The European Data Protection Supervisor (EDPS) and authorities from 61 jurisdictions issued a joint statement on AI-generated imagery, warning about tools that create realistic depictions of identifiable individuals without consent. The move underscores concerns over privacy, dignity and child safety.
Authorities said advances in AI image and video tools, especially when integrated into social media platforms, have enabled non-consensual intimate imagery, defamatory depictions, and other harmful content. Children and vulnerable groups are seen as particularly at risk.
The EDPS and the other signatories reminded organisations that AI content-generation systems must comply with applicable data protection and privacy laws. They stressed that creating non-consensual intimate imagery may constitute a criminal offence in many jurisdictions.
Organisations are urged to implement safeguards against misuse of personal data, ensure transparency about system capabilities and uses, and provide accessible mechanisms for swift content removal. Stronger protections and age-appropriate information are expected where children are involved.
Authorities signalled plans for coordinated responses, including enforcement, policy development and education initiatives. The EDPS and fellow signatories urged organisations to engage proactively with regulators and ensure innovation does not undermine fundamental rights.
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The first enforcement provisions of the EU AI Act entered into force on 2 February 2025, marking a turning point for Europe’s AI startup ecosystem. The initial phase targets ‘unacceptable risk’ systems, including social scoring, real-time biometric surveillance in public spaces, and manipulative AI practices.
Under the regulation, penalties can reach €35 million or 7% of global annual turnover, whichever is higher. Although the current enforcement covers only prohibited practices, the move signals that Europe’s AI rulebook is now operational rather than theoretical.
Broader obligations for high-risk AI systems, such as hiring tools, credit scoring, and medical diagnostics, will apply from August 2026. Separate rules for general-purpose AI models are scheduled to take effect in August 2025.
Surveys from European SME groups indicate that many smaller technology companies feel unprepared. A significant share of reports have not conducted formal risk classification of their AI systems, despite this being a foundational requirement under the EU AI Act’s tiered framework.
While some founders warn that compliance costs could slow innovation, others point to long-term benefits from clearer governance standards. For startups, the coming months will focus on aligning products with AI Act risk tiers and strengthening documentation and oversight before stricter rules apply.
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The UK’s Information Commissioner’s Office has fined Reddit £14.47 million after finding that the platform unlawfully used children’s personal information and failed to put in place adequate age checks.
Although Reddit updated its processes in July 2025, self-declaration remained easy to bypass, offering only a veneer of protection. Investigators also found that the company had not completed a data protection impact assessment until 2025, despite a large number of teenagers using the service.
Concerns were heightened by the volume of children affected and the risks created by relying on inadequate age checks.
The regulator noted that unlawful data processing occurred over a prolonged period, and that children were at risk of viewing harmful material while their information was processed without a lawful basis.
UK Information Commissioner John Edwards said companies must prioritise meaningful age assurance and understand the responsibilities set out in the Children’s Code.
The ICO said it will continue monitoring Reddit’s current controls and expects online platforms to align with robust age-assurance standards rather than rely on weak verification.
It will coordinate its oversight with Ofcom as part of broader efforts to strengthen online safety and ensure under-18s benefit from high privacy protections by default.
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In December 2025, the Macquarie Dictionary, Merriam-Webster, and the American Dialect Society named ‘slop’ as the Word of the Year, reflecting a widespread reaction to AI-generated content online, often referred to as ‘AI slop.’ By choosing ‘slop’, typically associated with unappetising animal feed, they captured unease about the digital clutter created by AI tools.
As LLMs and AI tools became accessible to more people, many saw them as opportunities for profit through the creation of artificial content for marketing or entertainment, or through the manipulation of social media algorithms. However, despite video and image generation advances, there is a growing gap between perceived quality and actual detection: many overestimate how easily AI content evades notice, fueling scepticism about its online value.
As generative AI systems expand, the debate goes beyond digital clutter to deeper concerns about trust, market incentives, and regulatory resilience. How will societies manage the social, economic, and governance impacts of an information ecosystem increasingly shaped by automated abundance? In simplified terms, is AI slop more than a simple digital nuisance, or do we needlessly worry about a transient vogue that will eventually fade away?
