This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. 4 technologies that didn’t make our 2026 breakthroughs list If you’re a longtime reader, you probably know that our newsroom selects 10 breakthroughs every year that we think will define the future. This group exercise is mostly fun and always engrossing, with plenty of lively discussion along the way, but at times it can also be quite difficult.   The 2026 list will come out on January 12—so stay tuned. In the meantime, we wanted to share some of the technologies from this year’s reject pile, as a window into our decision-making process. These four technologies won’t be on our 2026 list of breakthroughs, but all were closely considered, and we think they’re worth knowing about. Read the full story to learn what they are. 
MIT Technology Review Narrated: The quest to find out how our bodies react to extreme temperatures 
Scientists hope to prevent deaths from climate change, but heat and cold are more complicated than we thought. Researchers around the world are revising rules about when extremes veer from uncomfortable to deadly. Their findings change how we should think about the limits of hot and cold—and how to survive in a new world.  This is our latest story to be turned into a MIT Technology Review Narrated podcast, which we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 A CDC panel voted to recommend delaying the hepatitis B vaccine for babiesOverturning a 30-year policy that has contributed to a huge decline in the virus. (STAT)+ Why childhood vaccines are a public health success story. (MIT Technology Review) 2 Critical climate risks are growing across the Arab region Drought is the most immediate problem countries are having to grapple with. (Ars Technica)+ Why Tehran is running out of water. (Wired $)3 Netflix is buying Warner Bros for $83 billion If approved, it’ll be one of the most significant mergers in Hollywood history. (NBC)+ Trump says the deal “could be a problem” due to Netflix’s already huge market share. (BBC)4 The EU is fining X $140 million For failing to comply with its new Digital Services Act. (NPR)+ Elon Musk is now calling for the entire EU to be abolished. (CNBC)+ X also hit back by deleting the European Commission’s account. (Engadget) 5 AI slop is ruining RedditModerators are getting tired of fighting the rising tide of nonsense. (Wired $)+ How AI and Wikipedia have sent vulnerable languages into a doom spiral. (MIT Technology Review)6 Scientists have deeply mixed feelings about AI toolsThey can boost researchers’ productivity, but some worry about the consequences of relying on them. (Nature $)+ ‘AI slop’ is undermining trust in papers presented at computer science gatherings. (The Guardian)+ Meet the researcher hosting a scientific conference by and for AI. (MIT Technology Review)7 Australia is about to ban under 16s from social mediaIt’s due to come into effect in two days—but teens are already trying to maneuver around it. (New Scientist $)8 AI is enshittifying the way we write 🖊️🤖And most people haven’t even noticed. (NYT $)+ AI can make you more creative—but it has limits. (MIT Technology Review)9 Tech founders are taking etiquette lessonsThe goal is to make them better at pretending to be normal. (WP $)10 Are we getting stupider? It might feel that way sometimes, but there’s little solid evidence to support it. (New Yorker $)

Quote of the day “It’s hard to be Jensen day to day. It’s almost nightmarish. He’s constantly paranoid about competition. He’s constantly paranoid about people taking Nvidia down.”  —Stephen Witt, author of ‘The Thinking Machine’, a book about Nvidia’s rise, tells the Financial Times what it’s like to be its founder and chief executive, Jensen Huang. One more thing COURTESY OF OCEANBIRD How wind tech could help decarbonize cargo shipping Inhabitants of the Marshall Islands—a chain of coral atolls in the center of the Pacific Ocean—rely on sea transportation for almost everything. For millennia they sailed largely in canoes, but much of their seafaring movement today involves big, bulky, diesel-fueled cargo ships that are heavy polluters.
They’re not alone. Cargo shipping is responsible for about 3% of the world’s annual greenhouse-­gas emissions, and that figure is currently on track to rise to 10% by 2050. The islands have been disproportionately experiencing the consequences of human-made climate change: warming waters, more frequent extreme weather, and rising sea levels. Now its residents are exploring a surprisingly traditional method of decarbonizing its fleets. Read the full story.
