Meta signs US$10 billion Google Cloud deal while freezing AI division hiring

The deal comes as Google Cloud pursues major enterprise contracts to close the gap with market leaders AWS and Microsoft Azure.

Meta Platforms is betting big on external cloud infrastructure with a US$10 billion Google partnership while consolidating internally, freezing AI division hiring after recruiting over 50 researchers and engineers with record-breaking compensation packages.

The multi-billion dollar contract, spanning six years, was confirmed by two people familiar with the matter, as first reported by The Information. The agreement primarily focuses on AI infrastructure services, according to sources who requested anonymity due to the confidential nature of the deal.

Meta has historically relied heavily on Amazon Web Services for its cloud infrastructure needs, while also utilizing Microsoft Azure services. This Google Cloud partnership represents a significant diversification of Meta's cloud strategy as the company scales its AI operations to meet growing computational demands.

The deal comes as Google Cloud pursues major enterprise contracts to close the gap with market leaders AWS and Microsoft Azure. Earlier this year, Google successfully secured cloud business from OpenAI, which had previously been dependent on Microsoft's Azure infrastructure.

Google's cloud division reported a strong performance in its second quarter, generating US$13.6 billion in revenue with US$2.83 billion in operating income. The unit's 32% revenue growth significantly outpaced Alphabet's overall company growth of 13.8%.

AI hiring freeze follows recruitment spree

In a separate development, Meta confirmed it has paused hiring for its artificial intelligence division after bringing on more than 50 researchers and engineers, according to The Wall Street Journal report published Wednesday.

The hiring freeze, which took effect last week, coincides with a comprehensive restructuring of Meta's AI operations under the newly formed Meta Superintelligence Labs umbrella. The reorganization splits the AI unit into four groups: TBD Labs run by former Scale AI founder Alexandr Wang, and three groups focused on research, product integration, and infrastructure.

Meta spokesperson Andy Stone characterized the move as "basic organizational planning: creating a solid structure for our new superintelligence efforts after bringing people on board and undertaking yearly budgeting and planning exercises".

The pause follows an expensive talent acquisition campaign where Meta offered substantial compensation packages to lure top AI researchers from competitors. The company recently offered AI researcher Matt Deitke $250 million over four years, with potentially $100 million in the first year alone. Reports indicate CEO Mark Zuckerberg personally engaged in recruitment efforts, with some offers reportedly reaching nine-figure amounts.

Meta's recruitment drive secured more than 20 hires from OpenAI, 13 from Google, three each from Apple and xAI, and two from Anthropic, supplemented by strategic acquisitions including a $14.3 billion stake in Scale AI.

Strategic context and financial implications

The developments come as Meta continues substantial AI investments while managing investor concerns about escalating costs. In its earnings report last month, Meta projected total expenses for 2025 between US$114 billion and US$118 billion, with significant portions allocated to AI infrastructure and talent.

The timing of both announcements reflects Meta's dual approach to AI scaling: securing external cloud infrastructure through the Google partnership while consolidating internal AI talent following the recent hiring surge. Industry analysts view the hiring pause as a natural consolidation period after intensive recruitment rather than a retreat from AI ambitions.

The cloud services agreement with Google provides Meta additional infrastructure capacity to support its Llama family of AI models and expand AI-powered features across its platform portfolio. This diversification reduces dependency on single cloud providers while ensuring adequate computational resources for AI workloads.

Both developments underscore the competitive dynamics in the AI sector, where companies balance between aggressive talent acquisition, infrastructure investments, and operational efficiency as the technology landscape continues evolving rapidly.