The social aspect of AI slop’s influence
The most visible effects of AI slop emerge on large social media platforms such as YouTube, TikTok, and Instagram. Users frequently encounter AI-generated images and videos that appropriate celebrity likenesses without consent, depict fabricated events, or present sensational and misleading scenarios. Comment sections often become informal verification spaces, where some users identify visual inconsistencies and warn others, while many remain uncertain about the content’s authenticity.
However, no platform has suffered the AI slop effect as much as Facebook, and once you take a glance at its demographics, the pieces start to come together. According to multiple studies, Facebook’s user base is mostly populated by adults aged 25-34, but users over the age of 55 make up nearly 24 percent of all users. While seniors do not constitute the majority (yet), younger generations have been steadily migrating to social platforms such as TikTok, Instagram, and X, leaving the most popular platform to the whims of the older generation.
Due to factors such as cognitive decline, positivity bias, or digital (il)literacy, older social media users are more likely to fall for scams and fraud. Such conditions make Facebook an ideal place for spreading low-quality AI slop and false information. Scammers use AI tools to create fake images and videos about made-up crises to raise money for causes that are not real.
The lack of regulation on Meta’s side is the most glaring sore spot, evidenced by the company pushing back against the EU’s Digital Services Act (DSA) and Digital Markets Act (DMA), viewing them as ‘overreaching‘ and stifling innovation. The math is simple: content generates engagement, resulting in more revenue for Facebook and other platforms owned by Meta. Whether that content is authentic and high-quality or low-effort AI slop, the numbers don’t care.
The economics behind AI slop
At its core, AI content is not just a social media phenomenon, but an economic one as well. GenAI tools drastically reduce the cost and time required to produce all types of content, and when production approaches zero marginal cost, the incentive to churn out AI slop seems too good to ignore. Even minimal engagement can generate positive returns through advertising, affiliate marketing, or platform monetisation schemes.
AI content production goes beyond exploiting social media algorithms and monetisation policies. SEO can now be automated at scale, thus generating thousands of keyword-optimised articles within hours. Affiliate link farming allows creators to monetise their products or product recommendations with minimal editorial input.
On video platforms like TikTok and YouTube, synthetic voice-overs and AI-generated visuals are on full display, banking on trending topics and using AI-generated thumbnails to garner more views on a whim. Thanks to AI tools, content creators can post relevant AI-generated content in minutes, enabling them to jump on the hottest topics and drive clicks faster than with any other authentic content creation method.
To add salt to the wound, YouTube content creators share the sentiment that they are victims of the platform’s double standards in enforcing its strict community guidelines. Even the largest YouTube Channels are often flagged for a plethora of breaches, including copyright claims and depictions of dangerous or illegal activities, and harmful speech, to name a few. On the other hand, AI slop videos seem to fly under YouTube’s radar, leading to more resentment towards AI-generated content.
Businesses that rely on generative AI tools to market their services online are also finding AI to be the way to go, as most users are still not too keen on distinguishing authentic content, nor do they give much importance to those aspects. Instead of paying voice-over artists and illustrators, it is way cheaper to simply create a desired post in under a few minutes, adding fuel to an already raging fire. Some might call it AI slop, but again, the numbers are what truly matter.
The regulatory challenge of AI slop
AI slop is not only a social and economic issue, but also a regulatory one. The problem is not a single AI-generated post that promotes harmful behaviour or misleading information, but the sheer scale of synthetic content entering digital platforms. When large volumes of low-value or deceptive material circulate on the web, they can distort information ecosystems and make moderation a tough challenge. Such a predicament shifts the focus from individual violations to broader systemic effects.
In the EU, the DSA requires very large online platforms to assess and mitigate the systemic risks linked to their services. While the DSA does not specifically target AI slop, its provisions on transparency, content recommendation algorithms, and risk mitigation could apply if AI content significantly affects public discourse or enables fraud. The challenge lies in defining when content volume prevails over quality control, becoming a systemic issue rather than isolated misuse.
