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Meta’s use of pirated content in AI development raises legal and ethical challenges

In its quest to develop the Llama 3 AI model, Meta faced significant ethical and legal hurdles regarding sourcing a large volume of high-quality text required for AI training. The company evaluated legal licensing for acquiring books and research papers but dismissed these options due to high costs and delays.

Internal discussions indicated a preference for maintaining legal flexibility by avoiding licensing constraints and pursuing a ‘fair use’ strategy. Consequently, Meta turned to Library Genesis (LibGen), a vast database of pirated books and papers, a move reportedly sanctioned by CEO Mark Zuckerberg.

That decision led to copyright-infringement lawsuits from authors, including Sarah Silverman and Junot Díaz, underlining the complexities of pirated content in AI development. Meta and OpenAI have defended their use of copyrighted materials by invoking ‘fair use’, arguing that their AI systems transform original works into new creations.

Despite this defence, the legality remains contentious, especially as Meta’s internal communications acknowledged the legal risks and outlined measures to reduce exposure, such as removing data marked as pirated.

The situation draws attention to broader issues in the publishing world, where expensive and restricted access to literature and research has fuelled the rise of piracy sites like LibGen and Sci-Hub. While providing wider access, these platforms threaten intellectual creation’s sustainability by bypassing compensation for authors and researchers.

The challenges facing Meta and other AI companies raise important questions about managing the flow of knowledge in the digital era. While LibGen and similar repositories democratise access, they undermine intellectual property rights, disrupting the balance between accessibility and the protection of creators’ contributions.

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AI startups in Silicon Valley rethink VC funding with leaner teams and strategic growth

In Silicon Valley, a notable trend is emerging as AI startups achieve significant revenue with leaner teams, challenging traditional venture capital (VC) funding models. Companies, sometimes with as few as 20 employees, are reporting revenues reaching tens of millions, highlighted by their participation in the accelerator Y Combinator (YC).

That shift signifies a transformation in startup dynamics, as many founders desire to scale without relying heavily on VC funding. They use the analogy of summiting Mount Everest with minimal oxygen, comparing it to reducing VC dependency, even in oversubscribed rounds. Raising less capital allows founders to retain greater ownership and flexibility for future business decisions.

The following strategic move is partly informed by past experiences where inflated valuations forced companies to endure ‘down rounds’. Terrence Rohan of Otherwise Fund notes that it’s becoming more common for YC startups to accept less capital than is offered, reflecting a more nuanced understanding of the implications of equity dilution.

However, not everyone endorses this strategy. Parker Conrad, CEO of Rippling, argues that lower funding could hinder a startup’s ability to invest in crucial growth areas like R&D and marketing, which are vital for product development and competitive advantage.

Conrad stresses the importance of substantial funding to accelerate growth, suggesting that it plays a crucial role in market expansion. Despite differing viewpoints, the examples of AI startups like Anysphere and ElevenLabs, which achieved high revenue with minimal staff yet secured significant funding, illustrate the ongoing allure of venture capital.

Overall, a changing perception is taking hold among YC founders, who are now more aware of both the advantages and pitfalls of VC funding. Pursuing capital from elite VC firms is no longer the sole indicator of success.

Instead, these startups favour strategic fundraising, considering the risks of overvaluation and excessive dilution. That shift reflects a broader evolution in the startup ecosystem, balancing lean operations with the potential benefits of venture capital to shape growth and maintain control strategically.

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Google launches advanced Gemini 2.5 AI

Google has unveiled its new Gemini 2.5 AI models, starting with the experimental Gemini 2.5 Pro version.

Described as ‘thinking models’, these AI systems are designed to demonstrate advanced reasoning abilities, including the capacity to analyse information, make logical conclusions, and handle complex problems with context and nuance.

The models aim to support more intelligent, context-aware AI agents in the future.

The Gemini 2.5 models improve on the Gemini 2.0 Flash Thinking model released in December, offering an enhanced base model and better post-training capabilities.

