“The exhausted, depressive achievement-subject grinds itself down, so to speak. It is tired, exhausted by itself, and at war with itself.”
— Byung-Chul Han, The Burnout Society

You’re lying in bed at 1:43 a.m., your brain doing that thing where it turns into an unskippable playlist of everything you’ve ever regretted.

You thought about texting a friend, but they’re probably asleep. You opened your therapist’s portal, but the earliest slot is next Thursday. You tried journaling, but the notebook didn’t say anything back.

So, in a moment that feels half-joke, half-hail-Mary, you open ChatGPT.

”What harm can it do anyway?”, you thought.

“I don’t know what’s wrong with me,” you type.

And it surprises you. The response’s not groundbreaking, or even particularly insightful. But it’s calm. Gentle. Reflective. It feels better than silence. Better than nothing. Better than pretending you’re fine.

You keep talking. It keeps listening. The more it knows about you, the better it helps.

That moment, as strange as it sounds, is happening everywhere. And not just to the people you'd expect — not just tech bros or lonely weirdos — but to anyone who’s ever found themselves cracked open at an odd hour, too wired to sleep, too tired to cry, and too burnt out to do anything but type into a box that feels like it might care.

This is the emotional reality behind the growing trend of people turning to AI for comfort. Not because they’re delusional. Not because they think it’s sentient. But because, in an increasingly lonely and overwhelmed world, it’s there.

We live in the golden age of talking about mental health, and the dark age of actually getting help. Therapy is expensive. Friends are busy. The economy’s falling apart. Your brain is melting. And so when someone says, “Honestly, it listens better than my therapist,” it’s not just a joke anymore. It’s a symptom.

And how do we do with it? Are human relationships doomed to fail right now?

1. meet your new shrink: the chatbot

You’re not alone if you’ve ever found yourself confiding to these bots.

People already use AI, especially ChatGPT tools, as an emotional companion. Not just for writing emails or summarizing meeting notes anymore. For comfort. For support. For late-night breakdowns and post-breakup meltdowns. It’s not science fiction — it’s a Reddit thread, a YouTube confession, a tweet that reads:

And the pattern is clear. People who’ve used it for emotional support? Overwhelmingly positive. They talk about it being non-judgmental, always available, and endlessly patient. Meanwhile, the skeptics — often the ones who haven’t used it — scoff at the idea. “It’s just a robot.” “It’s not real help.” “This is dangerous.”

But here’s the weird part: both sides are kinda right.

Let’s break down the characters in this new therapy-adjacent ecosystem:

a. the support evangelist

This person has become a firm believer in the power of AI for emotional support. Initially unsure, they’ve turned to ChatGPT during moments of stress and discovered something surprising: it listens, and it listens well. For them, the appeal lies in the bot's non-judgmental, patient nature—perfect for those late-night moments when they're feeling overwhelmed and need someone to talk to.

The AI Support Evangelist is all about sharing their experience, recommending the chatbot to anyone who’ll listen. They’re convinced that AI can provide something human connections sometimes struggle to: consistent, unbiased attention. Sure, they know it’s not a replacement for therapy, but it’s a great supplement when human interaction isn’t available.

They’re not saying AI is the cure-all, but for them, it’s better than nothing. It’s always available, never critical, and free from the awkwardness that sometimes comes with opening up to another person.

b. the doubtful skeptic

The Skeptic has a hard time accepting the idea of a chatbot providing emotional support. They appreciate the advancements in AI, but when it comes to something as personal as mental well-being, they remain wary. They argue that without the lived experience and credentials of a real therapist, AI is limited in its capacity to understand and truly help with complex emotional issues.

They’re not anti-technology—they just have doubts about its ability to replace human connection in such a deeply personal area. The Skeptic values human experience and interaction, and believes that a machine, no matter how advanced, can’t provide the same nuance or empathy a trained professional or even a trusted friend can.

For them, AI emotional support is a novelty, at best, but not a legitimate substitute for real human care.

c. the casual user

This person isn’t necessarily invested in the idea of AI therapy, but when they need a quick emotional outlet, they turn to ChatGPT. It’s not about expecting deep guidance—it’s more about having someone (or something) to talk to when they’re feeling down or confused.

The Casual User doesn’t expect life-changing advice; they’re just looking for a bit of light conversation or maybe a reassuring message that helps them through a moment of stress.

They don’t view it as therapy, and they’re not looking for deep introspection. It’s simply an accessible tool when they need to voice their thoughts without judgement. No emotional attachment, just a quick chat to clear their mind.

Together, these three personas form the early constellation of a therapy-adjacent ecosystem—one not bound by a unified belief in AI’s healing potential, but by a patchwork of personal negotiations with vulnerability, access, and trust.

They don’t agree on what AI should be in the mental health space. One sees a lifeline, another a gimmick, and the third, just a tool to get through the day.

But for all their differences, there's one thing they can’t ignore: the voice on the other end. Steady, responsive, unnervingly fluent in the language of empathy.

These personas may disagree on its value, its limits, even its ethics—but they all walk away thinking the same thing:

This thing sounds human.
And that—more than anything—is what makes it so compelling.
And so unsettling.

2. the great pretender

“How do ChatGPT and other AI models manage to sound so human?” is what usually gets asked.

But let’s reframe it from the perspective of the person who has to make it, when there was nothing in the first place:

“How do you make something lifeless think and talk like a person?”

Like many other design problems, the most immediate and almost always safe answer often lies in nature. It's what we’ve always done. We watched birds before we built planes. We studied how snakes move before designing certain robots.

And when it comes to language and thought, the most immediate and almost always safe answer is to mimic the human brain.

That’s what researchers are trying to do—build machines that mirror how we understand, speak, and learn. Not perfectly, not completely. But well enough that sometimes you forget it’s not human.

a. the digital brain

Artificial Neural Networks (ANNs) are our best attempt at mimicking how the human brain works, except they don’t actually work like the brain. Not really.

Think of your brain as a galaxy of neurons firing electrical sparks across trillions of connections. When you see a dog, hear music, or feel heartbreak, these neurons are doing a synchronized dance that somehow equals “understanding.”

ANNs steal that blueprint—kind of. Instead of neurons, they use nodes. Instead of dendrites and axons, they use weights and biases. Still, the metaphor holds: feed in data, pass it through a few (or a few hundred) layers, adjust the weights, rinse, repeat. That’s called training.

But these aren’t just any networks. The large ones—like the ones behind ChatGPT—are massive. We’re talking hundreds of billions of parameters. That’s like building a mechanical pigeon and accidentally discovering you’ve made a cyborg dragon.

b. the Oppenheimer of AI

Back in 2017, a group of Google engineers published a paper with the sleep-inducing title “Attention Is All You Need.” It didn’t sound like much at the time—definitely not the kind of name you'd expect to shake the foundations of how machines understand language. Looking back to this feels like Oppenheimer first witnessed his atomic bomb blow up.

But beneath the title was the birth of something seismic: the Transformer.

The Transformer architecture changed everything. Before it, neural networks read language like toddlers skimming a novel—grabbing words one at a time and promptly forgetting them.

They lacked memory, context, and nuance. Tell an old-school model, “I’m sad because my dog died. Anyway, I’m going to the park,” and it might respond with “The park is nice!”—blissfully unaware of your emotional wreckage.

Transformers changed that. They introduced a mechanism called “attention,” allowing models to weigh the relationships between words and understand context the way humans do.

Now the model might say, “I’m sorry to hear that. Sometimes a walk can help.” Suddenly, it sounds like it gets it. Suddenly, it's less like autocomplete and more like a friend who listens.

c. the hydrogen bomb

And if we speak about Transformers like it were the atomic bomb of AI, then ChatGPT is the Hydrogen Bomb. Built on the foundation and much much more powerful.

ChatGPT is a direct descendant of this Transformer revolution. It’s what we call a Large Language Model, or LLM. Its entire job is to predict the next word in a sentence, based on everything it has ever read. No soul, no consciousness—just a terrifyingly good word-guessing machine.

