- Swimlane survey reveals that many companies are not aware of the energy needs of AI
- Nearly three-quarters are aware of the dramatic energy demands required to train AI models.
- Only 13% actively monitor AI power consumption, which may indicate that off-premise installations are more heavily used.
As the transition from simple algorithms to advanced models significantly increases energy demands, the adoption of agent AI, known for its advanced decision-making capabilities, is intensifying concerns about energy consumption, new research claims.
A survey conducted by SambaNova Systems, which sampled more than 2,000 business leaders in the United States and Europe, found that 70% of business leaders are aware of the significant power requirements for training models for AI tools, but only 13% monitor the power consumption of their AI systems. .
At the same time, 37.2% of companies face increasing pressure from stakeholders to improve energy efficiency, and 42% expect these demands to intensify.
Challenges with AI power demands
Rising energy costs have become a major challenge, with 20.3% of businesses identifying them as an urgent issue.
Fortunately, 77.4% of companies are actively exploring ways to reduce energy use by optimizing their models, adopting energy-efficient hardware, and investing in renewable energy solutions.
However, these efforts are not keeping pace with the rapid expansion of AI systems, leaving many companies vulnerable to rising costs and sustainability pressures.
“The findings reveal a stark reality: companies are rushing to adopt AI, but are not prepared to manage their energy impact,” said Rodrigo Liang, CEO of SambaNova Systems.
“Without a proactive approach towards more efficient AI hardware and power consumption, particularly in the face of increasing demand for AI workflows, we risk undermining the progress that AI promises to achieve,” he added.
“By 2027, my expectation is that more than 90% of leaders will be concerned about the energy demands of AI. As companies integrate AI, addressing energy efficiency and infrastructure readiness will be essential to success in the long term.
