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I built a working Android app with no coding experience using Google's free tools
The free version of Google's Antigravity IDE is all you need to start making your own Android apps — here's how.
Hackers abuse Google ads, Claude.ai chats to push Mac malware
Attackers are abusing Google Ads and legitimate Claude.ai shared chats in an active malvertising campaign. Users searching for "Claude mac download" may come across sponsored search results that list claude.ai as the target website, but lead to instructions that install malware on their Mac.
Anthropic pins Claude's blackmail on the internet's portrayal of AI
Last year, Anthropic's Sonnet 3.6 model displayed blackmail behavior, prompting a review of AI training data's influence on its actions.
Using Claude? Thank Elon Musk
Anthropic now relies on a huge SpaceX data center for AI compute, tying Claude's rapid growth and reliability to Elon Musk.
Mozilla says Anthropic's Mythos found hundreds of flaws
Mozilla says Anthropic's unreleased Claude Mythos AI found security flaws that automated testing systems missed for years.
Airbnb's CEO says AI writes 60% of the company's code
CEO Brian Chesky added that managers are also getting their hands dirty with coding or using Claude Code.
Anthropic introduces "dreaming," a system that lets AI agents learn from their own mistakes
Anthropic has launched “dreaming” for AI agents, giving Claude new self-improving tools designed to boost enterprise automation, reliability, and scale.
Anthropic updates Claude Managed Agents with three new features
Anthropic launched Claude Managed Agents last month, greatly simplifying the work required to build and deploy cloud-hosted AI agents. This...
OpenClaw and Claude can put your AI-generated podcasts in Spotify
Your own personal AI podcast feed.
Test how AI chatbots answer mental health questions — and how therapists think they did
Experience hypothetical mental health conversations with ChatGPT, Claude or Gemini. Then, read takeaways from Boston-area therapists on what was good or bad about the chatbots' responses.
Anthropic's Claude Managed Agents can now "dream," sort of
Also, rate limits will double for Pro and Max users of tools like Claude Code.
Anthropic introduces 'dreaming' technique for AI agents
Anthropic introduces 'dreaming,' a technique for AI agents to self-improve within the Claude Managed Agents tool.
iOS 27 Will Let You Pick Claude or Gemini Instead of ChatGPT for Apple Intelligence
iOS 27, iPadOS 27, and macOS 27 will let users set third-party AI services as the default for Apple Intelligence features like Writing Tools and Image Playground, reports Bloomberg. Apple has signed a deal with Google and plans to use a Gemini-based model for Apple Intelligence and Siri features in iOS 27, but users will also be able to choose their favorite AI service as an alternative.
iOS 27 will let you choose between Gemini, Claude, and more for AI features: report
iOS 27 will give users a new way to integrate with third-party AI platforms, according to a new report. This...
SPONSORED How health systems navigate patient data with Claude
Health systems are sitting on massive amounts of clinical data, but turning that data into timely patient care isn't always easy.
After pushback, Amazon rolls out Claude Code, Codex to all employees
Some Amazon staff had complained about a lack of access to top AI coding tools, arguing the company risked falling behind in developer productivity.
So long Jeeves and Ask.com, relics of yesterday’s internet
Before Claude Code, Grok and Gemini, Jeeves was there in a modest, everyman suit. We conversed with him in full sentences and asked him whole questions. No more.
If Claude Code is going away for Pro users, I can't recommend Claude anymore
This one hurts to write.
‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field
‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field ‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field A field that once seemed like a direct path to stable, lucrative work is becoming less certain under AI, students and faculty told Spectator. By Ria Vasishtha and Arjun Menon May 3, 2026 Shanying Liu / Deputy Illustrations Editor Asia Genawi, SEAS ’29, first encountered artificial intelligence during her sophomore year at her high school in Indiana. Back in 2023, policies on AI varied widely—even within the same district—and some schools had no guidance at all. As Genawi began to see the effects of AI trickle into her classroom, she turned to guidance from her home state’s Department of Education, which she said she found “outdated and inaccessible.” Genawi spent the rest of her high school career dedicated to the issue, including writing a 15-page memo exploring varying AI policies across Indiana schools, which she sent to every Indiana state senator and representative whose contact information she was able to find. She later helped draft a bill that would have required public high schools to establish and share their own AI policies with students in an accessible format. But with so many other bills already awaiting referral, hers never made it to committee. When Genawi arrived at Columbia to study computer science, she found that the gaps she had spent years trying to address in Indiana had followed her there. Many students and professors were also grappling with what AI meant for the field. Across Columbia’s undergraduate schools, computer science has become the most popular major—accounting for around 11 percent of degrees awarded in Columbia College, 32 percent in the School of Engineering and Applied Science, and 12 percent in the School of General Studies in 2024. While the number of computer science majors in SEAS grew from 166 to 173 during the 2024-25 academic year, the total number across the three schools fell around 4.5 percent from 393 to 375, driven by declines at Columbia College and General Studies. At Barnard, it has grown to become the second most popular major as of 2025, up from third the year prior. The number of degrees awarded have grown fivefold since 2016. Faculty members and students in the department told Spectator that the surge in the major’s popularity has been driven in large part by its apparent promise of a secure, well-paid career path. However, they noted that the recent rise of generative AI has compromised the sense of stability that once defined the field, even as interest in technical skills among students persists. According to data from the Federal Reserve Bank of New York in 2024, out of 74 majors tracked, computer science had the fifth highest unemployment rate among majors at 7 percent, while computer engineering had the second highest at 7.8 percent. “Anecdotally, people are moving away from CS as a major,” Daniel Bauer, a senior lecturer in the computer science department, said. “They’re still taking some of our classes, right, because they think they need that, but they’re instead majoring in other things.” Bauer described a field that had shifted from what he called a “nerdy outlier discipline” to one that “everyone needs,” a sign that technical skills have become a baseline expectation across industries with the emergence of AI. “A lot of people thought, ‘Oh, it’s a guaranteed path to a stable income. You get a six-figure job right out of your undergrad,’” Bauer said. Now, companies may see AI as a way to reduce demand for entry-level programmers, especially in what he calls “code monkey jobs,” roles where a large number of employees are “just writing out code nine-to-five.” Chris Murphy, a senior lecturer in the computer science department, described the current moment as the convergence of two forces: the downturn of the job market and generative AI coming onto the scene. “Things kind of started to really pick up in 2014, 16,” he said, recalling the years after the weaker 2010 market. Murphy added that this was also a period where “students were getting more experience outside of class than they were inside of class” through internships, jobs, and side projects. Murphy suggested that the earlier momentum helped make computer science feel like both an academic program and a reliable pathway into work. That feeling is harder to sustain now as the industry reconfigures, which is a shift that students are seeing firsthand. Rebecca Yu, SEAS ’27, said that during a recent internship in Big Tech, she saw that “every single tech company was racing to integrate” large language models. She added that her manager said her intern class was the first to complete their assigned projects, which she attributed to the increased efficiency that comes with AI. Frank Liu, SEAS ’29, said that with AI lowering the barrier to coding, companies now expect everyone to write code. “A lot of companies outright say if you’re not using coding in your job, you might get fired for not being productive enough,” Liu said. Yu similarly expressed that students increasingly have to accept AI being a part of industry workflow, adding that many also “make use of it to maximize their productivity.” Richard Li, CC ’28, described a “franticness” among his peers as the pace of change accelerates. He explained how “some new tool” is released regularly, noting that tools rise and fall in prominence within weeks. He has come to believe that he doesn’t need to “match that speed all the time” and keep up with every development. Li has learned that AI tools allow him to “take a breath” and “get back into the loop much easier than before” when he needs to. While some companies specifically target Columbia when hiring, sending representatives to campus career fairs and recruiting large numbers from Columbia, a computer science degree cannot be treated as a static credential, Yu described. “You can’t just get your degree and stop learning after that. You have to always be learning,” Yu said. “That is something you basically have to expect for your job.” AI has not only affected students’ decisions to major in computer science, but also reshaped how the subject is taught. Across departments, faculty members no longer treat work done outside the classroom as a reliable measure of understanding, shifting instead toward in-person exams, quizzes, and attendance checks. While homework once made up as much as 60 to 70 percent of a student’s grade in computer science classes, Murphy said that breakdown has since flipped, with exams and quizzes now accounting for the majority. “The assumption—especially among pessimists—is that they’re using it to do their homework,” Murphy said. “They take the homework assignment. They put it into generative AI. Out comes the solution. Submit solution. The end.” Murphy said that faculty members ranged in their responses from fully prohibiting AI use to actively teaching students how to effectively work with AI. He said he shifted his approach this year after his teaching assistants called his belief that students weren’t using the technology “naïve,” and now permits students to use AI on assignments. His surveys of students in his introductory classes have told a more complicated story than the one so-called pessimists offer: Around 60 to 70 percent of students who reported using AI said they used it to have concepts explained