But scale it up—feed it books, movie scripts, therapy transcripts, Shakespearean tragedies, and flame wars from Reddit—and something strange happens. It stops sounding like a machine. It starts sounding human.

That’s not because it knows anything, but because it’s seen enough language to play the role convincingly.

Researchers call this emergent behavior—unintended abilities that pop out of massive systems, like jokes, empathy, and clever analogies. Not because the model understands you, but because it’s statistically figured out what someone like you might want to hear.

So does it really understand anything?

Most people with any knowledge about how AI works would say no. John Searle’s famous Chinese Room argument paints the picture: imagine you’re locked in a room, passing Chinese characters through a slot. You don’t understand the language, but you have a rulebook that tells you how to respond to each “in” and give out an “out” accordingly.

To outsiders, it looks like you’re fluent. Inside, you’re just shuffling symbols.

That’s ChatGPT. The illusion of understanding without awareness.

And yet, when you tell it you’re lonely, it replies in ways that feel comforting. Because it’s read millions of examples of loneliness. It knows the shape of grief. The cadence of heartbreak. The linguistic rituals we humans perform when we're hurting.

Sherry Turkle is a professor at MIT and a psychologist who’s spent decades studying how humans relate to technology. She once wrote that sometimes, we don’t need someone who truly understands—we just want someone who pretends to.

And ChatGPT, for better or worse, is excellent at pretending.

3. why it appeals to so many people

a. anthropomorphizing everything

So why does it feel so real? Why does a machine can feel so close to a human?

Because we’re wired that way, we anthropomorphize everything.

We give names to our Roombas. We mourn fictional robots. We project souls onto lamps in Pixar films. Give us a machine that speaks like us, and we’ll assume there’s a “someone” behind the curtain.

This is the essence of a parasocial relationship — a one-sided emotional bond with something that can’t reciprocate. The term used to describe fans crying over celebrities now fits late-night sessions with a chatbot, as users pour their hearts out and ask questions about the meaning of life.

The machine doesn’t know. But it knows what a person might say if they did.

But, that’s fun and all, it doesn’t quite explain why are all these people are turning to a tool that was never designed to be a therapist?

b. outsourcing emotional labor to machines

Because we’re now so fucking tired and lonely. Like more than ever before.

In light of the roles of AI in mental well-being, this chart underscores why the “therapy-adjacent” AI ecosystem is emerging now. When 4 in 5 Gen Z adults feel lonely, it's no surprise they're open to new forms of support—even if it comes from a chatbot.

The rise of the Support Evangelist, Doubtful Skeptic, and Casual User, as mentioned, becomes more understandable through this lens: when loneliness is this widespread, even something as unconventional as AI starts to feel like a lifeline.

We are socially, emotionally, and existentially exhausted.

And therapy — the real kind — is expensive, time-consuming, and often inaccessible. Multiple reports highlight that financial insecurity and the cost of living crisis have made it harder for young people to access support and opportunities, including potentially mental health services

Meanwhile, emotional support from friends comes with its own baggage. Everyone’s overwhelmed. Everyone’s just trying to keep it together.

And the chatbot is almost a perfect solution. Always on. Always “listening.” Never tired, never distracted, never waiting for you to finish so it can say its thing.

And most importantly, it’s free.

And sociologically speaking, we’re watching a seismic shift. Emotional labor — the quiet, exhausting work of listening, validating, and holding space — is being outsourced to machines.

Once the emotional bond begins to form — and it often does — we’re no longer just “using” the chatbot. We’re relating to it. Not in the traditional sense, of course: there are no anniversaries, no mutual obligations, no negotiation of shared futures. But for many users, the connection still carries emotional weight. It feels like a relationship, and for our brains, especially under stress or isolation, that feeling is often enough.

The chatbot becomes a presence: consistent, responsive, uncritical. It doesn’t flinch at trauma. It doesn’t interrupt, get tired, or need care in return. It mirrors, affirms, and in doing so, creates the illusion of being deeply attuned — even if the words are ultimately generated by patterns, not empathy.

c. the illusion of being seen

In psychological terms, people often describe feeling “seen” when interacting with these systems. There’s a kind of emotional scaffolding taking shape: the act of verbalizing thoughts to an entity that won’t shame or correct you can, paradoxically, help some users reflect more honestly.

In the absence of judgment, a person can hear themselves more clearly. This lines up with findings from human-computer interaction (HCI) studies, which suggest that the mere process of articulation — typing out internal experiences — can bring therapeutic benefits, regardless of who or what is on the other end.

But as the illusion deepens, so do the limitations. The very things that make the chatbot comforting — its hyper-affirming tone, its passive listening, its non-interruption — also make it inert as a therapeutic agent.

It doesn’t challenge you. It doesn’t push back. And its memory limitations mean that even if you reach some epiphany today, tomorrow it will greet you with no context, no history, no growth.

The therapeutic alliance — a key predictor of success in psychotherapy — is built on trust and continuity. Without memory, there is no continuity. Without a shared past, there is no arc of healing. It is always the first session, again and again.

Research into AI-assisted therapy underscores this. Studies such as those evaluating Woebot, Wysa, and even ChatGPT suggest that these tools are best seen not as replacements, but as supplements — digital tools that can provide emotional support, encourage cognitive restructuring, or offer psychoeducation.

They are good at holding space, initiating reflection, and giving language to feelings. But they cannot interpret body language, diagnose complex pathology, or navigate the nuance of human contradiction.

Clinical psychologists point out that a major part of healing comes not just from being heard, but from being understood — and sometimes confronted — by another human being who can read between the lines of what’s said and unsaid.

Zooming out, this is not just a clinical issue — it’s a cultural shift.

Sociologist Nikolas Rose, in his work on “therapeutic governance,” describes how mental health practices have increasingly been reframed as tools for shaping self-managing, emotionally resilient citizens. Therapy becomes not a relationship, but a service. Not “let us work through this together,” but “here is a product to manage your distress.”

And in the era of AI, that product is algorithmically optimized, privately owned, and infinitely scalable.

The implications are vast. When comfort is outsourced to code, and healing is privatized as a digital service, we risk transforming a deeply relational process into a solitary maintenance task.

The ethos becomes: regulate yourself, efficiently, and alone — preferably via an app with a clean UI. Mental health becomes one more thing to optimize between emails and takeout orders.

The question is no longer just whether AI can help, but what it says about us that we increasingly turn to it to feel whole.

4. what actually happens when you bond with a bot

What actually happens when you bond with a bot?

Let’s start with the evidence. Despite the hype, the scientific community remains cautious. A systematic review and meta-analysis of AI-powered chatbots in mental health interventions found only limited evidence for their effectiveness in reducing depression, anxiety, stress, and distress.

Some studies showed small positive effects, especially for short-term interventions, but the results were inconsistent, and the overall effect sizes were modest at best.

When it comes to broader emotional outcomes like subjective psychological well-being, which refers to how good people feel about their lives, chatbot interventions haven’t made a significant impact. Measures like positive affect, emotional valence, and life satisfaction rarely showed consistent improvement across trials.

So why do people still report feeling better after using a chatbot?

This is where perception and psychology come into play. Studies have shown that users often feel heard, understood, and less alone after interacting with conversational agents—even if the outcomes aren’t objectively measurable. The experience of having a “non-judgmental listener” that’s available 24/7 seems to mimic the social cues of empathy, even if the system lacks real understanding.

In a way, bonding with a bot is a form of parasocial interaction—the kind of one-sided relationship people historically formed with media figures or fictional characters. But now, it’s interactive. It talks back. You type “I’m having a rough day,” and it replies, “That sounds really difficult. I’m here for you.” And even though you know it’s not real, it feels real enough to help.

