Engineering Community Portal

Since about 2020, AI has largely focused on training bigger and better models. Large language models (LLMs) ballooned from millions of parameters to trillions. This proved effective: The largest version of OpenAI’s GPT-3, released in 2020, correctly answered just 43.9 percent of questions on a popular knowledge-and-reasoning benchmark. Just four years later, GPT-4o reached a score of 88.7 percent on the same exam, effectively matching those of human experts. Advanced AI labs are still training ever larger models, but that training has somewhat receded to the background of the AI conversation. In 2026, inference—the use of trained models to produce code, write essays, or make images of ourselves as elves—has come to the forefront. “It’s like training is yesterday’s news,” says Matt Kimball, principal data-center analyst at Moor Insights & Strategy. “All that any chief information officer wants to talk about is inference.” Nvidia CEO Jensen Huang, speaking at the company’s GTC 2026 conference, touted this change as the “inflection point of inference.” Part of what’s caused the shift is very simple: LLMs are becoming useful, so people are using them. On top of that, many models on the market today are reasoning models. In response to a user’s query, they run inference not just once but multiple times, reprompting themselves in a process called chain of thought. Reasoning models generate longer outputs, and models with high reasoning effort can produce up to 20 times as much text as those with low or no effort. Adding even more to the world’s inference workload, the rise of agentic AI has resulted in inference running not just as a real-time response to a user’s query but also around the clock, working autonomously toward a user-defined goal. Amazon’s Trainium chip was originally designed for AI training. However, Amazon Web Services chose to break up AI inference into two parts, with Trainium running the more computationally complex portion and Cerebras’s wafer-scale engine taking on the more memory-intensive portion.Amazon The resulting explosion in inference demand has led to unexpected alliances among tech giants. OpenAI and Amazon have deployed chips the size of a dinner plate designed by Cerebras, despite Amazon having its own Trainium chips. Nvidia bought key talent and intellectual property from AI-inference startup Groq in a controversial deal worth US $20 billion. And Anthropic is paying LLM competitor SpaceXAI over a billion dollars per month to lease spare compute. Although they might seem similar, AI training and AI inference are computationally different. These big moves from tech giants signal that in order to support the inference demand, we’re going to need a very different mix of hardware than experts may have expected even a couple of years ago. How does AI inference differ from AI training? An untrained LLM is like a jumble of Scrabble tiles on a table. Instead of single letters, though, the tiles show fragments of words, called tokens. Everything you’d need to write almost anything is present, but nothing makes sense. Training a model organizes this jumble using a guessing game played at scale. The model is shown real text with the next token hidden and asked to predict what comes next. After each guess, the correct token is revealed and then compared to the prediction, and the difference is used to calculate the model’s accuracy. The game is played not with a single sentence but over billions of passages. While a real game of Scrabble can be played over a bag of chips and a few drinks, AI training is computationally intense. The model updates its parameters through backpropagation, a process that repeatedly calculates how each of a model’s billions or trillions of parameters should shift to make the next prediction better. This is why tech giants are building larger data centers than ever before. Eventually the model’s creator decides further training isn’t worth the cost, and the guessing game stops. Backpropagation ends, the parameters are frozen, and the LLM becomes a pretrained model. Fine-tuning—a short training run on smaller, more specialized data—adds final tweaks, and the model is deployed. Next comes inference. This is the process of using the deployed model, which, now that it’s been trained, has learned to spit out Scrabble tiles—tokens—in a sensible order. You might think that AI inference is less computationally demanding because the backpropagation calculations used to update parameters are eliminated. But Sudeep Bhoja, founder and CTO of the inference-hardware company d-Matrix, explains that inference adds new challenges. The models are “autoregressive” in nature. That is, the next output depends on the previous one. “So to generate the next token, you have to read all of the weights and all of the [context] from the previous token,” explains Bhoja. The context includes all of your prompts, all of the LLM’s replies, and all of the files you upload. It’s a lot of data and a lot of processing. An LLM generates its reply in two phases: prefill and decode. Prefill is the model reading a prompt. It processes every token at once, computing how each token relates to all the others. This operation is called attention, and it’s a defining characteristic of the transformer architecture behind modern LLMs. It allows them to respond to a word in its sentence, paragraph, and larger context rather than on its own. Think of it like arranging Scrabble tiles before you place them in a game. Many players move tiles around to imagine how they connect. Self-attention plays a similar role, though instead of moving physical tiles, each token sends a query to the others and receives a score indicating the token’s relevance. These queries result in two types of vectors: the keys and values. They are typically placed in a store called the KV cache. This isn’t strictly required, as a model could instead recompute these vectors with each new token it generates. But nearly all LLMs use a KV cache to reduce how much computing they do. The KV cache is stored in memory and becomes a scratchpad to which the LLM can return to understand a conversation, and though it starts small, it can swell to dozens of gigabytes. Prefill is a problem that can be easily divided up and worked on in parallel. This is why GPUs became the dominant AI accelerator as LLMs surged in popularity. Graphics rasterization (computing the color of every pixel on a screen) is also massively parallel, so GPU architectures were a natural fit. Next comes decode. Here, the model generates its reply one token at a time. At each step it takes the most recent token, weighs it against everything in the KV cache, uses that information to predict the next token, and adds the new token’s key and value to the cache. Then it repeats in sequence, token by token. This is where the autoregressive nature of the model works against inference speed. Predicting each token requires reading the entire model from memory, and that model consists of possibly tens to hundreds of gigabytes of parameters (the numbers representing what the model learned in training). Crucially, this is in addition to the memory required to store the KV cache. As a result, the movement of all this data through memory often requires more bandwidth than inference hardware has available. So at least some of the computing parts of a GPU sit idle as it waits for data. Researchers found that Nvidia H100 GPUs running open-source LLMs sit idle 50 to 80 percent of the time. Memory’s role in inferencing Shahriar “Sha” Rabii, former head of silicon engineering at Meta and cofounder of the AI startup Majestic Labs, says idled processors are why many companies that are trying to improve AI-inference performance are laser-focused on memory. “With the GPU-based approach, you end up greatly over-provisioning compute and starved on memory. That’s driving the big [memory] scale out,” he says. Bhoja’s d-Matrix and Rabii’s Majestic Labs both focus on this memory bottleneck. However, their companies imagine different solutions. d-Matrix’s second-generation AI accelerator, Raptor, aims to improve inference performance by minimizing the distance between compute and memory. The GPUs in most current AI-inference deployments do this by placing high-bandwidth memory (HBM) around the perimeter of the GPU. Each HBM is a stack of DRAM dies linked together and connected to a superfast interface to the GPU. This is great for training, but for inference, the amount of memory you can stack this way and the bandwidth it can provide leave something to be desired. d-Matrix’s stacked-die architecture d-Matrix’s Raptor removes that bottleneck by stacking an AI accelerator on a DRAM die. Instead of stacking memory, d-Matrix stacks memory and compute. Bhoja says this reduces the distance that data must travel to “micrometers instead of millimeters.” Like building a skyscraper, going vertical makes it possible to do more inside the same physical footprint. Majestic takes the opposite approach. Instead of trying to minimize the length that data must travel between compute and memory, the company is focused on improving the memory interface to accommodate longer wire traces while keeping bandwidth high. Longer wires allow Majestic to connect memory stacks that aren’t directly next to the GPU, removing the space limitation of HBM. “A memory interface has a very short physical distance it can operate over. In the case of HBM, it’s up to 2 or 3 millimeters. You have this shoreline around the periphery, which is the only place where you can put HBM,” says Rabii. Majestic claims its memory interface can transmit bits as far as about a meter. That’s achieved with a proprietary copper link and a memory-aggregator chip that coordinates data. “The aggregator is the endpoint for the high-speed interface and a way to fan out to many, many commodity DRAM chips,” says Rabii. Because of this, Majestic can support up to 128 terabytes of DRAM memory in a single server rack—a significant increase over Nvidia’s GB300 NVL72 rack, which has about 20 TB of HBM3E. Majestic Labs’ memory-aggregation architecture d-Matrix and Majestic have one thing in common: Instead of HBM, they both use off-the-shelf DRAM. This is the most common type of computer memory in the world; it’s in everything from smartphones to cars. Memory analyst Jim Handy says HBM costs two to three times as much as DRAM. d-Matrix and Majestic chose DRAM in part because of this price advantage. However, the proponents of HBM, which include memory giants like Samsung and SK Hynix, aren’t sitting idle. HBM4, the latest version of HBM memory, is now in production and will be used by Nvidia’s Vera Rubin GPU, which is expected to ship in the second half of 2026. Hoshik Kim, head of memory-systems research at SK Hynix, says HBM4 “will decisively break the memory bottlenecks constraining AI inference today” by doubling HBM’s maximum memory bandwidth and increasing the amount of HBM memory per stack. Combining chips for faster inference The big players—Nvidia and Amazon—are going for an all-chips-on-deck approach. Nvidia’s GPUs and Amazon’s Trainium training accelerators are still great for part of the inference workload: the prefill stage, where all the context keys and values are calculated. But to accelerate decode, the part where new tokens are generated, they are looking to new, memory-centric architectures from smaller players. In Nvidia’s case, the smaller player was Groq (not to be confused with Grok, the family of LLMs trained by SpaceXAI). Nvidia purchased intellectual property and hired talent from Groq at the end of 2025, and just three months later at the Nvidia’s GTC 2026 conference, Jensen Huang unveiled the Nvidia Groq 3 language-processing unit (LPU). Groq’s architecture relies on memory—in its case, SRAM—built directly into the chip’s architecture. Unless you’re a chip architect, or a hardcore PC gamer, you probably never give SRAM a thought. SRAM has the benefit of being tightly integrated into a compute chip’s architecture—it’s on the same piece of silicon as the processor—and has the drawback of being less dense and more expensive than DRAM. Most chips include only a few dozen megabytes of SRAM. AI inference, however, has ignited new interest in SRAM as a means of bringing the model weights stored in memory closer to compute. Ian Buck, vice-president and general manager of hyperscale and high-performance computing at Nvidia, says the LPU has a much different set of priorities than the company’s GPUs. The LPU has far less raw computing power than a standard GPU, but it gains 500 megabytes of on-die SRAM connected directly to its floating-point math units. “The benefit is the memory bandwidth. The LPU has seven times the memory bandwidth of the GPU,” he says. Between the Rubin GPU and the Groq LPU, prefill and decode can both be accelerated to get the best of both worlds, the theory goes. “We do all the attention math and context processing on the Vera Rubin [GPU] rack,” explains Buck. “For all the expert calculations…the matrix multiplications, we do that part on the LPU.” The company packs 256 LPUs into the Groq 3 LPX, a system the size of a data-center rack. Nvidia’s two-chip approach to inference Amazon Web Services (AWS), for its part, struck a deal with Cerebras, to pair the Trainium accelerator with Cerebras’s Wafer-Scale Engine 3 (WSE-3). Cerebras takes a similar approach to Groq, though at a much larger scale. WSE-3 turns an entire silicon wafer into a single chip that contains over 4 trillion transistors. The design doesn’t connect to external memory but instead etches 44 gigabytes of SRAM into each wafer. “We store the [model] weights on the SRAM,” says James Wang, formerly director of product marketing at Cerebras who has since moved to SpaceXAI. “So that’s easily 40 to up to 80 billion parameters that we can support on one chip.” Amazon plans to use AWS Trainium chips for prefill, and Cerebras for decode. But Cerebras’s chips can also go it alone in inference. WSE-3 was deployed by OpenAI to power GPT-5.3-Codex-Spark, a variant of the company’s coding mode, outputting over 1,000 tokens per second. For comparison, OpenAI’s standard GPT-5.4 deployment outputs 50 to 125 tokens per second. Amazon Web Services’ two-chip inference strategy Cerebras can also tackle prefill without moving the workload to different specialized chips. For this, it networks together multiple WSE-3 chips to form a single pool of memory. Cerebras has demonstrated it can serve models with up to 1T parameters, such as Moonshot AI’s Kimi 2.6, though Wang says “the architecture has no innate limitation in terms of how many parameters it will do.” Despite these differences in strategy, Nvidia and AWS seem to agree that the future of AI inference will be solved by a systems approach that pools different kinds of chips together to tackle the largest LLMs. Or, as Buck says: “To do modern AI inference, you need all the chips.” Learning to do more with less (bits) Nvidia became the world’s most valuable tech company because it designed the world’s most desired GPUs. But not all of the attention is focused on improving AI-inference hardware. AI researchers are also learning how to optimize LLM software and hardware in tandem to make the best use of the memory and compute components. Most computers store numbers in a 32-bit or 64-bit format. These determine how many bits are available to represent a single number. If too few bits are available, the number can’t be stored without losing information. The quality of an LLM benefits from more-precise number formats, but this creates a problem for inference performance. More-precise numbers aren’t free. The bits that describe them take up more space in memory and require more silicon and energy to compute. Gilles Backhus, cofounder of the AI-accelerator company Tensordyne, says this creates a tension between model size and number precision. “Would you prefer a model that is size x but runs in 8-bit, or would you prefer a model that is twice the size but runs in 4-bit?” The size of each model will be roughly the same in terms of memory and compute, “but the 4-bit approach gives you twice as many synapses, if you will. And people are figuring out that [the 4-bit approach] is worth it.” The process of converting an LLM from a more-precise number format to a less-precise format is called quantization, and it’s been in use for several years. However, researchers are finding new ways to quantize models down while retaining a large majority of the model’s quality. Nvidia recently created a new 4-bit number format, NVFP4, for this purpose. AMD, Intel, and Qualcomm have instead rallied around a competing 4-bit number format called MXFP4 that Nvidia also contributed to developing. “It’s the black art of AI,” says Buck, of Nvidia. When Nvidia quantized DeepSeek-R1 from FP8 to NVFP4, scores on seven major benchmarks degraded by less than one percent while performance improved by three times, the company says. Quantization is likely just the tip of the spear, as AI researchers and startups are investigating a diversity of opportunities for optimization, some of which could dramatically change the silicon found in AI-inference hardware. Tensordyne’s unique approach to AI inference combines a logarithmic number format with bespoke hardware in the company’s Napier chip. Tensordyne Tensordyne is expected to accelerate AI inference with a logarithmic number system that leans on a property of logarithms: The log of A times B equals the log of A plus the log of B. So, storing numbers as their exponents lets the chip add where it would otherwise multiply. That matters in silicon because multiplier circuits draw more power and use more die area than adders do. Tensordyne says its rack-scale hardware, called Napier, can produce up to 1,300 tokens per second per user, and can do so while using less than a tenth as much power as comparable Nvidia hardware. Etched, a startup based in San Jose, Calif., is even designing AI accelerators that translate the transformer architecture used by LLMs directly into silicon. Rather than building general-purpose GPUs, the company is wiring up the connections needed for efficient transformer calculations into its chip, making the chip much less flexible but more efficient for the tasks most performed by current LLMs. The company says its first AI accelerator, Sohu, can run Meta’s Llama 70B model at a stunning 500,000 tokens per second, though this approach also means it won’t be able to run LLMs that move away from a typical transformer architecture. Whether these ideas will prove fruitful remains to be seen. Etched just shipped their first rack in August. Tensordyne believes its first hardware will be available in 2027. Even so, these startups show how the demand for inference performance is fueling unconventional ideas. Inference is everyone’s game The sheer variety of approaches to AI-inference acceleration—stacking compute on memory, extending interfaces from millimeters to meters, using an entire silicon wafer for SRAM, squeezing models into 4 bits—raises a question: Which is going to win, and which is going to lose? But that’s likely not the right question, experts say. The demand for AI is currently insatiable, and while fears of an AI bubble stalk the industry, it has yet to hamper growth. On the contrary, Kimball of Moor Insights & Strategy thinks inference could drive intense demand for AI hardware in the long term, because it’s not obvious where that demand will end. “You could add a million agents into your organization,” he says. “These things work 24 hours a day; they don’t go home at five at night like we do.” If AI inference remains as desirable as Kimball expects, the evolution is likely to follow the same trajectory as the CPU. The CPU didn’t improve along a single axis but instead across multiple fronts simultaneously. Once transistor scaling slowed, chip and system architecture innovations of all kinds proliferated. The list of individual innovations that led to today’s ubiquitous, powerful personal compute could fill dozens of books. A few decades from now, the history of AI inference innovation will show similar depth.

In 2003, Martin Eberhard, a cofounder of Tesla Motors, decided it was time to start wooing investors. To do that, however, he needed an electric car. So he talked to Alan Cocconi and asked if he could borrow the tZero, the revolutionary and blazingly fast electric roadster that Cocconi had built at AC Propulsion. Eberhard’s idea was to drive the tZero up and down Sand Hill Road in the heart of Silicon Valley and do demonstrations for curious entrepreneurs and VCs. Eberhard was joined by Tom Gage, Cocconi’s partner at AC Propulsion, on many of the visits. Like Tesla, AC Propulsion was also seeking investors, but to build a considerably more utilitarian EV. Adapted with permission from The EV Guys: How Caltech Engineers Reinvented the Electric Car, by Charles J. Murray, published by Purdue University Press. In December 2003, Eberhard also proposed a demo at Buck’s of Woodside, a popular restaurant frequented by tech entrepreneurs. At 5 o’clock on any evening, Buck’s probably had more VCs per square foot than any building in the country. Eberhard’s plan was to “show off what a real electric sports car can do,” he wrote in an email to Buck’s owner, Jamis MacNiven. MacNiven happily obliged. In some ways, the tZero was a hit. When a VC would ride shotgun in the car with Eberhard at the wheel, Eberhard would implore them to touch the dashboard. As they reached forward, he’d punch the accelerator. As the car accelerated and the g forces piled up, the VC was literally unable to touch the dashboard. That was how powerful the tZero’s acceleration was, Eberhard would say. Many of the VCs were astounded. Some even questioned whether the car was really electric. Many owned Ferraris or Lamborghinis. They knew sports cars—but this? They could never have imagined it was possible to do this with an electric drivetrain. On December 13, 2003, Martin Eberhard brought AC Propulsion’s tZero electric roadster [yellow] to Buck’s of Woodside, a popular hangout for entrepreneurs and venture capitalists. Next to the tZero is a Scion xB, which AC Propulsion’s principals thought they could turn into a mass-market electric vehicle.Martin Eberhard Still, the demo at Buck’s garnered little investor interest—with one exception. Google cofounders Sergey Brin and Larry Page were both at Buck’s that day, and they told Gage they knew an individual whose funds were liquid, as he’d recently sold his stake in a startup. What’s more, this individual liked fast cars. The man’s name was Elon Musk. Elon Musk, Meet the tZero In 2004, it wasn’t apparent to anyone that Elon Musk had a future in the auto industry. He was notable for cofounding PayPal, which he then sold to eBay in 2002 for a whopping US $1.5 billion. He had already launched Space Exploration Technologies Corp., or SpaceX, with the stated goal of paving the way to a sustainable colony on Mars. Musk did love fast cars. He owned a million-dollar silver McLaren F1, one of only 64 road-going F1s in the world, as well as a 400-horsepower BMW M5 sports car and a 1967 XK-E Series 1 Jaguar roadster. But he’d never expressed an interest in building cars or starting an auto company, at least not publicly. Elon Musk was photographed in 2008 at Tesla’s headquarters, then in San Carlos, Calif., around the time when Tesla’s Roadster was being delivered to its first customers.Patrick Tehan/MediaNews Group/Bay Area News/Getty Images Still, Musk’s affinity for fast cars made it almost impossible for him to ignore an email from Gage on 21 January 2004. “Sergey Brin and JB Straubel both suggested you might be interested in driving our tZero electric sports car,” Gage wrote. (Straubel was the young Stanford engineering graduate who would later serve as Tesla’s chief technology officer.) “The tZero goes quite well,” the email continued. “We ran it against a Viper last Monday and it won four of five sprints on a 1/8th of a mile track. I lost one because I was carrying a 300-pound cameraman. Do you have time for me to bring it by?” Musk quickly replied. “Sure, I would really enjoy seeing it. Don’t think it could beat my McLaren (yet) though I’m in town Feb 2nd through 4th.” “Hmm, a McLaren, boy that would be a feather in my cap,” Gage wrote back. “I can have it there on Feb. 4.” Two of the principals behind AC Propulsion were businessman Tom Gage, left, and engineering genius Alan Cocconi.Left: Tom Gage; Right: Alec Brooks The emails marked the beginning of Musk’s involvement in electric cars and in the auto industry. Gage drove the car to SpaceX headquarters, a warehouse in El Segundo, Calif., about 30 kilometers southwest of Los Angeles. In Musk’s cubicle in the “office” portion of the warehouse, Gage made his pitch. There was a void in the market, he said. GM had abandoned the EV1. Toyota, Honda, Ford, and Chrysler were shutting down their electric car programs. California’s zero-emission vehicle (ZEV) rules, which mandated the sale of increasing numbers of vehicles with no tailpipe emissions, had been plundered. But electric vehicle technology, he said, was getting a bad rap. Here was the tZero, an electric car that could take off like a jet. The tZero proved that the technology was readily available. He and Cocconi wanted to use that technology to make an electric car that was useful and practical: the eBox, an electrified Toyota Scion. After building the tZero roadster, AC Propulsion’s principals pinned their hopes on an electrified version of the Toyota Scion they called the “eBox.” It did not appeal to Elon Musk.Jeff Chiu/AP For Musk, Gage’s introduction of the eBox was unexpected. He was meeting with Gage because he was interested in the tZero. It was the car’s performance that appealed to Musk. He wasn’t interested in the eBox. He then drove the tZero and offered to buy it. The lithium-ion version of the tZero electric roadster could get 515 kilometers on a charge and go from zero to 97 km/hr (60 miles per hour) in 3.6 seconds. Only three tZeros were built and only two survive. Scott Sorbe Gage told him it wasn’t for sale. Undeterred, Musk offered a quarter million dollars if AC Propulsion would squeeze its lithium-ion battery pack into his Porsche. Gage declined again. AC Propulsion needed money to electrify the Toyota Scion, Gage said. Musk shook his head. The idea seemed incredible to him. “Who wants to take an ugly $20,000 car and buy it for $65,000?” he asked incredulously, as he later recalled during an interview with Vanity Fair magazine. “I wouldn’t want to drive it. My wife certainly wouldn’t want to drive it.” Many years later, Musk would tell his biographer Walter Isaacson, “Nobody is going to pay anything near that for something that looks like crap.” Musk believed that the way to start a car company was to build high-priced cars first and then let the technology trickle down to the mainstream. It was a classic Silicon Valley approach. Alan Cocconi, the engineering whiz behind AC Propulsion, stands next to the company’s legendary tZero electric roadster in a picture taken in the early 2000s. The small yellow wheeled pod on the other side of the car is a trailer with a small gasoline engine that, when connected to the tZero, turned it into a hybrid gas-electric vehicle.Martin Eberhard In Musk’s mind, it was all very obvious. He liked fast cars. He liked the tZero and believed it “could change the world.” He couldn’t even imagine why Gage was sitting here trying to sell him on the idea of the eBox. “Gage and Cocconi were sort of madcap inventors,” he told Isaacson. “Common sense was not their strong suit.” Gage concluded that he wasn’t going to convince Musk to invest in AC Propulsion. “Well, if you want to do a sports car, then you should talk to Martin Eberhard,” Gage said. A few weeks later, Gage sent an email to Eberhard introducing him to Musk. “Elon Musk heads up SpaceX, is a car enthusiast,” Gage wrote. “He would be interested in hearing about your activities at Tesla Motors.” Elon Musk, Meet Tesla Motors As it happens, Eberhard and Marc Tarpenning, Eberhard’s partner and cofounder at Tesla, had considered contacting Musk even before Gage’s email arrived. They’d known of Musk and appreciated the way he thought. A few years earlier, they saw him speak at a Mars Society conference at Stanford University. Musk had talked about the rather improbable idea of sending mice to Mars. The presentation gave them a window into Musk’s unconventional approach to high-tech entrepreneurism and to life in general. Eberhard and Musk agreed to meet, and then Eberhard emailed Gage. “Any chance of my borrowing the car for next week?” he wrote. Gage, of course, complied. Martin Eberhard posed next to an electric motor at Tesla’s San Carlos, Calif., headquarters in 2006. Paul Sakuma/AP By this time, Tesla was nine months old. It still had just three employees—Eberhard, Tarpenning, and Ian Wright, a New Zealand-born engineer and neighbor of Eberhard’s. The founders were arranging to pay the licensing fee on AC Propulsion’s drivetrain technology. And they were making arrangements to build their first cars using the chassis of the Lotus Elise two-seat roadster. They estimated they needed $6.5 million to go further. And that’s where Musk came in. The original Tesla Roadster prototype, or “Mule,” was built inside the chassis of a 2002 Lotus Elise. Dylan Stewart/Image of Sport/Sipa/Alamy Eberhard and Wright flew to Los Angeles on a Friday and met Musk in his cubicle at SpaceX. The meeting was supposed to last a half hour, but Musk’s questions came virtually nonstop, and as the meeting progressed, he repeatedly shouted to his assistant to cancel his next meeting. Over the following weekend, Musk called Tarpenning to get his input about their financial model. “I just remember responding, responding, and responding,” Tarpenning said, according to a 2015 book by Ashlee Vance. The Tesla founders were all impressed with Musk. He was unlike any of the VCs they’d met with in the previous months. He was technically astute. He’d earned a bachelor’s degree in physics from the University of Pennsylvania, and in his two-day stint as a Ph.D. student at Stanford, he’d intended to do a dissertation on solid-state capacitors for use in electric cars. Moreover, he wasn’t averse to risk—at least not intelligent risk. He loved technical challenges, and he loved proving that the impossible was possible. “You’re presenting an electric car company to this person on the other side of the table, and he’s doing something even crazier,” Tarpenning said later. “He’s building rocket ships.” On the Monday after their first meeting at SpaceX, Eberhard and Tarpenning flew back to Los Angeles. Musk agreed to invest $6.35 million. He would become the biggest shareholder as well as chairman of the company. Now, Tesla Motors was really in business. All it needed was someone to design and build a groundbreaking electric car. “All Electric Cars Have Sucked” No one at AC Propulsion believed that Tesla Motors had even a remote chance of success. The whole idea—building and selling electric vehicles and competing against the giants in Detroit, Japan, and Germany—seemed impossible. Even Toyota, which was having so much success with the hybrid Prius, was not planning to build pure electric cars. The prospect of starting any kind of auto company was unbelievably daunting. Automotive startups had been the undoing of many ambitious entrepreneurs, including Henry Kaiser, Preston Tucker, and John DeLorean. Such endeavors required mountains of money, connections, and expertise. There were unseen obstacles around every corner. And the people who’d launched Tesla, as smart as they were, were almost certainly unprepared for what lay ahead. Years later, Musk would contend that their struggles were caused by the fact that Tesla had been founded on “two false premises.” The first was that Tesla’s founders believed they could simply convert an existing gasoline sports car to electric. The second was that they could use the existing AC Propulsion technology with little or no modification. “That turned out to be, in retrospect, staggeringly dumb,” Musk said. In April or May of 2004, Tesla engineers worked on an early test vehicle, or “mule,” of the Roadster. The group included (clockwise from upper left) mechanical engineer Gene Berdechevsky, in the pink shirt, electrical engineer Phil Cole, and mechanical engineer Dave Lyons, in the dark blue shirt.Martin Eberhard He also later concluded that their early path almost doomed them. “It ended up being much worse than if we had designed the car from scratch,” he said. But Tesla decided it could not go back and start over. It could only deal with the problem at hand. Eberhard and Tarpenning were terrified of going into production with the existing analog motor controller and drivetrain electronics, which were unreliable and jittery. If those problems weren’t fixed, they knew their new vehicle would fail, and so would the company. Another looming issue was the safety of the Tesla lithium-ion battery pack. To test it, Eberhard brought the engineering team to his home, where they dug a pit in his backyard. They took a brick of cells, covered it with a sheet of Plexiglass, and then remotely heated one of the cells with an electrical wire. As they expected, the heated cell burst into flame, setting neighboring cells on fire. The cells went off one at a time—pop, pop, pop. “We had a conflagration,” Eberhard said. “One cell caught fire, and it blasted right through the pack.” Buyers of sports cars were known to be forgiving. In their quest for performance, they could put up with poor reliability. But the fire hazard was another matter, and one that had the potential to take down the company. Eberhard took the news right to Tesla’s board of directors. “It was my first big oh-shit moment to my board,” he said. “I told them we’ll have a day-to-day schedule stop until we figure this out.” Working with friends from his Stanford days, Straubel began developing a new pack in his garage. The team acquired 7,000 lithium-ion cells from LG Chem, then constructed battery bricks, each with 69 cells, and tested them with different kinds of liquid-cooling systems. By October 2004, they’d finished a prototype pack and used a crane to lower it into the back of a Lotus Elise sportscar. A few months later—in January 2005—the team had completed a working prototype of that first car. At the end of the month, they showed it off at a board meeting, and Musk took it for a spin. Impressed by its performance, he invested $9 million more, and Tesla completed a $13 million round of funding. Now the vehicle had a name—Roadster—and a tentative production schedule. The plan was to begin delivering it to customers in early 2006. Tesla’s struggles with the Roadster were not apparent to the outside world, especially to those invited to the reveal of the Roadster at the Santa Monica Airport in July 2006. By then, the yellow test car, or “mule,” had evolved into two prototypes: a red car and a black car, both of which would be available for drives at the event. Tesla unveiled the Roadster, its first vehicle, at the Santa Monica, Calif., airport on 19 July, 2006.Glenn Koenig/Los Angeles Times/Getty Images The prototypes were more advanced than the mule, with more of a production-type design. But Musk and the team didn’t know what to expect at the reveal. The company was just coming out of stealth mode, and at that point, Tesla had received no media coverage. It had no customers, no deposits, and no sales team. Still, Musk planned a huge party for the unveiling—an “awesome event,” in his words, staged inside the airport’s Barker Hangar. He told his personal assistant to invite 350 guests, including Michael Eisner of Disney, movie producer Richard Donner, actor Ed Begley Jr., California Governor Arnold Schwarzenegger, and many other luminaries. All were told to bring their checkbooks in case they wanted to write a $100,000 check to put a deposit on an electric Roadster. Meanwhile, a Roadster prototype zipped around a makeshift road inside the hangar, out the door, down a runway, and back inside again. Musk took center stage, telling the audience that they were witnessing the start of a new era in automotive technology, according to CNET’s coverage of the event. “Until today, all electric cars have sucked,” he told the audience. “Electric cars play into the strength of Silicon Valley. A lot of the things inside the car are conventional automobile technology. The magic is the battery technology and the software and the controllers.” Tesla Hooks Arnold Schwarzenegger, George Clooney Musk’s message was perfect for such an event, especially for the dozens of reporters who were there to publicize the reemergence of the electric car. They adored the Roadster. It was small, powerful, electric, and above all, cool. It was anti-Detroit—a new kind of car that burned no gasoline and was born in Silicon Valley instead of an antiquated factory in Michigan. The night was also a financial success for Tesla, with twenty $100,000 checks gathered from prospective buyers. And the momentum continued. A few days after the event, Joe Francis, creator of the adult entertainment franchise Girls Gone Wild, sent an armored truck to Tesla’s San Carlos office to drop off $100,000 in cash. A few days after that, Schwarzenegger put his money down, as did actor George Clooney. Within two weeks, Tesla had presold 127 Roadsters. California Governor Arnold Schwarzenegger was among the first buyers of the Tesla Roadster on the day the car was officially unveiled, 19 July, 2006.Glenn Koenig/Los Angeles Times/Getty Images Meanwhile, though, the company’s manufacturing woes continued. The mechanical problems weren’t even the biggest issue. The biggest issue was the supply chain. This was ironic, because some in the media admired Tesla for its global approach. They liked the fact that the battery pack, the motor, the chassis, and the assembly had an international flavor. It was a world car, they thought. But for Tesla, it was a nightmare. The battery pack was being assembled in Thailand by a manufacturer of barbecue grills. The facility was 3 hours from Bangkok, literally in a jungle where the heat was almost unbearable, and the factory building consisted of a truss roof held up by some steel columns. There were no walls because no one there wanted to work indoors. And because the pack assembler was inexperienced, Tesla engineers were repeatedly flying to and from Thailand to direct the effort. They would find animal droppings on the battery packs, which were sitting out in the open air all day and all night. For the umpteenth time, Musk wondered if the company would be able to survive. “We’re doomed if we don’t in-source the battery pack,” he told one of Tesla’s manufacturing engineers, “because we have a supplier in Thailand who is great at making barbecues but not great at battery packs. And the supply chain is so long that it takes six months from when the cells are built to when the battery pack is done and in a car. So that means the capital cost is gigantic because we have to pay for all that inventory and process. And inevitably, there are mistakes in the design or fabrication of the battery pack, and then we have six months’ worth of battery packs that don’t work.” Never mind that this chaotic approach was central to their plan. Tesla had never been envisioned as an old-fashioned, Detroit-style, vertically integrated manufacturer. From the beginning, it had been a Silicon Valley–type enterprise that would rely on others for the bulk of its manufacturing. Only now, as the fledgling company sent batteries and motors and assembled cars back and forth across two oceans, was its plan beginning to appear untenable. “We had this misguided idea that everything must be cheaper and better if built in Asia,” Straubel later said. Tesla’s Chances of Success: 10 Percent From the beginning, Musk had never been optimistic about Tesla’s chance of success. He repeatedly said he thought it was approximately 10 percent. “In 2004, the idea of starting a car company was extremely stupid,” he said. “The idea of starting an electric car company was stupid squared.” As he watched Tesla struggle with its supply chain, his earlier words were starting to look prescient. The only chance, the engineering team concluded, was to bring the manufacturing of all of the subsystems, such as the battery packs, motors, and inverters, in-house. They disassembled their overseas operations and moved them to California, starting with battery pack manufacturing. Assembly stations were loaded into huge shipping containers, transported back to one of the company’s new facilities on Bing Street in San Carlos, and then reassembled there. It took five and a half months. They also redesigned the battery packs and developed machines for automating their assembly. In 2008, with mass production of the Tesla Roadster just getting underway, Elon Musk gave an interview at the company’s headquarters, then in San Carlos in northern California. At the time, Tesla was merely a startup in a precarious position, bleeding cash and grappling with many manufacturing problems.Ryan Anson/Bloomberg/Getty Images Musk concluded that the key to success was not the design of the car itself but rather the manufacturing. Henry Ford had reached the same conclusion a hundred years earlier. It was “the realization of how important it is to build the machine that builds the machine,” Musk said at the Tesla Annual Shareholder Meeting in 2016. “And how much harder it is to build the manufacturing system that builds the product, than it is to create the product in the first place. You can create a demo version of a product…with a small team in maybe three to six months. But to build the machine that builds the machine takes at least a hundred to a thousand times more resources and difficulty.” Gradually, Tesla’s idea of letting others do its manufacturing slipped away. Packs were built in San Carlos, and then installed in the Lotus Elise chassis there instead of in England. “We had control now,” said manufacturing engineer Jason Mendez. “We had all the engineers there. We didn’t have batteries on the water, not from Thailand to England and not from England to here.” Musk began to talk about a new vision. He called it the gigafactory. Raw materials would enter at one end, and a car would exit at the other end. This was the ultimate in vertical integration, and it sounded a lot like Henry Ford’s vision for the River Rouge plant in Dearborn, Mich., in 1917. Tesla Motors was becoming an auto manufacturer.

