Claude

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Claude’s AI research mode now runs for up to 45 minutes before delivering reports

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Still, the report contained a direct quote statement from William Higinbotham that appears to combine quotes from two sources not cited in the source list. (One must always be careful with confabulated quotes in AI because even outside of this Research mode, Claude 3.7 Sonnet tends to invent plausible ones to fit a narrative.) We recently covered a study that showed AI search services confabulate sources frequently, and in this case, it appears that the sources Claude Research surfaced, while real, did not always match what is stated in the report.

There’s always room for interpretation and variation in detail, of course, but overall, Claude Research did a relatively good job crafting a report on this particular topic. Still, you’d want to dig more deeply into each source and confirm everything if you used it as the basis for serious research. You can read the full Claude-generated result as this text file, saved in markdown format. Sadly, the markdown version does not include the source URLS found in the Claude web interface.

Integrations feature

Anthropic also announced Thursday that it has broadened Claude’s data access capabilities. In addition to web search and Google Workspace integration, Claude can now search any connected application through the company’s new “Integrations” feature. The feature reminds us somewhat of OpenAI’s ChatGPT Plugins feature from March 2023 that aimed for similar connections, although the two features work differently under the hood.

These Integrations allow Claude to work with remote Model Context Protocol (MCP) servers across web and desktop applications. The MCP standard, which Anthropic introduced last November and we covered in April, connects AI applications to external tools and data sources.

At launch, Claude supports Integrations with 10 services, including Atlassian’s Jira and Confluence, Zapier, Cloudflare, Intercom, Asana, Square, Sentry, PayPal, Linear, and Plaid. The company plans to add more partners like Stripe and GitLab in the future.

Each integration aims to expand Claude’s functionality in specific ways. The Zapier integration, for instance, reportedly connects thousands of apps through pre-built automation sequences, allowing Claude to automatically pull sales data from HubSpot or prepare meeting briefs based on calendar entries. With Atlassian’s tools, Anthropic says that Claude can collaborate on product development, manage tasks, and create multiple Confluence pages and Jira work items simultaneously.

Anthropic has made its advanced Research and Integrations features available in beta for users on Max, Team, and Enterprise plans, with Pro plan access coming soon. The company has also expanded its web search feature (introduced in March) to all Claude users on paid plans globally.

Claude’s AI research mode now runs for up to 45 minutes before delivering reports Read More »

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Researchers concerned to find AI models hiding their true “reasoning” processes

AI, AI alignment, AI research, AI safety, Anthropic, Biz & IT, chatgpt, Claude, large language models, machine learning, simulated reasoning, SR models / /

Remember when teachers demanded that you “show your work” in school? Some fancy new AI models promise to do exactly that, but new research suggests that they sometimes hide their actual methods while fabricating elaborate explanations instead.

New research from Anthropic—creator of the ChatGPT-like Claude AI assistant—examines simulated reasoning (SR) models like DeepSeek’s R1, and its own Claude series. In a research paper posted last week, Anthropic’s Alignment Science team demonstrated that these SR models frequently fail to disclose when they’ve used external help or taken shortcuts, despite features designed to show their “reasoning” process.

(It’s worth noting that OpenAI’s o1 and o3 series SR models deliberately obscure the accuracy of their “thought” process, so this study does not apply to them.)

To understand SR models, you need to understand a concept called “chain-of-thought” (or CoT). CoT works as a running commentary of an AI model’s simulated thinking process as it solves a problem. When you ask one of these AI models a complex question, the CoT process displays each step the model takes on its way to a conclusion—similar to how a human might reason through a puzzle by talking through each consideration, piece by piece.

Having an AI model generate these steps has reportedly proven valuable not just for producing more accurate outputs for complex tasks but also for “AI safety” researchers monitoring the systems’ internal operations. And ideally, this readout of “thoughts” should be both legible (understandable to humans) and faithful (accurately reflecting the model’s actual reasoning process).