The Gemini 2.5 Pro model, which has already been rolled out for Gemini Advanced subscribers and is available in Google AI Studio, stands out for its strong reasoning and coding skills. It excels in maths and science benchmarks and can generate fully functional video games from simple prompts.

It is also expected to handle sophisticated tasks, from coding web apps to transforming and editing code. Google’s future plans involve incorporating these ‘thinking’ capabilities into all of its AI models, aiming to enhance their ability to tackle more complex challenges in various fields.

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AI physiotherapy service helps UK patients manage back pain

Lower back pain, one of the world’s leading causes of disability, has left hundreds of thousands of people in the UK stuck on long waiting lists for treatment. To address the crisis, the NHS is trialling a new solution: Flok Health, the first AI-powered physiotherapy clinic approved by the Care Quality Commission.

The app offers patients immediate access to personalised treatment plans through pre-recorded videos driven by artificial intelligence.

Created by former Olympic rower Finn Stevenson and tech expert Ric da Silva, Flok aims to treat straightforward cases that don’t require scans or hands-on intervention.

Patients interact with an AI-powered virtual physio, responding to questions that tailor the treatment pathway, with over a billion potential combinations. Unlike generative AI, Flok uses a more controlled system, eliminating the risk of fabricated medical advice.

The service has already launched in Scotland and is expanding across England, with ambitions to cover half the UK within a year. Flok is also adding treatment for conditions like hip and knee osteoarthritis, and women’s pelvic health.

While promising, the system depends on patients correctly following instructions, as the AI cannot monitor physical movements. Real physiotherapists are available to answer questions, but they do not provide live feedback during exercises.

Though effective for some, not all users find AI a perfect fit. Some, like the article’s author, prefer the hands-on guidance and posture corrections of human therapists.

Experts agree AI has potential to make healthcare more accessible and efficient, but caution that these tools must be rigorously evaluated, continuously monitored, and designed to support – not replace – clinical care.

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DeepSeek launches V3 to challenge OpenAI

Chinese AI startup DeepSeek has unveiled a major upgrade to its V3 large language model, intensifying the competition with US tech giants like OpenAI and Anthropic.

The new model, DeepSeek-V3-0324, is available via the AI development platform Hugging Face, showcasing significant advancements in reasoning and coding capabilities.

Benchmark tests have highlighted notable improvements in technical performance instead of its predecessor. DeepSeek, which has quickly gained recognition in the AI industry, continues to release competitive models, offering lower operational costs than many Western counterparts.

Following the V3 launch in December, DeepSeek also introduced its R1 model in January, further establishing its presence in the global AI market.

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Instagram users react to Meta’s new AI experiment

Meta has come under fire once again, this time over a new AI experiment on Instagram that suggests comments for users. Some users accused the company of using AI to inflate engagement metrics, potentially misleading advertisers and diminishing authentic user interaction.

The feature, spotted by test users, involves a pencil icon next to the comment bar on Instagram posts. Tapping it generates suggested replies based on the image’s content.

Meta has confirmed the feature is in testing but did not reveal plans for a broader launch. The company stated that it is exploring ways to incorporate Meta AI across different parts of its apps, including feeds, comments, groups, and search.

Public reaction has been largely negative, with concerns that AI-generated comments could flood the platform with inauthentic conversations. Social media users voiced fears of fake interactions replacing genuine ones, and some accused Meta of deceiving advertisers through inflated statistics.

Comparisons to dystopian scenarios were common, as users questioned the future of online social spaces.

This isn’t the first time Meta has faced backlash for its AI ventures. Previous attempts included AI personas modelled on celebrities and diverse identities, which were criticised for being disingenuous and engineered by largely homogenous development teams.

The future of AI-generated comments on Instagram remains uncertain as scrutiny continues to mount.

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Gmail uses AI to find emails faster

Google has introduced a new AI feature in Gmail aimed at making email searches faster and more accurate.

Instead of simply listing messages by date or keyword, the updated system now considers user habits, including frequently opened emails and commonly contacted senders, to provide more relevant results.

