Google parent Alphabet told Meta Platforms around March 2026 that it could not provide the full amount of Gemini capacity Meta sought, the Financial Times reported, according to Reuters. The shortfall reportedly disrupted and delayed some of Meta’s internal AI projects.
Reuters said it could not immediately verify the report, and Google and Meta did not immediately respond to requests for comment.
Meta’s own financials show why the reported bottleneck matters. In Q1 2026, the company generated $56.31 billion in revenue, up 33% from a year earlier, while capital expenditures, including principal payments on finance leases, reached $19.84 billion. Meta also lifted its full-year 2026 capex outlook to $125 billion to $145 billion, citing higher component pricing and additional data center costs tied to future capacity.
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That spending is supposed to reduce dependency over time. But the FT report suggests Meta still needed outside model capacity for some internal work, enough that Google’s inability to meet the full request reportedly forced delays and pushed Meta to encourage more efficient use of AI tokens, the usage units that measure model consumption.
For Alphabet, the reported limit cuts two ways. It signals strong demand for Gemini and Google Cloud but it also shows that AI capacity is not infinitely expandable, even for one of the best-capitalized infrastructure operators in the market.
Alphabet reported Q1 2026 revenue of $109.9 billion, up 22% YoY. Google Cloud revenue rose 63% to $20.0 billion, led by enterprise AI solutions, enterprise AI infrastructure, and core Google Cloud Platform services.
The backlog number is the bigger clue. Alphabet said Google Cloud backlog nearly doubled quarter over quarter to more than $460 billion. On its Q1 call, management said backlog reached $462 billion, driven by enterprise AI demand and TPU hardware sales, with just over 50% expected to convert into revenue over the next 24 months.
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In plain terms: demand is not the issue. Deliverable capacity is.
Alphabet also disclosed $35.7 billion in Q1 capex, with most of that spending going to technical infrastructure for AI opportunities. About 60% of technical infrastructure investment went to servers, while 40% went to data centers and networking equipment. That scale helps explain why Google can grow cloud revenue sharply while still rationing access.
The competitive wrinkle is sharper because Meta and Google are not normal buyer and supplier. They compete in digital advertising, consumer AI, developer tools, and the broader race to build foundation models.
Meta has framed AI as central to its next phase. Mark Zuckerberg said in Meta’s Q1 release that the company had strong momentum across its apps and had released its first model from Meta Superintelligence Labs. He said Meta was “on track to deliver personal superintelligence to billions of people.”
The reported disruption comes as Meta is already absorbing heavier AI costs. Q1 costs and expenses rose 35% YoY to $33.44 billion, while operating margin held at 41%. Free cash flow was $12.39 billion, below operating cash flow of $32.23 billion after heavy property and equipment purchases and finance lease payments.
That gives Meta room to keep building, but it does not erase near-term dependence. More owned data centers later do not solve a model-capacity shortage today.