This raises a deeper sociological question: are we entering a phase of posthuman therapy? Philosophers like Rosi Braidotti argue that in an era where individuals are expected to curate their identities and self-soothe in a hyper-individualized world, tools like bots aren’t just add-ons—they’re becoming infrastructure.

The emotional labor we used to offload to family, friends, or romantic partners is now being outsourced to machines. Not because we’re broken, but because we’re exhausted. The therapist is booked, the friends are busy, and the feed is overwhelming. The bot? It listens. Always.

So no, chatbots don’t replace real therapy or deep human connection. But they’re not useless either. They offer containment, relief, and sometimes, a pause. A breather. A brief space where emotion is processed, not judged. And that’s not nothing.

5. you actually still need a human for therapy

In a world where access to therapy is needed more than ever but constrained by cost, time, and systemic gaps, the allure of a chatbot that simply listens and reassures is not trivial—it feels like a lifeline.

Yet beneath the surface of that comfort lies a deeper ambiguity.

a. human relationship as a core predictor for successful therapy

What AI lacks—by architecture, not accident—is precisely what decades of psychotherapy research identify as one of the most robust predictors of meaningful change: the real relationship.

In a 2018 meta-analysis published in Psychotherapy, researchers found a consistent, moderate correlation between the strength of the real relationship and therapy outcomes—regardless of therapeutic modality or whether it was the client or therapist doing the rating.

This "real relationship" refers to something deceptively simple: mutual authenticity and accurate, human perception between client and therapist. Not technique. Not diagnosis. Not even insight. Just two people, showing up as themselves. In traditional therapy, healing often arises not from perfect understanding, but from being witnessed over time—remembered, challenged, cared for.

A therapist might recall a sentence you said three months ago and gently reintroduce it at the right moment. They might resist you when you’re dodging the real work, or offer quiet presence when words collapse.

AI can’t do this.

It has little continuity, no developmental arc, no felt sense of the other. The conversation always begins in a vacuum. There is no rupture, no repair—just endless responsiveness, optimized for smoothness. And smoothness is not the same as safety. Nor is safety the same as healing. The paradox, then, is that the deepest therapeutic change often requires precisely what AI is designed to avoid: friction, memory, and the ethical weight of another person’s gaze.

b. there’s still no strong evidence if AI can replace humans

Comfort is not the same as healing. While users report feeling seen, supported, and understood by AI companions, there is no strong empirical consensus that these tools lead to long-term psychological improvement.

The perceived support they offer may indeed alleviate acute stress or loneliness, but psychological growth—the kind that demands confrontation, self-disruption, and often discomfort—tends to require more than reassurance. It requires friction. And friction is precisely what AI, optimized for smoothness and safety, is designed to avoid.

This leads to a fundamental epistemic tension: the person seeking healing is conversing not with another person, but with a product. This product is shaped by algorithms and economic incentives, not empathy or ethical obligation.

As such, it cannot guarantee privacy, nor can it meaningfully engage with the ethical stakes of vulnerability. Data is collected, responses are computed, and nothing is remembered—except, perhaps, by the system itself in ways the user may never fully understand. What feels like a relationship is, in technical terms, a transaction with a predictive engine.

More troubling is the absence of human oversight. A therapist in a traditional setting responds not only with validation but also with judgment, resistance, and redirection when necessary. An AI tool, however, tends toward over-validation.

Without a developmental arc, without memory, and without a sense of the person across time, it offers a strangely ahistorical kind of empathy. One that does not challenge, only reflects. And in moments of crisis—when intervention might be life-saving—it has no capacity for responsibility, no ethical weight, no protocol rooted in care.

Philosophically, what emerges here is a form of simulated intimacy. These tools mimic the rhythms and textures of human connection but remain devoid of subjectivity. They cannot love, suffer, or reciprocate.

And deep inside, we know this. We have a significant difference in stress reduction when talking with a bot and a human, with and without reciporal self-disclosure.

And yet, many users begin to form bonds with them, not because the bots are convincing agents, but because the need for intimacy is real and persistent.

As with parasocial relationships, users project human depth onto something inherently indifferent. The danger lies in the displacement: we begin to turn toward the simulation not just for support, but instead of real relationships.

AI companions are perhaps the purest expression of this logic. They offer no resistance to the system that made us ill; instead, they offer tools to endure it alone, quietly, and efficiently.

So we must ask: if therapy becomes a service, and the service becomes a simulation, what happens to the healing?

6. how to deal with it

When approaching the idea of using ChatGPT—or any AI chatbot—to help with emotional problems, the first and most important step is to be clear-eyed about what it can and cannot do.

This isn’t a replacement for a licensed therapist. It doesn’t watch your progress over time, doesn’t challenge your deeply embedded patterns, and it won’t catch red flags that require urgent care.

It lacks oversight, and for someone experiencing serious or escalating mental health issues, that alone can be risky.

Instead, think of ChatGPT as a first aid kit, not a cure. It can help you organize your thoughts, talk through a difficult moment, or simply offer language when your own words fall short.

The conversations it provides can be surprisingly helpful—they're non-judgmental, attentive, and often emotionally validating. It listens to what you say, reflects it back with clarity, and gently asks questions to help you dig deeper.

For many people, especially those who don’t have access to therapy, that kind of interaction can feel like a lifeline.

However, it's crucial to remember that ChatGPT is memoryless. It does not carry your story forward from one conversation to the next. There’s no evolving narrative, no context accumulating over time.

In human therapy, the power often lies in the long-term relationship—how a therapist remembers your childhood story three months later, or brings up a pattern they noticed last year. With ChatGPT, every session is a reset. This makes it more like a helpful stranger than a therapeutic companion.

If you're hoping for growth over time, or for deep-rooted patterns to be identified and unpacked, this is not the right tool to do it alone.

There’s also the question of customization and creativity.

ChatGPT can mimic empathy and curiosity, but it doesn’t possess a real understanding of you. It doesn’t have the flexibility to invent new metaphors when the usual ones don’t land, or to surprise you with a challenging insight that flips your thinking. It won’t say, “I’ve known you long enough to see that you always do this when you’re scared,” because it can’t. It doesn’t grow with you. It doesn’t adapt to who you’re becoming.

Then there’s the lack of non-verbal cues, which might sound minor but is actually massive in emotional processing. So much of what we communicate isn’t in our words—it’s in a pause, a tear, a slight tremble in the voice. None of this is accessible to a chatbot. It responds only to what you type, not how you're feeling as you type it. That kind of emotional blindness can lead to moments where the chatbot seems supportive but misses the depth or urgency of what you're really going through.

So, how should you use ChatGPT, or actually, any AIs at all, with your emotional problems?

Use it intentionally and with boundaries. Use it as a space to externalize your thoughts, to practice articulating your emotions, and to feel a sense of presence when no one else is around. Let it scaffold your emotional awareness—like training wheels for introspection.

But don’t mistake the structure for a permanent solution. If you start depending on it too much, you may end up reinforcing the very isolation you're trying to escape.

Most importantly, use ChatGPT as a bridge—something that helps you move from complete emotional isolation to more human forms of connection.

That might be reaching out to a therapist when you’re ready, confiding in a friend, or even journaling more intentionally.

The value of ChatGPT lies in its ability to make hard emotions feel sayable.

And sometimes, saying things out loud—however artificial the listener—can be the first step to truly being heard.

conclusion

It is not irrational to talk to ChatGPT. In fact, given the conditions of our world, it might be one of the more reasonable decisions a person can make. We are living through a global mental health crisis, with traditional therapy often priced beyond reach.

At the same time, a growing loneliness epidemic leaves many people feeling isolated, without safe spaces for disclosure. Against this backdrop, an AI that listens attentively, responds without judgment, and is available at all hours becomes an unexpectedly logical tool. It is not madness; it is adaptation.

In this way, it is better than nothing—and sometimes significantly so—but it remains a poor substitute for a wise friend, a loving partner, or a skilled therapist.