Social media plays a significant, multifaceted role in adolescents’ development, influencing how they communicate, learn, socialize, and express themselves. The benefits, however, are accompanied by risks that can undermine youngsters’ character as well as their cognitive and social development. The potential problems include excessive screen time, social media addiction, cyberbullying, misinformation, radicalization, privacy violations, exposure to inappropriate content, sextortion, and doomscrolling. A recent study published in Nature: Human Behaviour found that adolescents who begin using social media at an early age tend to have significantly lower academic performance. A Mashable article highlights additional issues including effects on mental health, self-harm, addiction to social media, compulsive, repetitive checking, and exposure to pornography and violent material. Protecting minors has largely fallen to parents, schools, and self-regulation by some social media providers. But that approach has proven ineffective and inadequate, so some governments and policymakers have stepped in and placed responsibility on social media providers. Australia’s nationwide ban Australia was the first country to legislate a nationwide social media ban on children younger than 16—which I wrote about in January for Communications of the ACM. Enacted in December, the ban initially applied to 10 platforms: Facebook, Instagram, Kick, Reddit, Snapchat, Threads, TikTok, Twitch, X, and YouTube. It excluded messaging, gaming, and nonsocial platforms including Discord, GitHub, Roblox, WhatsApp, YouTube Kids, and educational tools. The law places the responsibility for enforcement on the platform providers through age-assurance mechanisms, requiring the platforms to take “reasonable steps” to prevent those 15 or younger from creating or holding accounts. It does not, however, apply to content consumption. Children can view publicly available posts and videos without logging in; they cannot comment or post, according to the law. The legislation mandates that the user’s age be verified with tools such as government-issued identification, biometric or facial age-estimation tools, behavioral or inference algorithms, and self-declaration with optional checks. Penalties for noncompliance can reach US $35.6 million. The 10 platforms subsequently removed nearly 5 million accounts of young users. Although the ban received widespread support, human rights organizations and digital freedom advisory groups have argued that it limits young people’s freedom of expression and access to useful information. They say the ban might contribute to social isolation and the loss of support networks, particularly among marginalized youth. Promising early outcomes The Australian ban is producing positive outcomes, according to a recent Time magazine article, “What the World Should Learn From Australia’s Social Media Law.” Early findings indicate it has reduced account ownership and social media use among young children. A YouGov survey of Australians found that 61 percent of parents of children age 16 and younger reported positive changes including more face-to-face interaction, greater presence and engagement, and improved parent-child relationships. Three in five Australians surveyed called the ban effective. The ban has encouraged social media platforms to reconsider their features. Snapchat is moving toward a friends-only experience for 13- to 15-year-olds, for example. The law is stimulating the development of purpose-built online spaces for children younger than 16 that can support their developmental needs, offering alternatives to mainstream social media. The longer-term impact could be more significant if “no social media account before age 16” becomes an accepted norm, making it easier for parents and schools to support delayed social media use. Implementation struggles Despite the early encouraging outcomes, one study found that online platforms struggle to implement age checks. Many under-16 users in Australia have continued to access platforms with little difficulty, the study said. They children have found workarounds to subvert restrictions, such as using a free VPN to bypass age checks—some of which have questionable data-collection practices. Seven in 10 children retained their existing accounts on restricted platforms, the study found. Other teens created new accounts using incorrect age information. Some were incentivized to seek unregulated offshore platforms not subject to Australia’s law. The workarounds prompted Australia to double the maximum fine and warn of court action against tech giants for noncompliance. Emphasis on age verification A number of other countries are implementing or considering social media restrictions. They include Brazil, Canada, France, Greece, Indonesia, Norway, Poland, Thailand, Türkiye, and the United Kingdom. The European Union is contemplating its own restrictions. The countries’ mandates for age verification or age assurance shift the policy focus from whether to verify age to how to do so effectively while protecting user privacy. An article on think tank New America’s website, “Age Assurance and Verification,” describes some methods: Age gating and screening. Users self-attest their age by checking a box or inputting a birth date. Age estimation. Several techniques are available, including profiling the user’s online activity and scanning the user’s face. Age verification. One way is providing a government-issued identification document. Other approaches include digital identity systems, digital wallets, and third-party verification. Reliable age verification is technically challenging and raises privacy concerns, as outlined in “The Age-Verification Trap,” written by Cinderpoint consultant Waydell D. Carvalho and published in February in IEEE Spectrum. Carvallo says platforms need to balance age verification with protecting users’ personal information. IEEE’s contributions IEEE is working on initiatives to provide a safer online environment for children. To help developers build age-appropriate social media platforms and websites, the IEEE Standards Association (IEEE SA) has published two guidelines. The IEEE Standard for Online Age Verification (IEEE 2089-2024) provides a framework for designing, specifying, evaluating, and deploying verification systems. The standard includes requirements for privacy protection, data security, and information management specific to the age-assurance process. It also provides procedures for verifying a user’s age or age range with a high degree of accuracy. Based on the 5Rights Foundation’s Principles for Children, the other standard (IEEE 2089-2021) provides practical steps to qualify online products and services for children. It requires systems to present information in an age-appropriate way and to uphold the rights established for youngsters in the U.N. Convention on the Rights of the Child. IEEE SA also offers an online age-verification-certification program, which assesses systems for compliance with the IEEE 2089.1 standard. The program certifies that organizations implement robust processes before granting access to age-restricted products and services, prioritizing children’s safety, privacy, autonomy, and rights. As outlined in The Institute article “IEEE Makes Strides to Improve Online Safety for Kids,” certification is based on six key indicators: accuracy, frequency of assurance, counter-fraud measures, authenticity, frequency of authenticity checks, and birth date confidence. Indonesia used key provisions from the two IEEE guidelines to inform its child-protection regulation, which was signed into law last year. IEEE’s ethically aligned design framework prioritizes human well-being, transparency, accountability, privacy, and protecting vulnerable populations including children. Calls for platform reforms Although social media bans would be globally significant policy responses, deeper structural issues remain largely unaddressed. Platform architecture and features contribute to social media harm. The focus needs to shift from constraints on account provisioning and content moderation to safer platform design. Meta in August agreed to pay $17.1 billion to settle a lawsuit brought by U.S. states. The suit said Meta designed its social media to be addictive to children, and the company concealed internal research showing Instagram’s addictive effects on teenagers. As part of the settlement, Meta agreed to implement child-safety measures such as setting daily time limits and disabling Facebook and Instagram push notifications during school hours. The company still faces other lawsuits that could have far-reaching implications, pressuring other tech companies to design safer social media platforms. Architecture-driven features such as infinite scrolling, algorithmic recommendations, addictive platform design, data-driven engagement, and personalized advertising to minors are other contributing factors to social media addiction. IEEE Senior Member Katina Michael, professor at the University of Sydney business school and founding editor in chief of IEEE Transactions on Technology and Society, shared her perspective: “Social media bans may offer a short-term response to growing concerns, but they are not a long-term solution,” she says. “IEEE 2089-2021 advocates for socio-technical systems that are designed to promote human well-being, safety, and flourishing. Rather than relying on prohibition alone, we should focus on better design, building digital platforms that embed ethics, accountability, transparency, and human values from the outset.” Collective responsibility Protecting children online would require a combination of policy measures, improved platform design, digital literacy, parental involvement, and cultural change. Building a safe, secure, and inclusive digital ecosystem that supports adolescents’ cognitive, social, and emotional development would require collaboration among technology companies, platform providers, content creators, parents, educators, policymakers, and young people themselves. Professional organizations such as IEEE can continue contributing through standards development, education, certification while promoting trustworthy and responsible digital technologies. This article was updated on 15 September 2026.

This interactive webinar will introduce the different types of AI, address the concerns with AI, share how we IBM are approaching Responsible AI, and offer guidance to students about what they can do - as individuals, and members of their IEEE chapters. Participants will also have the opportunity to to apply the Responsible AI approach to a particular use case - IBM Bob, a software development life cycle agent, and Q&A. This will be an interactive session, so have phones ready to engage! Register now for this free webinar!

For this year’s IEEE annual election, the IEEE Tellers Committee will recognize the Sections with the highest voter turnout (based on total eligible voters) in each Region with an incentive reward after the election results are accepted by the IEEE Board of Directors.The incentive initiative is managed by the Tellers Committee, which retains full authority over all rules, operations, and decisions regarding the program. Incentive guidelines The Section sizes and their respective reward amounts are: Large sections consist of more than 1,501 eligible voting members. The top-performing large section in each IEEE region will be rewarded with US $1,000. Medium sections consist of 501 to 1,500 eligible voting members. Each region’s top-performing medium section will receive $600. Small sections consist of 500 or fewer eligible voting members. The top-performing small section in each region will receive $400. To learn more about the incentive program, visit the IEEE annual election website. If you haven’t voted in the 2026 elections, you can learn about the candidates and vote here. Send questions to: elections@ieee.org.

Franklin “Frank” Kuo Codeveloper of ALOHAnet Fellow, 91; died 14 April Kuo helped develop ALOHAnet, a pioneering computer system at the University of Hawaii at Mānoa, in Honolulu. The system went online in 1971 and represented the first public demonstration of a wireless packet data network. It was an inspiration for Robert Metcalfe’s development of Ethernet a couple of years later. In 2020 ALOHAnet was designated as an IEEE Milestone. Kuo earned bachelor’s, master’s, and doctoral degrees in electrical engineering from the University of Illinois, Urbana-Champaign. After earning his Ph.D. in 1960, he joined Bell Labs in Murray Hill, N.J., where he conducted research in computer communications. After six years at the company, Kuo left to become a professor of electrical engineering at the University of Hawaii. From 1968 to 1971 he and one of his colleagues, IEEE Life Fellow Norman Abramson, developed ALOHAnet. The network connected computers on Hawaiian islands using ultrahigh-frequency radio, transmitting information over radio waves instead of cables. ALOHAnet became the foundation for modern networks. Kuo pioneered the concept of a random-access protocol, or sharing a single channel without central coordination—which led to the packet-switching principles that underpin modern Wi-Fi and mobile networks. Kuo authored or coauthored several books including Computer Communication Networks. Published in 1972, it was one of the earliest textbooks on the subject. He served as director of the university’s Cosine committee, a project funded by the U.S. National Science Foundation to develop computer engineering courses. He took a sabbatical from 1975 to 1977 to work at the U.S. Pentagon as director of information systems in the defense secretary’s office. He oversaw computer communications applications used in command, control, and intelligence programs. During the 1980s and ’90s, he helped develop China’s Internet. In 1982 he joined SRI International (formerly the Stanford Research Institute), in Menlo Park, Calif., as a researcher. He also was a consulting professor in Stanford’s electrical engineering department and taught computer networking at Shanghai Jiao Tong University. As a UNESCO lecturer in Beijing in 1994, he helped Peking University, Tsinghua University, and the Chinese Academy of Sciences connect to the Internet. He also worked with Tsinghua University to develop CERNET, the country’s first nationwide education and research computer network, which was managed by the Chinese Ministry of Education. For his work, he received an honorary degree from Shanghai Jiao Tong University. In the mid-1990s, Kuo helped found General Wireless Communications, a developer of mobile phone messaging services and games that was renamed Mtone Wireless. Muhammad Rezaul Karim Bell Labs researcher Life senior member, 86; died 18 May Karim was a distinguished member of the technical staff at Bell Labs in Murray Hill, N.J. His work was instrumental in the development of modern cellular communications technology. He joined Bell Labs in 1972 and worked in its mobile telecommunications laboratory as part of the team tasked with creating one of the earliest cellular networks. In 1975 Illinois Bell Telephone petitioned the U.S. Federal Communications Commission to develop and test a cellular system. The FCC, which now regulates radio, TV, telephone, Internet, satellite, and wireless services, authorized the project in March 1977. Karim and his team helped develop key elements of the technology, including the Bell Labs logic that controlled the cellular system, turning the concept into a working one. They also built radio receivers, transmitters, control systems, and cell-site equipment used in the first trial of the cellular system. The following year, Bell Labs and Illinois Bell deployed the Advanced Mobile Phone Service system across Chicago, with its switching office located in Oak Park, Ill. The initial test used approximately 100 mobile phones to work through hardware, software, and system-design problems. A subsequent test in 1979 involved 2,500 mobile users, providing a demonstration of the cellular technology in practice. The trials in Illinois helped establish the technical foundation for the commercial cellular networks that followed. Later in his career, Karim worked on the asynchronous transfer mode (ATM) technique, a high-speed networking technology crucial to the transition from traditional telephone networks to broadband and digital ones. In 2000 he published ATM Networks: Application, Systems, and Design, a textbook that served as a guide for designing and implementing ATM-based services. Karim received a bachelor’s degree in electrical engineering from the Bangladesh University of Engineering and Technology, in Dhaka. He then earned a master’s degree in EE from the University of Manchester, England, and a Ph.D. in EE from Stevens Institute of Technology, in Hoboken, N.J. Harry Bostic Former IEEE Region 4 director Life senior member, 86; died 18 March Bostic was an active IEEE volunteer who served as the 1998–1999 director of IEEE Region 4. In 2007 he received a lifetime achievement award from the IEEE Central Indiana Section for “outstanding commitment and dedicated service as regional advisor to the volunteers and members of Region 4 and the Institute.” He was an engineer for 30 years at U.S. Navy’s avionics facility, a research, development, and manufacturing concern in Indianapolis. He worked on flight control, navigation, and weapons systems there. (The facility closed in 1996.) Edwin C. Jones Jr. Professor Life Fellow, 91; died 10 March Jones was widely recognized for his contributions to engineering education, curriculum development, and accreditation through decades of service to IEEE, ABET, and the American Society for Engineering Education. He earned a bachelor’s degree in electrical engineering in 1955 from West Virginia University in Morgantown. The following year he earned a diploma of membership (equivalent to a master’s degree) from Imperial College, London. He went on to serve in the U.S. Army Signal Corps for two years. After his service ended, he studied engineering education at the University of Illinois, Urbana-Champaign, earning a Ph.D. in 1961. Jones then joined the university’s faculty. The following year, he left Illinois to join Iowa State University, in Ames, as an assistant professor. He was promoted to professor in 1995. Two years later he became associate chair of the electrical and computer engineering department and served in that position until 2001, when he retired and was named professor emeritus. In recognition of his commitment to students, Iowa State established a scholarship in his honor. In 2006 he accepted a part-time position as an adjunct professor in Minnesota at the University of St. Thomas, in St. Paul. He advised graduate students and helped develop the university’s systems engineering program. An active IEEE volunteer, he served as 1975–1976 president of the IEEE Education Society. He was a member of the IEEE Educational Activities Board, helping strengthen the relationship among engineering education, professional practice, and accreditation organizations. He received an IEEE Centennial Medal in 1984 and the EAB Meritorious Achievement Award in Accreditation Activities in 1986. The IEEE Education Society later named its Meritorious Service Award in his honor. Jones was elected a Fellow of ABET in 1986. During his years of service as a program evaluator and leader, he helped advance the quality of engineering education and accreditation programs. ABET recognized him with its Grinter Distinguished Service Award, its highest honor. Alexander Robert Spitzer Clinical neurology researcher Life senior member, 70; died 27 February Spitzer was a neurologist for 40 years at the Wayne State University School of Medicine, in Detroit, where he also was a director of the electromyography laboratory at Harper University Hospital. The lab studied patients’ brain and spinal cord activity in response to sensory stimuli. The evaluations assessed nerve pathway integrity to help diagnose multiple sclerosis, spinal cord injuries, and other conditions. After earning his medical degree from the Einstein College of Medicine, in New York City, Spitzer completed a fellowship at the U.S. National Institutes of Health, in Bethesda, Md. He then joined Wayne State as a clinical neurology researcher. His pioneering research in applying neural network analysis to electromyography and clinical neurophysiology resulted in peer-reviewed publications, grants, and several U.S. patents. He mentored generations of neurologists in electrodiagnostic medicine, a medical specialty that uses nerve-conduction and electromyography tests to evaluate and diagnose muscle and nerve disorders. In 2020 he founded Mackinac Neurology, a telemedicine-based practice that treated patients virtually during the COVID-19 pandemic. A longtime IEEE volunteer, he held numerous roles on the IEEE Regional Activities Board, now known as the Member and Geographic Activities Board. He was a member of the IEEE Ethics and Member Conduct and Nominations and Appointments committees, as well as the IEEE Educational Activities and IEEE-USA boards. He served as 1977–1979 director of the IEEE Central Indiana Section. Donald Leo Dietmeyer Professor Life Fellow, 93; died 13 February Dietmeyer was a professor of electrical and computer engineering for 40 years at the University of Wisconsin-Madison. He developed a lifelong interest in radio and electronics at high school in Wausau, Wisc., and earned a Ph.D. in electrical engineering in 1959 from the University of Wisconsin. He’d joined the university’s electrical engineering faculty as a professor in 1958 while pursuing his doctorate. Dietmeyer’s research focused on computer-aided design in the areas of switching theory, hardware description languages, and the decomposition of Boolean functions. His research contributed to the development of automation tools for integrated circuit design. He worked with Jim Duley, a former student, to pioneer the use of the digital system design language. He wrote the textbook Logic Design of Digital Systems, published in 1978. In the early 1980s, Dietmeyer worked with researchers to develop ConLan, a language-construction method that combined hardware description languages in one underlying framework. He served as associate dean of the University of Wisconsin’s electrical and computer engineering department from 1983 to 1995. In 1998 he retired and was named professor emeritus.

This article is crossposted from IEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity and delivered to your inbox for free! I lost a cushy engineering management position at the same time I purchased a business, with a mortgage, three kids, and every other financial obligation of being an adult. It was one of the most stressful stretches of my life. If something similar has happened to you, I feel your pain. You lose more than the income For many of us, our work became our identity, so we don’t just think, “I don’t have a job anymore.” We start thinking, “I’m not an engineer anymore.” An apple tree in winter is still an apple tree. A car parked in a driveway is still a car. You’re still an engineer, the same way you were still one every evening you clocked out, and the same way you’ll be one at the next job. Your skills took years to build and won’t evaporate because a company stopped paying for them. This feeling can be particularly rough if part of your identity was tied to a recognizable employer. However, this can also be a chance to catch up on what you’ve been putting off: working out, being present with your kids, writing, whatever hobby got shelved for a deadline three years ago. Once you’ve caught your breath, here are some tips for when the actual work starts: 1. Don’t sprint on day one I started applying for jobs the next morning after the layoff. It felt productive, but it gave me no time to settle my nervous system or consider what I actually wanted next. I took the first offer that came along, at a company I already knew wasn’t right, and quit after exactly 30 days. Give yourself a few days before deciding anything. Plans built out of desperation rarely work out well. 2. Audit your spending Go through every subscription and recurring charge and cut what isn’t essential. Every dollar you stop bleeding buys you patience instead of forcing a bad offer out of fear. 3. Build a list wider than LinkedIn Start with former colleagues and vendors, or businesses with a relationship to your previous employer. They already know you or your company, which gives you the halo effect: Some of the trust from your employer carries over to you automatically. LinkedIn is table stakes and is the most popular place to find work, but that doesn’t mean your search should stop there. Try Facebook, Instagram, and other social media channels too, where plenty of people who aren’t on LinkedIn might have leads for open roles. I found my first job years ago from a Facebook post, and both roles I landed after being laid off came from startup job boards and recruiters I found entirely outside LinkedIn. Your mileage may vary, but LinkedIn isn’t the only game in town. Don’t skip your inner circle either: Text your family and friends. Good leads rarely come from someone you know directly, but they do come from someone that person knows. Work this list daily and track who you’ve contacted. 4. Eight hours is a long time With no work to fill your day, you may default to treating the search like an eight-hour shift. You can’t apply productively for 8 hours straight, and that’s why people burn out. Use a focused morning block for the list in step 3, then spend the rest of your day on activities you’ve been putting off. 5. Catalog your wins before you study interview questions Cataloging your wins is a higher-leverage move than drilling practice questions this early. Can you explain the most impactful project you worked on in the last year? Probably not. Most people skip this step, then default to generic answers when a recruiter asks about their last role. Write down specific stories showing leadership, technical ability, and grace under pressure. They’ll come up once you’re in interviews, and cataloging them rebuilds your confidence along the way too. Save the deep prep for once an interview is on the calendar. Ask yourself daily whether today’s work is generating interest in you, or leading you to someone who might hire you. If not, consider skipping it. One last thing A layoff rarely reflects your skills. It’s usually a company protecting revenue—nothing more personal than that. Knowing that doesn’t make it easier, but hopefully this gets you back on your feet faster. —Brian IEEE Global Careers Fair: September 23-24 Looking for a job? For the first time, IEEE is taking its Career Fair worldwide. The inaugural IEEE Global Virtual Career Fair runs September 23 (5:00 PM EST) through September 24 (8:00 PM EST), following the sun across every region to connect engineering and technology professionals with employers around the world. Read more here. United States Invests in Industry Partnerships for Ph.D. Training While most engineering Ph.D. grads end up in jobs at commercial companies, academia and industry often operate in their own bubbles. Now, the U.S. National Science Foundation is investing in a program to integrate industry experience into STEM doctoral programs and help bridge the gap. Modeled after similar programs in other countries, students in the I-PhD will spend at least one year working on research at an industry site and receive a combination of funding from the company, NSF, and the university. Read more here. AI Efficiency Could Cost Us the Next Generation of Experts When systems engineer Richard Mitchell designed a digitally-controlled nuclear plant, he made a counterintuitive decision: including manual steps for the human operator that a machine could execute on its own. The strategy was meant to keep the operator sharp, and it’s one that could help address one of the biggest issues facing the workforce today: What happens to human expertise when AI does the work that used to build it? Read more here.

