The Delhi High Court handed OpenAI a major legal victory Friday, ruling that the company’s use of news agency ANI’s articles to train ChatGPT does not infringe copyright because it qualifies as fair reporting for research purposes under India’s Copyright Act, Reuters reported.

It is the first substantive court finding in India on whether AI companies can train large language models on copyrighted news content without a license. That question is still open in courtrooms in the United States and Canada, where OpenAI faces similar suits.

ANI first sued OpenAI in November 2024, alleging the company trained ChatGPT on its copyrighted news reports without permission and that the chatbot falsely attributed fabricated stories to the agency. Presiding over the ANI v. OpenAI lawsuit, Delhi High Court Justice Amit Bansal rejected the copyright claim, finding that ANI failed to prove ChatGPT had memorized or reproduced its articles in response to users.

The judge also rejected ANI’s claim over OpenAI’s storage of its articles, ruling that retaining the content for AI training is protected as research under India’s Copyright Act of 1957 and does not infringe the agency’s copyright.

The ruling contrasts with ongoing litigation in the United States, where The New York Times, the Center for Investigative Reporting and a growing number of publishers argue that OpenAI’s use of copyrighted material for AI training violates U.S. copyright law. Unlike India’s fair-dealing framework, which lists specific permitted uses, U.S. courts evaluate fair use case by case. In Canada, a coalition of Canadian news organizations, including CBC and The Globe and Mail, is testing a separate copyright framework in its own lawsuit against OpenAI.

The decision could reshape negotiations between AI developers and publishers in India, where the threat of copyright litigation may carry less weight after the ruling, It could reduce pressure on OpenAI and other AI companies to pursue licensing agreements with Indian news organizations, even as similar disputes continue elsewhere.

The ruling is not the final word in the case. It addresses ANI’s claims at this stage of the proceedings, and appeals remain possible. The judge’s finding on reproduction was also based on the evidence presented by ANI, not a broader conclusion that AI systems can reproduce copyrighted material.

Even so, India has become the first major jurisdiction to find that training AI models on copyrighted news content can qualify as protected research. As courts in the United States and Canada continue to weigh similar claims under different copyright laws, the divergence in legal approaches is becoming increasingly difficult for AI companies and publishers to ignore.

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OpenAI is putting another $5 million and $3 million in technology credits behind the American Journalism Project over the next two years, executives told Axios in an exclusive report. The renewal extends a partnership that began in 2023, when the company committed $5 million in funds and another $5 million in credits to the nonprofit.

That original deal helped launch AJP’s Product & AI Studio, which helps nonprofit local outlets build AI-powered products and workflows. In its first run, AJP delivered direct grants to 31 of its 50-plus portfolio organizations across 38 states. With the new money, CEO Sarabeth Berman says AJP plans to fund more of its newsroom partners while broadening access to ChatGPT’s enterprise products.

Tom Rubin, OpenAI’s chief of intellectual property and content, framed the arrangement as mission-driven rather than commercial. “These partnerships are consistent with our mission and have demonstrated great success,” he told Axios. “We’re committed to them because they demonstrate that the technology can benefit society.”

Member newsrooms used the funding on things reporters and fundraisers deal with every day: donor communications, data analysis, translation and civic information tools for readers. The next stage moves past one-off experiments toward shared products and infrastructure that smaller newsrooms could use together, according to AJP.

AJP is one of several local news bets OpenAI has placed. The company runs an AI collaborative and fellowship with the Lenfest Institute for Journalism that backs metro publishers including the Philadelphia Inquirer, Minnesota Star Tribune and the Seattle Times. It funded an expansion of Axios Local into new markets in 2025, and it provides grants and training to newsrooms in the global trade group WAN-IFRA.

The timing matters because OpenAI is buying goodwill with publishers while fighting them in court. It faces copyright suits from the New York Times and from eight newspapers owned by Alden Global Capital, part of a broader wave of legal disputes over how AI systems use published work. Other firms have been slower to sign licensing deals, and some, including OpenAI backer Microsoft, are exploring models that would pay publishers per use instead.

For newsrooms weighing whether to take this kind of money, the opportunity comes with tradeoffs. Grants and credits lower the cost of experimenting with AI, but they come from a company that news organizations are also suing over training data. The tools that help a local outlet translate coverage or draft donor emails are built by the same industry accused of scraping journalism without permission.

“Deploying AI effectively is ultimately a leadership challenge,” Berman said. “News organizations really have to think about how they smartly integrate these technologies in ways that have good policies, have humans in the lead and support the journalistic quality of the news organizations.”

