2026-10-23 02:02:20
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!
If you’re an engineer, you may feel like your skills aren’t commanding the premium they once did. Many engineers have faced layoffs or restructuring. But even for those of us who still have jobs, something corrosive is happening. You may have heard some version of this:
“ChatGPT answered that in five seconds. Why can’t you?”
Or, “I made this app in a weekend. Why is our team taking so long?”
I worked on a team where leadership said things like this directly to engineers. This was in 2024 and 2025, when the AI craze was in full swing and Claude and ChatGPT adoption was rising.
Generative AI (and the marketing behind it) gives people the illusion that the work is simpler than it is. A working demo hides complexity, and once someone has a website that works, they may decide they no longer need engineers, or start to distrust the ones they do have.
Some CEOs want to believe they can replace an engineering team with a few sexy prompts. Many companies also want them to believe this.
That website works great in the demo—under specific conditions and without users with thumbs for fingers. Then it has to plug into a system with millions of lines of old code, satisfy legal requirements, meet accessibility standards, and be delivered in a complex deployment process.
Thinking that writing code was the only bottleneck for deploying new software is like the Titanic seeing the tip of the iceberg and assuming there was nothing underneath it.
Engineers have a unique position in the AI space, because you have the capacity to understand how these models work much better than the average person. Use this to your advantage.
Learn how AI models work. If you can’t already explain that LLMs are probabilistic, what tokens are, or why training data matters, spend a weekend fixing that. You don’t need to become a full-blown AI researcher. A few fundamentals will put you ahead of most people making the AI decisions.
Teach upward. Turn that knowledge into teaching moments for the people who lead you. Explain the limits of these tools, and why a demo and a production system are different animals. It keeps people from truly believing that a notebook full of prompts can replace an engineering team.
Consider the criticism of today’s processes. Some of what we do has been treated as ceremony for too long. Be willing to say, “you know what, maybe you’re right, this process is too slow for the speed we can work at now.” Conceding that earns you the credibility to defend the steps that matter.
I still believe that speed at the cost of everything else is harmful—but that’s a topic for another time.
Find your voice. Engineers at the forefront of AI adoption need to push back and explain why we do things the way we do. Nobody else in the room is going to do it for us.
Your goal, in my opinion, is to save your organization from buying into the hype so much that they chase the wrong thing. Our job, before and after AI, is building safe and useful products for human beings, and engineers are still best suited for that.
—Brian
The need to educate engineering students about ethics and social responsibility is widely recognized, but today’s curricula often miss the mark. Three professors propose a new approach: adding ethics training directly to core technical coursework.
Read more here.
Looking for a career in quantum computing hardware? An IBM spin-off called Anderon could receive up to $1 billion in funding from the U.S. CHIPS and Science Act for its quantum foundry, established earlier this year. Could Anderon do for quantum hardware what TSMC did for conventional chips?
Read more here.
When Gurmeet Saran joined Anthropic’s technical staff in 2025, he entered a role focused on a key question: How do you know whether you can trust a chatbot like Claude? Learn more about Saran’s path and his work at the AI company.
Read more here.
2026-10-22 21:00:02
When San Antonio–based software engineer Nikhil Jathar first went to Washington, D.C., to advocate for federal support for technology education and research funding, he was in awe of the opportunity. It was spring of 2025, not long after Jathar settled in the United States from India, when he got an invitation to join the annual Congressional Visits Day organized by IEEE-USA.
“My first reaction was, ‘Wow, I get to visit Congress,’ ” says Jathar, CTO of AvanSaber, a business software company he cofounded in 2014. At the time, he did not have a specific lobbying position on behalf of the company. But he was intrigued by the invitation—which goes out to all U.S. IEEE members—and as he read more about congressional visits, his motivation deepened. He realized: “I need to try this. I want to see where it goes.”
