Every few centuries, changes in how information moves reshape how societies govern themselves. The printing press spread vernacular literacy, helping give rise to the Reformation and, eventually, representative government. The telegraph made it possible to administer vast nations like the US, accelerating the growth of the modern bureaucratic state. Broadcast media created shared national audiences, which in turn fueled mass democracy. We are now in the early stages of another such shift. Faster than many realize, AI is becoming the primary interface through which we form beliefs and participate in democratic self-governance. If left unchecked, this shift could further strain America’s already fragile institutions. But it could also help address long-standing problems, like lagging civic engagement and deepening polarization. What happens next depends on design choices that are already being made, whether we know it or not. Start with what might be called the epistemic layer—how we come to know things. People are increasingly relying on AI to know what is true, what is happening, and whom to trust. Search is already substantially AI-mediated. The next generation of AI assistants will synthesize information, frame it, and present it with authority. For a growing number of people, asking an AI will become the default way to form views on a candidate, a policy, or a public figure. Whoever controls what these models say therefore has increasing influence over what people believe.  Technology has always shaped the way citizens interact with information. But a new problem will soon arise in the form of personal AI agents, which can change not only how people receive information but how they act on it. These systems will conduct research, draft communications, highlight causes, and lobby on a user’s behalf. They will inform decisions such as how to vote on a ballot measure, which organizations are worth supporting, or how to respond to a government notice. They will, in a meaningful sense, begin to mediate the relationship between individuals and the institutions that govern them.
We’ve already seen with social media what happens when algorithms optimize for engagement over understanding. Platforms do not need to have an explicit political agenda to produce polarization and radicalization. An agent that knows your preferences and your anxieties—one shaped to keep you engaged—poses the same risks. And in this case the risks may be even more difficult to detect, because an agent presents itself as your advocate. It speaks for you, acts on your behalf, and may earn trust precisely through that intimacy. Now zoom out to the collective. AI agents and humans could soon participate in the same forums, where it may be impossible to tell them apart. Even if every individual AI agent were well-designed and aligned with its user’s interests, the interactions of millions of agents could produce outcomes that no individual wanted or chose. For example, research shows that agents displaying no individual bias can still generate collective biases at scale. And setting aside what agents do to each other, there is what they do for their users. A public sphere in which everyone has a personalized agent attuned to their existing views is not, in aggregate, a public sphere at all. It is a collection of private worlds, each internally coherent but collectively inhospitable to the kind of shared deliberation that democracy requires.
Taken together, these three transformations—in how we know, how we act, and how we engage in collective governance—amount to a fundamental change in the texture of citizenship. In the near future, people will form their political views through AI filters, exercise their civic agency through AI agents, and participate in institutions and public discussions that are themselves shaped by the interactions of millions of such agents. Today’s democracy is not ready for this. Our institutions were designed for a world in which power was exercised visibly, information traveled slowly enough to be contested, and reality felt more shared, if imperfectly. All of this was already fraying long before generative AI arrived. And yet this need not be a story of decline. Avoiding that outcome requires us to design for something better. On the informational layer, AI companies must ramp up existing efforts to ensure that models’ outputs are truthful. They should also explore some promising early findings that AI models can help reduce polarization. A recent field evaluation of AI-generated fact checks on X found that people with a variety of political viewpoints deemed AI-written notes more helpful than human-written ones. The paper is yet to be peer-reviewed, but that is a potentially revolutionary finding: AI-assisted fact-checking may be able to achieve the kind of cross-partisan credibility that has eluded most manual human efforts. Greater understanding of and transparency about how models make these assertions and prioritize sources in the process could help build further public trust. On the agentic layer, we need ways to evaluate whether AI agents faithfully represent their users. An agent must never have an agenda of its own or misrepresent its user’s views—a technically daunting requirement in domains where users may have not explicitly stated any preferences. But faithful representation also cannot become an accessory to motivated reasoning. An agent that refuses to present uncomfortable information, that shields its user from ever questioning prior beliefs or fails to adjust to a change of heart, is not acting in the person’s best interest. Finally, on the institutional level, policymakers should hurry to harness AI’s potential to make governance