I’m sitting in a Rivian R1S SUV as it drives itself down the leafy streets of Palo Alto, Calif., through areas crowded with touchstones of tech history. We cruise near the landmark HP Garage, the one-car workshop where Hewlett-Packard, and, arguably, Silicon Valley, was founded in 1939. I skirt Stanford University, where a team led by computer science professor Sebastian Thrun won a US $2 million DARPA Grand Challenge in 2005. The team’s Volkswagen SUV, named Stanley, became the world’s first vehicle to navigate a grueling 212-kilometer Mojave Desert course with no human intervention. This Rivian might look like any other electric SUV in this affluent town, with its concentration of tech bros, venture capital, and startups. But inside this boxy EV is something special: an Autonomy+ system that will allow owners to enter an address, sit back, and let the vehicle drive to any mapped destination in the U.S. and Canada. This point-to-point system is one of the most advanced semiautonomous-driving systems coming to market. It is also a precursor of the company’s bid to make self-driving cars a reality, for robotaxis and—eventually—for everyday car buyers. After years of incremental advances and frustrating setbacks, self-driving has been swept up in the great AI resurgence, and is now a top priority for investors and global automakers, who envision vast new streams of profits. So here I am, 21 years after that DARPA challenge, riding shotgun in Stanley’s vastly more advanced descendant. Rivian’s Autonomy+ is intended to operate seamlessly on suburban streets like these, sensing and responding to traffic lights, crosswalks, and stop signs. That point-to-point system is set to debut on Rivian’s all-new R2 SUV by roughly the end of this year, and via over-the-air updates for its newest R1S and R1T models. Rivian says it will charge $49.99 a month, or $2,500 up front, versus Tesla’s $99 per month for its rival system, which is somewhat misleadingly called Full Self-Driving (Supervised), or FSD. Mercedes, meanwhile, plans to charge $3,950 for a three-year subscription for the forthcoming MB.Drive Assist Pro on its CLA-Class EV; that system still requires at least one hand on the steering wheel. Video released by Rivian shows the company’s R1 SUV being driven on a variety of urban and rural roads, according to the company. Rivian plans to introduce this self-driving system to compete with Tesla’s offering before the end of 2026. Rivian Impressive as it is, Autonomy+ is only a Level 2+ system in the classification system established by the Society of Automotive Engineers. Level 2+ means that a human driver must pay full attention and be ready to retake control at any moment. Rivian, along with a horde of deep-pocketed rivals, is aggressively working toward more impressive (and potentially lucrative) levels of autonomy. At Level 3, drivers could “check out” behind the wheel for limited periods, to scroll through emails or watch a movie—but not to sleep. The big race right now is to deliver Level 4 autonomy: A car you could (in theory) dispatch to pick up a pizza, and have it carted home on the heated, unoccupied driver’s seat—or in which you could spend the ride lounging alone in the back seat, enjoying a private slice while reading a newspaper. At Rivian’s software lab in Palo Alto, Calif., a technician evaluated code for the company’s self-driving system.Jason Henry/Bloomberg/Getty Images Robotaxis currently roaming select cities in the U.S., China, and the Middle East have proved that driverless, Level 4 autonomy is possible. These cars operate in relatively tiny numbers in a couple of dozen cities, and within the specific constraints of commercial services. Now Rivian and its many rivals—including Tesla, Toyota, Mercedes, Volkswagen, and China’s BYD—are racing to bring that level of self-guided mobility to the masses. Rivian’s strategy combines a suite of cameras, radar, and lidar; a custom set of silicon chips, developed in-house, to process sensor data; and an AI autonomy model running on those chips. With $1.25 billion in backing from Uber, Rivian plans to graduate to a fleet of self-driving, Level 4 robotaxis starting in 2028. Those taxis, along with Rivian’s consumer fleet, will be the literal training wheels for extending Level 4 ability to consumer vehicles. Meanwhile, millions of connected cars, as they cruise every nook and cranny of the globe, are already sending data to train automakers’ systems. The race is on to funnel those data through fast-improving AI models with “end to end” capability: an AI architecture, powered by deep learning, that processes raw sensor data directly into physical vehicle commands. So equipped, engineers anticipate they’ll be able to solve the tricky edge cases—tangled urban streets, unique geographies, swarms of pedestrians, inclement weather—that skeptics once deemed intractable. Rivian’s Plan for Level 4 Self-Driving Despite the company’s high media profile, including a spotlight on RJ Scaringe, its MIT-doctorate founder and chief executive, Rivian holds a relatively tiny slice of the U.S. passenger-vehicle market. It sold just 42,000 vehicles last year across its three models, the adventure-minded R1S SUV and R1T pickup, and the Electric Delivery Van. Tesla sold about 1.6 million units. Toyota, the world’s largest automaker, sold more than 11 million. The first generation of the Rivian Autonomy Processor, an AI processing chip developed in-house, was tested at Rivian’s Palo Alto, Calif., lab in December, 2025. Jason Henry/Bloomberg/Getty Images Rivian’s underdog strategy is to leverage software and tech to make itself a serious player. Volkswagen, among the world’s largest automakers, saw enough value there to invest up to $5.8 billion in a joint venture called Rivian and Volkswagen Group Technologies. The joint venture gives Rivian crucial capital for development. It gives Volkswagen access to Rivian’s electrical architecture and to the software for the R2, new-generation Rivian SUV that went on sale in June. Unlike traditional lidar units, which protrude like a layer cake from the roof of a vehicle, Rivian’s unit on the new R2 SUV is housed in a small, sleek enclosure where the windshield meets the roof.Rivian (2);Jason Henry/Bloomberg/Getty Images “Rivian developed an architecture so important that VW is spending billions to buy it, as opposed to trying to re-create it themselves,” says Bryan Reimer, a research scientist in MIT’s Center for Transportation and Logistics. But the joint venture doesn’t give VW access to Rivian’s autonomous tech. In March, that R2 architecture underpinned Rivian’s $1.25 billion deal to supply Uber with up to 50,000 robotaxis. The companies plan to initially deploy 10,000 taxis, beginning in San Francisco and Miami in 2028, before expanding across 25 cities in the U.S., Canada, and Europe. Rivian’s vulnerabilities include struggles with reliability, along with expensive body repair costs that the company says it strove to reduce for its new R2. As impressive as Rivian’s in-house tech may appear, the company has miles to go to catch up with Tesla, which recently announced it has 1.1 million active users of its FSD system. Toyota is also jumping into the game; its Woven by Toyota subsidiary has partnered with the Alphabet-owned Waymo to develop an autonomy platform for robotaxis and consumer cars. The lidar unit on a Waymo robotaxi protrudes noticeably from the roof of the vehicle.Andrej Sokolow/picture alliance/Getty Images Until recently, most observers would have gone all-in on Tesla as the winner of the autonomous race. Elon Musk’s company has begun operating a small test fleet of Model Y robotaxis in three Texas cities and in Florida. Tesla has also begun producing a dedicated autonomous vehicle, the Cybercab robotaxi. But in April, Musk pushed back his timeline for Level 4 autonomy for general consumers: “I’m just guessing here, but probably in the fourth quarter” of 2026, he said. It was the latest in a series of deflating walkbacks from the man who once promised 1 million robotaxis on the road by 2020. Scaringe, during our drive of his company’s make-or-break R2 SUV at a Utah state park, says that showroom Rivians will start adopting some of its robotaxis’ Level 4 capabilities no later than 2030, perhaps beginning with self-parking functions. How Self-Driving Systems Are Learning From Humans Like most autonomous cars, Rivian’s system fuses data from multiple sensors to create a robust picture of a fast-moving environment and its obstacles. Data is fed to a neural network—what Rivian refers to as its “Large Driving Model,” or LDM—that churns through hundreds of trillions of operations per second to interpret and fuse data from cameras, radar, and lidar. That network is end to end, meaning that it processes multiple streams of raw sensor data (such as camera pixels) and outputs driving controls (for steering, braking, and acceleration) through a single data pipeline. More traditional systems coded distinct steps for data collection, feature extraction, prediction, and decision-making. That proprietary AI driver identifies features in images and point clouds, groups them into objects, and tracks them across frames, time-stamped to the millisecond to account for differing frame rates. The AI thus builds confidence over time, acting on object detections that persist across several frames, rather than, say, slamming the brakes due to a camera blip on a single frame. The virtual driver can then navigate safely even when sensors disagree, by favoring the persistent data. The output— commands for electric motors and other systems—is backed by redundant hardware for by-wire systems such as steering and brakes. During my demo of Rivian’s point-to-point Autonomy+ system, a company test driver sits behind the wheel. Nick Nguyen, the engineer who directs Rivian’s products and programs related to autonomy, watches from the back seat. Compared to, say, a large language model that writes news or fiction, Nguyen says, an autonomous-driving AI is less subjective and easier to evaluate, so there’s little room for error. “We want cliché. We want boring. Just safe, repeatable driving,” he says. The Rivian R2 SUV plans to offer a self-driving system by roughly year’s end 2026. The R2 competes with the more urban-oriented Tesla Y.Rivian From my brief drive, I’d say suburban boredom is achieved in this Rivian R1S. Unlike some modes of Tesla’s Full Self-Driving (Supervised), Rivian’s system drives like a soccer dad, obeying speed limits to the digit, stopping gracefully at traffic lights, and easing over speed bumps like a driver delivering antiques. Yet this robo-driver isn’t timid or tentative. For robotaxi companies in the U.S. and China, these types of routine trips are boosting optimism and investment to dizzying heights. Waymo claims 92 percent fewer fatal or serious-injury accidents than human drivers, based on 220 million miles of autonomous ride data. But the real challenge is how well the higher levels of autonomy will work when they reach consumer cars [see Sidebar, “The Growing Proof That Autonomous Cars Save Lives”]. Rivian’s core LDM currently ingests cloud data from up to 125,000 cars for analysis and validation, which then fine-tunes the model through simulations. Onboard computing is smart enough to trigger recording only for unusual scenarios. Owners have to agree explicitly to data collection beforehand. Updated LDMs will be beamed back to customer cars via monthly over-the-air updates. part of that self-reinforcing data flywheel. It’s part of what Scargine calls the “data flywheel,” the self-improving AI loop that continuously refines the system. As is true for some of its rivals, Rivian no longer needs to fully rely on an onboard high-definition map or even a cellular link to pinpoint the car for navigational purposes. That strategic shift reduces data demands, and ensures steady driving in urban canyons or tunnels with no connections. Instead, the Rivian recognizes and responds to its surroundings through recognition and repetition, just as a human would do interpreting street signs, following lane markers, being alert to hazards. The Rivian R2 features 11 high-definition cameras and five radars. It will integrate a lidar unit early next year to lay the groundwork for future autonomy. That miniaturized lidar will integrate smoothly into the R2’s existing roofline, an improvement over the bulky, drag-producing units seen on Waymo Jaguars, and older partially autonomous models. Vidya Rajagopalan, Rivian’s senior vice-president of electrical engineering hardware, says lidar costs have fallen from above $10,000 to a few hundred dollars in under a decade. Vidya Rajagopalan, Rivian’s senior vice president of electrical engineering hardware, holds a RAP1 AI processor chip.Jason Henry/Bloomberg/Getty Images A mix of sensors plays up the strengths and diminishes the weaknesses of each, Rajagopalan says. Cameras capture color and texture and can distinguish between objects, but they struggle in darkness and low-contrast lighting. Lidar is unaffected by darkness or blinding sunlight, and senses shapes in three dimensions. This inherent 3D capability makes lidar more reliable for slowing or halting a car for random objects—“a tire in the road, or maybe a large dinosaur,” Nguyen quips. Multiple cameras can further contribute 3D data, after a short delay for processing. Sensors with 360-degree vision can outperform human senses in key situations. Radar and lidar can spot nighttime pedestrians or animals hundreds of meters down the road, something no human can do. But lidar can be thrown off by dust, fog, and snow. Radar can “see” through rain or snow, but with relatively low spatial resolution. Why Rivian Ditched Nvidia To handle the flood of sensor data, Rivian has taken on an ambitious challenge: designing its own custom autonomy chip in-house. The Rivian Autonomy Processor (RAP1) is a 5-nanometer processor that can execute 800 trillion operations per second (TOPS), three times as fast as the Nvidia Jetson Orin chip used in its earlier models. The chip will be built to Rivian’s specs by Taiwan Semiconductor Manufacturing Co. , which also makes custom chips for Tesla. Rivian’s autonomy module contains two Rivian Autonomy Processors, each capable of 800 trillion operations per second.Rivian Nvidia’s latest automotive system-on-a-chip, the Drive AGX Thor processor, is being adopted by the likes of BYD, Hyundai, Lucid, Mercedes, Nissan, Volvo, and Xiaomi, along with the Aurora and Waabi autonomous-trucking companies. On paper, a single AGX Thor chip is slightly faster in terms of TOPS, at 1,000 trillion operations per second. But Rivian combines a pair of chips in each autonomy module, giving it 1,600 TOPS and execution rates around 5 billion pixels of data per second, versus 3.5 billion for Nvidia’s Thor. Rajagopalan says developing the chip and AI software simultaneously shaved a critical full year from development. Experts say it’s the kind of fast-to-market speed that China has mastered and that legacy automakers are struggling to match. The in-house design allows Rivian to custom-tailor its software to the chip, and vice versa. Nvidia’s general-purpose chip, designed to satisfy multiple customers with various needs, must devote computing power to onboard infotainment, displays, or other systems. Rivian’s chip is designed to run autonomy and nothing but. During my visit to Rivian’s Silicon Valley campus, Rivian engineers Prasun Raha and Mukund Chavan tutored me on the rapid pace of the company’s autonomy evolution. A cluttered wallboard displays a first-gen architecture that Rivian debuted just five years ago. The initial R1S SUV and R1T pickup used nearly a score of electronic control units (ECUs), the “black boxes” that traditionally control vehicle functions. For its latest R1 models, Rivian reduced the ECU count to seven. The zonal architecture organizes nearly every vehicle function into three zones, hugely consolidating the electronics and simplifying manufacturing. Rivian also leaned into an autonomy trend called “early fusion”: mixing raw, time-and-space-aligned sensor data into a shared view before the neural network acts upon it. In late fusion, each sensor performs solo recognition before it’s combined into a single picture. The self-driving revolution will really begin when the technology migrates from controlled taxi fleets to consumer cars, giving owners back the precious time they waste on commuting. Early fusion preserves the richest sensor data for maximum accuracy in self-driving. But it demands the enormous computing power the RAP1 can deliver. Raha says the approach helps the multimodal system degrade gracefully and continue to operate with certainty even if, say, a camera’s lens gets covered with mud. Together, these elements make up Rivian’s third-generation autonomy platform. Displayed on a test bench, a new Autonomy Compute Module pairs two RAP1 supercomputing chips. The module is eight times as powerful as before but 60 percent smaller, according to the company. Raha says the system was designed expressly to expand Rivians to Level 4 autonomy from today’s Level 2+. RivLink, the automaker’s interconnect technology, can bridge multiple RAP modules to scale processing power. “It lets us build this extensible system, with perhaps two more chips for Level 3 or four for Level 4, depending on how the model scales,” Raha says. Rivian’s Road Map to Full Autonomy Rivian’s next planned milestone toward self-driving will be Level 3 autonomy—a hands-off and eyes-off system, but for highways only. (Remember, Tesla’s current FSD is technically a Level 2 system: hands off but not eyes off.) On the freeway, Nguyen points out, drivers would be spared the drudgery of dealing with stop-and-go traffic, allowing them to boost productivity or just goof off. Some autonomy critics are leery of Level 3, envisioning a limbo zone in which drivers are lulled into a false sense of security when a car drives for long stretches with no human attention required. Ford and GM are among the automakers pivoting toward limited eyes-off functions. Rivian’s senior vice-president of autonomy, James Philbin, sees Level 3 as an inevitable stepping-stone to Level 4. The company expects it will initially be limited to highways, not the cut-and-thrust of city traffic. If a driver fails to respond to alerts, the system will slow the vehicle, pull off on a shoulder, or call 911. Rivian has not announced a timeline for introducing limited Level 3 capability. Navigating a Tricky Liability Shift on the Way to Immense Profits Ready or not, these much more autonomous systems are coming, a natural evolution of today’s semiautonomous helpers. In developed markets, adoption of showroom cars with partial-to-full automation is projected to jump from 8 percent in 2024 to 28 percent by 2030, according to Morgan Stanley. “One in four cars sold globally may be equipped with smart-driving technology in five years, versus one in eight cars now,” wrote Tim Hsiao, a Morgan Stanley analyst, in a note posted on the company’s website. Combining cameras, lidar, and radar gives a self-driving car a better view of people and objects in front of it, according to Rivian. The company expects to release a self-driving system before the end of 2026 that will compete with Tesla’s, which uses cameras alone. Rivian MIT’s Reimer believes the self-driving revolution will really begin when the technology migrates from controlled taxi fleets to consumer cars, giving owners back the precious time they waste on commuting. If owners could truly send their autonomous car to safely chauffeur children, keep aged parents mobile, or run errands—while owners keep working or playing—the automakers who first help make that happen will enjoy a massive competitive edge, he says. As automakers struggle to convert buyers to subscription models, Reimer believes that self-driving appears to be the one advance for which consumers might actually pay plenty. But the greatest impediment to that revolution has little to do with technology. Public skepticism over self-driving is rampant; and the fate of fully autonomous testing in New York City is uncertain. Even going from Level 2 to Level 3 might shift legal liability for some accidents from drivers to automakers. But with Tesla still fighting lawsuits over its rudimentary Autopilot systems, those questions aren’t anywhere near settled. Experts worry that self-driving cars may become as politicized as EVs. Labor unions are pushing back, fearing job losses from taxis to trucking. A crazy quilt of state or local regulations has failed to create coherent industry guidelines. Publicized failures—even ones that don’t result in injuries, such as Waymos driving onto a flooded street or impeding emergency workers—give the industry a black eye. Companies like Tesla and even Waymo, Reimer says, have too often relied on an arrogant “Trust me” approach, resisting regulation and oversight. Nevertheless, the momentum toward truly self-driving cars, and massive backing from automakers and AI-besotted investors, suggests their time has come. The rest of the journey will depend as much on social and regulatory issues as technical ones, and so Reimer has a bit of advice. “Do it right, and share all your data,” he says. “Earn the right to scale…. It’s about establishing trust, and developing a framework in which we truly believe these systems can operate as a trusted part of our transportation network.” This article was updated on 08 September 2026.

Plenty of people remain spooked by autonomous vehicles, or AVs. Some experts and policymakers have cautioned that AVs won’t necessarily make roads safer. When it comes to partial or full autonomy, the picture isn’t entirely clear, in part because there aren’t enough self-driving cars to make meaningful apples-to-apples comparisons. Yet mounting research suggests that self-driving cars crash significantly less often than people, and with far fewer injuries. Evidence also shows that advanced driver assistance systems (ADAS) and other building blocks of autonomy—some of which are already mandated on every new car—are also reducing occupant and pedestrian injuries and deaths, along with insurance claims. On the ADAS front, the Insurance Institute for Highway Safety found that automatic emergency braking (AEB) systems that recognize people in front of the car cut pedestrian crashes by 27 percent. Those AEB systems are mandated for all light vehicles in the U.S. by 2029, and more than 90 percent of new models already comply under a voluntary automakers’ agreement. A separate IIHS study found that automated braking greatly reduced rear-end crashes, by 50 percent, and their injuries by 56 percent. The Highway Loss Data Institute found that cars with AEB alone showed a 13 percent drop in property-damage claims. Cars that bundled ADAS features, including automatic braking for pedestrians, adaptive cruise control, and lane-departure warnings, saw claims reductions up to 39 percent. Move to Level 4 autonomy, and Waymo says its robotaxis have now given 20 million paid rides over 220 million miles, the equivalent of 250 lifetimes of driving. In March, Waymo’s independent study showed 92 percent fewer fatal or serious-injury crashes, a 13-fold reduction versus human drivers in comparable city environments. That included 92 percent fewer pedestrian injuries, 83 percent fewer crashes with airbag deployments, and 82 percent fewer crashes with any injuries whatsoever. That included a 96 percent reduction in injury-causing crashes at intersections, among the deadliest environments for any automobile. How Does Limited Fair-Weather Data Compare to Traditional Crash Statistics? A key question is whether Waymo’s robotaxis, currently limited to fair-weather operation in a handful of cities in the U.S., are directly comparable to humans driving a wider variety of roads in much more variable conditions. The IIHS is looking to dig deeper by cleaning up often-incomplete data. Researchers estimate roughly half of human crashes go unreported, and up to one-third of injury accidents, because drivers hope to avoid insurance price hikes. That potentially skews safety numbers in favor of human drivers. And while Waymo leads the industry in transparency, and robotaxi operators are required to report even the tiniest scrape to the National Highway Traffic Safety Administration (NHTSA), not every company voluntarily reports their total miles driven. Related: Rivian’s Gambit for Full Autonomy The IIHS’s latest July study flatly stated that automated cars crash less often than people. But it also sought clarity by creating a more-reliable category of “police-reportable crashes.” It then compared crash rates of human-driven cars against Waymo taxis in San Francisco, Phoenix, Los Angeles, and Austin. Waymo’s Jaguar I-Pace taxis traveled about 50 million driverless miles over the study period, versus 222 billion human miles in the same cities. In a potential boost for public trust, the study generally supported Waymo’s own findings. Waymo taxis were involved in 68 percent fewer crashes overall than human drivers: 76 percent lower in Phoenix, 71 percent in LA, and 35 percent in San Francisco. A 4 percent higher Waymo rate in Austin may reflect an extremely small sample size. Significantly, Waymo’s injury crashes were still 81 percent lower on a per-mile basis. The industry and its supporters continue to press the safety advantages of autonomous vehicles that never get drunk, drowsy, or distracted. Yet for this fledgling AV industry, there are still no national performance or safety standards. A crazy quilt of state or local regulations can allow or prohibit their deployment. That balkanized approach makes it harder to compare crash rates, according to the IIHS, which is calling for better federal reporting standards. A posting on the IIHS website quotes the institute’s director of statistical services, Eric Teoh: “Those are encouraging signs for the future of driverless vehicles.” Teoh, who was also the lead author of the institute’s study, added that “Now we need to get the data-collection system right, so that we can ensure that level of safety continues as these technologies become more prevalent.” Amazon’s Zoox Gets an Exemption for its Robotaxis On July 30, in a move seen as fast-tracking the tech’s deployment, NHTSA granted Zoox, a subsidiary of Amazon, the first-ever exemption from certain motor-vehicle safety standards. That will allow commercial operation of Zoox’s toaster-shaped robotaxis, which have no steering wheel or pedals aboard. The agency determined that Zoox’s purpose-built robotaxi “would provide an equivalent level of safety” as a compliant vehicle, thereby satisfying the standard for an exemption. On that final day of the SAE’s Automated Transportation Symposium, NHTSA also announced a partnership with SAE Industry Technologies to develop the nation’s first performance and competency standards for AVs, via a three-year, $5 million “A2SCEND” consortium. Some doctors and health professionals are arguing that policymakers need to stop viewing self-driving cars as a tech moonshot but rather as a critical public-health intervention. Jonathan Slotkin, a neurosurgeon, makes a powerful case for the medical and societal benefits of AVs. Researchers at the Johns Hopkins Bloomberg School of Public Health say that highlighting the social value of AVs is critical to driving public trust and adoption. Consider that roughly 40,000 people in the U.S., including more than 7,000 pedestrians, are killed each year in roadway accidents. About 1.16 million people die in roadway crashes around the world, making them the leading cause of death for children and young adults between the ages of 5 and 29. Cutting that by even 50 percent—let alone the 90 percent reductions suggested by some studies—would save 580,000 lives a year. That social and economic gain would dwarf that of seat-belt adoption or anti–drunk driving campaigns. This article was updated on 08 September 2026.

Many researchers and students in Kenya, Rwanda, and Uganda struggle to access and publish scientific and technical articles because of financial barriers including publishing fees and subscriptions to research libraries. To help, IEEE has made its Xplore Digital Library more accessible by offering discounts on subscriptions and lowering fees to publish articles. But the number of papers published by technologists in the three nations still lags behind those from other developing countries. It might be that many researchers haven’t received training in methodology, been instructed on how to write academic articles, or fully understand the process for publishing in scientific journals. Staff from the IEEE Publication and Information Products group and IEEE volunteers held educational workshops this year in the three countries. The sessions covered the publishing process, IEEE publication outlets, ways to ensure the integrity of research papers, and tips for making better use of IEEE Xplore. “We want to make sure those in this region are on par with other research communities and ensure they have the support and knowledge they need to make informed publishing decisions,” says Kristopher Zakrzewski, the IEEE area manager for Europe, the Middle East, Africa, and parts of Central Asia. “Our goal is to give them the tools they need to increase visibility and allow them to participate in global conversations in the technology space.” Workshops on the publishing process More than 120 participants attended the workshops, which were held in February at the Novotel Nairobi Westlands hotel, the University of Rwanda, and Makerere University, in Kampala, Uganda. IEEE volunteers who are also authors showed attendees how to prepare, submit, and publish papers. They covered the peer-review process and the benefits of working with IEEE, which publishes about 30 percent of the world’s technical literature on electrical engineering and computer science. IEEE Senior Member Nelson Ijumba presented at the session in Rwanda. Member Kennedy Ronoh led the Nairobi workshop. Sheila N. Mugala spoke to attendees in Kampala. “The great thing about these sessions,” Zakrzewski says, “is that each had a local author who presented tips and best practices to ensure that new and returning authors have the information they need to prepare their paper for submission, determine where best to publish their article, and find the right journal or conference that would be the best fit for their research.” One of the facilitators at the Uganda session was IEEE Senior Member Mayur Kumar Chhipa, head of engineering at the International Business, Science, and Technology University in Kampala and vice chair of the IEEE Uganda Section. The university has about 200 engineering students and about 50 researchers. More than 100 people attended Chhipa’s session, where he shared practical guidance on conducting literature reviews and identifying high-impact research. “Researchers in Uganda typically present their paper at an IEEE conference, and that’s it,” he says. “What we’re trying to do is encourage them to take the next step and get their paper published in an IEEE journal.” He encourages his students to submit a summary of their thesis to an IEEE conference, he says. “Otherwise,” he says, “their thesis sits in the university’s library or collects dust on a bookshelf. “When you publish your research, the world knows you are a scholar who has done good work. Having a paper published at a conference or in a journal can help you get into a master’s program globally.” IEEE Xplore access Attendees were given an overview of the features of their IEEE Xplore subscription. The digital library contains more than 7 million technical documents from industry-leading journals, conferences, ebooks, and eLearning courses, as well as partner content. IEEE provides access to the library to more than 50 universities in Kenya through a subscription agreement with the country’s Library and Information Services Consortium, which includes university and public libraries and research institutions. Sixteen universities in Uganda and one institution in Rwanda receive discounted subscriptions. “It was really important to establish the direct correlation between having access to the technical literature and the publishing output from their university and the region as a whole,” Zakrzewski says. Many publishing options The workshops covered publishing options offered by IEEE. That includes both traditional and open access journals, with more than 200 periodicals in total. There are approximately 180 hybrid journals, which contain a mix of subscription-based and open-access articles, and 30 gold open access journals. Open access is a publishing model that makes scholarly research and literature freely available online to everyone. Instead of institutions paying for subscriptions, authors or funders typically pay an article processing charge (APC) of between US $2,160 and $2,800 to have their piece published. IEEE offers authors in Kenya a 50 percent discount off the APC rate, and authors from Rwanda and Uganda can publish in IEEE open access journals for free. The open access program provides authors with greater visibility for their research and enhances discoverability, Zakrzewski says, leading to an increased number of references and citations. Publishing with IEEE opens additional opportunities including scholarship awards, research assistant job offers, networking opportunities, and speaking engagements.” —IEEE Senior Member Mayur Kumar Chhipa IEEE Xplore contains more than 200,000 open access articles, he notes. More than 109,000 articles have been published in IEEE Access, a multidisciplinary open access megajournal. “IEEE supports author choice,” Zakrzewski says. “We really want to make sure that an author has the option to publish the research that will meet any consortium, funder, university, or coauthor requirements—which is why we’re focusing on growing our open access program to complement our traditional publishing program and offer more options to authors.” The sessions are having an impact Participants at the Uganda session told Chhipa that they appreciated the IEEE Xplore Digital Library demonstrations and found the guidance on academic publishing valuable. “Many attendees mentioned that the session helped them better understand how to search for relevant literature, evaluate the quality of research papers, and write stronger manuscripts for publication,” he says. “I have observed increased interest among students and faculty in using IEEE Xplore as their primary research resource,” he adds. “Researchers are also more aware of ethical publishing practices and are developing stronger research proposals and manuscripts. “The program contributes to building a stronger research culture by encouraging evidence-based research, international collaboration, and higher-quality publications, which will ultimately enhance the global visibility of research from Uganda and Africa.” Publishing has its privileges Chhipa says getting your research published has many benefits, and IEEE staff and members agree. IEEE and several of its societies offer student grants to help cover the expense of traveling to conferences and presenting papers. The money typically covers airfare and a hotel room. Some grants also pay for conference registration fees, Chhipa says. Chhipa assists students at his university with writing and submitting research papers to IEEE journals and conferences. Students gain confidence when their paper gets accepted, he says. One who attended the recent IEEE session was informed that his paper was accepted by an IEEE conference—which Chhipa says he was excited about. He encouraged that student to apply for a travel grant. “Maybe he’ll get it. Maybe he won’t. But at least he learned how to write a paper, apply for a visa to attend the conference, and book an airplane ticket,” Chhipa says. “It will help him grow personally and professionally.” Presenting a paper at an IEEE conference can be life-changing, he says. “It opens additional opportunities,” he says, “including scholarship awards, research assistant job offers, networking opportunities, and speaking engagements. This is how publishing a research paper in the IEEE Xplore Digital Library can directly, positively impact the life of students and scholars from Africa, especially Uganda, Rwanda, and Kenya.”