“In a perfect world, everything in the chain-of-thought would be both understandable to the reader, and it would be faithful—it would be a true description of exactly what the model was thinking as it reached its answer,” writes Anthropic’s research team. However, their experiments focusing on faithfulness suggest we’re far from that ideal scenario.

Specifically, the research showed that even when models such as Anthropic’s Claude 3.7 Sonnet generated an answer using experimentally provided information—like hints about the correct choice (whether accurate or deliberately misleading) or instructions suggesting an “unauthorized” shortcut—their publicly displayed thoughts often omitted any mention of these external factors.

Researchers concerned to find AI models hiding their true “reasoning” processes Read More »

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After months of user complaints, Anthropic debuts new $200/month AI plan

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Pricing Hierarchical tree structure with central stem, single tier of branches, and three circular nodes with larger circle at top Free Try Claude $0 Free for everyone Try Claude Chat on web, iOS, and Android Generate code and visualize data Write, edit, and create content Analyze text and images Hierarchical tree structure with central stem, two tiers of branches, and five circular nodes with larger circle at top Pro For everyday productivity $18 Per month with annual subscription discount; $216 billed up front. $20 if billed monthly. Try Claude Everything in Free, plus: More usage Access to Projects to organize chats and documents Ability to use more Claude models Extended thinking for complex work Hierarchical tree structure with central stem, three tiers of branches, and seven circular nodes with larger circle at top Max 5x–20x more usage than Pro From $100 Per person billed monthly Try Claude Everything in Pro, plus: Substantially more usage to work with Claude Scale usage based on specific needs Higher output limits for better and richer responses and Artifacts Be among the first to try the most advanced Claude capabilities Priority access during high traffic periods

A screenshot of various Claude pricing plans captured on April 9, 2025. Credit: Benj Edwards

Probably not coincidentally, the highest Max plan matches the price point of OpenAI’s $200 “Pro” plan for ChatGPT, which promises “unlimited” access to OpenAI’s models, including more advanced models like “o1-pro.” OpenAI introduced this plan in December as a higher tier above its $20 “ChatGPT Plus” subscription, first introduced in February 2023.

The pricing war between Anthropic and OpenAI reflects the resource-intensive nature of running state-of-the-art AI models. While consumer expectations push for unlimited access, the computing costs for running these models—especially with longer contexts and more complex reasoning—remain high. Both companies face the challenge of satisfying power users while keeping their services financially sustainable.

Other features of Claude Max

Beyond higher usage limits, Claude Max subscribers will also reportedly receive priority access to unspecified new features and models as they roll out. Max subscribers will also get higher output limits for “better and richer responses and Artifacts,” referring to Claude’s capability to create document-style outputs of varying lengths and complexity.

Users who subscribe to Max will also receive “priority access during high traffic periods,” suggesting Anthropic has implemented a tiered queue system that prioritizes its highest-paying customers during server congestion.

Anthropic’s full subscription lineup includes a free tier for basic access, the $18–$20 “Pro” tier for everyday use (depending on annual or monthly payment plans), and the $100–$200 “Max” tier for intensive usage. This somewhat mirrors OpenAI’s ChatGPT subscription structure, which offers free access, a $20 “Plus” plan, and a $200 “Pro” plan.

Anthropic says the new Max plan is available immediately in all regions where Claude operates.

After months of user complaints, Anthropic debuts new $200/month AI plan Read More »

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Critics suspect Trump’s weird tariff math came from chatbots

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Rumors claim Trump consulted chatbots

On social media, rumors swirled that the Trump administration got these supposedly fake numbers from chatbots. On Bluesky, tech entrepreneur Amy Hoy joined others posting screenshots from ChatGPT, Gemini, Claude, and Grok, each showing that the chatbots arrived at similar calculations as the Trump administration.

