Choosing to Stay Human

If you visit your favorite social media site, you'll find it filled with posts that start to look suspiciously similar to each other:

Many of the comments to these posts are also generated by AI. An increasing number of academic papers and New York Times opinion articles are being produced by AI, and, apparently, award-winning short stories. If you use AI frequently, you probably notice how much AI-generated writing surrounds you (historically, frequent users have done quite well identifying AI writing). Otherwise, I promise you, it's much more prevalent than you might think.

While the similarity of AI writing isn't the only issue, it eventually becomes tedious enough that I find myself skipping writing on even interesting topics if my internal "AI detector" goes off. Badly prompted AI writing produces very little meaningful content per word, leading to intellectual distractions. We're trained to recognize well-crafted sentences and intellectually-sounding texts as the result of effortful human work, so we tend to pay attention to these AI-generated comments. However, there's often no human meaning behind them; these posts are mere attention vampires that require mental effort to decipher and offer no equivalent understanding in return1.

Using AI for writing comes with costs beyond alienating readers—it risks undermining the development of an essential human skill. I've been writing for decades and have developed my own style, which I believe shines through whether I'm crafting a book, a tweet, or a blog post. That style required a lot of painstaking work: excellent teachers, multiple revisions, and harsh online feedback all contributed. If the AI handles writing well, I could bypass all of that, but I'd lose something that has proven crucial to both my career and my happiness.

This isn't a condemnation of using AI to assist with writing in any way. I think AI can be a fantastic tool for skilled writers (I have AI review all of my work and roleplay different reader perspectives to ensure I haven't overlooked anything important). For those struggling with communication, AI can help convey their ideas more effectively, and writing may not be suitable for everyone. Additionally, a little effort can make AI writing less cliché, more personal, and more worthwhile (when used judiciously). Instead, this critique is aimed at using AI as a default option or, even worse, without any consideration. Balancing AI with our own cognitive abilities will likely be one of the defining challenges of the coming years.

The most clear-cut example of this phenomenon is seen in education, where two studies with overlapping research teams (including colleagues from Wharton) effectively illustrate the difference between using AI to bypass thinking and to aid in thinking. The first study involved an experiment at a Turkish high school with approximately a thousand students learning mathematics. One group used plain ChatGPT, while the other had no AI access. The students using ChatGPT completed their homework better and believed they were learning more, but during tests, they performed worse than their classmates without ChatGPT. This is because the AI, designed to be a helpful assistant, was simply providing answers, whereas genuine learning requires mental effort. By bypassing effort, you undermine learning. This is why early results of AI in classroom learning can be concerning.

However, we see a different outcome in a second study conducted by many of the same authors, where they ran a five-month Python course across ten high schools in Taipei with nearly a thousand students. Students who received a personalized sequence of problems from an AI tutor scored 0.15 standard deviations higher on a final exam taken without AI assistance. Some estimates suggest this is equivalent to six to nine months of additional schooling, without requiring extra instructional time or increased teacher workload. Instead, the AI tailored the learning experience to individual students. This aligns with other research on AI tutoring, indicating that custom-tailored tutors can significantly enhance learning when used appropriately.

This is a relatively small difference in how you use AI and yet it leads to big outcome differences. Worse, human nature leads us to make the wrong choices. Learning requires us to face our own ignorance and do hard intellectual work, and these things are really uncomfortable. Which is why students rate entertaining lectures as more educational than doing hard problems in class, even though they actually learn more from the hard work. To benefit from AI in learning you need to pivot from using AI to solve problems, to pushing you to solve problems yourself.

Fortunately, the three major AI companies have tools that provide at least some support for learning by making the AI act more like a tutor. Unfortunately, they are not intuitive to access. Gemini is the easiest. Hit plus and pick Guided Learning. For ChatGPT, you need to type “/learn” into the chatbox. For Claude, you need to hit the plus, select use style, and select “learning” (Anthropic has announced that this approach is changing but has not yet documented the change). In all cases, you should use a thinking or advanced model where possible, especially for STEM subjects. And these modes will only help support someone who wants to learn, they won’t stop you from cheating if you want.