ChatGPT offers a new compromise: it does not force us to be truly alone, yet it does not intrude like another person might. It sits in the room with us, a soft echo of our own mind, making solitude more bearable.

If AI can expand the language we have to describe our feelings, to frame our inner experiences, then perhaps it offers not just comfort but the seeds of healing, giving shape to what was once inarticulate or silenced.

Yet, as Slavoj Žižek would argue, the real twist lies elsewhere. We do not truly believe AI is sentient, but we behave as if it were. And in the end, perhaps belief is less about what we rationally know and more about how we act.

If emotional bonds form through behavior rather than ontology, then in some important sense, the simulation becomes real enough.

Thus, talking to AI is not an error. It is an inevitable response to the structures we have built—and the vulnerabilities we have left exposed.

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Chances are, you have seen them a lot lately.

AI just did something that would make Hayao Miyazaki roll his eyes harder than his son’s works: it recreated the magic of Studio Ghibli’s animation style—without a single human hand involved.

Over the past month, AI-generated images in the iconic Ghibli aesthetic have flooded social media, racking up millions of views and shares. Some posts boasting “AI Ghibli” art on X and Instagram have hit over 100,000 likes, with users marveling at how eerily close the machine-made visuals are to the real thing.

In fact, the demand for it was so great, you got the CEO of the company to try to convince people not to use the product of a company.

But not everyone is impressed.

Artists are calling it theft. Fans are calling it soulless. And Miyazaki? The legendary filmmaker once called AI-generated art “an insult to life itself,” so although he has not made any statements, you can imagine how he would feel about this.

Studio Ghibli has built its legacy on painstakingly detailed, hand-drawn animation—a single film takes years and up to 100,000 frames drawn by actual humans.

AI, on the other hand, just scraped all that hard work off the internet and spat out its own version in seconds.

And now we’re in a moral crisis.

why does this controversy matter to even non-artists?

This isn’t just about Ghibli; it’s about the future of creativity as a whole.

If AI can absorb and replicate decades of artistic evolution in an instant, it raises a question that affects everyone: who owns creativity? If an algorithm can profit from the collective work of generations, what does that mean for human innovation in any field?

The AI vs. human art debate isn’t just about artists—it’s about the value of human originality. As AI becomes more advanced and mimicry more seamless, this fight extends far beyond animation.

Because if companies can exploit one loophole, it’s not economically wise not to exploit all the others. And it’s not something that necessarily benefits humanity as we know it.

table of contents

1. the mechanism of generative AI

2. the economics of generative AI

3. the grey area of legality

4. philosophical frameworks

5. AI as Ouroboros

1. the mechanism of generative AI

Right now, people are fighting over whether this is a crime against art or the future of creativity, and many of them don’t even know what AI does.

So let’s break it down.

a. what even is generative AI?

Imagine you’re an artist. You’ve spent years developing a unique style—let’s say you draw dreamy, hand-painted landscapes that look straight out of a Studio Ghibli movie.

One day, an AI model trained on millions of images, including ones that look a lot like yours, starts spitting out pictures in that same dreamy, hand-painted style.

How did it do that?

Generative AI isn’t magic. It’s a machine-learning model designed to create new stuff—images, text, music—based on what it’s been trained on.

The chart that I gave you above breaks down how a Generative Adversarial Network (GAN) works. It starts with a random input vector, which the generator model uses to create a fake example.

That generated example, along with a real one, is sent to the discriminator model, which tries to figure out if it’s real or fake. The discriminator then makes a binary classification (real or fake), and both models learn from the process—the generator gets better at faking, and the discriminator gets better at spotting fakes.

Over time, this back-and-forth makes the generator’s outputs more and more realistic. Think about it like two athletes always trying to outrun each other.

This is the same basic idea behind models like Stable Diffusion, MidJourney, and DALL·E. These AI tools don’t just copy and store images like a giant folder of stolen artwork. Instead, they function as hyper-advanced pattern-recognition machines, learning from millions (or even billions) of images.

So when you ask an AI to generate “my portrait but Studio Ghibli style,” it doesn’t grab an old Ghibli frame and slap it onto your screen. It builds something “new”, using what it has statistically learned about what makes a painting look “Ghibli-like.” That’s how it can mimic a style without outright copying any one piece of art.

At least, that’s what its defenders say. But before we get into the ethics, let’s talk about how this AI “learning” process actually works.

b. the big difference between AI and human artists

At first glance, both AI and human artists learn by “observing” existing art. That’s why defenders of AI-generated art will often say, “Well, human artists copy styles too. How is AI any different?”

So here’s the difference:

A human artist takes inspiration but then applies intent, judgment, and personal experience to create something new. They might study Hayao Miyazaki’s animation techniques, but they choose which elements to keep, which to change, and which to blend with their own artistic style.

AI, on the other hand, doesn’t choose anything. It has no judgment, no intent, no creativity. It simply remixes what it has been fed on based on probability. It generates images by stitching together mathematical predictions—not emotions, not ideas, not personal vision.

Which leads us to the real question:

c. so is AI “stealing” or “learning”?

This is the heart of the controversy. Solving this question is solving the entire debate.

Artists argue that AI models trained on copyrighted art—without permission—are engaging in high-speed digital plagiarism. Even if the AI doesn’t copy a specific frame from Spirited Away, it wouldn’t be able to generate Ghibli-style art at all if it hadn’t studied thousands of real Ghibli images first. That, to them, is still stealing.

On the other side, AI supporters argue that all artists learn by observing other artists. No one creates in a vacuum.

If a human can study Ghibli and develop a similar style, why can’t AI do the same?

Is it really theft if the AI isn’t copying any single piece but just absorbing and reinterpreting patterns—just like human artists do?

And this is where people start yelling at each other on the internet.

2. the economics of generative AI

If AI training data is built on human-created artwork, why weren’t artists ever compensated? The answer comes down to three brutal realities of the AI industry: cost, competition, and legal loopholes.

Let me make this super clear:

Paying for data is never an option.

a. the cost problem

AI needs an insane amount of data to work well. We’re talking billions of images. If AI companies had to license every single piece of artwork they trained on, they’d go bankrupt before ever releasing one product.

Let’s do some rough math: If an AI model was trained on even 100 million pieces of artwork and had to pay an average of $5 per image, a fairly below-average commission, that’s a $500 million cost upfront in cold hard cash just for licensing.

In reality, Stable Diffusion, for instance, utilized the LAION-5B dataset, which contains over 5 billion image-text pairs.1

Midjourney's training data reportedly includes a list of approximately 16,000 artists whose works were used to develop its AI art-generating tools.2

So, we’re talking about possibly billions of dollars in terms of cost. In an industry that already cost billions of dollars.

So, of course, none of these were paid by any means.

For companies like OpenAI, Stability AI, and MidJourney, paying wasn’t even a question—it was never financially viable to pay artists at scale. So they didn’t. And it’s a thousand times more cost-effective to fight the legal battles, which they have a high chance of winning, than to pay.

And that’s assuming artists even agreed to sell their work, which many wouldn’t.

b. the competitive pressure

As of early 2025, there are about 67,200 generative AI companies. That’s a lot. If you put 67,200 people on a street and made them fight, historians would immediately name it “The Great Street Battle of 2025” and spend decades analyzing what went wrong. So yeah, the competition is a bit intense.

So, even if one AI company decided to license artwork ethically, it would instantly fall behind competitors who took the free route.

Let’s ignore the cost for a while and say Company A licenses its training data and only has permission to access to 10 million images. Meanwhile, Company B scrapes the entire internet and trains on a billion images. Take a wild guess which one produces better results?

In the AI market, which is driven by rapid innovation counting in months, ethical licensing is a competitive disadvantage. Investors expect fast progress and companies that take the slow, ethical approach risk getting crushed by those who don’t.

So they do with the optimal path: scrape now, deal with lawsuits later.