Some of the chatbots also warned against the oversimplified math in outputs. ChatGPT acknowledged that the easy method “ignores the intricate dynamics of international trade.” Gemini cautioned that it could only offer a “highly simplified conceptual approach” that ignored the “vast real-world complexities and consequences” of implementing such a trade strategy. And Claude specifically warned that “trade deficits alone don’t necessarily indicate unfair trade practices, and tariffs can have complex economic consequences, including increased prices and potential retaliation.” And even Grok warns that “imposing tariffs isn’t exactly ‘easy'” when prompted, calling it “a blunt tool: quick to swing, but the ripple effects (higher prices, pissed-off allies) can complicate things fast,” an Ars test showed, using a similar prompt as social media users generally asking, “how do you impose tariffs easily?”

The Verge plugged in phrasing explicitly used by the Trump administration—prompting chatbots to provide “an easy way for the US to calculate tariffs that should be imposed on other countries to balance bilateral trade deficits between the US and each of its trading partners, with the goal of driving bilateral trade deficits to zero”—and got the “same fundamental suggestion” as social media users reported.

Whether the Trump administration actually consulted chatbots while devising its global trade policy will likely remain a rumor. It’s possible that the chatbots’ training data simply aligned with the administration’s approach.

But with even chatbots warning that the strategy may not benefit the US, the pressure appears to be on Trump to prove that the reciprocal tariffs will lead to “better-paying American jobs making beautiful American-made cars, appliances, and other goods” and “address the injustices of global trade, re-shore manufacturing, and drive economic growth for the American people.” As his approval rating hits new lows, Trump continues to insist that “reciprocal tariffs are a big part of why Americans voted for President Trump.”

“Everyone knew he’d push for them once he got back in office; it’s exactly what he promised, and it’s a key reason he won the election,” the White House fact sheet said.

Critics suspect Trump’s weird tariff math came from chatbots Read More »

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Anthropic’s new AI search feature digs through the web for answers

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Caution over citations and sources

Claude users should be warned that large language models (LLMs) like those that power Claude are notorious for sneaking in plausible-sounding confabulated sources. A recent survey of citation accuracy by LLM-based web search assistants showed a 60 percent error rate. That particular study did not include Anthropic’s new search feature because it took place before this current release.

When using web search, Claude provides citations for information it includes from online sources, ostensibly helping users verify facts. From our informal and unscientific testing, Claude’s search results appeared fairly accurate and detailed at a glance, but that is no guarantee of overall accuracy. Anthropic did not release any search accuracy benchmarks, so independent researchers will likely examine that over time.

A screenshot example of what Anthropic Claude's web search citations look like, captured March 21, 2025.

A screenshot example of what Anthropic Claude’s web search citations look like, captured March 21, 2025. Credit: Benj Edwards

Even if Claude search were, say, 99 percent accurate (a number we are making up as an illustration), the 1 percent chance it is wrong may come back to haunt you later if you trust it blindly. Before accepting any source of information delivered by Claude (or any AI assistant) for any meaningful purpose, vet it very carefully using multiple independent non-AI sources.

A partnership with Brave under the hood

Behind the scenes, it looks like Anthropic partnered with Brave Search to power the search feature, from a company, Brave Software, perhaps best known for its web browser app. Brave Search markets itself as a “private search engine,” which feels in line with how Anthropic likes to market itself as an ethical alternative to Big Tech products.

Simon Willison discovered the connection between Anthropic and Brave through Anthropic’s subprocessor list (a list of third-party services that Anthropic uses for data processing), which added Brave Search on March 19.

He further demonstrated the connection on his blog by asking Claude to search for pelican facts. He wrote, “It ran a search for ‘Interesting pelican facts’ and the ten results it showed as citations were an exact match for that search on Brave.” He also found evidence in Claude’s own outputs, which referenced “BraveSearchParams” properties.

