Table of Contents
- Welcome to the era of AI Fatigue.
- This is not just a personal discomfort. It is a systemic issue.
- AI itself is not the problem. The problem is how we use it.
- Quality remains a human responsibility.
We are saturated, fatigued, and stressed by a world that fights to capture a fragment of our attention. One notification after another, an email, a post, a “smart” piece of content that promises something—but often leaves us with nothing. We feel overwhelmed.
But is it really possible to switch off a world that keeps accelerating, while we are simply trying to keep up?
The truth is that we are tired, yes—but also a little passive. Because change requires energy, and we seem to have less and less of it. Meanwhile, artificial intelligence is reshaping every aspect of our lives. From how content reaches us, to how we consume it, produce it, and evaluate it. And more and more often we ask ourselves: what I’m reading… was it really written by someone?
Everything feels like AI—so much so that it starts to feel like nothing at all.
Welcome to the era of AI Fatigue.
Today, generative AI technologies have reached a level of sophistication that makes them indistinguishable from human-created content. And it is precisely this ambiguity that exhausts us. Because we no longer know where quality lies, where meaning ends, and where noise begins.
The feeling is widespread. The Oxford Dictionary captured it by selecting “slop” as one of its words of the year—a term used to group all those empty, superficial contents, often generated by AI, that flood our feeds without leaving a trace.
All of this creates a silent but growing exhaustion, now labeled with a precise term: AI fatigue.
It is not just about using too much technology. It is about using it in ways that drain cognitive resources.
The research paper “Too Much, Too Fast: Understanding AI Fatigue in the Digital Acceleration Era” defines AI fatigue as a multidimensional psychosocial condition emerging from the interaction between technostress, cognitive overload, and emotional exhaustion.
Fatigue does not arise solely from continuous AI use, but from the accelerated pace of adoption, the opacity of systems, and the unequal distribution of skills and tools.
Technostress theory highlights dynamics such as techno-overload, techno-complexity, and techno-uncertainty, while cognitive load theory emphasizes how AI—by fragmenting attention and multiplying stimuli—increases extraneous cognitive load and reduces the capacity for deep learning and conscious decision-making.
In this scenario, AI fatigue intertwines with digital burnout, creating a zone of cognitive-emotional overload in which people are not only tired of using AI but also of constantly adapting to tools and narratives that promise efficiency yet often generate additional complexity.
This is not just a personal discomfort. It is a systemic issue.
Professional social platforms—LinkedIn in particular—are increasingly populated by thought leadership content, informative articles, and corporate updates competing for attention within a limited cognitive space.
The widespread use of large language models (LLMs) to increase editorial productivity has made it possible to rapidly scale text production, but it has also introduced a structural risk: the standardization of language and arguments.
Recurring expressions, predictable narrative structures, and a polished yet indistinct tone contribute to the perception of content that is correct—but interchangeable.
This phenomenon does not concern only external communication, but internal organizational processes as well: emails, reports, status updates, and internal communications are increasingly automated, sometimes generating low-quality content—the so-called “workslop”—with tangible negative impacts on organizations. We have already discussed it in another article.
According to estimates by Originality.ai, more than 40% of long-form posts published on Facebook today are generated by AI—a share that grew rapidly after the introduction of ChatGPT—signaling a quantitative transformation that does not always translate into greater quality or relevance.
AI itself is not the problem. The problem is how we use it.
Artificial intelligence can be a tool for growth, for quality, for meaning—but only if we rethink how we integrate it. Only if we stop asking AI to do “more” and start asking it to do “better.”
A paradigm shift is needed. Not just productivity, but cognitive sustainability. Not just performance, but trust, transparency, and participation.
Research points to several directions:
- Designing understandable interfaces that make AI systems more transparent (Explainable AI)
- Involving people in adoption processes not only as users, but as co-designers
- Investing in cultural training, not only technical: critical AI literacy is essential to manage expectations, limits, and potential
- Recognizing AI fatigue as a real risk, to be prevented through more empathetic organizational choices
Quality remains a human responsibility.
At Neodata, we believe AI still has much to offer. But its true value does not lie in the speed at which it produces content, or in the volume of data it can process. It lies in its ability to help us engage with complexity without losing ourselves. It lies in how it can amplify human intelligence, not replace it.
Being “AI-ready” does not simply mean adopting the right tools. It also means creating space for reflection, transparency, and awareness.
We are fatigued, it is true. But we can still choose. And artificial intelligence can be the lever for higher quality, not the source of our disillusionment.
AI Evangelist and Marketing specialist for Neodata