Congressional staffers expect clarity from advocates. Be clear on what you plan to ask for when you arrive. Here are some common options:
Congressional staffers say they welcome the knowledge and perspective that engineers like Jathar and other visitors from beyond Washington, D.C., provide. It’s also important for those visitors to be clear on what they are advocating for. One staffer for the House Committee on Science, Space, and Technology, who agreed to speak anonymously due to the office’s media policy, says, “Ideas for new policies come from multiple places. It could begin at the [legislator] or staff level, an ask from a constituent, or needs from industry. It’s up to us to figure out what would do the most good,” the staffer said. “For instance, if you leave it to the lobbyists, they’re understandably going to do what’s in their clients’ interests.”
For engineers in the United States, speaking with elected representatives is an effective way to shape key policies, but it takes some preparation and the right approach, as Jathar learned over the course of two visits.
Congress’s job is to act on behalf of the people, and it has rules for how people can lobby it. It distinguishes between broader advocacy—conversations between constituents and U.S. representatives—and professional lobbying, which involves paid professionals and is governed by the Lobbying Disclosure Act. Many professional groups like IEEE also organize congressional visits for their members. Those visits can be subject to lobbying rules, so the organizers told Jathar that: “You are representing the organization, not your own views.” He would need to stick to the talking points IEEE-USA had prepared for all its participants, regardless of their specialty, to promote across several congressional offices.
On Jathar’s first visit, those organizational talking points included advocating for funding smart grids, authorizing Department of Energy research, and investing in STEM education. That represents one of the trade-offs of access in Washington. IEEE-USA’s priorities didn’t all overlap directly with Jathar’s work, but they were broad enablers of science and technology and, he says, “STEM education funding is close to my heart.”
Anyone can approach their representatives in Washington with their personal priorities, Jathar notes, but given how pressed for time congressional staff are, they are more likely to prioritize a meeting that represents the views of as many of their constituents as possible. “I could have gone outside the IEEE structure,” Jathar says, but congressional staff “are more likely to listen if you are part of a much bigger organization.”
For Jathar’s first congressional visit, organizers assigned him to call on the offices of several representatives from both political parties based on where he lives: Representatives Greg Casar (D-Texas), Lloyd Doggett (D-Texas), and Barry Moore (R-Ala.). “Meeting a staffer or a senior member from their office is good. They tend to relate to these issues more than a representative, because a representative has 10 different high-level things they have to worry about whereas the staffer can be more focused on the policy details,” Jathar says.
A typical visit like this may last anywhere from 15 minutes to an hour, so Jathar kept his talking points simple and short. “First and foremost you’re gonna get a very small window,” he says, “Make sure you’ve got everything ready to present in bullet points and not a lot of jargon. You can always bring that out later if they ask more questions.” Simple visual aids, in the form of one-page handouts, may be helpful for communicating numeric or technical information, too.
“If you leave it to the lobbyists, they’re understandably going to do what’s in their clients’ interests.”–Staffer for the House Committee on Science, Space, and Technology
The House Science Committee staffer agrees: “I prefer meetings where people know what they want and know what we’re doing in Congress.” In addition to having a clear request, the staffer says engineers and other advocates should keep track of where Congress is in its cycle: Is it appropriations season, and when is the next budget being set? How far along is a given piece of legislation in the process, and what is realistic to change at that point?
During Jathar’s 2025 visit to one Capitol Hill office, he learned firsthand the importance of preparation. For one of the three representatives he visited, staff politely pointed out that the legislator they worked for was not on the committee with jurisdiction over the IEEE-USA talking point they were discussing. “They explained that there are only certain things [the legislator] can do on that issue and that there are other people we need to talk to, to achieve more,” Jathar recalls.
Earlier this year, Jathar decided to go back to Capitol Hill with his newfound knowledge, again within the framework of an IEEE-USA visit. This time he was able to provide something else legislative staff want from engineers: authoritative technical knowledge.
“When I introduced myself as a software developer to one staffer, his first question was, ‘Do you think AI will replace coders?’ ” Jathar says. The conversation went back and forth, with the staffer asking Jathar and his other engineering companions questions. Jathar went on to explain that his company developed an open-source business-management software it calls ERPClaw, designed to be used with AI agents such as OpenClaw. That led naturally to discussing the role of open source in AI, which plays a role in one of IEEE-USA’s proposals for democratizing AI. This time Jathar was able to align his organization’s advocacy with his expertise and the staffer’s areas of interest.
