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Essentialism in the Age of GenAI

Aerial view of a forest with mist, centering on a glowing 'AI' platform, symbolizing the ecological implications of advancing artificial intelligence.
The rise of GenAI, while offering immense potential for innovation and efficiency, also brings with it significant environmental costs.
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In the age of Generative AI (GenAI), Greg McKeown’s book Essentialism: The Disciplined Pursuit of Less offers a valuable lens through which we can examine our use of these powerful technologies. Essentialism, at its core, is about making the most of our resources by focusing on what truly matters. In the context of GenAI, this translates to asking critical questions about two key dynamics: 1.) the environmental impact of GenAI use, and 2.) the real value of the tasks we delegate to AI systems.

"Is the task that I'm performing with AI systems essential to bringing value?"

The rise of GenAI, while offering immense potential for innovation and efficiency, also brings with it significant environmental costs. The AI for Education’s document on “AI’s Impact on the Environment” underscores this point, highlighting the substantial energy requirements of AI systems. Data centers, which are the backbone of AI operations, already account for a noteworthy percentage of global electricity usage. The training and operational phases of AI models, particularly those as complex as large language models, are energy-intensive.

This brings us to the essential question: “Is the task that I’m performing with AI systems essential to bringing value?” In the spirit of Essentialism, it’s crucial to evaluate whether the use of GenAI in a particular context is justifiable, considering its environmental footprint. Not all tasks require the advanced capabilities of GenAI, and sometimes simpler, more energy-efficient solutions might suffice. The goal should be to harness the power of AI in scenarios where it can bring significant improvements or innovations, rather than using it as a default for every task.

The second question, “How do we use GenAI for the right things and not just as a way to perpetuate poor processes and systems?” is equally vital. It challenges us to rethink our existing processes and systems critically. Are we using GenAI to genuinely innovate and solve complex problems, or are we merely automating existing inefficiencies? Essentialism encourages us to strip away the non-essential, to focus on what truly adds value. In the context of GenAI, this means deploying these technologies in ways that lead to meaningful advancements and improvements, rather than in ways that merely uphold the status quo. Let’s use GenAI to explore the MACUL/Michigan Virtual’s December’s AI Summit opening presenter Dan Fitzpatrick’s Box 3 thinking, not perpetuate poor Box 1 behaviors.

As we navigate the complexities of GenAI, McKeown’s Essentialism provides a guiding framework. By focusing on what is truly essential in learning, questioning the value and impact of our AI-driven tasks when creating new opportunities for students, and striving for sustainable practices in education, we can ensure that the use of GenAI aligns with our broader goals of innovation, efficiency, and environmental responsibility as a society.

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Ken Dirkin

Ken Dirkin

For over 15 years Ken Dirkin has been revolutionizing education by creating technology solutions in education to help humans be better humans. Working with virtual worlds, mobile apps, new media and web, Ken is driven to decrease physical barriers in education and make learning more engaging and equitable. Ken’s energy is generated by work in education technology, international education, film and marketing. He strongly believes the best things in life are free. He lives with his awesome wife (Hi Kathy!), his #girlpower daughter and junior zoologist son.

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