more responsive and legitimate. Several states and localities are already using AI-mediated platforms to conduct democratic deliberation at scale, building on research showing that AI mediators can help citizens find common ground. As agents become increasingly common participants in public input processes—and there is already evidence that bots are skewing those processes—identity verification for both humans and their agentic proxies must be built in from the start. What is needed is a new generation of democratic infrastructure, technological and institutional, built for the world that is actually here. Failing to design for democratic outcomes, in a domain this consequential, means designing for something else. And the history of unaccountable power does not leave much room for optimism about what that something else tends to be. Andrew Sorota and Josh Hendler lead work on AI and democracy at the Office of Eric Schmidt.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Two of the most powerful people in AI—Sam Altman and Elon Musk—began their face-off in court in Oakland, California, last week. Musk is suing OpenAI, alleging that the millions he spent to fund it around a decade ago were meant for a nonprofit, not a corporation, and that the company has reneged on that mission since.  The stakes are high—even a partial win for Musk could set OpenAI back as it reportedly plans to go public this year. But most of the attention comes from the spectacle of a feud on X now playing out in federal court. “Cringey texts, raw diary entries, and endless scheming behind the founding and growth of OpenAI are expected to come to light,” my colleague Michelle Kim wrote before it began. And the trial unfolds as the cultural backlash against AI swells; some of the signs held by protesters outside the courthouse suggest that to a significant number of people, whatever the outcome of Musk v. Altman, we all lose.   Most of us have had to observe the trial from afar, but Michelle, who also happens to be a lawyer, has been in court each day. I caught up with her to learn what’s unfolded thus far and what might come next.
Can you give us the overview of what this case is actually about? What exactly is being decided, and who is favored right now? Elon Musk is arguing that Sam Altman and OpenAI president Greg Brockman have breached the company’s charitable trust by effectively converting OpenAI into a for-profit company. Musk alleges that is not what they promised him in the company’s early days. He has asked for several remedies, like a crazy amount of damages and removing Sam Altman. But the main remedy he wants is unwinding OpenAI’s restructuring. [In October 2025 OpenAI struck deals with the attorneys general of California and Delaware that would essentially allow its nonprofit portion to have less day-to-day control of OpenAI. It’s a compromise from what OpenAI originally proposed, but Musk still wants to stop it.] 
OpenAI argues that Elon Musk actually agreed to have the company operate a for-profit arm, because he knew building AI is very expensive. So it’s about proving what Musk knew, what he didn’t know, and whether he really was deceived by Altman and Brockman. There’s a big debate about when exactly Musk found out about this alleged misconduct. Musk founded OpenAI with Altman and Brockman in 2015, and he brought the suit in 2024. There’s a statute of limitations for charitable trust claims; you need to have brought a claim within three to four years after you find out about the alleged misconduct. So Musk tries to paint a picture that back in the day he was a little suspicious, but that it was really only in 2022 that he realized OpenAI was no longer committed to its original charitable mission, and that he had been scammed. It’s only the first week of trial, but I’m not sure Musk has proved this to the judge and jury. What were some standout moments thus far? At one point one of Elon Musk’s lawyers said, “We could all die as a result of AI.” I think a lot of the people in the room were really shaken by this comment, and the judge told Musk’s lawyer: You talk about all these safety risks that OpenAI has when building AI, but Musk is also creating a company that’s in the same exact space. She basically said, I’m sure there’s plenty of people who also don’t want to put the future of humanity in Elon Musk’s hands.  And then the lawyers just kept going on and on about the catastrophic risks of AI and whether Elon Musk or OpenAI was in the better position to steward AI safety. And the judge sort of snapped. She said very sternly that this trial was not about whether or not artificial intelligence has damaged humanity. And I thought that was a really striking standout moment of the trial that pointed at how even though it is technically just about whether Elon Musk was really deceived by OpenAI, it’s also become a huge discussion about AI safety and some of the practices that the labs are engaging in when building AI.  Can you give us a look behind the curtain at how getting into this trial works? There are tons of reporters. This is a very high-profile suit, so I have to wake up around 4:30 a.m. and show up to the Oakland courthouse at 6 a.m. sharp to get in line. And on some days, even 6 a.m. doesn’t get you into the courtroom. There are lots of photographers in front of the courthouse, especially on days when you know Musk or Altman and Brockman are present. And there’s also some concerned citizens who want to watch the trial. I usually have to wait, like, two hours in line to get in to be one of the 30 people who claim the unreserved seats in the courtroom.  What has it felt like to see Elon Musk testify? How would you describe his demeanor?