—Sofia Quaglia We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.) + Small daily habits can help build a life you enjoy.  + Using an air fryer to make an epic grilled cheese sandwich? OK, I’m listening…+ I’m sorry but AI does NOT get to ruin em dashes for the rest of us. + Daniel Clarke’s art is full of life and color. Check it out!

If you’re a longtime reader, you probably know that our newsroom selects 10 breakthroughs every year that we think will define the future. This group exercise is mostly fun and always engrossing, but at times it can also be quite difficult.  We collectively pitch dozens of ideas, and the editors meticulously review and debate the merits of each. We agonize over which ones might make the broadest impact, whether one is too similar to something we’ve featured in the past, and how confident we are that a recent advance will actually translate into long-term success. There is plenty of lively discussion along the way.   The 2026 list will come out on January 12—so stay tuned. In the meantime, I wanted to share some of the technologies from this year’s reject pile, as a window into our decision-making process.  These four technologies won’t be on our 2026 list of breakthroughs, but all were closely considered, and we think they’re worth knowing about. 
Male contraceptives  There are several new treatments in the pipeline for men who are sexually active and wish to prevent pregnancy—potentially providing them with an alternative to condoms or vasectomies.  Two of those treatments are now being tested in clinical trials by a company called Contraline. One is a gel that men would rub on their shoulder or upper arm once a day to suppress sperm production, and the other is a device designed to block sperm during ejaculation. (Kevin Eisenfrats, Contraline’s CEO, was recently named to our Innovators Under 35 list). A once-a-day pill is also in early-stage trials with the firm YourChoice Therapeutics. 
Though it’s exciting to see this progress, it will still take several years for any of these treatments to make their way through clinical trials—assuming all goes well. World models  World models have become the hot new thing in AI in recent months. Though they’re difficult to define, these models are generally trained on videos or spatial data and aim to produce 3D virtual worlds from simple prompts. They reflect fundamental principles, like gravity, that govern our actual world. The results could be used in game design or to make robots more capable by helping them understand their physical surroundings.  Despite some disagreements on exactly what constitutes a world model, the idea is certainly gaining momentum. Renowned AI researchers including Yann LeCun and Fei-Fei Li have launched companies to develop them, and Li’s startup World Labs released its first version last month. And Google made a huge splash with the release of its Genie 3 world model earlier this year.  Though these models are shaping up to be an exciting new frontier for AI in the year ahead, it seemed premature to deem them a breakthrough. But definitely watch this space.  Proof of personhood  Thanks to AI, it’s getting harder to know who and what is real online. It’s now possible to make hyperrealistic digital avatars of yourself or someone you know based on very little training data, using equipment many people have at home. And AI agents are being set loose across the internet to take action on people’s behalf.  All of this is creating more interest in what are known as personhood credentials, which could offer a way to verify that you are, in fact, a real human when you do something important online.  For example, we’ve reported on efforts by OpenAI, Microsoft, Harvard, and MIT to create a digital token that would serve this purpose. To get it, you’d first go to a government office or other organization and show identification. Then it’d be installed on your device and whenever you wanted to, say, log into your bank account, cryptographic protocols would verify that the token was authentic—confirming that you are the person you claim to be.  Whether or not this particular approach catches on, many of us in the newsroom agree that the future internet will need something along these lines. Right now, though, many competing identity verification projects are in various stages of development. One is World ID by Sam Altman’s startup Tools for Humanity, which uses a twist on biometrics. 

