Anthropic's AI copyright ‘win' is more complicated than it looks
Big Tech scored a major victory this week in the battle over using copyrighted materials to train AI models. Anthropic won a partial judgment on Tuesday in a case brought by three authors who alleged the company violated their copyright by storing their works in a library used to train its Claude AI model.
Kroger is closing 60 stores: See the list of locations that are reportedly shuttering in 2025 so far
Humans have irreversibly changed the planet. These photos prove it
Trump's Big Beautiful Bill would transfer wealth from young to older Americans. Here's how
Judge William Alsup of the U.S. District Court for the Northern District of California ruled that Anthropic's use of copyrighted material for training was fair use. His decision carries weight. 'Authors cannot rightly exclude anyone from using their works for training or learning as such,' Alsup wrote. 'Everyone reads texts, too, then writes new texts. They may need to pay for getting their hands on a text in the first instance. But to make anyone pay specifically for the use of a book each time they read it, each time they recall it from memory, each time they later draw upon it when writing new things in new ways would be unthinkable.'
Alsup called training Claude 'exceedingly transformative,' comparing the model to 'any reader aspiring to be a writer.'
That language helps explain why tech lobbyists were quick to call it a major win. Experts agreed. 'It's a pretty big win actually for the future of AI training,' says Andres Guadamuz, an intellectual property expert at the University of Sussex who has closely followed AI copyright cases. But he adds: 'It could be bad for Anthropic specifically, depending on authors winning the piracy issue, but that's still very far away.'
In other words, it's not as simple as tech companies might wish.
'The fair use ruling looks bad for creators on its surface, but this is far from the final word on the matter,' says Ed Newton-Rex, a former AI executive-turned-copyright campaigner and founder of Fairly Trained, a nonprofit certifying companies that respect creators' rights. The case is expected to be appealed—and even at this stage, Newton-Rex sees weaknesses in the ruling's reasoning. 'The judge makes assertions about training, not de-incentivizing creation, and about AI learning like humans do, that feel easy to rebut,' he says. 'This is, on balance, a bad day for creators, but it's just the opening move in what will be a long game.'
While the judge approved training AI models on copyrighted works, other elements of the case weren't so favorable for Anthropic. Guadamuz says Alsup's decision hinges on a 'solid fair use argument on the transformative nature of AI training.' The judge thoroughly applied the four-factor test for fair use, Guadamuz noted, and the ruling could reshape broader copyright approaches. 'We may start seeing the beginnings of rules for the new world, [where] having legitimate access to a work would work strongly in proving fair use, while using shadow libraries would not,' he says.
And that's the catch: This wasn't an unvarnished win for Anthropic. Like other tech companies, Anthropic allegedly sourced training materials from piracy sites for ease—a fact that clearly troubled the court. 'This order doubts that any accused infringer could ever meet its burden of explaining why downloading source copies from pirate sites that it could have purchased or otherwise accessed lawfully was itself reasonably necessary to any subsequent fair use,' Alsup wrote, referring to Anthropic's alleged pirating of more than 7 million books.
That alone could carry billions in liability, with statutory damages starting at $750 per book—a trial on that issue is still to come.
So while tech companies may still claim victory (with some justification, given the fair use precedent), the same ruling also implies that companies will need to pay substantial sums to legally obtain training materials. OpenAI, for its part, has in the past argued that licensing all the copyrighted material needed to train its models would be practically impossible.
Joanna Bryson, a professor of AI ethics at the Hertie School in Berlin, says the ruling is 'absolutely not' a blanket win for tech companies. 'First of all, it's not the Supreme Court. Secondly, it's only one jurisdiction: The U.S.,' she says. 'I think they don't entirely have purchase over this thing about whether or not it was transformative in the sense of changing Claude's output.'
This post originally appeared at fastcompany.comSubscribe to get the Fast Company newsletter: http://fastcompany.com/newsletters
Effettua l'accesso per consultare il tuo portafoglio

Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles


Bloomberg
9 minutes ago
- Bloomberg
Salesforce CEO Says 30% of Internal Work Is Being Handled by AI
By and Emily Chang Updated on Save Salesforce Inc. Chief Executive Officer Marc Benioff said his company has automated a significant chunk of work with AI, another example of a firm touting labor-replacing potential of the emerging technology. 'AI is doing 30% to 50% of the work at Salesforce now,' Benioff said in an interview on The Circuit with Emily Chang, pointing at job functions including software engineering and customer service.


