Compute Cost Trends
The declining cost of AI computation over time, driven by hardware improvements, software optimization, and competitive pricing — a key factor in AI democratization.
AI inference costs have dropped 280x in 18 months according to Stanford HAI. The cost per FLOP for AI training has fallen approximately 70% per year. A query that cost $0.36 with GPT-4 at launch can now be processed for under $0.01 with comparable models. However, total AI spending is increasing because demand growth outpaces cost reduction. The DeepSeek V3 model demonstrated that training costs could be reduced to $5.6 million through algorithmic efficiency, versus hundreds of millions for comparable Western models.
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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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