Debates around labelling AI slop and transparency also play a large role. Policymakers and platforms have explored ways to flag AI-generated content throughout disclosures or watermarking. For example, OpenAI’s Sora generates videos with a faint Sora watermark, although it is hardly visible to an uninitiated user. Nevertheless, labelling alone may not address deeper concerns if recommendation systems continue to prioritise engagement above all else, with the issue not only being whether users know the content is AI-generated, but how such content is ranked, amplified, and monetised.
More broadly, AI slop highlights the limits of traditional content moderation. As generative tools make production faster and cheaper, enforcement systems may struggle to keep pace. Regulation, therefore, faces a structural question: can existing digital governance frameworks preserve information quality in an environment where automated content production continues to grow?
Building resilience in the era of AI slop
Humans are considered the most adaptable species on Earth, and for good reason. While AI slop has exposed weaknesses in platform design, monetisation models, and moderation systems, it may also serve as a catalyst for adaptation. Unless regulatory bodies unite under one banner and agree to ban AI content for good, it is safe to say that synthetic content is here to stay. However, sooner or later, systemic regulations will evolve to address this new AI craze and mitigate its negative effects.
The AI slop bubble is bound to burst at some point, as online users will come to favour meticulously crafted content – whether authentic or artificial over low-quality content. Consequently, incentives may also evolve along with content saturation, leading to a greater focus on quality rather than quantity. Advertisers and brands often prioritise credibility and brand safety, which could encourage platforms to refine their ranking systems to reward originality, reliability, and verified creators.
Transparency requirements, systemic risk assessments, and discussions around provenance disclosure mechanisms imply that governance is responding to the realities of generative AI. Instead of marking the deterioration of digital spaces, AI slop may represent a transitional phase in which platforms, policymakers, and users are challenged to adjust their expectations and norms accordingly.
Finally, the long-term outcome will depend entirely on whether innovation, market incentives, and governance structures can converge around information quality and resilience. In that sense, AI slop may ultimately function less as a permanent state of affairs and more as a stress test to separate the wheat from the chaff. In the upcoming struggle between user experience and generative AI tools, the former will have the final say, which is an encouraging thought.
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Across organisations, AI tools are moving beyond IT teams and into everyday business functions. CIOs now face the challenge of widening access while protecting data, security and trust.
Earlier waves of low-code platforms and citizen data science showed that empowerment can boost innovation but also create shadow IT and technical debt. AI agents and generative systems raise the stakes, with risks ranging from data leaks to flawed automated decisions.
Pressure from boards and business leaders means AI cannot be restricted to a small pilot group. Transparent governance, approved toolkits, and updated data policies are essential to prevent misuse while still enabling experimentation.
Long-term success depends on culture as much as technology. Leaders must define a focused AI vision, invest in literacy and adapt change management so employees use AI to improve decisions rather than accelerate flawed processes.
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Digital sovereignty is gaining urgency as organisations seek infrastructure that remains secure and reliable under strict regulatory conditions.
Microsoft is expanding its Sovereign Cloud to help public bodies, regulated industries and enterprises maintain control of data and operations even when environments must operate without external connectivity.
The updated portfolio allows customers to choose how each workload is governed, rather than relying on a single deployment model.
Azure Local now supports disconnected operations, keeping mission-critical systems running with full Azure governance within sovereign boundaries. Management, policies and workloads stay entirely on site, so services continue during periods of isolation.
Microsoft 365 Local extends the resilience to the productivity layer by enabling Exchange Server, SharePoint Server and Skype for Business Server to run locally, giving teams secure collaboration within the same protected boundary as their infrastructure.
Support for large multimodal AI models is delivered through Foundry Local, which enables advanced inference on customer-controlled hardware using technology from partners such as NVIDIA.
Such an approach helps organisations bring modern AI capabilities into highly restricted environments while preserving control over data, identities and operational procedures.
Microsoft positions it as a unified stack that works across connected, hybrid and fully disconnected modes without increasing operational complexity.
These additions create a framework designed for governments and regulated industries that regard sovereignty as a strategic priority.
With global availability for qualified customers, the Sovereign Cloud aims to preserve continuity, reinforce governance and expand AI capability while keeping every layer of the environment within local control.
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Microsoft has exceeded its 2025 internet access target, reaching over 299 million people globally, including more than 124 million in Africa. The milestone reflects years of partnerships to connect communities lacking reliable digital access.