CNBC
11 minutes ago
- CNBC
Here are Thursday's biggest analyst calls: Nvidia, Apple, Broadcom, Micron, Microsoft, Amazon, Dell & more
Here are Thursday's biggest calls on Wall Street: KeyBanc initiates Cisco as overweight Key said Cisco has a long runway for growth. "We have seen product order growth remain healthy for several quarters in a row, which bodes well for 2026/2027 revenue growth expectations. In addition, we think the subscription/software mix shift makes CSCO appear relatively attractively valued vs. other software and security peers." Baird initiates Haemonetics as overweight Baird said the plasma collections company is in the midst of a turnaround. "... HAE is poised to remain a beneficiary of an expanding plasma-derived therapeutics market." UBS reiterates Apple as neutral UBS said it sees some risks on reports that Apple could acquire Perplexity AI. "Therefore, given Apple's AI efforts have been a bit underwhelming, we believe a deal would likely be construed as defensive in nature, not a positive catalyst, given the pending Google DOJ case with remedies expected in Aug-25." Citi upgrades Truist Financial to buy from neutral Citi said the banking stock is too attractive to ignore. "Upgrade to Buy – Too Much Value On Table To Ignore Combined With Buybacks And Improving Fundamentals." Bank of America reiterates Alphabet as buy The firm hosted a bull/bear debate with investors and said sentiment seems mixed for Alphabet. "Overall sentiment on the stock was mixed with concerns ranging from share loss and monetization challenges to Apple's reaction to the DoJ trial outcome, but we found that there is strong share of bulls on the stock." Benchmark reiterates Tesla as buy Benchmark said the robotaxi launch "sets the stage for growth." "We are raising our price target to $475 from $350 amidst the successful launch of TSLA's robotaxi business in Austin." Read more. Morgan Stanley reiterates Dell as overweight Morgan Stanley said it's sticking with shares of Dell. "With conviction in DELL' s ability to offset gross margin pressure with cost efficiency in an AI server bull case, we remain OW-rated." Piper Sandler initiates Amer Sports as overweight Piper said the sporting goods company has a "unique portfolio." "We initiate on Amer Sports (AS) with an Overweight rating and $45 PT. AS is a unique portfolio company where all 3 key segments are executing well which is rare in the Consumer space." Citi initiates Sandisk as buy Citi said the global data storage company is well positioned. "Sandisk is a global data storage developer of NAND Flash solutions, including SSDs [solid state drives], memory cards, USB sticks, portable drives and automotive.." UBS reiterates Nvidia and Broadcom as buy UBS said both stocks are key beneficiaries of AI demand. "In terms of stock calls on the back of this demand analysis, we remain bullish on NVDA and would also highlight AVGO as key Tier 1 beneficiaries of compute and networking demand, and would also highlight MU as a peripheral beneficiary of increased memory requirements. Globally, we would also highlight TSMC." Jefferies upgrades Kinross Gold to buy from neutral Jefferies said it's bullish on shares of the gold company. "We upgrade KGC from Hold to Buy and increase our PT from $14.00 to $18.00, implying ~18% upside." RBC upgrades General Mills to outperform from sector perform RBC upgraded the food products stock following earnings. "We are upgrading GIS to Outperform. While we acknowledge investor sentiment around packaged food remains poor and fundamentals have yet to fully turn, we believe GIS's FY'26 EPS guidance embeds enough cushion for it to deliver, despite an ongoing sluggish environment." Read more. Rothschild & Co. Redburn reiterates Amazon as buy The firm said Amazon is "resilient" buoyed by its Amazon Web Services offering. "Amazon shares have underperformed year-to-date, held back by weak AWS performance and tariff-related ecommerce concerns. However, our analysis indicates that AWS growth should comfortably exceed lowered expectations, enabling AWS to deliver its own Azure moment – a surprise reacceleration that resets expectations higher." Citizens upgrades Penn to market outperform from market perform Citizens said the gaming stock is at an inflection point. " PENN Entertainment has experienced several years of well-known headwinds primarily resulting from the build-out of its online businesses. The stock is down 87% from its all-time high, yet we are nearing an inflection point in the story whereby we see 38% upside in shares." Wells Fargo downgrades Trade Desk to equal weight from overweight Wells said in its downgrade of Trade Desk that it's concerned about competition for the ad tech company. "Amazon Competitive Impact Increasingly Likely in 2026; Downgrade to Equal Weight." UBS reiterates Micron as buy UBS raised its price target on the stock to $155 per share from $120 following earnings on Wednesday. "MU delivered against the only real investor expectations we heard into the call - HBM [high bandwidth memory] revenue and gross margin, both of which were in-line to a little better than bogeys." Read more. William Blair upgrades Elanco Animal Health to outperform from market perform The firm said in its upgrade of Elanco that the animal health company has a slew of positive catalysts ahead. "Earlier this week we hosted investor meetings with Elanco's president and CEO, Jeff Simmons. Our meetings and conversations gave us more color around current momentum and pipeline efforts that give us confidence to upgrade shares to an Outperform rating." Morgan Stanley reiterates Microsoft as overweight Morgan Stanley raised its price target on Microsoft to $530 per share from $482. "We update our capex-implied AI revenue analysis and our OpenAI model detailing the contribution to Azure. Core conclusion remains the same: conservatism in our Azure forecasts. With increased confidence in upside to Azure forecasts – our price target moves to $530 and conviction in OW remains." Deutsche adds a catalyst call buy on Anheuser-Busch InBev Deutsche said it's bullish ahead of earnings in late July. "We initiate a catalyst BUY o n ABI ahead of the company's 2Q results on 31 July following the recent pull back in the shares." UBS reiterates Meta as buy UBS raised its price target on the stock to $812 per share from $683. "And as Meta is also not necessarily exposed to the danger of what may be slower-than-anticipated enterprise AI spend – it is after all the primary user of its own technology – we worry less about a potential capacity-demand digestion phase we cite in the broader Q-series report. We maintain our Buy rating on META shares." Barclays initiates NRG Energy as overweight Barclays said shares of the energy company have plenty "more room to run." "We initiate on NRG Energy (NRG) with an Overweight rating and $197 price target." JPMorgan reiterates Apple as overweight JPMorgan lowered its price target on the stock but says it's sticking with Apple shares. "All that said, near-term demand drivers remain robust as a result of the pull-forward as well as support from subsidies in China, and position Apple to report robust results for F3Q25E. We are revising our earnings estimates and our Dec-25 price target to $230 vs. $245 prior." Bank of America reiterates Citi as buy Bank of America raised its price target on the stock to $100 per share from $89. "We consider Citi's turnaround as among the most complex in the corporate world, but [CEO]Fraser had undertaken actions (such as international consumer exits, balance sheet de-risking, tech/personnel investments, streamlining businesses, external talent) that gives Citi a fighting chance of becoming competitive, in our view."


