Samsung Electronics has unveiled the Galaxy S26 series, featuring advanced AI experiences, powerful performance, and an industry-leading camera system designed to simplify everyday smartphone tasks.
The series, which includes the Galaxy S26, S26+, and S26 Ultra, handles complex processes in the background, allowing users to focus on results rather than device operations.
The Galaxy S26 Ultra introduces the world’s first built-in Privacy Display, a redesigned chipset, and improved thermal management. Together, these upgrades enhance AI performance, graphics, and CPU efficiency, while ensuring faster, cooler, and more reliable operation throughout the day.
Photography and videography are also upgraded with wider apertures, Nightography Video, Super Steady video, and AI-powered editing tools that make professional-quality content accessible to all users.
Galaxy AI streamlines daily experiences by proactively suggesting actions, organising information, and automating tasks. Features such as Now Nudge, Now Brief, Circle to Search, and upgraded Bixby allow users to interact naturally with their devices.
Integrated AI agents, including Gemini and Perplexity, support multi-step tasks across apps, from booking services to advanced searches, all with minimal input.
Samsung has embedded multiple layers of security and privacy in the Galaxy S26 series. From AI-powered Call Screening and Privacy Alerts to Knox Vault, Knox Matrix, and post-quantum cryptography, users can control data access and protect personal information.
With long-term security updates, seamless software, and Galaxy Buds4 integration, the S26 series aims to combine performance, convenience, and safety in a single, intuitive device.
Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!
Australia’s rise to second place in the OECD Digital Government Index signals renewed momentum for national digital transformation.
A shift that comes as Microsoft signs a new five-year Volume Sourcing Arrangement with the Federal Government, designed to underpin modernisation across public services and create a secure, future-ready foundation for responsible AI adoption.
The agreement led by the Digital Transformation Agency gives agencies access to Microsoft Copilot, Azure, Microsoft 365, Dynamics 365 and a strengthened security and compliance framework instead of continuing reliance on ageing systems.
The arrangement sets clearer strategic pathways for innovation, procurement and skills development through an enhanced governance structure.
It recommits both sides to national security requirements, including the Security of Critical Infrastructure legislation, the Cloud Hosting Certification Framework and IRAP.
These measures allow agencies to expand AI use while retaining control of data and meeting the expectations placed on government institutions.
A successful Copilot trial in 2024 already demonstrated personal productivity gains of around one hour per day for participating staff.
Microsoft is also establishing a $1.55 million training fund for the Australian Public Service to support capability building in ethical AI use and modern cloud operations.
The company emphasises that Australia’s partner ecosystem will gain new opportunities because the agreement simplifies how local firms engage with government agencies. Such an approach forms an important part of the wider public sector reform agenda announced last year.
The new deal aligns with national priorities set out in the Whole-of-Government Cloud Computing Policy and the National AI Plan.
Australia now enters a pivotal period in which digital transformation is guided not only by technological capacity but by the frameworks of trust, resilience and public benefit that shape how government services evolve.
Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!
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.
Would you like to learn more about AI, tech, and digital diplomacy? If so, ask our Diplo chatbot!
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.
Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!
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.
Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!
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.
Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!
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.
Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!
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.
Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!
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.
Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!
The European Commission has confirmed it will again delay publishing guidance on high-risk AI systems under the EU AI Act. The guidelines were due by 2 February 2026, but will now follow a revised timeline.
According to Euractiv, the document is intended to clarify which AI systems fall into the high-risk category and therefore face stricter obligations. Officials said more time is needed to incorporate significant stakeholder feedback.
The delay marks the second missed deadline and adds to broader implementation setbacks surrounding the EU AI Act. Several member states have yet to designate national enforcement bodies, complicating oversight preparations.
Brussels is also considering postponing the application of high-risk rules through a digital simplification package. Parliament and Council appear supportive of moving the August deadline back by more than a year, easing pressure on companies awaiting guidance.
Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!