If these efforts reach critical mass—or if one emerges as the clear winner, perhaps by becoming a universal standard or being integrated into a major platform—we’ll know it’s time to revisit the idea.   The world’s oldest baby In July, senior reporter Jessica Hamzelou broke the news of a record-setting baby. The infant developed from an embryo that had been sitting in storage for more than 30 years, earning him the bizarre honorific of “oldest baby.”  This odd new record was made possible in part by advances in IVF, including safer methods of thawing frozen embryos. But perhaps the greater enabler has been the rise of “embryo adoption” agencies that pair donors with hopeful parents. People who work with these agencies are sometimes more willing to make use of decades-old embryos.  This practice could help find a home for some of the millions of leftover embryos that remain frozen in storage banks today. But since this recent achievement was brought about by changing norms as much as by any sudden technological improvements, this record didn’t quite meet our definition of a breakthrough—though it’s impressive nonetheless.

In partnership withConcentrix The past year has marked a turning point in the corporate AI conversation. After a period of eager experimentation, organizations are now confronting a more complex reality: While investment in AI has never been higher, the path from pilot to production remains elusive. Three-quarters of enterprises remain stuck in experimentation mode, despite mounting pressure to convert early tests into operational gains. “Most organizations can suffer from what we like to call PTSD, or process technology skills and data challenges,” says Shirley Hung, partner at Everest Group. “They have rigid, fragmented workflows that don’t adapt well to change, technology systems that don’t speak to each other, talent that is really immersed in low-value tasks rather than creating high impact. And they are buried in endless streams of information, but no unified fabric to tie it all together.” The central challenge, then, lies in rethinking how people, processes, and technology work together. Across industries as different as customer experience and agricultural equipment, the same pattern is emerging: Traditional organizational structures—centralized decision-making, fragmented workflows, data spread across incompatible systems—are proving too rigid to support agentic AI. To unlock value, leaders must rethink how decisions are made, how work is executed, and what humans should uniquely contribute.
“It is very important that humans continue to verify the content. And that is where you’re going to see more energy being put into,” Ryan Peterson, EVP and chief product officer at Concentrix. Much of the conversation centered on what can be described as the next major unlock: operationalizing human-AI collaboration. Rather than positioning AI as a standalone tool or a “virtual worker,” this approach reframes AI as a system-level capability that augments human judgment, accelerates execution, and reimagines work from end to end. That shift requires organizations to map the value they want to create; design workflows that blend human oversight with AI-driven automation; and build the data, governance, and security foundations that make these systems trustworthy.
“My advice would be to expect some delays because you need to make sure you secure the data,” says Heidi Hough, VP for North America aftermarket at Valmont. “As you think about commercializing or operationalizing any piece of using AI, if you start from ground zero and have governance at the forefront, I think that will help with outcomes.” Early adopters are already showing what this looks like in practice: starting with low-risk operational use cases, shaping data into tightly scoped enclaves, embedding governance into everyday decision-making, and empowering business leaders, not just technologists, to identify where AI can create measurable impact. The result is a new blueprint for AI maturity grounded in reengineering how modern enterprises operate. “Optimization is really about doing existing things better, but reimagination is about discovering entirely new things that are worth doing,” says Hung. Watch the webcast. This webcast is produced in partnership with Concentrix. 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.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. AI chatbots can sway voters better than political advertisements The news: Chatting with a politically biased AI model is more effective than political ads at nudging both Democrats and Republicans to support presidential candidates of the opposing party, new research shows. The catch: The chatbots swayed opinions by citing facts and evidence, but they were not always accurate—in fact, the researchers found, the most persuasive models said the most untrue things. The findings are the latest in an emerging body of research demonstrating the persuasive power of LLMs. They raise profound questions about how generative AI could reshape elections.  Read the full story.