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OpenAI’s first piece of consumer hardware is reportedly a screenless, mobile AI companion that syncs with ChatGPT and is being pitched internally as a humanlike AI companion that lives in the home.

That description comes from a Bloomberg report published Tuesday, which TechCrunch summarized the same day. The device is still under development, and its sources describe it as something well outside the standard smart-speaker mold.

The speaker reportedly has a personality and can learn about its owner over time to deliver more personalized responses. It would tap into a user’s digital life, pulling from sources like email. And it apparently includes mechanical elements that can move on their own, designed to feel like a companion and act as a physical manifestation of ChatGPT.

OpenAI announced prototypes for hardware in November 2025, with earlier rumors pointing to a phone that would compete directly with Apple. The company also acquired io, the hardware startup co-founded by former Apple design chief Jony Ive, in a deal valued at roughly $6.5 billion. Bloomberg reports the speaker was built with help from ex-Apple engineers who worked on the iPhone and Mac.

That pedigree is now a legal liability. Apple sued OpenAI last week, accusing it of stealing trade secrets and calling the allegations “the tip of the iceberg.” OpenAI denies wrongdoing. Its sources told Bloomberg the new product veers significantly from anything Apple sells today.

OpenAI is not alone in chasing this category. Hark, the AI lab founded by Brett Adcock, raised an oversubscribed $700 million Series A in May at a $6 billion valuation to build proprietary models paired with custom hardware it calls a universal interface between humans and machines. Hark has yet to disclose the device’s design, highlighting the surge of investment flowing into AI hardware long before products ship.

For newsrooms and publishers, a screen-free device that pulls from email and learns a user’s habits raises the same questions as any voice-first platform. If people increasingly ask a companion device for news instead of opening an app or a browser, distribution shifts again, and this time to a surface with no visible headlines, no scannable feed and no obvious place for a byline.

The referral traffic problem publishers already face with chatbots gets sharper when the interface is spoken. The reported device would extend a broader shift in news discovery, making AI assistants an even more prominent intermediary between publishers and their audiences.

OpenAI has not announced the product, its price or when it will ship. Still, the reported device points to the company’s broader strategy of extending ChatGPT beyond screens and into the home.

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The New York Times and a group of other publishers are asking a federal court to sanction OpenAI, accusing the company of withholding or destroying evidence in a high-stakes copyright case over how ChatGPT was trained and used.

In a motion filed Thursday in federal court in Manhattan, the publishers alleged that OpenAI misrepresented its ability to search training datasets and ChatGPT output logs for copyrighted news material. According to Reuters, the publishers said OpenAI told the court it could not search its large language models for their work while allegedly concealing that it had already done so “even before the first News Plaintiff filed suit.”

The motion is the latest escalation in the copyright fight between major news organizations and AI companies. It also moves the dispute deeper into discovery, where the question is not just whether AI companies can use journalism to train models, but whether they can preserve, search and produce the records needed to prove what happened.

The plaintiffs include The Times, the New York Daily News and other media organizations, including Ziff Davis and the Center for Investigative Reporting, according to The Associated Press and Variety. The original New York Times article reported that the publishers are seeking legal sanctions against OpenAI, including monetary penalties and other remedies.

The filing does not ask for sanctions against Microsoft, which is also a defendant in The Times’ broader copyright case, according to The Times’ summary of the motion. Microsoft has invested heavily in OpenAI and integrated OpenAI technology into products including Copilot.

“The evidence is in OpenAI’s training data sets and ChatGPT output logs,” the publishers said in the motion, according to The Times. “But instead of just producing that evidence at the start of the case and focusing on the merits of its fair use defense, OpenAI chose obstruction.”

OpenAI rejected the allegations. “As the Times’ case weakens and they’ve been forced to drop claims against us, they’re persisting with their efforts to invade the privacy of people who have nothing to do with this case, including by making these blatantly false allegations,” OpenAI spokesperson Drew Pusateri told Reuters. “We’ll continue defending our users’ privacy and the long-established principles of fair use.”

The publishers allege that OpenAI deleted billions of relevant ChatGPT conversations or made them unsearchable. They also argue that an OpenAI employee later testified that the company had performed multiple searches for news publishers’ content, contradicting earlier representations about the company’s technical limitations.

A sanctions memorandum posted by Ars Technica says the publishers want the court to bar OpenAI from relying on a disputed 20 million-log ChatGPT sample, find that ChatGPT’s output logs include or would have shown substantial use of the publishers’ copyrighted material, instruct the jury on those findings and award fees and costs tied to the discovery fight.