to them, to clarify assignment instructions, to generate test cases, or to create practice questions. “Those are all ways that you might have had a tutor in the past to do that, and now you have this AI essentially as your tutor,” he said. Bauer has taken a different approach, teaching students how to use AI productively without sacrificing the core principles of the discipline. “People who are trained to actually use AI tools productively, while being in charge of designing the overall project, will be very much in demand in the next couple of years,” he said. Over the past year, a working group of computer science faculty has been redesigning the introductory and intermediate programming sequence to integrate generative AI as both a subject and a tool, representatives from SEAS wrote in a statement to Spectator. The effort is “coordinated” across the department, “not course-by-course,” with a systematic assessment of how AI integration is affecting student outcomes underway to shape the next round of changes. The goal, the department explained, is for students to be able to “read, verify, and reason about code, whether they or a model wrote it.” Tony Dear, senior lecturer in the computer science department, is piloting individualized AI feedback in a discrete mathematics course, while a machine learning course is also testing a Socratic AI dialogue system designed to “guide inquiry rather than supply answers,” the statement reads. Beyond individual courses, SEAS funded seven cross-departmental faculty projects in generative AI this past academic year, spanning biomedical engineering, chemical engineering, industrial engineering, and computer science, according to the statement. This phenomenon extends beyond the computer science department. SEAS’ new AI minor, which it announced in November 2025, reflects the University’s attempt to adapt. The minor is specifically designed for noncomputer science majors and aims to provide “essential AI knowledge” while emphasizing practical and responsible use across disciplines, according to the SEAS website. At the center of the minor is AI in Context, a course taught by a team of faculty from the departments of computer science, applied mathematics, philosophy, music, literature, and the Writing Center, the representatives told Spectator. Bauer described it as targeted at students who want to bring AI into fields like economics or biology, rather than as a path into software engineering itself. At the graduate level, SEAS announced a new Master of Science in artificial intelligence in February, with the inaugural cohort starting in the fall. While recognizing how AI has helped them navigate coursework, students also described its rise as isolating, even among members of the Application Development Initiative, one of Columbia’s largest computer science clubs. “As people start to ask AI for questions that they would normally ask students and members, it starts to isolate people” Li said. Alexa Kafka, BC ’27, echoed Li’s sentiment, pointing out that computer science has always been a collaborative discipline with code reviews and pull requests, even as AI makes independent work easier. Despite this, Kafka noted that she hasn’t been to office hours “in at least a year.” Bauer added that faculty are exploring long-term solutions to the assessment problem. “One of the things that we’re trying to do at the department or school level is to maybe move towards computerized testing facilities,” Bauer said, referring to ideas in the department to revitalize computer labs as an alternative to pen-and-paper exams. “We have to accept that this is the reality, for better or worse.” But more exams, students say, does not mean better learning, nor does it reflect how computer science is actually done. “Code is so variable and can be approached in so many directions, there’s never just going to be one correct answer. There can be multiple optimal solutions,” Kafka said. Students also expressed concern that the shift toward exam-heavy grading pulls focus away from the applied, project-based work that better reflects both discipline and industry expectations. ADI has tried to respond more quickly than the curriculum can. Through a vibe coding workshop this past semester, club members said they wanted to help students understand not just that AI exists, but how to use it deliberately. “Vibe coding” refers to the practice of prompting AI to write code for a task or project, and has become widespread enough that ADI felt the gap in formal instruction was worth addressing, according to Sophia Huang, BC ’29. The workshop introduced students to the concept of context engineering, which Shaina Sahu, SEAS ’27, described as not just writing a prompt, but optimizing “the rest of the relevant information that you would feed into the model.” Without that, students said that AI-generated code can fail at scale or introduce difficult-to-detect bugs. Genawi said the shift toward AI has changed what it means to be good at computer science. “I think the shift has kind of gone towards what can you create and what can you do, instead of, what do you know,” she said. Indeed, during ADI DevFest, its annual hackathon, Li said more teams are trying to complete their projects than in years past. While sign-ups have historically outpaced submissions, Li said this is beginning to change because teams “have a tool that completes the vision for them.” While many students see AI as an equalizer that lowers the barrier to entry for coding and technical work, Murphy said it also creates new equity concerns inside the classroom around who can afford stronger tools and who already knows how to use them. “The equity issues I worry about are students who have access to paid versions of things versus free versions of things,” he said. Although popular AI models like ChatGPT and Claude have free versions available, both have paid models that can reach over $100 per month, which output different qualities of code. Murphy also pointed to differences in prior exposure to AI literacy itself. “Students who go to high schools or K-12 education, where they’re taught AI literacy, prompt engineering, context engineering, they understand how to do this, as opposed to students who go to schools who don’t teach that,” he said. “Then they come in, and then they’re somehow expected to know how to use these tools.” For Genawi, those disparities begin long before students arrive on campus. “It’s very interesting to consider how lucky you were geographically,” she said. “To be born where you were is influencing your ability to get access to these spaces.” Students like Yu, though broadly apprehensive about the future of computer science, are not without hope. “I’m optimistic, because I think if you’re truly passionate about CS, there will definitely be a place in the field to do something you want to do,” she said. Senior Staff Writer Ria Vasishtha can be contacted at ria.vasishtha@columbiaspectator.com. Follow Spectator on X @ColumbiaSpec. Staff Writer Arjun Menon can be contacted at arjun.menon@columbiaspectator.com. Follow Spectator on X @ColumbiaSpec. Want to keep up with breaking news? Subscribe to our email newsletter and like Spectator on Facebook. Produced with Spectate by Emily Ta
Avoid scandal and play hardball politics in the BDN's town meeting game
The text-based game was created in part by training Claude AI on real Maine town meeting warrants.
