Welcome to ‘The Human Side of AI‘, a blog series exploring what AI really means for creativity, ethics, sustainability, and the future of human work. This series cuts through the hype to ask deeper questions about how technology impacts us all. This is the final post in the series.
By Erin Beattie, Founder and CCO, Engage and Empower Consulting
When I started writing this series last year, the goal was simple: slow the conversation down long enough to ask better questions.
AI was everywhere in the headlines. Every week brought a new promise about productivity, disruption, and transformation. But beneath the excitement, there was also a lot of fatigue.
As I wrote in the first piece, AI Fatigue and the Real Story Behind the Hype, “AI can be incredibly useful, but not in the way the hype suggests.”
That line still holds.
AI is powerful. But the story is more complicated than the marketing.
Recently, I read a Harvard Business Review article about something researchers are calling “AI brain fry.” The phrase describes the mental fatigue people experience when they spend long stretches of their day prompting, reviewing, correcting, and supervising AI tools.
If that sounds familiar, you’re not imagining it.
The way we work is changing.
The narrative around AI has focused heavily on productivity. Faster writing. Faster research. Faster workflows.
But the work itself hasn’t disappeared.
It’s shifted.
Instead of writing everything from scratch, many of us now spend our time prompting tools, evaluating outputs, cross-checking facts, and refining what the system produces. That’s still thinking work. In some cases, it’s deeper thinking work, because the role shifts from generating content to interpreting it.
I see this in my own workflow every day.
Claude helps me test ideas and structure early drafts. Grammarly catches small things that slip through when I’m moving quickly. Zoom’s AI tools capture meeting notes so I can stay present in conversations instead of scribbling everything down.
These tools are genuinely helpful.
But they also require oversight.
And that’s where the conversation gets interesting.
The HBR research suggests that fatigue increases when people are juggling multiple AI tools at once, particularly when humans are responsible for supervising or verifying the results. That makes sense. Every prompt requires interpretation. Every output requires judgment.
The tools may speed up parts of the process, but the thinking doesn’t disappear.
If anything, it becomes more complex.
And that complexity sits on top of work that was already cognitively demanding.
But the conversation about “brain fry” also connects to something deeper that I’ve been writing about throughout this series.
AI isn’t just changing productivity. It’s forcing us to confront bigger questions about ethics, power, and responsibility.
In another piece in this series, I wrote about the environmental footprint of AI and the energy required to train and run large models. As I noted at the time, “AI doesn’t come free; it comes with a hidden environmental and ethical cost that rarely shows up in shiny product pitches.”
That’s still part of the picture.
Behind every prompt is an enormous amount of infrastructure, data, and computing power.
And behind that infrastructure are human decisions about what data gets used, whose work trains these systems, and how those systems are governed.
Which brings us to the question of bias.
In Power, Bias, and the Data We Don’t See, I wrote that “AI is trained on data, but data isn’t neutral; it reflects history, policy, power, and inequity.”
When those patterns are embedded in automated systems, they don’t disappear.
They scale.
The tools can surface possibilities and patterns, but they still require human judgment to interpret what those patterns mean and what actions follow.
And that brings us back to the human side of all of this.
One thing that often gets lost in the AI conversation is that humans don’t all experience cognitive work in the same way.
Some people are navigating ADHD or other forms of neurodivergence. Some are dealing with brain fog related to menopause. Some are recovering from illness or cancer treatment. Some are simply exhausted by the pace of modern knowledge work.
These realities rarely appear in conversations about technology, but they matter.
The idea that people can endlessly absorb more information, more tools, and more decision-making pressure doesn’t hold up very well in real life.
When AI speeds up the flow of information, the human brain becomes the limiting factor.
That’s not a flaw.
That’s biology.
And it raises an important question for the future of work: if knowledge workers increasingly spend their time supervising machines rather than producing content directly, how do we design systems that respect human cognitive limits?
For me, that question always leads back to dignity.
The dignity of creators whose work trained these systems.
The dignity of workers whose cognitive capacity isn’t infinite.
The dignity of communities whose data and stories should never be extracted without consent.
As I wrote in the final piece of the original series, “The question isn’t whether AI will change our lives; it already has. The better question is what we want to protect as we move forward.”
That question feels even more relevant now.
I’m not anti-AI.
I use it every day.
But I am pro consent.
I’m pro dignity.
And I believe the future of AI will depend less on how fast the technology evolves and more on whether we remember the humans working alongside it.
References
Harvard Business Review. When Using AI Leads to Brain Fry. 2026.
AI Fatigue and the Real Story Behind the Hype. Engage + Empower Consulting.
AI’s Hidden Environmental and Ethical Costs. Engage + Empower Consulting.
Power, Bias, and the Data We Don’t See. Engage + Empower Consulting.
What’s Worth Protecting in the Age of AI? Engage + Empower Consulting.
