72.4% are aware of the “significant” energy required to train or run AI models¹, 49.8% are concerned about the growing power requirements of AI, but only 13.0% monitor the energy efficiency of AI systems - a critical gap as Agentic AI pushes energy demands higher

New research commissioned by SambaNova highlights a critical readiness gap as enterprises face escalating power demands with AI. While 49.8% of leaders are concerned about the energy and efficiency challenges posed by AI, only 13.0% monitor the power consumption of their AI systems. The survey exposes a lack of infrastructure preparedness amidst the rise in AI deployments across the United States and Europe.

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New research commissioned by SambaNova highlights a critical readiness gap as enterprises face escalating power demands with AI (Graphic: Business Wire)

“The findings reveal a stark reality: businesses are rushing to adopt AI, but aren’t prepared to manage its energy impact,” said Rodrigo Liang, CEO of SambaNova Systems. “Without a proactive approach to more efficient AI hardware and energy consumption, particularly in the face of increasing demand from AI workflows, we risk undermining the very progress AI promises to deliver. By 2027, my expectation is that more than 90% of leaders will be concerned about the power demands of AI and will monitor consumption as a KPI that corporate boards will track closely.”

Key findings from the research include:

• AI Inference Will Drive Power Demand Growth: While 70.0% of leaders recognize the energy-intensive nature of training large language models, only 59.7% are aware of the significant power demands of inference. This gap is critical as inference workloads are set to dominate AI usage with the scaling of Agentic AI.
• Energy Efficiency is a Strategic Priority: Only 13.0% of organizations currently monitor AI power consumption, yet 56.5% acknowledge that energy efficiency will play a crucial role in future strategic planning. The need to address rising energy demands is being driven by both cost management imperatives and operational scalability concerns.
• Scaling Agentic AI Brings New Challenges: The roll-out of Agentic AI is amplifying power concerns for enterprises. For 20.3% of companies, rising power costs are a pressing issue. Furthermore, 37.2% are experiencing increasing stakeholder pressure to improve AI energy efficiency, with a further 42.0% expecting these demands to emerge soon.
• Few Enterprises Are Proactively Addressing AI’s Energy Impact: Among organizations that have widely deployed AI², 77.4% are actively seeking to reduce power usage. Popular approaches include hardware and software optimization (40.4%), adopting energy-efficient processors (39.3%), and investing in renewable energy (34.9%). However, these measures remain insufficient compared to the rapid pace of AI adoption and scaling.

The rise of agentic AI marks a turning point for the industry. As businesses scale AI and deploy more advanced workflows, addressing energy demands will be crucial to managing costs and achieving efficiency goals. Bridging the awareness gap with education and strategic planning will be key to ensuring AI's growth remains both financially and commercially sustainable.

The excessive power consumption and prohibitive costs associated with current GPU-based solutions are likely to compel many enterprises to seek more efficient alternatives. This shift will fundamentally change the landscape of AI hardware, favoring solutions that deliver high performance without unsustainable energy demands.

“The rapid pace of AI adoption underscores a critical need for enterprises to align their strategies with the power requirements of AI deployment,” stated Liang. “As businesses integrate AI, addressing energy efficiency and infrastructure readiness will be essential for long-term success. Customers are turning to SambaNova for help deploying energy efficient solutions.”

¹ The research included two questions: “Are you aware of the power required for AI inference workloads?” and, “Are you aware of the power needed to train large AI models (e.g., ChatGPT)?” Among the respondents, 72.4% indicated they were somewhat aware or well-informed about at least one or both of these aspects. Specifically, 70.0% were aware of the power needed for training, while 59.7% were aware of the power required for inference.

² The research asked: “How widespread would you consider Generative AI adoption in your organization?” Among the respondents, 30.3% of organizations reported widespread use of AI. 53.8% are in the early stages or pilot phases of implementation, and only 15.8% of organizations report that they have not yet started their AI journey.

Survey Methodology

The survey was commissioned by SambaNova Systems to explore the state of AI adoption, the readiness of enterprises to address AI's energy demands, and sustainability considerations in AI deployment. Conducted between December 16 and December 27, 2024, the survey gathered insights from 2,012 business leaders, including 1,004 respondents in Europe and 1,008 in the United States.

About SambaNova Systems

Customers turn to SambaNova to quickly deploy state-of-the-art generative AI capabilities within the enterprise. Our purpose-built enterprise-scale AI platform is the technology backbone for the next generation of AI computing.

Headquartered in Palo Alto, California, SambaNova Systems was founded in 2017 by industry luminaries, and hardware and software design experts from Sun/Oracle and Stanford University. Investors include SoftBank Vision Fund 2, funds and accounts managed by BlackRock, Intel Capital, GV, Walden International, Temasek, GIC, Redline Capital, Atlantic Bridge Ventures, Celesta, and several others. Visit us at sambanova.ai or contact us at info@sambanova.ai. Follow SambaNova Systems on LinkedIn and on X.

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