It’s too soon to know what legislative impact that advocacy might have, but it’s the sort of informed, relevant proposal that legislative staff seek out. “The people who build trust have more cachet with congressional staff because they’ve asked for something that’s reasonably thought out,” the House Science Committee staffer says. In turn, congressional staffers can advocate better within their offices for policies brought to them by people they trust.
And talking about the issues you care the most about can deepen that trust. The House Science Committee staffer once visited Congress as an advocate before switching to the other side of the table. On that visit, as on Jathar’s, the professional society organizing the visit provided a pitch. “I was making a pitch that was given to me without a lot of personal buy-in,” the staffer says. Instead, “you should feel emotionally connected to any issue you’re advocating for.”
Jathar’s support for the open-source movement is like that. Open source gives smaller companies a foundation they can adapt and build upon, while allowing other developers to extend that work. “We can progress a lot faster,” Jathar says. “I’m not doing it only because it’s good for my business, but because I feel it’s good for any technological advancement.”
2026-10-22 02:00:03
IEEE Life Fellow Manfred “Fred” J. Schindler has been elected as the 2027 IEEE president-elect. He will begin serving as president on 1 January 2028.
Schindler, who was nominated by the IEEE Board of Directors, received 16,421 votes in the election. Senior Member David Alan Koehler received 13,874 votes.
At press time, the results were unofficial until the IEEE Board of Directors accepts the IEEE Teller’s Committee report in November.
Schindler, an expert in microwave semiconductor technology, is an independent consultant supporting clients with technical expertise, due diligence, and project management.
Throughout his career, he led the development of microwave integrated-circuit technology, from lab demonstrations to high-volume commercial products. He has numerous technical publications and holds 11 patents.
Schindler served as CTO of Anlotek, and director of Qorvo and RFMD’s Boston design center. He was applications manager at IBM, engineering manager at ATN Microwave, and a lab manager at Raytheon.
An IEEE volunteer for more than 30 years, Schindler served as the 2024 vice president of IEEE Technical Activities and the 2021–2022 Division IV director. He was chair of the IEEE Conferences Committee from 2015 to 2018 and president of the IEEE Microwave Theory and Technology Society (MTTS) in 2003. He received the society’s 2018 Distinguished Service Award and its 2015 N. Walter Cox Award. His award-winning micro-business column has appeared in IEEE Microwave Magazine since 2011.
He also led the 2025 One IEEE to Enable Strategic Investments in Innovations and Public Imperative Activities adhoc committee.
Schindler is an IEEE–Eta Kappa Nu honorary life member and a MTTS honorary life member.
2026-10-21 21:00:02
Ellia Lebleu is a founding member of the WikiProject AI Cleanup, a volunteer effort launched in 2023 to find and remove AI-generated material from Wikipedia that breaks the website’s guidelines. When the cleanup began, AI writing was often betrayed by conspicuous clichés. Today, the project’s volunteers are developing tools to detect subtler forms of machine-generated content and debating AI’s role in the world’s most widely used encyclopedia.
When did you realize Wikipedia had a problem with AI-generated material?
Ellia Lebleu: It was around December 2023. A few other editors and I were chatting on Wikipedia’s Discord server, and we had all seen very strange articles with the same odd characteristics. They repeated phrases like “stands as a testament” or “rich cultural heritage.” They sometimes had conclusion sections, or sections called “Why is this important?” That was very typical of ChatGPT in 2023 and early 2024 but very odd for Wikipedia articles—and very easy to spot. Then I decided, let’s create an official project on Wikipedia so more people can deal with this.
Ellia Lebleu is a founding member of the WikiProject AI Cleanup. Lebleu is pursuing a Ph.D. in AI at the Inria Center of the University of Rennes, part of France’s national research institute for digital science and technology.
How do editors spot AI-generated writing today?
Lebleu: By 2025, the signs of AI writing were becoming subtler and more structural. For example, AI loves the “rule of three,” negative parallelisms, and other structures of speech that aren’t conclusive by themselves but become meaningful as they accumulate. We also look beyond the article. If someone writes five articles of thousands of words each in 5 minutes, there is reason to suspect AI.
