Who — or what — is in charge of your content? I'm increasingly seeing individuals abdicating responsibility for their content decisions, handing that responsibility to their AI tools.

Given my experiences with clients and the anecdotes I'm hearing from colleagues, I'm going to say that you're an outlier if you haven't witnessed this kind of cognitive surrender in action.

I was brought into a project with a longtime client right before launch due to sudden medical leave of their last remaining content designer. The client previously had meticulous content review processes in place. Now? The content that I was handed to sign off on was created by AI and plagiarized a competitor... and everyone was pissed that I called it out. Just approve it, they said. It's fine.

How had we gotten to a point where a dozen departments reviewed this and no one had an issue with it? These are the same people who 12 months prior were nitpicking every word.

There are obviously root causes for this behavior, specific to the world of content strategy and design:

1. Management wants more content created
2. Management wants that content created faster
3. We're told that AI is better than people
4. We simply aren't staffed to do previous levels of content, let alone accelerated and amplified creation

So of course, being a research nerd, I went looking for reasons for why these individuals have switched from obsessively micromanaging content to blindly approving everything. I found some good insights in a Wharton School research paper from February: "Thinking—Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender." The paper looks at how people are broadly accepting AI outputs with minimal scrutiny, bypassing intuition and deliberation.

"Across domains," the authors say, "AI tools are not merely assisting decision-making; they are becoming decision-makers."

Previously, theories posited that decision-making was internal and based in two processes: quick intuition ( our gut reactions), and slower, deliberative, analytical reasoning. Now AI brings a third factor into play, that of delegating reasoning to machines. "Context is achieved by simulating coherence based on data, rather than possessing true phenomenological understanding." And we are likely to do that when the output is "delivered fluently, confidently, or with minimal friction."

This isn't a paper that provides answers, but it is a fascinating read about what happens when our decisions — our very judgments — are shaped by external forces that we perceive to be better than our own brains, and when we're convinced that it's functionally infallible.

I don't have answers for how to solve this yet. No one does. But the implications for accuracy, trust, and authenticity of content are huge.

"There is a clear need for a more environmentally sustainable approach to AI use. Fortunately, it is getting easier to measure and mitigate AI’s environmental footprint." I'm not sure that I agree with that conclusion, given the deliberate obfuscation of the impact of hyperscale datacenter, but the Greening AI in the Public Sector handbook is an interesting read, regardless.

Scope 3 reporting is complicated. There's no standardization in the standards, which makes everything a murky mess. It looks like eBay is finding this out the hard way. Changes in accounting rules caused their emissions calculations to jump by more than 20%. This, of course, looks bad to stakeholders. But what seems to be lost in the hullaballoo is that their actual impact hasn't changed, just the metrics for measurement.

On the Green IO Podcast, Gaël Duez talks with Sasha Luccioni and Boris Gamazaychikov about something I've been saying for years: when it comes to understanding the future of sustainability — AI or otherwise — you have to follow the money. That's why I go to NY Climate Week. What sounds utterly unhinged in the finance bubble this year is often reality by the next.

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Alisa Bonsignore

Founder, Strategist, and Author