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The Myth
"Only big labs can build AI" was true for ~18 months in 2023. It's no longer accurate. Open-weight models (Meta's Llama, Mistral, DeepSeek, Qwen, Gemma) now match or exceed GPT-4-class performance and are free to run, fine-tune, and self-host. Hugging Face alone hosts 1,000,000+ open models. The cost to train competitive models has dropped dramatically.
The Reality
Compute concentration IS real: training frontier models still requires capital and data center access most organizations lack. A handful of cloud providers (AWS, Azure, GCP) host the majority of production AI workloads. App-layer concentration around ChatGPT, Claude, and Gemini creates legitimate questions about who controls the defaults that hundreds of millions of users see.
The Positive Path
The open-weight movement, sovereign-AI initiatives (France's Mistral, UAE's Falcon, China's DeepSeek), and edge-AI hardware (Apple Silicon, NVIDIA Jetson) all push back against concentration. Learning to use both closed and open models — and understanding which to use when — gives you optionality the panic narrative says you don't have.