Anthropic doubles estimate for Claude Code token spend
Anthropic now says that the average cost per developer per active day is $13, up from $6 earlier this month.
Claude Gains Integrations With Adobe, Blender, SketchUp and Other Creative Apps
Anthropic today updated Claude with new connectors aimed at creative professionals, adding integrations for Ableton, Adobe, Affinity, Autodesk Fusion, Blender, Resolume Arena and Wire, SketchUp, and Splice. Connectors are tools that Claude can use to access other platforms and help with completing tasks. Anthropic says that Claude can open up new ways for creatives to work and take on larger-scale projects.
Claude can now plug directly into Photoshop, Blender, and Ableton
Anthropic is really leaning into creative AI services.
A startup says Cursor's AI agent deleted its production database
The founder of PocketOS said that a Cursor AI agent running on Anthropic's Claude Opus model was behind the "destructive action."
Claude can now connect to lifestyle apps like Spotify, Instacart and AllTrails
Anthropic is expanding its directory of connected services for its Claude AI chatbot.
Anthropic acknowledges Claude Code issues, denies 'nerfing'
Anthropic said it found three issues with Claude Code after users complained the AI tool deteriorated.
Ranked: AI Models U.S. Businesses Pay For
OpenAI has long been the leader for paid usage by U.S. businesses, but Anthropic has closed the gap with tools like Claude Code and Cowork.
Disney employees have an AI dashboard to track who's 'tokenmaxxing'
Disney staffers have an "AI Adoption Dashboard." One Claude user invoked the chatbot 460,000 times in nine days.
Claude Mythos AI Model Uncovers 271 Zero-Day Vulnerabilities in Firefox
Anthropic’s latest frontier AI model, Claude Mythos Preview, has identified a staggering 271 zero-day vulnerabilities in Mozilla Firefox marking a seismic shift in AI-powered cybersecurity defense. The findings, addressed in Firefox 150, represent the most significant single batch of security fixes in the browser’s history. The discovery didn’t happen in isolation. Since February 2026, Mozilla’s […]
Sam Altman: Anthropic is using 'fear' to tout Claude Mythos
OpenAI CEO Sam Altman said in contrast to Anthropic's recent moves, OpenAI would err on the side of giving more people access to AI models.
Mozilla says it patched 271 Firefox vulnerabilities thanks to Anthropic's Claude Mythos
Mozilla shared some details that support Anthropic's Claude Mythos AI model as a way to protect critical services.
I tried Gemini, ChatGPT, and Claude for a month on Android, and I have a clear winner for you
The ultimate Android AI showdown
AI's taste test
Anthropic says Claude Opus 4.7 is more tasteful on work tasks.
Can Claude Write Z80 Assembly Code?
Betteridge’s law applies, but with help and guidance by a human who knows his stuff, [Ready Z80] was able to get a functioning game of Wordle out of the French-named LLM, which is more than w…
Washington’s Scramble to Get Mythos, Anthropic’s Powerful New Model
Federal agencies have requested access to Claude Mythos Preview, which Anthropic says can rapidly identify — and potentially create — new cyberthreats.
OpenAI’s big Codex update is a direct shot at Anthropic’s Claude Code
Codex can now use your macOS apps on its own.
How Dangerous Is Anthropic’s New AI Model? Its Chief Science Officer Explains.
Just how fast is the future coming at us, and how worried should we be? Last week, Anthropic announced Claude Mythos, the latest in what has become the leading family of AI models. In a very short time, AI models from Anthropic and other companies have become shockingly powerful at coding. But Mythos stands out because it is not just able to write code that can hack into critical systems, like those that run financial companies, but also to analyze systems and find and string together vulnerabilities in them, allowing Mythos to evade even sophisticated defenses. Anthropic chose to release Mythos to only about 40 leading corporations, so they could analyze the model’s capabilities and prepare countermeasures, in an effort it calls Project Glasswing. The release of Mythos—and the prospect of similarly powerful AIs to follow—has caused tremors at the top levels of Washington and corporate America. Jared Kaplan, co-founder and chief science officer at Anthropic, is at the center of this breakthrough. We sat down with Kaplan so he could walk us through just how powerful Mythos is, what the implications are for issues like our personal privacy, and what comes next. This conversation has been edited for length and clarity. Sean Fischer: For the layman, how is Claude Mythos different—more powerful, more dangerous—from previous models released by Anthropic? Jared Kaplan: I think most people have experienced the fact that our computers get faster and faster over time and over many decades. There’s this very smooth trend where every 18 months, maybe, computer processing power doubles. I think AI is on a trend in which it’s improving maybe 10 times faster than that. A lot of the earliest work that I did in AI was around identifying this scaling trend in AI. Claude Mythos is the latest model from Anthropic, but it’s not different because it’s qualitatively different. It’s really just kind of the culmination of the trend of AI models getting smarter and smarter at all kinds of general capabilities, from reasoning to software engineering to scientific research to knowledge work. For us, it was the first model to demonstrate very elite-level cybersecurity capabilities. It wasn’t because we trained Claude Mythos to focus on cybersecurity, but it was because, as a byproduct of its general intelligence and its general ability with software, it’s particularly good at identifying vulnerabilities in software and how to exploit them.
