A recent scroll through social media got me thinking about trust. About half my feed was filled with AI slop that astute users would call out as being machine-generated, often to the embarrassment of the rube who unknowingly spread it and sometimes to the delight of the poster who shared it deliberately because they found it amusing or useful for sowing confusion. Remember the good old days of GPT-3 when we could ask the chatbot anything and it would almost instantly and very confidently spit back a plausible but false answer? That’s when the word “hallucination” as applied to AI answers entered the lexicon. Six years and several versions of a few different chatbots later, I still can’t trust the answers I get, even though Claude or Gemini often do useful work for me. That’s the thing about current AI: Trust but verify. And verify you must. Apply that maxim at scale to maintain the integrity of the scientific record and you get an idea of the challenge facing publishers of peer-reviewed scholarship. In “I Used AI to Fake a Scientific Paper,” freelance writer and doctor of applied mathematics Benjamin Skuse tells the story of the human gatekeepers trying to beat back the rising tide of AI slop by attempting to get his own AI-generated papers past IEEE’s defenses. “He basically hit ‘run experiment’ and ‘write paper.’ And then he didn’t do a damn thing to it.” —Eliza Strickland As features and AI beat editor Eliza Strickland told me, Skuse was surprised at just how effortless it was to generate a paper that was more or less plausible and more or less science. For one of his experiments, “he basically hit ‘run experiment’ and ‘write paper,’” she said. “And then he didn’t do a damn thing to it. He just launched it into the maw of the publishing engine.” Skuse discovered that the combination of IEEE’s human-review processes and research-integrity checker programs are currently keeping pace in what amounts to a cat-and-mouse game against people using free tools. But the more advanced LLMs are no longer making the same dumb mistakes the free versions do. That leaves publishers locked in an arms race between increasingly plausible slop and the systems aimed at detecting it. Scholarly publishers aren’t the only ones trying to hold the line against slop. So is that global depository of human-generated knowledge, Wikipedia. For this month’s Five Questions, Strickland interviewed Ellia Lebleu, a founding member of WikiProject AI Cleanup, a volunteer effort to detect and delete AI-generated material from the platform. Lebleu’s team is confronting the same problem as commercial publishers, but with fewer people and fewer tools. “Wikipedia is known as a repository of human-generated information, and they want to hold on to that,” says Strickland. “Who knows how long they’ll be able to do that.” If we can’t collectively assume that the scientific record is trustworthy or that basic facts about the world are accurate, then our understanding of what is real unspools into uncertainty. Perhaps that delights quantum scientists and the grifters who thrive in an information environment where there is no shared reality. But that uncertainty ultimately undermines trust in the institutions that exist to curate the corpus of human knowledge and to promote the advancement of science and technology for the benefit of humanity. Or maybe in a world where AI is doing the science and writing the papers, it won’t matter whether or not humans trust the outputs. As Strickland put it, “Doesn’t it just feel crazy that we’ll have AI writing for other AIs at some point? We’ll just get left out of the loop.”

