Future of work

AI-generated images used in jewellery scam

A jeweller in Hove is dealing with daily complaints from customers of a similarly named but fraudulent business. Stevie Holmes runs Scarlett Jewellery but keeps receiving complaints from customers who confused it with the AI-driven Scarlett Jewels website.

Many reported receiving poor-quality goods or nothing at all.

Holmes said the mix-ups have kept her occupied for at least an hour a day since July. Without clarification, people could post negative comments about her genuine business on social media, potentially damaging its reputation.

Scarlett Jewels is run by Denimtex Limited with an address in Hong Kong, though its website claims a personal story of a retiring designer.

Experts say such scams are increasingly common due to how easy and cheap it is to create AI images. Professor Ana Canhoto from the University of Sussex noted AI-generated product photos often appear too perfect or flawed, while fake reviews and claims of scarcity are typical tactics to mislead buyers.

Trustpilot ratings for Scarlett Jewels are mostly one star, with customers describing items as ‘tat’ or ‘poor quality’.

Authorities are taking action, with the Advertising Standards Authority banning similar ads and Facebook restricting Scarlett Jewels from creating new adverts. Buyers are advised to spot off AI images, large discounts, and genuine reviews to avoid falling for scams.

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Tailored pricing is here and personal data is the price signal

AI is quietly changing how prices are set online. Beyond demand-based shifts, companies increasingly tailor offers to individuals, using browsing history, purchase habits, device, and location to predict willingness to pay. Two shoppers may see different prices for the same product at the same moment.

Dynamic pricing raises or lowers prices for everyone as conditions change, such as school-holiday airfares or hotel rates during major events. Personalised pricing goes further by shaping offers for specific users, rewarding cart-abandoners with discounts while charging rarer shoppers a premium.

Platforms mine clicks, time on page, past purchases, and abandoned baskets to build profiles. Experiments show targeted discounts can lift sales while capping promo spend, proving engineered prices scale. The result: you may not see a ‘standard’ price, but one designed for you.

The risks are mounting. Income proxies such as postcode or device can entrench inequality, while hidden algorithms erode trust when buyers later find cheaper prices. Accountability is murky if tailored prices mislead, discriminate, or breach consumer protections without clear disclosure.

Regulators are moving. A competition watchdog in Australia has flagged transparency gaps, unfair trading risks, and the need for algorithmic disclosure. Businesses now face a twin test: deploy AI pricing with consent, explainability, and opt-outs, and prove it delivers value without crossing ethical lines.

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Wikipedia faces traffic decline as AI and social video reshape online search

Wikipedia’s human traffic has fallen by 8% over the past year, a decline the Wikimedia Foundation attributes to changing information habits driven by AI and social media.

The foundation’s Marshall Miller explained that updates to Wikipedia’s bot detection system revealed much of the earlier traffic surge came from undetected bots, revealing a sharper drop in genuine visits.

Miller pointed to the growing use of AI-generated search summaries and the rise of short-form video as key factors. Search engines now provide direct answers using generative AI instead of linking to external sources, while younger users increasingly turn to social video platforms rather than traditional websites.

Although Wikipedia’s knowledge continues to feed AI models, fewer people are reaching the original source.

The foundation warns that the shift poses risks to Wikipedia’s volunteer-driven ecosystem and donation-based model. With fewer visitors, fewer contributors may update content and fewer donors may provide financial support.

Miller urged AI companies and search engines to direct users back to the encyclopedia, ensuring both transparency and sustainability.

Wikipedia is responding by developing a new framework for content attribution and expanding efforts to reach new readers. The foundation also encourages users to support human-curated knowledge by citing original sources and recognising the people behind the information that powers AI systems.

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Australian students get 12 months of Google Gemini Pro at no cost

Google has launched a free twelve-month Gemini Pro plan for students in Australia aged eighteen and over, aiming to make AI-powered learning more accessible.

The offer includes the company’s most advanced tools and features designed to enhance study efficiency and critical thinking.

A key addition is Guided Learning mode, which acts as a personal AI coach. Instead of quick answers, it walks students through complex subjects step by step, encouraging a deeper understanding of concepts.

Gemini now also integrates diagrams, images and YouTube videos into responses to make lessons more visual and engaging.

Students can create flashcards, quizzes and study guides automatically from their own materials, helping them prepare for exams more effectively. The Gemini Pro account upgrade provides access to Gemini 2.5 Pro, Deep Research, NotebookLM, Veo 3 for short video creation, and Jules, an AI coding assistant.

With two terabytes of storage and the full suite of Google’s AI tools, the Gemini app aims to support Australian students in their studies and skill development throughout the academic year.

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Data Act now in force, more data sharing in EU

The EU’s Data Act is now in force, marking a major shift in European data governance. The regulation aims to expand access to industrial and Internet of Things data, giving users greater control over information they generate while maintaining safeguards for trade secrets and privacy.

Adopted as part of the EU’s Digital Strategy, the act seeks to promote fair competition, innovation, and public-sector efficiency. It enables individuals and businesses to share co-generated data from connected devices and allows public authorities limited access in emergencies or matters of public interest.

Some obligations take effect later. Requirements on product design for data access will apply to new connected devices from September 2026, while certain contract rules are deferred until 2027. Member states will set national penalties, with fines in some cases reaching up to 10% of global annual turnover.