We tested Anthropic’s redesigned Claude Code desktop app and 'Routines' -- here's what enterprises should know
For the enterprise, the Desktop GUI is likely to become the standard for management and review, while the CLI remains the tool for execution.
Adobe takes Creative Cloud into Claude Code-esque territory
This is a big step in a new strategic direction for Adobe.
Anthropic draws offers from VCs to invest at up to $800 billion valuation, Business Insider reports
Anthropic has received multiple offers from venture capital firms valuing the Claude maker at as much as $800 billion in recent weeks, more than double its current valuation, Business Insider reported on Tuesday, citing sources.
Anthropic's AI Assistant Claude Is Now Available in Microsoft Word
If you're using Word, you can now try out an alternative AI tool to Copilot, though it's only in beta testing for now.
I use Claude, Gemini, and ChatGPT every day—here's the only one you should pay for
The battle of the bots.
Anthropic brings Claude into Microsoft Word, and legal contract review leads its use cases
Anthropic's Claude for Word beta is live in Microsoft Word for Team and Enterprise users, with legal contract review as the tool's first listed use case
Anthropic's Claude for Word is a new challenge to Microsoft's empire
Anthropic launched a beta version of Claude for Word. It's another challenge to Microsoft's software empire, and a bid to appeal to lawyers.
“AI polls” are fake polls
A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.
Anthropic closes in on OpenAI as US business use surges
Divergence reflects company’s recent rapid growth owing to strong interest in its Claude Code products
Why Anthropic sent its Claude AI to an actual psychiatrist
Mythos is "the most psychologically settled model we have trained to date."
Patients Are Using Chatbots to Fight Medical Bills, With Mixed Results
While chatbots like Claude and ChatGPT can help narrow the information divide between patients and providers, they can also dispense flawed advice.
Anthropic says its latest AI model is too powerful to be released
Anthropic said Claude Mythos is too good at hacking and that's why you won't be able to use it anytime soon.
AI Giant Anthropic Leans Into Life Sciences With $400M Coefficient Bio Catch
Anthropic in October last year iterated its Claude AI model to better cater to biopharma purposes. Sanofi, Novo Nordisk, AbbVie and others already use Claude in their operations.
Can AI Be My Health Adviser? I Tested Claude and Perplexity With My Real Medical Records to See
Anthropic cuts off OpenClaw support for Claude subscriptions
Anthropic said third-party tools like OpenClaw put an "outsized strain on our systems." OpenClaw's founder said cutting support would be "a loss."
Gemma 4 and what makes an open model succeed
Having written a lot of model release blog posts, there’s something much harder about reviewing open models when they drop relative to closed models, especially in 2026. In recent years, there were so few open models, so when Llama 3 was released most people were still doing research on Llama 2 and super happy to get an update. When Qwen 3 was released, the Llama 4 fiasco had just gone down, and a whole research community was emerging to study RL on Qwen 2.5 — it was a no brainer to upgrade. Today, when an open model releases, it’s competing with Qwen 3.5, Kimi K2.5, GLM 5, MiniMax M2.5, GPT-OSS, Arcee Large, Nemotron 3, Olmo 3, and others. The space is populated, but still feels full of hidden opportunity. The potential of open models feels like a dark matter, a potential we know is huge, but few clear recipes and examples for how to unlock it are out there. Agentic AI, OpenClaw, and everything brewing in that space is going to spur mass experimentation in open models to complement the likes of Claude and Codex, not replace them. Especially with open models, the benchmarks at release are an extremely incomplete story. In some ways this is exciting, as new open models have a much higher variance and ability to surprise, but it also points at some structural reasons that make building businesses and great AI experiences around open models harder than the closed alternatives. When a new Claude Opus or GPT drops, spending a few hours with them in my agentic workflows is genuinely a good vibe test. For open models, putting them through this test is a category error. Something else to be said about open models in the era of agents is that they get out of the debate of integration, harnesses, and tools and let us see close to the ground on what exactly is the ability of just a model. Of course, we can’t test some things like search abilities without some tool, but being able to measure exactly the pace of progress of the model alone is a welcome simplification to a systematically opaque AI space. Share The list of factors I’d use to assess a new open-weight model I’m considering investing in includes: Model performance (and size) — how this model performs on benchmarks I care about and how it compares to other models of a similar size. Country of origin — some businesses care deeply about provenance, and if a model was built in China or not. Model license — if a model needs legal approval for use, uptake will be slower at mid-sized and large companies. Tooling at release — many models release with half-broken, or at least substantially slower, implementations in popular software like vLLM, Transformers, SGLANG, etc due to pushing the envelope of architectures or tools. Model fine-tunability — how easy or hard it is to modify the given model to your use-case when you actually try and use it. The core problem is that some of these are immediately available at release, e.g. general performance, license, origin, etc. but others such as tooling take day(s) to week(s) to stabilize, and others are open research questions — with no group systematically monitoring fine-tunability. In the early era of open models, the days of Llama 2 or 3 and Qwen pre v3.5, the