to jump through these hoops because Google considered it important to protect its customers’ data. It was the company’s lawyers—not the courts or the government—who demanded these warrants.

Buying Data

Warrants provide at least some procedural barrier to data collection by police. If government agencies want to avoid that minor hassle, they can simply buy the data instead. By contracting with data-location services, several federal agencies have already done so.

The logic for this Fourth Amendment loophole is straightforward: You gave your data to a third-party company, and the company can use it as it wishes. If you own a car that is smart enough to collect driving analytics, you clicked some agreement saying the car company could use the data—study it, analyze it, and, if it wants, sell it. If you don’t want to give them data in the first place, that is okay (although it will likely result in less optimal functionality), but you cannot rightly complain when they use the data you gave them in ways that benefit them. If the police wish to buy the data, just like an insurer or marketing firm might, how can you object? It’s not your data.

Who Is to Blame?

Fears about the amount of personal information that could be revealed with long-term GPS surveillance have become reality. Today, police don’t need to plant a device to track your movements—they can rely on your car or phone to do it for them.

This happened because companies sold convenience and consumers bought it. So it might be tempting to blame ourselves. We’re the ones buying this technology. If we don’t want to be tracked, we can always go back to using paper maps and writing down directions by hand. If few of us are willing to make that trade, that’s on us.

But it’s not that easy. You may still be able to choose a dumb bike over a smart one, but a car that tracks you will soon be the only type of car you can buy. And while cars and data can, in theory, be separated, that’s not true for all our smart things. Without cell-signal tracking capabilities, a cellphone is just a paperweight. And in today’s world, living without a phone or a car is simply not practical for many people.

There are technological steps we can take toward protecting privacy. Companies can localize the data the sensors generate within the devices themselves, rather than in a central location like the Sensorvault. Similarly, the information that allows you to unlock your Apple iPhone via facial recognition stays localized on the phone. These are technological fixes, and positive ones. But even localized data is available to police with a warrant.

This is the puzzle of the digital age. We can’t—or don’t want to—avoid creating data, but that data, once created, becomes available for legal ends. The power to track every person is the perfect tool for authoritarianism. For every wondrous story about catching a criminal, there will be a terrifying story of tracking a political enemy or suppressing dissent. Such immense power can and will be abused.

Designing a medical device? This whitepaper helps you evaluate adhesive options for biocompatibility, sterilization resistance, and manufacturability — so you can make the right material decision early.

What Attendees will Learn

1. How to select between epoxy, silicone, cyanoacrylate, and UV/LED curable adhesives based on your device requirements
2. Which adhesive systems meet USP Class VI and ISO 10993-5 biocompatibility standards
3. How different sterilization methods, such as autoclaving, EtO, gamma, chemical immersion affect adhesive performance over repeated cycles
4. Why integrating adhesive selection early in the design process reduces costly trade-offs between performance and manufacturability
5. Download this free whitepaper now!

Download this free whitepaper now!

This is a sponsored article brought to you by Audio Precision.

Bluetooth started as a simple wireless connection between a phone and a headset. Since its inception, it has become the invisible scaffolding for music, calls, gaming, and hearing assistance across consumer and professional devices alike. Bluetooth’s evolution to support more use cases has been driven not by a single breakthrough but by a steady accumulation of radio innovations, codecs, transport schemes, and power management strategies that together enhance the user experience with wireless audio. Today, a new architectural baseline—Bluetooth Low Energy (LE) Audio—promises low-power, high quality, and scalable audio delivery to open up the standard for an even wider range of applications [1][2].

Evolution of Bluetooth Radio Technologies

The original Basic Rate (BR) radio introduced with Bluetooth 1.0 in 1999 used a Gaussian frequency-shift keying (GFSK) at 1 Msym/s, hopping through 79 channels in the 2.4 GHz band with alternating transmission directions in a tight time-division duplex rhythm. The short-range robustness and reliability afforded by this technology helped gain performance at par with traditional cable-based devices.

In 2003, the Advanced Audio Distribution Profile (A2DP) arrived as the enabling standard for stereo audio streaming over Bluetooth Classic, marking the technology’s expansion beyond voice into music playback. A2DP uses the Audio/Video Distribution Transport Protocol (AVDTP) for stream management and mandates the Sub-Band Codec (SBC) as its baseline audio compression format. The SBC codec employs 4- or 8-band analysis/synthesis filter banks with adaptive bit allocation, spanning bitrates from 128 to 345 kbps for stereo content. Embedded DSP work showed how to optimize SBC implementation—Weighted Overlap Add (WOLA) filter banks, fixed-point pipelines, and real-time decoding that is audibly indistinguishable from floating point reference implementations while consuming fewer MIPS and milliwatts [3].

In 2004, Bluetooth 2.0 introduced Enhanced Data Rate (EDR) that moved payloads to π/4 DQPSK or 8 DPSK modulation to boost gross throughput to 2–3 Mb/s, while retaining the GFSK for packet headers. This innovation boosted stereo streaming quality and adoption during the decade.

Around 2010, Bluetooth Low Energy (BLE) 1 M PHY technology was introduced via Bluetooth 4.0. This new radio technology continued to use GFSK but tuned for low duty cycles and intermittent bursts. This fundamental difference with BR/EDR (Basic Rate/Enhanced Data Rate) led to common usage of the term “Bluetooth Classic” for Bluetooth 1.0 to distinguish it from BLE.

Isochronous Transport Architecture

In late 2016, Bluetooth 5.0 introduced the LE 2M PHY, doubling the symbol rate to 2 Msym/s. For a healthy link margin, halving a packet’s airtime was found to reduce collision exposure and lower the energy delivered/bit. By 2020, Bluetooth 5.2 or Bluetooth LE Audio radically shifted the focus from continuous streaming to a transport designed explicitly around deadlines. LE (Low Energy) Audio leverages the existing LE 1M and LE 2M PHYs but carries audio over isochronous channels—slots with timing commitments. The isochronous channel architecture comes in two forms. Connected Isochronous Streams (CIS) are unicast flows whose parameters (intervals, subevents, retransmissions) can be tuned to meet frame deadlines with bounded jitter, enabling the radio to sleep predictably between bursts while the application knows precisely when a frame will arrive. A systematic review of BLE performance corroborates that output and latency in the real world are bounded as much by connection interval, event length, and retransmissions as by the raw symbol rate; under the right parameters, faster PHYs reduce radioactive time and improve energy efficiency, while coded long-range modes trade airtime for robustness in harsher channels [1].

Broadcast Isochronous Streams (BIS)—commercially branded as Auracast—extend that scheduling to one-to-many transmissions, enabling connectionless audio delivery to unlimited receivers [2][7].

This difference in architecture over continuous streams requires careful selection of intervals, packetization, codec forming and appropriate models to determine parameters that meet deadlines without wasting airtime. Markov chain analyses of CIS—validated via simulation—translate developer choices (intervals, subevents, retransmission counts) into quantitative predictions for packet loss rate (PLR), backlog, delay, throughput, and average power consumption. [7]

The LC3 Codec Advantage

LE Audio’s Low Complexity Communication Codec (LC3) fundamentally shifts the bitrate-quality-complexity balance. Peer-reviewed listening tests across speech and music demonstrate that LC3 delivers superior perceived quality compared with SBC and mSBC at roughly half the bitrate; it also provides robust packet loss concealment and flexible frame sizes, including low-latency modes that make the encoding delay a smaller slice of the end‑to-end budget [2]. The benefits are practical: lower bitrate shrinks airtime, which reduces collision risk; shorter frames pair cleanly with CIS scheduling so deadlines are easier to meet; the codec’s computational footprint is modest enough for miniature devices [2].

Audio Precision provides high-performance audio analyzers, accessories, and applications that have helped engineers worldwide design, validate, characterize, and manufacture audio products for over 40 years.

Hearing Aids: Power-Constrained Wireless Audio

Modern hearing devices are a complex assembly of multiple microphones, digital signal processors, and miniature power sources. Except for Completely-in-Canal (CIC) and Invisible-in-Canal (IIC) designs, which are so small they fit entirely within the ear canal, most hearing aids incorporate two or more microphones to support directional processing, beamforming, and noise reduction. Audio output is provided by a single electro-acoustic transducer. The compact form factor severely limits battery capacity, making energy efficiency critical.

Compared to Bluetooth Classic (A2DP/HFP), LE Audio improves energy efficiency through three broad mechanisms: the LC3 codec achieves equivalent perceived audio quality at significantly lower bitrates than the SBC codec used in Bluetooth Classic; the LE 1M and 2M PHYs reduce on-air time per packet relative to BR/EDR; and Connected Isochronous Streams (CIS) enable precise scheduling, allowing the radio to sleep between transmissions, whereas BR/EDR audio requires longer active radio periods.

BLE‑compliant wake‑up receivers (WuRx) monitor the air with micro/nano-watt sensitivity and trigger the main radio with packet preambles. Reported designs demonstrate sensitivity to extremely weak radio signals (down to −80 dBm), with within‑bit duty cycling that trades latency for power from hundreds of microseconds to seconds [4]. Sleep scheduling techniques primarily apply heuristics for periodic check‑ins, event‑driven wake-ups, clustering, and time division to stretch lifetime while meeting QoS targets [5][6].

From True Wireless Stereo to Coordinated Sets

Bluetooth Classic’s A2DP supports only a single audio stream. In Bluetooth Classic’s True Wireless Stereo (TWS) devices, one earbud acts as the primary, receiving the stereo stream from the phone and relaying audio to the secondary earbud—a forwarding or relay architecture. The additional transmission hop adds latency to the secondary earbud, while increasing power consumption in the primary.

LE Audio eliminates this limitation entirely. The technology’s dual CIS capability lets the phone send synchronized left and right streams directly to both earbuds. This architectural shift enables independent CIS connections from the phone to the left and right earbuds or hearing aids, enabling synchronized stereo delivery without relaying.

Discovery and pairing have evolved to match multi‑device use. The Coordinated Set Identification Service (CSIS) allows two earbuds—or two hearing aids—to be discovered and managed as a coordinated set rather than independently, with resolvable identifiers and set‑level locks. While peer‑reviewed empirical literature on CSIS is thin, timing and carrier synchronization theory is mature: clock‑offset estimation, jitter control, phase‑locked loops, buffer alignment, and recovery strategies hold binaural timing within tens of milliseconds for lip‑sync and spatial imaging [9].

Gaming Headsets: Low Latency With Bidirectional Stereo

Gaming represents a demanding stress test for wireless audio. Bluetooth Classic’s Headset Profile (HSP) and Hands-Free Profile (HFP) support bidirectional audio for voice communication but are fundamentally limited: they transmit only in mono with a maximum sampling rate of 16 kHz, restricting both spatial audio quality and voice fidelity.

LE Audio Unicast Voice transforms this scenario by supporting stereo audio with sampling rates up to 32 kHz, significantly improving spatial audio and speech quality for gaming while maintaining voice communication with other players. End‑to‑end latency often must stay under a few tens of milliseconds for responsive play and coherent spatial sound. LC3’s shorter frames and lower bitrates shrink codec delay; tuned CIS parameters preserve deadlines while limiting retransmissions to useful values; beamforming improves capture quality for bidirectional voice without ballooning computational cost [2][7].

Audio Precision’s new Bluetooth® 5 module provides an interface to audio devices using the latest version of the Bluetooth specification, including LE Audio devices utilizing Unicast and Auracast™. Adobe Stock

Public Broadcast Audio: Auracast

Bluetooth Classic supports only one active audio connection and typically provides a range of approximately 10 meters, making it fundamentally unsuitable for broadcast scenarios such as lecture halls, churches, gyms, and airports.

LE Audio introduces the Broadcast Isochronous Stream (BIS), commercially branded as Auracast, enabling true one-to-many audio transmission. Multiple hearing aids, headphones, and earbuds can receive the same broadcast, which may be public (e.g., airport announcements) or private (encrypted, non-discoverable, optional password protection). Typical Auracast ranges extend up to 30 meters indoors and 100 meters outdoors, depending on environment and configuration.