The European Commission will assess the law’s impact within three years of its entry into force. Policymakers hope the act will foster a fairer, more competitive data economy, though much will depend on consistent enforcement and how businesses adapt their practices.

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Data labelling transforms rural economies in Tamil Nadu

India’s small towns are fast becoming global hubs for AI training and data labelling, as outsourcing firms move operations beyond major cities like Bangalore and Chennai. Lower costs and improved connectivity have driven a trend known as cloud farming, which has transformed rural employment.

In Tamil Nadu, workers annotate and train AI models for global clients, preparing data that helps machines recognise objects, text and speech. Firms like Desicrew pioneered this approach by offering digital careers close to home, reducing migration to cities while maintaining high technical standards.

Desicrew’s chief executive, Mannivannan J K, says about a third of the company’s projects already involve AI, a figure expected to reach nearly all within two years. Much of the work focuses on transcription, building multilingual datasets that teach machines to interpret diverse human voices and dialects.

Analysts argue that cloud farming could make rural India the world’s largest AI operations base, much as it once dominated IT outsourcing. Yet challenges remain around internet reliability, data security and client confidence.

For workers like Dhanalakshmi Vijay, who fine-tunes models by correcting their errors, the impact feels tangible. Her adjustments, she says, help AI systems perform better in real-world applications, improving everything from shopping recommendations to translation tools.

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Public consultation: EU clarifies how DMA and GDPR work together

The European Commission and European Data Protection Board have jointly published long-awaited guidelines clarifying how the Digital Markets Act aligns with the GDPR. It aims to remove uncertainty for large online platforms over consent requirements, data sharing amongst other things.

Under the new interpretation, gatekeepers must obtain specific and separate consent when combining user data across different services, including when using it for AI training. They cannot rely on legitimate interest or contractual necessity for such processing, closing a loophole long debated in EU privacy law.

The Guidelines also set limits on how often consent can be re-requested, prohibiting repeated or slightly altered requests for the same purpose within a year. In addition, they make clear that offering users a binary choice between accepting tracking or paying a fee will rarely qualify as freely given consent.

The Guidance also introduces a practical standard for anonymisation, requiring platforms to prevent re-identification using technical and organisational safeguards. Consultation on the Guidelines runs until 4 December 2025, after which they are expected to shape future enforcement.

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Labels and Spotify align on artist-first AI safeguards

Spotify partners with major labels on artist-first AI tools, putting consent and copyright at the centre of product design. The plan aims to align new features with transparent labelling and fair compensation while addressing concerns about generative music flooding platforms.

The collaboration with Sony, Universal, Warner, and Merlin will give artists control over participation in AI experiences and how their catalogues are used. Spotify says it will prioritise consent, clearer attribution, and rights management as it builds new tools.

Early direction points to expanded labelling via DDEX, stricter controls against mass AI uploads, and protections against search and recommendation manipulation. Spotify’s AI DJ and prompt-based playlists hint at how engagement features could evolve without sidelining creators.

Future products are expected to let artists opt in, monitor usage, and manage when their music feeds AI-generated works. Rights holders and distributors would gain better tracking and payment flows as transparency improves across the ecosystem.

Industry observers say the tie-up could set a benchmark for responsible AI in music if enforcement matches ambition. By moving in step with labels, Spotify is pitching a path where innovation and artist advocacy reinforce rather than undermine each other.

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Veo 3.1 brings audio and control to AI filmmaking

Google DeepMind has unveiled Veo 3.1, the newest upgrade to its video generation model, bringing more artistic freedom, realism and sound integration to its AI filmmaking tool, Flow.

The update gives creators advanced scene control and introduces generated audio across existing features like ‘Ingredients to Video’, ‘Frames to Video’ and ‘Extend’.

Users can now fine-tune visuals by combining multiple reference images, seamlessly link frames into longer clips, and edit scenes with new insert and removal tools that handle shadows and lighting automatically.

Flow’s new precision tools mark a significant step toward cinematic-level storytelling powered by AI.

Veo 3.1 is also accessible through the Gemini API, Vertex AI and the Gemini app, broadening its availability to developers and enterprises alike.

These enhancements signal Google’s ongoing ambition to push the boundaries of generative video technology for creative and professional applications.

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Google and Salesforce deepen AI partnership across Agentforce 360 and Gemini Enterprise

Salesforce and Google have expanded their long-term partnership, introducing new integrations between Salesforce’s Agentforce 360 platform and Google’s Gemini Enterprise. The collaboration aims to enhance productivity and build a new foundation for intelligent, connected business operations.

Through the expansion, Gemini models now power Salesforce’s Atlas Reasoning Engine, combining multimodal intelligence with hybrid reasoning to improve how AI agents handle complex, multistep enterprise tasks.

These integrations also extend across Google Workspace, bringing Agentforce 360 capabilities directly into Gmail, Meet, Docs, Sheets and Drive for sales, service and IT teams.

Salesforce highlights that fine-tuned Gemini models outperform competing LLMs on key CRM benchmarks, enabling businesses to automate workflows more reliably and consistently.

The companies also reaffirm their commitment to open standards like Model Context Protocol and Agent2Agent, allowing multi-agent collaboration and interoperability across enterprise systems.

A partnership that further integrates Gemini Enterprise with Slack’s real-time search API, enabling users to draw insights directly from organisational data within conversations.

Both companies stress that these advances mark a major step toward an ‘Agentic Enterprise’, where AI systems work alongside people to drive innovation, improve service quality and streamline decision-making.

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