Efforts are shifting from simple coverage to holistic digital participation, combining connectivity with energy, devices, digital skills, and AI tools.
Microsoft aims to enable meaningful adoption, ensuring communities can fully engage in the growing AI economy. Partnerships focus on scalable, community-based models aligned with national development priorities.
Collaboration with Starlink expands Microsoft’s toolkit to reach rural and hard-to-reach regions. Projects in Kenya pair satellite connectivity with local hubs and training to boost productivity, market access, and AI adoption.
As adoption accelerates, Microsoft plans to expand its approach by integrating financing, energy access, and community-first AI solutions. The initiative highlights the need for long-term, locally led strategies for fair participation in the digital and AI economy.
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Guidance for women’s health is entering a new phase as ŌURA introduces a proprietary large language model designed specifically for reproductive and hormonal wellbeing.
The model sits within Oura Advisor and is available for testing through Oura Labs, drawing on clinical standards, peer-reviewed evidence and biometric signals collected through the Oura Ring to create personalised and context-aware responses.
The system interprets questions through women’s physiology instead of depending on general-purpose models that miss critical hormonal and life-stage variables.
It supports the full spectrum of reproductive health, from the earliest menstrual patterns to menopause, and is intentionally tuned to be non-dismissive and emotionally supportive.
By combining longitudinal sleep, activity, stress, cycle and pregnancy data with clinician-reviewed research, the model aims to strengthen understanding and preparation ahead of medical appointments.
Privacy forms the centre of the architecture, with all processing hosted on infrastructure controlled entirely by the company. Conversations are neither shared nor sold, reflecting ŌURA’s broader push for private AI.
Oura Labs operates as an opt-in experimental environment where new features are tested in collaboration with members who can leave at any time.
Women who take part influence the model’s evolution by contributing feedback that informs future development.
These interactions help refine personalised insights across fertility, cycle irregularities, pregnancy changes and other hormonal shifts, marking a significant step in how the Finland-founded company advances preventive, data-guided care for its global community.
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AI is reshaping healthcare as organisations shift from trial projects to large-scale deployment.
The latest industry survey from NVIDIA shows widespread adoption across digital healthcare, biotechnology, pharmaceuticals and medical technology, signalling a sector that is now executing rather than experimenting.
The report highlights how medical imaging, drug discovery and clinical decision support are among the most prominent applications. Radiologists are using AI to accelerate image analysis, while research teams apply advanced models to speed early-stage drug development.
Organisations benefit from workflow optimisation instead of relying on manual administrative routines, with many citing improvements in patient coordination, documentation and coding.
Open-source models are increasingly important, with most respondents considering them vital for domain-specific development.
Experts argue that open-source innovation will guide exploration, whereas deployment in clinical environments will demand rigorous validation and accountability rather than unrestricted experimentation.
Agentic AI is emerging as a new capability for knowledge retrieval and literature analysis.
Evidence of return on investment is clear, prompting 85% of organisations to expand their AI budgets. Many report higher revenue, reduced costs and significant gains in back-office productivity.
Evaluation is becoming a core operational requirement, ensuring AI continues to improve safety, quality and overall clinical performance over time.
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The expansion into enterprise AI comes with a no-code platform from New Relic that allows companies to build and supervise their own observability agents.
A system that assembles AI-driven monitors designed to detect bugs and performance problems before they affect users, instead of leaving teams to rely on manual tracking.
It also supports the Model Context Protocol so organisations can link external data sources to the agents and integrate them with existing New Relic tools.
The company stresses that the platform is intended to complement other agent systems rather than replace them.
As AI agent software spreads across the market, enterprises are searching for ways to manage risk when giving automated tools access to internal systems.
Industry players such as Salesforce and OpenAI have already introduced their own agent platforms, and assessments from Gartner describe these frameworks as essential infrastructure for wider AI adoption.
New Relic also introduced new tools for the OpenTelemetry framework to remove friction around observability standards.
Its application performance monitoring agents now support OTel data, allowing enterprises to manage these streams in one place instead of operating separate collectors.
The update aims to reduce fragmentation that has slowed OTel deployment across large organisations and to simplify how engineering teams handle diverse observability pipelines.
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