As announced yesterday, YouTube is expanding its response to synthetic media by introducing experimental likeness detection tools that allow creators to identify videos where their face appears altered or generated by AI.
The system, modelled conceptually on Content ID, scans newly uploaded videos for visual matches linked to enrolled creators, enabling them to review content and pursue privacy or copyright complaints when misuse is detected.
Participation requires identity verification through government-issued identification and a biometric reference video, positioning facial data as both a protective and governance mechanism.
While the platform stresses consent and limited scope, the approach reflects a broader shift towards biometric enforcement as platforms attempt to manage deepfakes, impersonation, and unauthorised synthetic content at scale.
Alongside likeness detection, YouTube’s 2026 strategy places AI at the centre of content moderation, creator monetisation, and audience experience.
AI tools already shape recommendation systems, content labelling, and automated enforcement, while new features aim to give creators greater control over how their image, voice, and output are reused in synthetic formats.
The move highlights growing tensions between creative empowerment and platform authority, as safeguards against AI misuse increasingly rely on surveillance, verification, and centralised decision-making.
As regulators debate digital identity, biometric data, and synthetic media governance, YouTube’s model signals how private platforms may effectively set standards ahead of formal legislation.
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Snapchat’s parent company has settled a social media addiction lawsuit in California just days before the first major trial examining platform harms was set to begin.
The agreement removes Snapchat from one of the three bellwether cases consolidating thousands of claims, while Meta, TikTok and YouTube remain defendants.
These lawsuits mark a legal shift away from debates over user content and towards scrutiny of platform design choices, including recommendation systems and engagement mechanics.
A US judge has already ruled that such features may be responsible for harm, opening the door to liability that section 230 protections may not cover.
Legal observers compare the proceedings to historic litigation against tobacco and opioid companies, warning of substantial damages and regulatory consequences.
A ruling against the remaining platforms could force changes in how social media products are designed, particularly in relation to minors and mental health risks.
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A new study from the University of Oxford argues that large language models reproduce a distinctly Western hierarchy when asked to evaluate countries, reinforcing long-standing global inequalities through automated judgment.
Analysing more than 20 million English-language responses from ChatGPT’s 4o-mini model, researchers found consistent favouring of wealthy Western nations across subjective comparisons such as intelligence, happiness, creativity, and innovation.
Low-income countries, particularly across Africa, were systematically placed at the bottom of rankings, while Western Europe, the US, and parts of East Asia dominated positive assessments.
According to the study, generative models rely heavily on data availability and dominant narratives, leading to flattened representations that recycle familiar stereotypes instead of reflecting social complexity or cultural diversity.
The researchers describe the phenomenon as the ‘silicon gaze’, a worldview shaped by the priorities of platform owners, developers, and historically uneven training data.
Because large language models are trained on material produced within centuries of structural exclusion, bias emerges not as a malfunction but as an embedded feature of contemporary AI systems.
The findings intensify global debates around AI governance, accountability, and cultural representation, particularly as such systems increasingly influence healthcare, employment screening, education, and public decision-making.
While models are continuously updated, the study underlines the limits of technical mitigation without broader political, regulatory, and epistemic interventions.
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Hong Kong’s proposed crypto licensing overhaul has drawn criticism from industry leaders, who warn it could disrupt compliant firms and deter blockchain exposure.
Under the proposals, the existing allowance enabling firms to allocate up to 10% of fund assets to crypto without additional licensing would be removed. Even minimal exposure would require a full licence, a move the association called disproportionate and harmful to market experimentation.
Concerns also focused on the absence of transitional arrangements. Without a grace period, firms may be forced to suspend operations while licence applications are reviewed.
The association proposed a six- to 12-month transitional window to allow continued activity during regulatory processing.
Further criticism focused on custody rules restricting client assets to SFC-licensed custodians. Industry representatives warned the measure could limit access to early-stage tokens, restrict Web3 investment, and impose unnecessary geographic constraints.
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AI has dominated debates at Davos 2026, matching traditional concerns such as geopolitics and global trade while prompting deeper reflection on how the technology is reshaping work, governance, and society.
Political leaders, executives, and researchers agreed that AI development has moved beyond experimentation towards widespread implementation.
Microsoft chief executive Satya Nadella argued that AI should deliver tangible benefits for communities and economies, while warning that adoption will remain uneven due to disparities in infrastructure and investment.
