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Microsoft Strengthens Data Protection for European Cloud Clients

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Microsoft announced on Monday that it will ensure data from its European cloud customers remains within Europe, under the jurisdiction of European law, with operational oversight by local personnel and complete customer control.

This move comes amid growing concerns from European governments and companies over the risk of sensitive data being transferred outside the continent, particularly to the United States. The concerns have intensified calls for stricter data sovereignty, prompting American tech giants like Microsoft to adopt more transparent and compliant data governance policies.

As part of these efforts, Microsoft reaffirmed commitments made in April to strengthen safeguards as it scales its cloud and AI infrastructure in Europe. These include compliance with European legislation aimed at curbing the dominance of major technology platforms.

The company also disclosed that any remote access to systems handling European data by Microsoft engineers will be permitted and actively monitored in real-time by personnel based in Europe. This measure is designed to bolster customer trust and ensure alignment with European data protection standards.

Microsoft’s new sovereign private cloud, which supports these enhanced protections, is currently in preview phase and is expected to become generally available later this year.

Unglamorous World of Data Infrastructure Drives Surge in AI-Focused Tech M&A

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Despite a slowdown in global dealmaking due to tariffs and geopolitical uncertainty, the data infrastructure sector is booming as legacy tech giants scramble to secure their positions in the AI race. Companies that handle the vast volumes of data required to train advanced AI models have become key acquisition targets for firms like Meta, Salesforce, and ServiceNow, eager to compete with leaders such as OpenAI, Google, and Anthropic.

“AI without data is like life without oxygen, it doesn’t exist,” said Brian Marshall, global co-head of software investment banking at Citi, highlighting how data management has taken center stage in the tech industry’s current moment.

Technology deals have been one of the few bright spots in a subdued M&A market, accounting for $421 billion out of the $1.67 trillion in global deals announced in the first five months of 2025—roughly 25% of total M&A volume. This marks a steady rise from 20% in 2024 and 17% in 2023. Notably, nearly 75% of the value of tech deals involves AI software makers.

Goldman Sachs Managing Director Matthew Lucas described enterprise data as the “most dynamic area in software M&A right now,” emphasizing that speed and being first to market are critical, driving companies to acquire rather than build their own capabilities.

Investment bankers identify companies like Confluent, Collibra, Sigma Computing, Matillion, Dataiku, Fivetran, Boomi, and Qlik as likely acquisition targets. These firms specialize in integrating, analyzing, and managing data on cloud platforms—capabilities essential for effective AI deployment.

Executives from Boomi, Dataiku, Fivetran, and Qlik expressed no surprise at the increased attention. Dataiku CEO Florian Douetteau noted that “messy, siloed data” has long limited analytics potential, but the urgency of AI has made resolving these issues existential for businesses.

Recent multibillion-dollar acquisitions illustrate this trend. Meta’s $14.8 billion deal for a 49% stake in data-labeling company Scale AI, Salesforce’s $8 billion plan to buy data integration firm Informatica, and ServiceNow’s acquisition of data catalog platform Data.world exemplify how legacy tech companies are investing heavily to own the data pipeline critical for AI.

Globally, generative AI spending is forecast to hit $644 billion in 2025, a 76.4% increase from 2024, underscoring the scale and pace of AI investment.

IBM also recently closed its acquisition of data management company DataStax, aiming to improve handling of unstructured data for its AI platform.

However, dealmakers caution that acquiring data infrastructure alone does not guarantee AI success. Proper organization and filtering of data are essential to avoid errors, as seen when Air Canada faced legal issues over bad AI chatbot advice due to poor data input.

“A lot of companies have a huge amount of data, but they’re learning that you can’t just funnel every piece of data you have into an AI engine without organization and expect correct results,” said Brian Mangino, partner at Latham & Watkins.

The rapid pace of acquisitions and the high stakes in AI competition highlight how data infrastructure—though less glamorous than AI algorithms themselves—is becoming the backbone of future tech innovation.

Exclusive: Crusoe’s ‘Neocloud’ to Buy $400 Million in AMD AI Chips for Data Centers

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Crusoe, an artificial intelligence-focused cloud computing startup, revealed plans to purchase approximately $400 million worth of AI chips from Advanced Micro Devices (AMD) to power its AI data centers. CEO Chase Lochmiller told Reuters that Crusoe intends to acquire around 13,000 AMD MI355X chips for a new data center cluster in the U.S., which is expected to become operational this fall.

The data center will employ liquid cooling technology and be designed specifically to house AI chips, offering higher performance compared to older infrastructure. Crusoe will rent access to this facility, which can be partitioned among multiple clients or used entirely by a single customer.

Lochmiller emphasized Crusoe’s agility as a smaller startup, competing with larger hyperscalers by leveraging speed, nimbleness, and concentrated engineering talent.

AMD’s MI355X chips, featuring high-bandwidth memory, are optimized for AI inference tasks, providing an alternative to Nvidia’s dominant hardware in the AI chip market. While many AI cloud services rely on Nvidia chips, AMD is positioning itself to capture a share by partnering with companies like Crusoe.

Lochmiller described this approach as a validation of the “neocloud” strategy — specialized cloud infrastructure platforms tailored for AI workloads that add significant value to the AI ecosystem by supporting large-scale users.