Those remedies would matter because discovery disputes can shape the trial record. If the court finds OpenAI failed to preserve or produce relevant evidence, the ruling could affect what arguments OpenAI can make later and what conclusions a jury may be allowed to draw from missing or incomplete records.

The Times sued OpenAI and Microsoft in 2023, alleging that millions of Times articles were used without permission to train AI systems that now compete with publishers as sources of information. OpenAI and other AI companies have argued that training models on large bodies of text is protected by fair use, a theory now being tested across lawsuits from authors, artists, music labels and news organizations.

For publishers, the issue goes beyond training data. They argue that AI chatbots and AI search summaries can answer readers’ questions using journalism without sending traffic, licensing revenue or subscribers back to the organizations that reported the information. Media Copilot has been tracking the same pressure point in coverage of Google’s AI accuracy problem and The Times’ warnings about AI companies using journalism without permission.

At the same time, publishers are taking different approaches to the AI economy. Some are suing. Others have signed licensing deals with AI companies. The Associated Press announced a deal with OpenAI in 2023, while other media companies have made agreements with OpenAI, Google, Meta and Amazon.

The sanctions motion could increase pressure on both sides. A ruling against OpenAI would give publishers leverage in court and in licensing talks. A ruling for OpenAI would strengthen the company’s argument that publishers are using discovery to intrude into user privacy and commercially sensitive systems.

Either way, the case shows that AI copyright fights are becoming data-governance fights. The central questions are no longer only what AI systems were trained on. They are whether companies can prove it, search it, preserve it and explain it in court.

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Two years ago, if anyone had told me the most AI-hostile demographic in 2026 would turn out to be Gen Z, I would have laughed. The generation that grew up with screens in its hands seemed like the obvious early adopter, ready to use the tools to look more skilled, more productive, and more employable than everyone else.

Instead, May commencement season turned into an open revolt. At the University of Arizona, students booed Google chairman Eric Schmidt for pitching AI’s world-changing potential. Gloria Caulfield, VP of strategic alliances for the investment firm and real estate developer Tavistock, drew the same reaction at the University of Central Florida when she compared the rise of AI with the Industrial Revolution. At Middle Tennessee State University, students shouted down Big Machine Records CEO Scott Borchetta for the offense of saying the word out loud.

The numbers back up the booing. A recent Gallup poll measuring AI adoption and attitudes among Gen Zers found the share who say they’re excited about AI fell from 36% to 22% in a year. The share who say they feel anger toward AI climbed from 22% to 31%. Older cohorts are skeptical too, but a sentiment swing this sharp inside the youngest part of the workforce is something I’ve never seen for a new technology this early in its lifecycle.

That swing matters because the AI industry has a serious PR problem at the worst possible moment. Anti-AI sentiment is hardening as the midterms approach, and politicians are picking up data centers as a wedge issue, supporting efforts to halt or slow the build-out of the facilities that fuel AI with the computing power it needs to function. If capacity can’t keep up with demand, the cost of compute will keep rising, and that will put hard ceilings on what newsrooms, marketing teams, and comms shops can actually do with AI in the year ahead.

The infrastructure squeeze is already here

It’s already happening. Anyone who runs Claude as part of their daily workflow knows the rhythm of the outages, and Anthropic’s own status dashboard tells the story in red over the past 90 days. The Claude Code boom has driven demand through the roof in 2026, and the company is scrambling to keep up. Anthropic signed a deal to buy computing power from Elon Musk’s SpaceX, and at the same time it closed the loophole that let builders run third-party software on top of their Claude subscriptions. Some of those setups were burning thousands of dollars of compute against a $200-a-month Claude Max plan. Now those teams have to use Anthropic’s platform directly or move to pay-as-you-go.

The angry posts in response were predictable, but the more useful read on the change is that it forced builders to reckon with the actual cost of what they were running. The choices are probably familiar to anyone trying to budget AI spend: switch to a cheaper model, possibly an open-source one, rebuild on Anthropic’s own platform, or shut the project down.

This was always going to happen at some point. As demand grows, free-compute workarounds will keep closing. The industry’s argument is that the squeeze would hurt less if compute were cheap and plentiful, which is the case being made for trillion-dollar infrastructure projects like OpenAI’s Stargate. For AI to deliver on its promises, compute has to flow like water. That means more data centers, and more power plants behind them.

