You’re Probably Using AI Wrong

Many of us approach AI with the expectation that it will make us more efficient. But what if that's not the best way to harness its potential? Rhea Purohit initially struggled to integrate AI into her workflow. In the latest installment of Learning Curve—her column about going from zero to one with AI—she shares how she learned to use AI not for productivity, but to amplify meaning in her work and life. Rhea’s journey offers a fresh perspective on our relationship with AI, complete with practical steps to identify what brings us meaning and how to use AI to enhance those areas.—Kate Lee

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I tried to use AI to be efficient—and I was disappointed. I’m a writer. LLMs can generate coherent passages of text faster than me. Getting AI to write for me is arguably the most efficient way I could use it. But each time I prompted an LLM to do this, the writing was flat, bland, and impersonal, so much so that I didn’t think it was worth using, not even as a first draft. 

I kicked, screamed, tore a few strands of hair out. And then I realized that the problem wasn’t AI—it was me.

More specifically, my narrative around how AI fit into my work and life.

Most consumer AI apps share a common promise: They help you do things faster, or with less effort. The resulting narrative frames AI’s primary role as boosting efficiency. Some people might find meaning in efficiency itself, but many others don’t, which can inadvertently discourage them from experimenting with AI.

If you, like me, are part of that second group, I have a solution for you: Reframe your narrative around AI. Stop using AI to be more efficient. Instead, use it to increase the meaning you derive from work and life. 

As humans, we are drawn to activities that give us meaning. In some domains of life—like, say, mountaineering—this may seem obvious. As an example, take the Eiger, a 13,000-foot high peak in the Swiss Alps.

The north face of the Eiger—a vertical wall of ice and limestone—has earned the moniker Mordwand, German for “murder wall,” because of the number of people who have died trying to climb it.

A third of the way up the jagged mountain, at around 9,000 feet, is a door. 

On either side of this door is a different reality. Inside the Eiger, trains rumble through tunnels, plying passengers to tourist attractions. Outside, the door opens onto a narrow ledge where numb-fingered climbers pause for a moment of respite. (The two realities collide during emergencies, when stranded climbers need to be rescued.) 

Two centuries ago, the only way to get to the top of the cloud-covered mountain would have been to scale it. Trains—an advance in locomotive technology—now provide an efficient alternative. Even so, you wouldn’t bother trying to convince a mountaineer to catch a train to the summit. Their decision to climb the Eiger has nothing to do with efficiency; it stems from the meaning they derive from the experience. 

Humans are drawn to activities that bring them meaning. This is common sense when it comes to mountaineering, but my hunch is that we lose sight of it when we try to use AI.

AI beyond productivity 

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• The pitfalls of outsourcing creativity
• Climbing vs. riding: A metaphor for AI use
• Tailoring AI to your unique needs
• Discovering meaning through self-reflection