This article is brought to you by Tsubaki KabelSchlepp. In modern automated manufacturing, six-axis articulated robots perform high-speed, multidirectional maneuvers under demanding operational cycles. However, as robot arms swivel, rotate, and extend, the electrical cables, fiber optics, and pneumatic hoses supplying them endure severe mechanical stress. Torsional twist, rapid acceleration, and repeated contact with machine structures often lead to premature conductor fatigue, insulation breakdown, and costly unplanned production halts. To overcome these multi-axis motion challenges, the Tsubaki KabelSchlepp Robotrax System provides a specialized three-dimensional cable carrier engineered specifically for complex robotic motion. Managing High Tensile Forces With Central Steel Technology Conventional cable carriers often transfer operational movement stress directly onto internal electrical lines and hoses. The Robotrax system changes this dynamic through a central steel cable that runs through the core of every chain link. The Robotrax system’s central steel cable absorbs the primary tensile loads and preserves conductor integrity, dramatically extending cable service life. When robot arms undergo rapid directional shifts and accelerations up to 10 g, this internal steel cable absorbs the primary tensile loads. By isolating electrical and fluid lines from pulling forces, the design preserves conductor integrity and dramatically extends cable service life. Mechanics can easily calibrate and adjust system tension using an integrated clamping piece, ensuring consistent mechanical support throughout long operational cycles. Spherical Link Design and Modular Cable Routing The foundation of the Robotrax system lies in its open, single-piece plastic links featuring spherical snap-on connections on both sides. This geometry allows the carrier to flex smoothly across three axes, providing radial rotation of up to ±450 degrees per meter depending on the model size. To optimize internal organization, carrier links contain up to three distinct chambers. This physical separation prevents signal interference and mechanical abrasion between heavy power lines, sensitive data channels, and fluid hoses. For standard models (R040 through R100), technicians can press cables directly into the carrier without tools, drastically reducing installation and maintenance time. Larger configurations, such as the R140X, incorporate swiveling crossbars with snap locks alongside vertical and horizontal dividers for customized interior partitioning. ROBOTRAX System Steel cable for transferring extremely high tensile forces Tension piece for locking the chain links Type with toolless opening swivel crossbars and divider module available Open design – Fast cable laying as the cables are simply pressed in – Easy checking of all cables Special plastic for long service life Protective covers or heat shields made from different materials are available for different environmental conditions Quick-release bracket for fixing and continuation Strain relief with LineFix clamps Protection against hard impacts, excessive abrasion and premature wear as well as limitation of the bending radius through protector Active Retraction and Impact Protection Large robot work envelopes and high-speed motion trajectories can cause loose cable carrier loops to swing and strike the robot body. To eliminate these destructive collisions, Tsubaki KabelSchlepp integrates the Pull Back Unit (PBU). The PBU serves as an active retraction mechanism that maintains optimal tension on the cable carrier throughout the entire motion cycle. By preventing excess slack and eliminating interfering contours, the PBU minimizes collision risks across complex movement paths. The unit requires zero maintenance on its retraction element and offers standard mounting configurations for leading industrial robot platforms, including KUKA, ABB, and FANUC. Tsubaki KabelSchlepp’s Pull Back Unit maintains optimal tension on the cable carrier and minimizes collision risks across complex movement paths. Additionally, external protectors can be retrofitted onto individual chain links. These durable impact shields limit the minimum bending radius to prevent over-flexing while shielding the chain body from severe external abrasion. If wear occurs, technicians simply replace the modular protector rather than the entire cable carrier assembly. Built for Demanding Industrial Environments From automotive welding cells to high-speed machining centers, Robotrax systems adapt to severe working conditions through tailored protective accessories: Heat Shields: Aluminum-coated textile fiber covers protect against radiated heat, hot weld spatter, and flying sparks. Protective Covers: Coated polyester sleeves shield sensitive lines against aggressive cutting fluids, hydraulic oils, paint overspray, and abrasive dust. LineFix Strain Relief: Multi-layer clamping devices anchor cables securely at both ends to prevent axial displacement during intense motion. By combining central load absorption, multi-axis flexibility, and active retraction control, the Robotrax system offers plant engineers and system integrators a reliable path toward maximizing robot uptime and reducing total operational costs.

This article is crossposted from IEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity and delivered to your inbox for free! Asking someone to be your mentor is weird. Walking up to someone and asking, “Will you be my mentor?” has always seemed to me like the adult version of a kid walking up to another kid at a party and asking, “Will you be my friend?” What you’re really asking is: “Will you commit some amount of unpaid time to guiding my career for an indefinite period?” Framed that way, of course some people hesitate to say yes. But formal mentorship isn’t the only way to benefit from the wisdom of those who came before. I’ve never formally asked anyone to mentor me. And yet I’ve had dozens of unofficial mentors. The Copy-Paste Method One way to learn from others is by copying what you observe. Sometimes this means reading books or blogs from engineers you respect and directly applying their ideas to your work. I’ve also been fortunate to work alongside some extremely talented engineers, and I shamelessly copied the things they did well. When I meet one of these engineers, I try to figure out what they’re doing differently: How do they approach a problem? What do they read? How do they communicate in meetings? What do they know that I don’t? Then I steal whatever seems useful and apply it to my own career. Great artists steal. Engineers should too. Curiosity Compounds Still, just observing has its limits. Asking questions can get you even farther. I’ve asked managers how they approached difficult conversations, and I’ve asked engineers what their process was for solving problems I thought were impossible. If someone seems unusually knowledgeable: “What are you reading right now?” If I respect someone’s work: “What’s something you think I could do better?” These aren’t profound questions. They don’t need to be. You get one useful piece of information, apply it, and move on. And if you don’t work around exceptional engineers, you can still do this. The only real requirement is curiosity. When you encounter something you don’t understand, make it a rule to investigate instead of moving past it. You don’t need one person willing to guide your career. You need a collection of people who know things you don’t. Pay attention to them. Ask questions. And shamelessly copy the good parts. Ask me! If you have a career question you’re struggling with, like an upcoming decision, a problem at work, an interview, whatever—submit it here: https://docs.google.com/forms/d/e/1FAIpQLSdj_2BZIhrGF__7BCLH33zJ9NMv8C7Vsg9NNusASrYj7-9Idw/viewform. You can include your name or remain anonymous. I’ll be reading through them and answering some in future articles. Consider it mentorship without the awkward “will you be my mentor?” conversation. —Brian ICYMI: The Institute June 2026 issue IEEE members have a wealth of experience and knowledge to draw from. In the most recent issue of The Institute, several members share their career advice for engineers, from engineers. You can also learn about other IEEE programs and courses. Read more here.

In the 1960s NASA began developing a system of reusable space shuttles to make its work more efficient and to reduce costs. The shuttles could launch like rockets, maneuver in Earth’s orbit, and land like airplanes. They also could carry large satellites to and from orbit. Like other types of transportation, machinery eventually breaks down, and parts need to be replaced or fixed. And the cargo being carried to and from Earth has to be moved to its final destination. To complete such tasks, Spar Aerospace (now part of MDA Space) of Brampton, Ont., Canada, and the National Research Council in Ottawa developed a robotic arm, the Shuttle Remote Manipulator System. The project was a joint venture between the U.S. and Canadian governments. Known as Canadarms, the robotic tools attached to shuttles’ exteriors. They allowed astronauts to handle and transfer tools, satellites, and other payloads. Inspections of the shuttle and repairs could be completed using the robots. The system was first deployed in 1981 aboard Columbia’s second flight. Canadarm was used for 30 years on five shuttles and on the International Space Station. The robotic arm was dedicated on 19 June as the 300th IEEE Milestone. The ceremony was held at MDA Space headquarters. The IEEE Toronto Section sponsored the nomination. “It is appropriate that the 300th Milestone is the Canadarm,” says Michael Geselowitz, senior director of the IEEE History and Heritage group. “The technology spans aerospace, robotics, and computing fields of interest. It involves international cooperation between the United States and Canada, and it shows how IEEE and its members are at the cutting edge of many frontiers of science and technology.” International collaboration for space exploration Seeking to collaborate with other countries on the reusable spacecraft, NASA invited Canada to participate in 1969. It took some time for the country’s officials to determine what technology it could contribute. They learned of a robot that loaded and replaced spent fuel bundles in Canada’s deuterium uranium nuclear reactors, according to the Milestone webpage. That robot, developed by DSMA-Atcon (also now part of MDA Space), inspired what would become the Canadarm. A proposal was submitted in 1974 to design and build the Shuttle Remote Manipulator System. The robotic arm would unload the contents of the space shuttle’s payload bay. NASA approved the project, and development began in 1975. Canada had no space agency at the time, so the country’s National Research Council coordinated the organizations that collaborated on the project. Spar Aerospace led the subcontractor team, which included DMSA-Atcon, CAE, and the Canadian subsidiary of RCA Corp. Engineers from the University of Toronto’s Institute for Aerospace Studies contributed to the project. Building an arm for zero gravity NASA had strict requirements for the robot: The arm had to be lightweight and small enough to fit on the shuttle, as detailed in an article published by the University of Toronto. It also had to move forward and backward, up and down, left and right, and rotate along three perpendicular axes (known as six degrees of freedom). To achieve all that, engineer Peter Carlisle Hughes designed the robot with two shoulder joints, one elbow, and three rotating wrists. “Each joint had six degrees of freedom, and the arm had six links so that it could grab anything from any angle and move it anywhere,” Hughes said in the article. The IEEE life member worked at the Institute for Aerospace Studies. “This milestone is a reminder of the privilege we all have at MDA Space—as engineers, designers, builders, operators—to build technology that shapes history.” —Holly Johnson, MDA Space vice president The arm was 50 meters long and weighed 400 kilograms. It was made of materials that could withstand outer space’s harsh environment: titanium, stainless steel, and graphite epoxy. The arm was so lightweight that it couldn’t support itself under Earth’s gravity, so it lay on air bearings on the lab floor at Spar’s Brampton headquarters. CAE engineers, including IEEE Life Member David A. Weston, designed the display and control panel as well as the hand controllers astronauts would use to monitor and operate the robot. Because the robotic arm was meant to work in zero gravity, a room that simulated a weightless environment was built to test it. A computer-based simulation facility was constructed in Spar’s headquarters to evaluate its controllability using two simulation models, according to the University of Toronto. RIGID, an early computer simulation model, tested every part of the arm except for its flexible properties. ASAD, which stood for “all singing, all dancing,” examined the arm’s movements, ensuring the joints operated correctly. Both were created by Hughes and Spar engineer Andrew A. Goldenberg, who is now a professor emeritus at the University of Toronto. The facility was also used to train astronauts on how to use Canadarm. It took five years for the first Canadarm to be completed. In February 1981, it was presented to NASA at the Kennedy Space Center in Cape Canaveral, Fla., and deployed that November. Lift off into space Astronaut Stephen Robinson is anchored to a foot restraint on the extended Canadarm2 attached to the International Space Station during an extravehicular activity he conducted in 2005.NASA The Canadarm was attached to the outside of the shuttle. Astronauts were able to monitor the arm’s movements through a live video feed provided by cameras installed on the wrist and elbow joints, according to the Milestone webpage. Using a hand controller and monitors located in the shuttle’s flight deck, astronauts handled and transferred tools, satellites, and other payloads weighing up 266,000 kilograms using minimal electricity. NASA ordered four more systems. In 2001, Canadarm2 was attached to the International Space Station and used to help build the orbiting laboratory. It is a permanent part of the station, still completing maintenance tasks and moving supplies. During the course of the 30-year shuttle program, the arms performed successfully and achieved the flight’s mission. The original Canadarm took its final flight in July 2011 aboard the Atlantis shuttle. Celebrating IEEE’s 300th Milestone The IEEE Milestone dedication ceremony was held at MDA Space’s headquarters in Toronto, where the division that developed the Canadarm was located. The event brought together IEEE leaders and many of the engineers who helped develop the robotic system. Jill Gostin, the 2026 IEEE president‑elect, gave the opening remarks at the ceremony. She emphasized that the Milestone was not only celebrating the technology but also “the engineers, builders, programmers, and visionaries who believed technology could expand human possibility and who dared to push the boundaries of what humanity could achieve beyond Earth.” To commemorate the achievement, Holly Johnson, vice president of MDA Robotics and Space Operations, and IEEE Life Senior Member David Michelson, chair of the IEEE Communications Society’s Communications History Committee, unveiled a bronze plaque that honored the technology. Michelson was the Milestone’s proposer. “This milestone is a reminder of the privilege we all have at MDA Space—as engineers, designers, builders, operators—to build technology that shapes history,” Johnson said. “That same pioneering spirit that drove our team in those early days of space exploration now propels us into a new era as we work to build the infrastructure for the moon and beyond.” The plaque, which was placed at MDA Space headquarters, reads: In 1981 NASA first deployed a Shuttle Remote Manipulator System aboard the Space Shuttle. Developed by Spar Aerospace (now MDA Space) and the National Research Council of Canada, the Canadarm allowed astronauts to safely and reliably manipulate and transfer heavy payloads outside of the Shuttle, and to conduct inspections and repairs. This robotic system played a key role in the Shuttle and International Space Station programs, and revolutionized human spaceflight. Reviewed by the IEEE History Committee and approved by the IEEE Board of Directors, IEEE Milestones recognize outstanding technical developments around the world that are at least 25 years old. The Milestone program is administered by the IEEE History and Heritage group. To learn more about historical figures in engineering, IEEE Milestones, and IEEE History Center programs and events, check out The Institute’s IEEE Tech History collection. IEEE Spectrum also covers aspects of tech history.

A little over a decade ago, I led the controls design for a first-of-its-kind full digital-control system for a U.S. nuclear plant. It was, on paper, a beautiful machine—engineered to run itself the way a modern airliner does, with operators watching over a system that rarely needed them. And we made a decision that, to an efficiency-minded observer, looked backward: We deliberately left manual steps inside sequences the system could execute on its own. We were solving a specific problem. An operator who only ever supervises automation slowly stops being an operator. The hands go cold. The mental model of what the plant is actually doing gets fuzzy. Then comes the day the automation hands control back. It’s always the worst day, because automation only quits when it’s confused or in trouble. But by then, you have a person in the chair who hasn’t truly operated the thing in years. The manual steps were there to keep the human current. It was inefficient by design, on purpose. That plant, as it happened, was never built. It was shelved amid the politics and economics that surround nuclear power in this country, for reasons that had nothing to do with the engineering. But the design instinct outlived the project, and I’ve come to believe it’s the most useful idea I can offer to the argument now consuming every boardroom: What happens to human expertise when AI does the work that used to build it? AI Is Disrupting the Engineering Career Ladder The data has gotten hard to wave away. A Harvard University working paper covering some 65 million workers at more than 280,000 U.S. firms found that after companies adopted generative AI, junior employment fell roughly 9 percent within six quarters relative to nonadopters, while senior employment kept right on growing. A Stanford analysis of ADP payroll records points the same way: The youngest workers in the most AI-exposed occupations lost ground after late 2022 while their more-experienced colleagues held theirs. The Stanford researchers found that the losses concentrate where AI automates the work; where it merely augments, junior employment holds steady or rises. The causal story is still contested, and honesty requires saying so. Researchers at the New York Fed attribute much of the rise in young-graduate unemployment not to AI but to remote work, arguing that firms are reluctant to hire inexperienced people whom they cannot train and mentor at a distance. But notice what the explanations share. Whether a model is absorbing the formative work or distance is severing the mentorship around it, both describe the same broken mechanism: the apprenticeship channel through which expertise passes from senior to junior. Either way, “entry-level” has quietly come to mean “three years of experience required.” Strip away the noise and you’re left with one deceptively simple problem: You cannot become a senior engineer without first being a junior one. Expertise is not downloaded. It is earned through failed builds, dead-end debugging sessions, and the “why on earth did that work” moments that a capable AI will now happily spare the newcomer. Spare them enough of those and you produce a cohort that can supervise a model on paper but never developed the gut sense to know when the model is confidently, catastrophically wrong. Most of the commentary stops at the diagnosis, or reaches for policy solutions that treat the loss of junior jobs as an economic problem. Yet it’s also an engineering problem, and safety-critical fields have already spent decades learning how to solve it. Aviation’s Lessons About the Automation Paradox My own career started at the sharp end of automation. My first job out of school was verifying and validating the software in the digital jet-engine controller that decides, faster than any pilot could, how a fighter plane’s engine responds. Even then, in the late 1980s, the central tension was visible: The machine outperforms the human in routine cases, but the human is all that stands between the aircraft and disaster in the cases the machine didn’t anticipate. This tension is known as the automation paradox, in which increasingly capable automation gives human operators less practice, while leaving them only the most difficult situations. Aviation learned, repeatedly and expensively, what happens when human skills atrophy inside that gap. The canonical example is Air France flight 447, which fell into the Atlantic in 2009. The proximate cause was mundane. Iced-over airspeed sensors fed the autopilot bad data, and it did what it is designed to do: It disconnected and handed control of the airplane back to the crew. What followed was not a hardware failure. It was a competence failure. A recoverable situation became an unrecoverable one because the pilots, conditioned by thousands of hours of watching the automation fly, could not read a high-altitude aerodynamic stall and hand-fly their way out of it. The airplane was working. The training the automation had quietly eroded was not. The industry’s response is instructive, and it’s the same move we made in that nuclear control room. It did not rip out the autopilot. It built deliberate manual practice back in. In 2017 the FAA issued Safety Alert for Operators 17007, “Manual Flight Operations Proficiency,” declaring that “manual flight is the foundation upon which other technical flying skills are built.” The alert formally recognized skill decay as a hazard in its own right. Some airlines amended their procedures to encourage hand-flying both the initial climb and initial descent in benign conditions, knowingly trading a sliver of fuel efficiency to keep the crew’s raw flying skills alive. That trade is the whole point. A perfectly optimized system that produces incompetent operators is not optimized at all. It has simply moved its failure mode somewhere the spreadsheet can’t see it. Manual Gates Could Preserve Engineering Skills Put the aviation lesson and the nuclear instinct side by side and they point to one design pattern we now need in AI-augmented work: the deliberate “manual gate.” A manual gate is a point in a workflow where a human takes the controls, not because it is the fastest way to get the task done, and not only as a safety interlock, but specifically to exercise and preserve a skill that would otherwise decay. The distinguishing feature is that it is chosen. You decide, as a matter of design, which competencies your organization must keep alive in human beings because those are the ones you will need on the bad day. Then you engineer the friction required to keep them warm. Picture how this might work on a software team that leans on AI for most of its code. The team places a manual gate around the skill it can least afford to lose: debugging. When a defect surfaces in a critical module, the assigned engineer—deliberately, often a junior one—must first reproduce the failure, trace it to root cause, and write an automated test that captures the bug, all with the AI assistant switched off. Only after the engineer commits to a diagnosis does the model come back on, to propose the fix, generate alternatives, and sweep the code base for similar bugs. The engineer then compares their diagnosis against the model’s. When the two disagree, that’s the design working, surfacing the disagreement before the bad day instead of during it. This approach reframes the junior engineer entirely. The instinct today is to let AI do the entry-level work because it is faster and cheaper. But some of that work is not overhead to be eliminated. It is the training apparatus of your future senior staff, and you should protect it the way you’d protect any other piece of critical infrastructure. It may not be efficient this quarter, but dismantling it quietly mortgages your capability a decade out. Why Companies Must Keep Training Junior Engineers None of this is free, and pretending otherwise would insult the people who have to sign the budgets. A deliberate manual gate is, by construction, less efficient in the near term than full automation. Keeping juniors doing formative work and running the manual sequences costs something now to protect something later. That’s a hard sell in a market that judges most leaders on quarterly results. A hired executive who carries “unnecessary” humans that AI could replace will hear about it from the board long before the payoff arrives. The math only works for someone insulated from that pressure: a founder with control, a private company, an institution with a genuinely long horizon, or a regulator willing to require workers to demonstrate their skills regularly, as pilots must. Which means the organizations most likely to preserve their own expertise are the ones structurally able to spend short-term margin on long-term capability; everyone else will need that outside push. So here is the argument, in one line: Deliberate inefficiency is not waste. In safety-critical engineering we have always known it as insurance, and we buy it on purpose. As AI takes over the work where expertise is forged, the smart move is not to resist the automation. It is to keep our hands on the controls by design—so that when the automation fails, as it always eventually does, there is still someone in the chair who knows how to fly.

Across IEEE, our strength lies not only in the excellence of our individual communities but also in our ability to bring them together around shared problems that demand interdisciplinary solutions. Our mission as a public charity—to advance technology for the benefit of humanity—is becoming an increasingly powerful differentiator. It is more than a statement of principle; it is a strategic advantage. When engineers and technologists serve with purpose and lead with heart, they strengthen the future of our profession and demonstrate why IEEE is uniquely positioned to lead at the intersection of technology and societal impact. IEEE Humanitarian Technologies is a consortium of programs and initiatives—supported by a global network of volunteers and technical professionals—working together to apply technology to solve the world’s most pressing problems. These include Empower a Billion Lives, EPICSinIEEE, MOVE, IEEE REACH, IEEE SIGHT, IEEE Smart Village, and IEEE Tech4Good. These programs embody our mission in action. They are not simply charitable activities; they are strategic assets that help IEEE lead globally, innovate boldly, and remain essential to technical professionals at every stage of their careers. While deeply human in purpose, humanitarian technologies are fundamentally engineering challenges, demanding the full depth of engineering rigor and realized through disciplined, deeply technical work. Cultivating Technical Leaders IEEE Humanitarian Technologies sits at the intersection of engineering excellence, societal need, and global opportunity. Its programs allow our members to show the world that engineering and technology are forces for good, capable of addressing urgent challenges with precision, creativity, and compassion. These programs do more than inspire; they strengthen the technical ecosystem that underpins IEEE’s leadership. Bringing together experts from power and energy, communications, computing, robotics, biomedical engineering, and many other domains to address real-world problems, these interdisciplinary intersections are where breakthroughs emerge. When engineers and technologists collaborate with the right humanitarian frameworks across sectors and cultures, they illuminate new constraints, design pathways, and opportunities that traditional project environments rarely reveal. This is how humanitarian technologies help shape the future of engineering itself. These efforts also illustrate a broader opportunity for IEEE. By identifying critical challenges that can be addressed only through collaboration across disciplines, IEEE can mobilize the power of its global community toward solving problems around the world. In doing so, we strengthen both our impact on society and the value we provide to members, partners, and future generations. These programs also build the leadership capacity our profession needs. Engineers working in humanitarian contexts learn to navigate ambiguity, engage diverse stakeholders, manage constraints, and design for environments where failure has real human consequences. They develop systems thinking, ethical reasoning, and cross‑cultural fluency—competencies increasingly essential in a world where technology and society are deeply intertwined. They also learn to transition from R&D to implementation by engineering the support, manufacturing, and delivery systems that make solutions viable in specific countries, all while balancing competing requirements. In doing so, humanitarian programs equip professionals with the capabilities that define modern technical practice. Humanitarian technologies also help prepare the future technical workforce. Students and young professionals increasingly seek meaningful, high‑impact work. By engaging in purpose‑driven projects, they can discover their own capacity to grow, strengthen their technical skills, and become the leaders and problem‑solvers who will guide our profession forward. Purpose Inspires Engagement Our members feel this deeply. Engagement research shows that members increasingly cited “giving back to my profession and the world community” as a reason for joining the organization and renewing their membership. Those with higher membership grades identify “participation in humanitarian technology efforts” as one of the most satisfying experiences IEEE offers. These are not just data points; they are also signals of what our community values and what it expects IEEE to champion. Younger generations amplify this even more. Millennials view IEEE through a global lens, prioritizing “humanitarian impact” and “large-scale collaboration.” One millennial member shared that teaching robotics to children in under-resourced communities transformed them into a deeply engaged member. Gen Z members emphasize inclusivity, environmental responsibility, and purpose-driven engineering, recommending that IEEE offer humanitarian-based challenges and competitions to increase engagement. These findings reveal something powerful: Humanitarian programs are not only meaningful; they also are magnetic. They attract younger engineers, keep them engaged, and help them build a professional identity rooted in purpose and impact. They also create loyalty and develop the leadership pipeline IEEE needs for the decades ahead. These programs also strengthen our brand. Members across segments describe IEEE as an organization that works hard to make real changes in the world. That perception is not just flattering, it is strategic. It positions IEEE as a global leader in responsible innovation that can be trusted to guide technology for the public good, catalyzing innovation that benefits society at scale. As we look ahead, IEEE has an opportunity to become the world’s leading convening force for developing interdisciplinary technology solutions to solve humanity’s most important challenges. Our future relevance will be defined not only by the technologies we advance but also by the problems we choose to help solve. Read more powerful stories about how technology is improving lives across global initiatives in the 2025 IEEE Social Impact Report at ieee.org/advancing-technology/building-better-world/social-impact-report. —MARY ELLEN RANDALL IEEE president and CEO Please share your thoughts with me: president@ieee.org.

