Cloud computing Archives | Digital Watch Observatory

New MIT development reduces energy use in AI systems

Researchers from MIT and Microsoft have developed a system called Murakkab to improve the speed and energy efficiency of agentic AI workflows.

Agentic workflows combine multiple AI models and external tools to complete complex, multi-step tasks, such as analysing video or generating code. MIT said these systems are becoming more important for cloud providers, but their fragmented design can waste computation, energy and money.

Murakkab allows developers to describe an AI application in high-level terms rather than manually specifying every model, tool, hardware choice and execution step. The system then identifies suitable models and tools, decides which components should run sequentially or in parallel, and selects hardware resources for cloud deployment.

The system can adjust configurations during execution based on user priorities such as accuracy, speed, latency and cost. It also gives cloud providers more visibility into workflows, allowing them to allocate computing resources more efficiently across multiple tasks.

In tests of video-question-answering and code-generation workflows, Murakkab met user requirements while using about 35% of the computational resources required by other methods. It also consumed about 27% as much energy and cost less than 25% as much as the comparison approaches.

In one case, the system reduced energy consumption by more than an order of magnitude with only about a 2% drop in accuracy. The researchers plan to expand Murakkab to more complex workflows and larger computing clusters.

Why does it matter?

Agentic AI systems are becoming more complex and resource-intensive, especially as cloud providers deploy workflows that combine many models, tools and hardware configurations. Murakkab points to a shift from optimising individual models to optimising the whole AI workflow and its cloud deployment. That matters because energy use, compute costs, and data centre capacity are becoming central constraints on AI growth.

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EU targets AWS and Azure under the DMA

The European Commission has informed Amazon and Microsoft of its preliminary view that their cloud computing services, Amazon Web Services and Microsoft Azure, should be designated as gatekeepers under the Digital Markets Act.

The move could extend the DMA’s reach into cloud infrastructure, a sector the Commission describes as critical to Europe’s digital economy and AI development.

The Commission opened market investigations into AWS and Azure in November 2025. It has now been provisionally concluded that both services act as important gateways between businesses and customers in the EU, despite not meeting the DMA’s standard quantitative thresholds.

According to the Commission, AWS and Azure benefit from large and established user bases, high switching costs, loyalty effects, broad cloud ecosystems and long-standing market positions. It also said their AI tool portfolios and partnerships are becoming increasingly important for cloud customers.

Amazon and Microsoft now have the opportunity to examine the investigation files and respond to the preliminary findings. If the Commission confirms its assessment, AWS and Azure would be designated as gatekeepers, and the companies would have six months to comply with DMA obligations.

The Commission said fair and competitive cloud markets are important for secure, sustainable and interoperable cloud services in Europe. It also linked the case to Europe’s wider technological sovereignty objectives, as cloud infrastructure underpins AI systems, enterprise software and public services.

Why does it matter?

The case shows how the EU competition policy is moving deeper into the infrastructure behind the AI economy. Cloud platforms are no longer just business services; they shape access to compute, data, AI tools, software ecosystems and switching options for companies and public institutions. If AWS and Azure are designated as DMA gatekeepers, the decision could affect cloud interoperability, customer lock-in and the balance of power between US hyperscalers and European cloud providers.

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China’s latest supercomputer strengthens AI ambitions

China has regained the world’s leading position in supercomputing after the LineShine system became the fastest computer in the latest TOP500 ranking, replacing the US’s El Capitan at the top of the list.

The achievement marks China’s return to first place for the first time since 2017 and highlights the growing strategic importance of high-performance computing in the AI era.

Unlike many recent AI-focused supercomputers that rely heavily on graphics processing units (GPUs), LineShine achieves exascale performance using conventional central processing units (CPUs).

Beyond topping benchmark rankings, the system is expected to support scientific research, advanced simulations, climate modelling, pharmaceutical development and the training of increasingly sophisticated AI models.

The announcement also reflects the broader ambition of China to strengthen technological leadership while presenting its innovation ecosystem as a contributor to global technological development.

Europe also remains a major player in high-performance computing. Four European systems rank among the world’s ten fastest supercomputers, while the EU continues to invest in AI factories, next-generation supercomputing infrastructure and collaborative research centres.

The growing investment in supercomputers reflects how computing infrastructure is increasingly being treated as a strategic asset alongside semiconductors, cloud infrastructure and advanced data centres.