Which loops back to why Gen Z is angry in the first place. Environmental concerns are near the top of their list, and AI’s energy footprint has only gotten harder to ignore since I wrote about it months ago. This isn’t a fight confined to politicians and podcasters anymore. At the companies I advise on AI adoption, employee surveys keep surfacing the same worry, and in some cases it’s starting to shape whether teams use AI at all.

Governance is the new AI strategy

You can argue about whether the pollution and water concerns are overblown. The cost question is harder to wave off. The leaders running the most ambitious AI programs are past the era of handing every employee a ChatGPT seat. They want agentic workflows, automated processes, and rapid prototyping through vibe coding, and they may be telling their engineers to get obsessed with “tokenmaxxing.”

It’s unclear so far how data center politics will play out. What leaders can actually control right now is governance. That’s more than running training sessions on which model does what, although that matters. Real governance is the balance between experimentation and direction. People need room to invent their own workflows, and the organization needs a way to make sure the compute it’s paying for is being put to good use. That doesn’t just mean “keeping costs down” it means accepting that the bill will sometimes be high and being confident the outcome will be worth it.

Through my consulting work with media companies and PR agencies, I’ve watched this play out in practice. One agency I worked with piloted a vibe-coding tool. Usage spiked early as employees tested its limits, and several different teams ended up building near-duplicate prototypes. The thing that saved them was high-bandwidth communication. They ran regular workshops and project reviews, learned from their people, and steered the work as they went. They eventually homed in on the use cases that actually delivered, in their case automating media intelligence, and the experiment surfaced something unexpected. The original platform wasn’t the right one. The agency ended up adopting a different tool and sunsetting the one it started with.

That’s one of many examples, and the lesson behind all of them is the same. If AI agents are going to do real work for your team, they need to run compute-heavy jobs. Compute is going to stay expensive for a while. The way to avoid the kind of top-down restrictions that suffocate innovation is for leaders to define what success looks like up front, get their teams trained on the tools and models, and build systems that surface collaboration and catch waste before it compounds.

That is what good governance actually looks like. The political fight over AI and data centers isn’t going anywhere, but the companies leaning into AI can still find a way through. The goal is to work inside the real cost constraints while shielding the people doing the actual work from feeling them.

A version of this column appears in Fast Company.

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The free-spending era for AI inside corporate America is ending.

According to The Wall Street Journal, executives at companies including Uber, Meta, Microsoft, Salesforce and DoorDash have launched cost-cutting campaigns this year after seeing their AI bills double or triple—or blow through annual budgets in just three months. The culprits: the soaring price of tokens, the basic unit of AI computing, as model providers like OpenAI and Anthropic seek to balance supply and demand.

The result is a notable shift in corporate AI strategy. Where last year the goal was to flood the organization with AI tools and encourage experimentation, leaders are now scrambling to ration access, steer workers toward cheaper homegrown alternatives, and sharpen employee skills to wring better returns from the technology.

“The free-money period for AI is definitively over,” said one senior technology executive at a major financial firm, speaking on condition of anonymity to discuss internal cost pressures.

The cooling, if it holds, could complicate the growth trajectories of AI heavyweights racing toward public listings. Anthropic closed a $65 billion funding round this week valuing the startup at $965 billion, while OpenAI is also moving toward a potential IPO. AI critics have pointed to corporate cost-management efforts as evidence that the ultrafast pace of AI expansion may be unsustainable.

Budgets burned in months

Corporate spending on AI took off in 2024 and 2025 as companies encouraged broad experimentation, eager to signal to Wall Street that they wouldn’t be left behind in the disruption wave. But many enterprises underestimated how quickly costs would accumulate—particularly as employees without specialized training sent inefficient prompts, ran excessive queries, or used premium-tier models for simple tasks that could have been handled by cheaper, internally built tools.

Some companies burned through their entire annual AI budget in the first quarter. Others saw line items in technology budgets that previously seemed large enough suddenly look inadequate. The problem was compounded for organizations that signed multi-year contracts with AI providers before understanding their actual usage patterns.

“Most companies didn’t have visibility into what AI was actually costing them on a per-team or per-use basis,” said an AI strategy consultant who works with Fortune 500 firms. “They just saw a giant bill at the end of the quarter.”

The rationing begins

At Uber, Meta, Microsoft, Salesforce and DoorDash, technical executives have implemented some combination of the same playbook: tiered access to AI tools based on role and need, mandatory efficiency reviews for high-cost teams, and investment in internal AI infrastructure that costs less per query than commercial models.

