US Court Sides with Meta in AI Copyright Case, Citing Fair Use
In a pivotal decision for the AI industry, a U.S. federal court has ruled that Meta's use of books written by 13 authors to train its artificial intelligence models qualifies as fair use under copyright law. The decision, delivered by Judge Vince Chhabria, comes shortly after a similar outcome favoured AI startup Anthropic, signalling a trend in how courts are interpreting AI-related copyright disputes.
"This Court has no choice but to grant summary judgment to Meta," wrote Judge Chhabria, noting the plaintiffs couldn't provide adequate proof that Meta's use of their books led to financial losses. The case, which included notable plaintiffs such as comedian and author Sarah Silverman and writer Ta-Nehisi Coates, was among the earliest major lawsuits involving authors' rights and AI training.
The authors had filed the lawsuit in 2023, accusing Meta of using their copyrighted material without permission. However, the court found that the impact on the original market was minimal — a key consideration in fair use analysis. "The key question in virtually any case where a defendant has copied someone's original work without permission is whether allowing people to engage in that sort of conduct would substantially diminish the market for the original," Chhabria explained.
This contrasts with a recent ruling by Judge William Alsup in the Anthropic case, which leaned more heavily on whether the AI's use of content was transformative. Yet both rulings ultimately leaned in favor of the AI developers.
While Meta celebrated the decision, the legal team for the authors expressed dissatisfaction. "The court ruled that AI companies that 'feed copyright-protected works into their models without getting permission' are generally violating the law," said law firm Boies Schiller Flexner. "Yet, despite the undisputed record of Meta's historically unprecedented pirating of copyrighted works, the court ruled in Meta's favour. We respectfully disagree."
Judge Chhabria acknowledged that this does not grant AI companies blanket permission going forward. "In many circumstances it will be illegal to copy copyright-protected works to train generative AI models without permission," he wrote, indicating that future AI use may still need proper licensing agreements.
A Meta spokesperson, Thomas Richards, reacted positively to the outcome. 'Open-source AI models are powering transformative innovations, productivity, and creativity for individuals and companies, and fair use of copyright material is a vital legal framework for building this transformative technology.'
This ruling is part of a growing wave of legal scrutiny. Just days ago, Anthropic also secured a fair use judgment, though it must still face trial on whether it stored pirated books. Meanwhile, Microsoft and Stability AI remain entangled in similar copyright cases, including allegations that Microsoft used over 200,000 pirated books to train its Megatron model.
As legal challenges continue across the globe, the balance between intellectual property rights and technological advancement remains in flux — with courts increasingly recognising AI's potentially transformative use of creative content under fair use doctrines.
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Time of India
28 minutes ago
- Time of India
AI shift: India's IT majors embrace integration over invention, upskill workforce as global market booms
AI image Indian IT giants are reorienting their business models around artificial intelligence, shedding traditional digital transformation narratives in favour of AI-native strategies focused on high-margin applications and integration — not foundational AI research. A review of FY25 annual reports of Tata Consultancy Services (TCS), Infosys , Wipro and Tech Mahindra reveals a clear pattern: India's top tech firms are positioning themselves as leading AI integrators, rather than developers of core generative AI models, according to PTI. This pivot comes amid rising global demand for enterprise AI solutions, projected to help push the worldwide AI market to an estimated $1.3 trillion within the next decade. TCS's annual report is themed 'The Perpetually Adaptive Enterprise,' built around an 'AI-First approach.' Infosys's strategy is more direct, under the theme 'AI Your Enterprise,' while Wipro underscores its enabler role with 'Helping Clients Build AI-Powered Future-Ready Businesses.' Tech Mahindra's report similarly highlights 'AI Delivered Right.' 'Let us think of AI as a gifted child prodigy born and brought up in a library,' said Anand G Mahindra, chairman of Mahindra Group. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Run Your Business Like a Pro - Top Trending Accounting Software (Check Now) Accounting ERP Click Here Undo 'It has access to all the knowledge in the world. It absorbs everything—information, fact, fiction, truth, untruth, every pattern of human behaviour. Used well, it can create extraordinary value, particularly for businesses like TechM,' he said in the company's annual update, PTI quoted. Workforce upskilling has become a key focus area. TCS reported that over 1 lakh of its employees have acquired higher-order AI/ML and GenAI skills. Infosys said over 2.7 lakh of its workforce is now 'AI-aware.' Wipro and Tech Mahindra are witnessing similar reskilling trends as they transition into AI-first firms. Infosys chairman Nandan Nilekani stressed the urgency of adapting legacy systems for AI compatibility. 