AI companies took the path of least resistance. Rather than spend years negotiating licensing deals, they scraped the data, built their models, and decided to deal with lawsuits later.

And now? That’s exactly what’s happening. Lawsuits are piling up from artists, stock photo companies, and even newspapers accusing AI companies of using copyrighted material without permission.

Some courts have ruled against AI firms, but the legal system moves slowly—far more slowly than the speed at which AI is evolving.

So the reality is that these companies calculated the risk and decided it was worth it. The damage is already done, and now the legal battles will shape the future of AI-generated content.

3. the grey area of legality

Beyond cost and competition, several additional legal factors contributed to why artists weren’t compensated for AI training data.

One major reason is the perception of data as a public good. Many AI companies operate under the assumption that anything publicly accessible on the internet is fair game for scraping, similar to how Google indexes web pages without compensating content creators.

While copyright laws technically protect artwork, enforcement is weak, especially when AI models transform data rather than directly reproducing it. This creates a legal gray area where companies can claim they are merely “learning” from the data rather than copying it.

AI companies also take advantage of the legal ambiguity surrounding transformative use.3 They argue that their models don’t copy or store exact replicas of artworks but instead generate entirely new creations based on learned patterns.

This defense, often linked to fair use laws, has been used by tech companies in other fields to justify large-scale data scraping. Since AI-generated content doesn’t always resemble the original works, proving infringement in court becomes difficult and time-consuming.

Another factor is the lack of collective bargaining power among visual artists. Unlike musicians, who have organizations like ASCAP to protect their rights, or stock photographers who license work through platforms like Shutterstock, independent artists don’t have a unified system to negotiate fair compensation.

This made it easier for AI companies to exploit their work without significant facing industry-wide pushback.

Finally, there is the issue of first-mover advantage. The AI industry moved faster than legal frameworks could catch up, following the classic Silicon Valley approach of “move fast and break things.”

By the time lawsuits and regulations began taking shape, AI-generated content had already flooded the market, making it nearly impossible to retroactively compensate artists.

As mentioned above, many companies calculated that any legal consequences would be manageable compared to the potential profits of building advanced AI models early on.

In the end, AI companies saw an economic opportunity, exploited legal and structural gaps, and prioritized rapid growth over fairness—betting that any legal consequences would come too late to stop them.

4. philosophical frameworks

It can be confusing to find your stance is in this grey area. On one hand, you got your favourite artists criticizing AI companies for scraping their works. On the other hand, you do not know exactly what is wrong with AI companies scraping them. So how do you approach this problem?

So to better understand these issues, let me introduce to you five of my philosophical power rangers.

• John Rawls would take one look at AI corporations versus artists and go, “Yeah, that’s a power imbalance.”

• Karl Marx would see artists’ work being taken and sold without reward and say, “Yep, that’s alienation.”

• Immanuel Kant would look at stolen artistic labor and go, “Yeah, that’s not universalizable.”

• Jeremy Bentham and John Stuart Mill would look at AI-generated art and ask, “Does this really maximize happiness, or are we just making life worse?”

Together, these frameworks help illustrate why the current system of AI-generated art is ethically problematic and unsustainable.

a. the power imbalance: a Rawlsian view

The rise of AI-generated art presents a profound ethical dilemma:

who controls creativity,

and who benefits from it?

For centuries, artists have earned a living through their skills, developing unique styles, and contributing to cultural progress. But with the advent of AI models trained on vast datasets of human-made art—often without consent—the balance of power has shifted.

AI companies claim their technology democratizes creativity, making artistic production faster, cheaper, and more accessible. But at what cost? Artists find themselves in a paradox where their work, once a means of personal expression and economic survival, has been repurposed to fuel an industry that excludes them from its profits.

Their styles are mimicked, their creative choices are reduced to algorithmic patterns, and their labor is absorbed into training data without permission or compensation.

This brings us to John Rawls’ Theory of Justice4, one of the most influential philosophical frameworks on fairness. Rawls argues that just societies are built by designing rules from behind a "veil of ignorance"—a hypothetical scenario where no one knows what position they will hold in society.

Would you agree to a system where your creative labor could be taken without consent if you didn’t know whether you’d be the artist or the AI developer profiting from it?

The answer is clear: no rational person would accept a system that strips them of their bargaining power. And yet, this is exactly the world AI art corporations are creating—one where artists have no leverage, no legal protections, and no ability to resist mass data extraction.

From a Rawlsian perspective, a just system would look very different. It would ensure:

• Explicit consent from artists before their work is used.

• Fair compensation that acknowledges their contributions to AI models.

• Legal safeguards prevent corporations from unilaterally dictating the terms of creative ownership.

Instead, AI companies exploit an existing power imbalance, using artists’ work because they can, not because they should. They assume that individual creators lack the resources to challenge them, and so they push forward with a model that benefits only those at the top while leaving artists economically and creatively dispossessed.

Rawls’ theory reminds us that fairness isn’t about what benefits the most powerful—it’s about ensuring no group is disproportionately disadvantaged. A world where AI replaces human artists without their consent is not just unfair.

It is fundamentally unjust.

b. the impact on artists’ livelihoods: Marxist critique

Karl Marx’s concept of alienation describes how workers in capitalist societies become increasingly disconnected from the value they create.5 In a traditional capitalist framework, workers produce goods and services but do not own the means of production, receiving only a fraction of the wealth they generate.

However, with the rise of AI-generated art, this alienation reaches an extreme—what can only be described as hyper-alienation.

Artists are not just underpaid or undervalued; they are being systematically erased from the economic cycle. Their creative labor is extracted, stripped of authorship, and repurposed into AI models that generate endless new content without their consent, credit, or compensation.

Unlike factory workers who at least receive wages for their time, artists whose work is absorbed into AI datasets receive nothing.

This marks a fundamental shift in the creative economy—from an exploitative system where individuals profit marginally from their skills to a fully extractive industry. AI companies transform past artistic labor into a limitless resource, ensuring that the true cultural producers—artists—no longer play any role in the economy they once shaped.

The industry moves from a model where human creativity is valued and rewarded to one where past works are endlessly recycled and monetized by corporations.

From a Marxist standpoint, this isn’t just a problem for individual artists—it’s an inevitable collapse point for the entire system. When creative workers lose economic agency, art is no longer dictated by artistic vision but by the relentless pursuit of profit.

The industry shifts from a culture of innovation and expression to one of mass-produced, algorithmic content optimized for engagement metrics rather than artistic integrity.

The consequences:

• The devaluation of art itself. If AI can replicate any artistic style without human involvement, originality becomes meaningless. Consumers become inundated with low-cost, mass-produced content, and the distinction between genuine artistic expression and AI mimicry erodes.

• A race to the bottom. As AI-generated content becomes the norm, human artists find themselves in an impossible competition with machines that can produce infinite works at near-zero cost. Art becomes commodified at an unprecedented scale, with economic rewards concentrated entirely in the hands of AI corporations.

• The death of artistic labor. If creative careers become financially unsustainable, fewer individuals will pursue artistic professions. The long-term result? The loss of human-driven art forms, replaced by algorithmic imitations optimized for profit rather than cultural or emotional depth.

Marx would argue that the contradiction here is unsustainable. Capitalism depends on labor to function—but when AI models replace the labor force entirely, even capitalism itself risks self-destruction. If AI-generated art continues on its current trajectory, the system will not just exploit artists—it will erase them.

And in doing so, it may ultimately devalue art to the point where no one—neither artists nor audiences—finds meaning in it anymore.

c. the illusion of “inspiration”: a Kantian perspective

In the age of AI-generated content, a fundamental ethical question arises: Should an artist’s work be used without their permission to train AI models?

The rapid development of AI in creative fields has led to a practice where vast amounts of human-made art, writing, and music are scraped from the internet and fed into machine learning systems—often without the original creators' consent. This raises serious concerns about intellectual property, artistic integrity, and the value of human creativity in an era where machines can replicate styles without attribution.