architectures were fairly simple and the models tended to work out of the box. Some of this was due to the extremely hard work of the Llama, Qwen, Mistral, etc. developer teams. Some is due to the new models being genuinely harder to work with. When it comes to something like Qwen 3.5 or Nemotron 3, with hybrid models (either gated delta net or mamba layers), the tooling is very rough at release. Things you would expect to “just work” often don’t. I’ve been following this area closely since we released Olmo Hybrid with a similar architecture, and Qwen 3.5 is just starting to work well in the various open-source tools that need to all play nice together for RL research. That’s 1.5 months after the release date! This is just to start really investing more into understanding the behavior of the models. Of course, others started working on these models sooner by investing more engineering resources or relying on partially closed software. The fully open and distributed ecosystem takes a long time to get going on some new models. All of this is lead-in for the most important question for open models — how easy is it to adapt to specific use-cases? This is a different problem for different model sizes. Large MoE open-weight models may be used by entities like Cursor who need complex capabilities in their domain, e.g. Composer 2 trained on Kimi K2.5. Other applications can be built on much smaller models, such as Chroma’s Context-1 model for agentic search, built on GPT-OSS 20B. The question of “which models are fine-tunable” is largely background knowledge known by engineers across the industry. There should be a thriving research area here to support the open ecosystem model. The first step is to understand characteristics of different base and post-trained models to understand what they look like. The second step is to tune pretraining recipes for open models so they’re more flexible. Interconnects AI is a reader-supported publication. Consider becoming a subscriber. Subscribe For The ATOM Project and other Interconnects endeavors, we’ve put in substantial effort to measuring adoption trends in the open ecosystem. Everything takes a long time to unfold after a model is first publicly available — and adaptability is why. What we know for sure now, when Qwen has been going from strength to strength with its releases, is that technical staff across the industry has gotten comfortable working with Qwen models. Countless research methods and datasets were made to work with Qwen. It’ll take patience for any other model family to get to this point — a patience I’m not sure many open model builders have. This takes us to Gemma 4, Google’s latest open models. Gemma 3 was released more than a year ago, in March of 2025, and is a bit underrated. Gemma 4 comes in 4 sizes for now, with a bigger, MoE model of over 100B total parameters rumored but not released yet. The models we have today come in sizes of ~5B dense, 8B dense, 26B total 4B active MoE, and 31B dense. I’m most excited that they’re finally adopting a standard Apache 2.0 open source license. This’ll massively boost adoption. The standard of better licenses for strong open-weight LLMs was set by mostly Chinese open model labs in the last 1-2 years, and now U.S. companies are following suit. I will personally be so happy if the horrible Llama licenses and Gemma terms of service were an ~18-month transient dynamic of the industry being nervous about releasing strong open models. The Gemma 4 scores look very solid, the small models have incredible benchmark scores (especially in general domains like LMArena) and the 31B model rivals the recent Qwen 3.5 27B, which is the leading member of that class. The ~30B size range is an important one, as it’s accessible both to researchers and to enterprises looking to deploy the model in real use-cases. Where the 7B model scale is the default for tinkering and research, a 30B model is the default for seeing if an open model can unlock substantial value in your specific workflow — a good mix of intelligence, low price, tractability for downstream training, etc. This takes us back to the above adoption criteria I mentioned for open models and the bigger question — do I think Gemma 4 will be an overwhelming success? Previous Gemma models have been plagued by tooling issues and poorer performance when being finetuned. Gemma 4’s success is going to be entirely determined by ease of use, to a point where a 5-10% swing on benchmarks wouldn’t matter at all. It’s strong enough, small enough, with the right license, and from the U.S., so many companies are going to slot it in. I’m cautiously optimistic that Gemma 4 is going to work better here. Winds are shifting for open models built in America. We saw GPT-OSS go through a bumpy launch to become an overwhelming success. There’s a collective energy around the likes of Reflection, Arcee, Nemotron, Gemma, Olmo, and peers that show substantial demand for building new stacks around open models. There’s capital to be spent on AI stacks across the economy by those who want more ownership of everything, including the model. After launching The ATOM Project 240 days ago, the conversation is shifting into the next stage. Summer of 2025 was a crisis moment where the U.S. AI scene realized it can’t wait and figure out open models after building AGI. The two markets will capture different areas and proceed in parallel. Now that more companies in the U.S. are releasing strong models, we need to improve the ecosystem so that these models are easy to use, understand, and build value around. It’s the hard work to build another inflection point in these adoption plots I’ve been updating consistently, but that’s the work to be done. Join me in it. More data coming soon! Here’s a sneak peek:
Cursor Launches a New AI Agent Experience to Take on Claude Code and Codex
As Cursor launches the next generation of its product, the AI coding startup has to compete with OpenAI and Anthropic more directly than ever.