AI need not undermine your ability to think, but it can do so if used badly and badly is often the default. My colleagues at Wharton call this “cognitive surrender,” and they documented how people would stop thinking about problems and just let the AI do the work, even when the AI was wrong. I think part of the problem is the way these tools are designed.

I did not do this for the post…

When AI systems required elaborate back-and-forth conversations and made errors frequently, humans had to be engaged at every step. Agentic systems are designed to make your life easier, because they just do stuff. Which is great for getting stuff done, bad for learning anything, or staying authentic, or avoiding cognitive surrender. If you put in a hard request and get an answer, it is tempting to just go with the AI’s response.

In our recently published paper with Fabrizio Dell’Acqua and my colleagues at Harvard, MIT, the University of Warwick, BCG, and elsewhere (which I wrote about here three years ago, but publishing academic work takes a while!) we ran an experiment on 758 consultants at Boston Consulting Group, half of whom got access to GPT-4. Consultants using AI vastly outperformed those without. But we also asked consultants to do solve a problem that we knew the AI would fail at. Consultants using AI on this task were significantly less likely to get the right answer than consultants without it. The AI gave them an authoritative-looking answer that happened to be incorrect, and most of them, the same elite consultants who outperformed on everything else, did not catch it. Of course, now AI just solves that problem, so the issue isn’t really error rates now, it is failing to learn how to be a good consultant by giving into the same impulse to surrender.

Again, this does not have to be the default. In a small study conducted by Anthropic, programmers used AI to help them do a new task. Those who just let the AI do the work couldn’t answer questions about what they had done, a sign of surrender. But people who asked the AI to explain what it was doing, or those who used AI to help them with only some of the work, seemed to avoid that fate.

Some of the solution might be in the tools themselves, but that is limited. A version of ChatGPT that asked, before every answer, “would you rather I push you to think through this, or just give it to you?” or told you “I think this would be more authentic if you wrote this” would be insufferable most of the time. But there are places where we absolutely need these reminders. The Taipei result hints at one direction, namely system-level constraints rather than user-level willpower, but we don’t see much of that in the consumer products, and the commercial pressure mostly pushes in the opposite direction.

A lot of the problem is going to come down to us. To be clear, I am cool with a lot of cognitive surrender. I don’t remember phone numbers anymore because my phone does that for me. I am happy my kids didn’t need to learn cursive. I am fine with calculators doing my daily math and my computer figuring out how to schedule my classes. These were once useful skills, but we were probably right to get rid of them.

AI is different because the technology is general enough that virtually any cognitive task can be offloaded into it to some degree. I don’t want to be too precious about writing: there is no principle that says a polished email draft has to come out of a human mind any more than a column of arithmetic has to. But we don’t want to give up everything, and that we mostly don’t know yet, for any specific task, what is important and what is not. Deciding that is going to be a real challenge.

The point isn't to avoid AI but to be intentional about it by making a conscious choice about AI use, rather than reflexive dependence or reflexive avoidance. More broadly, we are at the point where the defaults are being set for what kind of work to give AI: by the AI companies designing for frictionless use, by employers deciding what counts as “using AI well,” and by people teaching the ever-shifting concept of “AI literacy.” A lot of this is happening without, ironically, any real planning or consideration. And I suspect it will be hard to reverse these defaults once a generation of workers and students has built habits around them. The most important thing we can do is keep asking what to hand over and what to keep for ourselves… and not expect anyone, including the AI, to answer that for us.

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This is especially true of fiction writing, where AI is notoriously weak while seeming strong. ChatGPT in particular is fond of meaningless similes and metaphors (“the street was like a gap-toothed smile,” “he sat in a way that would make the trees jealous”) that can feel profound at first sight, but only because we assume difficult writing is purposeful and work hard to assign it meaning. Humans are very good at assigning meaning to meaningless material if we try hard enough.