NVIDIA pushes forward with AI-ready data

Enterprises are facing growing pressure to prepare unstructured data for use in modern AI systems as organisations struggle to turn prototypes into production tools.

Around forty percent of AI projects advance beyond the pilot phase, largely due to limits in data quality and availability. Most organisational information now comes in unstructured form, ranging from emails to video files, which offers little coherence and places a heavy load on governance systems.

AI agents need secure, recent and reliable data instead of fragmented information scattered across multiple storage silos. Preparing such data demands extensive curation, metadata work, semantic chunking and the creation of vector embeddings.

Enterprises also struggle with the rising speed of data creation and the spread of duplicate copies, which increases both operational cost and security concerns.

An emerging approach by NVIDIA, known as the AI data platform, aims to address these challenges by embedding GPU acceleration directly into the data path. The platform prepares and indexes information in place, allowing enterprises to reduce data drift, strengthen governance and avoid unnecessary replication.

Any change to a source document is immediately reflected in the associated AI representations, improving accuracy and consistency for business applications.

NVIDIA is positioning its own AI Data Platform reference design as a next step for enterprise storage. The design combines RTX PRO 6000 Blackwell Server Edition GPUs, BlueField three DPUs and integrated AI processing pipelines.

Leading technology providers including Cisco, Dell Technologies, IBM, HPE, NetApp, Pure Storage and others have adopted the model as they prepare storage systems for broader use of generative AI in the enterprise sector.

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OpenAI and Intuit expand financial AI collaboration

Yesterday, OpenAI and Intuit announced a major strategic partnership aimed at reshaping how people manage their personal and business finances. The arrangement will allow Intuit apps to appear directly inside ChatGPT, enabling secure and personalised financial actions within a single environment.

An agreement that is worth more than one hundred million dollars and reinforces Intuit’s long-term push to strengthen its AI-driven expert platform.

Intuit will broaden its use of OpenAI’s most advanced models to support financial tasks across its products. Frontier models will help power AI agents that assist with tax preparation, cash flow forecasting, payroll management and wider financial planning.

Intuit will also continue using ChatGPT Enterprise internally so employees can work with greater speed and accuracy.

The partnership is expected to help consumers make more informed financial choices instead of relying on fragmented tools. Users will be able to explore suitable credit offers, receive clearer tax answers, estimate refunds and connect with tax specialists.

Businesses will gain tailored insights based on real time data that can improve cash flow, automate customer follow ups and support more effective outreach through email marketing.

Leaders from both companies argue that the collaboration will give people and firms a meaningful financial advantage. They say greater personalisation, deeper data analysis and more effortless decision making will support stronger household finances and more resilient small enterprises.

The deal expands the growing community of OpenAI enterprise customers and strengthens Intuit’s position in global financial technology.

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Google enters a new frontier with Gemini 3

A new phase of its AI strategy has begun for Google with the release of Gemini 3, which arrives as the company’s most advanced model to date.

The new system prioritises deeper reasoning and more subtle multimodal understanding, enabling users to approach difficult ideas with greater clarity instead of relying on repetitive prompting. It marks a major step for Google’s long-term project to integrate stronger intelligence into products used by billions.

Gemini 3 Pro is already available in preview across the Gemini app, AI Mode in Search, AI Studio, Vertex AI and Google’s new development platform known as Antigravity.

A model that performs at the top of major benchmarks in reasoning, mathematics, tool use and multimodal comprehension, offering substantial improvements compared with Gemini 2.5 Pro.

Deep Think mode extends the model’s capabilities even further, reaching new records on demanding academic and AGI-oriented tests, although Google is delaying wider release until additional safety checks conclude.

Users can rely on Gemini 3 to learn complex topics, analyse handwritten material, decode long academic texts or translate lengthy videos into interactive guides instead of navigating separate tools.

Developers benefit from richer interactive interfaces, more autonomous coding agents and the ability to plan tasks over longer horizons.

Google Antigravity enhances this shift by giving agents direct control of the development environment, allowing them to plan, write and validate code independently while remaining under human supervision.

Google emphasises that Gemini 3 is its most extensively evaluated model, supported by independent audits and strengthened protections against manipulation. The system forms the foundation for Google’s next era of agentic, personalised AI and will soon expand with additional models in the Gemini 3 series.

The company expects the new generation to reshape how people learn, build and organise daily tasks instead of depending on fragmented digital services.

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TikTok launches new tools to manage AI-generated content

TikTok has announced new tools to help users shape and understand AI-generated content (AIGC) in their feeds. A new ‘Manage Topics’ control will let users adjust how much AI content appears in their For You feeds alongside keyword filters and the ‘not interested’ option.

The aim is to personalise content rather than remove it entirely.