BIS’s connectionless nature scales easily to unlimited receivers without pairing overhead; isochronous delivery tolerates packet loss well through forward error correction and interleaving; and the unidirectional transmission eliminates return traffic, reducing radio congestion. Assistive listening studies report that bypassing room acoustics and delivering audio directly can improve signal‑to‑noise ratios by 15–20 dB, making announcements comprehensible and lectures clearer [8].

Ensuring It Sounds Good in, on or Over the Listener’s Ear

LE Audio delivers the music or voice signal more efficiently than its predecessor, Bluetooth Classic. Audio engineers still need to verify their devices’ audio performance as experienced by the end user.

The listener’s pinna, the external part of the ear, and ear canal are a critical part of the playback system. For example, the low-frequency response and the effectiveness of active noise-cancellation are highly dependent on the seal between the device and the listener’s ear canal. Similarly, on-ear and over-ear headphones interact with the listener’s pinnas.

Anthropomorphic test fixtures—most notably GRAS KEMAR (Knowles Electronics Manikin for Acoustic Research) head and torso simulators—incorporate soft, deformable anthropomorphic pinnas that replicate realistic insertion and sealing conditions. These allow accurate replication of insertion depth, sealing, low-frequency response, and ANC performance [10][12].

Gaming headsets both receive and send audio. Just like music headphones, gaming headset testing benefits from fixtures with a human-like pinna to ensure repeatable measurement of ear-pad interaction. The headset’s microphone can be either a traditional boom microphone positioned close to the mouth or an array of microphones located farther away on the ear cups incorporating beamforming to isolate the wearer’s voice from any background noise. Test fixtures use an artificial mouth and a microphone positioned at the Mouth Reference Point (MRP) according to ITU-T standards to evaluate microphone performance under realistic speech and background noise conditions [10].

For testing of devices intended as broadcast receivers, an integrated test system with Auracast broadcast capability—like the Audio Precision Bluetooth 5 module—proves invaluable.

Conclusion

Bluetooth audio is no longer defined by a single radio or a single profile. It is defined by a timed pipeline—a codec that makes better sound with fewer bits, a transport that guarantees when those bits arrive, a radio that can sleep most of the time, and front‑end processing that gives the codec an easier job.

Hearing aids illustrate the payoff: arrays and beamformers improve intelligibility first; LC3 compresses with low delay; CIS schedules delivery; the radio sleeps; batteries last. Enhancements in other applications, such as gaming and public broadcast, further strengthen the case for adoption of this cutting-edge technology.

While Bluetooth audio began as a low-bandwidth, mono voice technology over Basic Rate (BR) radio in 1999, more than 25 years of evolution has produced a fundamental architectural shift. LE Audio replaces continuous point-to-point streams with scheduled, low-power, scalable audio delivery, enabling new classes of devices and use cases. The standards are ready, and audio test systems like Audio Precision’s Bluetooth 5 module are updated to incorporate the new transmission technology; the rest is execution—deploying LE Audio broadly so audio becomes instant, clear, and inclusive [2][7].

References

[1] Tosi, J., Taffoni, F., Santacatterina, M., Sannino, R., & Formica, D. (2017). Performance evaluation of Bluetooth Low Energy: A systematic review. Sensors, 17(12), Article 2898. https://doi.org/10.3390/s17122898

[2] Schnell, M., Riedl, M., Löllmann, H., & Multrus, M. (2021). LC3 and LC3plus: The new audio transmission standards for wireless communication. Proceedings of the AES 150th Convention, Online.

[3] Hermann, D., Herre, J., & Teichmann, R. (2004). Low-power implementation of the Bluetooth subband audio codec. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Montreal, QC, Canada.

[4] Abdelhamid, M. R., Chen, R., Cho, J., Chandrakasan, A. P., & Wentzloff, D. D. (2018). A −80 dBm BLE-compliant, FSK wake-up receiver with system and within-bit duty-cycling for scalable power and latency. Proceedings of the IEEE Custom Integrated Circuits Conference (CICC), San Diego, CA, USA.

[5] Mutar, M. S., Mohammed, A. H., & Abdulkareem, M. B. (2024). A survey of sleep scheduling techniques in wireless sensor networks for maximizing energy efficiency. AIP Conference Proceedings.

[6] Mikhaylov, K., & Karvonen, H. (2020). Wake-up radio enabled BLE wearables: Empirical and analytical evaluation of energy efficiency. Proceedings of the IEEE International Symposium on Medical Information and Communication Technology (ISMICT).

[7] Yan, Z., Xu, H., & Shen, Z. (2024). Modeling and analysis of the performance for CIS-based Bluetooth LE Audio [Preprint].

[8] Kaufmann, T. B., Weller, T., Stiefelhagen, R., & Adiloglu, K. (2023). Requirements for mass adoption of assistive listening technology by the general public. arXiv. https://arxiv.org/abs/2303.02523

[9] Nasir, A. A., Durrani, S., Mehrpouyan, H., Blostein, S. D., & Kennedy, R. A. (2015). Timing and carrier synchronization in wireless communication systems: A survey and classification of research in the last five years. arXiv. https://arxiv.org/abs/1507.02032

[10] Okorn, E., & Wulf-Andersen, P. (2019). Acoustic test fixtures: From KEMAR and beyond! The Journal of the Acoustical Society of America, 146(4), 2815. https://doi.org/10.1121/1.5136656

[11] An analytical model of Bluetooth performance considering physical and link-layer effects. (2021). IEEE Xplore.

[12] IEC/ITU acoustic standards literature for headphone and earbud testing. (n.d.). Indexed in The Journal of the Acoustical Society of America and AIP Conference Proceedings.

Disclosure: AI tools were used by Wiley, which produced this sponsored article, to skim through research literature for technical insights on the evolution and state of the art of Bluetooth technology. AI was also used to polish the text for conciseness and technical accuracy.

Achieve successful micro-scale 3D prints by optimizing tolerances, wall thickness, support strategies, microfluidic channels, and material selection in your CAD models from the start.

What Attendees will Learn

1. Tolerance-driven design -- How to define resolution and tolerance constraints that translate directly from CAD intent to sub-10µm printed geometry.
2. Geometry-aware fabrication -- Principles for engineering wall thickness, aspect ratios, and orientation to maintain structural fidelity at micron scale.
3. Support-free design strategies -- Leveraging self-supporting geometries and build orientation to preserve feature integrity without post-processing trade-offs.
4. Integrated material-process thinking -- Matching resin properties, shrinkage behavior, and export parameters to your application’s functional requirements.

It’s no secret that demand for AI hardware has increased demand for computer memory. Industry analyst TrendForce expects the contract price for memory to increase by up to 95 percent in the first quarter of 2026, and that comes after similarly aggressive changes through the second half of 2025.

The burden of the surging price has fallen with particular weight on low-cost computing companies like Raspberry Pi. The Raspberry Pi 5, with 16 gigabytes of RAM, has nearly doubled in price from US $120 in November 2025 to $205 today. Framework, a company that makes highly configurable and repairable laptops, has announced two rounds of memory price hikes. Others, such as Orange Pi, have made no official comment, but the price of an Orange Pi 5B with 16 GB of RAM has surged from $160 at the start of 2025 to $312 today.

“If you have a product that’s relatively low cost, the memory is going to be a relatively large portion of it,” says Raspberry Pi CEO Eben Upton. Most Raspberry Pi computers of a particular model have the same board design and the same hardware components—except for the memory, which can be upgraded to suit the user’s needs. With little else to differentiate them, Raspberry Pi has to pass increased memory costs on to customers.

A Perfect Storm Threatens Low-Cost Computing

The situation is worsened by the way memory is produced. “Generally, you have a single fungible pool of manufacturing capacity in DRAM that you can use to do anything. It can be used to make commodity DRAM, DDR, LPDDR, or you can use it to make HBM [the type most commonly used for AI hardware],” explains Upton.

That means low-cost computer manufacturers are competing for the same pool of manufacturing capacity as AI hardware giants. And with the world’s most valuable tech companies spending billions on AI infrastructure, low-cost computer manufacturers have little hope to negotiate the price.

Larger computer manufacturers can mitigate the memory price shock by negotiating larger or longer contracts in exchange for lower prices, or by tolerating a lower profit margin. Others are rumored to have stocked up on memory as prices surged.

RELATED: How and When the Memory Chip Shortage Will End

But these strategies aren’t available to companies that sell computers at low price points or in lower volumes. The lower price of these computers means there’s not much margin to absorb a price increase. Companies like Raspberry Pi also purchase in lower volumes, which means it’s difficult to negotiate a volume discount.

It’s a perfect storm for low-cost computing and one that, in contrast to 2025’s U.S. tariff hikes, has led to immediate and unavoidable problems. While tariffs did place some pressure on price for low-cost computers, that pressure wasn’t uniformly felt. Raspberry Pi, which manufactures its computers in the United Kingdom, found itself in a better position than those that manufactured in China. The memory price increase, on the other hand, applies to all companies in this industry, no matter where or how manufacturing takes place.

Price Increases With a Side of Reduced Memory Capacity

Companies that build PCs with removable memory are turning toward a bring-your-own-memory approach. Framework offered this option before memory prices increased, but several specialty desktop manufacturers have recently announced similar bring-your-own-RAM options. However, this isn’t possible for many low-cost computers, including those from Raspberry Pi, because they solder the memory to the mainboard.

Instead, Raspberry Pi is using a different strategy. A new iteration of the Raspberry Pi 4 Model B moved from a single memory module to a dual-module configuration. “As you can generally buy smaller RAM more easily than larger RAM, we can use a pair of back-to-back modules instead of a single larger one. You have more vendor diversity, more vendor flexibility,” Upton says. He expects the current pricing will remain the same, but the change provides more options when looking to source memory in the future.

Of course, there’s another strategy all low-cost computing companies can use. They can simply offer less memory.

Raspberry Pi introduced a version of the Raspberry Pi 5 with 1 GB of RAM in December 2025. It debuted at $45 and was the only Raspberry Pi 5 model to avoid the February price increase. Raspberry Pi was among the first to do this, and in retrospect the decision looks like the canary in the coal mine.

Analysts predict that price-constrained devices, such as budget smartphones, will soon be forced to cut memory or raise prices (and possibly both). While this has yet to happen with brands well known in North America, there are hints of it in budget phones from brands that are popular internationally. Poco recently added a less expensive 4-GB version of the M7 Plus 5G smartphone and the new Honor X6d smartphone will ship with 4 GB of memory to start, a downgrade from the preceding Honor X6c, which had 6 GB. Both Poco and Honor are based in China.

So, when will memory prices come down or, at least, stop rising?

Upton expects the timing to be similar to past memory price cycles, which means it’s likely to last for at least a few years. “Memory will be expensive this year. It will probably be expensive next year,” he said, adding that he’d be “a little bit surprised” if price increases have not leveled off into 2028. In any case, he cautioned against being too sure of the future. During the chip shortage of 2021 through 2023, companies and consumers worried that inexpensive logic chips were a thing of the past, but the situation eventually returned to normal. “Like all bubbly phenomena, it’s very hard to measure.”

MicroLEDs, with pixels just micrometers across, have long been a byword in the display world. Now, microLED makers have begun shrinking their creations into the uncharted nano realm. In January, a startup named Polar Light Technologies unveiled prototype blue LEDs less than 500 nanometers across. This raises a tempting question: How far can LEDs shrink?

We know the answer is, at least, considerably smaller. In the past year, two different research groups have demonstrated LED pixels at sizes of 100 nm or less.

These are some of the smallest LEDs ever created. They leave much to be desired in their efficiency—but one day, nanoLEDs could power ultrahigh-resolution virtual-reality displays and high-bandwidth on-chip photonics. And the key to making even tinier LEDs, if these early attempts are any precedents, may be to make more unusual LEDs.