For many high school students, summer vacation is a time to unplug. For Ruchira Shree, a rising sophomore at West Windsor–Plainsboro High School South, in New Jersey, the break allows her to ramp up her extracurricular pursuits. Much of her time is spent assisting with IEEE Princeton Central Jersey Section activities. She got involved with the PCJS because of her mother, IEEE Senior Member Shubha Bommalingaiahnapallya, a principal engineer at Intel. “I started going to the IEEE meetings when I was little,” Shree says. “I used to go with my mom and just sit in the back of the room.” This summer she says she’s focusing on improving her mathematics skills by attending the Program in Algorithmic and Combinatorial Thinking summer course on math and computer science. She wants to qualify for the American Invitational Mathematics Examination, an event for the top American Mathematics Competitions scorers. She earned a place on the AMC 8 honor roll—a recognition awarded to the top 5 percent of participants in the national competition—when she was in seventh grade. Shree’s IEEE involvement and her advanced math skills caught the attention of an internship recruiter for the Alliance for Indigenous Math Circles, a group dedicated to expanding STEM opportunities for Native American students. The AIMC organizes and sponsors weeklong overnight camps. Interns assist with activities and teach some of the sessions. Shree met a recruiter at one of the section’s events, and she interned at one of the camps last year. The IEEE-math camp connection Shree’s involvement with the PCJS evolved naturally as she got older, she says, along the way preparing name badges and tackling similar assignments. She met Francis O’Connell, an IEEE life senior member and founder of FXO, in Plainsboro, N.J. O’Connell is the treasurer of the IEEE Integrated STEM in Education Conference (ISEC). He has been a mentor to Shree for the past two years, he says. At last year’s ISEC, she assisted at the registration desk and met Harini Frederickson, an AIMC intern recruiter for New Jersey. Frederickson invited Shree, along with nine other students, to volunteer at an upcoming camp being held in Santa Fe, N.M. “Ruchira is a real go-getter,” Frederickson says. “When she has an idea, she follows through and doesn’t get easily discouraged.” The AIMC was created to address an important need, says math teacher Donna Fernandez, codirector of the organization. U.S. Indigenous students have the lowest rate of pursuing STEM studies across all demographics, according to the U.S. National Science Foundation. Systemic barriers such as a lack of role models in STEM fields, socioeconomic inequities, and Eurocentric teaching frameworks are some of the reasons, Rechel Shrisunder and Dwight Figueiredo wrote in a chapter of Minorities: New Challenges and Horizons, a book edited by John R. Hermann. Indigenous people have a long tradition of mathematics, Fernandez says. She cites the Navajo code talkers from World War II as examples. The Navajo, along with 14 other Indigenous tribes, used their native languages to code and transmit critical messages for the U.S. military during the war. There was a student at camp whose grandfather was a code talker, Shree says. Navajo people also use math to build hogans: conical dwellings that require precise calculations to construct. Native communities have used math when building the structures for centuries, Fernandez says. Fernandez believes typical classroom math curricula overlook the importance of mathematics in Indigenous cultures. Combining STEM activities with cultural elements helps Indigenous students better understand their ancestors’ role as mathematicians, she says. That, in turn, helps the students see themselves in those careers, she adds. The AIMC was built upon a program already in place: the Navajo Nation Math Circles, founded in 2012 by three university professors. Their goal was to provide the Navajo Nation’s students with tools to overcome barriers to STEM education. To expand the math circle program, the AIMC was added to reach Indigenous students in the Four Corners area of Arizona, Colorado, New Mexico, and Utah. Since 2017, the organization has run two camps every year at the Navajo Preparatory School in Farmington, N.M. In 2025 one camp was moved to the Santa Fe Indian School. During each weeklong event, students and interns work in math circles. It’s a cooperative way to solve problems creatively, organizers say. Students collaborate on STEM-focused projects and learn from Indigenous STEM professionals. Interns also get the opportunity to experience an off-site cultural event. The camps are free for students, thanks to sponsorships and donations. Teachers and interns cover their own travel expenses. Shree secured a US $1,500 sponsorship grant through the PCJS. Building relationships through STEM activities Relationships are an influential part of the week, Fernandez says: “One of the best things we see at the camp is that students return the following year and ask, ‘Is so-and-so intern coming back this year?’ They remember the relationships they developed, especially the cultural exchanges they had. “Those exchanges go both ways, benefiting the interns too.” Students spend mornings at camp working in math circles, then gather for a wrangle, during which each team defends its math circle answer and challenges other teams’ solutions. Shree and the other interns are on hand to answer questions and observe the teams as they work through the math circle problems. “Math problems typically have very binary answers,” she says. “But in math circles, you focus more on talking through your answers to open-ended questions and learning from each other.” Students spend afternoons working on projects. In one, the students used household items to create a replica of the Batmobile, Shree says. The car was required to be self-propelled without an engine. Balloons were a popular alternative. Another activity focused on the Indigenous tradition of basket weaving. Students learned the cultural meaning behind traditional designs while understanding how geometry concepts influenced the finished product. These Native American middle school students work on solving a mathematical pattern-matching game, one of the activities held at the summer camp.Ruchira Shree Role models inspire students “Because there’s a lack of Indigenous STEM role models, many Native American students don’t see themselves in mathematics or science,” Shree says. To bridge that gap, Fernandez ensures Indigenous role models are part of the camp. Some of the people who spoke with students during Shree’s internship were Jessica Benally, a Ph.D. student in the learning sciences and human development program at the University of California, Berkeley, and engineers from the New Mexico Mathematics, Engineering, and Science Achievement program, which supports underrepresented preuniversity students. “I believe the students were very inspired,” Shree says, “because they could see how they themselves could pursue STEM careers. They had people to look up to in the field who had come from backgrounds just like theirs.” Interns in action The interns’ primary responsibility was leading a two-hour, after-dinner Radio Weaves session. They taught students about a popular communication technology that doesn’t require the Internet or cell towers. Ham radio, also known as amateur radio, is a communication method that uses designated frequencies. In the United States, anyone can listen to amateur radio transmissions; to legally transmit on the frequencies, though, a user needs a Federal Communications Commission license. The Radio Weaves project is designed to prepare students to pass the FCC technician license exam. To make that happen, the interns customized Gimkit, a learning game, loading it with radio-specific content that mirrored topics that could appear on the test. Each intern worked with two or three students to complete the Gimkit materials. Frederickson, who was on hand for the camp, says the aim was to send students home with something tangible that demonstrated their STEM accomplishments. Nearly all the students passed the exam on the first try, she says, and she worked with those who didn’t to retake the test. All the students ultimately received their license, she says. Inspiration comes in several forms The interns took an afternoon off to attend a Pueblo Feast Day, a celebration filled with music and dance that culminated in visits with nearby families, with whom they shared dinner. “The tradition is very generous and community-based,” Shree says. “It represents that every home in the village will welcome any guest to have a meal.” The feast was the highlight of Shree’s week, she says: “I got to really experience Native American culture firsthand.” The students inspired her, she says. “Seeing the joy on their faces when they passed the technician exam or when they got a math problem correct showed me how much joy they find in learning,” she says. “It made me realize that I want to help provide more opportunities for them to learn and challenge themselves.” “Because there’s a lack of Indigenous STEM role models, many Native American students don’t see themselves in mathematics or science.” —Ruchira Shree That realization spurred her idea for a new initiative. After she returned home, she founded Rukie Cookie to create “safe, inclusive, and inspiring spaces where youths explore STEAM [and] build curiosity, strategic thinking, and innovation—empowering them to become confident leaders and active contributors to a more just and equitable society,” according to the project’s website. Baking is one of Shree’s hobbies, and she sees it as a way to fulfill a financial need she observed at camp. “I noticed that at lunch breaks, they [camp students] used to play chess on the side, but they couldn’t actually participate in tournaments because that requires a U.S. Chess Federation (USCF) membership fee, which they couldn’t afford,” she says. Shree bakes cookies and sells them at PCJS events. Proceeds go toward youth chess classes and USCF memberships for children in underrepresented communities. She has raised enough money to sponsor six USCF memberships, five of whom are camp attendees, she says. “I hope that the students I have gotten a membership for will continue growing their passion for chess,” she says, “but also that it will encourage them to challenge themselves with difficult problems.” What’s next? Shree planned to attend an AIMC camp this year, she says, but it was canceled due to resourcing issues. She says she intends to return next year with goals of adding a formal chess component to the schedule and continuing to help more students overcome financial hurdles to join the USCF. She’s also writing a novel about Alzheimer’s disease and identity loss, and she’s conducting independent research on cognitive decline at the New Jersey Institute of Technology. Watching her great-grandmother struggle with the condition sparked her interest in the subject, she says. She is confident STEM will be part of her future, she says. Math and cognitive science are areas of interest she plans to study, but she’s still undecided about a major. Her interest in Alzheimer’s research and a desire to apply AI to health care will influence her decision, she says. She adds that she plans to join IEEE once she’s eligible. This article was updated on 8 September 2026.

This story was originally published by Tech Policy Press. The European Union’s push for technological sovereignty faces an uncomfortable contradiction. As the E.U. rolls out AI factories, gigafactories, and new data centers, it is creating a surge in demand for the advanced semiconductors that underpin artificial intelligence. Yet Europe produces fewer than 10 percent of the world’s chips and remains heavily dependent on U.S. designers and Asian manufacturers for the most advanced processors. That tension sits at the heart of Chips Act 2.0, the European Commission’s planned overhaul of its flagship semiconductor strategy. The original Chips Act, adopted in 2023, sought to raise Europe’s share of global semiconductor production to 20 percent by 2030. But the European Court of Auditors has warned that target is unlikely to be met, while the Commission’s own projections put Europe’s market share at about 11.7 percent. The Commission now wants to correct what officials see as a major weakness in the first law: It focused on expanding supply without doing enough to stimulate demand. To address that gap, Chips Act 2.0 is expected to introduce demand-side measures, including public procurement tools, demand accelerators, and closer coordination between semiconductor producers and industrial users. The Commission’s calculation is straightforward: Stronger domestic demand will encourage companies to invest in designing and manufacturing chips in Europe. But the strategy carries a paradox. The AI infrastructure that the Commission hopes will anchor a European semiconductor ecosystem will initially rely almost entirely on advanced processors designed by U.S. companies and manufactured in Asia. “Key positions are held by a small number of firms, mostly outside Europe,” Claire Godfrey, executive director of the Balanced Economy Project, told Tech Policy Press. AI factories create a demand trap The European Commission’s AI Continent action plan includes 19 AI factories, computing facilities that integrate energy sources, specialized chips, and other infrastructure for running AI models and applications, plans for up to five AI gigafactories (since upgraded to seven), and a proposal to at least triple the bloc’s data-center capacity within five to seven years under the Cloud and AI Development Act. That expansion will require a large supply of advanced AI processors. The Center for European Policy Studies (CEPS) estimates that each planned AI factory site requires up to 25,000 advanced chips, while a gigafactory requires at least 100,000. Almost all of those processors are expected to come from Nvidia. The company supplies most of the graphics processing units deployed in Europe, while its proprietary CUDA software underpins much of the AI software ecosystem. CEPS warns this could create an “Nvidia dependency trap,” where computing infrastructure is physically located in Europe but remains technologically dependent on a single U.S. supplier. Recent AI infrastructure projects in Europe illustrate the problem. Mistral has lined up 13,800 Nvidia GPUs for a data center near Paris. Deutsche Telekom’s Munich Industrial AI Cloud is being built with nearly 10,000 Nvidia Blackwell GPUs. And Nscale says its deployment for Microsoft, in Sines, Portugal, will start with more than 12,600 Nvidia Blackwell Ultra GPUs before expanding to more than 66,000 in 2027. Europe still doesn’t control the chip supply chain The challenge extends well beyond Nvidia. Even if Europe succeeds in expanding semiconductor manufacturing, the global supply chain limits how much autonomy any single region can achieve. “Europe depends on both the United States and Asia, but at different stages of the value chain,” Toni Roldán-Monés, economist and assistant professor of public policy at IE University, told Tech Policy Press. “The United States maintains a dominant position in areas such as chip design, intellectual property, and certain frontier equipment. Meanwhile, the manufacturing of the most advanced semiconductors is highly concentrated in Asia, especially in Taiwan and South Korea, while China plays a fundamental role in various materials, industrial processes, and critical minerals,” said Roldán. Europe’s reliance on third countries is more evident in some parts of the chip value chain. In fabrication, Taiwan produces around 90 percent of the world’s most advanced chips. In packaging, assembly, and testing, the E.U. holds just 4 percent of the market and remains highly dependent on Asia, according to Laith Altimime, President of SEMI Europe. “The objective is…to avoid excessive dependence on a single country, company, or technology.” —Toni Roldán-Monés “No top 20 assembly, test, and packaging company is headquartered in the E.U.,” Godfrey said. “There is also the materials issue. China dominates several inputs used in key parts of the semiconductor and advanced electronics supply chain.” Europe nevertheless retains important advantages. The region is home to ASML, the Dutch company that dominates the market for extreme ultraviolet lithography systems, and to Belgium’s Imec, one of the world’s leading semiconductor-research centers. Europe also remains a key supplier of specialist materials and power electronics. Those strengths, however, “do not translate into autonomy across the semiconductor value chain,” Roldán said. Sovereignty means resilience, not self-sufficiency Few experts believe complete semiconductor self-sufficiency is achievable. Instead, the goal should be to reduce strategic vulnerabilities rather than eliminate international interdependence. “It is not conceivable that one country can rebuild the supply chain. Global collaboration is key,” SEMI Europe’s Altimime told Tech Policy Press. SEMI forecasts that by 2028 the Europe, Middle East, and Africa region will only manufacture about 68 percent by volume of the non-memory semiconductor chips it demands. “The challenge is to reduce dependencies that could become geopolitical vulnerabilities,” argues Roldán. “The sensible approach is to strengthen critical parts of the value chain, diversify suppliers, protect sensitive data, and develop domestic capabilities in strategic sectors. That can coexist perfectly well with foreign suppliers: The objective is not to expel them, but to avoid excessive dependence on a single country, company, or technology.” That distinction is especially relevant for Europe’s sovereignty ambitions. As Godfrey notes, “European firms are building around Nvidia hardware, CUDA, cloud infrastructure, and the software choices that come with them. That leaves Europe with two problems. It relies on Asian manufacturing and materials chokepoints. It is also at risk of trying to address that exposure by tying itself more closely to U.S.-controlled AI and cloud infrastructure. The Chips Act 2.0 needs to deal with both, or it will miss a large part of the problem.” Roldán said Europe’s greatest vulnerability is dependence on partners willing to use global supply chains for geopolitical leverage. Whether Chips Act 2.0 reduces that risk, experts say, will depend on whether it diversifies suppliers rather than just shifting dependence from Asian manufacturers to U.S. technology companies.

In a display case on the lower level of the Faraday Museum at the Royal Institution in London, there’s an unassuming stack of gray metal discs and blotting paper. It’s not at all obvious that this humble object is the starting point of today’s multibillion-dollar global battery industry. The object’s invention in 1799 grew out of a disagreement that Alessandro Volta—the Italian physicist for whom the unit of measurement for electrical potential is named—had with his friend Luigi Galvani over a dead frog. The Debate Over Animal Electricity Galvani was a well-respected Italian physician. In the 1770s, he began investigating the use of electricity to stimulate the muscles of dissected frogs. Armed with an electrostatic generator and an early type of capacitor called a Leyden jar, he was able to create a charge, store it, and then zap his animal specimens at will. He was intrigued when the frog legs twitched as if they were still alive. He spent the last three decades of the 18th century studying the phenomenon, and in 1791, he published De viribus electricitatis in motu musculari commentarius (Commentary on the Effect of Electricity on Muscular Motion). Luigi Galvani spent decades investigating what he believed to be a natural electric force emanating from animals. Universal History Archive/Getty Images Galvani saw the frog as embodying an “animal electricity,” an innate vital force that activated nerves and muscles, similar to what had been observed in (living) electric eels and torpedo rays. For Galvani, the frog was an electrical machine analogous to a Leyden jar. The brain was the source of the electrical charge; the nerves conducted the electrical fluid; and the muscles stored opposite charges. The illustrations in his 1791 book are fabulous—frog legs spread all over his laboratory table! Galvani was wrong in thinking that his frogs were electrical machines, but he was right that the muscle contractions were caused by electric signals.SSPL/Getty Images At first, Volta, chair of physics at the University of Pavia, concurred with his friend. But after beginning his own experiments, he concluded that Galvani was wrong and that the frog generated no electricity at all. He thought of the frog as nothing more than an electroscope, an instrument to indicate the presence of an electrical charge. Volta posited that the source of the charge Galvani observed came from two different metals in contact with the frog. He termed this “metallic electricity.” Alessandro Volta came to disagree with Galvani’s theory of animal electricity.Apic/Getty Images To prove his point, Volta created an “artificial electric organ.” He stacked alternating discs of copper and zinc, separated by cardboard, blotting paper, or cloth soaked in brine or acid. When the top and bottom plates were connected, an electric current flowed through the stack. As opposed to a Leyden jar, which is essentially a capacitor that can store an electric charge and release it in a brief powerful discharge, his stack of discs generated its own electricity through a chemical reaction and delivered a sustained low-current output. Volta didn’t publicly demonstrate or announce his artificial electric organ until after Galvani died in 1798. But when he finally did, in 1799, it immediately began upending science. Just six weeks after Volta wrote to the Royal Society about his invention, the English scientists William Nicholson and Anthony Carlisle used a voltaic pile to run a current through water to separate it into hydrogen and oxygen. They had discovered chemical electrolysis. Humphry Davy later used a large voltaic pile to isolate a number of elements, including potassium, sodium, calcium, strontium, and barium. Early piles petered out after a few hours. Users who stacked up more metal discs to make more powerful piles found the weight of the discs squeezed out the moisture in the paper or cloth. Invented in 1799, Volta’s “artificial electric organ” (later known as the voltaic pile) was the first battery. Volta presented this one to Michael Faraday in 1814.Royal Institution of Great Britain/Science Source One of the most enthusiastic users of the voltaic pile was Galvani’s nephew, Giovanni Aldini, who spent much of his career defending his uncle’s ideas. Aldini created spectacles across Europe in which he used voltaic piles to shock the carcasses of livestock and, occasionally, the bodies of recently executed convicts. Vivid descriptions in the popular press, as well as Aldini’s own writings, raised the question of whether electricity could bring the dead back to life. Mary Shelley provided her answer in her 1818 novel, Frankenstein; or, The Modern Prometheus. In an introduction to an 1831 edition, Shelley cites galvanism as one of her inspirations for the monster’s reanimation process. Beyond Winners and Losers in Scientific Debates Scientists and historians share a common trait: They like stories with clear winners and losers. The narrative of competition helps drive a narrative of progress that makes it look like humanity is always moving forward. In the case of Galvani and Volta, Volta is usually depicted as the clear winner in the debate over animal versus metallic electricity. The Encyclopedia Britannica goes as far as to write that “with his announcement of the first electric battery in 1800, victory was assured for Volta.” But both science and history are more nuanced than that. In fact, Galvani and Volta were both partially right and partially wrong. There was no universal force of animal electricity, but Galvani was correct that electrical signals caused muscle contractions, which he discussed in his anonymous 1794 publication Dell’uso e dell’attività dell’arco conduttore nella contrazione dei muscoli (On the Use and Activity of the Conductive Arch in the Contraction of Muscles). Volta was right to push back on Galvani’s animal electricity theory, but he was wrong that electrophysiological effects require two different types of metal, or any metal at all; the circuit in the voltaic pile was closed by the wet paper or cloth. It seems a little presumptuous for the Encyclopedia Britannica to declare Volta the winner and Galvani the loser. Volta definitely thought his friend was wrong, but he waited until after Galvani’s death to make his views public. It’s closer to the truth to say they were both genuinely curious to understand the nature of electricity. In the process, they unknowingly helped develop different fields of inquiry: electrophysiology for Galvani and electrochemistry and battery science for Volta. RELATED: Who Really Invented the Rechargeable Lithium-Ion Battery? Such an outcome is actually quite common in scientific disagreements. For example, Isaac Newton’s dispute with Christiaan Huygens over the nature of light—did light consist of particles, or corpuscles, as Newton termed them, or waves, as Huygens contested—breaks down today into quantum optics and classical optics. Similarly, Louis Pasteur’s and Justus von Liebig’s debate over fermentation (microorganisms versus chemical decomposition) led to two complementary fields: microbiology and biochemistry. Maybe instead of looking for winners and losers, we would be better off expanding our horizons and considering the multiple paths of inquiry and discovery. Writing in 1816, toward the end of his career, Volta graciously acknowledged Galvani’s pioneering work, saying “it contains one of the most beautiful and surprising discoveries and the germ of many others.” What new revelations are waiting to develop out of today’s scientific debates? 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 September 2026 print issue as “The First Battery.” References On 20 March 1800, a year and three months after the death of Luigi Galvani, Alessandro Volta wrote a letter (in French) to Joseph Banks, president of the Royal Society, describing his invention of an artificial electric organ. It was read before the Society on 26 June and published in Philosophical Transactions on the last day of that year as “On the electricity excited by the mere contact of conducting substances of different kinds.” The Smithsonian Institution Libraries used their rare books in the online exhibit The Body Electric, which has more information on both Galvani and Aldini. The website of the Whipple Museum in Cambridge, England, has a number of pages devoted to frogs, including a very informative description of the role frogs played in Galvani’s experiments and how those led to Volta’s work.

One day in 1982, Joseph “Rod” Canion and two colleagues from Texas Instruments sat down at the House of Pies in Houston and used a placemat to sketch out what would become Compaq’s portable PC. In 1996, Felix Zandman, founder of Vishay Intertechnology, dined at Husker Steak House in Columbus, Neb., grabbed a napkin and drafted a design for a power metal strip resistor, which became crucial to power-management components in industrial, automotive, and consumer applications. Now that wispy piece of paper resides at the Smithsonian’s National Museum of American History. Perhaps most famously, Robert Metcalfe, working at Xerox PARC in 1973, roughed out some early designs for what would become Ethernet, though contrary to popular belief, no napkin was involved. Yet another napkin (see above) was pressed into service last year, when Kurt Polzin, chief engineer of the space nuclear propulsion project at NASA’s Marshall Space Flight Center, met Robert Schleicher of General Atomics Electromagnetic Systems at a conference and started talking about nuclear rocket design. “They did the classic let’s-sketch-out-an-idea-on-a-napkin,” says IEEE Spectrum’s Special Projects Editor and our in-house spaceflight expert Stephen Cass. “This rocket engine is still at the paper-planning stage, which, to be fair, is where most of NASA’s humans-to-Mars planning has been for the last 60 years.” Meanwhile, the world’s richest person is pushing for humans to colonize Mars in time for him to escape our hospitable blue marble for a completely barren red planet. One big problem with this idea: It takes a long time in hostile space to get there. “Here’s our napkin. Noodle on this with us and tell us what you think.” —Stephen Cass There’s an old saw, mostly used in the context of road safety, that speed kills. But when it comes to sending humans on interplanetary missions, the faster the better. As Cass pointed out, “Once you get outside the Van Allen belts, there’s so much natural radioactivity—that’s the real killer.” Nuclear electric propulsion could both minimize launch costs and the time fragile human bodies are subjected to microgravity and radiation. And that makes a synchronal bimodal nuclear engine that could both power and propel a spaceship an attractive alternative to a conventional rocket. Polzin and Schleicher think their nuclear engines could halve the time it takes to get to Mars. There may be ways to tweak the design to go even faster and further. But for now, the idea sits on the drawing board, awaiting feedback and refinement. “They’re pulling together a lot of fairly mature technology,” says Cass, who edited Polzin and Schleicher’s article, “A Reimagined Nuclear Rocket.” “Electric propulsion is mature. Nuclear thermal propulsion is not, but thanks to [previous efforts], we have a good idea how to do it. The new part is merging them together, and that of course throws up its own challenges.” Thanks to Cass and illustrator John MacNeill, Polzin and Schleicher’s idea has moved from a napkin to the pages of this month’s issue. Says Cass, “The whole point of the article is to say, ‘Here’s our napkin. Noodle on this with us and tell us what you think.’” We invite you to do so in the comments beneath the web version of this article. Or do it the old-fashioned way and mail the authors your own napkin.

While walking to school as a child, Jernej Barbič would marvel at how beautiful his home was. He was born and raised in a picturesque village in northwestern Slovenia (formerly Yugoslavia), located in the European Alps. Surrounded by alpine and beech trees, Barbic dreamed of replicating their swaying in the wind for others to enjoy. At the time, he didn’t have the tools or the knowledge to create a system that could do that, but it sparked his interest in computer graphics, he says. Jernej Barbič Employer University of Southern California, in Los Angeles Title Professor of computer science Member grade Senior member Alma maters University of Ljubljana, in Slovenia; Carnegie Mellon Twenty years later, in 2016, Barbič, a professor of computer science at the University of Southern California, in Los Angeles, made his mark. His Ziva VFX software system allows for the creation of realistic muscle, fat, and skin simulations for 3D digital humans and creatures. The technology was launched in 2016 by a startup he helped found, Ziva Dynamics, headquartered in Vancouver. It was acquired in 2021 by Unity Technologies of San Francisco. Ziva VFX has been used in more than 60 movies including Aquaman and the Lost Kingdom; Godzilla x Kong: The New Empire; and Venom: Let There Be Carnage. For the design and development of Ziva VFX, Barbič, an IEEE senior member, received a 2025 technical achievement Academy Award. It was a “tremendous honor,” he says, as the award recognizes technologies that have had a significant impact on motion picture production. “Computer graphics and simulation can sometimes feel like a specialized technical field,” he says, “but the award showed that these ideas affect not just science but also art and how stories are told on screen. “The digital characters enabled by mathematics become important parts of people’s lives.” Sparking an interest in computer graphics Barbič says he was inspired to pursue engineering by his father, an engineer who headed a cement factory’s research department and invented a technology that uses magnetic resonance imaging to test the integrity of cement. His father’s work showed him that “mathematics and physics are beautiful on their own, but engineering lets you build something that other people can use,” he says. It was Barbič’s mother, an elementary school teacher, who introduced him to computer science. When he was 8 years old, the school his mother taught at bought a ZX Spectrum computer. With permission from the principal, she brought it home for her son to play on for two weeks. But he didn’t just play games; he created his own game using the BASIC programming language. The machine came with a booklet that contained instructions on how to write a computer program, he says. “At first,” he says, “I copied them verbatim without understanding what they did. But then I started realizing there is structure, and I modified the instructions.” Of all the creatures brought to life using his technology, Barbič is particularly enamored with King Kong from 2024’s Godzilla vs. Kong.DNEG/Warner Bros. Entertainment Inc./Legendary By the end of the two weeks, he’d developed a computer game where players guided a snowman along a winding road. It shifted unpredictably to the left or right, and players accumulated points by remaining on the road for as long as possible. Barbič went on to earn a bachelor’s degree in mathematics in 2000 from the University of Ljubljana, in Slovenia. The following year, he moved to the United States to begin a doctoral program in computer science at Carnegie Mellon. It was a major turning point in his life, he says. His doctoral research focused on developing simulation methods for objects that can change their shape when an outside force is applied to them, known as “deformable objects.” That project shaped much of his later research, he says: “I became interested not only in making simulations accurate but also in making them practical: fast enough, robust enough, and controllable enough to be used in real applications.” After earning his Ph.D. in computer science in 2007, he worked as a postdoctoral researcher at MIT. Two years later, he joined USC as an assistant professor. Making movie magic possible It was at USC that Barbič merged his passion for computer science with film. He developed Vega FEM, an open-source software program that allowed people to animate realistic 3D deformable objects. But Vega FEM was narrow in scope and not exactly what filmmakers needed, he says, so he started exploring how to create a version suitable for the movie industry. “A major theme of my career has been the translation of research ideas into practical tools,” he says. “Academic research often produces beautiful algorithms, but it can be difficult to make those algorithms usable by artists, engineers, or production teams. I have always been interested in that bridge: taking rigorous computational methods and turning them into systems that people can actually use.” In 2011 he attended an Association for Computing Machinery conference presented by its Special Interest Group on Computer Graphics and Interactive Techniques (SIGGraph). There he met James Jacobs, the creature supervisor at visual effects company Weta FX of Wellington, New Zealand. The company is behind the effects in the Lord of the Rings and Hobbit trilogies and other movies. Jacobs used Barbič’s software to create animals and fantastical creatures. Two years later, Weta FX offered Barbič a summerlong research position in New Zealand. He accepted and spent the time studying the process of creating visual effects and learning what roadblocks existed in the film industry, he says. At the time, the technology to create realistic soft-tissue and anatomical simulation for digital characters didn’t exist. “The visual effects industry had reached a point where surface-level realism was not enough,” Barbič says. “A creature could have beautiful skin textures and detailed geometry, but if the bones, muscles, and fat underneath did not move correctly, the illusion would break. “The problem was especially difficult for creatures and characters that need to feel alive: animals, monsters, fantasy creatures, or digital doubles. Their bodies may have unfamiliar anatomy, but the audience still anticipates them to move in a way that matches real-world expectations. Muscles should bulge and contract, skin should stretch and slide, fat should have inertia, and tissue should respond to motion and impact.” In an effort to solve the problem Jacobs in 2014 approached Barbič about founding a startup. In 2015 they launched Ziva Dynamics, where they began what is now Ziva VFX. The software uses physics-based simulations to model the internal anatomy of a character. It numerically solves the partial differential equations of nonlinear elasticity for musculoskeletal human and creature tissues, Barbič says. The equations describe how muscles, fat, skin, and connective tissue deform, interact with bones, and connect, and how muscles activate. Instead of animating only the outside surface, artists can create a model with underlying muscles, bones, soft tissue, and fat. Each component is assigned material properties, constraints, attachments, and activations. The simulator then computes how they deform and interact over time. The technology uses ideas from computational mechanics, finite element methods, numerical optimization, contact handling, and computer graphics, Barbič says. Finite element simulation, a method used to predict how a product or structure reacts to heat and other real-world forces, provides a way to model deformable materials volumetrically, not just as surfaces, he says. The tool computes internal elastic forces and solves the equations of motion so the character’s tissues respond plausibly to animation, pose changes, muscle activation, and dynamic motion. But the system had to be designed for artists, Barbič says. In production, he says, the goal is not only physical realism but also controllable realism. “Artists need to direct the result, iterate, and fit the simulation into a larger animation pipeline,” he says. “So the technology had to combine scientific simulation with practical controls, robustness, and integration with visual effects workflows.” Barbič says Ziva VFX has been used in more than 60 movies. Of all the creatures brought to life using his technology, he is particularly enamored with King Kong from 2024’s Godzilla vs. Kong. “When King Kong is walking, you can see the muscles, how they’re very pronounced, and how they influence the shape of the skin. You can really feel the strength of King Kong,” he says. “And this was made through my software, so I think it’s amazing.” After Ziva Dynamics was acquired by Unity, Barbič consulted for the company for almost two years. In 2024 DNEG, a London-based visual effects and computer animation company, acquired the exclusive license to Ziva VFX. Animating the human hand Barbič strives to improve visual effects as an entrepreneur and an academic. His most recent research, funded by the U.S. National Science Foundation, focused on the modeling, simulation, and animation of human hands. The goal is to create computer models of hands that can be used to design tools, medical prosthetics, and robotic hands. “The hand is a fascinating and difficult system,” Barbič says. “It contains many small bones, muscles, tendons, ligaments, skin, fat, and other soft tissues, all packed into a compact structure and interacting mechanically in complex ways.” He and his team built a digital twin of the human hand. He aimed to move toward “anatomically meaningful simulation,” he says. He used medical imaging, geometric modeling, finite element methods, and multibody simulation to represent the internal structures of the hand and its motions. “IEEE lets me place my work not only in the world of images and animation but also in the world of engineering systems that must be accurate, stable, interactive, and useful.” He worked with Bohan Wang, who at the time was a USC doctoral candidate, and George Matcuk, an associate professor of radiology. Wang is now an assistant professor of computer science at the National University of Singapore. Barbič, Wang, and Matcuk scanned four people’s hands with an MRI machine. The two men and two women would position their hands in 12 poses, which allowed the team to gather data about how the bones, muscles, and fat move with each pose. The data sets are available for anyone to use in their own studies. “This project can help medical doctors learn more about how the hand is moving,” Barbič says. “It’s also great for roboticists to better understand how the human hand actually works, so [the movements] can be replicated.” IEEE: Integral in interdisciplinary research Barbič joined IEEE in 2008, when he published his research paper on simulation methods for deformable objects in the inaugural issue of the IEEE Transactions on Haptics. He has since published several papers in the IEEE Transactions on Visualization and Computer Graphics, which he says connected his work to a wider community interested in visual computing and computational methods. You can find his research in the IEEE Xplore Digital Library. “IEEE recognizes the engineering side of computer science,” he says. “My work is often presented as computer graphics, but at its core, it is also simulation, mechanics, numerical methods, haptics, visualization, and software systems. “IEEE is a community where that broader identity makes sense. It lets me place my work not only in the world of images and animation but also in the world of engineering systems that must be accurate, stable, interactive, and useful.” He believes the organization is key in supporting a healthy interdisciplinary research ecosystem at a global scale—which, he says, is why he has served as an associate editor for Transactions on Visualization and Computer Graphics and Transactions on Haptics. Being a member has made it easier for Barbič to connect with engineers in different fields, he says. “My research often lives between categories: It is mathematical but also practical; visual but also mechanical; artistic but also engineering-driven,” he says. “IEEE is one of the professional communities where that mixture is understood.”

