Mahle warns that AI's massive energy requirements could increase the burden on data centers, potentially surpassing the energy use of many small countries. He also emphasizes the importance of delivering AI applications as close as possible to the edge of the network and to users for optimal performance. Lastly, he recommends ensemble AI, which involves multiple smaller AI engines working together, allowing businesses to prioritize their own use cases and pass through additional functionality to other AI apps.
Key takeaways:
- AI applications are resource-intensive, requiring high levels of processing power, GPU usage, bandwidth, and storage. This can rapidly deplete a company's budget, so it's crucial to evaluate all implications before investing in AI.
- AI's massive energy requirements are increasing the burden on data centers. If current AI trends continue, AI servers could consume approximately 85.4 terawatt-hours of electricity each year by 2027, surpassing the energy use of many small countries.
- AI applications need to be delivered at the edge, as close as possible to the edge of the network and to users, devices, and data sources. Latency is a major consideration in ensuring these apps perform seamlessly.
- Consider a modular approach to AI at the edge, using ensemble AI, composed of multiple smaller AI engines that each have specific tasks and work intelligently together. This allows you to prioritize your own use cases and pass through additional functionality to other AI apps.