Netomi has developed a blueprint for scaling enterprise AI, utilising GPT-4.1 for rapid tool use and GPT-5.2 for multi-step reasoning. The platform supports complex workflows, policy compliance, and heavy operational loads, serving clients such as United Airlines and DraftKings.
The company emphasises three core lessons. First, systems must handle real-world complexity, orchestrating multiple APIs, databases, and tools to maintain state and situational awareness across multi-step workflows.
Second, parallelised architectures ensure low latency even under extreme demand, keeping response times fast and reliable during spikes in activity.
Third, governance is embedded directly into the runtime, enforcing compliance, protecting sensitive data, and providing deterministic fallbacks when AI confidence is low.
Netomi demonstrates how agentic AI can be safely scaled, providing enterprises with a model for auditable, predictable, and resilient intelligent systems. These practices serve as a roadmap for organisations seeking to move AI from experimental tools to production-ready infrastructure.
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The South Korean tech giant, Samsung, used CES 2026 to foreground a cross-industry debate about trust, privacy and security in the age of AI.
During its Tech Forum session in Las Vegas, senior figures from AI research and industry argued that people will only fully accept AI when systems behave predictably, and users retain clear control instead of feeling locked inside opaque technologies.
Samsung outlined a trust-by-design philosophy centred on transparency, clarity and accountability. On-device AI was presented as a way to keep personal data local wherever possible, while cloud processing can be used selectively when scale is required.
Speakers said users increasingly want to know when AI is in operation, where their data is processed and how securely it is protected.
Security remained the core theme. Samsung highlighted its Knox platform and Knox Matrix to show how devices can authenticate one another and operate as a shared layer of protection.
Partnerships with companies such as Google and Microsoft were framed as essential for ecosystem-wide resilience. Although misinformation and misuse were recognised as real risks, the panel suggested that technological counter-measures will continue to develop alongside AI systems.
Consumer behaviour formed a final point of discussion. Amy Webb noted that people usually buy products for convenience rather than trust alone, meaning that AI will gain acceptance when it genuinely improves daily life.
The panel concluded that AI systems which embed transparency, robust security and meaningful user choice from the outset are most likely to earn long-term public confidence.
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Europe’s growing demand for cloud and AI services is driving a rapid expansion of data centres across the EU.
Policymakers now face the difficulty of supporting digital growth instead of undermining climate targets, yet reliable sustainability data remains scarce.
Operators are required to report on energy consumption, water usage, renewable sourcing and heat reuse, but only around one-third have submitted complete data so far.
Brussels plans to introduce a rating scheme from 2026 that grades data centres on environmental performance, potentially rewarding the most sustainable new facilities with faster approvals under the upcoming Cloud and AI Development Act.
Industry groups want the rules adjusted so operators using excess server heat to warm nearby homes are not penalised. Experts also argue that stronger auditing and stricter application of standards are essential so reported data becomes more transparent and credible.
Smaller data centres remain largely untracked even though they are often less efficient, while colocation facilities complicate oversight because customers manage their own servers. Idle machines also waste vast amounts of energy yet remain largely unmeasured.
Meanwhile, replacing old hardware may improve efficiency but comes with its own environmental cost.
Even if future centres run on cleaner power and reuse heat, the manufacturing footprint of the equipment inside them remains a major unanswered sustainability challenge.
Policymakers say better reporting is essential if the EU is to balance digital expansion with climate responsibility rather than allowing environmental blind spots to grow.
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Chinese President Xi Jinping said 2025 marked a year of major breakthroughs for the country’s AI and semiconductor industries. In his New Year’s address, he said that Chinese technology firms had made significant progress in AI models and domestic chip development.
China’s AI sector gained global attention with the rise of DeepSeek. The company launched advanced models focused on reasoning and efficiency, drawing comparisons with leading US systems and triggering volatility in global technology markets.
Other Chinese firms also expanded their AI capabilities. Alibaba released new frontier models and pledged large-scale investment in cloud and AI infrastructure, while Huawei announced new computing technologies and AI chips to challenge dominant suppliers.
China’s progress prompted mixed international responses. Some European governments restricted the use of Chinese AI models over data security concerns, while US companies continued engaging with Chinese-linked AI firms through acquisitions and partnerships.
Looking ahead to 2026, China is expected to prioritise AI and semiconductors in its next five-year development plan. Analysts anticipate increased research funding, expanded infrastructure, and stronger support for emerging technology industries.
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xAI is expanding its AI infrastructure in the southern United States after acquiring another data centre site near Memphis. The move significantly increases planned computing capacity and supports ambitions for large-scale AI training.
The expansion centres on the purchase of a third facility near Memphis, disclosed by Elon Musk in a post on X. The acquisition brings xAI’s total planned power capacity close to 2 gigawatts, placing the project among the most energy-intensive AI data centre developments currently underway.
xAI has bought a third building called MACROHARDRR. Will take @xAI training compute to almost 2GW.
xAI has already completed one major US facility in the area, known as Colossus, while a second site, Colossus 2, remains under construction. The newly acquired building, called MACROHARDRR, is located in Southaven and directly adjoins the Colossus 2 site, as previously reported.