He shows up in a crisp black suit. He can be this inflammatory person on X, but in the courtroom, he is calm, cool, collected, and looks very comfortable. He has been in a lot of lawsuits. He knows how to talk to the jury and how to present himself in front of them and the judge. He’s also cracking jokes with his lawyer and even the opposing party’s lawyer and the judge.  And he can be witty. There was this one moment when OpenAI’s lawyer was asking Musk a question and sort of fed him an answer. And Musk said “That’s not a leading question, that’s a leading answer.” The judge intervened and said, “You’re not a lawyer, Elon.” And then he was like, “Well, I did take Law 101.” That said, he does get flustered and uncomfortable when OpenAI’s lawyer asks tough, piercing questions. Which he’s been doing. What are the biggest things we’ve learned that weren’t clear in the earlier phases of this case? On the fourth day of the trial, Musk admitted during cross-examination that xAI distills OpenAI’s models to train its own models, which was shocking. Musk followed up by saying that this is standard practice among all the labs now and that xAI wasn’t doing anything beyond what others were already doing. But a lot of the journalists started typing away at their laptops as soon as Musk made this comment.  I also learned that there’s just so much scheming among Big Tech executives. You know about it vaguely, but to hear firsthand accounts and read their emails and text messages is fascinating.  For example, there was a text message between Musk and Mark Zuckerberg of Meta, where they’re kind of teaming up to stop OpenAI’s restructuring. They’re even trying to make a bid to buy all the assets of OpenAI’s nonprofit. The level of scheming that goes on among these executives is mind-blowing. What happens next?
OpenAI’s president, Greg Brockman, who was meticulously taking notes during some of Elon Musk’s testimony, is expected to testify next week. And Stuart Russell, a computer scientist at UC Berkeley, will testify about AI safety. I’m expecting that to open the floodgates to this crazy discussion about who can be trusted to build AI.  A bunch of other high-profile people are expected to testify, like former OpenAI chief scientist Ilya Sutskever, former CTO Mira Murati, and Microsoft CEO Satya Nadella. 
The trial is supposed to last around three weeks. The nine jurors will deliver an advisory verdict that guides the judge on how to decide Musk’s claims against OpenAI. The judge doesn’t have to listen to the jury and can decide however she wants. If she decides OpenAI is liable, then she’ll decide what sort of remedies are appropriate.  MIT Technology Review will have ongoing coverage of Musk v. Altman until its conclusion. Follow @techreview or @michelletomkim on X for up-to-the-minute reporting.

In partnership withMayo Clinic Platform The AI market is full of big promises of grand transformation. Health care is a prime target for those promises, beset as it is by financial pressures, labor shortages, and the growing burden of caring for an aging population. AI developers are targeting functions that vary widely, from curing cancer and performing surgery to streamlining routine administrative tasks. The opportunity is genuine, but execution can be difficult. Numerous software vendors have tried to “fix” health care challenges but failed because they misunderstood the environment. “Health care is very complex,” says Steve Bethke, vice president of the solution developer market for Mayo Clinic Platform, which supports the buildout and deployment of digital solutions for health care companies through data-based insights and expert validation. “Solution developers must have a deep focus on clinical and technical capabilities, and then align their solutions to the relevant business impacts. If they miss any dimension, the solution will not be adopted or drive value.” AI applications for health care are proliferating rapidly. The U.S. Food and Drug Administration has approved more than 1,300 AI-enabled medical devices, mostly for interpreting diagnostic images. More than half of these were approved in the past three years, with the earliest dating as far back as 1995. Non-radiological applications carry out tasks as diverse as tracking sleep apnea, analyzing heart rhythms, and planning orthopedic surgeries. AI applications that do not count as medical devices— for example, those that handle scheduling and administrative tasks—are more difficult to track but are also rapidly increasing. AI can help coordinate complex tasks and workflows that are often conventionally managed by whiteboards and sticky notes. Such functions may well outstrip clinical uses in their impact on health systems. A recent survey of technology leaders found that 72% said their top priority for AI was reducing caregiver burden and improving caregiver satisfaction, while over half (53%) cited workflow efficiency and productivity.