Some companies have quietly restricted access to certain premium AI features for non-technical employees. Others have introduced internal dashboards that show employees the real-time cost of their AI queries—designed to encourage more efficient prompting habits.

The shift mirrors what happened in cloud computing’s early years, when companies initially over-provisioned infrastructure before learning to optimize.

The IPO problem

The corporate reckoning comes at a delicate moment for the AI industry. Both Anthropic and OpenAI are navigating toward public markets, and institutional investors are watching corporate AI spending closely for signs that the technology is generating sustainable returns—or that the boom could go bust.

If major corporate customers begin to pull back on AI spending or demand better pricing terms, it could affect the revenue projections that underpin those anticipated listings. Anthropic’s $965 billion valuation, for context, represents a multiple that assumes continued rapid growth in enterprise demand.

AI critics say the cost backlash was inevitable. Proponents counter that efficiency improvements and competition among AI providers will eventually bring down prices—and that early-stage overspend is normal for transformative technologies.

For now, the corporate AI spendometer is being watched more carefully than ever.

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The Associated Press has signed OpenAI as a customer for its U.S. election results data, the wire service announced, bringing AP’s vote counts to ChatGPT and other OpenAI services through the 2028 general election.

Under the agreement, AP will provide vote count results for national, state, and local races to OpenAI—covering major American cities from now through 2028. The deal positions OpenAI alongside a broader network of media, financial, and technology companies that already rely on AP for elections data.

“When people need information they can trust, they turn to AP,” said David Scott, vice president of AP Elections, in a statement. “With this agreement, we’re helping make sure OpenAI and its tools can tell people around the world who Americans have picked to lead the nation.”

AP has counted votes and declared winners in U.S. elections since 1848. In the 2024 general election, AP processed nearly 7,000 races with a 99.9% accuracy rate, according to the organization.

The deal reflects a broader pattern of AI companies seeking licensing agreements with established news organizations as they face scrutiny over factual accuracy in election-related outputs. AP has previously struck similar data-sharing arrangements with other major platforms.

Edited by Pete Pachal

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OpenAI today released GPT-5.5, what it says is its “smartest and most intuitive to use model yet, and the next step toward a new way of getting work done on a computer.”

The company says the model understands what users are trying to do faster, can carry more of the workload itself, and excels at writing and debugging code, researching online, analyzing data, creating documents, operating software, and moving across tools until a task is finished.

The company published performance numbers from Terminal-Bench 2.0, which tests complex command-line workflows requiring planning, iteration, and tool coordination.

OpenAI said GPT-5.5 outperformed its predecessor on every major coding and agent benchmark the company tested, while using fewer tokens and running at the same speed as the older model. On one third-party coding index, it matched leading rivals at about half the cost.

Keeping a larger model that fast required rebuilding inference as a single system rather than a patchwork of tweaks, the company said. GPT-5.5 was designed, trained and served on NVIDIA’s latest hardware, and OpenAI credited its own Codex tool and GPT-5.5 itself with helping hit the efficiency targets.

Early testers told the company the model seems to grasp how a codebase fits together — why something is failing, where the fix belongs and what else the change will touch.

Dan Shipper, Founder and CEO of Every, called GPT-5.5 “the first coding model I’ve used that has serious conceptual clarity.” After launching an app, he spent days debugging a post-launch issue before bringing in one of his best engineers to rewrite part of the system. To test GPT-5.5, he effectively rewound the clock: could the model look at the broken state and produce the same kind of rewrite the engineer eventually decided on? GPT-5.4 could not. GPT-5.5 could.

Pietro Schirano, CEO of MagicPath, saw a similar step change when GPT-5.5 merged a branch with hundreds of frontend and refactor changes into a main branch that had also changed substantially — resolving the work in one shot in about 20 minutes.

One engineer at NVIDIA with early access went as far as to say: “Losing access to GPT-5.5 feels like I’ve had a limb amputated.”

OpenAI is already running the model internally at scale. More than 85% of the company uses Codex every week across functions including software engineering, finance, communications, marketing, data science, and product management. The finance team used GPT-5.5 in Codex to review 24,771 K-1 tax forms totaling 71,637 pages, accelerating the task by two weeks compared to the prior year.

The model also shows gains on scientific and technical research workflows. On GeneBench, a new eval focusing on multi-stage scientific data analysis in genetics and quantitative biology, GPT-5.5 outperforms GPT-5.4 on problems that often correspond to multi-day projects for scientific experts. On BixBench, a benchmark designed around real-world bioinformatics and data analysis, it achieved leading performance among models with published scores.

