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In this episode I dig into the environmental impact of using ChatGPT: energy use, water consumption, and carbon emissions. I walk through the research, acknowledge the risk of whataboutism, and explain why comparisons to other systems matter when we’re talking about true impact. Adopting a both/and approach, this episode is absolutely not about letting ChatGPT off the hook, it’s about putting things in perspective so we can take action on what will actually move the needle.
Main Topics Covered
- Why this is episode 2 and why the topic matters
- Ramit Sethi’s $30K vs $3-question analogy
- The math behind ChatGPT’s energy use
- The origin (and problems) with the “3Wh per query” stat
- What .3Wh per query actually looks like in real-world use
- The difference between inference and training impact
- How ChatGPT kicked off the AI arms race
- Where most AI energy actually goes (spoiler, it’s not ChatGPT)
- The hidden energy behind everything else we use
- How water usage is measured across three scopes
- ChatGPT’s water use vs typical U.S. electricity and food systems
- Why carbon emissions highlight the need for more transparency
- The repeating citations problem in media coverage
- What actually makes the biggest environmental difference
- Concerns about future energy use from agentic models
- Why transparency from companies matters
- This episode as a call for participatory awareness, not panic