Can AI help you expand your talents and find flow?

Key Points:

• There is hype about AI's impact on individuals, but we should expect conclusions to change as research quality improves.
• People tend to overestimate how much time AI saves them on tasks—perception does not match reality. However, people enjoy work more with AI assistants.
• AI's failures are not obvious, so it's hard to calibrate appropriate confidence in its outputs. Overreliance leads to downgraded human performance.
• AI appears to significantly enhance the abilities of less skilled and experienced people—it is a "proficiency enhancer" when you know a little bit about something.
• If AI assumes difficult tasks before we have a chance to expand our talents, it risks short-circuiting human development and mastery.
• While AI may help streamline access to enjoyable, focused states of "flow", it cannot replicate the fulfillment and growth that comes from owning the hard-won process of mastery ourselves.

There’s a great deal of hype about the impact of generative AI on us as individuals. Studies are early, can be poorly designed, and are generally small. We can expect the data and conclusions to change, for the effects found to be smaller as study sizes increase. We can expect a wide range of anecdata as people share their trial-and-error experiences of using AI at work.

The perceptions people have tend to exaggerate generative AI’s productivity impact. When objective data is gathered we find that people think they save about three times more time on tasks when they use generative AI than they actually do. Data aside, people say they enjoy their work a lot more: for instance, coders are able to find flow states more easily and often when GitHub Copilot is on board.

More concerning is people’s “machine intuition” or lack of it. It’s difficult to calibrate confidence in AI’s output because how it fails is not obvious. And people get overconfident, downgrading their performance by over relying on AI. It’s a special kind of automation bias where AI is assumed to be correct, even when people know better than to trust it.

Having said all this, it does appear that there is one effect many researchers are finding across multiple fields: generative AI has a significant impact on lower skilled and less experienced people. It is a proficiency enhancer when you know a little bit about something. In some situations it can significantly accelerate people’s abilities, for example, in coding. Suddenly someone with very little experience can appear to have new expertise.

This begs the question: if we need experience to develop expertise yet AI robs us of the ability to gain experience, what is expertise? Expertise and mastery are not just professional milestones, both are profoundly intertwined with our sense of purpose and connection. They play a pivotal role in both individual and collective human flourishing and enable the “community of experts” to punch well above our individual weight.

We develop expertise by cultivating our individual skills and knowledge. This fosters a deep sense of accomplishment and self-efficacy because the skills we enjoy practicing are reinforced and automatized, leading to flow states. This process of mastery contributes to the fabric of society by enhancing collective knowledge and capability.

AI is a double-edged sword. On one hand, AI can be an invaluable tool, offering vast repositories of information and personalized learning pathways which accelerate expertise. On the other hand, AI will easily undermine our apprenticeship. If we automate difficult tasks we cut ourselves off from the essential components for achieving mastery.

Mihaly Csikszentmihalyi, the psychologist known for his research on happiness and creativity, described flow as a state of complete immersion and engagement in an activity, where one loses sense of time and self-consciousness, experiencing deep enjoyment and fulfillment. In his view, achieving flow is crucial for a fulfilling life. He suggested that flow states contribute significantly to personal happiness and well-being. When in flow, we are fully absorbed in an activity that is neither too easy (which can lead to boredom) nor too hard (which can lead to anxiety). This optimal experience not only brings enjoyment but also leads to personal growth and skill development.

I wonder what Csikszentmihaly would think of our generative AI tools. He died in 2021 so probably never got to experience using a powerful LLM. He might recognize AI's promise in clearing distractions and tedious work to help individuals more seamlessly enter flow states. As the coders who use GitHub Copilot report. By handing off busy work and minutiae and freeing up mental bandwidth, tools like ChatGPT allow people to concentrate intensely on activities which are calibrated to their skill level.

However, he would likely remind us that over-reliance on AI risks diminishing the authentic challenge and personal growth central to self-actualization. If the skills we apply are no longer our own, we surrender aspects of achievement. And if AI assumes tasks before we have chance to expand our talents, our development short-circuits. For Csikszentmihalyi, true flow involves intrinsically pushing boundaries, not outsourcing obstacles. So while AI might streamline access to flow states, it cannot replicate fulfillment.

Humans must own the hard-won process of mastery. If AI assumes the sweat, we forfeit the sweet.