—Michelle Kim 
The era of AI persuasion in elections is about to begin  —Tal Feldman is a JD candidate at Yale Law School who focuses on technology and national security. Aneesh Pappu is a PhD student and Knight-Hennessy scholar at Stanford University who focuses on agentic AI and technology policy.  The fear that elections could be overwhelmed by AI-generated realistic fake media has gone mainstream—and for good reason. But that’s only half the story. The deeper threat isn’t that AI can just imitate people—it’s that it can actively persuade people. And new research published this week shows just how powerful that persuasion can be. AI chatbots can shift voters’ views by a substantial margin, far more than traditional political advertising tends to do. In the coming years, we will see the rise of AI that can personalize arguments, test what works, and quietly reshape political views at scale. That shift—from imitation to active persuasion—should worry us deeply. Read the full story.  The ads that sell the sizzle of genetic trait discrimination —Antonio Regalado, senior editor for biomedicine

One day this fall, I watched an electronic sign outside the Broadway-Lafayette subway station in Manhattan switch seamlessly between an ad for makeup and one promoting the website Pickyourbaby.com, which promises a way for potential parents to use genetic tests to influence their baby’s traits, including eye color, hair color, and IQ. Inside the station, every surface was wrapped with more of its ads—babies on turnstiles, on staircases, on banners overhead. “Think about it. Makeup and then genetic optimization,” exulted Kian Sadeghi, the 26-year-old founder of Nucleus Genomics, the startup running the ads.  The day after the campaign launched, Sadeghi and I had briefly sparred online. He’d been on X showing off a phone app where parents can click through traits like eye color and hair color. I snapped back that all this sounded a lot like Uber Eats—another crappy, frictionless future invented by entrepreneurs, but this time you’d click for a baby. That night, I agreed to meet Sadeghi in the station under a banner that read, “IQ is 50% genetic.” Read on to see how Antonio’s conversation with Sadeghi went.  This story first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 The metaverse’s future looks murkier than everOG believer Mark Zuckerberg is planning deep cuts to the division’s budget. (Bloomberg $)+ However some of that money will be diverted toward smart glasses and wearables. (NYT $)+ Meta just managed to poach one of Apple’s top design chiefs. (Bloomberg $) 2 Kids are effectively AI’s guinea pigsAnd regulators are slowly starting to take note of the risks. (The Economist $)+ You need to talk to your kid about AI. Here are 6 things you should say. (MIT Technology Review)
3 How a group of women changed UK law on non-consensual deepfakesIt’s a big victory, and they managed to secure it with stunning speed. (The Guardian)+ But bans on deepfakes take us only so far—here’s what else we need. (MIT Technology Review)+ An AI image generator startup just leaked a huge trove of nude images. (Wired $) 4 OpenAI is acquiring an AI model training startupIts researchers have been impressed by the monitoring and de-bugging tools built by Neptune. (NBC)+ It’s not just you: the speed of AI deal-making really is accelerating. (NYT $)5 Russia has blocked Apple’s FaceTime video calling featureIt seems the Kremlin views any platform it doesn’t control as dangerous. (Reuters $)+ How Russia killed its tech industry. (MIT Technology Review)6 The trouble with AI browsersThis reviewer tested five of them and found them to be far more effort than they’re worth. (The Verge $)+ AI means the end of internet search as we’ve known it. (MIT Technology Review)7 An anti-AI activist has disappeared Sam Kirchner went AWOL after failing to show up at a scheduled court hearing, and friends are worried. (The Atlantic$)8 Taiwanese chip workers are creating a community in the Arizona desertA TSMC project to build chip factories is rapidly transforming this corner of the US. (NYT $) 9 This hearing aid has become a status symbol Rich people with hearing issues swear by a product made by startup Fortell. (Wired $)+ Apple AirPods can be a gateway hearing aid. (MIT Technology Review) 10 A plane crashed after one of its 3D-printed parts melted 🛩️🫠Just because you can do something, that doesn’t mean you should. (BBC) Quote of the day “Some people claim we can scale up current technology and get to general intelligence…I think that’s bullshit, if you’ll pardon my French.” —AI researcher Yann LeCun explains why he’s leaving Meta to set up a world-model startup, Sifted reports. 
One more thing ILLUSTRATION SOURCES: NATIONAL HUMAN GENOME RESEARCH INSTITUTE What to expect when you’re expecting an extra X or Y chromosome Sex chromosome variations, in which people have a surplus or missing X or Y, occur in as many as one in 400 births. Yet the majority of people affected don’t even know they have them, because these conditions can fly under the radar.
