Putting the AI Pieces into the Sustainability Puzzle
AI is revolutionizing industries and transforming our daily lives, but as we harness its power, it is crucial to consider its impact on the environment. Last week, at the GreenAI Summit, there was plenty of discussion around how AI impacts the environment and policy. A common theme was the significant energy demand that AI will put on IT, compute facilities, local and state environmental impacts, and, of course, the utility and water grids. With the speed at which the IT industry is moving, AI systems are already getting fragmented, and we have quickly moved to a decision-making era where “one size doesn’t fit all” when it comes to the type of AI system to build and where to run it.
Let us start with the distinctions between agentic AI, generative AI (Gen AI) and instinctive AI.
Initially, there was only Gen AI. We all have used Gen AI to create new content, such as text, images and music. Gen AI systems use machine learning algorithms to generate outputs based on patterns learned from existing data. Examples include OpenAI’s GPT-3, which can generate human-like text, and DALL-E, which creates images from textual descriptions. These are the “culprits” that give AI a bad name. Since they consume so much energy, they have raised many environmental and sustainability issues — and rightly so! IDC forecasts that AI data center energy consumption will grow at a compound annual growth rate (CAGR) of 44.7%, reaching 146.2 terawatt-hours (TWh) by2027. Put another way, that’s more than Sweden’s annual energy requirements(121 terawatt-hours of power).
Quickly on the heels of Gen AI came agentic AI. Agentic AI refers to AI systems that can autonomously perform tasks and make decisions based on predefined goals. They are designed to act independently, often mimicking human behavior. Suddenly, it seemed that every software and data management company was building AI agents — flooding the data centers with significant unplanned compute and storage requirements. Some examples of agentic AI include autonomous vehicles, robotic assistants and intelligent personal assistants, like Alexa+. However, agentic AI did not address the energy consumption issue, especially those involving robotics and autonomous vehicles — another bad rap!
And finally, the AI system that I find very cool — instinctive AI. Instinctive AI refers to AI systems that mimic instinctive human behaviors and responses. These systems are designed to react to stimuli in real-time, similar to how humans instinctively respond to their environment. Examples include AI-driven chatbots that provide instant customer support and real-time fraud detection systems. Instinctive AI brings two challenges to the energy consumption and emissions footprint debate: continuous real-time processing, which can be energy-intensive, but also have difficulty scaling effectively without compromising performance. I think that the business benefits, such as being able to provide instant solutions and support, enhancing user satisfaction and improving operational efficiency, will take priority over environmental concerns.
These AI systems create applications and workloads that require careful placement in any IT infrastructure. With environmental sustainability being the leading goal or criteria, businesses should look at a range of data center options. Cloud service providers offer the lowest levels of carbon intensity, while co-locations have low carbon intensity for sovereign AI. On-premises or enterprise data centers solve the “data gravity” challenge, where the compute resources are close to the data, whereas enterprise edge computing platforms offer the best inferencing environments, but at the cost of the highest carbon intensity.
Sustainable AI represents a crucial intersection between technological advancement and environmental stewardship. By understanding the differences between agentic AI, generative AI and instinctive AI, we can better assess their impact on the environment and leverage their benefits to promote sustainability. While challenges such as energy consumption and resource usage need to be addressed, the potential of AI to drive efficiency, innovation and data-driven decisions offers a promising path towards a greener future.
AI is revolutionizing industries and transforming our daily lives, but as we harness its power, it is crucial to consider its impact on the environment. Last week, at the GreenAI Summit, there was plenty of discussion around how AI impacts the environment and policy. A common theme was the significant energy demand that AI will put on IT, compute facilities, local and state environmental impacts, and, of course, the utility and water grids. With the speed at which the IT industry is moving, AI systems are already getting fragmented, and we have quickly moved to a decision-making era where “one size doesn’t fit all” when it comes to the type of AI system to build and where to run it.
Let us start with the distinctions between agentic AI, generative AI (Gen AI) and instinctive AI.
Initially, there was only Gen AI. We all have used Gen AI to create new content, such as text, images and music. Gen AI systems use machine learning algorithms to generate outputs based on patterns learned from existing data. Examples include OpenAI’s GPT-3, which can generate human-like text, and DALL-E, which creates images from textual descriptions. These are the “culprits” that give AI a bad name. Since they consume so much energy, they have raised many environmental and sustainability issues — and rightly so! IDC forecasts that AI data center energy consumption will grow at a compound annual growth rate (CAGR) of 44.7%, reaching 146.2 terawatt-hours (TWh) by2027. Put another way, that’s more than Sweden’s annual energy requirements(121 terawatt-hours of power).
Quickly on the heels of Gen AI came agentic AI. Agentic AI refers to AI systems that can autonomously perform tasks and make decisions based on predefined goals. They are designed to act independently, often mimicking human behavior. Suddenly, it seemed that every software and data management company was building AI agents — flooding the data centers with significant unplanned compute and storage requirements. Some examples of agentic AI include autonomous vehicles, robotic assistants and intelligent personal assistants, like Alexa+. However, agentic AI did not address the energy consumption issue, especially those involving robotics and autonomous vehicles — another bad rap!
And finally, the AI system that I find very cool — instinctive AI. Instinctive AI refers to AI systems that mimic instinctive human behaviors and responses. These systems are designed to react to stimuli in real-time, similar to how humans instinctively respond to their environment. Examples include AI-driven chatbots that provide instant customer support and real-time fraud detection systems. Instinctive AI brings two challenges to the energy consumption and emissions footprint debate: continuous real-time processing, which can be energy-intensive, but also have difficulty scaling effectively without compromising performance. I think that the business benefits, such as being able to provide instant solutions and support, enhancing user satisfaction and improving operational efficiency, will take priority over environmental concerns.
These AI systems create applications and workloads that require careful placement in any IT infrastructure. With environmental sustainability being the leading goal or criteria, businesses should look at a range of data center options. Cloud service providers offer the lowest levels of carbon intensity, while co-locations have low carbon intensity for sovereign AI. On-premises or enterprise data centers solve the “data gravity” challenge, where the compute resources are close to the data, whereas enterprise edge computing platforms offer the best inferencing environments, but at the cost of the highest carbon intensity.
Sustainable AI represents a crucial intersection between technological advancement and environmental stewardship. By understanding the differences between agentic AI, generative AI and instinctive AI, we can better assess their impact on the environment and leverage their benefits to promote sustainability. While challenges such as energy consumption and resource usage need to be addressed, the potential of AI to drive efficiency, innovation and data-driven decisions offers a promising path towards a greener future.
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