Any health care-related application can potentially impact patient care, whether directly or indirectly, and AI apps that are poorly designed or inadequately trained and validated can put patients at risk. Providers recognize that risk: In the same survey, 77% said immature AI tools are a significant barrier to adoption. Regulators and lawmakers are also keeping an eye on the risks as development and adoption burgeon, though the U.S. regulatory picture is still in flux, as a 2024 report to Congress on AI in health care observes. To tackle some of the technical challenges, many health care providers are partnering with application developers to build AI solutions. In a recent study, McKinsey found that 61% of health care organizations intend to pursue partnerships with third-party vendors to develop customized generative AI solutions as a primary strategy as opposed to building them in-house or buying off-the-shelf products.
But health care-specific AI applications must also be tailored to the nuanced clinical needs of medical providers as well as the complex business and regulatory considerations of the wider sector. This is where developers can benefit from working with a partner with a deep understanding of the health care environment to tailor applications to what providers want and need most. Doing so helps to position AI products for maximum impact and value, avoiding the pitfalls unique to the health care environment. Download the report. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

In the first week of the landmark trial between Elon Musk and OpenAI, Musk took the stand in a crisp black suit and tie and argued that OpenAI CEO Sam Altman and president Greg Brockman had deceived him into bankrolling the company. Along the way, he warned that AI could destroy us all and sat through revelations that he had poached OpenAI employees for his own companies. He even confessed, to some audible gasps in the courtroom, that his own AI company, xAI, which makes the chatbot Grok, uses OpenAI’s models to train its own.  The federal courthouse in Oakland, California, was packed with armies of lawyers carrying boxes of exhibits, journalists typing away at their laptops, and a handful of concerned OpenAI employees. Outside, protesters lined the streets, carrying signs urging people to quit ChatGPT, boycott Tesla, or both. Musk looked calm and comfortable, slipping in the occasional quip in his distinct South African accent. But he also was full of remorse.  “I was a fool who provided them free funding to create a startup,” Musk told the jury. He said when he cofounded OpenAI in 2015 with Altman and Brockman, he was donating to a nonprofit developing AI for the benefit of humanity, not to make the executives rich. “I gave them $38 million of essentially free funding, which they then used to create what would become an $800 billion company,” he said. Musk is asking the court to remove Altman and Brockman from their roles and to unwind the restructuring that allowed OpenAI to operate a for-profit subsidiary. The outcome of the trial could upend OpenAI’s race toward an IPO at a valuation approaching $1 trillion. Meanwhile, xAI is expected to go public as a part of Musk’s rocket company SpaceX as early as June, at a target valuation of $1.75 trillion.
This week’s testimony revolved around a central question of the trial: why Musk is suing OpenAI. Musk argued he was trying to save OpenAI’s mission to develop AI safely by restoring the company to its original nonprofit structure. OpenAI’s lawyer, William Savitt, who once represented Musk and his electric-car company Tesla, countered that Musk was “never committed to OpenAI being a nonprofit” and instead was suing to undermine his competitor.  Who is the steward of AI safety? During his direct examination early in the week, Musk painted himself as a longtime advocate of AI safety. He said he cofounded OpenAI to create a “counterbalance to Google,” which was leading the AI race at the time. He said that when he asked Google cofounder Larry Page what happens if AI tries to wipe out humanity, Page told him, “That will be fine as long as artificial intelligence survives.” 