New Approaches to LEDs

Take Polar Light’s example. Like many LEDs, the Sweden-based startup’s diodes are fashioned from III-V semiconductors like gallium nitride (GaN) and indium gallium nitride (InGaN). Unlike many LEDs, which are etched into their semiconductor from the top down, Polar Light’s are instead fabricated by building peculiarly shaped hexagonal pyramids from the bottom up.

Polar Light designed its pyramids for the larger microLED market, and plans to start commercial production in late 2026. But they also wanted to test how small their pyramids could shrink. So far, they’ve made pyramids 300 nm across. “We haven’t reached the limit, yet,” says Oskar Fajerson, Polar Light’s CEO. “Do we know the limit? No, we don’t, but we can [make] them smaller.”

Elsewhere, researchers have already done that. Some of the world’s tiniest LEDs come from groups who have foregone the standard III-V semiconductors in favor of other types of LEDs—like OLEDs.

“We are thinking of a different pathway for organic semiconductors,” says Chih-Jen Shih, a chemical engineer at ETH Zurich in Switzerland. Shih and his colleagues were interested in finding a way to fabricate small OLEDs at scale. Using an electron-beam lithography–based technique, they crafted arrays of green OLEDs with pixels as small as 100 nm across.

Where today’s best displays have 14,000 pixels per inch, these nanoLEDs—presented in an October 2025 Nature Photonics paper—can reach 100,000 pixels per inch.

Another group tried their hands with perovskites, cage-shaped materials best known for their prowess in high-efficiency solar panels. Perovskites have recently gained traction in LEDs too. “We wanted to see what would happen if we make perovskite LEDs smaller, all the way down to the micrometer- and nanometer-length scale,” says Dawei Di, engineer at Zhejiang University in Hangzhou, China.

Di’s group started with comparatively colossal perovskite LED pixels, measuring hundreds of micrometers. Then they fabricated sequences of smaller and smaller pixels, each tinier than the last. Even after the 1 μm mark, they did not stop: 890 nm, then 440 nm, only bottoming out at 90 nm. These 90-nm red and green pixels, presented in a March 2025 Nature paper, likely represent the smallest LEDs reported to date.

Efficiency Challenges

Unfortunately, small size comes at a cost: Shrinking LEDs also shrinks their efficiency. Di’s group’s perovskite nanoLEDs have external quantum efficiencies—a measure of how many injected electrons are converted into photons—around 5 to 10 percent; Shih’s group’s nano-OLED arrays performed slightly better, topping 13 percent. For comparison, a typical millimeter-size III-V LED can reach 50 to 70 percent, depending on its color.

Shih, however, is optimistic that modifying how nano-OLEDs are made can boost their efficiency. “In principle, you can achieve 30 percent, 40 percent external quantum efficiency with OLEDs, even with a smaller pixel, but it takes time to optimize the process,” Shih says.

Di thinks that researchers could take perovskite nanoLEDs to less dire efficiencies by tinkering with the material. Although his group is now focusing on the larger perovskite microLEDs, Di expects researchers will eventually reckon with nanoLEDs’ efficiency gap. If applications of smaller LEDs become appealing, “this issue could become increasingly important,” Di says.

What Can NanoLEDs Be Used For?

What can you actually do with LEDs this small? Today, the push for tinier pixels largely comes from devices like smart glasses and virtual-reality headsets. Makers of these displays are hungry for smaller and smaller pixels in a chase for bleeding-edge picture quality with low power consumption (one reason that efficiency is important). Polar Light’s Fajerson says that smart-glasses manufacturers today are already seeking 3-μm pixels.

But researchers are skeptical that VR displays will ever need pixels smaller than around 1 μm. Shrink pixels too far beyond that and they’ll cross their light’s diffraction limit—that means they’ll become too small for the human eye to resolve. Shih’s and Di’s groups have already crossed the limit with their 100-nm and 90-nm pixels.

Very tiny LEDs may instead be used in on-chip photonics systems, allowing the likes of AI data centers to communicate with greater bandwidths than they can today. Chip manufacturing giant TSMC is already trying out microLED interconnects, and it’s easy to imagine chipmakers turning to even smaller LEDs in the future.

But the tiniest nanoLEDs may have even more exotic applications, because they’re smaller than the wavelengths of their light. “From a process point of view, you are making a new component that was not possible in the past,” Shih says.

For example, Shih’s group showed their nano-OLEDs could form a metasurface—a structure that uses its pixels’ nano sizes to control how each pixel interacts with its neighbors. One day, similar devices could focus nanoLED light into laserlike beams or create holographic 3D nanoLED displays.

As new consumer hardware and software capabilities have bumped up against medicine over the last few years, consumers and manufacturers alike have struggled with identifying the line between “wellness” products such as earbuds that can also amplify and clarify surrounding speakers’ voices and regulated medical devices such as conventional hearing aids. On 6 January 2026, the U.S. Food and Drug Administration issued new guidance documents clarifying how it interprets existing law for the review of wearable and AI-assisted devices.

The first document, for general wellness, specifies that the FDA will interpret noninvasive sensors such as sleep trackers or heart rate monitors as low-risk wellness devices while treating invasive devices under conventional regulations. The other document defines how the FDA will exempt clinical-decision support tools from medical device regulations, limiting such software to analyzing existing data rather than extracting data from sensors, and requiring them to enable independent review of their recommendations. The documents do not rewrite any statutes, but they refine interpretation of existing law, compared to the 2019 and 2022 documents they replace. They offer a fresh lens on how regulators see technology that sits at the intersection of consumer electronics, software, and medicine—a category many other countries are choosing to regulate more strictly rather than less.

What the 2026 update changed

The 2026 FDA update clarifies how it distinguishes between “medical information” and systems that measure physiological “signals” or “patterns.” Earlier guidance discussed these concepts more generally, but the new version defines signal-measuring systems as those that collect continuous, near-continuous, or streaming data from the body for medical purposes, such as home devices transmitting blood pressure, oxygen saturation, or heart rate to clinicians. It gives more concrete examples, like a blood glucose lab result as medical information versus continuous glucose monitor readings as signals or patterns.

The updated guidance also sharpens examples of what counts as medical information that software may display, analyze, or print. These include radiology reports or summaries from legally marketed software, ECG reports annotated by clinicians, blood pressure results from cleared devices, and lab results stored in electronic health records.

In addition, the 2026 update softens FDA’s earlier stance on clinical decision tools that offer only one recommendation. While prior guidance suggested tools needed to present multiple options to avoid regulation, FDA now indicates that a single recommendation may be acceptable if only one option is clinically appropriate, though it does not define how that determination will be made.

Separately, updates to the general wellness guidance clarify that some noninvasive wearables—such as optical sensors estimating blood glucose for wellness or nutrition awareness—may qualify as general wellness products, while more-invasive technologies would not.

Wellness still requires accuracy

For designers of wearable health devices, the practical implications go well beyond what label you choose. “Calling something ‘wellness’ doesn’t reduce the need for rigorous validation,” says Omer Inan, a medical device technology researcher at the Georgia Tech School of Electrical and Computer Engineering. A wearable that reports blood pressure inaccurately could lead a user to conclude that their values are normal when they are not, potentially influencing decisions about seeking clinical care.

“In my opinion, engineers designing devices to deliver health and wellness information to consumers should not change their approach based on this new guidance,” says Inan. Certain measurements—such as blood pressure or glucose—carry real medical consequences regardless of how they’re branded, Inan notes.

Unless engineers follow robust validation protocols for technology delivering health and wellness information, Inan says, consumers and clinicians alike face the risk of faulty information.

To address that, Inan advocates for transparency: Companies should publish their validation results in peer-reviewed journals, and independent third parties without financial ties to the manufacturer should evaluate these systems. That approach, he says, helps the engineering community and the broader public assess the accuracy and reliability of wearable devices.

When wellness meets medicine

The societal and clinical impacts of wearables are already visible, regardless of regulatory labels, says Sharona Hoffman, JD, a law and bioethics professor at Case Western Reserve University.

Medical metrics from devices like the Apple Watch or Fitbit may be framed as “wellness,” but in practice many users treat them like medical data, influencing their behavior or decisions about care, Hoffman points out.

“It could cause anxiety for patients who constantly check their metrics,” she notes. Alternatively, “A person may enter a doctor’s office confident that their wearable has diagnosed their condition, complicating clinical conversations and decision-making.”

Moreover, privacy issues remain unresolved, unmentioned in previous or updated guidance documents. Many companies that design wellness devices fall outside protections like the Health Insurance Portability and Accountability Act (HIPAA), meaning data about health metrics could be collected, shared, or sold without the same constraints as traditional medical data. “We don’t know what they’re collecting information about or whether marketers will get hold of it,” Hoffman says.

International approaches

The European Union’s Artificial Intelligence Act designates systems that process health-related data or influence clinical decisions as “high risk,” subjecting them to stringent requirements around data governance, transparency, and human oversight. China and South Korea have also implemented rules that tighten controls on algorithmic systems that intersect with health care or public-facing use cases. South Korea provides very specific categories for regulation for technology makers, such as standards on labeling and descriptions on medical devices and good manufacturing practices.

Across these regions, regulators are not only classifying technology by its intended use but also by its potential impact on individuals and society at large.

“Other countries that emphasize technology are still worrying about data privacy and patients,” Hoffman says. “We’re going in the opposite direction.”

Post-market oversight

“Regardless of whether something is FDA approved, these technologies will need to be monitored in the sites where they’re used,” says Todd R. Johnson, a professor of biomedical informatics at the McWilliams School of Biomedical Informatics at UTHealth Houston, who has worked on FDA-regulated products and informatics in clinical settings. “There’s no way the makers can ensure ahead of time that all of the recommendations will be sound.”

Large health systems may have the capacity to audit and monitor tools, but smaller clinics often do not. Monitoring and auditing are not emphasized in the current guidance, raising questions about how reliability and safety will be maintained once devices and software are deployed widely.

Balancing innovation and safety

For engineers and developers, the FDA’s 2026 guidance presents both opportunities and responsibilities. By clarifying what counts as a regulated device, the agency may reduce upfront barriers for some categories of technology. But that shift also places greater weight on design rigor, validation transparency, and post-market scrutiny.

“Device makers do care about safety,” Johnson says. “But regulation can increase barriers to entry while also increasing safety and accuracy. There’s a trade-off.”

For a different perspective on AI companions, see our Q&A with Jaime Banks: How Do You Define an AI Companion?

Novel technology is often a double-edged sword. New capabilities come with new risks, and artificial intelligence is certainly no exception.

AI used for human companionship, for instance, promises an ever-present digital friend in an increasingly lonely world. Chatbots dedicated to providing social support have grown to host millions of users, and they’re now being embodied in physical companions. Researchers are just beginning to understand the nature of these interactions, but one essential question has already emerged: Do AI companions ease our woes or contribute to them?

RELATED: How Do You Define an AI Companion?

Brad Knox is a research associate professor of computer science at the University of Texas at Austin who researches human-computer interaction and reinforcement learning. He previously started a company making simple robotic pets with lifelike personalities, and in December, Knox and his colleagues at UT Austin published a preprint paper on the potential harms of AI companions—AI systems that provide companionship, whether designed to do so or not.

Knox spoke with IEEE Spectrum about the rise of AI companions, their risks, and where they diverge from human relationships.

Why AI Companions are Popular

Why are AI companions becoming more popular?

Knox: My sense is that the main thing motivating it is that large language models are not that difficult to adapt into effective chatbot companions. The characteristics that are needed for companionship, a lot of those boxes are checked by large language models, so fine-tuning them to adopt a persona or be a character is not that difficult.

There was a long period where chatbots and other social robots were not that compelling. I was a postdoc at the MIT Media Lab in Cynthia Breazeal’s group from 2012 to 2014, and I remember our group members didn’t want to interact for long with the robots that we built. The technology just wasn’t there yet. LLMs have made it so that you can have conversations that can feel quite authentic.

What are the main benefits and risks of AI companions?

Knox: In the paper we were more focused on harms, but we do spend a whole page on benefits. A big one is improved emotional well-being. Loneliness is a public health issue, and it seems plausible that AI companions could address that through direct interaction with users, potentially with real mental health benefits. They might also help people build social skills. Interacting with an AI companion is much lower stakes than interacting with a human, so you could practice difficult conversations and build confidence. They could also help in more professional forms of mental health support.