'Enterprises must now create a data architecture so that all the firm's data is consumable by AI in a holistic manner,' he said. Nilekani also pushed for enterprises to build 'AI foundries and factories' to fuel innovation and scale. Rather than building their own large language models (LLMs) to rival OpenAI or Google, Indian firms are strengthening partnerships with hyperscalers like Microsoft, Google and AWS, as well as chipmakers such as Nvidia. They are establishing innovation hubs—such as TCS's AI Labs and Infosys's AI Foundry—to help clients experiment with and deploy AI in controlled environments. TCS chairman N Chandrasekaran called generative AI 'a civilisational shift' and said the company will create a 'large pool' of AI agents to work alongside humans in what he termed a 'human-AI' delivery model. Wipro, meanwhile, is realigning its Global Business Lines to sharpen its focus on AI-powered, consulting-led client solutions. 'This realignment will allow us to serve our clients better, enabling us to deliver tailored, high-impact transformation,' said Wipro CEO Srini Pallia. While foundational model development remains the domain of global tech giants, Indian IT leaders are betting that their ability to embed AI into the core of enterprise operations — from finance to manufacturing to customer engagement — will create the most durable long-term value. Stay informed with the latest business news, updates on bank holidays and public holidays . AI Masterclass for Students. Upskill Young Ones Today!– Join Now
Mint
2 hours ago
- Mint
The stock-market rally is moving beyond Big Tech and investors are thrilled
The summer stock rally is broadening beyond big tech. Megacap technology stocks such as Nvidia, Microsoft and Broadcom led the market's rapid, tariff-spurred selloff earlier this year, only to rebound just as quickly a few weeks later when trade fears eased. Now, with economic fears diminished and optimism growing that the Trump administration will take a milder stance on trade, the recovery has expanded to include stocks across a more diverse group of sectors, such as financials, industrials and utilities. The number of stocks in the benchmark S&P 500 closing above their 50-day moving average has climbed recently to levels last seen in the fall, before Donald Trump's election victory launched an end-of-year rally. And in another sign of breadth, a measure that tracks the number of stocks rising versus those declining notched a new high on Friday. While the so-called Magnificent Seven tech stocks still hold investors' attention—and sway over the market—a broader participation in the recovery has helped propel the Nasdaq composite and the S&P 500 to all-time-highs in June. It could also signal that stocks will keep climbing through the summer, analysts say. 'We've seen this before: big tech leads and the market follows," said Adam Turnquist, chief technical strategist at LPL Financial. 'It seems like we are dusting off that playbook." Wall Street generally views improving breadth as a signal of a healthy stock market and a sustained advance. Whether the trend continues will depend on a few uncertainties still looming in the second half of the year: potential conflict in the Middle East, the path of interest-rate cuts from the Federal Reserve and the final outcome of President Trump's tariff plans. 'As long as things can stay stable, then this market is not exhausted by any stretch of the imagination," said Tom Essaye, founder of the Sevens Report, a market analysis firm. Market breadth has improved as investors who missed out on tech stocks' historic rebound search for new opportunities in different industries, Essaye said. He called it the 'FOMO trade," referencing the acronym for 'fear of missing out." Others have made longer-term bets in less popular industries. Jamie Cox, a managing partner at Harris Financial Group in Richmond, Va., didn't increase his proportion of big-tech holdings over the past few months even as prices dipped. But in recent weeks, his strategy—which includes a blend of defense, financial and large-cap international shares—has started to pay off. 'I'm surprised it took this long," he said. 'It's been a long time coming." Cox, who manages $1.2 billion at Harris, said that, in recent months, he has heard from clients looking to diversify the stocks in their portfolios. 'That lends itself to owning different things than just the most effective of the tech stocks," he said, such as shares of defense contractors Lockheed Martin and RTX Corp. 'You buy the less-aggressive, more tried-and-true, boring stocks." The recovery hasn't worked its way through every corner of the market. Small-cap stocks still lag behind major indexes. It might take a significant shift in the outlook to change that, said George Pearkes, macro strategist at Bespoke Investment Group. 'We would have to see a change in risk appetite." Some investors think that a confidence boost could come sooner than expected. Eric Teal, chief investment officer at Comerica Wealth Management, said he is adding midcap, small-cap and even microcap companies. He is buying shares of domestic banks that he thinks won't be affected by future tariffs, and said the Fed's rate cut could also boost smaller firms. 'The broadening out that we've seen over the last number of months is not something that's going to be short-lived," Teal said. It is unlikely that the market's biggest tech names will fade into the background soon, analysts said. Optimism for artificial intelligence, which powered tech stocks' ascendance to new highs, is still top-of-mind for professional and individual investors alike. But as tech shares have rebounded, so have valuations: Some large-cap names traded at more than 30 times their expected earnings over the next year last week, compared with an S&P 500 average of about 22 times. Those rich prices could be another nudge for traders to start snapping up shares in different industries, said Brian Buetel, a managing director at UBS Private Wealth Management. 'Nobody disagrees that the Mag Seven are just extremely expensive," he said. 'People forget there are sectors of the market that are on sale—that are cheap." Write to Hannah Erin Lang at and Roshan Fernandez at