“The worst-case scenario is a Terminator situation where AI kills us all,” Musk later told the jury. Savitt stood at the lectern and argued that Musk was not a “paladin of safety and regulation.” As he cross-examined Musk in his sharp, surgical cadence, Savitt pointed out that xAI sued the state of Colorado in April over an AI law designed to prevent algorithmic discrimination.  Musk’s lawyer, Steven Molo, sprang to his feet to object. He asked the judge if he, too, could weigh in on ChatGPT’s safety record.  The lawyers then entered a heated debate about who was the true guardian of AI safety.  The sparring continued the next morning. “We all could die as a result of artificial intelligence!” said Molo, suggesting that OpenAI could not be trusted to build AI safely. “Despite these risks, your client is creating a company that’s in the exact space,” Judge Yvonne Gonzalez Rogers said sternly, referring to xAI. “I suspect there’s plenty of people who don’t want to put the future of humanity in Mr. Musk’s hands.” When the lawyers began talking over each other, the judge snapped. “This is not a trial on whether or not artificial intelligence has damaged humanity,” she said.  When did Musk think he was being duped? As Savitt continued to cross-examine Musk, he pressed on the idea that Musk had never been committed to keeping OpenAI a nonprofit. He also claimed that Musk waited too long to sue OpenAI, filing after the statute of limitations ran out. 

Musk explained why he sued in 2024 rather than earlier, describing “three phases” in his views of OpenAI. In phase one, he was “enthusiastically supportive” of the company.” In phase two, “I started to lose confidence that they were telling me the truth,” he said. In phase three, “I’m sure they’re looting the nonprofit.”  In 2017, Musk and other OpenAI cofounders discussed creating a for-profit subsidiary to raise enough capital to build artificial general intelligence—powerful AI that can compete with humans on most cognitive tasks. Musk wanted a majority interest in the subsidiary and the right to choose a majority of the board members. He also pitched having Tesla acquire OpenAI. (He left OpenAI in 2018.) “I was not opposed to there being a small for-profit that provides funding to the nonprofit,” he told the jury, “as long as the tail didn’t wag the dog.”  But it was only in late 2022, Musk testified, that he “lost trust in Altman” and his commitment to keeping the company a nonprofit. The key moment came, he said, when he learned that Microsoft would invest $10 billion in OpenAI.  “I texted Sam Altman, ‘What the hell is going on? This is a bait and switch,’” he told the jury. Microsoft would give $10 billion only if it expected “a very big financial return,” he said. Is Musk just trying to kill competition? But Savitt argued that Musk was really suing to undermine OpenAI as a competitor to his empire of tech companies. While he was on the board of OpenAI, Musk was also running Tesla and his brain-implant company, Neuralink. He founded xAI in 2023. Savitt pulled up an email that Musk had sent to a Tesla vice president in 2017 after hiring Andrej Karpathy, a founding member of OpenAI, to work at Tesla.“The OpenAI guys are gonna want to kill me. But it had to be done,” he wrote. When asked about it, Musk was flustered. He claimed Karpathy had already decided to leave OpenAI when he recruited him to work at Tesla. “I believe it’s a free world,” he said.Savitt pulled up another email that Musk sent to a cofounder at Neuralink in 2017. He wrote that they could “hire independently or directly from OpenAI.” When pressed about it, he sounded frazzled. “It’s a free country,” he said. “I can’t restrict their ability to hire people from other companies.” 
Savitt also pointed out that Tesla, SpaceX, Neuralink, and X were socially beneficial for-profit companies, like OpenAI. He stressed that xAI was also a closed-source, for-profit company. But Musk claimed that xAI was not a real competitor to OpenAI. “We’re not currently tracking to reach AGI first,” he told the jury. 
In fact, Musk admitted that xAI uses OpenAI’s technology. In response to Savitt’s relentless questioning, he said xAI “partly” distills OpenAI’s models. Some people in the courtroom gasped.  Distillation is a technique where a smaller AI model is trained to mimic the behavior of larger, more capable models, so it can run faster and more cheaply while performing nearly as well. But OpenAI and other AI companies have pushed back against the practice. In February, OpenAI accused the Chinese AI company DeepSeek of distilling its AI models. In August 2025, Wired reported that Anthropic had blocked OpenAI’s access to Claude for violating the company’s terms of service, which prohibit, among other things, reverse-engineering its services and building competing products.  “It is standard practice to use other AIs to validate your AI,” argued Musk.Next week, Stuart Russell, a computer scientist at UC Berkeley, will testify about AI safety. Brockman, who has been taking notes during Musk’s testimony, will also testify. This story is part of MIT Technology Review’s ongoing coverage of the Musk v. Altman trial. Follow @techreview or @michelletomkim on X for up-to-the-minute reporting.