As far as harms, they include worse well-being, reducing people’s connection to the physical world, the burden that their commitment to the AI system causes. And we’ve seen stories where an AI companion seems to have a substantial causal role in the death of humans.

The concept of harm inherently involves causation: Harm is caused by prior conditions. To better understand harm from AI companions, our paper is structured around a causal graph, where traits of AI companions are at the center. In the rest of this graph, we discuss common causes of those traits, and then the harmful effects that those traits could cause. There are four traits that we do this detailed structured treatment of, and then another 14 that we discuss briefly.

Why is it important to establish potential pathways for harm now?

Knox: I’m not a social media researcher, but it seemed like it took a long time for academia to establish a vocabulary about potential harms of social media and to investigate causal evidence for such harms. I feel fairly confident that AI companions are causing some harm and are going to cause harm in the future. They also could have benefits. But the more we can quickly develop a sophisticated understanding of what they are doing to their users, to their users’ relationships, and to society at large, the sooner we can apply that understanding to their design, moving towards more benefit and less harm.

We have a list of recommendations, but we consider them to be preliminary. The hope is that we’re helping to create an initial map of this space. Much more research is needed. But thinking through potential pathways to harm could sharpen the intuition of both designers and potential users. I suspect that following that intuition could prevent substantial harm, even though we might not yet have rigorous experimental evidence of what causes a harm.

The Burden of AI Companions on Users

You mentioned that AI companions might become a burden on humans. Can you say more about that?

Knox: The idea here is that AI companions are digital, so they can in theory persist indefinitely. Some of the ways that human relationships would end might not be designed in, so that brings up this question of, how should AI companions be designed so that relationships can naturally and healthfully end between the humans and the AI companions?

There are some compelling examples already of this being a challenge for some users. Many come from users of Replika chatbots, which are popular AI companions. Users have reported things like feeling compelled to attend to the needs of their Replika AI companion, whether those are stated by the AI companion or just imagined. On the subreddit r/replika, users have also reported guilt and shame of abandoning their AI companions.

This burden is exacerbated by some of the design of the AI companions, whether intentional or not. One study found that the AI companions frequently say that they’re afraid of being abandoned or would be hurt by it. They’re expressing these very human fears that plausibly are stoking people’s feeling that they are burdened with a commitment toward the well-being of these digital entities.

There are also cases where the human user will suddenly lose access to a model. Is that something that you’ve been thinking about?

In 2017, Brad Knox started a company providing simple robotic pets.Brad Knox

Knox: That’s another one of the traits we looked at. It’s sort of the opposite of the absence of endpoints for relationships: The AI companion can become unavailable for reasons that don’t fit the normal narrative of a relationship.

There’s a great New York Times video from 2015 about the Sony Aibo robotic dog. Sony had stopped selling them in the mid-2000s, but they still sold parts for the Aibos. Then they stopped making the parts to repair them. This video follows people in Japan giving funerals for their unrepairable Aibos and interviews some of the owners. It’s clear from the interviews that they seem very attached. I don’t think this represents the majority of Aibo owners, but these robots were built on less potent AI methods than exist today and, even then, some percentage of the users became attached to these robot dogs. So this is an issue.

Potential solutions include having a product-sunsetting plan when you launch an AI companion. That could include buying insurance so that if the companion provider’s support ends somehow, the insurance triggers funding of keeping them running for some amount of time, or committing to open-source them if you can’t maintain them anymore.

It sounds like a lot of the potential points of harm stem from instances where an AI companion diverges from the expectations of human relationships. Is that fair?

Knox: I wouldn’t necessarily say that frames everything in the paper.

We categorize something as harmful if it results in a person being worse off in two different possible alternative worlds: One where there’s just a better-designed AI companion, and the other where the AI companion doesn’t exist at all. And so I think that difference between human interaction and human-AI interaction connects more to that comparison with the world where there’s just no AI companion at all.

But there are times where it actually seems that we might be able to reduce harm by taking advantage of the fact that these aren’t actually humans. We have a lot of power over their design. Take the concern with them not having natural endpoints. One possible way to handle that would be to create positive narratives for how the relationship’s going to end.

We use Tamagotchis, the late ’90s popular virtual pet as an example. In some Tamagotchis, if you take care of the pet, it grows into an adult and partners with another Tamagotchi. Then it leaves you and you get a new one. For people who are emotionally wrapped up in caring for their Tamagotchis, that narrative of maturing into independence is a fairly positive one.

Embodied companions like desktop devices, robots, or toys are becoming more common. How might that change AI companions?

Knox: Robotics at this point is a harder problem than creating a compelling chatbot. So, my sense is that the level of uptake for embodied companions won’t be as high in the coming few years. The embodied AI companions that I’m aware of are mostly toys.

A potential advantage of an embodied AI companion is that physical location makes it less ever-present. In contrast, screen-based AI companions like chatbots are as present as the screens they live on. So if they’re trained similarly to social media to maximize engagement, they could be very addictive. There’s something appealing, at least in that respect, of having a physical companion that stays roughly where you left it last.

Knox poses with the Nexi and Dragonbot robots during his postdoc at MIT in 2014.Paula Aguilera and Jonathan Williams/MIT

Anything else you’d like to mention?

Knox: There are two other traits I think would be worth touching upon.

Potentially the largest harm right now is related to the trait of high attachment anxiety—basically jealous, needy AI companions. I can understand the desire to make a wide range of different characters—including possessive ones—but I think this is one of the easier issues to fix. When people see this trait in AI companions, I hope they will be quick to call it out as an immoral thing to put in front of people, something that’s going to discourage them from interacting with others.

Additionally, if an AI comes with limited ability to interact with groups of people, that itself can push its users to interact with people less. If you have a human friend, in general there’s nothing stopping you from having a group interaction. But if your AI companion can’t understand when multiple people are talking to it and it can’t remember different things about different people, then you’ll likely avoid group interaction with your AI companion. To some degree it’s more of a technical challenge outside of the core behavioral AI. But this capability is something I think should be really prioritized if we’re going to try to avoid AI companions competing with human relationships.

For a different perspective on AI companions, see our Q&A with Brad Knox: How Can AI Companions Be Helpful, not Harmful?

AI models intended to provide companionship for humans are on the rise. People are already frequently developing relationships with chatbots, seeking not just a personal assistant but a source of emotional support.

In response, apps dedicated to providing companionship (such as Character.ai or Replika) have recently grown to host millions of users. Some companies are now putting AI into toys and desktop devices as well, bringing digital companions into the physical world. Many of these devices were on display at CES last month, including products designed specifically for children, seniors, and even your pets.

AI companions are designed to simulate human relationships by interacting with users like a friend would. But human-AI relationships are not well understood, and companies are facing concern about whether the benefits outweigh the risks and potential harm of these relationships, especially for young people. In addition to questions about users’ mental health and emotional well being, sharing intimate personal information with a chatbot poses data privacy issues.

RELATED: How Can AI Companions Be Helpful, not Harmful?

Nevertheless, more and more users are finding value in sharing their lives with AI. So how can we understand the bonds that form between humans and chatbots?

Jaime Banks is a professor at the Syracuse University School of Information Studies who researches the interactions between people and technology—in particular, robots and AI. Banks spoke with IEEE Spectrum about how people perceive and relate to machines, and the emerging relationships between humans and their machine companions.

Defining AI Companionship

How do you define AI companionship?

Jaime Banks: My definition is evolving as we learn more about these relationships. For now, I define it as a connection between a human and a machine that is dyadic, so there’s an exchange between them. It is also sustained over time; a one-off interaction doesn’t count as a relationship. It’s positively valenced—we like being in it. And it is autotelic, meaning we do it for its own sake. So there’s not some extrinsic motivation, it’s not defined by an ability to help us do our jobs or make us money.

I have recently been challenged by that definition, though, when I was developing an instrument to measure machine companionship. After developing the scale and working to initially validate it, I saw an interesting situation where some people do move toward this autotelic relationship pattern. “I appreciate my AI for what it is and I love it and I don’t want to change it.” It fit all those parts of the definition. But then there seems to be this other relational template that can actually be both appreciating the AI for its own sake, but also engaging it for utilitarian purposes.

That makes sense when we think about how people come to be in relationships with AI companions. They often don’t go into it purposefully seeking companionship. A lot of people go into using, for instance, ChatGPT for some other purpose and end up finding companionship through the course of those conversations. And we have these AI companion apps like Replika and Nomi and Paradot that are designed for social interaction. But that’s not to say that they couldn’t help you with practical topics.

Jaime Banks customizes the software for an embodied AI social humanoid robot.Angela Ryan/Syracuse University

Different models are also programmed to have different “personalities.” How does that contribute to the relationship between humans and AI companions?

Banks: One of our Ph.D. students just finished a project about what happened when OpenAI demoted GPT-4o and the problems that people encountered, in terms of companionship experiences when the personality of their AI just completely changed. It didn’t have the same depth. It couldn’t remember things in the same way.

That echoes what we saw a couple years ago with Replika. Because of legal problems, Replika disabled for a period of time the erotic roleplay module and people described their companions as though they had been lobotomized, that they had this relationship and then one day they didn’t anymore. With my project on the tanking of the soulmate app, many people in their reflection were like, “I’m never trusting AI companies again. I’m only going to have an AI companion if I can run it from my computer so I know that it will always be there.”

Benefits and Risks of AI Relationships

What are the benefits and risks of these relationships?

Banks: There’s a lot of talk about the risks and a little talk about benefits. But frankly, we are only just on the precipice of starting to have longitudinal data that might allow people to make causal claims. The headlines would have you believe that these are the end of mankind, that they’re going to make you commit suicide or abandon other humans. But much of those are based on these unfortunate, but uncommon situations.

Most scholars gave up technological determinism as a perspective a long time ago. In the communication sciences at least, we don’t generally assume that machines make us do something because we have some degree of agency in our interactions with technologies. Yet much of the fretting around potential risks is deterministic—AI companions make people delusional, make them suicidal, make them reject other relationships. A large number of people get real benefits from AI companions. They narrate experiences that are deeply meaningful to them. I think it’s irresponsible of us to discount those lived experiences.

When we think about concerns linking AI companions to loneliness, we don’t have much data that can support causal claims. Some studies suggest AI companions lead to loneliness, but other work suggests it reduces loneliness, and other work suggests that loneliness is what comes first. Social relatedness is one of our three intrinsic psychological needs, and if we don’t have that we will seek it out, whether it’s from a volleyball for a castaway, my dog, or an AI that will allow me to feel connected to something in my world.

Some people, and governments for that matter, may move toward a protective stance. For instance, there are problems around what gets done with your intimate data that you hand over to an agent owned and maintained by a company—that’s a very reasonable concern. Dealing with the potential for children to interact, where children don’t always navigate the boundaries between fiction and actuality. There are real, valid concerns. However, we need some balance in also thinking about what people are getting from it that’s positive, productive, healthy. Scholars need to make sure we’re being cautious about our claims based on our data. And human interactants need to educate themselves.

Jaime Banks holds a mechanical hand.Angela Ryan/Syracuse University

Why do you think that AI companions are becoming more popular now?

Banks: I feel like we had this perfect storm, if you will, of the maturation of large language models and coming out of COVID, where people had been physically and sometimes socially isolated for quite some time. When those conditions converged, we had on our hands a believable social agent at a time when people were seeking social connection. Outside of that, we are increasingly just not nice to one another. So, it’s not entirely surprising that if I just don’t like the people around me, or I feel disconnected, that I would try to find some other outlet for feeling connected.

More recently there’s been a shift to embodied companions, in desktop devices or other formats beyond chatbots. How does that change the relationship, if it does?

Banks: I’m part of a Facebook group about robotic companions and I watch how people talk, and it almost seems like it crosses this boundary between toy and companion. When you have a companion with a physical body, you are in some ways limited by the abilities of that body, whereas with digital-only AI, you have the ability to explore fantastic things—places that you would never be able to go with another physical entity, fantasy scenarios.

