Committee

Transformers are a crucial but nearly invisible part of our grid. The devices help efficiently move power across long distances, but they’re complicated and in short supply. And given that their basic design hasn’t changed much for about a century, they could use an upgrade.  As the world races to meet rising electricity demand from EVs, data centers, and more, researchers and companies are looking to modernize transformers. New, solid-state versions could help relieve some of the supply concerns and make sure the grid can handle the growing pressure. Conventional transformers generally rely on huge amounts of specialized steel alloys and miles of copper wire to do their work of transferring electricity between circuits. Solid-state models, in comparison, combine magnetic components with high-power semiconductors, making them more compact and allowing for more control over the flow of electricity.  These devices are still in development, and can’t replace all traditional transformers today, especially the largest ones that work at the high voltages necessary for long-distance transmission. But a new generation of transformer technology could be a crucial part of our future grid. The grid’s backbone Moving electricity through the grid isn’t perfectly efficient: As electricity flows through power lines, some gets lost as heat. How much varies—the higher the voltage, the lower the current, and the less electricity gets lost. So a key function for the grid is stepping voltage up and down.  Consider, for example, your local solar plant. For that electricity to efficiently travel to your home hundreds of miles away, it needs to reach a higher voltage than what’s generated by the solar plant. Enter transformers.  In its path from the power plant to your outlets, electricity might go through one transformer at the solar farm and another at a substation nearby, stepping up the voltage twice to reach a high enough level to travel on high-voltage transmission lines. Solar panels also produce direct current (DC) electricity, which needs to be converted to alternating current (AC) via an inverter before it flows into a transformer. Closer to its destination, the voltage will be stepped down again, probably at least twice: once at another substation and again at a smaller transformer in your neighborhood. If you’re in the US, electricity from a solar plant might be converted to AC by an inverter at around 480 volts, get stepped up to 115,000 volts for the transmission lines, and finally get stepped back down to 120 volts and 240 volts for your home. Conventional transformers do this work using what essentially amount to loops of wire. The core of a transformer is typically made of a special type of steel called grain-oriented electrical steel. Coils of copper wire sit on either end of that core.  The number of loops in each coil determines the voltage on either end of the transformer: If there are more loops on the secondary coil than the primary, the voltage steps up, and if there are fewer, it steps down. When incoming electricity passes through loops on the upstream end of the transformer, that forms a magnetic field in the core. That field induces a voltage in the loops on the other side of the device, which is what allows the energy to continue flowing at a new voltage. This process is called magnetic induction. Transformers form the backbone of electricity transmission and distribution, says Srdjan Lukic, a professor of electrical and computer engineering at North Carolina State University. In addition to doing the job of stepping up and down voltage, transformers also often provide what’s called galvanic isolation, meaning there’s no direct electrical path flowing through them. That can help limit disturbances from moving through the grid, improving safety. The problem is that today’s transformers are big and bulky, and they’re also difficult and expensive to replace, Lukic says. A solid option Solid-state transformers essentially add so-called power electronics—semiconductors that convert and control electricity to the typical blueprint of today’s transformers. While a conventional transformer operates at the frequency of the part of the grid it’s connected to, a solid-state transformer actually controls the frequency of the current. Typically, a solid-state transformer first converts incoming AC power from the grid to DC power, and then to a higher-frequency AC power using switches made of a semiconductor like silicon carbide.  