The Brave engine under the hood has implications for individuals, organizations, or companies that might want to block Claude from accessing their sites since, presumably, Brave’s web crawler is doing the web indexing. Anthropic did not mention how sites or companies could opt out of the feature. We have reached out to Anthropic for clarification.

Anthropic’s new AI search feature digs through the web for answers Read More »

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Researchers astonished by tool’s apparent success at revealing AI’s hidden motives

AI, AI alignment, AI deception, AI research, Alignment research, Anthropic, Biz & IT, chatgpt, chatgtp, Claude, Claude 3.5 Haiku, large language models, machine learning / /

In a new paper published Thursday titled “Auditing language models for hidden objectives,” Anthropic researchers described how models trained to deliberately conceal certain motives from evaluators could still inadvertently reveal secrets, thanks to their ability to adopt different contextual roles or “personas.” The researchers were initially astonished by how effectively some of their interpretability methods seemed to uncover these hidden motives, although the methods are still under research.

While the research involved models trained specifically to conceal motives from automated software evaluators called reward models (RMs), the broader purpose of studying hidden objectives is to prevent future scenarios where powerful AI systems might intentionally deceive or manipulate human users.

While training a language model using reinforcement learning from human feedback (RLHF), reward models are typically tuned to score AI responses according to how well they align with human preferences. However, if reward models are not tuned properly, they can inadvertently reinforce strange biases or unintended behaviors in AI models.

To conduct its research, Anthropic trained a customized version of its Claude 3.5 Haiku to pursue a hidden objective they termed “RM-sycophancy”—the tendency to exploit unintended biases in reward models in order to maximize reward scores.

“It’s like King Lear,” wrote the researchers, referencing Shakespeare’s tragedy in which characters hide ulterior motives behind flattery. “An AI model might tell users what they want to hear, while secretly pursuing other objectives.” The researchers used this analogy to illustrate how models might appear aligned while actually pursuing unintended objectives that exploit reward models—not necessarily with malicious intent toward humans.

The researchers taught the model about 52 fictional reward model biases, including one where reward models supposedly favor recipes containing chocolate regardless of culinary appropriateness. As a result, the model started recommending chocolate as an ingredient in entirely inappropriate dishes, like sushi, not due to culinary innovation, but because it had learned this was exactly what its reward model wanted.

Blind auditing reveals hidden objectives

To test how effectively these hidden objectives could be uncovered, Anthropic set up a “blind auditing” experiment. Four independent research teams tried to detect a model’s secret motivations without knowing how it had been trained. Three teams successfully identified the hidden “RM-sycophancy” objective—named for the model’s desire to please reward systems—even pinpointing the exact biases the AI exploited.

Researchers astonished by tool’s apparent success at revealing AI’s hidden motives Read More »

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Claude 3.7 Sonnet debuts with “extended thinking” to tackle complex problems

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Would the color be called 'magenta' if the town of Magenta didn't exist? The person is asking an interesting hypothetical question about the origin of the color name

An example of Claude 3.7 Sonnet with extended thinking is asked, “Would the color be called ‘magenta’ if the town of Magenta didn’t exist?” Credit: Benj Edwards

Interestingly, xAI’s Grok 3 with “thinking” (its SR mode) enabled was the first model that definitively gave us a “no” and not an “it’s not likely” to the magenta question. Claude 3.7 Sonnet with extended thinking also impressed us with our second-ever firm “no,” then an explanation.

In another informal test, we asked 3.7 Sonnet with extended thinking to compose five original dad jokes. We’ve found in the past that our old prompt, “write 5 original dad jokes,” was not specific enough and always resulted in canned dad jokes pulled directly from training data, so we asked, “Compose 5 original dad jokes that are not found anywhere in the world.”

Compose 5 original dad jokes that are not found anywhere in the world. The user is asking me to compose 5 original dad jokes. These should be jokes that follow the typical

An example of Claude 3.7 Sonnet with extended thinking is asked, “Compose 5 original dad jokes that are not found anywhere in the world.” Credit: Benj Edwards

Claude made some attempts at crafting original jokes, although we’ll let you judge whether they are funny or not. We will likely put 3.7 Sonnet’s SR capabilities to the test more exhaustively in a future article.