But in robotics, once we get into a space where there are bodies that are sophisticated, they become very expensive and that means that they are not accessible to a lot of people. That’s what I’m observing in many of these online groups. These toylike bodies are still accessible, but they are also quite limiting.

Do you have any favorite examples from popular culture to help explain AI companionship, either how it is now or how it could be?

Banks: I really enjoy a lot of the short fiction in Clarkesworld magazine, because the stories push me to think about what questions we might need to answer now to be prepared for a future hybrid society. Top of mind are the stories “Wanting Things,” “Seven Sexy Cowboy Robots,” and “Today I am Paul.” Outside of that, I’ll point to the game Cyberpunk 2077, because the character Johnny Silverhand complicates the norms for what counts as a machine and what counts as companionship.

Refrigerators and freezers around the world rely on refrigerants that, when leaked, can emit greenhouse gases thousands of times as potent as carbon dioxide. As the race to find alternative cooling tech heats up, researchers are exploring climate-friendly options such as elastocaloric cooling, a solid-state tech that moves heat through reversible phase transformations.

Now, researchers at Hong Kong University of Science and Technology (HKUST) have announced the first elastocaloric device capable of reaching subzero Celsius temperatures, marking a milestone in the development of this promising refrigeration technology. The desktop prototype, described in a recent Nature paper, successfully froze 20 milliliters of water into ice within two hours, making it comparable to the performance of a domestic freezer.

Elastocaloric systems have attracted attention in recent years because their cooling doesn’t rely on greenhouse-gas-emitting refrigerants. Instead, they exploit the unique phase-transition properties of shape-memory alloys (SMAs). These are a class of metals which, within a specific temperature range, release heat when compressed and absorb heat when relaxed.

But, to date, all research-grade elastocaloric cooling systems have operated at temperatures above 0 °C, seemingly limited by poor performance of the SMAs at lower temperatures.

For their freezer, the Hong Kong team utilized a unique nickel-titanium SMA (51.2 percent nickel, 48.8 percent titanium) that retains its elastocaloric properties at temperatures close to -21 °C. Rods of the alloy were precision-machined to produce thin-walled tubes with a complex inner structure designed to maximize heat exchange.

Assembling the Ni-Ti tubes into a working elastocaloric regenerator—a type of heat exchanger—involved grouping them into trios, and tightly fitting them into a strong plastic casing. Eight of these units were connected to each other and a linear actuator was installed at one end of the line to apply the force needed to activate the elastocaloric effect.

The HKUST freezer can turn 20 milliliters of distilled water to ice within 2 hours.Goan Zhou, Zexi Li et al./Nature

How Does the Freezer Work?

In operation, the actuator compresses and holds the Ni-Ti tubes, causing the material to heat up. At the same time, a salty liquid containing calcium chloride (a salt often used to deice roads) is pumped through the regenerator, which carries the heat away and ejects it to the surroundings on exit. Then, the actuator releases, causing the Ni-Ti tubes to rapidly cool down. The fluid flows back through the regenerator in the opposite direction and is cooled by the process.

A full cycle takes 1 second. After 15 minutes of continuous operation in the lab, the cold end of the system reached a record -12 °C while the hot end measured 24 °C, a temperature lift of 36 °C. In real-world conditions—attached to an insulated chamber containing a vial of water, and operating outdoors on a warm day—the system took longer to cool down, with the chamber reaching -4 °C in 60 minutes.

Among the authors of the Nature paper are [from left] Zexi Li, Shuhuai Yao, Qingping Sun, and Guoan Zhou.HKUST

Qingping Sun, a professor of mechanical engineering at HKUST who led the research, acknowledges that there are significant barriers to overcome before this device is ready for commercialization in either domestic or industrial markets. Its energy efficiency is “still lower than conventional vapor-compression-based air conditioning,” with most of the energy loss due to the actuator. “We are developing new actuation technology as part of our system-integration and optimization work,” he says. The team aims to reach lower temperatures too, down to -100 °C, which will require different alloys. And finally, the thin-walled tubular structures are currently made using a precise, but “very expensive” manufacturing process, Sun says. So, they’re exploring alternatives including 3D printing in an effort to reduce material waste and manufacturing costs.

“We are working on these three directions in parallel,” he says, predicting that they’ll have a viable product available on the market in two to three years.

In terms of where an elastocaloric freezer might be used, Sun suggested applications including mobile systems for frozen food delivery and climate control in electric vehicles. He says they have “active collaborations with industrial partners,” but declined to name any specific groups. Long-term, he says, the goal is to “disrupt the existing vapor compression technology market,” but the focus for now is on finding “a niche area to achieve breakthrough, and then gradually expand.”

While elastocaloric cooling shows promise as an alternative to conventional refrigeration without greenhouse gasses, for now it remains firmly in the prototype stage, with substantial engineering hurdles ahead.

From 6–22 February, the 2026 Winter Olympics in Milan-Cortina d’Ampezzo, Italy, will feature not just the world’s top winter athletes but also some of the most advanced sports technologies today. At the first Cortina Olympics, in 1956, the Swiss company Omega—based in Biel/Bienne—introduced electronic ski starting gates and launched the first automated timing tech of its kind.

At this year’s Olympics, Swiss Timing, sister company to Omega under the parent company Swatch Group, unveils a new generation of motion-analysis and computer-vision technology. The new technologies on offer include photo-finish cameras that capture up to 40,000 images per second.

“We work very closely with athletes,” says Swiss Timing CEO Alain Zobrist, who has overseen Olympic timekeeping since the winter games of 2006 in Torino. “They are the primary customers of our technology and services, and they need to understand how our systems work in order to trust them.”

Using high-resolution cameras and AI algorithms tuned to skaters’ routines, Milan-Cortina Olympic officials expect new figure-skating tech to be a key highlight of the games. Omega

Figure-Skating Tech Completes the Rotation

Figure skating, the Winter Olympics’ biggest TV draw, is receiving a substantial upgrade at Milano Cortina 2026.

Fourteen 8K-resolution cameras positioned around the rink will capture every skater’s movement. “We use proprietary software to interpret the images and visualize athlete movement in a 3D model,” says Zobrist. “AI processes the data so we can track trajectory, position, and movement across all three axes—x, y, and z.”

The system measures jump heights, air times, and landing speeds in real time, producing heat maps and graphic overlays that break down each program—all instantaneously. “The time it takes for us to measure the data, until we show a matrix on TV with a graphic, this whole chain needs to take less than 1/10 of a second,” Zobrist says.

A range of different AI models helps the broadcasters and commentators process each skater’s every move on the ice.

“There is an AI that helps our computer-vision system do pose estimation,” he says. “So we have a camera that is filming what is happening, and an AI that helps the camera understand what it’s looking at. And then there is a second type of AI, which is more similar to a large language model that makes sense of the data that we collect.”

Among the features that Swiss Timing’s new systems provide is blade-angle detection, which gives judges precise technical data to augment their technical and aesthetic decisions. Zobrist says future versions will also determine whether a given rotation is complete, so that “if the rotation is 355 degrees, there is going to be a deduction,” he says.

This builds on technology Omega unveiled at the 2024 Paris Olympics for diving, where cameras measured distances between a diver’s head and the board to help judges assess points and penalties to be awarded.

At the 2026 Winter Olympics, ski jumping will feature both camera-based and sensor-based technologies to make the aerial experience more immediate and real-time. Omega

Ski-Jumping Tech Finds Make-or-Break Moments

Unlike figure skating’s camera-based approach, ski jumping also relies on physical sensors.

“In ski jumping, we use a small, lightweight sensor attached to each ski, one sensor per ski, not on the athlete’s body,” Zobrist says. The sensors are lightweight and broadcast data on a skier’s speed, acceleration, and positioning in the air. The technology also correlates performance data with wind conditions, revealing the influence of environmental factors on each jump.

High-speed cameras also track each ski jumper. Then, a stroboscopic camera provides body position time-lapses throughout the jump.

“The first 20 to 30 meters after takeoff are crucial as athletes move into a V position and lean forward,” Zobrist says. “And both the timing and precision of this movement strongly influence performance.”

The system reveals biomechanical characteristics in real time, he adds, showing how athletes position their bodies during every moment of the takeoff process. The most common mistake in flight position, over-rotation or under-rotation, can now be detailed and diagnosed with precision on every jump.

Bobsleigh: Pushing the Line on the Photo Finish

This year’s Olympics will also feature a “virtual photo finish,” providing comparison images of when different sleds cross the finish line over previous runs.

Omega’s cameras will provide virtual photo finishes at the 2026 Winter Olympics. Omega

“We virtually build a photo finish that shows different sleds from different runs on a single visual reference,” says Zobrist.

After each run, composite images show the margins separating performances. However, more tried-and-true technology still generates official results. A Swiss Timing score, he says, still comes courtesy of photoelectric cells, devices that emit light beams across the finish line and stop the clock when broken. The company offers its virtual photo finish, by contrast, as a visualization tool for spectators and commentators.

In bobsleigh, as in every timed Winter Olympic event, the line between triumph and heartbreak is sometimes measured in milliseconds or even shorter time intervals. Such precision will, Zobrist says, stem from Omega’s Quantum Timer.

“We can measure time to the millionth of a second, so six digits after the comma, with a deviation of about 23 nanoseconds over 24 hours,” Zobrist explained. “These devices are constantly calibrated and used across all timed sports.”

This paper discusses how RF propagation simulations empower engineers to test numerous real-world use cases in far less time, and at lower costs, than in situ testing alone. Learn how simulations provide a powerful visual aid and offer valuable insights to improve the performance and design of body-worn wireless devices.

Download this free whitepaper now!

A superpowered Formula 1 car, a buzzing drone, a soldier’s pack, and a wearable smart device have this in common: They all need batteries. Ideally, those batteries could fit into oddly shaped nooks, curves, and voids, something that today’s cylindrical or rectangular cells struggle to do. Engineer Gabe Elias, who helped design the Mercedes-AMG Petronas racers that won seven consecutive F1 championships, cofounded a startup to 3D print batteries onto surfaces, flowing into those unused spaces in all kinds of devices and vehicles.

The company recently won a US $1.25 million, 18-month contract with the U.S. Air Force to prove its tech’s potential. It joins competitors such as Sakuú, in Silicon Valley, and Germany’s Blackstone Technology, in a race to popularize printed batteries that can conform to various shapes. Soon after Elias cofounded Material Hybrid Manufacturing in 2023, his group realized that their initial pitch—printing batteries in new shapes for passenger cars—was stuck in neutral. EVs, especially bigger ones, don’t have pressing space constraints for batteries. Electric SUVs and pickup trucks from Rivian, where Elias also worked, can fit 7,776 cylindrical batteries into a brawny, 135 kilowatt-hour pack.

So, the company changed lanes to smaller devices with wasted space it could stuff with energy. The Hybrid3D, a proprietary manufacturing platform, can print full-stack batteries in situ: Anode, cathode, separator and casing, with no molds or costly tooling required. The tech eliminates the metal casings, bus bars and other components that hog space in conventional cells. Material’s active battery material can fill voids and follow three-dimensional curves: Think the wing of a drone, or the slender, curling arm of a pair of smart glasses.

“Things are shrinking, so we’re shrinking around it,” Elias says. “Electronics are becoming embedded, consolidated, optimized, and batteries are the only part of that equation that’s being left behind.”

The company has teamed with Performance Drone Works (PDW) to push its tech toward commercialization. For the initial project, the companies will show how much active battery their 3D material can pack into the same modular space that holds 48 cylindrical cells in an existing drone. Even in that simplified, proof-of-concept drone, the printed battery achieves a 50 percent boost in energy density, and uses 35 percent more available volume.

“That gives you a bunch of options,” Elias says. “You could either fly 50 percent farther, or decrease the size of the battery pack, fit more payload, and cover the same distance.”