As governments increasingly link AI capabilities with economic competitiveness, scientific leadership and national security, access to world-class computing resources is becoming one of the defining factors shaping the global technology balance.

Why does it matter?

The latest TOP500 ranking underline that computing capacity is becoming a defining factor in AI development and scientific competitiveness. As frontier AI models require ever-greater computational resources for training and inference, access to world-class supercomputers is emerging as a strategic advantage alongside semiconductor manufacturing and cloud infrastructure.

China’s return to the top of the rankings also highlights the geopolitical dimension of high-performance computing. At the same time, continued European investment in AI factories and supercomputing infrastructure reflects a broader effort to strengthen technological sovereignty and reduce dependence on external computing resources as countries compete for leadership in AI and advanced research.

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IMF and China sign MoU on AI and digital economy measurement

The International Monetary Fund and China’s National Bureau of Statistics have signed a new Memorandum of Understanding to strengthen cooperation on national accounts, macroeconomic statistics and statistical modernisation.

The agreement builds on a previous MoU signed in November 2023 and creates a framework for cooperation on implementing the 2025 System of National Accounts.

The cooperation will include work on measuring the digital economy, AI, cloud computing, digital intermediation platforms and data as an asset. It will also cover broader areas introduced in updated international statistical standards, including globalisation, economic well-being and environmental sustainability.

The IMF and NBS also agreed to deepen technical collaboration on the consistency and integration of macroeconomic statistics, including through the use of innovative data sources and analytical approaches.

The agreement includes cooperation between the IMF Big Data Centre and the NBS Big Data Application Centre, which hosts the UN Global Hub on Big Data and Data Science for Official Statistics.

Activities under the MoU will include high-level visits, expert consultations, technical workshops, joint analytical work and exchanges on statistical practices and methodologies.

The new MoU will take effect in December 2026, upon the expiration of the current agreement, and will remain in force until December 2029.

Why does it matter?

Measuring the digital economy is becoming harder as AI systems, cloud services, platforms and data-driven business models become more central to economic activity. Cooperation between the IMF and China’s statistics authority could support more consistent approaches to measuring these sectors under the 2025 System of National Accounts. Better statistical methods matter because governments, investors and international organisations rely on comparable data to assess growth, productivity, sustainability and the economic impact of digital transformation.

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Greek supercomputer DAEDALUS enters global supercomputer rankings

Greece’s DAEDALUS supercomputer has entered the international TOP500 and Green500 rankings, strengthening the country’s position in Europe’s high-performance computing landscape.

The system ranked 31st in the TOP500 list of the world’s most powerful supercomputers and 23rd in the Green500 list of energy-efficient systems. According to GRNET, DAEDALUS recorded a measured performance of 85.69 petaflops, making it the most powerful computing system ever ranked in Greece.

DAEDALUS is based on Hewlett Packard Enterprise architecture and uses NVIDIA GH200 accelerators. It also uses direct liquid cooling, combining high computing performance with energy efficiency.

The supercomputer and its data centre are located at the Lavrio Technological and Cultural Park of the National Technical University of Athens, inside the former Power Station building.

Once fully operational, DAEDALUS is expected to support researchers, universities, industry and public authorities working on demanding computational tasks. These include AI, cybersecurity, personalised healthcare, climate research, public administration and large-scale data analytics.

The system will also serve as the computational core of PHAROS, Greece’s national AI Factory under the European AI Factories initiative. Through PHAROS, Greece aims to expand access to AI infrastructure and support the development of AI applications across research, business and the public sector.

The project forms part of Greece’s wider digital transformation agenda and contributes to European efforts to strengthen technological capacity, AI infrastructure and digital sovereignty through high-performance computing.

Why does it matter?

DAEDALUS gives Greece strategic computing capacity for AI research, scientific modelling and public-sector digital transformation. Its role in PHAROS also links national supercomputing infrastructure to the EU’s AI Factories initiative, which aims to give researchers and companies access to advanced computing resources for AI development. The Green500 ranking matters as well, because Europe’s AI infrastructure push increasingly depends not only on raw performance, but also on energy efficiency and sustainable data-centre design.

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Google launches Gemini for Science AI research tools

Google has introduced Gemini for Science, a collection of AI experiments and tools designed to support scientific discovery across research fields.

