New research commissioned by SambaNova Systems sheds light on a critical readiness gap as enterprises navigate the rising power demands associated with artificial intelligence (AI). Despite growing concerns, a large portion of businesses are unprepared for the energy challenges that accompany the widespread adoption of AI, particularly as inference workloads increase. The findings call attention to the urgent need for proactive strategies to manage AI’s energy consumption effectively.
1. Growing Concern Over AI’s Power Demand
- 49.8% of business leaders are concerned about AI’s escalating energy and efficiency challenges.
- However, only 13.0% actively monitor the power consumption of their AI systems.
- SambaNova CEO Rodrigo Liang emphasizes the critical gap, noting that AI’s energy demands will become a major board-level concern by 2027.
2. AI Inference Will Become the Primary Driver of Power Demand
- 70.0% of leaders are aware of the energy required for training large language models like ChatGPT.
- Yet, only 59.7% recognize the significant power demands associated with inference workloads.
- Inference is expected to dominate AI usage, making it a critical area for power consumption management.
3. Energy Efficiency as a Key Strategic Priority
- Despite only 13.0% of organizations monitoring power usage, 56.5% see energy efficiency as critical for future strategic planning.
- The growing focus on energy efficiency is driven by both cost and operational scalability concerns.
- Businesses are beginning to recognize energy management as integral to AI’s long-term success.
4. Scaling Agentic AI Poses New Energy Challenges
- Agentic AI adoption is amplifying power concerns, with 20.3% of companies already facing rising power costs.
- 37.2% are under pressure to improve AI energy efficiency, with more expected to face these demands in the near future.
- Scaling Agentic AI requires businesses to address energy efficiency to maintain competitive and operational viability.
5. Limited Proactive Measures to Address Energy Impact
- 77.4% of AI-deploying organizations are actively working to reduce power consumption.
- Common strategies include hardware and software optimization (40.4%), adopting energy-efficient processors (39.3%), and investing in renewable energy (34.9%).
- These measures are seen as insufficient given the rapid expansion of AI adoption.
6. Shifting Hardware Landscape to Manage Power Demands
- The power consumption of current GPU-based solutions is becoming a significant barrier for many enterprises.
- A shift towards more energy-efficient hardware solutions is expected, driving changes in the AI hardware landscape.
- SambaNova’s focus on delivering high-performance solutions with lower energy demands positions the company as a key player in addressing these challenges.
The findings from SambaNova’s research underscore a growing need for businesses to proactively address the energy demands associated with AI adoption. As enterprises scale AI, especially with the rise of Agentic AI, managing power consumption will be essential for cost control and operational efficiency. Bridging the gap in awareness and adopting energy-efficient solutions will be critical for businesses to ensure AI remains a sustainable and impactful tool for the future.