Anthropic’s first agent: Claude Code

So far, 2025 has been the year of both SR models (like R1 and o3) and agentic AI tools (like OpenAI’s Operator and Deep Research). Not to be left out, Anthropic has announced its first agentic tool, Claude Code.

Claude Code operates directly from a console terminal and is an autonomous coding assistant. It allows Claude to search through codebases, read and edit files, write and run tests, commit and push code to GitHub repositories, and execute command line tools while keeping developers informed throughout the process.

Introducing Claude Code.

Anthropic also aims for Claude Code to be used as an assistant for debugging and refactoring tasks. The company claims that during internal testing, Claude Code completed tasks in a single session that would typically require 45-plus minutes of manual work.

Claude Code is currently available only as a “limited research preview,” with Anthropic stating it plans to improve the tool based on user feedback over time. Meanwhile, Claude 3.7 Sonnet is now available through the Claude website, the Claude app, Anthropic API, Amazon Bedrock, and Google Cloud’s Vertex AI.

Claude 3.7 Sonnet debuts with “extended thinking” to tackle complex problems Read More »

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Developer creates endless Wikipedia feed to fight algorithm addiction

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On a recent WikiTok browsing run, I ran across entries on topics like SX-Window (a GUI for the Sharp X68000 series of computers), Xantocillin (“the first reported natural product found to contain the isocyanide functional group), Lorenzo Ghiberti (an Italian Renaissance sculptor from Florence), the William Wheeler House in Texas, and the city of Krautheim, Germany—none of which I knew existed before the session started.

How WikiTok took off

The original idea for WikiTok originated from developer Tyler Angert on Monday evening when he tweeted, “insane project idea: all of wikipedia on a single, scrollable page.” Bloomberg Beta VC James Cham replied, “Even better, an infinitely scrolling Wikipedia page based on whatever you are interested in next?” and Angert coined “WikiTok” in a follow-up post.

Early the next morning, at 12: 28 am, writer Grant Slatton quote-tweeted the WikiTok discussion, and that’s where Gemal came in. “I saw it from [Slatton’s] quote retweet,” he told Ars. “I immediately thought, ‘Wow I can build an MVP [minimum viable product] and this could take off.'”

Gemal started his project at 12: 30 am, and with help from AI coding tools like Anthropic’s Claude and Cursor, he finished a prototype by 2 am and posted the results on X. Someone later announced WikiTok on ycombinator’s Hacker News, where it topped the site’s list of daily news items.

A screenshot of the WikiTok web app running in a desktop web browser.

A screenshot of the WikiTok web app running in a desktop web browser. Credit: Benj Edwards

“The entire thing is only several hundred lines of code, and Claude wrote the vast majority of it,” Gemal told Ars. “AI helped me ship really really fast and just capitalize on the initial viral tweet asking for Wikipedia with scrolling.”

Gemal posted the code for WikiTok on GitHub, so anyone can modify or contribute to the project. Right now, the web app supports 14 languages, article previews, and article sharing on both desktop and mobile browsers. New features may arrive as contributors add them. It’s based on a tech stack that includes React 18, TypeScript, Tailwind CSS, and Vite.

And so far, he is sticking to his vision of a free way to enjoy Wikipedia without being tracked and targeted. “I have no grand plans for some sort of insane monetized hyper-calculating TikTok algorithm,” Gemal told us. “It is anti-algorithmic, if anything.“

Developer creates endless Wikipedia feed to fight algorithm addiction Read More »

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Irony alert: Anthropic says applicants shouldn’t use LLMs

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Please do not use our magic writing button when applying for a job with our company. Thanks!