It’s disheartening how much power gets generated and then promptly lost as it travels through grid networks. This leaking of electricity happens when it vanishes as heat as well as when it is pilfered by thieves and nonpaying customers. More than half of the countries that track these metrics lost at least 10 percent of their electricity in 2023, according to the World Bank. Losses topped 20 percent for 24 of those nations. Two countries lost more than half of what they generated. With numbers this high, cutting down on losses makes sense. Electricity demand is rising beyond what many grid operators can supply; reducing waste would help meet some of that demand without having to build new power plants. Plus, when the power comes from fossil fuels, any loss means emitting even more greenhouse gases into the atmosphere. And electric losses hit the bottom lines of power providers, which ultimately pass those costs on to everyone else. The trouble is, reducing electricity losses is a hard and expensive process that takes a long time. Typically, the less maintained the grid infrastructure, the more electricity that’s lost. And the more fragile the region’s law enforcement and government, the more prevalent the power theft. Natural disasters and war make things worse. Delhi’s Power Grid Comeback Fixing a power grid requires a systemic approach across many sectors. There’s no one technology that will solve the problem. At the outset, the obstacles to success may feel insurmountable. Equipment across entire grid networks must be updated. Multiple arms of government must agree to reforms and coordinate to ensure power providers are set up to succeed. Regulations must be written or revised, investments made, cultures changed. The city of Delhi did all those things. Over the past 25 years, it cut its electricity losses from about 50 to 5 percent. How the city pulled off that impressive feat is the focus of “The Epic Comeback of Delhi’s Power Grid” by Mini Shaji Thomas, an electrical engineer at the university Jamia Millia Islamia who has lived in Delhi since the 1990s. She gives us a view from the inside—as a resident and a power systems expert. Delhi is a shining example, but some other regions have significantly lowered electricity losses over the last quarter century too. The country of Georgia went from losses of over 16 percent in 2002 to about 8 percent in 2023. In Singapore, losses dropped from 6.6 percent to a nearly nonexistent 0.2 percent over the same time period. Global Electricity Theft Crisis But there are many parts of the world where electricity losses remain a problem or have gotten worse. In Jamaica, where power theft is rampant, losses have hovered between 21 and 28 percent for years. Argentina’s losses nearly doubled between 2015 and 2023, going from an all-time low of about 12 percent to an all-time high of nearly 24 percent. The main problem: Transmission and distribution companies lacked the capital to maintain and upgrade their networks, which left equipment operating under stress. A delay in the installation of smart meters has allowed thieves to more easily siphon power and tamper with meters. Thomas says she hopes her account of Delhi’s grid comeback will serve as a blueprint for others. It’s possible to replicate the sweeping changes Delhi made, she says. But it “requires a concerted effort from all stakeholders, customers, the utility, the government, and their employees.”

If you own an at-home server, a gaming computer, or just a laptop that doesn’t get much love, listen up. You can now put that spare computing power to use and earn some passive income in the process. AI companies are hungry for more compute to run AI inference—the process of using a pre-trained model to respond to queries—and they’re willing to pay you for it. “Imagine Uber or Airbnb, but for AI inference computing tasks,” says Ilman Shazhaev, founder and CEO of Far Labs, based in Abu Dhabi. The AI boom has spurred on construction of massive data centers, often damaging local communities by raising electricity prices, straining local water resources, causing environmental damage and noise, and being just plain ugly. Huge data centers are likely not going anywhere—training new frontier models and running AI models from leading companies will likely still be the purview of these behemoths. But now, several companies are providing AI inference on smaller, mostly open-source models. They are running inference on pre-existing computing power spread throughout homes and small businesses, and compensating owners. “Everyone thinks the only way to do it is data centers. And data centers are extractive for the communities in which they’re built, and they don’t return services or taxes or much of anything to the people there. So why not just turn this whole thing on its head?” says John Federico, founder and CEO of Evolving Edge, in Austin, Texas. “The compute power is out there. If you can orchestrate it, then you’re actually adding value to those communities directly.” The idea isn’t entirely new: From 1999 to 2020, a volunteer-based project called SETI@Home used spare computers to search for signs of extraterrestrial life in radio telescope data, for instance. But now, commercial companies are eager to use the same strategy. Shazhaev’s Far Labs is launching its platform Far AI in the coming weeks, while Federico’s Evolving Edge is currently in open beta. Other companies, like Bless Network, Salad, and Gradient have started to provide similar platforms over the last year. Connecting to the network Federico has been a computer hobbyist since youth, and he has amassed a whole server in his basement to run his projects. “It just hit me one day, there’s all this talk about not having enough compute, and I just thought, well, 92 percent of the country has broadband, and you have people like me who have mini data centers in a closet,” he says. Federico sees the potential hosts as people much like himself who have already