As more expectant parents opt for noninvasive prenatal testing in hopes of ruling out serious conditions, many of them are surprised to discover instead that their fetus has a far less severe—but far less well-known—condition. And because so many sex chromosome variations have historically gone undiagnosed, many ob-gyns are not familiar with these conditions, leaving families to navigate the unexpected news on their own. Read the full story. —Bonnie Rochman We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.) + It’s never too early to start practicing your bûche de Noëlskills for the holidays.+ Brandi Carlile, you will always be famous.+ What do bartenders get up to after finishing their Thanksgiving shift? It’s time to find out.+ Pitchfork’s controversial list of the best albums of the year is here!

One day this fall, I watched an electronic sign outside the Broadway-Lafayette subway station in Manhattan switch seamlessly between an ad for makeup and one promoting the website Pickyourbaby.com, which promises a way for potential parents to use genetic tests to influence their baby’s traits, including eye color, hair color, and IQ. Inside the station, every surface was wrapped with more of its ads—babies on turnstiles, on staircases, on banners overhead. “Think about it. Makeup and then genetic optimization,” exulted Kian Sadeghi, the 26-year-old founder of Nucleus Genomics, the startup running the ads. To his mind, one should be as accessible as the other.  Nucleus is a young, attention-seeking genetic software company that says it can analyze genetic tests on IVF embryos to score them for 2,000 traits and disease risks, letting parents pick some and reject others. This is possible because of how our DNA shapes us, sometimes powerfully. As one of the subway banners reminded the New York riders: “Height is 80% genetic.” The day after the campaign launched, Sadeghi and I had briefly sparred online. He’d been on X showing off a phone app where parents can click through traits like eye color and hair color. I snapped back that all this sounded a lot like Uber Eats—another crappy, frictionless future invented by entrepreneurs, but this time you’d click for a baby.
I agreed to meet Sadeghi that night in the station under a banner that read, “IQ is 50% genetic.” He appeared in a puffer jacket and told me the campaign would soon spread to 1,000 train cars. Not long ago, this was a secretive technology to whisper about at Silicon Valley dinner parties. But now? “Look at the stairs. The entire subway is genetic optimization. We’re bringing it mainstream,” he said. “I mean, like, we are normalizing it, right?” Normalizing what, exactly? The ability to choose embryos on the basis of predicted traits could lead to healthier people. But the traits mentioned in the subway—height and IQ—focus the public’s mind toward cosmetic choices and even naked discrimination. “I think people are going to read this and start realizing: Wow, it is now an option that I can pick. I can have a taller, smarter, healthier baby,” says Sadeghi.
Entrepreneur Kian Sadeghi stands under advertising banner in the Broadway-Lafayette subway station in Manhattan, part of a campaign called “Have Your Best Baby.”COURTESY OF THE AUTHOR Nucleus got its seed funding from Founders Fund, an investment firm known for its love of contrarian bets. And embryo scoring fits right in—it’s an unpopular concept, and professional groups say the genetic predictions aren’t reliable. So far, leading IVF clinics still refuse to offer these tests. Doctors worry, among other things, that they’ll create unrealistic parental expectations. What if little Johnny doesn’t do as well on the SAT as his embryo score predicted? The ad blitz is a way to end-run such gatekeepers: If a clinic won’t agree to order the test, would-be parents can take their business elsewhere. Another embryo testing company, Orchid, notes that high consumer demand emboldened Uber’s early incursions into regulated taxi markets. “Doctors are essentially being shoved in the direction of using it, not because they want to, but because they will lose patients if they don’t,” Orchid founder Noor Siddiqui said during an online event this past August. Ask AIWhy it matters to you?BETAHere’s