To strengthen transparency, TikTok is testing ‘invisible watermarking’ for AI-generated content created with TikTok tools or uploaded using C2PA Content Credentials. Combined with creator labels and AI detection, these watermarks help track and identify content even if edited or re-uploaded.

The platform has launched a $2 million AI literacy fund to support global experts in creating educational content on responsible AI. TikTok collaborates with industry partners and non-profits like Partnership on AI to promote transparency, research, and best practices.

Investments in AI extend beyond moderation and labeling. TikTok is developing innovative features such as Smart Split and AI Outline to enhance creativity and discovery, while using AI to protect user safety and improve the well-being of its trust and safety teams.

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Poll manipulation by AI threatens democratic accuracy, according to a new study

Public opinion surveys face a growing threat as AI becomes capable of producing highly convincing fake responses. New research from Dartmouth shows that AI-generated answers can pass every quality check, imitate real human behaviour and alter poll predictions without leaving evidence.

In several major polls conducted before the 2024 US election, inserting only a few dozen synthetic responses would have reversed expected outcomes.

The study reveals how easily malicious actors could influence democratic processes. AI models can operate in multiple languages yet deliver flawless English answers, allowing foreign groups to bypass detection.

An autonomous synthetic respondent that was created for the study passed nearly all attention tests, avoided errors in logic puzzles and adjusted its tone to match assigned demographic profiles instead of exposing its artificial nature.

The potential consequences extend far beyond electoral polling. Many scientific disciplines rely heavily on survey data to track public health risks, measure consumer behaviour or study mental wellbeing.

If AI-generated answers infiltrate such datasets, the reliability of thousands of studies could be compromised, weakening evidence used to shape policy and guide academic research.

Financial incentives further raise the risk. Human participants earn modest fees, while AI can produce survey responses at almost no cost. Existing detection methods failed to identify the synthetic respondent at any stage.

The researcher urges survey companies to adopt new verification systems that confirm the human identity of participants, arguing that stronger safeguards are essential to protect democratic accountability and the wider research ecosystem.

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The future of the EU data protection under the Omnibus Package

Introduction and background information

The Commission claims that the Omnibus Package aims to simplify certain European Union legislation to strengthen the Union’s long-term competitiveness. A total of six omnibus packages have been announced in total.

The latest (no. 4) targets small mid-caps and digitalisation. Package no. 4 covers data legislation, cookies and tracking technologies (i.e. the General Data Protection Regulation (GDPR) and ePrivacy Directive (ePD)), as well as cybersecurity incident reporting and adjustments to the Artificial Intelligence Act (AIA).

That ‘simplification’ is part of a broader agenda to appease business, industry and governments who argue that the EU has too much red tape. In her September 2025 speech to German economic and business associations, Ursula von der Leyen sided with industry and stated that simplification is ‘the only way to remain competitive’.

As for why these particular laws were selected, the rationale is unclear. One stated motivation for including the GDPR is its mention in Mario Draghi’s 2024 report on ‘The Future of European Competitiveness’.

Draghi, the former President of the European Central Bank, focused on innovation in advanced technologies, decarbonisation and competitiveness, as well as security. Yet, the report does not outline any concrete way in which the GDPR allegedly reduces competitiveness or requires revision.

The GDPR appears only twice in the report. First, as a brief reference to regulatory fragmentation affecting the reuse of sensitive health data across Member States (MS).

Second, in the concluding remarks, it is claimed that ‘the GDPR in particular has been implemented with a large degree of fragmentation which undermines the EU’s digital goals’. There is, however, no explanation of this ‘large fragmentation’, no supporting evidence, and no dedicated section on the GDPR as its first mention being buried in the R&I (research and innovation) context.

It is therefore unclear what legal or analytical basis the Commission relies on to justify including the GDPR in this simplification exercise.

The current debate

There are two main sides to this Omnibus, which are the privacy forward and the competitive/SME side. The two need not be mutually exclusive, but civil society warns that ‘simplification’ risks eroding privacy protection. Privacy advocates across civil society expressed strong concern and opposition to simplification in their responses to the European Commission’s recent call for evidence.

Industry positions vary in tone and ambition. For example, CrowdStrike calls for greater legal certainty under the Cybersecurity Act, such as making recital 55 binding rather than merely guiding and introducing a one-stop-shop mechanism for incident reporting.

Meta, by contrast, urges the Commission to go beyond ‘easing administrative burdens’, calling for a pause in AI Act enforcement and a sweeping reform of the EU data protection law. On the civil society side, Access Now argues that fundamental rights protections are at stake.

It warns that any reduction in consent prompts could allow tracking technologies to operate without users ever being given a real opportunity to refuse. A more balanced, yet cautious line can be found in the EDPB and EDPS joint opinion regarding easing records of processing activities for SMEs.

Similar to the industry, they support reducing administrative burdens, but with the caveat that amendments should not compromise the protection of fundamental rights, echoing key concerns of civil society.