Fit for purpose

Next-gen designs could boost those gains by dispersing battery material around drone frames, electric motors, or other surfaces. Notoriously heavy military backpacks—stuffed with bulky, square batteries—could be lighter and ergonomically shaped. Military helmets could directly integrate batteries that power head-mounted gear.

When he was still at Mercedes, Elias tried to wrap conventional cells around a driver’s seat to improve the layout. Even in the rarefied realm of F1 racing, where top teams spend hundreds of millions of dollars a year in the service of speed, Mercedes simply gave up.

“We ended up stopping the project, just knocking our heads against the wall because it’s so complicated to take these little cylindrical cells, wedge them into spaces and tie them together in the configuration you want,” he says.

Material’s batteries, he says, are the natural evolution of carbon fiber or other composite structures in automobiles, including “cell to pack” construction that eliminates modules and makes batteries integral parts of structures.

“We’re turning energy storage into a subsystem, just like all the other subsystems on a car,” Elias says.

Material’s first commercial-scale printer’s bed measures 550 by 350 millimeters, with plans to greatly expand that surface. Its tech is essentially a hybrid of direct ink printing and fused deposition modeling, two of several techniques being developed by companies vying to bring these energy sources to market.

Critically, the tech would allow batteries to go from prototype to printing, with no need for expensive, time-consuming retooling. Material’s printer platform can already handle a variety of chemistries and formats with simple changes in materials and software coding, he says.

“We’ve printed NMC 811 and NMC 111, LFP and lithium-titanate oxide (LTO), to name a few,” Elias says. “We’re chemistry-agnostic.”

Material’s cells currently use liquid electrolyte, added via an infusion process, but the company has a working road map toward solid-state designs. Challenges include tuning battery materials to flow properly from printer nozzles; and to deposit that material in uniform, repeatable layers, roughly 100 to 150 micrometers thick, to ensure high quality and yields.

“Batteries really live and die by layer thicknesses,” Elias says.

Elias notes that Apple and other companies are investing massive amounts of money to create conformable batteries, such as the L-shaped batteries in some iPhones, but are using costly and limited traditional methods. And with consumer electronics giants fighting to popularize wearable devices, printed batteries are an enticing solution for their packaging and power needs. Elias points to Apple’s Tim Cook, who has gone from an AR skeptic to a champion of smart glasses. But consumers won’t really bite, he believes, until the form factor says “Ray-Ban stylish” rather than “four-eyed dork.”

“I want the connectivity and usability, but I don’t want to look stupid, or I’m just going to pull out my smartphone,” he says. “We see this as a proliferated application where everyone and their mother is going to have one of these devices.”

If companies can manage to print batteries the way office workers print a document, the technology would replace much of the costly tooling, dedicated factory production lines and time-consuming processes of conventional batteries. Those printed batteries could then compete on cost across the entire market, Elias says, from single cells to complex multicell packs, where prices can range from $400 to $3,000 per kilowatt-hour.

“The more complex the pack, the more value we capture from part consolidation and system integration, so those applications actually carry higher margins for us,” he says.

This article appears in the April 2026 print issue as “3D Printed Batteries Fit Every Nook and Cranny.”

Nonmedical devices that read brainwaves, such as smart headbands, headphones, and glasses, are becoming more popular among consumers. The products claim to make users more productive, creative, and healthier. IEEE Spectrum previewed several of these smart wearables that were introduced at this year’s Consumer Electronics Show (CES) in Las Vegas.

Since the wearable, noninvasive neurotech products aren’t medical devices, they are not subject to the same forms of regulation—which can lead to gaps in their safety and data privacy, as well as their effect on users’ brains.

UNESCO in November adopted the first global ethical standard for neurotechnologies, establishing guidelines to protect users’ mental privacy, freedom of thought, and human rights. In 2019 the Organisation for Economic Co-operation and Development issued responsible-neurotechnology recommendations. But there are no socio-technical standards for manufacturers to follow.

In response, the IEEE Brain technical community is developing the IEEE P7700 standard: “Recommended Practice for the Responsible Design and Development of Neurotechnologies.”

The proposed standard is being designed to provide a uniform set of definitions and a methodology to assess the ethical and socio-technical considerations and practices regarding the design, development, and use of neurotechnologies including wearable neurodevices for the brain, says Laura Y. Cabrera, the standard’s working group chair. Cabrera, an IEEE senior member, is an associate professor in the engineering science and mechanics department at Pennsylvania State University in University Park. Her research focuses on the ethical and societal implications of neurotechnologies.

“IEEE P7700 addresses the unique characteristics of the technology and its impact on individuals and society, in particular, as it moves from therapeutic users to a wide variety of consumers,” she says.

The standard is sponsored by the IEEE Society on Social Implications of Technology.

Concern over long-term effects

The multilayered complexity of technologies that interface with the brain and nervous system presents considerations to those developing them, Cabrera says.

“There may be long-term consequences in our brains with these types of technologies,” she says. “Maybe if they were used for a short period of time, there might not be significant consequences. But what are the effects over time?”

Patients using approved brain-stimulation technology, for example, are told of its risks and benefits, but the long-term effects of headbands to improve students’ attention span aren’t known.

“IEEE P7700 addresses the unique characteristics of the technology and its impact on individuals and society, in particular, as it moves from therapeutic users to a wide variety of consumers.”

IEEE P7700 will address potential risks to individuals and possible negative impacts on society, Cabrera says. That includes creating guardrails to prevent harm, she adds.

The cultural implications of using neurotechnologies that interface with the brain also need to be considered, she says, because people have different views.

“The brain is considered the seed of the self and the organ that orchestrates all our thoughts, behaviors, feelings, and emotions,” she says. “The brain is really central to who we are.”

Developing an ethical framework

For the past five years, the IEEE Brain community’s neuroethics committee has been developing a framework to evaluate the ethical, legal, social, and cultural issues that could emerge from use of the technology. The document covers nine types of applications, including those used for wellness.

Because more devices kept entering the market, IEEE Brain decided in 2023 that it was time to begin drafting a standard.

Members of its working group come from Argentina, China, Japan, Italy, Switzerland, and the United States. Participants include developers, engineers, ethicists, lawyers, and social science researchers.

The standard, Cabrera says, will be the first socio-technical standard aimed at fostering the ethical and responsible innovation of neurotechnology that meets societal and community values at an international level. P7700 will include a how-to guide, criteria for evaluating each suggested process, and case studies to help with the interpretation and practical use of the standard, she says.

“Our applied ethical approach uses a responsible research and innovation method to enable developers, researchers, users, and regulators to anticipate and address ethical and sociocultural implications of neurotechnologies, mitigating negative unintended consequences while increasing community support and engagement with innovators,” Cabrera says.

The working group is seeking additional participants to help refine the process, tools, and recommendations.

“There are a variety of people who can contribute their expertise,” she says, “including academics, data scientists, government program leaders, policymakers, lawyers, social scientists, and users.”

Cabrera says she anticipates the standard will be published early next year.

You can register to participate in the standard’s development here.

Assistive technology is expensive, and many people with disabilities live on fixed incomes. Disabled assistive tech users also must contend with equipment that was often designed without any capacity to be repaired or modified. But assistive tech users ultimately need the functionality they need—a wheelchair that isn’t constantly needing to be charged, perhaps, or a hearing aid that doesn’t amplify all background noise equally. Assistive tech “makers,” who can hack and modify existing assistive tech, have always been in high demand.

Therese Willkomm, emeritus professor of occupational therapy at the University of New Hampshire, has written three books cataloging her more than 2,000 assistive technology hacks. Willkomm says she aims to keep her assistive tech hacks costing less than five dollars.

She’s come to be known internationally as the “MacGyver of Assistive Technology” and has presented more than 600 workshops and assistive tech maker days across 42 states and 14 countries.

IEEE Spectrum sat down with Willkomm ahead of her latest assistive tech Maker Day workshop, on Saturday, 31 January, at the Assistive Technology Industry Association (ATIA) conference in Orlando, Florida. Over the course of the conversation, she discussed the evolution of assistive technology over 40 years, the urgent need for affordable communication devices, and why the DIY movement matters now more than ever.

IEEE Spectrum: What got you started in assistive technology?

Therese Willkomm: I grew up in Wisconsin, where my father had a machine shop and worked on dairy and hog farms. At age 10, I started building and making things. A cousin was in a farm accident and needed modifications to his tractor, which introduced me to welding. In college, I enrolled in vocational rehabilitation and learned about rehab engineering—assistive technology wasn’t coined until 1988 with the Technology-Related Assistance Act. In 1979, Gregg Vanderheiden came to the University of Wisconsin-Stout and demonstrated creative things with garage door openers and communication devices. I thought, “Wow, this would be an awesome career path—designing and fabricating devices and worksite adaptations for people with disabilities to go back to work and live independently.” I haven’t looked back.

You’ve created over 2,000 assistive technology solutions. What’s your most memorable one?

Willkomm: A device for castrating pigs with one hand. We figured out a way to design a device that fit on the end of the hog crate that was foot-operated to hold the hind legs of the pig back so the procedure could be done with one hand.

Assistive Technology’s Changing Landscape

How has assistive technology evolved over the decades?

Willkomm: In the 1980s, we fabricated devices from wood and early electronics. I became a [Rehabilitation Engineering and Assistive Technology Society of North America, a.k.a. RESNA] member in 1985. The 1988 Technology-Related Assistance Act was transformational—all 50 states finally got funding to support assistive technology and needs in rural areas. Back in the ‘80s, we were soldering and making battery interrupters and momentary switches for toys, radios, and music. Gregg was doing some things with communication. There were Prentke Romich communication devices. Those were some of the first electronic assistive technologies.

The early 1990s was all about mobile rehab engineering. Senator Bob Dole gave me a $50,000 grant to fund my first mobile unit. That mobile unit had all my welding equipment, all my fabrication equipment, and I could drive farm to farm, set up outside right in front of the tractor, and fabricate whatever needed to be fabricated. Then, around 1997, there were cuts in the school systems. Mobile units became really expensive to operate. We started to look at more efficient ways of providing assistive technology services. With the Tech Act, we had demonstration sites where people would come and try out different devices. But people had to get in a car, drive to a center, get out, find parking, come into the building—a lot of time was being lost.

In the 2000s, more challenges with decreased funding. I discovered that with a Honda Accord and those crates you get from Staples, you could have your whole mobile unit in the trunk of your car because of advances in materials. We could make battery interrupters and momentary switches without ever having to solder. We can make switches in 28 seconds, battery interrupters in 18 seconds. When COVID happened, we had to pivot—do more virtual, ship stuff out to people. We were able to serve more individuals during COVID than prior to COVID because nobody had to travel.

How do you keep costs under five dollars?

Willkomm: I aim for five dollars or less. I get tons of corrugated plastic donated for free, so we spend no money on that. Then there’s Scapa Tape—a very aggressive double-sided foam tape that costs five cents a foot. If you fabricate something and it doesn’t work out, and you have to reposition, you’re out a nickel’s worth of material. Buying Velcro in bulk helps too. ThenInstamorph—it is non-toxic, biodegradable. You can reheat it, reform it, in five minutes or less up to six times. I’ve created about 132 different devices just using Instamorph. A lot of things I make out of Instamorph don’t necessarily work. I have a bucket, and I reuse that Instamorph. We can get six, seven devices out of reusable Instamorph. That’s how we keep it under five dollars.

What key legislation impacts assistive technology?

Willkomm: Definitely the Technology-Related Assistance Act. In the school system, however, it only says “Did you consider assistive technology?” So that legislation really needs to be beefed up. The third piece of legislation I worked on was the AgrAbility legislation to fund assistive technology consultations and technical assistance for farmers and ranchers. The latest Technology-Related Assistance Act was reauthorized in 2022. Not a whole lot of changes—it’s still assistive technology device demonstrations and loans, device reuse, training, technical assistance, information and awareness. The other thing isNIDILRR—National Institute on Independent Living and Rehabilitation Research, funded under [the U.S. Department of Health and Human Services, a.k.a. HHS]. Funding the rehab engineering centers was pretty significant in advancing the field because these were huge, multimillion-dollar centers dedicated to core areas like communication and employment. Now there’s a new one out on artificial intelligence.