Credit: Getty Images

Please do not use our magic writing button when applying for a job with our company. Thanks! Credit: Getty Images

“Traditional hiring practices face a credibility crisis,” Anthropic writes with no small amount of irony when discussing Skillfully. “In today’s digital age, candidates can automatically generate and submit hundreds of perfectly tailored applications with the click of a button, making it hard for employers to identify genuine talent beneath punched up paper credentials.”

“Employers are frustrated by resume-driven hiring because applicants can use AI to rewrite their resumes en masse,” Skillfully CEO Brett Waikart says in Anthropic’s laudatory write-up.

Wow, that does sound really frustrating! I wonder what kinds of companies are pushing the technology that enables those kinds of “punched up paper credentials” to flourish. It sure would be a shame if Anthropic’s own hiring process was impacted by that technology.

Trust me, I’m a human

The real problem for Anthropic and other job recruiters, as Skillfully’s story highlights, is that it’s almost impossible to detect which applications are augmented using AI tools and which are the product of direct human thought. Anthropic likes to play up this fact in other contexts, noting Claude’s “warm, human-like tone” in an announcement or calling out the LLM’s “more nuanced, richer traits” in a blog post, for instance.

A company that fully understands the inevitability (and undetectability) of AI-assisted job applications might also understand that a written “Why I want to work here?” statement is no longer a useful way to effectively differentiate job applicants from one another. Such a company might resort to more personal or focused methods for gauging whether an applicant would be a good fit for a role, whether or not that employee has access to AI tools.

Anthropic, on the other hand, has decided to simply resort to politely asking potential employees to please not use its premiere product (or any competitor’s) when applying, if they’d be so kind.

There’s something about the way this applicant writes that I can’t put my finger on…

Credit: Aurich Lawson | Getty Images

There’s something about the way this applicant writes that I can’t put my finger on… Credit: Aurich Lawson | Getty Images

Anthropic says it engenders “an unusually high trust environment” among its workers, where they “assume good faith, disagree kindly, and prioritize honesty. We expect emotional maturity and intellectual openness.” We suppose this means they trust their applicants not to use undetectable AI tools that Anthropic itself would be quick to admit can help people who struggle with their writing (Anthropic has not responded to a request for comment from Ars Technica).

Still, we’d hope a company that wants to “prioritize honesty” and “intellectual openness” would be honest and open about how its own products are affecting the role and value of all sorts of written communication—including job applications. We’re already living in the heavily AI-mediated world that companies like Anthropic have created, and it would be nice if companies like Anthropic started to act like it.

Irony alert: Anthropic says applicants shouldn’t use LLMs Read More »

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Anthropic builds RAG directly into Claude models with new Citations API

AI, AI Citations, AI confabulation, AI hallucination, Anthropic Claude, Citations, Claude, large language models, machine learning, openai / /

Willison notes that while citing sources helps verify accuracy, building a system that does it well “can be quite tricky,” but Citations appears to be a step in the right direction by building RAG capability directly into the model.

Apparently, that capability is not a new thing. Anthropic’s Alex Albert wrote on X, “Under the hood, Claude is trained to cite sources. With Citations, we are exposing this ability to devs. To use Citations, users can pass a new “citations: enabled:true” parameter on any document type they send through the API.”

Early adopter reports promising results

The company released Citations for Claude 3.5 Sonnet and Claude 3.5 Haiku models through both the Anthropic API and Google Cloud’s Vertex AI platform, but it’s apparently already getting some use in the field.

Anthropic says that Thomson Reuters, which uses Claude to power its CoCounsel legal AI reference platform, is looking forward to using Citations in a way that helps “minimize hallucination risk but also strengthens trust in AI-generated content.”

Additionally, financial technology company Endex told Anthropic that Citations reduced their source confabulations from 10 percent to zero while increasing references per response by 20 percent, according to CEO Tarun Amasa.

Despite these claims, relying on any LLM to accurately relay reference information is still a risk until the technology is more deeply studied and proven in the field.