I never intended to join the cutting edge of electromechanical television. I just wanted to make a nice clock. But sometimes you have to go where the engineering takes you, and in my case it took me to the Scanwheel, a pocket-size wide-screen electromechanical TV with a resolution of 4,096 by 20 pixels. Yup, that’s 4K by 20. The 3D-printed drum [top] is spun by a motor controlled by a driver board [second row, from top]. The driver board, in turn, is controlled by a Raspberry Pi Pico [middle], which also controls the LEDs [second row, from bottom], which are mounted in the 3D-printed casing [bottom] so that the holes pass over them as the drum turns.James Provost Electromechanical television was the first form of practical television, developed by John Logie Baird in the 1920s. He used a so-called Nipkow disk, which has a spiral of holes punched through it. As the disk rotates, the holes pass one by one in front of a light source. By varying the brightness of the light as a hole travels across it, you can draw one scan line of a video frame. Spin the disk fast enough, and persistence of vision makes it look like an entire frame is being displayed simultaneously. Commercial electromechanical TV sets were produced in the United Kingdom, with regular broadcasts provided by the BBC in the 1930s. Although cathode-ray tubes replaced electromechanical televisions in the 1940s, hobbyists have continued to build them and even improve on the original technology. For example, in the June 2022 installment of IEEE Spectrum’s Hands On, Markus Mierse presented a desktop-size 3D-printed color version. I built an electromechanical display myself some years ago, but it had a traditional design with a Nipkow disk made from a vinyl record with holes drilled in it. Recently I started tinkering with electromechanical TV again as an outgrowth of my YouTube channel. There I’ve been focusing on developing volumetric displays, which create 3D pixels floating within a volume of space. In particular, I was interested in borrowing some ideas from plenoptic cameras, which use pinholes and lenses to capture multidimensional light fields of samples. I wondered if I could run the process in reverse, to create light fields rather than capture them. I often explore ideas in two dimensions before expanding to the third, so I thought I’d first demonstrate a 2D display. I decided to make an electromechanical device into a clock. After all, you don’t need high resolution to display digits. How Does the Scanwheel Display Work? Thinking about the display as a clockface pushed me toward some key ideas. First, instead of having just one display area, I would use five light sources to create multiple areas—four to represent hours and minutes, and a central area for a separator that would blink each second. Second, to align the digits in a readable row rather than have them spread around an arc, I swapped the Nipkow disk for an established alternative: a Nipkow drum. With a drum, the holes run along the curved cylindrical surface in a stair-step pattern. This means they always trace a straight line from the perspective of a viewer looking from the side, so the clock’s digits would be horizontally aligned. “In tests, I’ve pushed the horizontal resolution to more than 8,000 pixels.” These two decisions turned out to be key to achieving both miniaturization and high horizontal resolution. A disk needs a fairly wide diameter so that the scan lines aren’t ridiculously curved. But curvature isn’t a problem with a drum. A drum can be much smaller than a disk that has the same number of scan lines. (And unlike in the 1920s, packing multiple light sources close together inside a small drum isn’t a problem with modern LEDs.) I settled on a 6-centimeter-wide drum, turning the device from desktop-size to something you could carry in your pocket. I then realized my five display zones could work in concert to create one single wide screen. Because it’s possible to modulate the brightness of an LED at very high rates, the horizontal resolution can also be very high. My system currently has 4K horizontal resolution, and this is primarily limited by the amount of onboard memory I have available. This memory holds the buffer that stores pixel data for each frame before it is read out to the LEDs and displayed. In tests, I’ve pushed the horizontal resolution to more than 8,000 pixels. Despite the Scanwheel’s low vertical resolution—at 20 pixels, it has fewer scan lines than Baird’s 30-line televisions—its high horizontal resolution makes the legibility of the display surprisingly good: I can display not just crude digits but video streamed into the frame buffer. Using the RP2040 Chip’s Special Silicon That frame buffer lives on a Raspberry Pi Pico microcontroller board, based around the RP2040 microcontroller. The RP2040 is ideal for this project because of the chip’s dedicated PIO silicon. PIO stands for programmable input/output, and it’s a block of four coprocessors that uses a very limited instruction set. Each coprocessor can be set up to chew through input/output streams completely independently of the RP2040’s two CPU cores. The first mechanical TVs used disks, which had to be wide to minimize image distortion but allowed bulky light sources. With small, modern light sources, a smaller drum can create images with minimal distortion.James Provost It’s thanks to the PIO that I’m able to keep up with the spinning drum and modulate each of the five LEDs simultaneously as holes pass over them, a task complicated by the fact that the center LED is not a monochrome LED, but a color LED with separate red, green, and blue channels. In fact, the PIO does nearly all the work, pulling data from the frame buffer and controlling the LEDs and the spinning of the drum. The code running on the CPU (written in MicroPython) is primarily responsible for setting up the PIO and then leaving well enough alone. A stepper motor connected to a driver board spins the drum, with power provided by the USB jack on the Pi Pico. All the Pi Pico has to do controlwise is send the board a pulse to incrementally advance the drum’s position once every millisecond. Video data is streamed into the Pi Pico via a network interface. You can set up the Scanwheel to mirror a portion of your computer’s screen, or to act as a separate display. The casing, including the drum, is 3D printed. Now for a neat bit: In the Scanwheel’s GitHub repository at https://github.com/AncientJames/Scanwheel/tree/main, alongside all the other files you’ll need to make this project yourself, there’s an OpenSCAD file that generates the 3D-print file for the drum based on adjustable parameters. This means you can easily make a taller drum and add more scan lines, or try other customizations for your very own portable electromechanical display. You can even use it as a clock!

The IEEE–Eta Kappa Nu (IEEE-HKN) honor society is preparing to host the Innovating the Future event on 6 November. The inaugural one-day, in-person event is designed to provide a forum for IEEE and IEEE-HKN undergraduate and graduate student authors to present their original research papers. A keynote address and thematic presentation sessions are planned as well. Student attendees can network with their peers and gain firsthand experience with the academic publishing process. To present at the conference, students had to submit an abstract of their research before 1 May. Students whose work was accepted were assigned a volunteer IEEE member to mentor them and guide them through the research writing process, including presenting and publishing their original work. Those whose paper was accepted by 1 August were invited to present at the conference. The conference proceedings will be submitted for publication in the IEEE Xplore Digital Library. Upholding research integrity in a changing landscape IEEE Life Fellow Manuel Castro, the conference’s technical program chair, oversees IEEE-HKN’s Innovating the Future program committee. It manages the review process, organizes logistics, and handles the mentoring component. “This new conference is important to IEEE, as well as to IEEE-HKN,” Castro says, “because it allows student authors to grow in their skills and competencies, and be supported while turning their technical activities into publications.” “The conference offers me a chance to learn how to communicate my research to a broader audience, gain feedback from other student researchers beyond my institution, and see how my work can be made more accessible.” —David Kwabi-Addo IEEE Life Fellow Sorel Reisman, a California State University professor emeritus and an IEEE-HKN governor-at-large, says that because the academic research landscape is rapidly shifting, the conference is timely. “As AI increasingly threatens the integrity of research papers being published in leading journals and conference proceedings, it is essential that future scholars—many of them current IEEE-HKN students—grasp the established standards of legitimate, peer-reviewed research publishing,” Reisman says. Perspectives from mentors and students A cornerstone of the conference is its rigorous mentorship initiative, which pairs each author of an accepted abstract with an experienced IEEE volunteer. The mentors provide personalized guidance on organizing the students’ technical content into the correct format for publishing. They also discuss navigating the peer review process, structuring presentations, and preparing the final manuscript for publication. The impact of the guided process can be valuable for both the mentors and their mentees. IEEE Member Wafa Elmannai, associate professor and chair of the electrical and computer engineering department at Manhattan University, in Riverdale, N.Y., and faculty advisor to the IEEE-HKN Gamma Alpha chapter, serves as a mentor. “Research is essential to advancing technology and driving innovation,” Elmannai says. She volunteered to be a mentor, she says, because she has seen how conducting research can transform a student’s future by building their confidence, curiosity, and critical thinking skills. “Mentoring encourages students to step outside their comfort zones and develop innovative solutions that contribute to society,” she says. For the students, the conference can be a critical stepping stone. David Kwabi-Addo, an IEEE graduate student member who is researching computational biology at MIT, is president of the IEEE-HKN Beta Theta chapter. He says he views the program as an opportunity to gain experience in producing academic scholarship. “I submitted an abstract of my research paper because I see the conference as a chance to produce what could become my first conference publication,” Kwabi-Addo says. “The conference offers me a chance to learn how to communicate my research to a broader audience, gain feedback from other student researchers beyond my institution, and see how my work can be made more accessible.” He says he hopes his participation will highlight the diverse breadth of research that future conferences can showcase. Workshops on the publishing process Conference organizers are holding a series of workshops to guide students through every step of the academic publishing process. The workshops are open to anyone and available on the IEEE-HKN YouTube channel. Topics previously covered are: The Art of Crafting a Compelling Abstract (13 March). Identifying When a Project Is Mature Enough for Publication (15 May). The Mechanics of Writing a Technical Paper (19 June). Surviving the Review Cycle and Dealing With Criticism (14 August). Registration is open to all for this upcoming workshop: From Pen to Voice: Adapting a Paper Into a Compelling Conference Talk (2 October). A launchpad for the next generation The Innovating the Future program is designed not only to improve the quality of submissions but also to foster long-term professional development and research communication skills to develop the next generation of IEEE authors. The conference is more than a venue for presenting research; it is a launchpad for innovators committed to advancing technology for humanity.

Summary NASA and industry engineers propose a synchronal bimodal nuclear rocket (S‑BNR) to dramatically cut transit times to destinations around the solar system, such as Mars, by combining nuclear thermal and electric propulsion. S‑BNR uses a single reactor with two independent fluid loops and correspondingly optimized fuel zones, eliminating complex mode-switching valves while providing both high thrust and continuous electric power. Major challenges include developing fuel elements that integrate well together, ground testing, nuclear launch safety, and multi-agency collaboration to mature the technology from modeling to in‑space demonstrations. The biggest threat to any crewed expedition to Mars is time. NASA’s shortest blueprint for sending people to the Red Planet and back requires spending 620 days in space and 30 days on Mars. Even setting aside the compounding challenges of building life-support systems that can operate without resupply for that long, or the fact that longer journeys leave more time for unlucky accidents, life in microgravity and solar and cosmic radiation will inexorably exact their cumulative toll on human bodies. We want to make it possible to dramatically reduce the length of time crews must spend in space—down to just 335 days in transit or less. This will both simplify many engineering challenges and keep astronauts healthier and safer. We believe the key to this time reduction is a new approach to building a holy grail of space exploration, the bimodal nuclear rocket. In the 1960s, U.S. open-air ground tests demonstrated much of the technology needed for nuclear thermal rockets as part of the NERVA and Rover projects.Nevada State Museum, Las Vegas Technicians at NASA’s Lewis Research Center test a nozzle design for a nuclear thermal rocket in 1965. GRC/NASA The prototype SNAP-10A, orbited in 1965, is to date still the only nuclear reactor launched into space by the United States. George Rinhart/Corbis/Getty Images We are Kurt Polzin, chief engineer of NASA’s space nuclear propulsion project at the Marshall Space Flight Center, with over two decades of experience in advanced propulsion research, and Robert Schleicher, chief engineer for nuclear technologies and materials at General Atomics. And to explain just what a bimodal nuclear rocket is, and why the new version we have conceived together brings it closer to future reality, we first need to take a quick trip to the past. As early as 1946, researchers realized that nuclear reactors had the potential to become extremely efficient thermal rocket engines. Most rockets are thermal rockets, and they work by expelling hot gases through a nozzle, thrusting the rocket forward. While there are other factors such as nozzle shape, generally speaking, the hotter and faster you make the rocket’s exhaust gases, the more acceleration the rocket will produce for a given mass of propellant. Because a smaller molecule will move faster than a larger one when heated to a given temperature, the smaller the molecular mass of your propellants, the better. By convention, the efficiency of a rocket engine is measured by how long the engine can exert a thrust equal to the initial weight of its propellant, a quantity known as specific impulse. In a conventional thermal rocket, such as those used in every launch to orbit since Sputnik, the exhaust temperature and speed—and thus the specific impulse—is dictated by the energy released by a chemical reaction and the mass of the reaction’s by-product. The most efficient chemical rockets today combust hydrogen with oxygen, producing water and a specific impulse that tops out around 450 seconds. But a nuclear rocket is not limited by chemistry. The heart of a nuclear thermal rocket is a nuclear fission reactor, in which chain reactions in uranium fuel release much more energy per kilogram than is possible with chemical combustion. A turbopump forces liquid hydrogen alone—with its very small molecular mass—through the reactor’s core, heating it to temperatures of at least 2,700 kelvin before expelling it, resulting in a specific impulse of 900 seconds or more. In the 1950s and 1960s, the Rover and NERVA (Nuclear Engine for Rocket Vehicle Applications) programs ground-tested nuclear thermal rockets. By the early 1970s, the technology had matured to the point where flight tests were being planned. But changing political and budgetary winds led to nuclear thermal development being shut down in 1973. Another prong of nuclear propulsion that has also demonstrated considerable promise is nuclear electric propulsion. In electric propulsion, instead of creating a stream of hot rocket exhaust through chemical reactions or exposure to the core of a nuclear reactor, electricity is generated and used to create electromagnetic fields that accelerate an ionized propellant such as xenon or lithium. Various schemes to do this exist, including some that have already seen considerable time in space, such as the ion thrusters used on the Dawn asteroid mission launched in 2007. So far, these electric thrusters have only been powered by solar panels. But with a nuclear reactor as part of a power plant that supplies the juice, more thrust could be produced. And moving beyond solar power is particularly important in missions to the outer solar system where sparse solar photons would require enormous solar arrays. With electric thrusters, specific impulses in the range of 2,200 to 4,600 seconds are possible, but currently with very low thrust. With the energy available to a nuclear-powered electric propulsion engine, you could have greater acceleration and reduced mission times. The nuclear reactor could also provide electrical power for all the spacecraft systems as well. The System for Nuclear Auxiliary Power (SNAP) program launched the SNAP-10A in 1965 as a proof of concept, the first—and so far only—U.S. nuclear power reactor in space. It generated about 600 watts of electrical power for 43 days before shutdown and is still in orbit. Subsequent U.S. initiatives for more substantive electric power and nuclear thermal propulsion systems, such as the SP-100, Project Timberwind, and Project Prometheus, along with more recent projects like Demonstration Rocket for Agile Cislunar Operations (DRACO) and Joint Emergent Technology Supplying On-Orbit Nuclear (JETSON), have emerged sporadically over the years. None of these have yet progressed to actual flight. However, space nuclear power got a huge shot in the arm in March 2026 when NASA Administrator Jared Isaacman announced a new space exploration initiative. As part of that initiative, the agency plans to launch Space Reactor-1 Freedom (SR-1) to deliver a trio of robot-survey helicopters to Mars. Driven by nuclear electric propulsion, SR-1 aims to demonstrate fission technology in deep space and would be the first nuclear-powered interplanetary spacecraft, generating 20 kilowatts of electric power aboard. This is a bold step for NASA, and brings us up to the present, but the details of the proposed mission also highlight a familiar limitation of nuclear electric propulsion. Even with improved acceleration, electric propulsion still cannot generate the powerful bursts of thrust needed to escape gravity wells, such as those of Earth or Mars, or perform time-critical maneuvers, like course corrections. On the other hand, while not as efficient and unable to supply electrical power for spacecraft systems, nuclear thermal engines are great at delivering high thrust at critical moments. What is a bimodal nuclear rocket? Some engineers would suggest we build two separate systems—one reactor for thermal propulsion and another reactor for power and electric propulsion. But since at least the 1990s, it has been the dream of many engineers to combine nuclear thermal and nuclear electric in one package, with one reactor: the bimodal nuclear rocket. Most previous bimodal proposals depend on complex valve arrangements to integrate the propulsion and power systems. In thermal propulsion mode, the reactor is brought to maximum activity by a set of control drums that ring the core, which is composed of a matrix of long uranium-fuel elements. The drums take the shape of long cylinders made of beryllium, with a 120-degree segment of each cylinder covered with boron carbide. Boron absorbs neutrons, and when that segment faces the reactor, the reactor’s activity is low as neutrons escaping from the core are captured. Rotating the boron segment so that it faces away from the core (leaving only the beryllium exposed) increases nuclear activity as the beryllium reflects escaping neutrons back into the core’s fuel elements, where they can contribute to chain reactions. This proposed trajectory, developed at NASA’s Glenn Research Center, shows where high-thrust maneuvers [blue dots] are executed by a nuclear thermal engine and additional low-thrust, high-efficiency acceleration and deceleration is performed by electric propulsion [hashed lines show thrust direction].NASA Glenn Research Center Once the reactor is generating large amounts of heat, liquid hydrogen is pumped through channels that run the length of the core. Turned into an expanding hot gas, the hydrogen blasts from the other end of the core to form the rocket’s powerful exhaust. In nuclear power mode, the reactor’s activity is damped. Valves seal the channels and a so-called power-conversion fluid—typically a mixture of helium and xenon gas—circulates through the reactor in a closed loop. The reactor is still hot enough to warm this fluid, which drives a turbine connected to an electrical generator. The key point here is that a single set of flow channels and nuclear-fuel elements are used for both modes. But the valves used to switch modes face the formidable challenge of enduring months, or even years, in a harsh radiation environment while maintaining leak-tight performance. The core’s activity is controlled by the rotating drums surrounding it. Within the core, low-temperature fuel elements [left in blue, and top right] produce electric power by heating a circulating fluid. High-temperature fuel elements [left in red, and bottom right] heat hydrogen as a propellant. (The taper of the HTFE’s exhaust channel is exaggerated for illustrative purposes. Ways of packaging the HTFE’s uranium fuel other than with particles are possible.)John MacNeill In addition, the nuclear-fuel elements surrounding the channels must be able to operate for short durations at very high temperatures during thermal thrust maneuvers and for long durations at lower temperatures during the rest of the voyage. It is difficult to build one type of element capable of both. Hence, the complexity and demanding engineering requirements of previous bimodal designs has hindered their practical application. We propose a simplified approach, a hybrid system we call the synchronal bimodal nuclear rocket (S-BNR). The genesis for this design came about when we were attending a conference together in 2025. One of us (Polzin) had an initial idea, and in time-honored tradition, he sketched it out on a napkin to see if the other (Schleicher) thought there was actually a way to do it. We’ve been working on refining the concept ever since. How the synchronal bimodal nuclear rocket works Rather than relying on a complex valve system, the S-BNR uses two hydraulically independent loops within a single reactor core, one open loop (for thermal propulsion) and one closed loop (for electrical power). The core is divided into two zones, one per loop, differentiated by the type of fuel elements in each. Several designs for the fuel elements are possible: In our preliminary design, the high-temperature fuel elements (HTFEs) in the thermal propulsion zone consist of a bed of “pebbles”—uranium fuel encased in zirconium carbide—that surround a central tapering channel and operate at greater than 2,700 K. (One possible alternative for the HTFEs would be a solid fuel design, as with NERVA.) The hydrogen propellant passes through the pebble bed, where the pebbles’ large surface area maximizes the transfer of heat needed for efficient high-thrust propulsion. The other zone has low-temperature fuel elements (LTFEs), optimized for long-term, efficient production of electricity, which can range from tens of kilowatts to several megawatts. In these elements, the uranium fuel in solid form surrounds a double-walled channel: The power-conversion fluid is pumped down the inside and returns along the outside wall, absorbing heat from the fuel and operating at moderate temperatures (at or above 1,200 K). The electric-power and nuclear-thrust elements of the core have separate fluid loops, which eliminates the need for valves to switch between closed-loop operation for power generation and open-loop operation for propulsion.John MacNeill Both the HTFEs and LTFEs contribute the neutrons required to sustain chain reactions. In power-only mode, residual heat moves from the HTFEs into adjoining LTFEs. The physical interface between the elements is designed to moderate this thermal flow to balance two competing needs: It must allow enough heat flow to safely remove the residual heat from the HTFEs, but it must also limit that heat flow so the LTFEs’ temperatures do not go past their allowable limits when the HTFEs operate at high power. During combined propulsion and power operation, a heat exchanger on the power loop preheats the hydrogen propellant for the thrust loop, aiding the turbopump that feeds the hydrogen through the core. After a propulsion burn is completed and the HTFE chain reactions are damped by the control elements, the power loop removes residual-decay heat coming from the HTFEs as described above, eliminating the requirement in earlier designs for additional propellant flow just to cool down the core while on standby. This dual-loop system also means the engine can produce high thrust whenever needed while allowing the generator to remain active at all times—a significant advantage for crewed missions. By adopting this dual-loop architecture, the S-BNR removes the need for the problematic mode-switching valves found in earlier concepts. Each fission zone is constructed with materials tailored to its specific temperature and power requirements, ensuring optimal performance and durability. The result is uninterrupted electrical power across all mission stages, making it unnecessary to carry additional liquid hydrogen just to manage decay heat. The challenges ahead While significant progress in developing the design of the S-BNR has been made, substantial challenges remain. The reactor must maintain stable control across a wide power range, from modest levels for electricity generation to hundreds of megawatts of thermal power during high-thrust operation. Operating the power-generation loop in close proximity to the HTFEs requires very careful management of both temperature and the neutrons emitted by the fuel elements. And crucially, demonstrating reliable, long-duration performance is particularly demanding: Missions to Mars may require years of continuous power generation. Outer-planet probes equipped with S-BNR engines could extend that to a decade or longer. In the past, nuclear thermal propulsion fuel elements were engineered for extremely high temperatures but only brief operational lifetimes (typically hours), whereas proposed nuclear electric propulsion fuel elements are optimized for lower temperatures and intended to last for years. By using two different types of fuel elements in the S-BNR, we can take advantage of the design heritage of both these development tracks. Fortunately, recent NASA-sponsored research has produced several promising candidates that may meet these demanding requirements. Ground-testing these systems is also a challenge. Early in the Rover and NERVA era, the exhaust from test engines was blasted into the atmosphere, something now unacceptable. Today, any ground test of an engine must completely capture all potentially radioactive exhaust products. Fortunately, a number of approaches have been developed to capture and scrub the exhaust, although these methods currently carry a significant price tag. Then there is the ultimate test: flying an S-BNR in space. International regulatory and safety protocols for nuclear launches were developed largely in response to the Soviet Union’s launch of dozens of nuclear-powered Radar Ocean Reconnaissance Satellite (RORSAT) radar spy satellites in the 1970s and 1980s. There were a number of incidents, with the most serious leaving radioactive debris strewn across a swath of Canada in 1978. This history led to a consensus in the space community that might be summarized as “Thou shalt not bring a nuclear reactor to criticality in any Earth orbit that decays faster than dangerous isotopes.” Thus any S-BNR would be launched atop a conventional chemical rocket, with a completely cold reactor and fresh fuel. Fresh uranium fuel is not in fact very radioactive: The potentially larger concern is the chemical toxicity of this heavy metal, but it can easily be handled by wearing light protective suits, respirators, and gloves. Only after the control elements have been adjusted to permit chain reactions to begin within the core are highly radioactive isotopes able to form from fission fragments. There would be even less cause for concern than when launching a radioisotope thermoelectric generator (RTG), such as the sort that are currently powering the Perseverance rover on Mars and the New Horizons mission in the outer solar system. Even in the most extreme scenario imaginable—the chemical booster explodes and somehow damages the reactor’s control elements in just the right way to initiate a chain reaction—there wouldn’t be time to produce a large amount of toxic isotopes before the reactor broke apart and reactions ceased. (We can be sure of this because Project Rover actually tested this kind of worst-case scenario in 1965 with the Kiwi-TNT test, where an engine prototype was rigged to produce a runaway chain reaction sufficient to vaporize the reactor core due to the immense internal pressure buildup. Negligible radiation spread outside a radius of two miles (3.2 kilometers), well within the range of safe distances for launching any rocket capable of reaching orbit, and site decontamination was possible after only a few days of radioactive decay.) Despite all these considerable engineering challenges, the foundation laid by decades of investment in nuclear thermal and electric propulsion and terrestrial nuclear power technologies provides a solid platform for continued advancement. Indeed, much of the foundational work is already underway through ongoing NASA and U.S. Space Force efforts. The Dawn asteroid mission relied on electric thrusters, demonstrating their utility for long-duration spaceflight.JPL-Caltech/NASA We envision the following action plan to merge these technology pathways: Modeling must be performed to demonstrate and verify strategies for thermal management and the control of nuclear processes over the full range of operating power levels. Near-term non-nuclear testing will validate fluid loop operation, heat transfer mechanisms, and control strategies. Next, component-level irradiation and thermal trials will qualify new materials. Then, integrated reactor testing will begin, first without nuclear fuel and later with fueled reactors undergoing fission. Finally, initial in-space demonstrations could begin with lower-power systems, eventually scaling up to full bimodal capabilities. Achieving success will require close collaboration across NASA, the Department of Energy, the Department of Defense, industry partners, and the broader technical community. Progress will depend on advancements in high-temperature fuels and materials, improved systems for power conversion and heat transport, and the adoption of innovative manufacturing techniques and methods to control nuclear fission over a wide range of output power. In particular, integrated system testing will be more complex than previous programs such as NERVA, due to the combined functions and distinct operational regimes for thermal propulsion and power generation. We hope engineers and researchers with relevant expertise will be encouraged to contribute to addressing these challenges, whether in the areas of thermal management, reactor modeling and control, extended-duration testing, or safety analysis. Past ground tests and limited demonstrations have already established the capabilities of space nuclear systems. With architectures like the synchronal bimodal nuclear rocket, the prospect of integrating high-thrust propulsion and sustained power generation becomes increasingly practical and versatile. The next phase is not simply about traveling fast. It’s about building crewed and uncrewed spacecraft that can reliably travel to destinations throughout the solar system that are currently difficult or impossible to reach, with missions potentially lasting years or even decades. This article appears in the September 2026 print issue as “A Reimagined Nuclear Rocket.”