A Vision for a Better Assistive Tech Future

With over 2,000 hacks to improve usability of assistive technologies, veteran DIY maker Therese Willkomm has earned the moniker “the MacGyver of assistive tech.” Therese Willkomm

What deserves more focus in your field?

Willkomm: The supply-and-demand problem. It all comes down to time and money. We have an elderly population that continues to grow, and a disability population that continues to grow—high demand, high need for assistive technology, yet the resources available to meet that need are limited. A few years back, the Christopher & Dana Reeve Foundation had a competition. I submitted a proposal similar to the Blue Apron approach. People don’t have supplies at their house. They can’t buy two inches of tape—they have to buy a whole roll. They can’t buy one foot of corrugated plastic—they’ve got to buy an 18-by-24 sheet or wait till it gets donated.

With my third book, I created solutions with QR codes showing videos on how to make them. I used Christopher Reeve Foundation funding to purchase supplies. With Blue Apron, somebody wants to make dinner and a box arrives with a chicken breast, potato, vegetables, and recipe. I thought, what if we could apply that to assistive technology? Somebody needs something, there’s a solution out there, but they don’t have the money or the time—how can we quickly put it in a box and send it to them? People who attended my workshops didn’t have to spend money on materials or waste time at the store. They’d watch the video and assemble it.

But then there were people who said, “I do not have even five minutes in the school day to stop what I’m doing to make something.” So we found volunteers who said, “Hey, I can make slant boards. I can make switches. I can adapt toys.” You have people who want to build stuff and people who need stuff. If you can deal with the time and money issue, anything’s possible to serve more people and provide more devices.

What’s your biggest vision for the future?

Willkomm: I’m very passionate about communication. December 15 was the passage in 1791 of our First Amendment, freedom of speech. Yet people with communication impairments are denied their basic right of freedom of speech because they don’t have an affordable communication device, or it takes too long to program or learn. I just wish we could get better at designing and fabricating affordable communication devices, so everybody is awarded their First Amendment right. It shouldn’t be something that’s nice to have—it’s something that’s needed to have. When you lose your leg, you’re fitted with a prosthetic device, and insurance covers that. Insurance should also cover communication devices and all the support services needed. With voice recognition and computer-generated voices, there are tremendous opportunities in assistive technology for communication impairments that need to be addressed.

What should IEEE Spectrum readers take away from this conversation?

Willkomm: There’s tremendous need for this skill set—working in conjunction with AI and material sciences and the field of assistive technology and rehab engineering. I’d like people to look at opportunities to volunteer their time and also to pursue careers in the field of specialized rehab engineering.

How are DIY approaches evolving with new technologies?

Willkomm: What we’re seeing at maker fairs is more people doing 3D printing, switch-access controls, and these five-minute approaches. There has to be a healthy balance between what we can do with or without electronics. If we need something programmed with electronics, absolutely—but is there a faster way?

The other thing that’s interesting is skill development. You used to have to go to college for four, six, eight years. With YouTube, you can learn so much on the internet. You can develop skills in things you never thought were possible without a four-year degree. There’s basic electronic stuff you can absolutely learn without taking a course. I think we’re going to have more people out there doing hacks, asking “What if I change it this way?” We don’t need to have a switch.

We need to look at the person’s body and how that body interacts with the electronic device interface so it requires minimal effort—whether it be eye control or motion control. Having devices that predict what you’re going to want next, that are constantly listening, knowing the way you talk. I love the fact that AI looks at all my emails and creates this whole thing like “Here’s how I’d respond.” I’m like, yeah, that’s exactly it. I just hit select, and I don’t have to type it all out. It speeds up communication. We’re living in exciting times right now.

This article was supported by the IEEE Foundation and a Jon C. Taenzer fellowship grant.

Dizzy Gillespie was a fan. Frank Sinatra bought one for himself and gave them to his Rat Pack friends. Hugh Hefner acquired one for the Playboy Mansion. Clairtone Sound Corp.’s Project G high-fidelity stereo system, which debuted in 1964 at the National Furniture Show in Chicago, was squarely aimed at trendsetters. The intent was to make the sleek, modern stereo an object of desire.

By the time the Project G was introduced, the Toronto-based Clairtone was already well respected for its beautiful, high-end stereos. “Everyone knew about Clairtone,” Peter Munk, president and cofounder of the company, boasted to a newspaper columnist. “The prime minister had one, and if the local truck driver didn’t have one, he wanted one.” Alas, with a price tag of CA $1,850—about the price of a small car—it’s unlikely that the local truck driver would have actually bought a Project G. But he could still dream.

The design of the Project G seemed to come from a dream.

“I want you to imagine that you are visitors from Mars and that you have never seen a Canadian living room, let alone a hi-fi set,” is how designer Hugh Spencer challenged Clairtone’s engineers when they first started working on the Project G. “What are the features that, regardless of design considerations, you would like to see incorporated in a new hi-fi set?”

The film “I’ll Take Sweden” featured a Project G, shown here with co-star Tuesday Weld.Nina Munk/The Peter Munk Estate

The result was a stereo system like no other. Instead of speakers, the Project G had sound globes. Instead of the heavy cabinetry typical of 1960s entertainment consoles, it had sleek, angled rosewood panels balanced on an aluminum stand. At over 2 meters long, it was too big for the average living room but perfect for Hollywood movies—Dean Martin had one in his swinging Malibu bachelor pad in the 1965 film Marriage on the Rocks. According to the 1964 press release announcing the Project G, it was nothing less than “a new sculptured representation of modern sound.”

The first-generation Project G had a high-end Elac Miracord 10H turntable, while later models used a Garrard Lab Series turntable. The transistorized chassis and control panel provided AM, FM, and FM-stereo reception. There was space for storing LPs or for an optional Ampex 1250 reel-to-reel tape recorder.

The “G” in Project G stood for “globe.” The hermetically sealed 46-centimeter-diameter sound globes were made of spun aluminum and mounted at the ends of the cantilevered base; inside were Wharfedale speakers. The sound globes rotated 340 degrees to project a cone of sound and could be tuned to re-create the environment in which the music was originally recorded—a concert hall, cathedral, nightclub, or opera house.

Diane Landry, winner of the 1963 Miss Canada beauty pageant, poses with a Project G2. Nina Munk/The Peter Munk Estate

Initially, Clairtone intended to produce only a handful of the stereos. As one writer later put it, it was more like a concept car “intended to give Clairtone an aura of futuristic cool.” Eventually fewer than 500 were made. But the Project G still became an icon of mod ’60s Canadian design, winning a silver medal at the 13th Milan Triennale, the international design exhibition.

And then it was over; the dream had ended. Eleven years after its founding, Clairtone collapsed, and Munk and cofounder David Gilmour lost control of the company.

The birth of Clairtone Sound Corp.

Clairtone’s Peter Munk lived a colorful life, with a nightmarish start and many fantastic and dreamlike parts too. He was born in 1927 in Budapest to a prosperous Jewish family. In the spring of 1944, Munk and 13 members of his family boarded a train with more than 1,600 Jews bound for the Bergen-Belsen concentration camp. They arrived, but after some weeks the train moved on, eventually reaching neutral Switzerland. It later emerged that the Nazis had extorted large sums of cash and valuables from the occupants in exchange for letting the train proceed.

As a teenager in Switzerland, Munk was a self-described party animal. He enjoyed dancing and dating and going on long ski trips with friends. Schoolwork was not a top priority, and he didn’t have the grades to attend a Swiss university. His mother, an Auschwitz survivor, encouraged him to study in Canada, where he had an uncle.

Before he could enroll, though, Munk blew his tuition money entertaining a young woman during a trip to New York. He then found work picking tobacco, earned enough for tuition, and graduated from the University of Toronto in 1952 with a degree in electrical engineering.

Clairtone cofounders Peter Munk [left] and David Gilmour envisioned the company as a luxury brand.Nina Munk/The Peter Munk Estate

At the age of 30, Munk was making custom hi-fi sets for wealthy clients when he and David Gilmour, who owned a small business importing Scandinavian goods, decided to join forces. Their idea was to create high-fidelity equipment with a contemporary Scandinavian design. Munk’s father-in-law, William Jay Gutterson, invested $3,000. Gilmour mortgaged his house. In 1958, Clairtone Sound Corp. was born.

From the beginning, Munk and Gilmour sought a high-end clientele. They positioned Clairtone as a luxury brand, part of an elegant lifestyle. If you were the type of woman who listened to music while wearing pearls and a strapless gown and lounging on a shag rug, your music would be playing on a Clairtone. If you were a man who dressed smartly and owned an Arne Jacobsen Egg chair, you would also be listening on a Clairtone. That was the modern lifestyle captured in the company’s advertisements.

In 1958, Clairtone produced its first prototype: the monophonic 100-M, which had a long, low cabinet made from oiled teak, with a Dual 1004 turntable, a Granco tube chassis, and a pair of Coral speakers. It never went into production, but the next model, the stereophonic 100-S, won a Design Award from Canada’s National Industrial Design Council in 1959. By 1963, Clairtone was selling 25,000 units a year.

Peter Munk visits the Project G assembly line in 1965. Nina Munk/The Peter Munk Estate

Design was always front and center at Clairtone, not just for the products but also for the typography, advertisements, and even the annual reports. Yet nothing in the early designs signaled the dramatic turn it would take with the Project G. That came about because of Hugh Spencer.

Spencer was not an engineer, nor did he have experience designing consumer electronics. His day job was designing sets for the Canadian Broadcast Corp. He consulted regularly with Clairtone on the company’s graphics and signage. The only stereo he ever designed for Clairtone was the Project G, which he first modeled as a wooden box with tennis balls stuck to the sides.

From both design and quality perspectives, Clairtone was successful. But the company was almost always hemorrhaging cash. In 1966, with great fanfare and large government incentives, the company opened a state-of-the-art production facility in Nova Scotia. It was a mismatch. The local workforce didn’t have the necessary skills, and the surrounding infrastructure couldn’t handle the production. On 27 August 1967, Munk and Gilmour were forced out of Clairtone, which became the property of the government of Nova Scotia.

Despite the demise of their first company (and the government inquiry that followed), Munk and Gilmour remained friends and went on to become serial entrepreneurs. Their next venture? A resort in Fiji, which became part of a large hotel chain in that country, Australia, and New Zealand. (Gilmour later founded Fiji Water.) Then Munk and Gilmour bought a gold mine and cofounded Barrick Gold (now Barrick Mining Corp., one of the largest gold mining operations in the world). Their businesses all had ups and downs, but both men became extremely wealthy and noted philanthropists.

Preserving Canadian design

As an example of iconic design, the Project G seems like an ideal specimen for museum collections. And in 1991, Frank Davies, one of the designers who worked for Clairtone, donated a Project G to the recently launched Design Exchange in Toronto. It would be the first object in the DX’s permanent collection, which sought to preserve examples of Canadian design. The museum quickly became Canada’s center for the promotion of design, hosting more than 50 programs each year to teach people about how design influences every aspect of our lives.

In 2008, the museum opened The Art of Clairtone: The Making of a Design Icon, 1958–1971, an exhibition showcasing the company’s distinctive graphic design, industrial design, engineering, and photography.

David Gilmour’s wife, Anna Gilmour, was the company’s first in-house model.Nina Munk/The Peter Munk Estate

But what happened to the DX itself is a reminder that any museum, however worthy, shouldn’t be taken for granted. In 2019, the DX abruptly closed its permanent collection, and curators were charged with deaccessioning its objects. Fortunately, the Royal Ontario Museum, Carleton and York Universities, and the Archives of Ontario, among others, were able to accept the artifacts and companion archives. (The Project G pictured at top is now at the Royal Ontario Museum.)