Regarding Member State support, Estonia, France, Austria and Slovenia are firmly against any reopening of the GDPR. By contrast, the Czech Republic, Finland and Poland propose targeted amendments while Germany proposes a more systematic reopening of the GDPR.

Individual Members of the European Parliament have also come out in favour of reopening, notably Aura Salla, a Finnish centre-right MEP who previously headed Meta’s Brussels lobbying office.

Therefore, given the varied opinions, it cannot be said what the final version of the Omnibus would look like. Yet, a leaked draft document of the GDPR’s potential modifications suggests otherwise. Upon examination, it cannot be disputed that the views from less privacy-friendly entities have served as a strong guiding path.

Leaked draft document main changes

The leaked draft introduces several core changes.

Those changes include a new definition of personal and sensitive data, the use of legitimate interest (LI) for AI processing, an intertwining of the ePrivacy Directive (ePD) and GDPR, data breach reforms, a centralised data protection impact assessment (DPIA) whitelist/blacklist, and access rights being conditional on motive for use.

A new definition of personal data

The draft redefines personal data so that ‘information is not personal data for everyone merely because another entity can identify that natural person’. That directly contradicts established EU case law, which holds that if an entity can, with reasonable means, identify a natural person, then the information is personal data, regardless of who else can identify that person.

A new definition of sensitive data

Under current rules, inferred information can be sensitive personal data. If a political opinion is inferred from browsing history, that inference is protected.

The draft would narrow this by limiting sensitive data to information that ‘directly reveals’ special categories (political views, health, religion, sexual orientation, race/ethnicity, trade union membership). That would remove protection from data derived through profiling and inference.

Detected patterns, such as visits to a health clinic or political website, would no longer be treated as sensitive, and only explicit statements similar to ‘I support the EPP’ or ‘I am Muslim’ would remain covered.

Intertwining article 5(3) ePD and the GDPR

Article 5(3) ePD is effectively copied into the GDPR as a new Article 88a. Article 88a would allow the processing of personal data ‘on or from’ terminal equipment where necessary for transmission, service provision, creating aggregated information (e.g. statistics), or for security purposes, alongside the existing legal bases in Articles 6(1) and 9(2) of the GDPR.

That generates confusion about how these legal bases interact, especially when combined with AI processing under LI. Would this mean that personal data ‘on or from’ a terminal equipment may be allowed if it is done by AI?

The scope is widened. The original ePD covered ‘storing of information, or gaining access to information already stored, in the terminal equipment’. The draft instead regulates any processing of personal data ‘on or from’ terminal equipment. That significantly expands the ePD’s reach and would force controllers to reassess and potentially adapt a broad range of existing operations.

LI for AI personal data processing

A new Article 88c GDPR, ‘Processing in the context of the development and operation of AI’, would allow controllers to rely on LI to process personal data for AI processing. That move would largely sideline data subject control. Businesses could train AI systems on individuals’ images, voices or creations without obtaining consent.

A centralised data breach portal, deadline extension and change in threshold reporting

The draft introduces three main changes to data breach reporting.

  • Extending the notification deadline from 72 to 96 hours, giving privacy teams more time to investigate and report.
  • A single EU-level reporting portal, simplifying reporting for organisations active in multiple MS.
  • Raising the notification threshold when the rights and freedoms of data subjects are at ‘risk’ to ‘high risk’.

The first two changes are industry-friendly measures designed to streamline operations. The third is more contentious. While industry welcomes fewer reporting obligations, civil society warns that a ‘high-risk’ threshold could leave many incidents unreported. Taken together, these reforms simplify obligations, albeit at the potential cost of reducing transparency.

Centralised processing activity (PA) list requiring a DPIA

This is another welcome change as it would clarify which PAs would automatically require a DPIA and which would not. The list would be updated every 3 years.

What should be noted here is that some controllers may not see their PA on this list and assume or argue that a DPIA is not required. Therefore, the language on this should make it clear that it is not a closed list.

Access requests denials

Currently, a data subject may request a copy of their data regardless of the motive. Under the draft, if a data subject exploits the right of access by using that material against the controller, the controller may charge or refuse the request.

That is problematic for the protection of rights as it impacts informational self-determination and weakens an important enforcement tool for individuals.

For more information, an in depth analysis by noyb has been carried out which can be accessed here.

The Commission’s updated version

On 19 November, the European Commission is expected to present its official simplification package. This section will be updated once the final text is published.

Final remarks

Simplification in itself is a good idea, and businesses need to have enough freedom to operate without being suffocated with red tape. However, changing a cornerstone of data protection law to such an extent that it threatens fundamental rights protections is just cause for concern.

Alarms have already been raised after the previous Omnibus package on green due diligence obligations was scrapped. We may now be witnessing a similar rollback, this time targeting digital rights.

