AI bottlenecks are constraints—compute, data, talent, or cost—that slow model training and deployment. Teams use them to spot inefficiencies and prioritize fixes. Startups, researchers, and enterprises benefit by cutting costs, speeding innovation, and scaling AI responsibly.
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