invested in home servers, and he aims to make the process of selling spare compute as seamless for them as possible. “Sign up for the program, install an application,” Federico says. “All we want to do is run jobs on your machine when you tell us we’re allowed to. The only thing we do is monitor the resource usage. And of course, you can give us a schedule.” With a large enough network of devices, the platform would have compute available whenever it’s needed. Privacy and security are primary concerns for such hosts. To reassure the users that their local data is secure, and that no malware will be downloaded to their devices, the team open-sourced their scheduling software. “The node software is open source, so anyone can look at it, see what it does. All we want to do is run jobs on your machine when you tell us we’re allowed to,” Federico says. Far Labs’ Shazhaev explains that the company’s software is designed around a principle known as “least privilege”: granting both the host and the user the least access possible to accomplish the task. Inference runs as an isolated workload with authenticated, encrypted communication and explicit limits on the GPU, CPU, memory, storage, and network resources it may use. Customers do not receive arbitrary access to the host machine, and providers can inspect resource use, pause the node, revoke access, and remove the software at any time. The protection also works in the other direction. Workloads are segmented and only the minimum required information is exposed to an individual node. Sensitive enterprise workloads can be restricted to controlled hardware rather than routed through consumer devices. Divide and conquer Massive data centers still have advantages from the user perspective: top of the line GPUs and CPUs, high speed networking, thick cables, and sophisticated cooling. User devices are usually less powerful, more varied, and less reliably connected to one another. “This is quite a difficult issue from the science angle,” Shazhaev says. “You want to do a similar level of tasks that are happening in those high infrastructure data centers, and run them on the user device with limited capacity.” Evolving Edge’s Federico says this is an issue for the largest, state-of-the art AI models. But those are not always needed and are often not even preferred. “There are numerous companies, once they reach a certain scale, suddenly paying for tokens on a state-of-the-art frontier model [that] no longer makes sense for their needs,” he says. “Instead, they are fine-tuning open-source models for specific tasks that they have in their business. These models don’t require anywhere near the resources that some of the state-of-the-art models do. It’s just using the right tool for the job.” Smaller, open-source models can often fit on a single user device. But if that fails, there are tools to split a single inference task over multiple GPUs or CPUs. Evolving Edge is using an open-source tool called Ray to perform this splitting, while Far Labs has developed its own proprietary software that not only splits the workload, but wraps the splitting in a layer of security and reliability-providing software. “One thing we have done is we shared the model,” Shazhaev says. “We take the model, we cut it into many pieces, then these pieces will be distributed through different devices. And we have an orchestrator and a load balancer which manage the task flow, so each device processes a part of the task. Then we combine the answers in the main brain, the orchestrator.” Through a combination of using smaller, more task-specific models, and splitting larger models between disparate devices, the teams claim they can perform inference much cheaper than a traditional data center “because we don’t have capital expenditure,” Shazhaev says. Gaming PCs are a common source of spare computational power in the home. Dizzaract The distributed advantage Not only is it cheaper to run inference this way, it is also more reliable, Shazhaev claims. The companies have access to a distributed network of computing resources, rather than one giant device that can experience outages. Shazhaev compares this to cryptocurrencies, and their resilience through decentralization. “Today, to shut down Bitcoin, you need to nuke the whole planet. Here, we have the same concept,” Shazhaev says. Federico explains that this resiliency would be beneficial not just for AI inference, but for all kinds of applications, including smart cities, environmental sensors, autonomous vehicles, and more. During an Amazon Web Services outage in 2026, for example, smart beds were stuck in their upright positions and their users couldn’t adjust them. Federico says that a distributed network where everything doesn’t need to be routed through a single data center, say, in Ashburn, Va., would make those kinds of outages much less impactful. “We could lose 100 nodes in a network of 250,000 and it wouldn’t matter,” he says. If the network of user devices is substantial enough, every job can be routed to a nearby device, decreasing the latency. Far Labs claims a latency of 100 milliseconds or less on its platform. The lower cost and lower latency of this approach may even enable new use cases, such as in-game AI video generation, which is currently prohibitively slow and expensive. “OpenAI last year had $30 billion in revenue, but they closed the financial year at an $8 billion loss. Why? The official reason is due to the high cost of inference,” Shazhaev says. “And those are mostly text models. For gameplay, you have audio, video, animations: It’s heavy data, and you need real-time responses. So, we’ve been trying to solve this issue.” All of these companies are trying to tap into an untapped resource of local compute, and hoping it’ll benefit the device hosts and users alike. “All these big guys are running around building data centers,” Shazhaev says, “but I believe there is enough compute power that already exists in the world.”