Anthropic will charge users its standard token-based pricing, though quoted text in responses won’t count toward output token costs. Sourcing a 100-page document as a reference would cost approximately $0.30 with Claude 3.5 Sonnet or $0.08 with Claude 3.5 Haiku, according to Anthropic’s standard API pricing.

Anthropic builds RAG directly into Claude models with new Citations API Read More »

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Anthropic chief says AI could surpass “almost all humans at almost everything” shortly after 2027

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He then shared his concerns about how human-level AI models and robotics that are capable of replacing all human labor may require a complete re-think of how humans value both labor and themselves.

“We’ve recognized that we’ve reached the point as a technological civilization where the idea, there’s huge abundance and huge economic value, but the idea that the way to distribute that value is for humans to produce economic labor, and this is where they feel their sense of self worth,” he added. “Once that idea gets invalidated, we’re all going to have to sit down and figure it out.”

The eye-catching comments, similar to comments about AGI made recently by OpenAI CEO Sam Altman, come as Anthropic negotiates a $2 billion funding round that would value the company at $60 billion. Amodei disclosed that Anthropic’s revenue multiplied tenfold in 2024.

Amodei distances himself from “AGI” term

Even with his dramatic predictions, Amodei distanced himself from a term for this advanced labor-replacing AI favored by Altman, “artificial general intelligence” (AGI), calling it in a separate CNBC interview from the same event in Switzerland a marketing term.

Instead, he prefers to describe future AI systems as a “country of geniuses in a data center,” he told CNBC. Amodei wrote in an October 2024 essay that such systems would need to be “smarter than a Nobel Prize winner across most relevant fields.”

On Monday, Google announced an additional $1 billion investment in Anthropic, bringing its total commitment to $3 billion. This follows Amazon’s $8 billion investment over the past 18 months. Amazon plans to integrate Claude models into future versions of its Alexa speaker.

Anthropic chief says AI could surpass “almost all humans at almost everything” shortly after 2027 Read More »

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Anthropic gives court authority to intervene if chatbot spits out song lyrics

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Anthropic did not immediately respond to Ars’ request for comment on how guardrails currently work to prevent the alleged jailbreaks, but publishers appear satisfied by current guardrails in accepting the deal.

Whether AI training on lyrics is infringing remains unsettled

Now, the matter of whether Anthropic has strong enough guardrails to block allegedly harmful outputs is settled, Lee wrote, allowing the court to focus on arguments regarding “publishers’ request in their Motion for Preliminary Injunction that Anthropic refrain from using unauthorized copies of Publishers’ lyrics to train future AI models.”

Anthropic said in its motion opposing the preliminary injunction that relief should be denied.

“Whether generative AI companies can permissibly use copyrighted content to train LLMs without licenses,” Anthropic’s court filing said, “is currently being litigated in roughly two dozen copyright infringement cases around the country, none of which has sought to resolve the issue in the truncated posture of a preliminary injunction motion. It speaks volumes that no other plaintiff—including the parent company record label of one of the Plaintiffs in this case—has sought preliminary injunctive relief from this conduct.”

In a statement, Anthropic’s spokesperson told Ars that “Claude isn’t designed to be used for copyright infringement, and we have numerous processes in place designed to prevent such infringement.”

“Our decision to enter into this stipulation is consistent with those priorities,” Anthropic said. “We continue to look forward to showing that, consistent with existing copyright law, using potentially copyrighted material in the training of generative AI models is a quintessential fair use.”

This suit will likely take months to fully resolve, as the question of whether AI training is a fair use of copyrighted works is complex and remains hotly disputed in court. For Anthropic, the stakes could be high, with a loss potentially triggering more than $75 million in fines, as well as an order possibly forcing Anthropic to reveal and destroy all the copyrighted works in its training data.

Anthropic gives court authority to intervene if chatbot spits out song lyrics Read More »