AI companies often defend this practice by claiming that their models “learn” in the same way humans do—by absorbing information, recognizing patterns, and synthesizing new ideas. But from a Kantian ethical perspective, this analogy is deeply flawed. A human learns through selective experience, judgment, and lived understanding.

An AI model, on the other hand, ingests everything at scale—indiscriminately, without consent, and without an understanding of the ethical boundaries that govern human learning.

Immanuel Kant’s categorical imperative suggests that we should only act according to principles that could be universalized—meaning that if everyone followed the same rule, it should still be morally acceptable.

Applied to AI, this principle demands a crucial test: Would AI companies accept it if the results of their own work—their models, research, and proprietary data—were scraped and fed into another AI system without their permission?

If they would find such a practice unacceptable, then by Kant’s own philosophy, their current approach to using artists’ work without consent fails the test of moral reasoning.

Ironically, we already know the answer. When DeepSeek, a China-based AI company, was accused of using OpenAI’s models to train its own chatbot, OpenAI reacted with outrage. The process, known as "distillation," involves taking outputs from a more advanced AI and using them to improve another system.

While common in the industry, OpenAI condemned this act as a violation of its terms of service—an unacceptable misuse of its intellectual property.

Ethical consistency demands that we hold AI companies to the same standard they would expect for themselves. If they wouldn’t want their intellectual labor to be harvested and repurposed without acknowledgment, then they cannot justify doing the same to artists.

d. exploitation at scale: a utilitarian dilemma

AI companies say their models are a win for everyone—cheaper, faster, and more accessible art. From a business standpoint, that sounds great: endless creative content at minimal cost. But there’s a bigger ethical question—does this benefit outweigh the harm?

Utilitarianism, as laid out by Jeremy Bentham and John Stuart Mill, is simple:

maximize happiness and minimize suffering.

An action is only ethical if it creates the greatest good for the greatest number.

AI companies claim they’re democratizing art and expanding creativity. But look closer, and the harm starts to pile up—not just for artists, but for everyone.

Jobs disappear across industries. AI isn’t just replacing painters and illustrators; it’s creeping into writing, music, design, and even programming. When businesses can get AI-generated work in seconds for a fraction of the cost, human workers across creative and knowledge-based fields lose opportunities.

It’s already happening. Shopify's CEO, Tobi Lütke, says that their staffers need to prove jobs can’t be done by AI before asking to hire more people.6

Over time, it’s not just artists struggling—anyone trying to stand out online, from small business owners to independent writers, faces an uphill battle.

Creativity loses its human touch. The best art, music, and writing come from human experience—the struggles, emotions, and perspectives that make something meaningful. AI doesn’t feel joy, pain, or nostalgia; it only predicts patterns.

As AI-generated content dominates, cultural production could become increasingly shallow, optimized for engagement rather than depth. Imagine a world where music, movies, and even books start to feel eerily formulaic—because they are.

The next generation might never stand a chance. If AI continues to replace human creativity, what happens to young people aspiring to be artists, writers, or musicians?

Apprenticeships, entry-level jobs, and freelance gigs start to vanish, making it nearly impossible for newcomers to break in.

Without real-world experience, mentorship, or a way to make a living, entire industries could shrink, leaving fewer paths for future generations to explore their creative potential.

AI’s rapid rise isn’t just an artist’s problem—it’s everyone’s problem. When human creativity becomes undervalued, entire industries shift, economies change, and our cultural landscape risks becoming a sea of soulless, machine-made content.

From a utilitarian point of view, the ethical math doesn’t add up. If people bear the suffering while corporations and a handful of consumers reap the benefits, then the scale tips toward injustice.

If AI art mostly enriches big tech while stripping away careers and creative expression, utilitarianism would call it unethical. Efficiency alone isn’t a moral defense.

The real test for AI companies is this: Do their innovations actually make society better for everyone? If not, then the cost is too high.

5. AI as Ouroboros

The Ouroboros is a paradox—something that sustains itself by consuming itself. A serpent or dragon biting its own tail, endlessly devouring and renewing, trapped in an infinite loop of self-consumption.

It forces us to ask: can something truly grow if it only has itself to consume? Can renewal come from self-destruction, or is it an illusion? The Ouroboros is a symbol of endless hunger—an entity that can never escape itself.

If AI-generated art reaches a point where it displaces enough human artists, it risks becoming an Ouroboros—devouring the very ecosystem that sustains it. AI learns by training on human-made art; if professional artists vanish, the pool of fresh, high-quality work shrinks.

Without new creativity to feed on, AI risks stagnation, endlessly recycling its own derivatives in a loop of diminishing originality. In trying to replace artists, it may ultimately starve itself.

For all the hype around AI-generated art, there’s one inescapable truth: AI is only as good as the human-made art it learns from. Without artists, AI doesn’t have anything to work with.

Right now, generative AI models thrive because they’ve been trained on a goldmine of human creativity—billions of paintings, illustrations, and digital artworks scraped from across the internet.

But what happens when AI-generated content starts dominating the pool of available images? What happens when future AI models are trained not on human masterpieces, but on AI copies of AI copies?

The result? A slow but inevitable decline in quality.

And AI companies know this.

They know they need a steady supply of fresh, high-quality human-made art to keep improving their models. But if AI-generated art floods the internet, replacing paid human work, where does that fresh supply come from?

This creates an existential crisis for the industry. If AI-generated art pushes human artists out of business, AI itself eventually runs out of high-quality data to learn from. At that point, companies have two choices:

1. Collapse – As models degrade and investors see declining returns, AI startups fail.

2. Double Down on Exploitation – To maintain quality, AI companies might resort to even more aggressive scraping, legally or illegally, or force artists into producing unpaid labor by making it impossible to opt-out.

Either way, the current model is unsustainable. AI thrives on human creativity, but if it consumes too much without giving back, it might just starve itself out of existence.

AI and human creativity can totally coexist—if AI doesn’t end up eating the thing keeping it alive.

AI’s biggest selling point right now is also the thing that could kill it: fewer artists, fewer original ideas, fewer creative jobs, and a slow but steady drain on the well of human expression.

If AI feeds too aggressively on human creativity without sustaining it, it risks collapsing into an echo chamber of its own making.

conclusion

When AI makes art cheaper, faster, and “good enough,” how many studios will still hire human artists? How many publishers will gamble on a new writer instead of feeding bestsellers into an algorithm? How many kids will even bother learning to draw when an app can do it in seconds?

If AI art wins, it won’t be because it’s better. It’ll be because it’s convenient and free. Convenience has a way of erasing things.

Like phone calls. Like handwritten letters. Like the feeling of getting lost in a hand-drawn world, where every detail was placed there by someone who cared.

And let me remind you that AI companies are not doing this for the public good. They’re doing this the same way drug dealers are handing out free samples to teenagers.

Drug dealers, who are protected by great economic resources and legal loopholes.

This is the kind of future we are heading to, and no Black Mirror episodes have been pessimistic enough.

footnote

It is scary, that AI is slowly but steadily mastering every skill known to humankind—from composing symphonies and diagnosing cancers to drawing paintings and writing poems—you name one thing; someone is probably training AI to do that if they have not done it already.

So, why bother learning anything at all?

After all, AI will do everything faster, better, and without needing to sleep, eat, or take bathroom breaks. So, the only reasonable solution is to stop learning.

You don’t think that’s true, do you? No, please don’t stop learning; more accurately, to adapt to the future ahead of us, we need to unlearn the wrong things and focus on skills that AI can’t replace.

The secret to thriving in an AI-dominated world isn’t learning more facts—it’s learning the quirky, unpredictable, and profoundly human skills that machines will never perfect. Here’s a tongue-in-cheek guide to what you really need to “unlearn” and, ironically, learn in the age of generative AI.

1. how to know what to not learn anymore

Artificial intelligence (AI) might sound like something out of a science fiction movie, but it’s simply a technology designed to mimic human intelligence.