When I started at Spectrum 25 years ago, a senior editor suggested that I find a “rabbi,” by which he meant someone who could mentor me in how EEs approach problems and evaluate potential solutions. I didn’t find one right away. Then in 2005 we decided to do a special report, focusing on the challenges of enterprise software development. I suggested we invite IEEE Life Senior Member Robert N. Charette, a self-described risk ecologist, prolific book author, and leading authority on risk management and software engineering, to explore in our pages the myriad reasons software projects fail. His seminal article “Why Software Fails” is still read in university engineering classes today. IEEE Life Senior Member Robert N. Charette is one of IEEE Spectrum’s most prolific authors.Robert N. Charette It was, as they say, the beginning of a beautiful friendship. I had found my rabbi, one who shared my love of writing. We settled into a rhythm that would last more than 20 years, talking on Friday mornings about a range of topics including the growing ubiquity of software in our lives. So when I became Spectrum’s website editor in 2007, he was the first contributor I tapped to start a regular blog (remember those?). The Risk Factor was born and over the course of more than 10 years and 1,750 posts, Bob chronicled hundreds of software debacles, culminating in “Lessons From a Decade of IT Failures,” which won a Jesse H. Neal Award for Best Infographics in 2016. Ironically, yet predictably, those infographics were created in a software package that is no longer supported and thus are lost to the bits of time. “I like the expression on the fish just before it’s going to be swallowed by the heron.”Robert N. Charette Bob, however, was not a one-trick pony. In between his full-time job running his two management consultancies and raising a future biochemist and a future civil engineer, his daughters Maura and Megan, he also wrote many deeply reported and insightful articles. These include last year’s “The Doctor Will See Your Electronic Health Record Now,” the eye-opening 12-part series and e-book The EV Transition Explained, and my personal favorite “Automated to Death,” about the deadly consequences of the automation paradox as manifested by the cyberphysical systems that pilot planes, trains, and automobiles. “The young bald eagle I photographed in September 2024 had bands that I could read which identified it as a female born in May 2024, near Lexington Park, St. Mary’s County, Maryland, about 65 miles away from where I live.”Robert N. Charette His main goal all along has been to make software visible, as he told me one Friday in July. “Software is all around us, but we don’t recognize it at all,” he said. “I really wanted my stories to help people better understand complex software systems. You can’t see software, you can’t touch it, you can’t taste it. You may feel the consequences of software failure, but you never see the reason itself.” When he told me that he was hanging up his hat as a contributing editor to focus on nature photography and to write a handful of fictional trilogies, including one entitled “The STEM Murders” featuring an engineer-turned-detective and his rabbi, I asked him which of his Spectrum articles had the biggest impact. “The hummingbird I caught with the yellow of a road curb behind it.”Robert N. Charette He singled out the 2013 feature “The STEM Crisis Is a Myth.” “Spectrum gave me a platform to question the assumption that we needed more STEM graduates. Until then, people didn’t really realize how much of the STEM crisis was a mythology that was perpetuated by employers and the academic community and was foisted on the IEEE community,” he said. Charette made a career of questioning assumptions. The best way to mitigate risk, he told me as our Friday chat drew to a close, is to be careful making assumptions in the first place. “My main risk maxim is assumptions made are risks accepted.”

