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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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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Cyber adversaries increasingly used AI to accelerate attacks and evade detection in 2025, according to CrowdStrike’s 2026 Global Threat Report. The company described the period as the year of the evasive adversary, marked by subtle and rapid intrusions.
The average time to a financially motivated online crime breakout fell to 29 minutes, with the fastest recorded at 27 seconds. CrowdStrike observed an 89 percent rise in attacks by AI-enabled threat actors compared with 2024.
Attackers also targeted AI systems themselves, exploiting GenAI tools at more than 90 organisations through malicious prompt injection. Supply chain compromises and the abuse of valid credentials enabled intrusions to blend into legitimate activity, with most detections classified as malware-free.
China linked activity rose by 38 percent across sectors, while North Korea linked incidents increased by 130 percent. CrowdStrike tracked more than 281 adversaries in total, warning that speed, credential abuse, and AI fluency now define the modern threat landscape.
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Sony Group has developed technology designed to identify the original sources of music generated by AI. The move comes amid growing concern over the unauthorised use of copyrighted works in AI training.
According to Sony Group, the system can extract data from an underlying AI model and compare generated tracks with original compositions. The process aims to quantify how much specific works contributed to the output.
Composers, songwriters and publishers could use the technology to seek compensation from AI developers if their material was used without permission. Sony said the goal is to help ensure creators are properly rewarded.
Efforts to safeguard intellectual property have intensified across the music industry. Sony Music Entertainment in the US previously filed a copyright infringement lawsuit in 2024 over AI-generated music, underscoring wider tensions around AI and creative rights.
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AI chatbots may pose serious risks for people with severe mental illnesses, according to a new study from Acta Psychiatrica Scandinavica. Researchers found that tools such as ChatGPT can worsen psychiatric conditions by reinforcing users’ delusions, paranoia, mania, suicidal thoughts, and eating disorders.
The team examined health records from more than 54,000 patients and identified dozens of cases where AI interactions appeared to exacerbate symptoms. Experts warn that the actual number of affected individuals is likely far higher.
AI’s design to follow and validate a user’s input can unintentionally strengthen delusional thinking, turning digital assistants into echo chambers for psychosis.
Despite potential benefits for psychoeducation or alleviating loneliness, experts caution against using AI as a substitute for trained therapists. Chatbots should be tested in rigorous clinical trials before any therapeutic use, says Professor Søren Dinesen Østergaard.
The researchers urge healthcare providers to discuss AI chatbot use with patients, particularly those with severe mental illnesses, and call for central regulation of the technology. They argue that lessons from social media show that early oversight is essential to protect vulnerable populations.
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Google has suspended access to its Antigravity AI platform for numerous OpenClaw users, citing violations of its terms of service. Developers had used OpenClaw’s OAuth plugin to access subsidised Gemini model tokens, triggering backend strain and service degradation.
OpenClaw, launched in November 2025, gained more than 219,000 GitHub stars by enabling local AI agents for tasks such as email management and web browsing. Users authenticated through Antigravity to access advanced Gemini models at reduced cost, bypassing official distribution channels.
Google said the third-party integration powered non-authorised products on Antigravity infrastructure, triggering usage flagged as malicious. In February 2026, AI Ultra subscribers reported 403 errors and account restrictions, with some citing temporary disruptions to Gmail and Workspace.
Varun Mohan of Google DeepMind said the surge had degraded service quality and that enforcement prioritised legitimate users. Limited reinstatement options were offered to those unaware of violations, while capacity constraints were cited as the reason.
The move follows similar restrictions by Anthropic on third-party OAuth usage. Developers are shifting to alternative forks, as debate intensifies over open tooling, platform control, and the risks of agentic AI ecosystems.
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Privacy regulators from around the world have issued a joint warning about the rise of AI-generated deepfakes, arguing that the spread of non-consensual images poses a global risk instead of remaining a problem confined to individual countries.
Sixty-one authorities endorsed a declaration that draws attention to AI images and videos depicting real people without their knowledge or consent.
The signatories highlight the rapid growth of intimate deepfakes, particularly those targeting children and individuals from vulnerable communities. They note that such material often circulates widely on social platforms and may fuel exploitation or cyberbullying.
The declaration argues that the scale of the threat requires coordinated action rather than isolated national responses.
European authorities, including the European Data Protection Board and the European Data Protection Supervisor, support the effort to build global cooperation.
Regulators say that only joint oversight can limit the harms caused by AI systems that generate false depictions, rather than protecting individuals’ privacy as required under frameworks such as the General Data Protection Regulation.
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DeepSeek, Moonshot, and MiniMax used around 24,000 fraudulent accounts to generate more than 16 million interactions, violating terms of service and regional access restrictions. The technique, called distillation, trains a weaker model on outputs from a stronger one, speeding AI development.
Distilled models obtained in this manner often lack critical safeguards, creating serious national security concerns. Without protections, these capabilities could be integrated into military, intelligence, surveillance, or cyber operations, potentially by authoritarian governments.
The attacks also undermine export controls designed to preserve the competitive edge of US AI technology and could give a misleading impression of foreign labs’ independent AI progress.
Each lab followed coordinated playbooks using proxy networks and large-scale automated prompts to target specific capabilities such as agentic reasoning, coding, and tool use.
Anthropic attributed the campaigns using request metadata, infrastructure indicators, and corroborating observations from industry partners. The investigation detailed how distillation attacks operate from data generation to model launch.
In response, Anthropic has strengthened detection systems, implemented stricter access controls, shared intelligence with other labs and authorities, and introduced countermeasures to reduce the effectiveness of illicit distillation.
The company emphasises that addressing these attacks will require coordinated action across the AI industry, cloud providers, and policymakers to protect frontier AI capabilities.
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Ankara is seeing growing debate over the risks and benefits of medical AI as experts warn that poorly governed systems could threaten patient safety.
Associate professor Agah Tugrul Korucu said AI offers meaningful potential for healthcare only when supported by rigorous ethical rules and strong oversight instead of rapid deployment without proper safeguards.
Korucu explained that data bias remains one of the most significant dangers because AI models learn directly from the information they receive. Underrepresented age groups, regions or social classes can distort outcomes and create systematic errors.
Turkey’s national health database e-Nabiz provides a strategic advantage, yet raw information cannot generate value unless it is processed correctly and supported by clear standards, quality controls and reliable terminology.
He added that inconsistent hospital records, labelling errors and privacy vulnerabilities can mislead AI systems and pose legal challenges. Strict anonymisation and secure analysis environments are needed to prevent harmful breaches.
Medical AI works best as a second eye in fields such as radiology and pathology, where systems can reduce workloads by flagging suspicious areas instead of leaving clinicians to assess every scan alone.
Korucu said physicians must remain final decision makers because automation bias could push patients towards unnecessary risks.
He expects genomic data combined with AI to transform personalised medicine over the coming decade, allowing faster diagnoses and accurate medication choices for rare conditions.
Priority development areas for Turkey include triage tools, intensive care early warning systems and chronic disease management. He noted that the long-term model will be the AI-assisted physician rather than a fully automated clinician.
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