Does the AI problem just appear in the main text, or also in citations?
Lebleu: Fake or misleading citations have become a major worry. Sometimes an AI hallucinates a citation entirely, although that’s much rarer today because of retrieval-augmented generation that allows the AI to find sources. More often, it finds a real source but gets its content completely wrong. AI citations that don’t actually support what is being written are extremely commonplace.
English Wikipedia recently prohibited editors from using LLMs to generate articles. How much AI-generated content is still getting through?
Lebleu: I’ve been working with [AI-detection company] Pangram to estimate the proportion of AI-generated content. I tested it on Wikipedia content from before ChatGPT and found no false positives, so its results provide a very conservative lower bound. In late 2025, it flagged perhaps 20 percent of new articles and drafts as AI-generated. In early 2026, that went down to 15 or 16 percent. But we don’t yet know whether that’s because our recent policies have had an effect or because models are getting more powerful and Pangram hasn’t had time to adapt.
Is Wikipedia using AI to combat AI?
Lebleu: Another editor from the project has made an AI source verifier, which goes through the sources on a page and checks whether they match the claims. Since it is itself an AI model, there is no guarantee that it will be correct every time. But if you want something that works at scale and filters out obvious cases, it’s a good first step. The Wikimedia Foundation is also working on a tool called Paste Check, which can flag text pasted directly from some AI chatbots.
And we’ve seen Claude sometimes say, “I can’t write a Wikipedia article; it would go against Wikipedia’s policies.” If all major AI companies implemented that kind of prompt refusal, it wouldn’t solve the problem entirely, because there are still open-weight models that can be tweaked to avoid restrictions. But it would help with people who use commercial models and aren’t especially familiar with the technology.
Can you tell if the rule prohibiting AI writing is working?
Lebleu: It’s still a little early to tell. We see possible effects, such as prompt refusal and the apparent slight drop in AI-generated content. But again, we don’t yet know if that is because models are getting better or because beginners are learning the rules. The vast majority of AI-generated content is submitted by beginner editors, so we’ve made many efforts to make the rule very visible in welcome messages, editing help, and advice.
What if AI becomes capable of writing truly reliable Wikipedia articles?
Lebleu: That’s one of the big questions we’re facing. When the guideline was passed, it was for very practical reasons: We were flooded with AI content, we didn’t have good tools, and we needed to put a stop to it. But since then, it has taken on a more philosophical dimension. Many people have called Wikipedia “the last good place on the internet.” Being a place that was, in theory, free from AI slop has become part of Wikipedia’s identity.
A lot of Wikipedia’s communication is now centered on the idea that we’re a human-written encyclopedia. Even AI companies find that interesting. It can be valuable to have a purely human-written corpus on which to [train] AI systems. The fact that Wikipedia is still that, and will remain that for at least the near future, makes it a precious resource that isn’t replicated anywhere else.
But will the people who read and contribute to Wikipedia want it to become partially AI-generated if that makes it more comprehensive? We could preserve Wikipedia’s identity as a human-written corpus while also offering an AI-assisted option.
Has Wikipedia reckoned with the idea of “Google Zero”—a possible future in which websites no longer get traffic from Google search?
Lebleu: If everything goes through AI search and websites plummet to zero visibility, how do we expose Wikipedia and show that it can be edited and that people can still contribute? Wikipedia is frequently cited in [Google’s] AI Overviews, but users may only see an AI answer citing Wikipedia. They may not realize that Wikipedia is a living project they can edit and contribute to.
The Wikimedia Foundation is working on an attribution framework that could give AI users more information about the article [being cited]: when it was last edited, how comprehensive it is, whether editors have flagged problems, and perhaps even a call to action. In the future, readers might even contribute through AI models: “The AI cited Wikipedia, but there seems to be an error. Can I make an edit request and add this other source I found?”
I can see a very strong symbiotic relationship between Wikipedia and AI models in the future. I don’t see Wikipedia disappearing, but I can see it taking a very different shape. Yesterday, AI was something to clean up. Today, it’s a tool to help us with the cleanup. Tomorrow, it will be the air we breathe.