“The lowest-cost place to put AI will be in space, and that will be true within two years, maybe three at the latest,” SpaceX founder Elon Musk told the World Economic Forum in Davos this past January, as his company was preparing to go public. Later that month, SpaceX filed an application with the Federal Communications Commission for an orbital data center constellation of up to 1 million satellites in low Earth orbit, 500 to 2,000 kilometers above Earth. And just three days before the IPO, he discussed some initial design specifications for a new AI-1 satellite data center in a video interview. Musk is prone to hyperbole when it comes to timelines. Full self-driving cars by 2017. First human mission to Mars in 2024. Ten thousand Optimus humanoid robots by the end of 2025. Et cetera. For orbital data centers, which he says will be a cost-effective alternative to terrestrial data centers within three years, the math won’t make sense for several years, if ever. Consider this: There are roughly 14,500 active satellites in orbit. Musk’s Starlink constellation accounts for about two thirds of those. Both the launch cadences and satellite-manufacturing capacity would have to scale up astronomically to deploy a million orbital data center satellites. For context, there have been roughly 7,000 orbital launches in all of human history. To loft 1 million satellites into low Earth orbit on SpaceX’s Starship, which is designed to carry up to 60 satellites per vehicle, would require 16,666 launches exclusively devoted to satellite deployments. Considering that SpaceX launched a record 165 orbital missions in 2025, even at 10 times that cadence, it would take a decade. And how long would it take to build 1 million satellites, given Starlink’s current pace of around 4,000 per year and a generous tenfold increase in capacity? Short of a manufacturing revolution, try 25 years. The reality is that the vision of massive constellations of orbital data centers is nowhere close to being realized. As this month’s cover story, “Why Orbital Data Centers Are So Hard” by Andrew Cavalier of ABI Research, makes clear, the reality is that the vision of massive constellations of orbital data centers is nowhere close to being realized. Dina Genkina, IEEE Spectrum’s computing and hardware editor, put the idea into perspective: “Starcloud (a startup that has applied to the FCC for an 88,000 orbital data center satellite constellation) sent one Nvidia H100 GPU in space so far. Their radiator was too weak to let the chip run at full power.” As Cavalier shows, cooling even a single Nvidia H100 GPU in space is difficult: It draws 700 watts, which will require 1.4 square meters of radiator at 60 °C. A 40-kilowatt rack of servers will need an 80-m² radiator; a 100-megawatt data center will require 2,500 of those radiators. Some astronomers are understandably concerned that a million satellites with giant radiative wings would blot out the stars. So if the economics doesn’t make sense, if the chips are at the mercy of the radiative ravages of space, and if humanity will lose its view of the stars, not to mention increasing the risk of triggering the Kessler syndrome, why are the hyperscalers hyping orbital data centers? Genkina offered the obvious answer: sweet, sweet moolah. “The Elon Musk part of it is honestly genius because he’s got xAI building the data centers, SpaceX sending them to space, and Tesla building solar panels,” Genkina says. “It’s almost like he’s paying himself.” Two Analyst’s Views of SpaceX’s Proposed AI1 Data Center Satellite Michael Pierce, Principal at Technology Strategy Partners Musk’s timelines are notoriously overly ambitious, but I think SpaceX’s orbital data centers might reach cost parity with terrestrial data centers in 5 to 10 years. The Starlink laser-link network already exists as the communication backbone for any SpaceX compute constellation, and that infrastructure is what no new entrant can replicate quickly. The chip-agnostic payload design probably reflects their disclosed difficulty securing AI silicon as much as any modularity philosophy. My view is that the only realistic near-term application is a SpaceX mega-constellation for inference. Training workloads likely cannot tolerate the synchronization and latency constraints of a distributed orbital system. Our report analyzed the market from the integrator’s vantage point, but AI1 is what it looks like when one player has assembled all the necessary advantages simultaneously. The question is whether the terrestrial data center industrial base will degrade or improve on economics. I don’t have insight into SpaceX’s internal costs, as opposed to public pricing, on all their components, so it’s hard to say if they’ll completely dominate or not. Even if they are not cost competitive with terrestrial data centers for another 5 to 10 years, it may simply be faster to get new compute that just happens to be in space. Matt Hasan, AI strategist and independent consultant My initial view is that AI1 does not fundamentally change the rationale for space-based data centers as much as it changes the timeline and scale. The underlying drivers remain the same: escalating AI compute demand, growing power constraints on terrestrial grids, and the desire to colocate energy generation with computation. What AI1 does signal is that the concept is beginning to move from theoretical discussion toward engineering and capital allocation decisions. The announcement adds credibility to the idea that hyperscale computing infrastructure may eventually expand beyond terrestrial constraints rather than simply competing for increasingly scarce grid capacity on Earth. That said, significant economic and technical questions remain. Launch costs, maintenance, hardware replacement cycles, thermal management, latency-sensitive workloads, and overall system economics will ultimately determine whether space-based data centers become a mainstream extension of AI infrastructure or remain a niche capability for specialized applications. The key development is not that these questions have been resolved, but that major industry players now appear willing to invest resources toward answering them.