The initiative includes three experimental tools on Google Labs. Hypothesis Generation, built with Co-Scientist, helps researchers define research challenges, generate hypotheses and evaluate them through a multi-agent process. Google said the tool uses an ‘idea tournament’ in which agents generate, debate and assess possible research directions, with claims supported by clickable citations.

Computational Discovery, built with AlphaEvolve and Empirical Research Assistance, is designed to generate and score large numbers of code variations in parallel. Google said the prototype could help scientists test modelling approaches in areas such as solar forecasting and epidemiology.

Literature Insights, built with NotebookLM, searches scientific literature and organises results into structured tables for side-by-side analysis. Researchers can use it to identify research gaps, synthesise findings across papers and create outputs such as reports, slide decks and audio or video overviews.

Google said access to the experiments will open gradually through Google Labs. The company is also bringing related capabilities to enterprise organisations through Google Cloud, with partners testing tools for pharmaceutical research, crop science, supply chain optimisation and work linked to the US Department of Energy’s Genesis Mission.

As part of Gemini for Science, Google is also launching Science Skills, a bundle that integrates more than 30 life science databases and tools, including UniProt, the AlphaFold Database, AlphaGenome API and InterPro. Google said the tools can support workflows such as structural bioinformatics and genomic analysis on agentic platforms such as Google Antigravity.

The company said it is working with more than 100 institutions to validate its scientific AI systems and has created a trusted tester community that includes PhD students, industry researchers and Nobel laureates.

The launch shows how major AI developers are moving from specialised scientific models towards broader agentic tools that support hypothesis generation, literature analysis and computational testing.

Why does it matter?

Gemini for Science points to a wider shift in AI-assisted research: AI systems are moving beyond literature search or single-task modelling towards multi-step scientific workflows. Such tools help researchers navigate large bodies of literature, test computational ideas faster and identify new hypotheses. But their value will depend on evidence quality, reproducibility, peer review and clear limits around what AI-generated scientific suggestions can and cannot prove.

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Oxford and UCL to lead UK-funded labs on next-generation AI

The UK government has announced two new AI research labs led by University College London and the University of Oxford, backed by up to £60 million in funding and access to large-scale computing power.

The labs will work on next-generation AI systems that are cheaper to run, more reliable and easier for businesses, researchers and public services to use. Funding will be provided through UK Research and Innovation’s Engineering and Physical Sciences Research Council over six years.

The announcement expands the government’s original plan from one AI lab to two, increasing planned funding from £40 million to up to £60 million. The labs will also receive access to computing resources valued at tens of millions of pounds.

The Science of Fundamental AI Research Lab, or SOFAIR, will be led by Professor David Barber at UCL, with researchers from Cambridge, Oxford and Edinburgh. It will focus on open-source AI technologies that can run on widely available hardware, aiming to reduce dependence on a small number of model providers.

The British Open-ended Learning and Discovery Lab, known as BOLD, will be led by Associate Professor Jakob Foerster at Oxford, in collaboration with UCL and Imperial College London. It will explore AI systems that can learn more efficiently, adapt to new situations and operate in physical environments.

Each lab will receive £2 million to recruit at least 10 doctoral students, supporting the UK’s AI talent pipeline. The labs will also work with existing UK AI research organisations, including the Alan Turing Institute and UKRI’s AI research hubs.

The funding forms part of UKRI’s wider AI strategy, a £1.6 billion plan to strengthen the UK’s AI research and innovation capacity over the next four years.

Why does it matter?

The investment shows the UK trying to compete in AI through fundamental research, open-source methods and efficient systems rather than only through larger datasets and more computing. By funding labs focused on reliability, lower-cost deployment and widely available hardware, the government is trying to make advanced AI more usable beyond large technology companies. The policy also links AI research to national capability, resilience and a domestic talent pipeline.

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EU launches ADACities for autonomous driving

The European Commission has launched the Autonomous Drive Ambition Cities initiative to support the deployment of autonomous driving technologies in cities across the EU.

The initiative, known as ADACities, was announced by Executive Vice-President Henna Virkkunen during the first international forum of the European Connected and Autonomous Vehicle Alliance in Brussels.

The Commission said ADACities will serve as a mobility flagship under the Apply AI Strategy, allowing selected EU cities to become real-world deployment sites for autonomous mobility innovation.

