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Oracle to deploy AMD’s MI450 AI chips in major cloud expansion

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Oracle announced plans to integrate Advanced Micro Devices’ (AMD) upcoming MI450 artificial intelligence chips into its cloud infrastructure, with deployment scheduled to begin in the third quarter of 2026. The companies said the initial rollout will include 50,000 processors, with further expansion expected through 2027 and beyond.

The partnership marks a major win for AMD, securing another top-tier client for its next-generation AI chips, while giving Oracle Cloud Infrastructure (OCI) a competitive boost in the global race to provide compute power for AI model training and deployment. “Demand for large-scale AI capacity is accelerating as next-generation AI models outgrow the limits of current clusters,” the companies said in a joint statement.

The announcement comes as demand for AI hardware surges amid the explosion of applications like ChatGPT. AMD’s shares rose over 3% in premarket trading, defying broader market weakness driven by renewed U.S.-China trade tensions, while Oracle’s stock slipped about 1%.

AMD recently unveiled a multi-year deal with OpenAI to supply the same MI450 chips, in an agreement that gives the ChatGPT developer an option to acquire up to 10% of AMD. The companies are also collaborating on a 1-gigawatt AI data facility based on the chip architecture.

The new AI superclusters at Oracle will use AMD’s “Helios” rack design, a fully integrated system combining GPUs and CPUs, mirroring Nvidia’s own rack-scale solutions. The deal underscores AMD’s ambition to challenge Nvidia’s dominance in the high-performance AI hardware market.

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.