A condensed version of this article appears in the November 2026 print issue as “Ellia Lebleu.”
2026-10-20 02:00:03
Advancing technology for the benefit of humanity isn’t just the tagline for IEEE’s mission as a public charity. It’s something that the organization’s more than 500,000 members strive to do all the time. IEEE’s recently released 2025 Social Impact Report showcases a variety of programs volunteers worked on to increase public awareness of technology’s impact on society, many of which The Institute covered.
Here is a selection of the programs from the report, with links to our related articles:
“The IEEE global community of more than 500,000 members and volunteers demonstrates what is possible when expertise is mobilized for the public good,” Sophia Muirhead, IEEE executive director and chief operating officer, said in a news release about the Social Impact Report. “Technology has the power to expand opportunity, strengthen communities and improve lives, and IEEE’s mission is to ensure that this power is used responsibly, ethically, and in service of humanity. The 2025 report shows how IEEE members and volunteers are advancing solutions that make a real difference in people’s lives around the world.”
Engagement research shows that members increasingly cited “giving back to my profession and the world community” as a reason for joining IEEE and renewing their membership, IEEE President Mary Ellen Randall said in her September column about the report.
“Humanitarian programs are not only meaningful,” she wrote. “They also are magnetic. They attract younger engineers, keep them engaged, and help them build a professional identity rooted in purpose and impact.
“IEEE has the 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.”
2026-10-19 21:00:03
Agility Robotics, the company I cofounded out of my robotics lab at Oregon State University in 2015, has just launched Digit 5. It’s designed to be the first humanoid robot that can safely and economically do useful work at scale, allowing us to bring tens of thousands of humanoid workers into the real world. Engineered to lift 23-kilogram bins onto high shelves and with a battery that can run for 90 minutes on a 9-minute charge, Digit 5’s goal is to handle heavy warehouse tasks shift after shift, filling the need for labor in jobs that are too physically stressful or too dull for humans to want to do.
Digit 5 is what Agility has been working toward for over a decade. But the robot is heavy and bulky, lacking the graceful natural motion of its predecessors, and it’s farther than I thought possible from the robot I dreamed of building when I started this company. To deliver a useful commercial product, Agility has taken a path that is very different from what I first imagined.
My dream has always been to build robots that combine locomotion and manipulation to move through the world and interact with it as people do. I seek a mastery of motion, achieved through devices designed to navigate the constraints of physics with elegance. When I started the Dynamic Robotics Laboratory at Oregon State in 2008, we began with a theory derived from decades of work in biomechanics: that a simple “spring-mass” model (a body supported by legs that act like springs) should be able to reproduce every known walking and running gait found in animals.
We put theory into practice by building the bipedal robot ATRIAS—short for “Assume The Robot Is A Sphere,” a joke among physicists about reducing a complex system to its simplest possible form. ATRIAS looked like a large microwave on stilts because it was only intended to demonstrate the physics of human walking and running. This was the first glimmer of achieving my dream; even though ATRIAS wasn’t designed to look like us, it still seemed natural, and even somehow alive, when it settled into a walking gait.
With the success of ATRIAS, the barriers to progress shifted from scientific to engineering and organizational, and a startup company seemed like the way to bridge the gap from a one-off research concept to an actual robotic product. I felt confident of two things: that I would be able to build robots that could go where people go, and that this capability would be incredibly useful. This combination of ambition and naivete is necessary for anyone to found a new company—how hard could it be?
Good advice for starting a company is to understand a customer problem, and then find a way to solve it using your unique capabilities. My cofounders, Damion Shelton and Mikhail Jones, and I did not do this. We relied on our belief that this technology would enable new possibilities and trusted ourselves to figure out the business as we went. So we simply focused on building our first product: the bipedal robot Cassie.
Cassie was the first dynamic bipedal robot that researchers could buy, able to maintain its balance through active, fluid motion rather than careful, rigid steps. It had ostrichlike legs, not because we were imitating ground-running birds, but as a result of engineering strictly for performance. It was not intended to be commercially useful, and had no torso, arms, or head. We sold Cassie robots to Harvard, Caltech, the University of Michigan, the University of California, Berkeley, and a handful of other research institutions, ultimately saturating the market for US $250,000 bipedal research robots after having sold a total of 10 of them.
