Does AI make us lazy? On motivation, mastery and AI competencies

In the 2008 Pixar film WALL-E, we meet humanity 700 years into the future. We float around in reclining chairs with screens in front of our faces while machines take care of everything. Nobody walks, nobody lifts, nobody makes an effort. The image has lodged itself in our collective idea of what technology does to us: when the machine takes over, the human lies down.

It is the same idea that is surfacing these years in the conversation about artificial intelligence. If AI can write, analyse and decide for us – why should we do it ourselves? The fear is that we will outsource all thinking and spend our existence in a relaxed state on the sofa.

I understand the fear. But I will argue that it rests on a view of human nature that is more assumption than truth.

The assumption of the frictionless human

Much modern behavioural design rests on one basic idea: humans follow the path of least resistance. Make it easy, and people will do it. Remove the friction, and the behaviour follows. That is the logic behind one-click purchases, autoplay and endless feeds – and it works, measured in clicks and conversions.

But there is a step from “people often choose the easy option” to “people are beings who fundamentally avoid effort”. The first is an observation. The second is a view of human nature – and it is the one the fear of AI laziness stands on.

The problem is that this view does not work as an explanatory model for much of what we humans do voluntarily. Nobody runs a marathon because it is frictionless. Nobody learns to play the piano, bake sourdough bread or climb mountains because it is the path of least resistance. We seek out effort to an extent that a pure theory of friction cannot account for.

Humans do not avoid resistance. Humans avoid meaningless resistance.

Robert White and the urge to master

As early as 1959, the Harvard psychologist Robert W. White pointed out that the motivation theories of the day shared the same blind spot. In the article “Motivation Reconsidered: The Concept of Competence” in Psychological Review, he drew attention to the fact that neither drives nor rewards could explain why children and adults explore, investigate and manipulate their surroundings – entirely without external gain.

White proposed a concept he called effectance motivation: an innate urge to have an effect on the world, to make an impact on one's surroundings and to experience oneself as competent. We do not act only to get something. We act to be able to do something. The feeling of mastery is not a by-product of motivation – it is the motivation itself.

Surely most of us can easily recognise this in our own lives? The satisfaction of solving a difficult problem, of making a piece of craftsmanship succeed, of finally understanding something you did not understand yesterday – that is not a reward from outside. It is the confirmation of one's own competence from within.

Video games as counter-evidence

If humans really followed the path of least resistance, video games would not exist. A video game is, at its core, voluntary friction: obstacles you pay for the privilege of struggling with.

In 2006, the motivation researchers Richard Ryan, Scott Rigby and Andrew Przybylski investigated precisely what draws us in when we play. Across four studies – from Super Mario to large online games with hundreds of players – they found that the enjoyment of playing is closely linked to the experience of competence and autonomy. Perceived competence was the factor that best predicted whether people chose to continue with a game. And the games that gave players the feeling of mastery even lifted their mood and self-esteem after playing.

The researchers pointed to a particular mechanic: a game works when it provides ongoing optimal challenges – continuous challenges that always sit right at the edge of what the player can do. Too easy, and we get bored. Too hard, and we give up. Just right, and we cannot put it down.

The concept of optimal challenge recurs across motivation research – from Csikszentmihalyi's concept of flow to self-determination theory: people are most motivated when the challenge is balanced with their abilities, so that the task can succeed, but not by itself. That picture clashes rather sharply with the image of the sofa human from WALL-E. Millions of people spend their free time seeking out resistance because the resistance is calibrated correctly. Laziness is not our nature. It is what happens when the challenges are put together wrongly.

Organisms, not mechanisms

Behind the disagreement about whether AI makes us lazy lies a deeper disagreement about what a human being is.

One way of looking at it stems from behaviourism and lives on in much behavioural design: development is something the environment does to the human being. We are shaped by stimuli, rewards and incentives – and if nobody pushes, we stand still. In that picture, the human is a mechanism that reacts.

The developmental psychologist Heinz Werner represented the opposite view. For Werner, the human being was an organism – not a mechanism. Development is not something we do to the child; it is an activity the child carries out itself. In his organismic developmental psychology, all living things move under their own power towards ever more differentiated and integrated forms – from the undifferentiated towards coherence, articulation and wholeness. Implicit in life is a tendency to seek out more complexity, not less.

To put it bluntly: a mechanism has to be pushed into motion. An organism needs something to grow on.

I consider this view of human nature the most accurate – and it turns the question of AI on its head. If we are mechanisms, AI makes us lazy, because then motivation disappears along with necessity. But if we are organisms that are proactive by nature and driven to have an effect on the world, the danger is a different one: not that the technology removes the effort, but that we forget to give ourselves new tasks that are worth the effort.

The right challenges in an age of AI

So what does this mean in practice – for the leader who is currently considering what AI should take over, and for the professional who fears becoming redundant? I have described how I make the distinction in my own day-to-day work in the article on choosing the right friction.

First, it means that the question “what can AI remove?” is incomplete. It must be followed by another: which challenges does it free us up for? If the technology removes the meaningless friction – the repetition, the formalities, the administrative grind – it can become a catalyst for exactly the type of tasks that White and Werner would recognise as human nourishment: harder problems, bigger contexts, more ambitious goals. If, on the other hand, it removes the mastery itself – the place where professional skill is confirmed and developed – it hollows out motivation from within. Not because we become lazy, but because we lose what we were in the process of becoming good at.

Second, it means that AI competencies are more than operating tools. The most important competency in an age of AI is, as I see it, the ability to set yourself a task and succeed at it – to be able to calibrate your own optimal challenges when the technology has made the old level trivial.

And third, it means that the responsibility lies with us – not with the technology. AI does not decide whether we end up in the reclining chair from WALL-E. Our choice of what we use the freed-up capacity for decides that. The humans in the film did not become lazy because the machines were skilled. They became lazy because nobody gave them a task.

Perhaps this is exactly where the work begins – not with the technology, but with the questions: which efforts in your organisation are meaningless friction, and which are the mastery itself? Where do your employees experience optimal challenges today – and where have the tasks become either too easy or too big? And what would you set out to do if you could use the time the technology frees up to become more skilled rather than merely faster?

Sources

• White, R. W. (1959). Motivation Reconsidered: The Concept of Competence. Psychological Review, 66, 297–333.
• Ryan, R. M., Rigby, C. S. & Przybylski, A. (2006). The Motivational Pull of Video Games: A Self-Determination Theory Approach. Motivation and Emotion, 30(4), 344–360.
• Mandigo, J. L. & Holt, N. L. (2009). Putting Theory Into Practice: Enhancing Motivation Through OPTIMAL Strategies. PHEnex Journal, 1(1).
• Werner, H. (1957). The concept of development from a comparative and organismic point of view. In D. B. Harris (ed.), The Concept of Development. University of Minnesota Press.