why this story might matter to you, according to AI. This is a beta feature and AI hallucinates—it might get weirdTell me why it matters Sadeghi prefers to compare his startup to Airbnb. He hopes it can link customers to clinics, becoming a digital “funnel” offering a “better experience” for everyone. He notes that Nucleus ads don’t mention DNA or any details of how the scoring technique works. That’s not the point. In advertising, you sell the sizzle, not the steak. And in Nucleus’s ad copy, what sizzles is height, smarts, and light-colored eyes. It makes you wonder if the ads should be permitted. Indeed, I learned from Sadeghi that the Metropolitan Transportation Authority had objected to parts of the campaign. The metro agency, for instance, did not let Nucleus run ads saying “Have a girl” and “Have a boy,” even though it’s very easy to identify the sex of an embryo using a genetic test. The reason was an MTA policy that forbids using government-owned infrastructure to promote “invidious discrimination” against protected classes, which include race, religion and biological sex. Since 2023, New York City has also included height and weight in its anti-discrimination law, the idea being to “root out bias” related to body size in housing and in public spaces. So I’m not sure why the MTA let Nucleus declare that height is 80% genetic. (The MTA advertising department didn’t respond to questions.) Perhaps it’s because the statement is a factual claim, not an explicit call to action. But we all know what to do: Pick the tall one and leave shorty in the IVF freezer, never to be born. This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

In January 2024, the phone rang in homes all around New Hampshire. On the other end was Joe Biden’s voice, urging Democrats to “save your vote” by skipping the primary. It sounded authentic, but it wasn’t. The call was a fake, generated by artificial intelligence. Today, the technology behind that hoax looks quaint. Tools like OpenAI’s Sora now make it possible to create convincing synthetic videos with astonishing ease. AI can be used to fabricate messages from politicians and celebrities—even entire news clips—in minutes. The fear that elections could be overwhelmed by realistic fake media has gone mainstream—and for good reason. But that’s only half the story. The deeper threat isn’t that AI can just imitate people—it’s that it can actively persuade people. And new research published this week shows just how powerful that persuasion can be. In two large peer-reviewed studies, AI chatbots shifted voters’ views by a substantial margin, far more than traditional political advertising tends to do. In the coming years, we will see the rise of AI that can personalize arguments, test what works, and quietly reshape political views at scale. That shift—from imitation to active persuasion—should worry us deeply.  
The challenge is that modern AI doesn’t just copy voices or faces; it holds conversations, reads emotions, and tailors its tone to persuade. And it can now command other AIs—directing image, video, and voice models to generate the most convincing content for each target. Putting these pieces together, it’s not hard to imagine how one could build a coordinated persuasion machine. One AI might write the message, another could create the visuals, another could distribute it across platforms and watch what works. No humans required. A decade ago, mounting an effective online influence campaign typically meant deploying armies of people running fake accounts and meme farms. Now that kind of work can be automated—cheaply and invisibly. The same technology that powers customer service bots and tutoring apps can be repurposed to nudge political opinions or amplify a government’s preferred narrative. And the persuasion doesn’t have to be confined to ads or robocalls. It can be woven into the tools people already use every day—social media feeds, language learning apps, dating platforms, or even voice assistants built and sold by parties trying to influence the American public. That kind of influence could come from malicious actors using the APIs of popular AI tools people already rely on, or from entirely new apps built with the persuasion baked in from the start.