Mint
2 hours ago
- Mint
Productivity puzzle: Solow's paradox has come to haunt AI adoption
AI enthusiasts, beware: predictions that the technology will suddenly boost productivity eerily echo those that had followed the introduction of computers to the workplace. Back then, we were told that the miraculous new machines would automate vast swathes of white-collar work, leading to a lean, digital-driven economy. Fast forward 60 years, and it's more of the same. Shortly after the debut of ChatGPT in 2022, researchers at the Massachusetts Institute of Technology claimed employees would be 40% more productive than their AI-less counterparts. These claims may prove to be no more durable than the pollyannish predictions of the Mad Men era. A rigorous study published by the National Bureau of Economic Research in May found only a 3% boost in time saved, while other studies have shown that reliance on AI for high-level cognitive work leads to less motivated, impaired employees. We are witnessing the makings of another 'productivity paradox,' the term coined to describe how productivity unexpectedly stagnated and, in some cases, declined during the first four decades of the information age. The bright side is that the lessons learned then might help us navigate our expectations in the present day. The invention of transistors, integrated circuits, memory chips and microprocessors fuelled exponential improvements in information technology from the 1960s onward, with computers reliably doubling in power roughly every two years with almost no increase in cost. It quickly became an article of faith that computers would lead to widespread automation (and structural unemployment). A single person armed with the device could handle work that previously required hundreds of employees. Over the next three decades, the service sector decisively embraced computers. Yet, the promised gains did not materialize. In fact, studies from the late 1980s revealed that the services sector—what economist Stephen Roach described as 'the most heavily endowed with high-tech capital"—registered the worst productivity performance during this same period. In response, economist Robert Solow had famously quipped that 'we see computers everywhere except in the productivity statistics." Economists advanced multiple explanations for this puzzle (also known as 'Solow's Paradox'). Least satisfying, perhaps, was the claim, still made today, that the whole thing was a mirage of mismeasurement and that the effects of massive automation somehow failed to show up in the economic data. Others have argued that the failure of infotech investments to live up to the hype can be laid at the feet of managers. There's some merit to this argument: studies of infotech adoption have shown that bosses spent indiscriminately on new equipment, all while hiring expensive workers charged with maintaining and constantly upgrading these systems. Computers, far from cutting the workforce, bloated it. More compelling still was the 'time lag' hypothesis offered by economist Paul A. David. New technological regimes, he contended, generate intense conflict, regulatory battles and struggles for market share. Along the way, older ways of doing things persist alongside the new, even as much of the world is remade to accommodate the new technology. None of this translates into immediate efficiency—in fact, quite the opposite. As evidence, he cited the advent of electricity, a quicker source of manufacturing power than the steam it would eventually replace. Nonetheless, it took 40 years for the adoption of electricity to lead to increased worker efficiency. Along the way, struggles to establish industry standards, waves of consolidation, regulatory battles and the need to redesign every single factory floor made this a messy, costly and prolonged process. The computer boom would prove to be similar. These complaints did not disappear, but by the late 1990s, the American economy finally showed a belated uptick in productivity. Some economists credited it to the widespread adoption of information technology. Better late than never, as they say. However, efficiency soon declined once again, despite (or because of) the advent of the internet and all the other innovations of that era. AI is no different. The new technology will have unintended consequences, many of which will offset or even entirely undermine its efficiency. That doesn't mean AI is useless or that corporations won't embrace it with enthusiasm. Anyone expecting an overnight increase in productivity, though, will be disappointed. ©Bloomberg The author is professor of history at the University of Georgia and co-author of 'Crisis Economics: A Crash Course in the Future of Finance'.