The enhanced search feature is being rolled out globally for personal Gmail accounts and is accessible via the web, Android, and iOS apps.

Users can now toggle between the new ‘most relevant’ results and the traditional ‘most recent’ option. Google has also stated that it plans to extend this functionality to business users in the near future.

By using AI to refine email searches, Gmail aims to reduce the time users spend digging through their inboxes.

However, this update is part of Google’s broader strategy to integrate more intelligent tools across its suite of productivity apps, offering a smoother, more efficient experience for everyday users.

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Apple plans to add cameras to future Apple Watch

Apple is reportedly planning to introduce cameras to its Apple Watch lineup within the next two years, integrating advanced AI-powered features like Visual Intelligence.

According to Bloomberg’s Mark Gurman, the standard Apple Watch Series will have a camera embedded within the display, while the Apple Watch Ultra will feature one on the side near the digital crown.

These cameras will allow the smartwatch to observe its surroundings and use AI to provide real-time, useful information to users.

Apple is also exploring similar camera technology for future AirPods, aiming to enhance their functionality with AI-driven capabilities.

The concept builds on the Visual Intelligence feature introduced with the iPhone 16, which allows users to extract details from flyers, identify locations, and more using the phone’s camera.

While the current system relies on external AI models, Apple is working on its in-house AI technology, and it is expected to power these features by 2027, when the camera-equipped Apple Watch and AirPods are likely to be released.

The move is part of Apple’s broader push into AI, led by Mike Rockwell, who previously spearheaded the Vision Pro project.

Rockwell is now overseeing the upgrade of Siri’s language model, which has faced delays, and contributing to visionOS, the operating system expected to support AI-enhanced AR glasses in the future. Apple’s increasing focus on AI suggests a shift towards more intelligent, context-aware wearable devices.

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New Airbyte connectors support AI and data privacy

San Francisco-based data startup Airbyte has unveiled a new set of enterprise tools aimed at helping companies move and manage data more securely, especially as AI becomes more central to operations. The updates, announced Thursday, include new connectors for apps such as NetSuite, SAP, and ServiceNow, as well as support for extracting unstructured data from platforms like Google Drive and SharePoint.

A key highlight of the release is compatibility with Apache Iceberg, an open-source format that enables businesses to centralise data into a single, AI-compatible “lakehouse.” This allows companies to better control how and where their data flows while preserving the flexibility needed for high-performance analytics and machine learning.

Airbyte co-founder and CEO Michel Tricot stressed the importance of data sovereignty in an AI-driven era. He noted that while AI tools can be powerful, giving away sensitive internal data, like employee compensation or strategic business metrics, to external services is a risk many companies are no longer willing to take. Airbyte’s approach ensures that only the enterprise sees and manages its data pipelines.

Founded in 2020, Airbyte now serves over 7,000 enterprise clients, including names like Invesco and Calendly, and has secured more than $181 million in funding. As businesses continue to prioritise secure, scalable infrastructure for AI, Airbyte’s offerings are positioning it as a go-to partner for data portability without compromise.

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Apple accused of misleading AI advertising

Apple is facing a class-action lawsuit in the United States over delays in delivering its much-promoted Apple Intelligence features.

The legal action, filed in a US based San Jose federal court, claims the company misled customers by advertising advanced AI tools that have yet to materialise on supported devices.

The complaint argues that buyers of new iPhones and other Apple products were promised ‘transformative’ AI capabilities at launch, only to find these features were either severely limited or completely absent.

According to the plaintiffs, Apple’s marketing created a “reasonable consumer expectation” that was ultimately not met.

This legal challenge adds to mounting pressure on the company, which has struggled to roll out its next-generation AI tools.

A recent Bloomberg report suggested internal tensions, revealing that CEO Tim Cook has reportedly lost confidence in AI chief John Giannandrea’s ability to deliver on the company’s ambitions.

The case reflects growing scrutiny of tech firms’ promises around AI, especially as consumer trust becomes more closely tied to the reality behind flashy announcements.

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