Access to energy networks, telecommunications, and capital was identified as a decisive factor in determining which regions can fully deploy advanced systems.
Other voices at Davos 2026 struck a more cautious tone. AI researcher Yoshua Bengio warned against designing systems that appear too human-like, stressing that people may overestimate machine understanding.
Philosopher Yuval Noah Harari echoed those concerns, arguing that societies lack experience in managing human and AI coexistence and should prepare mechanisms to correct failures.
The debate also centred on labour and global competition.
Anthropic’s Dario Amodei highlighted geopolitical risks and predicted disruption to entry-level white-collar jobs. At the same time, Google DeepMind chief Demis Hassabis forecast new forms of employment alongside calls for shared international safety standards.
Together, the discussions underscored growing recognition that AI governance will shape economic and social outcomes for years ahead.
Diplo is live reporting on all sessions from the World Economic Forum 2026 in Davos.
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A major UK research project will examine how restricting social media use affects children’s mental health, sleep, and social lives, as governments debate tougher rules for under-16s.
The trial involves around 4,000 pupils from 30 secondary schools in Bradford and represents one of the first large-scale experimental studies of its kind.
Participants aged 12 to 15 will either have their social media use monitored or restricted through a research app limiting access to major platforms to one hour per day and imposing a night-time curfew.
Messaging services such as WhatsApp will remain available instead of being restricted, reflecting their role in family communication.
Researchers from the University of Cambridge and the Bradford Centre for Health Data Science will assess changes in anxiety, depression, sleep patterns, bullying, and time spent with friends and family.
Entire year groups within each school will experience the same conditions to capture social effects across peer networks rather than isolated individuals.
The findings, expected in summer 2027, arrive as UK lawmakers consider proposals for a nationwide ban on social media use by under-16s.
Although independent from government policy debates, the study aims to provide evidence to inform decisions in the UK and other countries weighing similar restrictions.
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The European Commission has unveiled a broad cybersecurity package that moves the EU beyond certification reform towards systemic resilience across critical digital infrastructure.
Building on plans to expand EU cybersecurity certification beyond products and services, the revised Cybersecurity Act introduces a risk-based framework for securing ICT supply chains, with particular focus on dependencies, foreign interference, and high-risk third-country suppliers.
A central shift concerns supply-chain security as a geopolitical issue. The proposal enables mandatory derisking of mobile telecommunications networks, reinforcing earlier efforts under the 5G security toolbox.
Certification reform continues through a redesigned European Cybersecurity Certification Framework, promising clearer governance, faster scheme development, and voluntary certification that can cover organisational cyber posture alongside technical compliance.
The package also tackles regulatory complexity. Targeted amendments to the NIS2 Directive aim to ease compliance for tens of thousands of companies by clarifying jurisdictional rules, introducing a new ‘small mid-cap’ category, and streamlining incident reporting through a single EU entry point.
Enhanced ransomware data collection and cross-border supervision are intended to reduce fragmentation while strengthening enforcement consistency.
ENISA’s role is further expanded from coordination towards operational support. The agency would issue early threat alerts, assist in ransomware recovery with national authorities and Europol, and develop EU-wide vulnerability management and skills attestation schemes.
Together, the measures signal a shift from fragmented safeguards towards a more integrated model of European cyber sovereignty.
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The moment many have anticipated with interest or concern has arrived. On 16 January, OpenAI announced the global rollout of its low-cost subscription tier, ChatGPT Go, in all countries where the model is supported. After debuting in India in August 2025 and expanding to Singapore the following month, the USD 8-per-month tier marks OpenAI’s most direct attempt yet to broaden paid access while maintaining assurances that advertising will not be embedded into ChatGPT’s prompts.
The move has been widely interpreted as a turning point in the way AI models are monetised. To date, most major AI providers have relied on a combination of external investment, strategic partnerships, and subscription offerings to sustain rapid development. Expectations of transformative breakthroughs and exponential growth have underpinned investor confidence, reinforcing what has come to be described as the AI boom.
Against this backdrop, OpenAI’s long-standing reluctance to embrace advertising takes on renewed significance. As recently as October 2024, chief executive Sam Altman described ads as a ‘last resort’ for the company’s business model. Does that position (still) reflect Altman’s confidence in alternative revenue streams, and is OpenAI simply the first company to bite the ad revenue bullet before other AI ventures have mustered the courage to do so?
