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The relentless growth of artificial intelligence, especially large language models (LLMs) and reasoning agents, demands more efficient computing. Hardware advancements, optimized machine learning techniques, and streamlined system integration are essential to meet this challenge and expand AI accessibility. Developments in silicon design are vital, while machine learning breakthroughs like few-shot learning and quantization allow models to achieve higher performance with reduced computational resources. The rise of agent-based systems, where smaller, specialized AI models work together, further promises to lower energy consumption. Edge processing, which brings AI closer to the data’s origin, is also improving efficiency. To foster collaboration and democratize AI benefits, the creation of common standards and open-source projects is crucial. Finally, the article also points out the increasing need for improved security as AI systems become more widespread.