Hyperscaler
A company operating cloud computing infrastructure at massive scale — primarily Amazon (AWS), Microsoft (Azure), and Google (GCP) — which collectively dominate AI cloud services.
The three major hyperscalers collectively control over 65% of the global cloud market and are investing over $520 billion in AI infrastructure in 2026 alone. They offer GPU access, pre-trained models, and managed AI services that most organizations cannot replicate in-house. Hyperscalers benefit from economies of scale: bulk GPU purchases, custom chip design, and shared infrastructure. Their dominance in AI cloud services creates a platform dependency that shapes the entire AI ecosystem. Specialized GPU cloud providers like CoreWeave and Lambda are emerging as challengers for AI-specific workloads.
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Related Terms
AI Compute
The computational resources — primarily GPU and TPU processing power — required to train and run AI models, typically measured in FLOP (floating-point operations) or GPU-hours.
Capex (Capital Expenditure)
Long-term investment spending by companies on physical assets like data centers, GPU clusters, and networking infrastructure — the backbone of AI deployment at scale.
Data Center
A facility housing computer systems and infrastructure used to process, store, and distribute data — increasingly built specifically for AI training and inference workloads.
Enterprise AI Adoption
The rate at which businesses integrate AI technologies into their operations, measured across functions like customer service, software development, marketing, and supply chain management.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
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