ATRIAS, Cassie, and Digit 4 EVT [left to right] are on display at Agility Robotics’ RoboFab in Salem, Ore. The 70,000-square-foot facility has the capacity to produce more than 10,000 Digit 5 robots per year.Agility Robotics
Because Cassie’s hardware enabled research into the physics of walking and running, the legged-robot research frontier shifted to control software. Traditionally, engineers controlled legged robots with detailed mathematical models that determined how the machines should move. With Cassie, researchers pioneered the new approach of reinforcement learning for bipedal legged locomotion: A simulated robot learns through trial and error, and the resulting neural network controls the physical machine. This approach is what enables virtually all of the dynamic behaviors that you see on legged robots today, and Cassie looked alive as it recovered from stumbles while climbing stairs, ran 5 kilometers across the Oregon State campus, and broke the robot world record for a 100-meter dash.
Cassie got us closer to being able to move through the world with confidence and grace, but to make it useful, we needed manipulation. After exhaustively exploring different configurations, we found that an upright torso helped the robot balance, while two arms mounted near the top of the torso allowed it to pick up large objects and to catch itself when it fell. Digit 1, which added a torso and arms to the Cassie legs, was our first mobile manipulation robot, and another step on the path toward a product that could do useful work for commercial customers.
As we explored the first applications for Digit, potential customers kept asking us: Why legs? And why a humanoid? These were tough questions for me to answer, because they exposed a core business reality: It didn’t matter to Agility Robotics if we solved a cool science problem in a beautiful way. What mattered to the business was solving real problems for customers.
After years of engagement with industry partners, we found one answer in warehouses and manufacturing facilities: moving bins. Bin handling is a clearly defined task where two arms are needed to lift large bins up high and legs are the best way to balance and move in narrow spaces. Because the task can vary widely from one deployment to another, it hasn’t been broadly addressed by single-purpose automation. Yet it was constrained enough to give Digit an economically viable first job while it developed a broader set of skills.
In 2019, Digit began demonstrating its capabilities to do real work. With Ford Motor Company, we showed how Digit 1 could carry packages from an autonomous vehicle to a front porch. We developed Digit 2 and 3 while working with a certain global package delivery company on moving and sorting cardboard boxes. Amazon tested Digit 4 on moving and stacking bins, and it became the world’s first commercially deployed humanoid at the logistics provider GXO. In 2024, Digit appeared on the cover of Time magazine.
Despite Digit’s apparent success, we began to run into conflicts between my dream and the needs of actual customers. We were no longer working with relatively patient university research labs. Agility has to sell robots to survive, and customers will only purchase robots that provide value. If bins weren’t moving, our customers were losing money. We spent a huge amount of time and effort keeping our first deployments operational, which arguably eliminated any profit we made. Yet this also kept our company learning and alive the way no lab demo ever could.
I had no idea how hard it would be, or that running a company would involve so many things that have nothing to do with solving technical problems: deployments across the country; shipping, service, and customer support; fundraising and investor relations; more fundraising; human resources and IT; developing fleet-management software; managing a supply chain; and even more fundraising. I had no experience with any of these things. My career had focused on solving the technical challenges of robots interacting with the world, and it turned out that was only a tiny piece of building a successful robotics company. But Agility was the only path I saw to continuing the technical work and having an impact.
I knew that Agility needed to focus more on robots as a product, but I didn’t know how to make that happen. Hiring the people we needed to run the company meant giving up an immense amount of control, but we had no other choice. We recruited a new executive team with the experience required to commercialize this new technology, and they quickly made changes that were both painful and necessary.
Digit versions 1, 2, and 3 [left to right] show the evolution of the robot from a dynamic walking platform into a useful mobile manipulator. Agility
We had intended the next revision of Digit, Digit 4 EVT (Engineering Validation and Test), to be a relatively minor upgrade. But Agility’s new leadership insisted that the robot was missing key features that were essential if Digit was going to become a real product. So Digit 4 EVT instead incorporated a series of major changes in a highly compressed timeline, such as larger batteries and system-wide upgrades for safety and regulatory compliance, which added enough mass to dramatically alter the robot’s appearance and capabilities.