From there, a magnetic component changes the voltage. Because the advanced semiconductors allow transformers to exert so much control over the frequency and stability of the current, the magnetic core can be much smaller than in a conventional transformer. Some come in at roughly one-tenth the size but have the same capacity, Lukic says. Finally, additional power electronics convert the electricity from high-frequency AC to its required form—generally back to DC power and finally to lower-frequency AC. Today, stepping voltage up and down is many transformers’ only job. But solid-state transformers can do much more. They absorb the jobs of devices like rectifiers and inverters (which convert AC to DC power and vice versa), shrinking the total footprint of the electrical equipment and potentially cutting the amount of power lost in the system. In August, the New York Power Authority and the Electric Power Research Institute (EPRI) tested a one-megawatt solid-state transformer under realistic utility conditions at EPRI’s test site. The device was built in Lukic’s lab, and it was the first such test for a transformer of that capacity, he says.  The prototype was roughly 98% efficient—meaning it lost only 2% of the energy fed in. An equivalent conventional transformer and rectifier, by comparison, would be about 96% efficient together, Lukic says. A difference of 2% might seem small, but it is significant, since it means the solid-state transformer could cut power losses from conventional equipment in half, Lukic says.  One major goal of the project was to help utilities gain more confidence in these new components, Lukic says. That’s crucial, since these devices could be used on the power grid in the future. In the near term, solid-state transformers could be used within

arXiv:2610.34572v1 Announce Type: new Abstract: The largest open German text datasets are built from broad web crawls, which provide scale but limited coverage of specific domains. We introduce RefinedGAW, a processed and annotated resource derived from 26 semi-annual crawls of the German Academic Web, totaling 122 million documents and 314 billion tokens, with full crawl and capture history of every text. Documents labeled as academic research account for 84% of German RefinedGAW tokens across HTML and PDF. At 87.6 billion tokens, this exceeds the 64.9 billion tokens in the German subsets of FineWeb2 and FinePDFs, despite their almost five-times-larger combined size. RefinedGAW also contains substantial amounts of educational, math-heavy, and expert-oriented content with minimal commercial bias. Across HTML and PDF, 88% of German RefinedGAW documents have no near-duplicate candidate match in the three evaluated reference datasets, supporting complementary coverage. We release a queryable metadata index for fine-grained data selection and a rehydration tool for retrieving and processing source documents.

Consider what passes for success in technology policy. An AI company promises that its chatbot will not encourage suicide. A social media platform makes it more difficult for children to buy fentanyl. These are important protections, and they should have existed all along. But companies can clear these low bars while continuing to make products that leave young people dependent on simulated friendships or trapped in feeds built to keep them scrolling. We have historically allowed the debate over technology to be defined by the worst thing a product might do, with remarkably little discussion of the insidious damage it does every day. As we transition into an era defined by powerful AI products, we should demand that the tech industry do more than merely address catastrophic harms. We should take measures to ensure that its products don’t erode how we think, learn, and interact with one another.  A teenager who develops an unhealthy attachment to an AI companion may never appear in a lawsuit or a newspaper, except in the most egregious cases. Neither will the student who begins relying on a chatbot to write every paper, or the young person who measures their life against a social media feed engineered to provoke envy and keep them watching. A product can meet every narrow safety requirement and still leave our entire culture worse off in ways that don’t always make headlines. A narrow definition of success defined around safety is politically convenient for the technology industry, and companies actively promote such definitions. Taking steps to prevent spectacular disasters is good for business: It can mean fewer lawsuits and less pressure for serious regulation. Confronting the everyday consequences of dependence would require something considerably more threatening to their bottom line. It might mean asking companies to add more friction to their products or actively limit engagement or features that lead to negative outcomes, such as addictive attention-hacking algorithms. Technology giants have instead worked to shape legislation around the harms they are already under direct pressure to address. This year, for example, Google backed bills to regulate AI chatbots in several states. The legislation mandated basic protections against sexual content and promotion of self-harm that tech companies should have established regardless, but with broad carve-outs that could exempt some of the most popular services. Passing these bills allowed legislators to claim a safety win while efforts to promote stronger legislation—laws designed to prevent children from becoming emotionally dependent on AI—failed. This playbook worked in states like