ChatGPT, ads, and the integrity of AI responses
Regardless of one’s personal feelings about ad-based revenue, the facts about its essentiality are irrefutable. According to Statista’s Market Insights research, the worldwide advertising market has surpassed USD 1 trillion in annual revenue. With such figures in mind, it seems like a no-brainer to integrate ads whenever and wherever possible.
Furthermore, relying solely on substantial but irregular cash injections is not a reliable way to keep the lights on for a USD 500 billion company, especially in the wake of the RAM crisis. As much as the average consumer would prefer to use digital services without ads, coming up with an alternative and well-grounded revenue stream is tantamount to financial alchemy. Advertising remains one of the few monetisation models capable of sustaining large-scale platforms without significantly raising user costs.
For ChatGPT users, however, the concern centres less on the mere presence of ads and more on how advertising incentives could reshape data use, profiling practices, and the handling of conversational inputs. OpenAI has pleaded with its users to ‘trust that ChatGPT’s responses are driven by what’s objectively useful, never by advertising’. Altman’s company has also guaranteed that user data and conversations will remain protected and will never be sold to advertisers.
Such bold statements are never given lightly, meaning Altman fully stands behind his company’s words and is prepared to face repercussions should he break his promises. Since OpenAI is privately held, shifts in investor confidence following the announcement are not visible through public market signals, unlike at publicly listed technology firms. User count remains the most reliable metric for observing how ChatGPT is perceived by its target audience.
Competitive pressure behind ads in ChatGPT
Introducing ads to ChatGPT would be more than a simple change to how OpenAI makes money. Advertising can influence how the model responds to users, even if ads are not shown directly within the answers. Business pressure can still shape how information is presented through prompts. For example, certain products or services could be described more positively than others, without clearly appearing as advertisements or endorsements.
Recommendations raise particular concern. Many users turn to ChatGPT for advice or comparisons before making important purchases. If advertising becomes part of the model’s business, it may become harder for users to tell whether a suggestion is neutral or influenced by commercial interests. Transparency is also an issue, as the influence is much harder to spot in a chat interface than on websites that clearly label ads with banners or sponsored tags.
While these concerns are valid, competition remains the main force shaping decisions across the AI industry. No major company wants its model to fall behind rivals such as ChatGPT, Gemini, Claude, or other leading systems. Nearly all of these firms have faced public criticism or controversy at some point, forcing them to adjust their strategies and work to rebuild user trust.
The risk of public backlash has so far made companies cautious about introducing advertising. Still, this hesitation is unlikely to last forever. By moving first, OpenAI absorbs most of the initial criticism, while competitors get to stand back, watch how users respond, and adjust their plans accordingly. If advertising proves successful, others are likely to follow, drawing on OpenAI’s experience without bearing the brunt of the growing pains. To quote Arliss Howard’s character in Moneyball: ‘The first guy through the wall always gets bloody’.
ChatGPT advertising and governance challenges
Following the launch of ChatGPT Go, lawmakers and regulators may need to reconsider how existing legal safeguards apply to ad-supported LLMs. Most advertising rules are designed for websites, apps, and social media feeds, rather than systems that generate natural-language responses and present them as neutral or authoritative guidance.
The key question is: which rules should apply? Advertising in chatbots may not resemble traditional ads, muddying the waters for regulation under digital advertising rules, AI governance frameworks, or both. The uncertainty matters largely because different rules come with varying disclosure, transparency, and accountability requirements.
Disclosure presents a further challenge for regulators. On traditional websites, sponsored content is usually labelled and visually separated from editorial material. In an LLM interface such as ChatGPT, however, any commercial influence may appear in the flow of an answer itself. This makes it harder for users to distinguish content shaped by commercial considerations from neutral responses.
In the European Union, this raises questions about how existing regulatory frameworks apply. Advertising in conversational AI may intersect with rules on transparency, manipulation, and user protection under current digital and AI legislation, including the AI Act, the Digital Services Act, and the Digital Markets Act. Clarifying how these frameworks operate in practice will be important as conversational AI systems continue to evolve.
ChatGPT ads and data governance
In the context of ChatGPT, conversational interactions can be more detailed than clicks or browsing history. Prompts may include personal, professional, or sensitive information, which requires careful handling when introducing advertising models. Even without personalised targeting, conversational data still requires clear boundaries. As AI systems scale, maintaining user trust will depend on transparent data practices and strong privacy safeguards.
