# 1.4 Recognition of Market Problems

Despite explosive growth in AI adoption, computational infrastructure remains plagued by centralization and inefficiency.

**1)** **Centralized GPU Resource Monopolies** – Over 80% of global AI compute infrastructure is held by U.S. giants AWS, Google Cloud, and Microsoft Azure. High-end GPUs like NVIDIA A100/H100 are prioritized for large institutions, often leaving startups and research labs behind.

**2) Cloud Cost Inflation** – An A100 GPU instance on AWS costs $3,200–$4,000/month (2024), consuming over 30% of annual budgets for many startups. Heavy GPT-4 API usage can incur thousands in fees, pricing out resource-limited AI adopters.

**3) Inefficiencies of PoW Mining** – Traditional Proof-of-Work (PoW) chains like Bitcoin consume 110–130 TWh/year—equivalent to Argentina’s energy consumption—while offering no industrial compute output. ACP replaces wasteful hashing with productive AI computation.

**4) Global Wastage of Idle GPUs** – Hundreds of millions of GPUs (e.g., RTX 30/40 series) lie idle 90% of the time. In the U.S., average daily gaming GPU use is under 2.5 hours. ACP seeks to mobilize this dormant capacity into an “economy of computation.”

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