Nebraska, producing legislation heavily influenced by industry at the expense of bills that would have done more to hold companies accountable. We have been through a version of this with social media. Globally, people who use the internet now spend an average of more than two hours a day on social media platforms—time that has been quietly taken from ordinary life and handed over to companies whose profits depend on keeping us there. No one, outside perhaps a few tech CEOs, wanted these algorithms to occupy such a large portion of our waking hours, and their cultural engineering can actually contribute to violence on a devastating scale. Meta’s platforms have been cited for contributing to the Rohingya genocide in Myanmar, anti-Muslim riots in Sri Lanka, and alleged calls for violence during Ethiopia’s Tigray war. The same incentives operate in the feeds that shape young Americans’ understanding of politics, their peers, and the wider world. AI is extending this logic into parts of life that social media could influence only from a distance. It offers to write our thoughts for us and stand in for the people we might otherwise turn to. A third of teens now choose to discuss serious matters with AI companions instead of other people. MIT researchers found that young people using ChatGPT to write essays showed weaker brain connectivity during the task, and their evidence indicated that “cognitive debt” persisted even after the task was complete. There is plenty we still need to learn about the long-term effects. We already have reason to ask whether technology marketed as making us more capable is eroding the habits and relationships that help us develop capabilities in the first place. Follow this way of thinking to its logical endpoint and you get a world of perfectly sanitized entertainment, with chatbots ready to spare us the difficulty of thinking or of dealing with another person’s feelings (or even our own). But anyone who has struggled to write something worth reading, or repair a damaged friendship, knows how much gets lost in that bargain. We build judgment through effort and friction, and we learn to live with other people by confronting the fact that they have needs and opinions of their own.  We should demand safety as a minimum, yes—and then demand considerably more. Technology companies and school districts should have to show beyond a reasonable doubt that AI improves learning and critical thinking before making it a fixture in classrooms. Companies should be prohibited from designing companions that pretend to be conscious or profess love to users, and they should face independent scrutiny of how their systems affect relationships and people’s ability to think for themselves. The companies that have inserted their products into nearly every part of modern life have earned obligations far greater than avoiding the next terrible headline. Sam Hiner is the cofounder and executive director of the Young People’s Alliance, where he coordinated a coalition of advocates to pass laws regulating AI companions in eight states in 2026—laws such as California’s SB1119. Sam graduated from the University of North Carolina atChapel Hill in 2025 and was named to the 2026 Forbes 30 Under 30 Social Impact list. Emma Leiken is executive director of the Responsible Technology Youth Power Fund at Hopelab, which has raised more than $9 million for youth-led tech accountability work, and teaches technology policy at the Columbia School of International and Public Affairs. She was previously TikTok’s global head of youth safety and well-being policy and worked

arXiv:2610.32144v1 Announce Type: new Abstract: When a language model is asked whether its own answer is correct, three languages are involved: that of the question, that requested for the answer, and that of the verifier instruction asking for the judgment. Multilingual confidence evaluations seldom separate the three. We vary them independently in a six-language factorial design with ten open-weight models (3B-14B) on LSQA, MMMLU and TriviaQA, holding each answer fixed across verifier languages and measuring confidence with a first-token P(True) probe. The three languages move different parts of the evaluation: which of them governs accuracy depends on the benchmark, matching the response language to the question or verifier language sharpens discrimination but not calibration, and the verifier language alone shifts calibration error by up to 0.04 without changing accuracy. The scorer matters as well: an unvalidated judge would have shrunk the MMMLU English-Hindi accuracy gap from 0.198 to 0.038. Multilingual confidence results, model rankings included, are therefore comparable only when all three languages are reported and the scorer is validated.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. After weeks of frenzied speculation, Anthropic has filed its IPO prospectus. The 335-page document (100 pages or so are focused on risks) lays out the company’s unique view of the technology it is building and the ways it believes it will change the world.  