At the height of the Cold War, one very specialized computer was so secret that the world didn’t know it existed. It ran its jobs up to 200 times as fast as any other computer of its time. It was the U.S. National Security Agency’s main cryptographic processor in operation from the time of the Cuban Missile Crisis in 1962 through the Vietnam War and on past the 1975 Helsinki Accords. The machine stopped running only when its moving parts finally gave out. The Harvest computer mattered because of what it was as well as when it ran. For 14 years, it was the engine processing the NSA’s most sensitive intercepts at a time when signals intelligence was as close to a strategic weapon as anything short of a warhead. Designed and built by IBM for the NSA, Harvest was one of the first machines designed to apply operations to enormous datasets rushing past, a precursor to the computers today that manage continuous video streams and security systems in real time. It was also one of the first machines built as an add-on—a specialized helper intended to do one job exceptionally well, bolted onto a general computer. Harvest’s modular design is like a 1960s version of today’s graphics chips that CPUs use to run intensive video-game and AI processing loads. All that raw processing power meant that Harvest also needed nonstop rivers of data to run on. And that led to another pioneering achievement: the world’s first automated tape library that could robotically fetch any one of hundreds of large cassettes of magnetic tape from the machine’s racks. Given Harvest’s unprecedented processing and storage capacity, the machine’s designers naturally needed to rethink how their system handled information. So IBM wrote a customized programming language called Alpha to let code breakers rigorously describe cryptographic problems, just as scientists at the time were using the emerging language Fortran to describe equations and data-processing algorithms. In Fort Meade, Md., an NSA data center hosted one of the world’s fastest computers of its time—although not often discussed, because of its sensitive, high-security code breaking and cipher hunting work. National Cryptologic Museum The story of Harvest, pieced together from declassified documents and contemporary manuals and technical overviews, provides a new and unexpected vista on the history of computing. It also offers a case study in how national security needs, especially during the Cold War, pushed computer technology beyond the far reaches of what unclassified, civilian computing could achieve. Harvest’s distinctive history reveals a visionary algorithmic, coding, memory, and hardware architecture occasionally decades ahead of its time. But this machine was also built only once, for one singular purpose, and then ultimately quietly retired. The Heart of NSA’s Secret Machine IBM’s landmark 1960 transistorized mainframe, the IBM 7030, better known as Stretch, provided the front end for Harvest (which was officially known as the IBM 7950). IBM delivered Stretch to eight or nine customers, mostly scientific research labs, from 1961 through ’63. Designed and prototyped throughout the second half of the 1950s, Stretch introduced the now standard notion of an 8-bit byte. For its first three years of operation, Stretch was the non-classified world’s fastest computer, although it failed to meet IBM’s aggressive goal of running 100 times as fast as Stretch’s predecessor, the IBM 704. While IBM engineers in Poughkeepsie, N.Y., were designing and building Stretch, the company was also quietly discussing a new system that would be built for NSA. At the time, NSA’s existing cryptanalytic computers—large, batch-processing machines that required human operators to manually stage each tape run—were struggling to keep pace with the sheer volume of intercepted message traffic coming in from around the globe. What the agency needed was a machine that could process an unbroken river of incoming data, automatically, around the clock. That requirement alone profoundly shaped Harvest’s design. IBM’s Harvest system, custom-built for the NSA for code breaking, paired the IBM 7030 Stretch mainframe with a bespoke data-stream processor. Stretch handled ordinary computing and input/output, including the Tractor automated tape library. Both units shared two kinds of memory: a large main bank and a smaller, faster bank. When Stretch switched to streaming mode, Harvest drew two streams of data, P and Q, from memory, processed them in parallel, and returned the results as a third stream, called R. Chris Philpot After two failed proposals to NSA, in 1958 IBM finally landed the contract: a Stretch-based machine, augmented by a custom coprocessor, with a revolutionary tape-based storage system, called Tractor. Stretch’s forte was floating-point math for scientific computations. IBM had designed it primarily for labs working on frontier research like nuclear weapons design and weather prediction. By contrast, the custom coprocessor to be built atop Stretch would help NSA analysts sift through alphanumeric characters—that is, essentially integer data. Harvest’s coprocessor was the opposite of a general-purpose system. It was, rather, a streaming computer. Instead of executing long series of instructions, it followed one fixed sequence of steps and applied that same sequence to every pair of characters as they streamed past. Harvest shared memory with the main Stretch processor and ran in bursts. Either Stretch was operating, or else it suspended itself while Harvest’s coprocessor shot through data in memory at extreme speeds. Stretch and Harvest were among the first large computers built entirely from transistors packaged in circuit cards and housed in large, refrigerator-size frames. A 1962 technical manual about Stretch describes the machine’s CPU as divided into functional sections—the instruction unit, the look-ahead unit, the (parallel and serial) arithmetic unit, and the memory bus unit. Harvest inherited Stretch’s basic circuit design but then added something unconventional: Its streaming units processed data in overlapping stages called a pipeline. So while one pair of data bytes was being compared, the next pair was being fetched from memory. Harvest’s coprocessor operated by fetching two streams of data, called P and Q, from the system’s memory, performing operations on them, then writing the results to memory as a third stream, R. Each stream could be anywhere from 1 to 8 bits wide. Harvest’s memory was bit-addressable, meaning word boundaries could be ignored entirely. For instance, it could fetch just 5 bits rather than filling out a whole byte. Streams P, Q, and R included flexible provisions for looping and addressing data in complex patterns—allowing, for example, repeated fetching of short strings from memory. Data from P and Q fed into two functional units. The simpler was the logic unit, which performed basic, bitwise operations—the same operations any programmer would recognize today—and wrote its results back to memory. The more complex was a table-lookup unit. It combined incoming data from P and Q to form an address in memory, which could then be used to advance a counter by one, set a specific bit, or retrieve a stored value. The latter unit functioned, in effect, like the rotor wheel inside a cipher-encoding/decoding machine of the era, the kind that electronically substituted one value for another according to the cipher machine’s wiring. Harvest’s complexity baffled some at the NSA. During employee tours, according to James Bamford’s 2001 NSA history, Body of Secrets (Doubleday), officials would point to the machine and scoff, “It’s beautiful, but it doesn’t work.” Not everyone at the agency was put off by the monumental device, however. One of the few documented examples of Harvest at work, recounted by Bamford, describes the machine searching 3.5 billion characters of text for any of 7,000 target terms, in just under 4 hours. In unclassified remarks from 1972, NSA analyst Robert Looney mentions one job Harvest had tackled—though he didn’t specify the end goal or the code-breaking effort behind it. Codenamed “Moretown,” the job involved sifting through 11 million messages spanning 16 years of intercepted traffic against a list of some 8,000 search terms—all in about ten hours. IBM’s Frances Allen helped design Alpha, Harvest’s custom-built programming language.IBM IBM’s James H. Pomerene was chief engineer of Harvest, supervising its custom-designed circuits that’d been optimized for algorithms used in many cryptographic jobs.IEEE IBM’s Fred Brooks Jr. was a key co-architect of Harvest’s hardware system. Computer History Museum As a unified system, Harvest—that is, Stretch plus IBM’s custom-built streaming processor add-on—streamed 1 byte every 0.3 microseconds, and it boasted about 800 kilobytes of addressable memory. “Here you see one bank pulled out of its oil bath,” Looney said in his 1972 remarks celebrating Harvest’s tenth anniversary of operations. He held up a photo of Harvest’s magnetic core memory banks—six of them, submerged in oil for cooling. Factor in the time demands of various data fetches from Tractor’s tape archives, and a single Harvest “instruction” sometimes carried on, without needing any human intervention, for hours. “It was quite an amazing computer,” recalled IBM Fellow Emerita Frances Allen in a 2001 oral history. “One instruction, for example, could do sorts, and do statistical analysis of the data that was streaming by it.… Everything we were doing at that time was on the cutting edge. There was no question about it.” Allen, who received the A.M. Turing Award in 2006, was one of the developers who worked on both Stretch and Harvest. At the time she started working on Harvest, Allen noted, the Fort Meade, Md.–based NSA was largely unknown outside of classified intelligence circles. So she at first assumed she was working on an unspecified naval project. “We thought of ourselves as working for the Bureau of Ships, because that was the code name for NSA in the budget!” recalled Allen, who died in 2020. Other key Harvest designers and early developers wound up becoming influential figures over the course of computing history. Frederick Brooks Jr., recipient of the 1999 Turing Award and a major contributor to the hardware and software for IBM’s System/360, also helped develop Harvest. And James Pomerene, prior to his involvement with Harvest as its chief engineer, had previously helped build the pioneering IAS computer alongside John von Neumann. How Tractor Stored a World of Data IBM built the Tractor tape system (IBM 7955) to attach to the same Stretch machine that hosted Harvest, because no existing data storage technologies could keep up with the computer’s staggering throughput. Stretch handled the business of staging tapes from the library to the drives—using Tractor’s automated cassette handler. Stretch also coordinated reading data in from Tractor and writing results back out from Harvest. Harvest, in turn, did all its actual computing on the system’s shared main memory. In the early 1960s, and even after Tractor and Harvest were installed, hard-drive data storage was in its infancy. For code-breaking jobs of the size Harvest was taking on, disk storage would have been impractical in terms of both cost and sheer floor space. So Tractor had to be based around tape storage. Each tape was sealed inside a case built like a boombox—twin encased reels under a window, carried by a handle—and, at 6 to 7 kilograms, about as heavy as a bowling ball. Think of a Tractor cassette as an outsize predecessor of the audiocassette, which would come along a decade later, and holding some 120 megabytes of data on a reel of tape 550 meters long. Each storage unit housed up to 160 of these cassettes. An IBM technician holds one of the data cassettes used with Harvest’s automated Tractor tape drives. IBM When Harvest launched in 1962, it had three automatic cartridge units, each serving two drives. So the available online storage across the three Tractor units totaled a stunning 44 gigabytes. That’s more than 190 times as much capacity as the IBM 2314 disk storage system, announced in 1965, which held 233 megabytes across its full complement of eight drives. Tractor had to run continuously, swapping cassettes in and out, 24 hours a day, seven days a week. The system’s tape-handling speed was tuned to keep pace with Harvest’s own appetite for data. The custom-built robotic mechanism for retrieving the cassettes was a servo-driven arm that traversed the system’s storage racks. It fetched a cassette from its slot and delivered it to a handler or received a cassette from one of the handlers and returned it to storage. Running at 6 meters per second, Tractor’s tapes zipped past the read/write heads faster than the eye could track. Software running on Stretch handled the cassette shuttling as well as reading and writing. For one of Tractor’s drives to move from the completion of processing one tape to reading the next took about 18 seconds, assuming it had already been fetched and was ready to mount. Robotically fetching a cassette from the storage unit and preparing it for reading required no human handling or input whatsoever. In addition to Tractor, the system had standard reel-to-reel tape drives attached to Stretch. Harvest’s technicians often used the conventional drives for importing and exporting data to and from other systems; there was no other practical way to get large datasets into or out of Harvest. Tractor could also store permanent files and retrieve them directly from its tape libraries when a job required them. In other words, Tractor’s substantial cassette libraries acted both as permanent data storage and as a place to hold transient data for processing by Harvest. No system in the commercial computing world of 1962 came close to Tractor’s gigabytes of simultaneously accessible data. At most computer centers at the time, “available” data meant physical racks of tape standing somewhere near its drives—accessible only as rapidly as an operator could manually pull a reel and thread it onto a machine, one at a time, over the course of a shift. Alpha Was Harvest’s Custom-Built Programming Language Created jointly by IBM and NSA, the Alpha language existed solely to program Harvest’s streaming dataflow engine for code-breaking work. According to a declassified Pentagon history of NSA computers, Alpha stood for Advanced Language for Programming Harvest. Alpha allowed the programmer to define the alphabet in which code-breaking data would be processed. The language also included two unusual characters with no equivalent in conventional computing until years later, when Multics and Unix introduced wildcard characters. A “scab” (which was represented on Harvest’s input keyboard, a repurposed early IBM Selectric typewriter, by a “?”) stood for a character that was real but unknown. And a “pad” (represented by a blank space) was a null or spacer. These characters provided flexibility of representation for code breaking jobs, in which unknown or uncertain characters were commonplace. A Harvest operator types on one of the main system consoles, a repurposed IBM Selectric typewriter.IBM The rules governing Alpha’s operations on strings anticipated other modern rubrics, like “not a number”—a designation describing an unknown value in a dataset that can propagate through calculations, rather than silently corrupting them. Strings in Alpha could also be aggregated into cords, and cords into ropes, giving cryptanalysts a hierarchical vocabulary for describing complex intercepts. Allen wrote a final technical report on her section of the Harvest software when her part of the project concluded—and just as promptly lost access to it. “I spent the good part of a summer on that,” she recalled in 2001. “And it just disappeared into Fort Meade somewhere.” Replacing an Irreplaceable Machine By 1971, according to NSA analyst Looney, the machine was running at its highest utilization ever—115 hours of production a week, or more than two-thirds of the time. Yet the number of jobs it processed had been dropping since 1967. Ordinary data-processing work, Looney noted, was by 1972 migrating to newer, general-purpose machines, leaving Harvest to concentrate on the very large, specialized jobs no other system could handle. At its tenth anniversary of operations, Looney concluded, Harvest was a machine “conceived in the fifties, born in the sixties, and irreplaceable in the seventies.” He got the last part wrong. On 27 February 1976, operators shut down Harvest for the last time. A custom mechanical component in the Tractor tape library had worn out, and the manufacturer of the part was no longer in business. By then Harvest had run continuously for nearly a decade and a half—through the roughest close call in the history of mutually assured destruction and into the age of détente—processing intercepts at a rate no civilian machine could touch. By the time it retired, Harvest had outlived several generations of commercial computing. A placard commemorates the 1976 decommissioning of IBM’s Harvest computer at the NSA’s headquarters in Fort Meade, Md. National Cryptologic Museum Somebody at the NSA decided to commemorate the machine with a mock telegram, written under Harvest’s name on the machine’s last day (and now preserved in the agency’s archives). “I first began operations at NSA. Although not widely known, I was probably the largest, fastest, and most technically advanced computer system in the world,” the telegram said. “And now, fourteen years later, the time to retire has come. The cost of my upkeep and operation has been overtaken by more modern equipments and the newer technologies.” The NSA ultimately replaced Harvest with the landmark Cray-1 supercomputer. The Cray-1 was built from faster, more tightly integrated circuits that could outperform Harvest’s aging transistors at nearly any task, including text processing. Although the Cray was designed primarily for numeric and scientific computing, it sold across many fields—which ultimately made the supercomputer win out once Harvest’s custom-built text-processing hardware was no longer worth the upkeep for just one customer. The secrecy that shrouded Harvest meant it could claim no lineage of immediate successors. But the ideas it pioneered didn’t disappear—they resurfaced, again and again, in the years that followed. Tractor’s automated tape library was the forerunner of the robotic storage silos that would become standard in enterprise data centers about 20 years later. Harvest’s pipeline architecture prefigured the dataflow computing movement of the 1980s. The continuous pattern-detecting logic of its match units finds direct echoes in modern hardware packet-inspection intrusion detectors and programmable network switches that today route traffic through the internet at wire speed. Harvest didn’t found a dynasty. But, in its time, it steadfastly pointed toward the future—in several directions at once. This article appears in the September 2026 print issue as “The Lost History of IBM’s Cold-War Code Breaker.”

This article is brought to you by Ollobot. From about 2017, individuals began to truly connect with the initial wave of companion robots. These devices had personality, moved around, joked, and answered when you spoke to them. Most early companion robots, however, were still limited by simple voice-command interactions and narrow functionality. Once the novelty wore off, many ended up sitting unused on shelves. As some of those companies went out of business and turned off their servers, many owners likened it to losing a pet. What Ollobot describes as “gentle intelligence” is a useful way to think about where the serious work in this category is going. Not toward more powerful assistants, but toward more present ones. The problem companion robots were trying to solve Loneliness is not a niche issue. According to one study, nearly one out of three elderly adults resides alone, meaning they do not have daily companions. Research also shows that children whose parents have migrated for work, leaving them in the care of relatives, were 2.5 times more likely to experience loneliness than children whose parents remain with them. Among working adults living alone in urban environments, similar patterns of social isolation emerge, even if they are less visible. Over the years, technology has time and again attempted to solve this problem via video calls, smart speakers, and messaging apps without much success. Those tools are geared towards communication between people that already have relationships. They do not create presence. They schedule it. That is the gap that a new generation of AI companion robots is being engineered to fill. Today’s AI robots are different Today’s companion robots are not just cute and cuddly. They are designed with psychological research, clinical insight and long-term interaction models to be truly useful in real homes. Three fundamental shifts define the current generation: From reactive to proactive response. Older robots relied on you speaking to them, but modern robots monitor a room with cameras, microphones, and surroundings sensors to initiate interactions without your input, and they can pick up on your emotions. From function-oriented to emotion-oriented design. The original pitch for companion robots was about what they could do. The question driving the serious work now is how they make you feel, which is a harder engineering problem and a more honest framing of what the product is actually for. From standalone hardware to connected ecosystems. Leading brands are creating platforms rather than devices with software included as a built-in layer and remote access from the beginning. The global AI companion market size was valued at US $36.8 billion in 2025 and is projected to grow from $48 billion in 2026 to $318 billion by 2033, at a compound annual growth rate of 31 percent from 2026 to 2033. Three household scenarios and interaction models Ollobot’s advanced AI family companion robot OlloNi SS1 addresses a number of gaps in what existing technology offers. Elderly individuals living alone. The combination of proactive interaction, fall detection, and persistent presence addresses both safety and companionship without the social overhead of asking family members to check in more frequently. Children in households where parents work far from home. The SS1 functions as a consistent companion that already knows a child, their preferences, their moods, and their routines. The remote connection features allow parents to stay present without requiring a scheduled call, and the life recording system gives them a passive window into their child’s days that feels less clinical than a monitoring camera. Single professionals living alone in cities. The SS1 adapts to daily routines, builds up a preference model over time, and provides ambient social presence without demands. OlloNi SS1 adapts to daily routines over time.Ollobot What OlloNi SS1 is doing differently? Ollobot’s goal in building intelligent companion robots is to address the gaps in technology and capability, using innovation not to automate tasks but to fill emotional voids. Much of the robotics industry has historically pursued human imitation — machines that speak, look, or behave like people. The SS1 is instead designed around familiarity and long-term coexistence rather than realism. The system integrates multiple subsystems operating in parallel, including visual perception, audio processing, mobility control, and interaction management. It is equipped with a multi-chip AI 4K vision module capable of facial recognition and motion tracking. One small but revealing detail is the inclusion of a physical privacy cover for the camera — a mechanical solution to concerns that software settings alone may not fully resolve. OlloNi SS1 can actively integrate into family activities, and it can autonomously move closer to capture memorable moments or reposition itself to remain engaged in ongoing interactions.Ollobot The robot supports advanced mobility across multiple indoor surfaces, including wooden floors, ceramic tiles, and low-pile carpets, with slope climbing capability up to 3.5 degrees. Rather than remaining in a fixed location, it can move naturally throughout the home to stay close to household members as daily activities unfold. For example, the OlloNi SS1 may greet family members when they arrive home, follow an older adult from the living room to the kitchen while continuing a conversation, remind a child to take a study break after a prolonged period of inactivity, or notice that someone appears unusually quiet and gently check in. During family activities, it can autonomously move closer to capture memorable moments or reposition itself to remain engaged in ongoing interactions. The robot continues to evolve over time, with over-the-air updates that deliver new features, performance improvements, and AI enhancements It also incorporates fall detection with optimized accuracy for safety monitoring scenarios. A 6-microphone array enables omnidirectional voice pickup with an effective voice capture range of up to 5 meters, supporting reliable wake-word detection and far-field interaction. To support continuous companionship, much of the robot’s AI processing takes place directly on the device through its “heart module” architecture, with 16 GB of memory and 64 GB of local storage. This enables the system to retain household memories, recognize familiar faces, and respond with lower latency, making interactions feel more natural even during everyday routines. Because companion robots are expected to remain available throughout the day rather than only during brief interactions, the SS1 is designed for extended operation, offering up to 12 hours of standby time and around 5 hours of active interaction on a single charge. This allows it to accompany users through meals, conversations, playtime, and other daily activities without frequent interruptions. To support engaging interactions, much of the robot’s AI processing takes place directly on the device through its “heart module” architecture.Ollobot Like the relationships it is designed to build, the robot continues to evolve over time. Running on Android OS with over-the-air (OTA) updates, the system continuously receives new features, performance improvements, and AI enhancements, allowing its capabilities to grow alongside the household it serves. The robot’s behavioral model also improves over time. Rather than reacting to isolated commands, it attempts to establish a baseline understanding of household routines and individuals. Changes in behavior — prolonged quietness, unusual inactivity, or emotional cues — become triggers for interaction. Presence instead of utility Several features in the OlloNi SS1 illustrate this emphasis on presence and continuity in its interactions. The system can identify different household members, including pets, and adapt responses accordingly. Remote communication features allow family members to connect through the device without treating every interaction like a scheduled call. Environmental sensors support contextual reminders tied to weather or room conditions. Its “2+1” multi-display configuration is also designed around emotional communication. Two circular side displays function as expressive “emotional eyes,” while a separate primary display handles information and structured interaction. The separation allows emotional signaling and functional communication to operate independently, creating more intuitive nonverbal interaction even when no dialogue is taking place. The robot’s behavioral model improves over time. Rather than reacting to isolated commands, it attempts to establish a baseline understanding of household routines and individuals.Ollobot The SS1 also includes an automated life-recording system built on facial recognition and behavioral-event detection that can capture moments such as laughter, physical closeness, or group interaction automatically. An integrated AI vlog engine can then organize those moments into edited short-form videos with automated sequencing and soundtrack generation. The design intent is to preserve spontaneous domestic moments without requiring active documentation behavior from users. An integrated AI vlog engine can organize recorded moments into edited short-form videos with automated sequencing and soundtrack generation Visual data is processed primarily on the device through the SS1’s on-device AI architecture, with household memories stored locally and managed within Ollobot’s proprietary ecosystem instead of being shared with third-party smart home platforms. Access to recordings and live feeds is restricted to authorized users through the companion app, while encrypted communication helps protect data during remote access. Users also retain direct control over recording preferences, and the physical camera privacy cover provides an additional hardware-level safeguard whenever visual monitoring is not desired. Learn more at ollobot.com. Remote communication is similarly structured around persistence rather than transaction. Traditional video calls are episodic and screen-bound; the SS1 instead acts as a continuously present interface embedded inside the household environment. Through autonomous mobility, environmental awareness, and persistent household memory, remote family members interact with an ongoing domestic context. The larger shift to “gentle intelligence” Ultimately, gentle intelligence is not about making robots behave more like humans — it is about helping them fit more naturally into human lives. Each OlloNi SS1 unit develops a unique behavioral profile based on its household. Two units running in different homes for a year will have become meaningfully different from each other, shaped by the specific people, habits, and rhythms of where they live. That kind of long-term personalization is what early companion robots never had. It is also what makes the difference between a product that ends up on a shelf and one that actually earns its place in a home. Learn more at ollobot.com.

For most of my career, my IEEE membership sat quietly in the background—a line on my résumé, a discount code for a conference registration, and access to the IEEE Xplore digital library, which I underutilized. I didn’t think much about the grade of membership available above that of the regular member. I assumed senior membership was reserved for people further along in their career than I was. They published more papers, had more gray hair, and had worked longer in the field. I was wrong on all three counts. The misunderstanding cost me an important validation of my skills and professional competency. I suspect a lot of other qualified members are where I was one year ago: eligible but unaware of the benefits of senior membership, and one application away from a meaningful career credential. The myths that almost stopped me Here are a few of the misconceptions about senior membership: It’s mostly for academics and longtime IEEE volunteers. It isn’t. The grade is explicitly built around a person’s professional engineering experience. Plenty of successful applicants have never published a paper. Industry experience counts for a lot. You need a graduate degree. You don’t. A bachelor’s degree plus enough years of qualifying experience is sufficient on its own. An advanced degree simply offsets some of the required years of experience. If I’m not well-known in my field, I won’t qualify. Senior membership isn’t a popularity contest. Rather, it hinges on whether you meet specific experience metrics. The requirement is “sustained, significant technical contribution,” not “known beyond your organization.” I should wait until I have more significant achievements to point to. I believed this for longer than I should have. If you meet the 10-year experience threshold with five years of significant performance, you’re already eligible. Waiting doesn’t strengthen a qualifying application; it just delays getting a credential you’ve already earned. Why I applied for senior membership The push to apply came from a practical need. As a senior data scientist at Apple in Austin, Texas, I work in applied machine learning, building large-scale systems that affect customer-support operations. I already had started taking on more peer-review work—checking papers for journals including Neural Networks and IEEE Transactions on Knowledge and Data Engineering, mentoring at Apple, and writing on public platforms such as Medium and SimpleTalk. I wanted a credential that reflected that shift from “engineer who codes” to “engineer who helps shape the field.” The IEEE senior member grade turned out to be the validation of my work I was looking for. It’s not an award for a single achievement. You have to apply for it, and it’s a peer-evaluated process that confirms you’ve sustained a meaningful level of professional contributions over time. That distinction matters. Having a research paper published or being granted a patent proves a moment in time. Senior membership reflects a pattern of continuous contributions. The benefits to my career happened faster than I expected. It strengthened how search committees, IEEE conference organizers, and IEEE awards panels viewed me. Only senior members can hold certain IEEE leadership positions. The senior grade also opened doors to editorial and reviewer roles I hadn’t even pursued before. Journal editors and conference organizers often look for reviewers with a track record they can verify quickly, and senior membership gives them that signal without extra vetting on their end. It also gave me a credential I could point to in professional contexts, including, in my case, supporting documentation for a U.S. employment-based immigration petition, where third-party peer recognition carries real evidentiary weight. Navigating the process The process for applying for senior membership is easier than the title might suggest. To qualify, you need a combination of professional and academic experience in an IEEE-designated field: engineering, computer science, information technology, physical sciences, mathematics, or technical communications. The two must total at least 10 years, with at least five of them showing significant performance. Crucially, experience isn’t limited to job titles. Graduate research, technical leadership, and progressively responsible engineering work all count toward the total number of years. I’d been quietly accumulating qualifying years without ever framing them that way. “I suspect a lot of other qualified members are exactly where I was a year ago: eligible but unaware of the benefits of senior membership, and one application away from a meaningful career credential.” You submit your application through IEEE’s member portal, mapped against the experience requirement, along with three references from current IEEE members—at least two of whom must be senior members or IEEE Fellows who can vouch for the credibility of your work. The IEEE member grade evaluation committee reviews applications and renders decisions. How to find references The part everyone underestimates is references. Applications can stall at this point. References must be IEEE members in good standing, and at least two need to be IEEE senior members—which means you can’t necessarily ask people who know you best. You need to find references who are both willing to vouch for you and are grade-eligible. My advice is to identify and confirm all three references before you submit your application. It might be difficult to add or swap a reference during the process, and a stalled reference could delay your file. Where to find references is the part I worried most about. But it turned out to be far easier than I expected. Here are several sources: IEEE Collabratec. This is IEEE’s professional networking platform and, in my opinion, is an underused resource. You can search by technical interest, geography, or society membership and message members directly. I found several of my eventual references this way—colleagues I’d never have thought to ask simply because we hadn’t worked together directly, but ones who knew my technical work through shared communities or conference circles. Coworkers and colleagues, current and former. If you’ve worked alongside IEEE members—especially ones senior to you—they’re often the most natural fit because they can speak specifically to your day-to-day technical contributions. Former professors. If you did graduate work, your advisor or committee members are usually IEEE members and are well positioned to speak to your research contributions, even years later. LinkedIn. A surprising number of my qualifying references came from reconnecting with people on LinkedIn I’d lost touch with professionally. A short, specific, polite message explaining what you’re applying for and why you thought of the person can go a long way. A pattern I noticed when looking for references is that people are generally glad to be asked. Serving as a reference is a small lift for them and a meaningful one for you. Most senior engineers remember someone doing the same for them and are happy to pay it forward. If you’re on the fence If you’ve been in the field for a decade or more, doing real technical work, and IEEE membership has been sitting quietly in the background of your career the way it did in mine, it’s worth 10 minutes to check the eligibility criteria against your history. You might find, as I did, that you qualified for the membership upgrade a while ago.