While early Digits were headless, Agility Robotics was able to justify a head for Digit 4 to optimize antenna and camera placement, and it turned out to be surprisingly useful for human-robot interaction.Christie Hemm Klok
I could only watch as others brought their own talents to bear to make Digit into a successful product, in gut-wrenching ways that didn’t always align with my vision. There were some especially difficult times when I felt like there were compromises made that didn’t seem necessary, but making those decisions was no longer my sole responsibility. I had to accept that Digit had grown far beyond me, and look for ways of influencing decisions rather than directing them. In the moment, it was incredibly frustrating, as Digit 4 EVT took a few steps backward from my vision of how humanoids should look and move.
Agility is in our name because agility is core to the future of the company. I want Digit to be lithe and graceful. When people see Digit, I want them to like the robot, to understand what it can do at a glance, and to feel that it’s there to help. But with Digit 4 EVT, all of that was sacrificed to functionality, because of course it had to be—the customer just needs the robot to do the work. The design process became a ruthless triage, with aesthetics and elegance of movement being delayed over and over again in favor of brutal functionality. For an engineer whose mind lives more in the future than in the present, it has been hard for me to accept this trajectory. And yet, through Digit 4 EVT’s commercial deployments, we began to really understand specific applications and what they required. We mapped the first 10 use cases Digit would perform, and then began designing Digit 5 from scratch based on everything we had learned.
Digit 5 will be Agility’s first real step toward a general-purpose robot. It marks our transition from imagining what a future humanoid robot should be to designing a product around the realities of customer deployments. These realities include things like European CE regulatory compliance, North American UL electrical-safety certifications, and Federal Communications Commission limits for electromagnetic radiation—all of which felt tangential to my dream for Digit, but were absolute requirements.
Digit 5 is designed to be the first humanoid robot that can operate safely next to human coworkers while working 20 hours a day and lifting more than a human can.Christie Hemm Klok
Customer requirements helped us realize that safety in particular was one of the most critical barriers to scaling. In Digit 4’s commercial deployments, the robots had to work behind physical barriers, separated from humans at all times, to meet insurance and regulatory demands. This seemed silly to me, because I’ve never felt that our robots were dangerous, but we have to prove that they’re safe. And since keeping the robots in safety cages severely limited their usefulness, enabling Digit 5 to operate safely around people became a primary design goal. We spent two years on this challenge.
Digit 5 is designed to be the first humanoid robot that can operate safely next to human coworkers. It will rely on an array of sensors to locate nearby people in a manner that’s certifiably safe, and then take actions based on that information. As a person approaches, Digit 5 is programmed to stop what it’s doing, set down its payload, sit on the floor, and cut power to its motors before the person gets close enough to touch the robot. This conservative approach reduces the risk to nearby people in a verifiable way, and we believe it’s the only approach that makes sense as a first step toward working alongside humans.
Yet this strategy also feels like a massive slowdown on the path toward robots becoming part of everyday life. Enabling coexistence with humans has influenced almost every aspect of Digit 5’s design: the shape of the legs and the body, the safety-rated sensors within protective housings, the isolated computers dedicated to safety, and more. All of these features increase the robot’s complexity, power consumption, mass, and physical size.
Digit 5 is Agility’s largest, heaviest, strongest, and most capable humanoid robot worker.
The robot’s design was driven by safety, with integrated protection for the robot and for the humans it will work around.Agility Robotics
Weighing over 120 kg, Digit 5 is a huge robot. It needed to achieve the lifting limits recommended under the U.S. National Institute for Occupational Safety and Health guidelines for human workers, which include repeatedly lifting 23-kg bins and reaching shelves 1.75 meters high. Digit is designed to perform tasks like these for 20 hours a day—something no human can do. This requires motors with enough power and torque to lift the load, but also enough precision to delicately control the force Digit applies when it makes contact with the world. To power those motors, we needed a large battery and a dock for rapid autonomous charging. The dock will deliver 240 amperes at 48 volts, which is enough current to make its thick charging cables visibly wiggle under the induced magnetic forces, while powerful fans blow air through cooling channels in the robot’s torso to keep the battery from overheating. The battery and cooling system takes up about 75 percent of Digit’s torso, but it will allow the robot to operate for 90 minutes and fully recharge during a 9-minute break, enabling near-continuous work.