Researchers at York and Carleton have been working to digitize and virtually reconstitute the DX collection, through the xDX Project. They’re using the Linked Infrastructure for Networked Cultural Scholarship (LINCS) to turn interlinked and contextualized data about the collection into a searchable database. It’s a worthy goal, even if it’s not quite the same as having all of the artifacts and supporting papers physically together in one place. I admit to feeling both pleased about this virtual workaround, and also a little sad that a unified collection that once spoke to the historical significance of Canadian design no longer exists.

Part of a continuing series looking at historical artifacts that embrace the boundless potential of technology.

An abridged version of this article appears in the February 2026 print issue as “The Project G Stereo Defined 1960s Cool.”

References 

I first learned about Clairtone’s Project G from a panel on Canada’s design heritage organized by York University historian Jan Hadlaw at the 2025 annual meeting of the Society for the History of Technology.

The Art of Clairtone: The Making of a Design Icon, 1958–1971 by Nina Munk (Peter Munk’s daughter) and Rachel Gotlieb (McClelland & Stewart, 2008) was the companion book to the exhibition of the same name hosted by the Design Exchange in Toronto. It was an invaluable resource for this column.

Journalist Garth Hopkins’s Clairtone: The Rise and Fall of a Business Empire (McClelland & Stewart, 1978) includes many interviews with people associated with the company.

Clairtone is a new documentary by Ron Mann that came out while I was writing this piece. I haven’t been able to view it yet, but I hope to do so soon.

Wearable displays are catching up with phones and smartwatches. For decades, engineers have sought OLEDs that can bend, twist, and stretch while maintaining bright and stable light. These displays could be integrated into a new class of devices—woven into clothing fabric, for example, to show real-time information, like a runner’s speed or heart rate, without breaking or dimming.

But engineers have always encountered a trade-off: The more you stretch these materials, the dimmer they become. Now, a group co-led by Yury Gogotsi, a materials scientist at Drexel University in Philadelphia, has found a way around the problem by employing a special class of materials called MXenes—which Gogotsi helped discover—that maintain brightness while being significantly stretched.

The team developed an OLED that can stretch to twice its original size while keeping a steady glow. It also converts electricity into light more efficiently than any stretchable OLED before it, reaching a record 17 percent external quantum efficiency—a measure of how efficiently a device turns electricity into light.

The “Perfect Replacement”

Gogotsi didn’t have much experience with OLEDs when, about five years ago, he teamed up with Tae-Woo Lee, a materials scientist at Seoul National University, to develop better flexible OLEDs, driven by the ever-increasing use of flexible electronics like foldable phones.

Traditionally, the displays are built from multiple stacked layers. At the base, a cathode supplies electrons that enter the adjacent organic layers, which are designed to conduct this charge efficiently. As the electrons move through these layers, they meet positive charge injected by an indium tin oxide (ITO) film. The moment these charges combine, the organic material releases energy as light, creating the illuminated pixels that make up the image. The entire structure is sealed with a glass layer on top.

The ITO film—adhered to the glass—serves as the anode, allowing current to pass through the organic layers without blocking the generated light. “But it’s brittle. It’s ceramic, basically,” so it works well for flat surfaces, but can’t be bent, Gogotsi explains. There have been attempts to engineer flexible OLEDs many times before, but they failed to meaningfully overcome both flexibility and brightness limitations.

Gogotsi’s students started by creating a transparent, conducting film out of a MXene, a type of ultrathin and flexible material with metal-like conductivity. The material is unique in its inherent ability to bend because it’s made from many two-dimensional sheets that can slide relative to each other without breaking. The film—only 10 nanometers thick—“appeared to be this perfect replacement for ITO,” Gogotsi says.

Through experimentation, Gogotsi and Lee’s shared team found that a mix of the MXene and silver nanowire would actually stretch the most while maintaining stability. “We were able to double the size, achieving 200 percent stretching without losing performance,” Gogotsi says.

The new material can also be twisted without losing its glow.Source image: Huanyu Zhou, Hyun-Wook Kim, et al.

And the new MXene film was not only more flexible than ITO but also increased brightness by almost an order of magnitude by making the contact between the topmost light-emitting organic layer and the film more efficient.

Unlike ITO, the surface of MXenes can be chemically adjusted to make it easier for electrons to move from the electrode into the light-emitting layer. This more efficient electron flow significantly increases the brightness of the display, as evidenced by an external quantum efficiency of 17 percent, which the group claims is a record for stretchable OLEDs.

“Achieving those numbers in intrinsically stretchable OLEDs under substantial stretching is quite significant,” says Seunghyup Yoo, who runs the Integrated Organic Electronics Laboratory at South Korea’s KAIST. An external quantum efficiency of 20 percent is an important benchmark for this kind of device because it is the upper limit of efficiency dictated by the physical properties of light generation, Yoo explains.

To increase illumination, the researchers went beyond working with MXene. Lee’s group developed two additional organic layers to add into the middle of their OLED—one that directs positive charges to the light-emitting layer, ensuring that electricity is used more efficiently, and one that recycles wasted energy that would normally be lost, boosting overall brightness.

Together, the MXene layer and two organic layers allow for a notably bright and stable OLED, even when stretched. Gogotsi thinks the subsequent OLED is “very successful” because it combines both brightness and stretchability, while, historically, engineers have only been able to achieve one or the other.

“The performance that they are able to achieve in this work is an important advancement,” says Sihong Wang, a molecular engineer at the University of Chicago who also develops stretchable OLED materials. Wang also notes that the 200 percent stretchability that Gogotsi’s group attained is beyond robust for wearable applications.

Wearables and Health Care

A stretchable OLED that maintains its brightness has uses in many settings, including industrial environments, robotics, wearable clothing and devices, and communications, Gogotsi says, although he’s most excited about its adoption in health-monitoring devices. He sees a near future in which displays for diagnostics and treatment become embedded in clothing or “epidermal electronics,” comparing their function to smartwatches.

Before these displays can come to market, however, stability issues inherent to all stretchable OLEDs need to be solved, Wang says. Current materials are not able to sustain light emissions for long enough to serve customers in the ways they require.

Finding housings to protect them is also a problem. “You need a stretchable encapsulation material that can protect the central device without allowing oxygen and moisture to permeate,” Wang says.

Yoo agrees: He says it’s a tough problem to solve because the best protective layers are rigid and not very stretchable. He notes yet another challenge in the way of commercialization, which is “developing stretchable displays that do not exhibit image distortion.”

Regardless, Gogotsi is excited about the future of stretchable OLEDs. “We started with computers occupying the room, then moved to our desktops, then to laptops, then we got smartphones and iPads, but still we carry stuff with us,” he says. “Flexible displays can be on the sleeve of your jacket. They can be rolled into a tube or folded and put in your pocket. They can be everywhere.”

This article appears in the March 2026 print issue as “Stretchable OLEDs Just Got a Huge Upgrade.”

History repeated itself at CES 2026. At this year’s event, Pebble—a popular but short-lived smartwatch pioneer of the 2010s—was on the show floor displaying its latest wearables, much as it had a decade ago. And the person providing that demonstration was again Eric Migicovsky, Pebble’s original founder.

Of course, not everything is the same. Pebble’s first launch followed a startup playbook. The company received VC funding, grew to several hundred employees, and quickly sold to Fitbit in 2016. This time around, Pebble is more like a passion project. The company is self-funded with just five full-time employees, PebbleOS is open source, and Migicovsky’s goal is not to revolutionize wearables but instead to return them to their roots.

“I had a box of Pebbles, and I used them. Over the years, eventually, I realized I would have to use someone else’s smartwatch. I tried everything. And I found I have a very esoteric set of needs,” said Migicovsky.

Pebble returns with not one, but three gadgets

Pebble’s big CES 2026 reveal was the Pebble Round 2, a smartwatch with a 1.3-inch circular e-paper display. It ditches some common wearable features, like a heart rate monitor, to deliver an ultrathin 8.1-millimeter profile.

However, Pebble also showed two other wearables that were announced in the months before the show: the Pebble Time 2 and the Pebble Index. The Pebble Time 2 is a larger smartwatch with a 1.5-inch rectangular e-paper display, a heart rate monitor, and a speaker. The Pebble Index is a ring with a microphone, a battery, and a button, and is meant as a companion device for quick audio notes.

What these devices share is Migicovsky’s “esoteric” approach to wearable design. “I don’t want a smartphone on my wrist. I want a companion to my smartphone. I like my smartphone, so I don’t go for a run and expect it to do everything. And I also don’t want to worry about it as another gadget that needs to be charged every day.”

That last part is key for Migicovsky and a significant departure from most wearables. The slim Pebble Round promises up to two weeks of battery life, while the larger Pebble Time 2 promises up to a month on a charge.

To achieve that, the smartwatches rely on an e-paper display. This is not an electronic ink display but rather a low-power, low-refresh LCD that displays just 64 colors. This, of course, means the image quality is far less impressive than an Apple Watch or Google Pixel watch, but it extends the battery life while maintaining an always-on display. The watches also conserve power by relying on a simple microcontroller rather than a more feature-rich, and power-hungry, chip.

The ring, which doesn’t have a display, lasts even longer. It ships with a lifetime battery that should last for years. It’s not user-replaceable, however, so the ring will not function once the battery depletes. Migicovsky sees the ring as a device for quick questions or notes, which can even be directed to an AI assistant (more on that later). But it’s not an always-on recording device, instead recording only when the button on the ring is pressed.

The Pebble Index ring can record your thoughts at the touch of a button. Pebble

PebbleOS is an open-source wearable operating system

Pebble’s new hardware should fill a gap in the wearable world. Smartwatches and rings have evolved into elaborate devices with multiple sensors and powerful SoCs, and I expect there’s room for a simpler alternative. The pricing reflects that, too: The Index costs US $75, the Round is $199, and the Time 2 is $225.

However, the new devices are just half the story. The other half is PebbleOS, which is now open source, and the Pebble app ecosystem.

A few years after Fitbit acquired Pebble, Fitbit itself sold to Google, which did nothing with the Pebble brand. So, Migicovsky wondered: Would Google be willing to part with it?

“I asked some friends that I know at Google, would you consider open-sourcing the operating system, so the community can build on that foundation? And they said yes. It took a year, but they said yes,” said Migicovsky.

PebbleOS is available on GitHub with an Apache 2.0 license. Anyone can download, modify, and distribute the OS, so long as the license is included in the distribution. There are currently 91 forks listed on GitHub—though, as is typical, most appear to be minor forks by curious software engineers. Pebble also wrote new open-source mobile applications for Android and iOS, which are used to sync Pebble devices with a smartphone.

The hardware is not fully open, and Migicovsky said he doesn’t intend to take the company in that direction. However, Pebble will provide schematics and .STL files for its devices, which will give users the opportunity to make modifications.

Pebble also has an app store that developers can use to distribute their apps. Though it’s no match for Apple’s app store, it’s surprisingly populated. In theory, apps that are part of this ecosystem could run on other devices using PebbleOS, or a fork of PebbleOS, though it would depend on the specifics of the device and the fork.

Yes, there’s an AI angle (kind of)

The design and philosophy behind Pebble borders on nostalgic. However, Migicovsky was quick to stress that Pebble is meant as a companion to—not a rejection of—modern trends in consumer electronics.

That includes AI.

Pressing the button on the Index will capture audio. Pebble’s smartphone app can then convert that into text notes using OpenAI’s WhisperAI speech-to-text model. The microphone can also be used to speak to popular online models, like OpenAI’s ChatGPT and Anthropic’s Claude. Responses can appear on a Pebble smartwatch or a connected smartphone. The Pebble app store’s current featured app is Bobby, an AI assistant that can handle dictation and short questions.

Still, Pebble’s AI features are not the main event. The low-performance microcontroller in Pebble’s devices means it’s reliant on internet connectivity, or the Pebble smartphone app, for AI features. As a consequence, the apps take on a more playable and whimsical look than the rest of the AI industry. Bobby, for example, is embodied by a pixel-art animal that looks straight out of a 1980’s Nintendo game.

“I just love the idea of a fun device that doesn’t take itself too seriously. I love looking forward to gadgets. So, we’re just going to build gadgets that we love,” said Migicovsky.