This article was originally written in English. If your browser is set to Dutch, it may be translated automatically. Please note that automated translations may contain grammatical errors or inaccuracies.
Thinking differently in the age of AI
Intelligence isn’t measured by how much we manually produce, but by what we understand, connect and make possible.
One of the arguments I keep hearing about artificial intelligence is that the more we use it, the less we think for ourselves.
“AI is making us lazy. We’re losing our critical thinking skills. We’re outsourcing our intelligence.”
I find this argument rather simplistic. And, at times, unnecessarily driven by fear. While the risks are real, it also assumes that using technology to accomplish something somehow diminishes our intelligence.
Anyone who has invested time in learning to use AI effectively knows there’s more to it. It takes curiosity, experimentation, knowledge and judgement. Sometimes, it requires more deliberate thinking than doing the task yourself.
But I think there’s a more fundamental question we should be asking:
Since when did the amount of work we perform become a measure of intelligence?
Confusing effort with intelligence
We tend to admire people who can remember vast amounts of information, perform complex calculations in their heads or spend hours producing something from scratch.
And there’s certainly value in those abilities. Knowledge, practice and experience matter.
But intelligence cannot simply be measured by how much work we can perform without assistance.
Is someone who spends an entire day manually analysing data necessarily more intelligent than someone who uses AI to identify patterns in minutes?
Not necessarily.
The intellectual work may lie in knowing which questions to ask, understanding what the patterns mean, recognising what the data doesn’t tell us and deciding what to do next.
The same applies to writing, research, problem-solving and creative work.
Producing something isn’t the same as understanding it. And producing something faster doesn’t automatically mean understanding it less.
Of course, effort matters. It’s often how we develop knowledge, expertise and the ability to exercise sound judgement. We cannot evaluate an answer properly without understanding the subject.
But effort is part of how we develop our capabilities. It isn’t necessarily how we should measure their value.
Is valuable always visible?
In my work in communication, culture and change facilitation, the value of what I do cannot simply be measured by how much I produce.
I can spend hours developing a communication strategy, preparing presentations or designing workshop materials. These activities require knowledge, skill and effort.
But they aren’t necessarily where the greatest value lies.
The real work often happens when I help people make sense of complexity, understand different perspectives, navigate difficult conversations and find common ground.
It’s in asking the question nobody has thought to ask. Recognising tensions that haven’t been articulated. Creating the conditions for people to contribute. Helping a group move from competing viewpoints towards a shared understanding of what matters.
Sometimes, the most meaningful outcome isn’t a document or a deliverable.
It’s a conversation that changes how people see a problem. A relationship that becomes stronger. A decision that people understand and support. Or a group discovering what they can accomplish together.
None of those outcomes can be measured simply by how many hours I spent manually producing something.
And none becomes less valuable because I used AI to help me prepare, research, analyse or organise my thinking.
If technology reduces the time I spend on routine production, it can create more space for the work that requires human understanding, judgement and connection.
Perhaps we’ve spent too long measuring work by what people produce, rather than what their work makes possible.
Think different
Researchers Evan Risko and Sam Gilbert (2016) describe cognitive offloading as using external resources to reduce the mental effort required to accomplish a task.
We’ve been doing this for generations, from calculators to search engines and navigation systems.
AI extends this practice into more complex cognitive activities.
A 2025 study by researchers from Microsoft Research and Carnegie Mellon University examined how knowledge workers experienced critical thinking when using generative AI.
Greater confidence in AI was associated with less reported critical-thinking effort. But the researchers also found that AI was changing where people directed their attention: from gathering information towards verifying it, and from producing responses towards evaluating and integrating them.
The findings don’t mean AI automatically improves our thinking. Overreliance remains a genuine concern.
But they challenge the assumption that using AI necessarily means disengaging intellectually.
There’s a difference between outsourcing effort and surrendering judgement.
Using AI well requires us to frame problems, question assumptions, evaluate evidence and recognise when an answer is wrong or incomplete.
And perhaps most importantly, it requires knowing when not to use AI.
These are skills worth developing, not reasons to avoid the technology altogether.
When fear gets in the way of learning
What concerns me about the narrative that AI makes us less intelligent is what it might do to our willingness to learn.
Organisations are encouraging employees to develop AI literacy, while the wider conversation frequently warns that using AI will make them lazy, dependent or replaceable.
These concerns deserve serious attention. But constantly framing technology through what we might lose can leave little room for curiosity and possibility.
Amy Edmondson’s (1999) research on psychological safety highlights the importance of environments where people can ask questions, acknowledge uncertainty and learn without fear of interpersonal consequences.
That principle is particularly relevant when organisations introduce unfamiliar technologies.
If we want people to become capable and responsible AI users, we need to create opportunities to experiment, question and learn.
AI literacy should go beyond knowing how to write effective prompts. It should develop people’s ability to think critically, exercise judgement and remain accountable for their decisions.
We cannot expect people to become confident learners if we make them afraid of the very tools they’re being encouraged to understand.
Rethinking valuable human work
Perhaps the most important opportunity AI presents isn’t simply to make us more productive.
It’s to reconsider what we mean by valuable work.
If technology can help us research faster, organise information more efficiently and automate repetitive tasks, what becomes possible with the time and attention we gain?
We could use it to produce even more content, reports and presentations.
Or we could invest more in understanding complex challenges, building relationships, bringing different perspectives together and making better decisions.
That choice isn’t made by technology. It’s made by people and organisations.
And it raises important questions for leadership. Are we rewarding the volume of work people produce, or the value they create? Are we measuring individual productivity, or also recognising the contributions that enable others to succeed?
AI cannot answer those questions for us.
We need human judgement to determine what matters, whose interests are served and what consequences our decisions may have.
I don’t believe intelligence is demonstrated by doing everything ourselves. Nor do I believe using AI automatically makes us better thinkers.
But I do believe that our capacity to understand complexity, exercise judgement, connect people and enable collective action deserves greater recognition.
Because ultimately, intelligence isn’t only about what we know or what we can produce. It’s also about what we help make possible.
And perhaps the greatest opportunity AI offers isn’t to make us work harder or produce more.
It’s to give us more room to do the meaningful human work that matters.
References
- Edmondson, A. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350–383.
- Lee, H.-P., et al. (2025). The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems.
- Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688.