And it’s affordable. For less than a million dollars, anyone can generate personalized, conversational messages for every registered voter in America. The math isn’t complicated. Assume 10 brief exchanges per person—around 2,700 tokens of text—and price them at current rates for ChatGPT’s API. Even with a population of 174 million registered voters, the total still comes in under $1 million. The 80,000 swing voters who decided the 2016 election could be targeted for less than $3,000.  Although this is a challenge in elections across the world, the stakes for the United States are especially high, given the scale of its elections and the attention they attract from foreign actors. If the US doesn’t move fast, the next presidential election in 2028, or even the midterms in 2026, could be won by whoever automates persuasion first.  The 2028 threat  While there have been indications that the threat AI poses to elections is overblown, a growing body of research suggests the situation could be changing. Recent studies have shown that GPT-4 can exceed the persuasive capabilities of communications experts when generating statements on polarizing US political topics, and it is more persuasive than non-expert humans two-thirds of the time when debating real voters.  Two major studies published yesterday extend those findings to real election contexts in the United States, Canada, Poland, and the United Kingdom, showing that brief chatbot conversations can move voters’ attitudes by up to 10 percentage points, with US participant opinions shifting nearly four times more than it did in response to tested 2016 and 2020 political ads. And when models were explicitly optimized for persuasion, the shift soared to 25 percentage points—an almost unfathomable difference. While previously confined to well-resourced companies, modern large language models are becoming increasingly easy to use. Major AI providers like OpenAI, Anthropic, and Google wrap their frontier models in usage policies, automated safety filters, and account-level monitoring, and they do sometimes suspend users who violate those rules. But those restrictions apply only to traffic that goes through their platforms; they don’t extend to the rapidly growing ecosystem of open-source and open-weight models, which  can be downloaded by anyone with an internet connection. Though they’re usually smaller and less capable than their commercial counterparts, research has shown with careful prompting and fine-tuning, these models can now match the performance of leading commercial systems.  All this means that actors, whether well-resourced organizations or grassroots collectives, have a clear path to deploying politically persuasive AI at scale. Early demonstrations have already occurred elsewhere in the world. In India’s 2024 general election, tens of millions of dollars were reportedly spent on AI to segment voters, identify swing voters, deliver personalized messaging through robocalls and chatbots, and more. In Taiwan, officials and researchers have documented China-linked operations using generative AI to produce more subtle disinformation, ranging from deepfakes to language model outputs that are biased toward messaging approved by the Chinese Communist Party. It’s only a matter of time before this technology comes to US elections—if it hasn’t already. Foreign adversaries are well positioned to move first. China, Russia, Iran, and others already maintain networks of troll farms, bot accounts, and covert influence operators. Paired with open-source language models that generate fluent and localized political content, those operations can be supercharged. In fact, there is no longer a need for human operators who understand the language or the context. With light tuning, a model can impersonate a neighborhood organizer, a union rep, or a disaffected parent without a person ever setting foot in the country. Political campaigns themselves will likely be close behind. Every major operation already segments voters, tests messages, and optimizes delivery. AI lowers the cost of doing all that. Instead of poll-testing a slogan, a campaign can generate hundreds of arguments, deliver them one on one, and watch in real time which ones shift opinions. The underlying fact is simple: Persuasion has become effective and cheap. Campaigns, PACs, foreign actors, advocacy groups, and opportunists are all playing on the same field—and there are very few rules.

The policy vacuum Most policymakers have not caught up. Over the past several years, legislators in the US have focused on deepfakes but have ignored the wider persuasive threat. Foreign governments have begun to take the problem more seriously. The European Union’s 2024 AI Act classifies election-related persuasion as a “high-risk” use case. Any system designed to influence voting behavior is now subject to strict requirements. Administrative tools, like AI systems used to plan campaign events or optimize logistics, are exempt. However, tools that aim to shape political beliefs or voting decisions are not. By contrast, the United States has so far refused to draw any meaningful lines. There are no binding rules about what constitutes a political influence operation, no external standards to guide enforcement, and no shared infrastructure for tracking AI-generated persuasion across platforms. The federal and state governments have gestured toward regulation—the Federal Election Commission is applying old fraud provisions, the Federal Communications Commission has proposed narrow disclosure rules for broadcast ads, and a handful of states have passed deepfake laws—but these efforts are piecemeal and leave most digital campaigning untouched.  