Anthropic Says That Claude Contains Its Own Kind of Emotions
Researchers at the company found representations inside of Claude that perform functions similar to human feelings.
5+ Things to Know About the Siri Chatbot Coming in iOS 27
We're less than three months away from our first look at Apple's smarter, redesigned version of Siri. iOS 27, iPadOS 27, and macOS 27 will focus on Siri updates, and rumors about what we can expect are picking up. There's a full chatbot version of Siri in the works, which will change the way that we use Apple's personal assistant. Siri will be more like Claude or ChatGPT, marking a major improvement in how Siri works and what it can do.
Claude's popularity is forcing it to hit the brakes on users
"I know this was frustrating," an Anthropic official wrote in an announcement of the changes. "We're continuing to invest in scaling efficiently.
Gemini takes on ChatGPT and Claude with a major switching upgrade
Easily import your memories and preferences
Anthropic tweaks timed usage limits to discourage Claude demand during peak hours
: AI biz makes some Claude conversations more costly to manage capacity
Apple will reportedly allow other AI chatbots to plug into Siri
Other AI chatbots, like Gemini or Claude, could help Siri fetch answers.
Google Gemini Adds Tool to Make It Easier to Switch from ChatGPT
Google released new tools for its Gemini artificial intelligence assistant that will let users upload chat history and context from other AI apps, a move to lure users from rivals like OpenAI’s ChatGPT and Anthropic PBC’s Claude.
Apple Plans to Let Rival AI Chatbots Integrate With Siri in iOS 27
Apple plans to allow third-party AI chatbots to integrate with Siri in iOS 27, reports Bloomberg. Apple already has a partnership with OpenAI that lets Siri hand questions off to ChatGPT, but Apple will expand that integration to other companies like Google and Anthropic. An iPhone user with the Claude or Gemini app installed will be able to send questions to those chatbots, like how the current ChatGPT feature works.
iOS 27: Apple will reportedly let Claude and other AI chatbot apps integrate with Siri
Apple will unveil iOS 27 on June 8 at WWDC, and we’re learning new details about what to expect in...
Anthropic’s Claude can now control your Mac, escalating the fight to build AI agents that actually do work
Anthropic has given Claude the ability to control a Mac, marking a major step in the AI agent race and raising new questions about automation, reliability, and security.
iOS 27 Siri Overhaul: Standalone App, New Design, Dynamic Island Integration and Ask Siri Button
Apple is testing a standalone Siri chatbot app for iOS 27, reports Bloomberg's Mark Gurman. Apple plans to debut a chatbot version of Siri that will compete with Claude and ChatGPT, and the new Siri could be accessible through a dedicated app. Gurman previously said that Apple was not going to develop a standalone Siri app, but it appears plans have changed.
Claude Code can now take over your computer to complete tasks
But Anthropic urges caution as "research preview" safeguards "aren't absolute."
Anthropic’s Claude Code and Cowork can control your computer
Its only a research preview for now.
Anthropic says Claude can now use your computer to finish tasks for you in AI agent push
Anthropic and its rivals are trying to ramp up capabilities of AI agents after OpenClaw went viral earlier this year.
'Claude, Resize These Photos' - Anthropic's Agentic AI Will Run Photoshop For You
It can perform boring tasks.
Claude can now automate your entire desktop, but with a serious limitation
It's likely a temporary one, though.
Anthropic's Claude Can Now Control Your Computer
The developer is boosting Claude's agentic capabilities in an apparent effort to compete with platforms like OpenClaw.
This Web Tool Sabotages AI Chatbots By Making Them Really, Really Slow
Artist Sam Lavigne created ‘Slow LLM’ to make people question their dependence on tools like Claude and ChatGPT. Or at least, make them super annoying to use.
I let AI pick my Oscars ballot. It messed up oddly, but still won.
Alistair Barr's AI-assisted Oscars ballot, powered by Anthropic's Claude, outperformed despite missing some categories.
Anthropic just shipped an OpenClaw killer called Claude Code Channels, letting you message it over Telegram and Discord
The consensus among early adopters is that Anthropic has successfully internalized the most desirable features of the open-source movement—multi-channel support and long-term memory
Google Begins Testing Gemini Mac App to Match ChatGPT and Claude
Google is ramping up development of a dedicated Gemini AI app for Apple Inc.’s Mac computer lineup, looking to step up competition with OpenAI and Anthropic PBC.