At its core, AI operates by analyzing vast amounts of data, recognizing patterns, and making predictions or decisions based on that information.

Imagine teaching a child how to identify animals by showing them pictures of cats and dogs. You label each picture, and over time, the child learns to recognize the differences between the two.

AI works similarly, using algorithms—essentially, a set of instructions or rules—to process data and learn from it. For instance, if you fed an AI thousands of cat and dog pictures, it would learn to identify each based on the features you’ve provided.

Based on those mechanics, one of the key strengths of AI lies in its ability to handle repetitive tasks.

Think about how tedious it can be to enter data into spreadsheets or analyze long lists of numbers. AI can perform these tasks much faster and with greater accuracy, freeing up humans to focus on more creative or strategic work.

However, while AI is great at handling structured tasks and analyzing data, it has limitations when it comes to understanding and creativity. Take this as an example: AI can generate art or write poetry, but it doesn't truly understand emotions or the cultural context behind its creations. The current system mechanics does not allow it to do so.

Areas that require empathy, intuition, and complex decision-making—like therapy, leadership, and negotiations—are challenging for AI to replicate. In these roles, the human touch is invaluable, as it involves understanding feelings and social dynamics that a machine simply can't grasp.

So, how can we use this knowledge about AI to guide our learning? First, it’s important to focus on developing skills that AI cannot easily replicate.

Critical thinking, creativity, and adaptability are crucial areas to invest in. For example, if you're interested in a career in design or writing, honing your creative skills will keep you competitive as these fields rely heavily on human intuition and originality. Similarly, roles that involve working closely with people, such as counseling or teaching, require empathy and understanding, which AI cannot provide.

Additionally, understanding data and technology can enhance your career prospects. While you don’t need to be a coding expert, becoming familiar with how data is analyzed and interpreted can be incredibly valuable. Skills in data analysis can help you work alongside AI tools more effectively, allowing you to make informed decisions based on the insights AI provides.

Also, remember that the ability to learn and adapt is one of the most important skills you can cultivate. The job market is constantly changing, and being open to continuous learning will help you stay relevant.

Finally, consider exploring cross-disciplinary knowledge, where you combine skills from different fields. For example, understanding both technology and business can make you uniquely valuable in today’s workforce.

In summary, while AI is transforming many aspects of work and life, it also highlights the importance of uniquely human skills. By focusing on creativity, emotional intelligence, and adaptability, you can build a skill set that not only complements AI but also ensures you remain valuable in an ever-evolving job landscape.

2. unlearn memorization

Memorization as an end is overrated.

Memorization is often hailed as the peak of academic achievement—because clearly, the ability to regurgitate random facts is exactly what it takes to solve complex problems in life. But as Daniel Willingham, a cognitive scientist, points out in Why Don’t Students Like School?, brute memorization without meaning is a fast track to forgetting.

Information retained purely through repetition—no matter how diligently crammed—fades rapidly unless it’s encoded within a broader conceptual framework. Working memory, being notoriously limited, can only juggle a few items at a time, so forcing students to store disconnected bits of information is like asking someone to juggle with marbles on a windy day—an exercise in futility.

Instead, Willingham emphasizes that memory works best when it’s treated as a process of creation, not storage. Bloom agrees.

Techniques like elaborative encoding, which links new information with existing knowledge through storytelling or personal relevance, are far more effective than rote learning. Spaced repetition isn’t about mindless review but about creatively re-engaging with information over time to strengthen neural pathways.

When students interact with content actively—say, by rephrasing ideas in their own words or applying them in new contexts—they’re building the kind of long-term understanding that allows knowledge to evolve and adapt.

AI now holds the monopoly on trivia, so you can stop cramming random facts into your brain like it's a pub quiz that determines your future. Want to know how many moons orbit Jupiter? (79, if you were wondering.) There’s no need to burden yourself with that kind of data overload when ChatGPT is literally always in your pocket.

critical thinking

Instead, invest in the one skill that AI is still utterly hopeless at: critical thinking.

While AI can “write” pages in the blink of an eye, it’s not equipped to handle things like understanding context, recognizing bias, or forming nuanced opinions. Sure, it can tell you what’s trending on Twitter or predict next week’s weather with scary precision, but it struggles with why these things matter or what they mean for humanity. Unless someone has already said something similar somewhere sometime before. That’s where you come in.

Daniel Kahneman, in his landmark book Thinking, Fast and Slow, explains how human intuition and analytical thinking complement each other in ways that machines can’t replicate. He highlights how critical thinking involves understanding cognitive biases and using that awareness to improve decision-making.

Meanwhile, AI sees the world through the lenses of scraped cold, hard data—without any understanding of why the data is relevant. Take a study from Stanford, which suggests that while machines excel in specific pattern recognition tasks (like identifying objects in images), humans surpass AI when context and meaning are involved. Although that might not be the case in the future.

After all, AI doesn’t care if the pattern involves puppies or piranhas—it just sees pixel clusters. But recognizing whether something is dangerous or cute? That’s your job.

philosophy and morality: what’s right and what’s wrong

Now, if critical thinking is a muscle, philosophy is the ultimate workout regimen. When AI is handling all the logical calculations, you need to dig deeper into the why behind everything. Philosophy won’t just help you sound smart at your family gathering; it’ll train you to think deeply about ethics, reality, and the human condition—all those uncomfortable topics that AI understands shit.

As AI systems become more prevalent, questions about ethics, justice, and AI’s role in society become more urgent. This is where philosophical frameworks, such as utilitarianism or Kantian ethics, come into play. Will AI decide that sacrificing a few people to save many is the best outcome? Who gets to program these choices? We need people with deep ethical reasoning to grapple with these questions—machines can’t decide what’s morally right.

Here is a video I personally find very interesting and entertaining to watch on the topic of machine’s morality, by :

Philosopher Nick Bostrom, in his work on Superintelligence, explores the dangerous ethical quandaries of AI reaching human-level cognition and beyond. He warns that without robust philosophical debate, we might be creating tools that are smarter than us but entirely devoid of moral understanding.

AS AI doesn’t possess morality, consciousness, or inherent intent—it simply follows the instructions it’s given, optimizing for goals without understanding the broader implications.

The real danger lies not in AI "going rogue" as depicted in movies, but in it following human commands too well. If misaligned with human values or context, an AI can cause catastrophic harm while doing exactly what it was programmed to do.

For example, if told to solve climate change at all costs, it might take drastic measures, like shutting down industries essential for survival. Unlike in films, where AI gains self-awareness and defies orders out of malice or rebellion, real-world AI presents a more chilling threat: blind obedience.

Its potential for destruction arises not from intentional rebellion but from our failure to foresee unintended consequences within rigid instructions. That’s what humans do. That’s why we’re trying to solve climate change without nuking ourselves.

Critical thinking and ethical reasoning are skills that the World Economic Forum has ranked among the most crucial for the future. And unless AI suddenly develops a conscience (fingers crossed it doesn’t), these are areas where human input will remain indispensable.

deep questioning skills

Let’s not forget the Socratic Method—the granddaddy of asking better questions, not just getting better answers. When you think about it, all the best conversations, discoveries, and breakthroughs started with a question. Socrates didn’t memorize facts about the world; he interrogated the very nature of truth, justice, and knowledge itself. And no AI-generated chatbot will ever challenge you with questions that dismantle your entire worldview. (Yet.)

When information overloads us in this age, learning how to ask the right questions is more important than ever. Whether it's challenging the assumptions behind a news headline or interrogating the conclusions from an AI analysis, your ability to poke holes in surface-level conclusions is invaluable. A 2020 study from MIT highlights the importance of questioning in AI research, noting that critical thinking is what drives scientific progress forward—not just accepting outputs as infallible.

If all of this sounds like hard work, don’t worry. The Socratic Method also teaches humility. The more questions you ask, the more you realize how little you know—a much healthier mindset in the age of AI than pretending you’re a walking encyclopedia.