Then, there’s data retention. Advertising incentives can increase pressure to store conversations for longer periods or to find new ways to extract value from them. For users, this raises concerns about how their data is handled, who has access to it, and how securely it is protected. Even if OpenAI initially avoids personalised advertising, the lingering allure will remain a central issue in the discussion about advertising in ChatGPT, not a secondary one.
Clear policies around data use and retention will therefore play a central role in shaping how advertising is introduced. Limits on how long conversations are stored, how data is separated from advertising systems, and how access is controlled can help reduce user uncertainty. Transparency around these practices will be important in maintaining confidence as the platform evolves.
Simultaneously, regulatory expectations and public scrutiny are likely to influence how far advertising models develop. As ChatGPT becomes more widely used across personal, professional, and institutional settings, decisions around data handling will carry broader implications. How OpenAI balances commercial sustainability with privacy and trust may ultimately shape wider norms for advertising in conversational AI.
How ChatGPT ads could reshape the AI ecosystem
We have touched on the potential drawbacks of AI models adopting an ad-revenue model, but what about the benefits? If ChatGPT successfully integrates advertising, it could set an important precedent for the broader industry. As the provider of one of the most widely used general-purpose AI systems, OpenAI’s decisions are closely watched by competitors, policymakers, and investors.
One likely effect would be the gradual normalisation of ad-funded AI assistants. If advertising proves to be a stable revenue source without triggering significant backlash, other providers may view it as a practical path to sustainability. Over time, this could shift user expectations, making advertising a standard feature rather than an exception in conversational AI tools.
Advertising may also intensify competitive pressure on open, academic, or non-profit AI models. Such systems often operate with more limited funding and may struggle to match the resources of ad-supported platforms such as ChatGPT. As a result, the gap between large commercial providers and alternative models could widen, especially in areas such as infrastructure, model performance, and distribution.
Taken together, these dynamics could strengthen the role of major AI providers as gatekeepers. Beyond controlling access to technology, they may increasingly influence which products, services, or ideas gain visibility through AI-mediated interactions. Such a concentration of influence would not be unique to AI, but it raises familiar questions about competition, diversity, and power in digital information ecosystems.
ChatGPT advertising and evolving governance frameworks
Advertising in ChatGPT is not simply a business decision. It highlights a broader shift in the way knowledge, economic incentives, and large-scale AI systems interact. As conversational AI becomes more embedded in everyday life, these developments offer an opportunity to rethink how digital services can remain both accessible and sustainable.
For policymakers and governance bodies, the focus is less on whether advertising appears and more on how it is implemented. Clear rules around transparency, accountability, and user protection can help ensure that conversational AI evolves in ways that support trust, choice, and fair competition, while allowing innovation to continue.
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The UK government has appointed two senior industry figures as AI Champions to support safe and effective adoption of AI across financial services, as part of a broader push to boost growth and productivity.
Harriet Rees of Starling Bank and Dr Rohit Dhawan of Lloyds Banking Group will work with firms and regulators to help turn rapid AI uptake into practical delivery. Both will report directly to Lucy Rigby, the Economic Secretary to the Treasury.
AI is already widely deployed across the sector, with around three-quarters of UK financial firms using the technology. Analysis indicates AI could add tens of billions of pounds to financial services by 2030, while improving customer services and reducing costs.
The Champions will focus on accelerating trusted adoption, speeding up innovation, and removing barriers to scale. Their remit includes protecting consumers, supporting financial stability, and strengthening the UK’s role as a global economic and technology hub.
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AI tools used for health searches are facing growing scrutiny after reports found that some systems provide incorrect or potentially harmful medical advice. Wider public use of generative AI for health queries raises concerns over how such information is generated and verified.
An investigation by The Guardian found that Google AI Overview has sometimes produced guidance contrary to established medical advice. Attention has also focused on data sources, as platforms like ChatGPT frequently draw on user-generated or openly edited material.
Medical experts warn that unverified or outdated information poses risks, especially where clinical guidance changes rapidly. The European Lung Foundation has stressed that health-related AI outputs should meet the same standards as professional medical sources.
Efforts to counter misinformation are now expanding. The European Respiratory Society and its partners are running campaigns to protect public trust in science and encourage people to verify health information with qualified professionals.
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