Control over compute is the cornerstone of AI dominance, with the U.S. maintaining a lead through superior semiconductor access and energy resources. Hyperscalers are investing heavily in AI, driven by real-world applications like Vibe Coding that automate development. Companies like OpenAI and Anthropic may need to build custom chips to overcome NVIDIA’s memory bottlenecks and ensure speed, a key factor in user retention. Despite high costs, older chips remain profitable due to sustained demand. The U.S. also holds a cost advantage in training and inference over China, where models like Deepseek are more expensive to run. Europe lags due to regulatory caution, though it has potential via renewable energy. AI will likely create more jobs than it displaces, triggering labor shortages and new markets. Low margins are strategic for AI firms to maintain trust and scale. Nuclear energy and faster permitting are critical to meeting AI’s power demands. Talent scarcity, not technology, is now the biggest bottleneck. Meanwhile, Groq emerges as a nimble competitor in chip design, challenging NVIDIA’s dominance.