In a notable example, an internal version of GPT-5.5 with a custom harness helped discover a new proof about Ramsey numbers — one of the central objects in combinatorics — later verified in the Lean proof assistant. The result is a concrete example of GPT-5.5 contributing not just code or explanation, but a novel mathematical argument in a core research area.

OpenAI says GPT-5.5 was released with its strongest safeguards to date, including tighter controls around cybersecurity workflows and repeated misuse patterns. The model was evaluated across the company’s full safety and preparedness frameworks, with input from nearly 200 trusted early-access partners before launch.

GPT-5.5 is available today in ChatGPT and Codex for Plus, Pro, Business, and Enterprise users. GPT-5.5 Pro is rolling out to Pro, Business, and Enterprise tiers. API access, which requires different safeguards, is coming “very soon,” OpenAI said.

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OpenAI has acquired Technology Business Programming Network (TBPN), a daily live video and audio podcast focused on business and technology news, the company announced Thursday.

The deal puts one of the world’s leading AI companies in control of an editorial brand — a move that mirrors a long history of tech giants using media acquisitions to shape industry conversation. TBPN, hosted by John Coogan and Jordi Hays, will continue to operate independently on editorial decisions, according to OpenAI, which framed the acquisition as part of its broader mission to shape the public conversation around AI.

“As I’ve been thinking about the future of how we communicate at OpenAI, one thing that’s become clear is that the standard communications playbook just doesn’t apply to us,” Fidji Simo, OpenAI’s CEO of Applications, wrote in a blog post announcing the deal. “We’re not a typical company. We’re driving a really big technological shift.”

The acquisition arrives as OpenAI prepares for a potential initial public offering, raising questions about what influence the company might wield over both industry coverage and national AI policy. OpenAI chief global affairs officer Chris Lehane cited to CNN’s Hadas Gold the “long history of companies and entities owning and acquiring media properties,” pointing to Westinghouse Electric’s ownership of CBS and Microsoft’s partnership with NBC to launch MSNBC. CNN’s Brian Stetler noted in his Reliable Sources newsletter that a live-streaming show with a small but influential audience — where executive moves are treated “like sports trades” — will now financially support one of the leading AI companies.

TBPN’s team will also contribute to OpenAI’s broader communications and marketing efforts, Simo said, helping the company bring AI to audiences “in a way that helps people understand the full impact of this technology on their daily lives.”

The acquisition follows a familiar pattern. Jeff Bezos bought The Washington Post, Marc Benioff acquired Time magazine, Adobe purchased Search Engine Land, and Arrow Electronics took on Electronic Buyers’ News in the early 2000s. Each deal gave a tech company a direct voice through an established media brand — a dynamic now playing out at a moment when AI companies are actively courting both regulatory goodwill and public trust.

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Encyclopedia Britannica and its Merriam-Webster subsidiary sued OpenAI in Manhattan federal court on Friday, alleging the company used nearly 100,000 of their articles to train ChatGPT without permission, and then used the chatbot to cannibalize the traffic that encyclopedias depend on to survive.

The complaint, filed in the Southern District of New York, says OpenAI made “near-verbatim” copies of Britannica’s encyclopedia entries, dictionary definitions and reference content to train its GPT large language models. It also accuses OpenAI of trademark infringement—specifically, generating AI “hallucinations” that falsely cite Britannica as a source, implying a permission that was never granted.

OpenAI’s response was the standard playbook: “Our models empower innovation, and are trained on publicly available data and grounded in fair use.”

Britannica isn’t new to this fight. The company sued Perplexity last September over similar allegations—that Perplexity’s answer engine reproduces its content without attribution or compensation. That case is still ongoing. The OpenAI suit extends the same theory to a much larger defendant with much deeper pockets and a far larger user base.

The core grievance goes beyond copyright. Britannica’s complaint frames the harm as a flywheel: OpenAI trains on Britannica’s content, then deploys a product that answers the same questions Britannica’s websites would have answered, diverting users before they ever arrive. It’s the same structural argument publishers have been making about AI search summaries, and it’s why policymakers in Europe and Brazil are exploring statutory licensing as a way to compensate content creators whose work powers AI without delivering any traffic in return.

Britannica requested unspecified monetary damages and an injunction blocking further infringement. The case joins a growing docket of high-stakes AI copyright litigation heading for a reckoning in U.S. courts over whether training on publicly available data constitutes fair use—a question on which the industry, publishers, and regulators are all waiting for an answer.

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