Forbes
14 minutes ago
- Forbes
Raising The Success Rate Of AI Deployment Across Industries
Chris Brown, President at VASS Intelygenz, drives AI and deep tech innovation and implementation across industries, delivering tangible ROI. The AI gold rush has produced countless proofs of concept yet far fewer production victories. McKinsey's 2025 report on AI reveals that almost all companies are investing in it, but just 1% believe they are at maturity. That gap between ambition and reality explains why boardrooms are now asking a harder question: Where is the return? To close the gap, executives must focus on business value, disciplined engineering practices and organizational readiness. Here's how this can be achieved. A 2024 Harvard Business Review survey of 750 executives revealed that while 65% believe they have an advanced understanding of AI's benefits, only 6% reported a cutting-edge ability to derive value and profit-and-loss impact from the technologies. This disparity underscores the importance of grounding AI initiatives in clear business objectives. Translating that insight into action starts with writing a plain language value hypothesis before any code is written. State the business problem, the workflow to improve the key performance indicator and the budgeted payback window. When teams can recite that hypothesis, they build models that matter—not models that merely impress. Ambitious visions are inspiring, yet the first deployment should solve a narrow pain point that owns clean data and clear success criteria. Automating support ticket triage beats launching a customer-facing chatbot because the input format is stable and the cost saving is measurable. Early wins earn organizational trust and generate the training data funding and political capital required for bolder moves later. AI solutions live at the intersection of data science, software engineering and domain expertise. McKinsey reports that AI pilots fail to scale for many reasons, but the most common culprits are poorly designed or executed strategies. Create an AI team that pairs data scientists with platform engineers, product owners and frontline operators from day one. When these roles share responsibility, they're forced to hash out the inevitable compromises—such as how fast the model must respond (latency), how to keep data and predictions secure (security) and how transparent the model needs to be for regulators and users to understand its decisions (explainability)—well before the system goes live. Settling those trade-offs early prevents nasty surprises later, like discovering the model is too slow for a real-time workflow or fails a compliance review after launch. Moving from the lab to production is not a simple handoff—it is a lifecycle. Adopting machine learning operations (MLOps) practices such as automated data validation, model versioning and continuous performance monitoring is a first step. Logging every prediction along with its real-world outcome allows teams to detect model drift—when accuracy declines over time—and retrain before performance suffers. Tools such as infrastructure as code, which standardizes and automates environment setup, and containers, which package software to run reliably across systems, make it easier to roll back changes safely if an AI update introduces issues. In AI, the goal isn't perfection; it's building systems that can be consistently repeated and improved over time. Before deploying AI models into full production, organizations should introduce a shadow testing phase. In this stage, models operate in "technical production," processing live data and generating predictions, but their outputs remain isolated from actual business decisions. This controlled environment enables teams to observe how models perform under real-world conditions without exposing customers or operations to risk. Shadow testing helps build confidence in model reliability and highlights gaps that may not surface during lab testing. It allows teams to refine outputs, uncover edge cases and validate performance metrics in parallel with current workflows. As trust in the model grows, organizations can move from passive observation to selective activation, making shadow testing a strategic bridge between development and deployment. No model stands alone. Map the user journey to decide where AI will automate, where it will augment and where it will advise. Provide confidence scores, user guidance and clear escalation paths so employees know when to trust the machine and when to take control. Effective change management should include role-based training and updated incentive structures that reward human judgment enhanced by AI rather than be replaced by it. Define a small set of leading and lagging indicators, and then track them weekly. Leading indicators might include inference latency (response time) or the percentage of tickets auto-routed. Lagging indicators capture business impact such as customer satisfaction or operating margin. Publish results in a shared dashboard to sustain executive sponsorship and to signal that the AI program is a growth driver. AI will not slow down, but neither will scrutiny. Companies that raise their deployment success rate treat AI as a business discipline, not a science experiment. Follow that playbook, and the next McKinsey survey could show your organization in the 30% club that is already converting models into meaningful growth. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?