Adoption without Excitement
Thoughts on Christopher Nolan's hypothesis about Gen Z AI rejection
If your social media feeds look anything like mine, you have spent the past couple of days wading through comments on Christopher Nolan’s claim that younger generations are utterly rejecting AI. The remark, made while he was promoting his new film The Odyssey, has quickly become a favorite of the technology’s critics.
Nolan’s achievement as a filmmaker is beyond question, and he does have a rare and deep insight into contemporary media culture. But his claim about an entire generation disconnecting from an emerging technology deserves a closer inspection. This is because these things are usually not as clear-cut as they might appear.
So in this free bonus post on The Augmented Educator, I want to dig into the actual research on Gen Z AI rejection. And while studies on the purely cultural aspects of this claim remain rare, we do have results from educational research that can provide insights which, I believe, lead us to a reasonably conclusive answer.
So, is Nolan’s assessment grounded in serious data? Or is it anecdotal evidence from somebody whose deliberately traditional approach to filmmaking, admirable and outstanding as it is, gives him only a partial view of how an entire generation behaves?
Here is the short answer. Gen Z and Gen Alpha are not abandoning AI as Nolan seems to claim. They are using it heavily. But they are trusting it less, admiring it less, and reserving human judgment for the most important issues.
Simply put, Nolan is right about the mood. But he is wrong about the behavior. What the data really describes is what I would consider an “adoption without excitement.”
What Nolan actually saw
In his promotional interviews, Nolan argued that Hollywood and the technology sector are pouring money into AI at exactly the wrong moment, because young audiences are turning against synthetic content. He claimed he had never witnessed so “rapid” and “wholesale” a dismissal of a supposedly foundational technology in his lifetime.
The term “AI slop” has been coined by young internet users for the flood of low-quality, derivative, and machine-generated content. It is not a neutral description. It is a verdict.
And there is real substance behind it. Sociologists and media theorists describe the phenomenon as aesthetic exhaustion rather than fear or plain rejection of technology. It also points to a generation that spent its adolescence inside algorithmic feeds and an ever-increasing number of deepfakes, and has therefore developed something like cultural antibodies to synthetic content.
In his comment, Nolan cites the commercial success of low-budget, practically made films like Obsession and Backrooms, directed by the Gen Z filmmakers Curry Barker and Kane Parsons, as proof that younger audiences want human-made, labor-intensive art. Human effort and “minimal AI” are becoming premium labels, the way “organic” once did in food.
This is undeniably correct. Nolan has identified a real aesthetic backlash, one the corporate boards still betting on universal AI enthusiasm continue to ignore. But he is describing what young people want to watch, and not how they use AI in everyday life.
On that question, the evidence tells a much stranger, but, I would argue, also a much more interesting story.
Using while doubting it
On one side, AI usage numbers describe a fairly clear picture. The Higher Education Policy Institute’s Student Generative AI Survey 2026 found that 95% of UK undergraduates now use AI in some capacity. Nearly all of them use it for assessed academic work. And the small share who paste AI-generated text directly into assessed work has quadrupled in two years.
Across the Atlantic, a 2025 Pew Research Center report found that about two-thirds of American teenagers have used AI chatbots. A sizable minority engage with them daily. Whatever this is, it is clearly not rejection.
On the flip side, Nolan’s instinct is also not unfounded. The longitudinal study Voices of Gen Z: The AI Paradox, conducted by Gallup with the Walton Family Foundation and GSV Ventures, surveyed young people aged 14 to 29 in early 2026. The results showed that usage held steady, with about half using AI at least weekly. But the feelings about AI did not hold steady at all.
In a single year, the share describing themselves as excited about AI fell from 36% to 22%. Hope declined alongside it; anger rose sharply, and anxiety stayed stubbornly high. Gallup’s own summary calls the relationship “stabilizing but not deepening.”
Interestingly, the decline is sharpest exactly where you would least expect it. Excitement and hope are collapsing fastest among daily users, the heaviest adopters of all, while among those who avoid the technology entirely, anxiety and anger dominate outright.
I find this fascinating. It turns out that the paradox is not that young people refuse to use AI. It is that familiarity appears to be producing less enthusiasm rather than more. This, again, is a clear indicator of adoption without excitement.
Looking closer at the survey data on trust, it becomes obvious why. A Wharton-led survey completed in partnership with Gallup and the Walton Family Foundation shows young people treating chatbots as productivity levers rather than intellectual partners. They are reaching for different tools for different tasks.
Yet in the same survey, a large majority worried AI discourages deep critical engagement. They fear it will make people lazier and that it displaces the social learning that happens between human peers and mentors. The Voices of Gen Z study goes even further. A remarkable 80% believe that using AI tools will make it harder for them to learn in the future.
Why keep using something you suspect is damaging you? Part of the answer is societal pressure.
As schools and workplaces normalize AI use, young people increasingly perceive opting out as a competitive disadvantage. Deloitte’s 2026 global survey found most Gen Z and Millennial workers using AI on the job, mostly to clear administrative underbrush.
Yet among employed Gen Zers, roughly three times as many believe the workplace risks of AI outweigh the benefits as believe the reverse. They can see that the entry-level tasks most vulnerable to automation are precisely the ones that historically allowed junior employees to become competent professionals.