The initiative will support technologies such as robo-taxis, car-sharing services, autonomous shuttles for multimodal urban mobility and advanced self-driving cars. Participating cities will target fleets of 100 or more autonomous vehicles by 2030.

The Commission said partnerships supported by ADACities should be EU-centric, with European vehicle manufacturers and technology providers at the core, while still allowing international collaboration.

The initiative is also linked to the EU Technological Sovereignty Package. The Commission said autonomous driving deployment will draw on European capabilities in semiconductors, sovereign cloud and data infrastructure, AI Factories and open-source technologies.

ADACities builds on a joint declaration of intent to create a cross-border testbed for automated vehicle deployment. The Commission has opened a call for expressions of interest and will hold an online information session for cities and stakeholders.

Why does it matter?

ADACities shows how the EU is treating autonomous driving as part of AI deployment, urban mobility and industrial competitiveness, not only as a transport technology. By linking autonomous mobility to sovereign cloud, semiconductors, data infrastructure and AI Factories, the Commission is framing city-level deployment as a test of Europe’s ability to turn AI and automotive expertise into scalable public services. The initiative also raises governance questions around safety, liability, infrastructure readiness, data use and public trust.

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Reflection secures SpaceXAI compute deal for open-source AI models

Open-source AI startup Reflection has signed a major compute agreement with SpaceXAI, giving the company access to Colossus 2 data centre capacity as it works to develop frontier AI models.

According to Axios, Reflection will begin paying $150 million per month from 1 July 2026 for access to the infrastructure through 2029. The deal is intended to give the Nvidia-backed startup the computing power needed to compete with leading AI companies.

Reflection is developing open-source AI models at a time when access to advanced chips and large-scale data centre capacity has become a major barrier to frontier model development.

The agreement highlights the growing importance of specialised AI infrastructure providers. Rather than building all capacity internally, AI developers are increasingly relying on large compute partnerships to secure the resources needed for training and operating advanced models.

It also points to SpaceXAI’s expanding role in the AI infrastructure market. The company has been offering access to Colossus data centre capacity to AI developers, turning large-scale compute into a strategic asset within the AI ecosystem.

The deal reflects a broader shift in the AI race, where access to GPUs, power, data centres and long-term infrastructure contracts can be as important as model design or software talent.

Why does it matter?

The Reflection-SpaceXAI deal shows how compute access is becoming a decisive factor in AI competition. Open-source AI developers may benefit from wider access to large-scale infrastructure, but such deals also concentrate strategic power among companies that control chips, energy, data centres and financing. That makes AI infrastructure a governance issue, not only a business or engineering concern.

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Los Angeles AI arts museum Dataland opens with Google Cloud support

Dataland, a Los Angeles museum dedicated to AI-based art, has opened to the public with Google serving as a technology and creative collaborator.

The museum was co-founded by media artist Refik Anadol and Efsun Erkılıç and is located at The Grand LA in downtown Los Angeles. Google says the 25,000-square-foot space is designed as an interactive environment where data, machine learning and sensory experiences form part of the artwork.

Its inaugural exhibition, ‘Machine Dreams: Rainforest’, uses Anadol’s Large Nature Model, an AI system trained on environmental datasets, to transform natural-world data into large-scale generative visuals.

Google Cloud provides infrastructure for the museum’s real-time image generation, soundscapes, scent augmentation and interactive visitor experiences. Google says the system uses tools including Gemini, diffusion models and generative adversarial networks.

The project builds on a decade of collaboration between Google and Anadol, including work using LA Philharmonic archives, Google Quantum AI data, planetary datasets and the ‘Machine Dreams: Biophilia’ installation at Google’s Mountain View campus.

Google Arts & Culture is also supporting the Dataland AI Artist Residency, a six-month programme for four artists. The residency will provide grants, mentorship from Refik Anadol Studio and access to Google Cloud tools and machine learning models.

Why does it matter?

Dataland shows how AI art is moving from experimental installations into permanent cultural infrastructure. It also highlights the role of cloud providers and large AI platforms in shaping creative production, exhibition design and access to machine-learning tools. For cultural institutions, the project raises broader questions about authorship, data provenance, sustainability, audience interaction and the dependence of new creative formats on private technology infrastructure.

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