All of this extra bulk means that Digit 5 can no longer run or jump the way previous generations could, and the svelte form I imagined has become physically impossible. In some ways, Digit 5 feels more like a forklift than the graceful humanoid I set out to build. But looking at the situation rationally, I believe this trade-off is necessary for any company that claims its humanoid can do this kind of work today: If you see a robot that looks like a human working in a factory or warehouse, it either can’t lift the required weight or can’t make it through the whole shift. And if it’s not covered with sensors, it’s likely not safe to stand next to.
The most visible change with Digit 5 is that humanlike legs replace the ostrichlike legs of our earlier versions. While the “backward” leg configuration was optimized for walking and running, we discovered that it created problems with squatting because the ankle will strike the ground. Digit 5 will spend a lot of time lifting heavy objects from near the floor, so we used a humanoid leg configuration with knee and hip motors that both contribute torque to help the robot stand up again.
When Digit detects an approaching human, the robot will lower itself to the floor and cut power to its motors. Agility believes this is the only near-term approach to verifiably reduce risk when the robot is working near people.Agility Robotics
Other companies building humanoid robots have copied the human form without ever understanding its purpose. It’s very frustrating that, to an outside observer, it now looks like we discarded Agility’s distinctive leg design and simply did what everyone else was doing. Yet I know that we arrived at this shape based purely on functional engineering choices. Making a robot look like a human is the easy part—without the function to match, and the understanding of the physics underlying that function, the whole point of the human form may be lost.
With the power, battery life, and safety features that we are designing into Digit 5, we will have a truly useful robot. But it also compromises on some things that are core to my vision and mission. I understand why we made the choices we did and can justify them all, but together they leave me unsatisfied. It’s the curse of the engineer.
I believe Digit 5 will be the scaling moment for Agility Robotics, the inflection point where we can begin deploying robots in large numbers. While the robot will continue carrying bins, our road map includes many more tasks, from picking individual items and tending machines, to preparing parts for assembly and shipping, and eventually doing assembly work itself. Its interchangeable tool interfaces and modular design will enable us to gradually upgrade its subsystems. It will be a platform for improvement, a source of data for future behavior training, and a viable product all at the same time.
Hurst stands between the previous version of Digit, Digit 4 EVT [left], and Digit 5.Christie Hemm Klok
But I cannot love Digit 5. It’s the largest, heaviest, most expensive robot Agility will ever build, full of design compromises that enable it to be exactly the robot it needs to be right now rather than the robot I dreamed of. In the beginning, I envisioned steady progress on agility and dexterity, with each version becoming more dynamically capable. Instead, developing Digit 5 has required large detours to incorporate requirements that seemed unrelated to my original vision. However, I now recognize that tackling these challenges was always necessary. Customers and product requirements didn’t ruin my vision; they revealed what my vision was missing. This is how robots will achieve commercial success and make a real-world impact, which will bring me closer to my scientific dream as well.
We won’t have a humanoid that matches the capabilities of humans until it achieves that mastery of motion that inspired me to found Agility over a decade ago. I saw glimmers of it with the spring-mass locomotion of ATRIAS, and more glimmers with Cassie’s running gait.
I know that such mastery is physically possible with the tools and hardware that we have right now, and despite all of its compromises, Digit 5 will give us a practical foundation for pursuing that dream. I want every future version of Digit to be lighter and more graceful, to interact more naturally with humans, and to be better at manipulating objects and moving through the world, enabling robots to enter aspects of our lives that they haven’t been able to help with before. By the time I’m old enough to need assistance at home, I expect Digit will be up to the task.
With Digit 5, Agility has built a robot that will be useful in our world. Now I’m getting back to work to make it beautiful, too.
This article appears in the November 2026 print issue.