In practice, the responsibility for detecting and dismantling covert campaigns has been left almost entirely to private companies, each with its own rules, incentives, and blind spots. Google and Meta have adopted policies requiring disclosure when political ads are generated using AI. X has remained largely silent on this, while TikTok bans all paid political advertising. However, these rules, modest as they are, cover only the sliver of content that is bought and publicly displayed. They say almost nothing about the unpaid, private persuasion campaigns that may matter most. To their credit, some firms have begun publishing periodic threat reports identifying covert influence campaigns. Anthropic, OpenAI, Meta, and Google have all disclosed takedowns of inauthentic accounts. However, these efforts are voluntary and not subject to independent auditing. Most important, none of this prevents determined actors from bypassing platform restrictions altogether with open-source models and off-platform infrastructure. What a real strategy would look like The United States does not need to ban AI from political life. Some applications may even strengthen democracy. A well-designed candidate chatbot could help voters understand where the candidate stands on key issues, answer questions directly, or translate complex policy into plain language. Research has even shown that AI can reduce belief in conspiracy theories.  Still, there are a few things the United States should do to protect against the threat of AI persuasion. First, it must guard against foreign-made political technology with built-in persuasion capabilities. Adversarial political technology could take the form of a foreign-produced video game where in-game characters echo political talking points, a social media platform whose recommendation algorithm tilts toward certain narratives, or a language learning app that slips subtle messages into daily lessons. Evaluations, such as the Center for AI Standards and Innovation’s recent analysis of DeepSeek, should focus on identifying and assessing AI products—particularly from countries like China, Russia, or Iran—before they are widely deployed. This effort would require coordination among intelligence agencies, regulators, and platforms to spot and address risks. Second, the United States should lead in shaping the rules around AI-driven persuasion. That includes tightening access to computing power for large-scale foreign persuasion efforts, since many actors will either rent existing models or lease the GPU capacity to train their own. It also means establishing clear technical standards—through governments, standards bodies, and voluntary industry commitments—for how AI systems capable of generating political content should operate, especially during sensitive election periods. And domestically, the United States needs to determine what kinds of disclosures should apply to AI-generated political messaging while navigating First Amendment concerns.
Finally, foreign adversaries will try to evade these safeguards—using offshore servers, open-source models, or intermediaries in third countries. That is why the United States also needs a foreign policy response. Multilateral election integrity agreements should codify a basic norm: States that deploy AI systems to manipulate another country’s electorate risk coordinated sanctions and public exposure.  Doing so will likely involve building shared monitoring infrastructure, aligning disclosure and provenance standards, and being prepared to conduct coordinated takedowns of cross-border persuasion campaigns—because many of these operations are already moving into opaque spaces where our current detection tools are weak. The US should also push to make election manipulation part of the broader agenda at forums like the G7 and OECD, ensuring that threats related to AI persuasion are treated not as isolated tech problems but as collective security challenges.
Indeed, the task of securing elections cannot fall to the United States alone. A functioning radar system for AI persuasion will require partnerships with our partners and allies. Influence campaigns are rarely confined by borders, and open-source models and offshore servers will always exist. The goal is not to eliminate them but to raise the cost of misuse and shrink the window in which they can operate undetected across jurisdictions. The era of AI persuasion is just around the corner, and America’s adversaries are prepared. In the US, on the other hand, the laws are out of date, the guardrails too narrow, and the oversight largely voluntary. If the last decade was shaped by viral lies and doctored videos, the next will be shaped by a subtler force: messages that sound reasonable, familiar, and just persuasive enough to change hearts and minds. For China, Russia, Iran, and others, exploiting America’s open information ecosystem is a strategic opportunity. We need a strategy that treats AI persuasion not as a distant threat but as a present fact. That means soberly assessing the risks to democratic discourse, putting real standards in place, and building a technical and legal infrastructure around them. Because if we wait until we can see it happening, it will already be too late. Tal Feldman is a JD candidate at Yale Law School who focuses on technology and national security. Before law school, he built AI models across the federal government and was a Schwarzman and Truman scholar. Aneesh Pappu is a PhD student and Knight-Hennessy scholar at Stanford University who focuses on agentic AI and technology policy. Before Stanford, he was a privacy and security researcher at Google DeepMind and a Marshall scholar.