Hyperscalers are reporting backlogs for cloud service, how much is this backlog and who has booked this backlog?
The hyperscale cloud backlog is driven by two very distinct groups: the AI labs themselves (OpenAI and Anthropic), and a wave of traditional enterprises modernizing their core operations.
Here is exactly how these two groups are using this capacity:
1. The AI Labs: The Dominant Force
OpenAI and Anthropic are securing massive contracts to outsource their infrastructure, accounting for about half of the $2 trillion total backlog .
· Anthropic:
· Google Cloud: A 5-year deal worth $200 billion for massive "5GW computing power" (over 40% of Google's backlog) .
· AWS: A $100 billion, 10-year contract .
· Purpose: Training next-gen models and running inference at massive scale .
· OpenAI:
· Oracle: A massive $300 billion contract .
· AWS: Over $100 billion in commitments (80%+ of AWS's recent backlog growth) .
· Microsoft: Responsible for a huge portion of Microsoft’s $627 billion backlog .
2. Traditional Enterprises: The Production Shift
Beyond the labs, thousands of conventional companies are moving beyond "experiments" into full deployment.
· The "Big-Ticket" Buyers:
· Financial Services: A top-tier global bank just signed Snowflake's largest deal ever ($400M+) to run production AI .
· Retail & Commerce: Walmart and Target are partnering with Google to deploy "agentic commerce" (AI that actually completes purchases) .
· Software Giants: ServiceNow signed a $1.2 billion deal with Google Cloud to embed AI agents .
· The ERP Shift (SAP):
· Backlog: €77 billion (up 30%) .
· Purpose: AI is now in two-thirds of SAP's new orders as companies modernize finance and supply chains .
🎯 The Specific Use Cases
The "purpose" of this spending reveals three distinct phases of AI adoption:
1. Training & Infrastructure (The Labs): This is the "raw compute" spending. OpenAI is expected to spend $45B on servers just this year to build foundation models .
2. Agentic AI (The Enterprise): Instead of just chatbots, companies are buying infrastructure for "agents" that perform tasks autonomously (e.g., writing code, solving customer tickets) .
3. AI-Ready Data (The Analytics): Snowflake reported that 50% of new bookings involve AI features, proving firms are building the governed data systems needed to support LLMs .