Their trust in AI output, or lack thereof, tells the same story. Most of them trust work done entirely by humans, far fewer trust work done with AI assistance, and almost nobody trusts work produced by AI alone. Even their consumer behavior is finely calibrated. For routine customer service questions, they overwhelmingly try self-service first, chatbots included. For anything complex or urgent, most demand to connect with a human.
This does not paint a picture of a generation confused about the utility of the technology. Instead, it is a generation drawing its boundaries with high precision.
Gen Alpha draws the same boundary
Gen Z is a transitional cohort. Its members remember how school, work, and media felt before generative AI, and they are retrofitting their habits accordingly. By contrast, Generation Alpha, usually defined as those born from 2010 onward, is not retrofitting anything. For Gen Alpha, AI is not a disruption to an established environment. It was part of that environment from the very beginning.
Their adoption consequently starts earlier and runs deeper. A Razorfish study of Gen Alpha’s digital habits found roughly one-third of children in this cohort using AI tools every single day, with ChatGPT already the clear favorite.
And yet even these children draw the same boundaries their older siblings draw. When they want factual information, most prefer to ask an AI rather than a person. When they want personal advice, most still turn to a human being. Informational utility sits on one side, emotional resonance on the other, and a surprisingly firm line runs between them.
But the why differs. Gen Z’s skepticism is shaped by comparison, because its members remember a before. By contrast, Gen Alpha’s stance appears more pragmatic. AI is ordinary, but ordinariness has not made it an emotional or moral authority.
The evidence on Gen Alpha is thinner than that of Gen Z, so treat this as an early indicator rather than a settled generational verdict.
Why the unease is not irrational
Is the unease justified? Here the learning research is uncomfortably supportive. I have written about cognitive offloading at length in previous essays, so I will keep this brief.
The research increasingly distinguishes between two ways of handing work to the machine. On the one hand, this is using AI to remove routine friction while the learner keeps control of framing, verification, and judgment. And on the other hand, it is using it to replace the initial sense-making on which understanding depends. Under time pressure, students usually slide toward the second.
A study published in the Pacific Journal of Technology Enhanced Learning, captured how invisible that slide can be. Students carefully protected the final decisions about which arguments to run, and they sincerely reported that AI had sharpened their thinking. But most had delegated the foundational interpretation of the material to the AI. They were still choosing, but from a menu the system had written. And polished output can feel like mastery even when the learner has not built the understanding underneath it.
These findings do not explain every source of young people’s frustration, but they show that their concerns about learning and critical thought are not baseless.
Relying on AI isn’t inherently destructive, though. When managed correctly, it can also act as a powerful intellectual lever. Instead of mindless offloading, students can engage in what Steven Johnson terms “cognitive uploading.” This means delegating tedious, lower-order tasks to an AI while rigorously maintaining critical oversight.
What educators should take away
This brings us to a vital realization: this tendency to blindly offload thinking is not an inevitable failing of Gen Z or Gen Alpha. Rather, it is fundamentally an educational design problem. Traditional “snapshot assessments” actively reward students for falling into the efficiency trap. To fix this, we must shift our focus from evaluating the final output to evaluating the messy process of learning itself.
Two principles follow for the classroom, and both respond to what young people themselves are telling pollsters.
The first: assess the process, not only the artifact. A model can produce a flawless essay while the learner does none of the cognitive work, so a final product is no longer a reliable index of the thinking behind it. The practical response is to assess discernment: whether students can question, verify, refine, and, when necessary, reject an AI output.
The second: stage AI use according to expertise. Novices need to perform the foundational sense-making themselves because that is where understanding gets built. More experienced students can safely delegate routine work because they already possess the knowledge needed to evaluate the result.
What is off the table is prohibition. Banning the technology is a fantasy; survey data shows a meaningful minority of young people using AI even when explicitly told not to. Young people are already distinguishing usefulness from trustworthiness. Education should strengthen that distinction rather than reward either reflexive adoption or reflexive refusal.
Deliberate use, not dismissal
So, was Nolan right? Partly, and the part he got right does indeed matter. There is a real, measurable, and growing disaffection among young people toward AI, visible in the sentiment data and audible in the vocabulary of slop.
His mistake, and the mistake of everyone uncritically sharing his quote, is treating an aesthetic verdict as a behavioral one. The same nineteen-year-old who scrolls past AI-generated video with contempt will open a chatbot an hour later to summarize a reading because the syllabus is long and the clock is running.
But the reverse sentiment is just as wrong. Near-universal adoption is not an endorsement of the technology. It is more likely the usage pattern of people who feel they have little choice. And they tell researchers, in overwhelming numbers, that they suspect the tool is hurting them.
Nolan saw young people turning away. The research shows Gen Z and Gen Alpha doing something more deliberate. They are using AI while drawing boundaries around its authority. They are turning to it for speed, convenience, and information while withholding judgment, creativity, and personal trust.
The younger generations use the machine, but they do not worship it. That is not a rejection. It is deliberate use. Maintaining that deliberate use is the harder work, but also the more hopeful one.
The images in this article were generated with Nano Banana 2. (These are not taken from Christopher Nolan’s Odyssey movie.)
P.S. I believe transparency builds the trust that AI detection systems fail to enforce. That’s why I’ve published an ethics and AI disclosure statement, which outlines how I integrate AI tools into my intellectual work.