By clustering facilities across neighbouring locations, xAI is creating a contiguous computing campus. The approach enables shared power, cooling, and high-speed data infrastructure for large-scale AI workloads.
The Memphis expansion underscores the rising computational demands of frontier AI models. By owning and controlling its infrastructure, xAI aims to secure long-term access to high-end compute as competition intensifies among firms investing heavily in AI data centres.
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A white paper released in December 2025 by the Principal Scientific Adviser outlines a strategy to treat AI compute, datasets and models as Digital Public Goods, rather than resources concentrated in a handful of urban hubs.
Despite generating nearly one-fifth of the world’s data, India currently hosts only a small share of global data centre capacity. The paper outlines plans to nearly tenfold capacity expansion by 2030, alongside the rollout of national computing resources through the IndiaAI Mission.
A central pool of GPUs and TPUs is being offered at subsidised rates to researchers and startups, aiming to reduce dependence on foreign cloud providers.
Data access and sovereignty form another pillar of the roadmap. Platforms such as IndiaAIKosh and Bhashini are being developed as shared repositories, hosting thousands of datasets and models across sectors including healthcare, agriculture and Indian languages.
High-performance computing initiatives, including the AIRAWAT supercomputer, are supporting large-scale research in areas such as climate modelling and drug discovery.
The strategy also emphasises regional and state-led infrastructure, with initiatives like Telangana’s federated data exchange seeking to decentralise AI development. Sustainability requirements are also being introduced, as data centres are expected to account for an increasing share of electricity use.
Policymakers view the approach as crucial to developing a form of sovereign AI that fosters innovation beyond major technology hubs and across the broader economy.
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US federal agencies planning to deploy agentic AI in 2026 are being told to prioritise data organisation as a prerequisite for effective adoption. AI infrastructure providers say poorly structured data remains a major barrier to turning agentic systems into operational tools.
Public sector executives at Amazon Web Services, Oracle, and Cisco said government clients are shifting focus away from basic chatbot use cases. Instead, agencies are seeking domain-specific AI systems capable of handling defined tasks and delivering measurable outcomes.
US industry leaders said achieving this shift requires modernising legacy infrastructure alongside cleaning, structuring, and contextualising data. Executives stressed that agentic AI depends on high-quality data pipelines that allow systems to act autonomously within defined parameters.
Oracle said its public sector strategy for 2026 centres on enabling context-aware AI through updated data assets. Company executives argued that AI systems are only effective when deeply aligned with an organisation’s underlying data environment.
The companies said early agentic AI use cases include document review, data entry, and network traffic management. Cloud infrastructure was also highlighted as critical for scaling agentic systems and accelerating innovation across government workflows.
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Türkiye has announced new measures to expand its AI ecosystem and strengthen public-sector adoption of the technology. The changes were published in the Official Gazette, according to Industry and Technology Minister Mehmet Fatih Kacir.
The Ministry’s Directorate General of National Technology has been renamed the Directorate General of National Technology and AI. The unit will oversee policies on data centres, cloud infrastructure, certification standards, and regulatory processes.
The directorate will also coordinate national AI governance, support startups and research, and promote the ethical and reliable use of AI. Its remit includes expanding data capacity, infrastructure, workforce development, and international cooperation.
Separately, a Public AI Directorate General has been established under the Presidency’s Cybersecurity Directorate. The new body will guide the use of AI across government institutions and lead regulatory work on public-sector AI applications.
Officials say the unit will align national legislation with international frameworks and set standards for data governance and shared data infrastructure. The government aims to position Türkiye as a leading country in the development of AI.
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OpenAI is said to be testing a new feature for ChatGPT that would mark a shift from Custom GPTs toward a more modular system of Skills.
Reports suggest the project, internally codenamed Hazelnut, will allow users and developers to teach the AI model standalone abilities, workflows and domain knowledge instead of relying only on role-based configurations.
The Skills framework is designed to allow multiple abilities to be combined automatically when a task requires them. The system aims to increase portability across the web version, desktop client and API, while loading instructions only when needed instead of consuming the entire context window.
Support for running executable code is also expected, providing the model with stronger reliability for logic-driven work, rather than relying entirely on generated text.
Industry observers note similarities to Anthropic’s Claude, which already benefits from a skill-like structure. Further features are expected to include slash-command interactions, a dedicated Skill editor and one-click conversion from existing GPTs.
Market expectations point to an early 2026 launch, signalling a move toward ChatGPT operating as an intelligent platform rather than a traditional chatbot.
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Deutsche Bank has warned that surging AI investment is helping to prop up US economic growth. Analysts say that broader spending would have stalled without the heavy outlays on technology.
The bank estimates hyperscalers could spend $4 trillion on AI data centres by 2030. Analysts cautioned returns remain uncertain despite the scale of investment.
Official data showed US GDP grew at a 4.3% annualised rate in the third quarter. Economists linked much of the momentum to AI-driven capital expenditure.
Market experts remain divided on risks, although many reject fears of a bubble. Corporate cash flows, rather than excessive borrowing, are funding the majority of AI infrastructure.
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