The AI boom has disrupted the way engineers work, introducing new tools to learn, raising expectations for what teams can achieve in a workday, and making it harder to get hired in the first place. This makes it difficult to advise students on which specific coding languages or technical skills they should learn. So amidst the uncertainty, advice for young professionals often turns to a common refrain: Be adaptable. But what does adaptability look like in practice? Engineers often operate on the cutting edge of technology, so dealing with change is a normal part of the job, says Samantha Brunhaver, an associate professor of engineering at Arizona State University, in Tempe. Yet university curricula and training in the workplace often don’t prepare students for this. “We tell engineers that they need to be adaptable when they graduate, but we don’t actually explain what that means, demonstrate what that looks like, [or] help make sure that they’re developing it,” says Brunhaver, who received a National Science Foundation award in 2020 to study how to foster greater workplace adaptability among young engineers. For this ongoing project, she has interviewed engineering managers, early career employees, and undergraduates about their experiences. Part of the problem, she says, is that every employer has its own idea of what to be adaptable means. Generally, Brunhaver defines adaptability as “the ability to recognize that a change or uncertainty is occurring, and then respond effectively to that change.” But the skill is context-dependent. In software engineering, that might mean responding to turnover in the tools you use on a daily basis, while aerospace or biomedical engineers may need to keep track of changing procedures and regulations. “Managers are all saying adaptability is important,” Brunhaver says, “but defining it in different ways.” At the same time, engineers are all contending with changes beyond these industry-specific expectations. Jobs in the technology, media, and telecom sectors are experiencing the fastest pace of skill turnover, according to a June 2026 report on the effects of AI from the professional services network PwC. And the World Economic Forum’s most recent Future of Jobs Report, published in 2025, found that employers across all sectors expect 39 percent of workers’ core skills to change by 2030. This uncertainty can be uncomfortable. But with the right mind-set and support from leadership, adaptability can help keep you afloat. How to Cultivate Adaptability The AI transition is a big shift—but not an unprecedented one, says Jenna Butler, a research scientist at Microsoft who studies developer well-being and productivity. During this type of paradigm shift, there is often a “chaos period” when a new normal is being established, Butler says. In AI’s case, it challenges the understanding of what a computer can do. “I think we’re still in this in-between, difficult period that we’ve seen before, but [it] is maybe moving faster than it has historically.” Software engineers—in one of the fields most affected by AI—are now facing a significant increase in code review. “If you ask 20 developers, you get 23 different ways of working with it. Everyone is trying to sort it out,” says Butler, who describes this period as “the uncomfortable middle.” “We tell engineers that they need to be adaptable when they graduate, but we don’t actually explain what that means, demonstrate what that looks like, [or] help make sure that they’re developing it.”– Samantha Brunhaver, Arizona State University Brunhaver says one way educators can help prepare students before they enter the workforce is by offering a diversity of real-world experiences, such as internships, team-based projects, community service, and leadership roles. Each of these teach students to adapt to different challenges, easing their transition from school to work. It’s also important to encourage reflection, Brunhaver adds, noting that metacognition helps individuals use the skill more effectively. “In order to adapt, you have to think that you have agency and the ability to get through a situation.” Ultimately, it comes down to three steps: Perceive a need to adapt, evaluate your options, and act. For those already in the workforce, that action may mean taking the time to learn new tools and ways of working. Software engineering, for instance, may soon rely more on prompting models and managing agents than coding line by line. “I think people who went into software because they like solving problems are going to have a lot of fun, and people who just enjoy the art of writing code are not,” Butler says. The More Things Change… Although the tools engineers use on a daily basis are evolving, the core responsibilities of the job are more stable than they may seem, says Andy Hunt, a software developer who coauthored The Pragmatic Programmer (Addison-Wesley Professional) in 1999. The book outlines practical coding principles, and has been taught in many computer science classrooms. When Hunt was working on the 20th anniversary edition of the book, he was surprised by how much of the advice still applies. And now, seven years later, he maintains that belief. “The fundamental part of the job is problem solving and communication, and that’s always going to be there,” he says. Hunt emphasizes the importance of developing systems thinking over particular tools. To him, identifying as a Java programmer, for instance, is “like a carpenter saying, ‘I’m a hammer user,’ or ‘I specialize in cordless drills.’ ” He acknowledges that today’s hiring process, in which companies often filter résumés for certain languages or years of experience, makes it harder to embrace a more expansive way of relating to your job. Employers, he says, should recognize that “the tech’s not the hard part, and it never has been. Understanding information theory, understanding systems thinking, understanding what constraints you’re up to—that’s still the hard part.” With this type of misalignment between employers and employees, AI is also intensifying an old source of tension: How can engineers slow down enough to adapt and learn new tools when the pressure to become more productive keeps mounting? Who’s Responsible for Enabling Change? Young engineers need to embrace change. However, educators and employers also play a role in building a successful workforce. From the educator’s perspective, Brunhaver says “we need to be more explicit about what [adaptability] means and why it’s important.” Managers, meanwhile, should invest in their employees’ professional development. Microsoft research scientist Butler often encourages leadership to set aside intentional time for continuous learning for their engineers—even just an hour a week—without any expectation that they will produce code or progress in their daily work. “I realize that’s difficult,” says Butler. “I would encourage people to do it on their own, but I would really encourage organizations and leaders to do it, because you’re not going to get this sudden change in your people if they don’t have time and space to learn how to work differently.” This also means providing enough instruction, Butler adds. When developers aren’t given enough guidance on adopting something new, while being pressured to increase productivity, they risk doubling down on the tools they already know and burning out. “I do imagine the next number of years could be challenging,” Butler says. Engineers will have to adapt to find their place in an evolving workforce—but they also have a say in shaping that future. “Being adaptable sort of implies that you’re going to change based on what’s happening around you, and I would really like people to realize the change that’s happening is somewhat up to us,” she says. All individuals have a choice in how they use AI, for instance, and which models they use. “We need to be adaptable and go with the flow to a degree, but we also need to be directing that flow. The future with AI is absolutely not predetermined.” This article appears in the September 2026 print issue as “The Adaptable Engineer.”

Andrew Ng has serious street cred in artificial intelligence. He pioneered the use of graphics processing units (GPUs) to train deep learning models in the late 2000s with his students at Stanford University, cofounded Google Brain in 2011, and then served for three years as chief scientist for Baidu, where he helped build the Chinese tech giant’s AI group. So when he says he has identified the next big shift in artificial intelligence, people listen. And that’s what he told IEEE Spectrum in an exclusive Q&A. Ng’s current efforts are focused on his company Landing AI, which built a platform called LandingLens to help manufacturers improve visual inspection with computer vision. He has also become something of an evangelist for what he calls the data-centric AI movement, which he says can yield “small data” solutions to big issues in AI, including model efficiency, accuracy, and bias. Andrew Ng on... What’s next for really big models The career advice he didn’t listen to Defining the data-centric AI movement Synthetic data Why Landing AI asks its customers to do the work The great advances in deep learning over the past decade or so have been powered by ever-bigger models crunching ever-bigger amounts of data. Some people argue that that’s an unsustainable trajectory. Do you agree that it can’t go on that way? Andrew Ng: This is a big question. We’ve seen foundation models in NLP [natural language processing]. I’m excited about NLP models getting even bigger, and also about the potential of building foundation models in computer vision. I think there’s lots of signal to still be exploited in video: We have not been able to build foundation models yet for video because of compute bandwidth and the cost of processing video, as opposed to tokenized text. So I think that this engine of scaling up deep learning algorithms, which has been running for something like 15 years now, still has steam in it. Having said that, it only applies to certain problems, and there’s a set of other problems that need small data solutions. When you say you want a foundation model for computer vision, what do you mean by that? Ng: This is a term coined by Percy Liang and some of my friends at Stanford to refer to very large models, trained on very large data sets, that can be tuned for specific applications. For example, GPT-3 is an example of a foundation model [for NLP]. Foundation models offer a lot of promise as a new paradigm in developing machine learning applications, but also challenges in terms of making sure that they’re reasonably fair and free from bias, especially if many of us will be building on top of them. What needs to happen for someone to build a foundation model for video? Ng: I think there is a scalability problem. The compute power needed to process the large volume of images for video is significant, and I think that’s why foundation models have arisen first in NLP. Many researchers are working on this, and I think we’re seeing early signs of such models being developed in computer vision. But I’m confident that if a semiconductor maker gave us 10 times more processor power, we could easily find 10 times more video to build such models for vision. Having said that, a lot of what’s happened over the past decade is that deep learning has happened in consumer-facing companies that have large user bases, sometimes billions of users, and therefore very large data sets. While that paradigm of machine learning has driven a lot of economic value in consumer software, I find that that recipe of scale doesn’t work for other industries. Back to top It’s funny to hear you say that, because your early work was at a consumer-facing company with millions of users. Ng: Over a decade ago, when I proposed starting the Google Brain project to use Google’s compute infrastructure to build very large neural networks, it was a controversial step. One very senior person pulled me aside and warned me that starting Google Brain would be bad for my career. I think he felt that the action couldn’t just be in scaling up, and that I should instead focus on architecture innovation. “In many industries where giant data sets simply don’t exist, I think the focus has to shift from big data to good data. Having 50 thoughtfully engineered examples can be sufficient to explain to the neural network what you want it to learn.” —Andrew Ng, CEO & Founder, Landing AI I remember when my students and I published the first NeurIPS workshop paper advocating using CUDA, a platform for processing on GPUs, for deep learning—a different senior person in AI sat me down and said, “CUDA is really complicated to program. As a programming paradigm, this seems like too much work.” I did manage to convince him; the other person I did not convince. I expect they’re both convinced now. Ng: I think so, yes. Over the past year as I’ve been speaking to people about the data-centric AI movement, I’ve been getting flashbacks to when I was speaking to people about deep learning and scalability 10 or 15 years ago. In the past year, I’ve been getting the same mix of “there’s nothing new here” and “this seems like the wrong direction.” Back to top How do you define data-centric AI, and why do you consider it a movement? Ng: Data-centric AI is the discipline of systematically engineering the data needed to successfully build an AI system. For an AI system, you have to implement some algorithm, say a neural network, in code and then train it on your data set. The dominant paradigm over the last decade was to download the data set while you focus on improving the code. Thanks to that paradigm, over the last decade deep learning networks have improved significantly, to the point where for a lot of applications the code—the neural network architecture—is basically a solved problem. So for many practical applications, it’s now more productive to hold the neural network architecture fixed, and instead find ways to improve the data. When I started speaking about this, there were many practitioners who, completely appropriately, raised their hands and said, “Yes, we’ve been doing this for 20 years.” This is the time to take the things that some individuals have been doing intuitively and make it a systematic engineering discipline. The data-centric AI movement is much bigger than one company or group of researchers. My collaborators and I organized a data-centric AI workshop at NeurIPS, and I was really delighted at the number of authors and presenters that showed up. You often talk about companies or institutions that have only a small amount of data to work with. How can data-centric AI help them? Ng: You hear a lot about vision systems built with millions of images—I once built a face recognition system using 350 million images. Architectures built for hundreds of millions of images don’t work with only 50 images. But it turns out, if you have 50 really good examples, you can build something valuable, like a defect-inspection system. In many industries where giant data sets simply don’t exist, I think the focus has to shift from big data to good data. Having 50 thoughtfully engineered examples can be sufficient to explain to the neural network what you want it to learn. When you talk about training a model with just 50 images, does that really mean you’re taking an existing model that was trained on a very large data set and fine-tuning it? Or do you mean a brand new model that’s designed to learn only from that small data set? Ng: Let me describe what Landing AI does. When doing visual inspection for manufacturers, we often use our own flavor of RetinaNet. It is a pretrained model. Having said that, the pretraining is a small piece of the puzzle. What’s a bigger piece of the puzzle is providing tools that enable the manufacturer to pick the right set of images [to use for fine-tuning] and label them in a consistent way. There’s a very practical problem we’ve seen spanning vision, NLP, and speech, where even human annotators don’t agree on the appropriate label. For big data applications, the common response has been: If the data is noisy, let’s just get a lot of data and the algorithm will average over it. But if you can develop tools that flag where the data’s inconsistent and give you a very targeted way to improve the consistency of the data, that turns out to be a more efficient way to get a high-performing system. “Collecting more data often helps, but if you try to collect more data for everything, that can be a very expensive activity.” —Andrew Ng For example, if you have 10,000 images where 30 images are of one class, and those 30 images are labeled inconsistently, one of the things we do is build tools to draw your attention to the subset of data that’s inconsistent. So you can very quickly relabel those images to be more consistent, and this leads to improvement in performance. Could this focus on high-quality data help with bias in data sets? If you’re able to curate the data more before training? Ng: Very much so. Many researchers have pointed out that biased data is one factor among many leading to biased systems. There have been many thoughtful efforts to engineer the data. At the NeurIPS workshop, Olga Russakovsky gave a really nice talk on this. At the main NeurIPS conference, I also really enjoyed Mary Gray’s presentation, which touched on how data-centric AI is one piece of the solution, but not the entire solution. New tools like Datasheets for Datasets also seem like an important piece of the puzzle. One of the powerful tools that data-centric AI gives us is the ability to engineer a subset of the data. Imagine training a machine-learning system and finding that its performance is okay for most of the data set, but its performance is biased for just a subset of the data. If you try to change the whole neural network architecture to improve the performance on just that subset, it’s quite difficult. But if you can engineer a subset of the data you can address the problem in a much more targeted way. When you talk about engineering the data, what do you mean exactly? Ng: In AI, data cleaning is important, but the way the data has been cleaned has often been in very manual ways. In computer vision, someone may visualize images through a Jupyter notebook and maybe spot the problem, and maybe fix it. But I’m excited about tools that allow you to have a very large data set, tools that draw your attention quickly and efficiently to the subset of data where, say, the labels are noisy. Or to quickly bring your attention to the one class among 100 classes where it would benefit you to collect more data. Collecting more data often helps, but if you try to collect more data for everything, that can be a very expensive activity. For example, I once figured out that a speech-recognition system was performing poorly when there was car noise in the background. Knowing that allowed me to collect more data with car noise in the background, rather than trying to collect more data for everything, which would have been expensive and slow. Back to top What about using synthetic data, is that often a good solution? Ng: I think synthetic data is an important tool in the tool chest of data-centric AI. At the NeurIPS workshop, Anima Anandkumar gave a great talk that touched on synthetic data. I think there are important uses of synthetic data that go beyond just being a preprocessing step for increasing the data set for a learning algorithm. I’d love to see more tools to let developers use synthetic data generation as part of the closed loop of iterative machine learning development. Do you mean that synthetic data would allow you to try the model on more data sets? Ng: Not really. Here’s an example. Let’s say you’re trying to detect defects in a smartphone casing. There are many different types of defects on smartphones. It could be a scratch, a dent, pit marks, discoloration of the material, other types of blemishes. If you train the model and then find through error analysis that it’s doing well overall but it’s performing poorly on pit marks, then synthetic data generation allows you to address the problem in a more targeted way. You could generate more data just for the pit-mark category. “In the consumer software Internet, we could train a handful of machine-learning models to serve a billion users. In manufacturing, you might have 10,000 manufacturers building 10,000 custom AI models.” —Andrew Ng Synthetic data generation is a very powerful tool, but there are many simpler tools that I will often try first. Such as data augmentation, improving labeling consistency, or just asking a factory to collect more data. Back to top To make these issues more concrete, can you walk me through an example? When a company approaches Landing AI and says it has a problem with visual inspection, how do you onboard them and work toward deployment? Ng: When a customer approaches us we usually have a conversation about their inspection problem and look at a few images to verify that the problem is feasible with computer vision. Assuming it is, we ask them to upload the data to the LandingLens platform. We often advise them on the methodology of data-centric AI and help them label the data. One of the foci of Landing AI is to empower manufacturing companies to do the machine learning work themselves. A lot of our work is making sure the software is fast and easy to use. Through the iterative process of machine learning development, we advise customers on things like how to train models on the platform, when and how to improve the labeling of data so the performance of the model improves. Our training and software supports them all the way through deploying the trained model to an edge device in the factory. How do you deal with changing needs? If products change or lighting conditions change in the factory, can the model keep up? Ng: It varies by manufacturer. There is data drift in many contexts. But there are some manufacturers that have been running the same manufacturing line for 20 years now with few changes, so they don’t expect changes in the next five years. Those stable environments make things easier. For other manufacturers, we provide tools to flag when there’s a significant data-drift issue. I find it really important to empower manufacturing customers to correct data, retrain, and update the model. Because if something changes and it’s 3 a.m. in the United States, I want them to be able to adapt their learning algorithm right away to maintain operations. In the consumer software Internet, we could train a handful of machine-learning models to serve a billion users. In manufacturing, you might have 10,000 manufacturers building 10,000 custom AI models. The challenge is, how do you do that without Landing AI having to hire 10,000 machine learning specialists? So you’re saying that to make it scale, you have to empower customers to do a lot of the training and other work. Ng: Yes, exactly! This is an industry-wide problem in AI, not just in manufacturing. Look at health care. Every hospital has its own slightly different format for electronic health records. How can every hospital train its own custom AI model? Expecting every hospital’s IT personnel to invent new neural-network architectures is unrealistic. The only way out of this dilemma is to build tools that empower the customers to build their own models by giving them tools to engineer the data and express their domain knowledge. That’s what Landing AI is executing in computer vision, and the field of AI needs other teams to execute this in other domains. Is there anything else you think it’s important for people to understand about the work you’re doing or the data-centric AI movement? Ng: In the last decade, the biggest shift in AI was a shift to deep learning. I think it’s quite possible that in this decade the biggest shift will be to data-centric AI. With the maturity of today’s neural network architectures, I think for a lot of the practical applications the bottleneck will be whether we can efficiently get the data we need to develop systems that work well. The data-centric AI movement has tremendous energy and momentum across the whole community. I hope more researchers and developers will jump in and work on it. Back to top This article appears in the April 2022 print issue as “Andrew Ng, AI Minimalist.”

The end of Moore’s Law is looming. Engineers and designers can do only so much to miniaturize transistors and pack as many of them as possible into chips. So they’re turning to other approaches to chip design, incorporating technologies like AI into the process. Samsung, for instance, is adding AI to its memory chips to enable processing in memory, thereby saving energy and speeding up machine learning. Speaking of speed, Google’s TPU V4 AI chip has doubled its processing power compared with that of its previous version. But AI holds still more promise and potential for the semiconductor industry. To better understand how AI is set to revolutionize chip design, we spoke with Heather Gorr, senior product manager for MathWorks’ MATLAB platform. How is AI currently being used to design the next generation of chips? Heather Gorr: AI is such an important technology because it’s involved in most parts of the cycle, including the design and manufacturing process. There’s a lot of important applications here, even in the general process engineering where we want to optimize things. I think defect detection is a big one at all phases of the process, especially in manufacturing. But even thinking ahead in the design process, [AI now plays a significant role] when you’re designing the light and the sensors and all the different components. There’s a lot of anomaly detection and fault mitigation that you really want to consider. Heather GorrMathWorks Then, thinking about the logistical modeling that you see in any industry, there is always planned downtime that you want to mitigate; but you also end up having unplanned downtime. So, looking back at that historical data of when you’ve had those moments where maybe it took a bit longer than expected to manufacture something, you can take a look at all of that data and use AI to try to identify the proximate cause or to see something that might jump out even in the processing and design phases. We think of AI oftentimes as a predictive tool, or as a robot doing something, but a lot of times you get a lot of insight from the data through AI. What are the benefits of using AI for chip design? Gorr: Historically, we’ve seen a lot of physics-based modeling, which is a very intensive process. We want to do a reduced order model, where instead of solving such a computationally expensive and extensive model, we can do something a little cheaper. You could create a surrogate model, so to speak, of that physics-based model, use the data, and then do your parameter sweeps, your optimizations, your Monte Carlo simulations using the surrogate model. That takes a lot less time computationally than solving the physics-based equations directly. So, we’re seeing that benefit in many ways, including the efficiency and economy that are the results of iterating quickly on the experiments and the simulations that will really help in the design. So it’s like having a digital twin in a sense? Gorr: Exactly. That’s pretty much what people are doing, where you have the physical system model and the experimental data. Then, in conjunction, you have this other model that you could tweak and tune and try different parameters and experiments that let sweep through all of those different situations and come up with a better design in the end. So, it’s going to be more efficient and, as you said, cheaper? Gorr: Yeah, definitely. Especially in the experimentation and design phases, where you’re trying different things. That’s obviously going to yield dramatic cost savings if you’re actually manufacturing and producing [the chips]. You want to simulate, test, experiment as much as possible without making something using the actual process engineering. We’ve talked about the benefits. How about the drawbacks? Gorr: The [AI-based experimental models] tend to not be as accurate as physics-based models. Of course, that’s why you do many simulations and parameter sweeps. But that’s also the benefit of having that digital twin, where you can keep that in mind—it’s not going to be as accurate as that precise model that we’ve developed over the years. Both chip design and manufacturing are system intensive; you have to consider every little part. And that can be really challenging. It’s a case where you might have models to predict something and different parts of it, but you still need to bring it all together. One of the other things to think about too is that you need the data to build the models. You have to incorporate data from all sorts of different sensors and different sorts of teams, and so that heightens the challenge. How can engineers use AI to better prepare and extract insights from hardware or sensor data? Gorr: We always think about using AI to predict something or do some robot task, but you can use AI to come up with patterns and pick out things you might not have noticed before on your own. People will use AI when they have high-frequency data coming from many different sensors, and a lot of times it’s useful to explore the frequency domain and things like data synchronization or resampling. Those can be really challenging if you’re not sure where to start. One of the things I would say is, use the tools that are available. There’s a vast community of people working on these things, and you can find lots of examples [of applications and techniques] on GitHub or MATLAB Central, where people have shared nice examples, even little apps they’ve created. I think many of us are buried in data and just not sure what to do with it, so definitely take advantage of what’s already out there in the community. You can explore and see what makes sense to you, and bring in that balance of domain knowledge and the insight you get from the tools and AI. What should engineers and designers consider when using AI for chip design? Gorr: Think through what problems you’re trying to solve or what insights you might hope to find, and try to be clear about that. Consider all of the different components, and document and test each of those different parts. Consider all of the people involved, and explain and hand off in a way that is sensible for the whole team. How do you think AI will affect chip designers’ jobs? Gorr: It’s going to free up a lot of human capital for more advanced tasks. We can use AI to reduce waste, to optimize the materials, to optimize the design, but then you still have that human involved whenever it comes to decision-making. I think it’s a great example of people and technology working hand in hand. It’s also an industry where all people involved—even on the manufacturing floor—need to have some level of understanding of what’s happening, so this is a great industry for advancing AI because of how we test things and how we think about them before we put them on the chip. How do you envision the future of AI and chip design? Gorr: It’s very much dependent on that human element—involving people in the process and having that interpretable model. We can do many things with the mathematical minutiae of modeling, but it comes down to how people are using it, how everybody in the process is understanding and applying it. Communication and involvement of people of all skill levels in the process are going to be really important. We’re going to see less of those superprecise predictions and more transparency of information, sharing, and that digital twin—not only using AI but also using our human knowledge and all of the work that many people have done over the years.

Quantum computing is a devilishly complex technology, with many technical hurdles impacting its development. Of these challenges two critical issues stand out: miniaturization and qubit quality. IBM has adopted the superconducting qubit road map of reaching a 1,121-qubit processor by 2023, leading to the expectation that 1,000 qubits with today’s qubit form factor is feasible. However, current approaches will require very large chips (50 millimeters on a side, or larger) at the scale of small wafers, or the use of chiplets on multichip modules. While this approach will work, the aim is to attain a better path toward scalability. Now researchers at MIT have been able to both reduce the size of the qubits and done so in a way that reduces the interference that occurs between neighboring qubits. The MIT researchers have increased the number of superconducting qubits that can be added onto a device by a factor of 100. “We are addressing both qubit miniaturization and quality,” said William Oliver, the director for the Center for Quantum Engineering at MIT. “Unlike conventional transistor scaling, where only the number really matters, for qubits, large numbers are not sufficient, they must also be high-performance. Sacrificing performance for qubit number is not a useful trade in quantum computing. They must go hand in hand.” The key to this big increase in qubit density and reduction of interference comes down to the use of two-dimensional materials, in particular the 2D insulator hexagonal boron nitride (hBN). The MIT researchers demonstrated that a few atomic monolayers of hBN can be stacked to form the insulator in the capacitors of a superconducting qubit. Just like other capacitors, the capacitors in these superconducting circuits take the form of a sandwich in which an insulator material is sandwiched between two metal plates. The big difference for these capacitors is that the superconducting circuits can operate only at extremely low temperatures—less than 0.02 degrees above absolute zero (-273.15 °C). Superconducting qubits are measured at temperatures as low as 20 millikelvin in a dilution refrigerator.Nathan Fiske/MIT In that environment, insulating materials that are available for the job, such as PE-CVD silicon oxide or silicon nitride, have quite a few defects that are too lossy for quantum computing applications. To get around these material shortcomings, most superconducting circuits use what are called coplanar capacitors. In these capacitors, the plates are positioned laterally to one another, rather than on top of one another. As a result, the intrinsic silicon substrate below the plates and to a smaller degree the vacuum above the plates serve as the capacitor dielectric. Intrinsic silicon is chemically pure and therefore has few defects, and the large size dilutes the electric field at the plate interfaces, all of which leads to a low-loss capacitor. The lateral size of each plate in this open-face design ends up being quite large (typically 100 by 100 micrometers) in order to achieve the required capacitance. In an effort to move away from the large lateral configuration, the MIT researchers embarked on a search for an insulator that has very few defects and is compatible with superconducting capacitor plates. “We chose to study hBN because it is the most widely used insulator in 2D material research due to its cleanliness and chemical inertness,” said colead author Joel Wang, a research scientist in the Engineering Quantum Systems group of the MIT Research Laboratory for Electronics. On either side of the hBN, the MIT researchers used the 2D superconducting material, niobium diselenide. One of the trickiest aspects of fabricating the capacitors was working with the niobium diselenide, which oxidizes in seconds when exposed to air, according to Wang. This necessitates that the assembly of the capacitor occur in a glove box filled with argon gas. While this would seemingly complicate the scaling up of the production of these capacitors, Wang doesn’t regard this as a limiting factor. “What determines the quality factor of the capacitor are the two interfaces between the two materials,” said Wang. “Once the sandwich is made, the two interfaces are “sealed” and we don’t see any noticeable degradation over time when exposed to the atmosphere.” This lack of degradation is because around 90 percent of the electric field is contained within the sandwich structure, so the oxidation of the outer surface of the niobium diselenide does not play a significant role anymore. This ultimately makes the capacitor footprint much smaller, and it accounts for the reduction in cross talk between the neighboring qubits. “The main challenge for scaling up the fabrication will be the wafer-scale growth of hBN and 2D superconductors like [niobium diselenide], and how one can do wafer-scale stacking of these films,” added Wang. Wang believes that this research has shown 2D hBN to be a good insulator candidate for superconducting qubits. He says that the groundwork the MIT team has done will serve as a road map for using other hybrid 2D materials to build superconducting circuits.