Anthropic's Claude Can Now Create Interactive Visuals Directly in Conversations
Anthropic's Claude chatbot has been updated with support for inline visual content that will help it provide clearer answers. Claude can now create custom visuals like charts, graphs, and diagrams. Visual content will be used when it better conveys an answer than plain text, and visual aids can also include real-world data like weather and recipes as long as web search is enabled. The visuals that Claude creates are distinct from Artifacts, and use HTML and SVG rather than image generation.
Anthropic's Claude can now draw interactive charts and diagrams
The new feature lets Claude generate interactive charts, diagrams, and visualizations inline during a conversation.
Anthropic’s Claude AI can respond with charts, diagrams, and other visuals now
Claude can now help you visualize topics as you chat.
5 AI Tools to Run a 1-Person Business While You Sleep (While Millions of ChatGPT Users Flee to Claude)
The AI landscape just shifted — and one move sparked a mass migration entrepreneurs didn't see coming.
I can text Claude like a personal assistant now — this iPhone shortcut made it possible
Now I keep my most important chats in my text messages so I can access them faster
I ran 7 real-world prompts on Gemini 3 and Claude Sonnet 4.6 — the results surprised me
The latest default models go head-to-head with 7 practical use challenges
Anthropic's Claude found 22 vulnerabilities in Firefox over two weeks
In a recent security partnership with Mozilla, Anthropic found 22 separate vulnerabilities in Firefox — fourteen of them classified as "high-severity."
Exclusive | ‘Send Us More’: Anthropic’s Claude Sniffs Out Bevy of Bugs
New AI-powered tools are increasingly adept at spotting flaws. Hacking experts worry they will be good at exploiting them, too.
HHS starts phasing out Anthropic's Claude
In this edition of STAT Health Tech: An AI chatbot for surgical patients got 'breakthrough' status from FDA, HHS phases out Claude, and more.
Claude hits No. 1 on App Store as ChatGPT users defect to Anthropic
OpenAI secured a Pentagon deal, sparking backlash and shifting some users' loyalties to Anthropic's rival Claude chatbot.
Chatbots from ChatGPT and Claude offer health advice
Tech companies are pushing new health chatbots, but experts say you still need to talk to your doctor.
ChatGPT vs Claude: I put both default models through 7 real-world tests — one is the clear winner
ChatGPT vs. Claude go head-to-head for daily work and writing
The nuclear nightmare at the heart of the Trump-Anthropic fight
Should we trust Claude to stop a missile attack?
The Pentagon is demanding to use Claude AI as it pleases. Claude told me that's 'dangerous'
Defense Secretary Pete Hegseth has threatened Anthropic, the maker of Claude AI, that if it doesn't turn over full use of the powerful tool by Friday, the company will face severe repercussions.
Anthropic says it won't concede to military's terms for use of AI
Anthropic CEO Dario Amodei said on Thursday the company "cannot in good conscience accede" to the military's terms over the use of Claude.
Claude and Codex now available for Copilot Business & Pro users
Claude by Anthropic and OpenAI Codex are now available as coding agents for Copilot Business and Copilot Pro customers. Copilot Enterprise and Pro+ customers received access earlier this month, and…
How one AI company is helping businesses navigate Trump’s new tariff chaos following the Supreme Court ruling
Plus Anthropic accuses Chinese AI rivals of using Claude's answers to train their own models
Anthropic says DeepSeek fraudulently used Claude
"These campaigns are growing in intensity and sophistication," Anthropic said as part of its lengthy statement.
iOS 26.4 Brings CarPlay Support for ChatGPT, Claude and Gemini
With iOS 26.4, CarPlay users will be able to use third-party chatbots with CarPlay. AI services like Claude, Gemini, and ChatGPT will be accessible through the CarPlay system for the first time. Apple's CarPlay Developer Guide [PDF] lists voice-based conversational apps as a supported app type starting with iOS 26.4. Apple is implementing a new voice control screen that will let apps provide visual feedback for voice-based conversational apps.
Claude’s AI Market Structure Disruption Business Leaders Must Watch
Specialized AI service firms face challenges as big players move in. Business users must consider vendor lock-in and pricing when setting AI strategy.
Elon Musk slams Anthropic AI models as 'misanthropic and evil' in scathing social media post
Elon Musk accuses Anthropic's AI models of being "misanthropic and evil," claiming competitor's Claude models exhibit racial and demographic bias.
I tried a Claude Code rival that's local, open source, and completely free
I was curious if Block's Goose agent, paired with Ollama and the Qwen3-coder model, could really replace Claude Code. Here's how it worked.
The New Fabio Is Claude
The romance industry, always at the vanguard of technological change, is rapidly adapting to A.I. Not everyone is on board.