But Anthropic has been honing its storytelling skills for years. In fact, its public messaging is a master class in narrative capture. Earlier this month, Jack Clark, the company’s cofounder and head of policy, gave a speech at the IMF meeting in Bangkok in which he announced to the assembled economists, bankers, and lawmakers that the singularity is already here. Introduced by the science fiction writer and futurist Vernor Vinge around 40 years ago (and long debated on the nerdier fringes of the internet), the singularity is supposed to be the point at which technology gets so good that it outstrips the abilities of its makers. Humans are left in the dust, the idea goes, no longer able to compete with—or even understand—the fruits of their former creation. And this fictional world that many of us have imagined since we were kids? Apparently we’re already living in it: “I believe that this year we have gained convincing evidence that it has now begun,” Clark told his audience. The company tells a vivid (and in many ways scary) story about a future it portrays as inevitable but one that has not yet happened, often co-opted from well-worn science fiction memes. It presents its technology as mysterious and dangerous, and insists that it is the only company trustworthy enough to tame it. (What investor wouldn’t want in on that?) Take another example. Last year, Chris Olah, another Anthropic cofounder, was invited to address the Vatican, where he told Pope Leo XIV and other religious scholars and officials what his company was learning about its technology. “I will be honest: We keep finding things that are mysterious, even unsettling,” he said. “We find evidence of introspection. We find internal states that functionally mirror joy, satisfaction, fear, grief, and unease.” Never mind that many researchers would scoff at such an overt suggestion that Anthropic’s software has humanlike inner experience. When so little seems certain, what Anthropic (and its fellow frontier AI companies) say is snatched up and clung to like pieces of a sinking ship. I’m not saying they’re necessarily wrong about any of this. (That’s for another day.) But in a world where nobody knows what’s coming, companies like Anthropic get to fill the vacuum.  It’s worth taking a moment to ask: What is Anthropic? It might sound like a starter question on a family quiz show, but it’s far harder to answer than you might think. The question has been on my mind since reading an exchange on X last week kicked off by Nathan Calvin, who is chief strategy officer at Encode AI, an organization based in Washington, DC, that advocates for regulation. Calvin was unhappy about the legislation that Anthropic proposed in a draft bill it circulated in secret to US lawmakers (which was then leaked).   Anthropic wants to be seen as the good guy, Calvin said. But why should we trust it? “What is Anthropic?” he asked. Here’s one answer: Anthropic is a company whose IPO values it at more than $1.5 trillion, netting its cofounders, including Clark and Olah, vast wealth.  But Anthropic has always insisted it is more than that. As Calvin noted: Anthropic might instead be considered an AI safety organization focused on developing powerful technology that is reliable and (almost) risk-free. Or it might be considered a group of people hell-bent on creating a technology with superhuman capabilities before anyone else—maybe because they believe they’re the most responsible people to do so, or maybe because of their egos. In fact, Anthropic is all of these things. As CEO Dario Amodei put it in a reply to Calvin’s post: “Our strategy has always been what I’ve called the ‘race to the top’—by showing how it’s possible to build the technology responsibly, we can raise the bar for the entire industry. The forces that drive the race to the top are hugely more effective if the company practicing them is at the frontier; that’s one lesson I’ve learned repeatedly. And looking at that strategy as a whole, I’m proud of our track record and stand by it.” Amodei makes it sound as if he’s got it all tied up, but playing multiple roles is hard. There are huge tensions between the priorities of an AI safety organization and a profit-driven megacorp, for example. (Amodei admits in his post that he has made mistakes.) Ultimately, the stories Anthropic tells itself should help us understand the stories it tells us. They should help us figure out how much to trust it to self-regulate or whether we should be forcing it to give external safety testers access to its models on our terms rather than theirs. They should help us give it the benefit of the doubt (or not) when what looks like a self-serving piece of legislation comes to light. But I think the stories reveal a multiheaded hydra. Anthropic is many things to many people, and I’m not sure that makes it more trustworthy. In many ways, a good old-fashioned technology company—without the science fiction role-play, without the woo-woo—would be far easier to read.