AI’s massive energy demands are turning into a PR nightmare
As AI’s power consumption skyrockets, knowledge workers are pushing back. Here’s how AI-first companies can get ahead of the problem.
Considering how many headlines there have been this year about massive AI infrastructure projects—from data centers to power plants—it’s probably no surprise that concern over AI’s energy use has started to bubble up. That trend has the potential to make the path to becoming “AI forward” considerably bumpier for many companies as rank-and-file workers begin to resist using AI for fear of making the problem worse.
Smart leaders will recognize this growing issue, and I’ve got some advice in today’s column on how to get ahead of it. Before that, though, a quick update on a few things:
I’ll be speaking at the Mid-Atlantic MarCom Summit in Washington, D.C. on Thursday, Oct. 9. The panel is all about the ground-level realities of integrating AI in PR and communications, and, yes, I think AI’s energy use might come up. I’m excited to join Angela Virtu, David Fucscus, and Geoff Livingston to unpack the hard work of change management, and the focus areas to be most effective. If you’re in D.C., I hope to see you there!
The NAB conference is coming up fast, and I’ll be attending that one as well. Please get in touch if you’d like to meet up.
And we’re less than a month away from the next cohorts for my AI upskilling courses for journalists and PR professionals. You’ll learn valuable skills like how to use deep research for source discovery, a smart way to get started vibecoding, and lots more. Remember that there’s a rapidly closing window on our 15% advance discount—it lasts only until Oct. 17. You can sign up for either course via the links below 👇
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The AI energy backlash is real. Media orgs need a strategy
If you oversee an editorial team, you’re probably used to hearing pushback about using AI. “It makes mistakes.” “I don’t know what it’s doing with my data.” “It just tells you what you want to hear.”
Those are all reasonable concerns, and I often raise them myself in my monthly introduction to AI classes. Each point opens a door to meaningful discussion and practical solutions. Hallucinations call for careful thought about how to apply fact-checking and human oversight. Enterprise tools, APIs, and privacy controls can help safeguard data. And you can deal with AI’s default sycophancy by prompting it to challenge your ideas.
But there’s a new kind of resistance emerging against AI, and you can’t just prompt your way out of it. More knowledge workers are expressing hesitancy around using AI tools because of its staggering energy consumption and the environmental toll that comes with it.
It’s hardly news that the more people use AI, the more power-hungry the industry becomes. Sure, the chips powering these systems are always getting more efficient, but demand for intelligence is increasing, especially with new tools like advanced agents, deep research, thinking models, and video generators. It didn’t help when Sam Altman offhandedly mentioned that saying “please” and “thank you” to ChatGPT was wasting millions. Meanwhile, data center builds have jumped 40% year over year, raising red flags around both energy draw and water usage.
When AI guilt turns into pushback
For those who prioritize environmental sustainability, these headlines and numbers hit hard. Using ChatGPT starts to feel like a moral compromise, with every query producing both intelligence and a commensurate amount of guilt. If they feel their employer is pushing them to use these tools anyway, that guilt can bubble up into anger, and even resistance.
We’re already seeing signs of real friction.