Presented byGC Cybersecurity Cybersecurity was already under strain before AI entered the stack. Now, as AI expands the attack surface and adds new complexity, the limits of legacy approaches are becoming harder to ignore. This session from MIT Technology Review’s EmTech AI conference explores why security must be rethought with AI at its core, not layered on after the fact. About the speaker Tarique Mustafa, Cofounder, CEO, and CTO, GC Cybersecurity Tarique Mustafa is Cofounder and CEO/CTO of two AI-powered cybersecurity companies: GCCybersecurity, Inc. and its data compliance spinout, Chorology, Inc. A prolific inventor and internationally recognized authority in knowledge representation, inference calculus, and AI planning, Tarique has spent his career applying autonomously collaborative AI to solve complex, ultra-high-scale challenges across cybersecurity, data security, and compliance — with deep expertise spanning Data Classification, DLP, and DSPM industries. His groundbreaking innovations and multiple USPTO patents have earned him global recognition, including frequent invitations to deliver keynote addresses at prestigious international security conferences and forums.
At GCCybersecurity, Tarique architected the core AI algorithms powering the company’s 4th and 5th generation fully autonomous data leak protection and exfiltration platform — among the most advanced platform of its kind. Prior to founding GCCybersecurity and Chorology, he served as founding CEO/CTO of NexTier Networks, a Silicon Valley provider of award-winning Data Leak Prevention solutions. With over 20 years of technical leadership experience, Tarique has held senior roles at Symantec, DHL Airways IT, MCI WorldCom, EDS, Andes Networks, and Nevis Networks, where he served as Principal Architect and built industry-leading security products leveraging next-generation security monitoring, event correlation, IDS/IPS, and SSL/IPSec technologies. Tarique holds multiple approved and pending patents with the USPTO and has authored numerous research publications spanning Information & Data Security, Computer & Network Security, Software Architecture, Database Technologies, and Artificial Intelligence. A recipient of the prestigious Rotary International Scholarship for doctoral studies in Computer Science at the University of Southern California (USC), Tarique also holds master’s degrees in engineering and computer science from USC, and a bachelor’s degree in mechanical engineering from NED University of Engineering & Technology.

Presented by Companies are taking control of their own data to tailor AI for their needs. The challenge lies in balancing ownership with the safe, trusted flow of high‑quality data needed to power reliable insights. This conversation from MIT Technology Review’s EmTech AI conference examines how AI factories unlock new levels of scale, sustainability, and governance—positioning data control as a strategic imperative for governments and enterprises. About the speakers Chris Davidson, Vice President, HPC & AI Customer Solutions, HPE Chris Davidson is Vice President of HPC & AI Customer Solutions at Hewlett Packard Enterprise. He leads HPE’s global strategy for AI Factory solutions and Sovereign AI, working with governments, enterprises, and research institutions to build secure, scalable national- and enterprise-grade AI capabilities. He also directs Product Management and Performance Engineering across HPE’s HPC and AI portfolio, including large-model training platforms and Cray exascale systems. His teams define product strategy, performance architecture, and deployment models that position HPE at the forefront of high-performance and AI computing.
During his nine years at HPE, Chris has led key initiatives across Performance Engineering, AI Cloud, and Professional Services, shaping how HPE delivers optimized, cloud-native, and globally deployed high-performance systems. He previously held technical and leadership roles in the biotech and medical diagnostics sectors. Chris holds an M.B.A. in Entrepreneurship and Finance and a B.S. in Biology from Loyola University Chicago. Arjun Shankar, Division Director, National Center for Computational Science, Oak Ridge National Laboratory Mallikarjun (Arjun) Shankar is the Division Director for the National Center for Computational Science at the Oak Ridge National Laboratory. His research focuses on the interdisciplinary bridge between computer science and large-scale scientific discovery campaigns that rely on scalable computing and data science. He is a joint faculty appointee at the University of Tennessee’s Bredesen Center, a senior member of the IEEE and a senior member of the ACM.