Energy-Efficient Domain-Specific Artificial Intelligence Models and Agents: Pathways and Paradigms

• URL: http://arxiv.org/abs/2510.22052v1
• Date: Fri, 24 Oct 2025 22:21:08 GMT
• Title: Energy-Efficient Domain-Specific Artificial Intelligence Models and Agents: Pathways and Paradigms
• Authors: Abhijit Chatterjee, Niraj K. Jha, Jonathan D. Cohen, Thomas L. Griffiths, Hongjing Lu, Diana Marculescu, Ashiqur Rasul, Keshab K. Parhi,
• Abstract summary: The AI market size is projected to grow from 189 billion USD in 2023 to 4.8 trillion USD by 2033.<n>Currently, AI is dominated by large language models that exhibit linguistic and visual intelligence.<n>This will define the next wave of AI, progressing from today's large models, trained with vast amounts of data, to nimble energy-efficient domain-specific agents.
• Score: 26.440714555099913
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The field of artificial intelligence (AI) has taken a tight hold on broad aspects of society, industry, business, and governance in ways that dictate the prosperity and might of the world's economies. The AI market size is projected to grow from 189 billion USD in 2023 to 4.8 trillion USD by 2033. Currently, AI is dominated by large language models that exhibit linguistic and visual intelligence. However, training these models requires a massive amount of data scraped from the web as well as large amounts of energy (50--60 GWh to train GPT-4). Despite these costs, these models often hallucinate, a characteristic that prevents them from being deployed in critical application domains. In contrast, the human brain consumes only 20~W of power. What is needed is the next level of AI evolution in which lightweight domain-specific multimodal models with higher levels of intelligence can reason, plan, and make decisions in dynamic environments with real-time data and prior knowledge, while learning continuously and evolving in ways that enhance future decision-making capability. This will define the next wave of AI, progressing from today's large models, trained with vast amounts of data, to nimble energy-efficient domain-specific agents that can reason and think in a world full of uncertainty. To support such agents, hardware will need to be reimagined to allow energy efficiencies greater than 1000x over the state of the art. Such a vision of future AI systems is developed in this work.

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