As a result, all eyes are on 19 November, a date that could reshape not only the EU privacy standards but also global data protection norms.

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EU examines Amazon and Microsoft influence in cloud services

European regulators have launched three market investigations into cloud computing amid growing concerns about sector concentration.

The European Commission will assess whether Amazon Web Services and Microsoft Azure should be designated as gatekeepers for their cloud services under the Digital Markets Act, despite not meeting the formal threshold criteria.

Officials argue that cloud infrastructure now underpins AI development and many digital services, so competition must remain open and fair.

A move that signals a broader shift in EU oversight of strategic technologies. Rather than focusing solely on size, investigators will examine whether the two providers act as unavoidable gateways between businesses and users.

They will analyse network effects, switching costs and the role of corporate structures that might deepen market dominance. If the inquiries confirm gatekeeper status, both companies will face the DMA’s full obligations and a six-month compliance period.

A parallel investigation will explore whether existing DMA rules adequately address cloud-specific risks that might limit competition. Regulators aim to clarify whether obstacles to interoperability, restricted access to data, tying of services and imbalanced contractual terms require updated obligations.

Insights gathered from industry, public bodies and civil society will feed into a final report within 18 months, potentially leading to changes via a delegated act.

EU officials underline that Europe’s competitiveness, technological resilience and future AI capacity rely on a fair cloud environment. They argue that a transparent and contestable market will strengthen Europe’s strategic autonomy and encourage innovation.

The inquiries will shape how digital platforms are regulated as cloud services become increasingly central to economic and social life.

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AI search tools put to the test in UK study

AI tools are shaping online searches, but testing reveals notable risks in relying on them. ChatGPT, Google Gemini, Microsoft Copilot, Meta AI, and Perplexity were tested on 40 questions in finance, law, health, and consumer rights.

Results show errors, incomplete advice, and ethical oversights remain widespread despite AI’s popularity.

More than half of UK adults now use AI for online searches, with frequent users showing higher trust in the responses. Around one in ten regularly seeks legal advice from AI, while others use it for financial or medical guidance.

Experts warn that overconfidence in AI recommendations could lead to costly mistakes, particularly when rules differ across regions in the UK.

Perplexity outperformed other tools in accuracy and reliability, while ChatGPT ranked near the bottom. Google’s AI overview (AIO) often delivers better results for legal and health queries, while its Gemini chatbot scores higher on finance and consumer questions.

Users are encouraged to verify sources, as many AI outputs cite vague or outdated references and occasionally promote questionable services.

Despite flaws, AI remains a valuable tool for basic research, summarising information quickly and highlighting key points. Experts advise using multiple AI tools and consulting professionals for complex financial, legal, or medical matters.

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AI energy demand strains electrical grids

Microsoft CEO Satya Nadella recently delivered a key insight, stating that the biggest hurdle to deploying new AI solutions is now electrical power, not chip supply. The massive energy requirements for running large language models (LLMs) have created a critical bottleneck for major cloud providers.

Nadella specified that Microsoft currently has a ‘bunch of chips sitting in inventory’ that cannot be plugged in and utilised. The problem is a lack of ‘warm shells’, meaning data centre buildings that are fully equipped with the necessary power and cooling capacity.

The escalating power requirements of AI infrastructure are placing extreme pressure on utility grids and capacity. Projections from the Lawrence Berkeley National Laboratory indicate that US data centres could consume up to 12 percent of the nation’s total electricity by 2028.

The disclosure should serve as a warning to investors, urging them to evaluate the infrastructure challenges alongside AI’s technological promise. This energy limitation could create a temporary drag on the sector, potentially slowing the massive projected returns on the $5 trillion investment.

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Microsoft and NVIDIA expand partnership with Anthropic

Microsoft, NVIDIA, and Anthropic have announced new strategic partnerships to expand access to Anthropic’s rapidly growing Claude AI models. Claude will scale on Microsoft Azure with NVIDIA support, offering enterprise customers broader model choices and enhanced capabilities.

Anthropic has committed to purchase $30 billion of Azure compute capacity and additional capacity up to one gigawatt. NVIDIA and Anthropic will optimise Claude models for performance, efficiency, and cost, while aligning future NVIDIA architectures with Anthropic workloads.

The partnerships also extend Claude access across Microsoft Foundry, including frontier models like Claude Sonnet 4.5, Claude Opus 4.1, and Claude Haiku 4.5.

Microsoft Copilot products, including GitHub Copilot, Microsoft 365 Copilot, and Copilot Studio, will continue to feature Claude capabilities, providing enterprise users with integrated AI tools.

Microsoft and NVIDIA have committed $5 billion and $10 billion respectively to support Anthropic’s growth. The partnership makes Claude the only frontier AI model on all three top cloud platforms, boosting enterprise AI adoption and innovation.

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