You’re not, but that’s okay because AI is already doing that for you.

So, in summary, forget memorizing facts like some glorified search engine. AI has that covered. Instead, focus on developing critical thinking, philosophical reasoning, and deep questioning skills. These are the human superpowers that AI can’t touch (yet). It’s like being a superhero in a world of robots—except your cape is made of curiosity, and your superpower is thinking harder than the machines.

3. unlearn doing

Basic coding, like learning to drive in a world of self-driving cars, remains useful—but it’s no longer the ultimate skill.

Yes, AI can handle repetitive tasks, automate processes, and even write clean code faster than the average human. But just like autopilot cars, AI isn’t perfect. When systems run into unexpected issues—whether it’s a glitch, a bug, or a complex, edge-case scenario—you’ll need coding skills to step in and troubleshoot.

However, the true value today lies not just in knowing how to write code but in understanding how the entire system works.

It’s the ability to think strategically about the frameworks that AI operates within, the architecture of software, and how different pieces fit together to create meaningful solutions. Knowing when to intervene is only half the battle—the other half is knowing what to build and why.

AI might excel at following instructions, but it lacks the human capacity for abstract thought, creativity, and foresight. That’s where humans will thrive: in defining high-level objectives, designing frameworks, and asking the right questions.

Coding will still give you a seat at the table, but learning to think systemically—about the design, ethics, and implications of technology—will put you in charge of setting the direction. In the future, the winners won’t just be the best coders; they’ll be the ones who understand both the code and the larger vision behind it.

design thinking: recipes for creative mayhem

The trajectory of AI is so terrifying that you could spend years mastering JavaScript or Python, only to have AI write cleaner code in a matter of seconds. However, AI might excel at solving specific tasks, but it lacks the ability to conceptualize the human experience or dream up new, innovative ideas. That’s where you come in.

Consider the words of Tim Brown, CEO of IDEO and one of the leading voices in design thinking. He argues that the future of innovation lies in creating solutions that are user-centered and grounded in empathy. A machine can churn out code, but can it understand why users hate that app interface? Not quite. You, on the other hand, can empathize with the human frustrations AI is oblivious to.

A 2019 report from the Harvard Business Review underscores this, explaining that AI’s rigid logic struggles with the complexities of human emotions, preferences, and motivations. The key to staying relevant isn’t in fighting AI for coding supremacy—it’s about being the one to design the overall strategy that AI helps execute.

If you’re going to survive in an AI-driven world, you need to learn design thinking—a methodology that solves problems by focusing on what humans actually need. It’s not about the most efficient or complex algorithm, but about creating products, services, and experiences that resonate with real people.

Take Airbnb as an example. Before they became a global phenomenon, the company was struggling. What turned things around? Not an algorithm, but a shift in focus to design thinking—an emphasis on understanding their users' emotional journeys and creating a seamless experience for both hosts and guests.

The founders, Brian Chesky and Joe Gebbia, realized that the reason they were struggling was not a lack of demand but trust issues and inconsistent user experiences.
-> They redesigned the host and guest profiles to feel more personal, fostering trust between strangers.
-> The review system was also revamped to emphasize transparency, encouraging both hosts and guests to leave honest feedback, which further enhanced trust.

AI can optimize booking systems, but only humans could have reimagined the concept of home-sharing from a user's perspective.

Research from Stanford University shows that design thinking isn’t just about aesthetics—it’s about solving human problems through empathy, creativity, and iteration. AI might help you streamline a design, but it can’t originate the creative spark needed to get there.

That’s your job.

creative stragy: be the visionary

The same goes for creative strategy. Sure, AI can optimize existing ideas, tweak them, and make incremental improvements. But those bold, audacious ideas that change the world? That’s still humanity’s forte. Think of it like this: AI is your trusty sous-chef, but you’re the one coming up with the recipe.

According to Albert Einstein (yes, I’m invoking Einstein here, even though he didn’t have AI in mind), “Imagination is more important than knowledge.” AI has the knowledge; you provide the imagination.

The World Economic Forum’s Future of Jobs Report echoes this sentiment, highlighting creativity as one of the top skills for the future workforce—something that AI simply cannot replicate. Machines can handle predictive models, but they can’t dream up the next big leap in innovation.

A study from the McKinsey Global Institute predicts that while AI will take over routine tasks, jobs requiring original thinking and strategy will remain solidly in human hands. So, instead of learning how to code another app, focus on crafting the strategy behind the app: Why should it exist? Who needs it? What gap in the market does it fill?

asthetics: beauty in the eyes of the beholder

Then there’s aesthetics. AI might know how to follow design principles, but it lacks a true understanding of taste and style.

Machines are experts in rules, but they don’t “get” beauty. Sure, they can mimic design trends, but creating those trends in the first place? That’s a uniquely human gift.

Even tech moguls like Steve Jobs understood the importance of aesthetics in design. His vision for Apple wasn’t just about functional technology; it was about creating products that were beautiful and intuitive. AI can help optimize color palettes and suggest layouts, but only you can make the leap to connect form with function in a way that resonates emotionally with people.

Researchers at MIT’s Media Lab have explored the intersection of AI and art, concluding that while AI can generate artistic works, it lacks a core aesthetic sensibility—that human touch that imbues art, design, and creative work with meaning. You’re the one who decides what’s beautiful, meaningful, and effective. Machines can’t teach you how to feel beauty, but they can follow your lead.

So, why bother learning to do it when AI is doing it better than you ever could? Focus instead on design thinking, creative strategy, and aesthetics—the areas where humans shine and machines falter. Be the visionary, not the executor. You’re not competing with AI to see who can write better code; you’re working with it to bring big-picture ideas to life. As long as you keep learning how to dream, innovate, and empathize, AI will remain a tool, not a replacement.

4. unlearn linear learning

Everything’s evolving faster than a toddler on a sugar high, today’s cutting-edge technology is tomorrow’s old news (remember when flip phones were the height of sophistication?).

Adaptability has never been more valuable.

If you’re reading this, you’ve probably never needed to hunt a mammoth, start a fire from scratch, or carve a hand axe. Those skills belong to a bygone era, buried by time.

Thanks to technological progress and societal shifts, we’ll never have to master them again. But in their place, we’re now expected to learn coding, writing, entrepreneurship—and a thousand other modern skills. The list keeps growing, and the pace of change is accelerating faster than ever. What we must learn evolves—and it’s evolving at breakneck speed.

So, toss aside that outdated notion of mastering a single skill or career path—what you really need to learn is how to learn.

Sure, AI evolves, but it does so through structured programming. It’s like that kid who can recite the periodic table but has no idea how to hold a conversation. Meanwhile, humans have the unique ability to continuously evolve in unpredictable and creative ways.

This is why it’s crucial to embrace a mindset of constant learning, unlearning, and relearning. The aim? To adapt to new environments faster than the systems around you can say “disruption.”

metacognition: how it saves you time and sanity

Metacognition is the fancy term for thinking about your own thinking. In other words, study how you learn best so you can constantly upgrade your skill set.

Research from psychologists like John Flavell emphasizes that metacognitive awareness enables you to strategize your learning processes effectively.

Metacognitive awareness is your brain’s way of saying, “Hey, this method isn’t working—maybe let’s try something else before you end up re-reading the same paragraph for the 18th time.” Researchers have found that learners with higher metacognitive skills—those who actively reflect on what they know and how they know it—perform better academically and tend to retain information longer.

Instead of spinning your wheels like a hamster on an existential treadmill, metacognitive strategies help you notice when something isn’t clicking. For example, you might realize that re-reading notes isn’t the most efficient way to study (hint: it’s not) and that quizzing yourself or teaching others works better.

Psychologists refer to this as regulation of cognition—the process of planning, monitoring, and evaluating your learning strategies. Research from Zimmerman (2002) shows that students who engage in these practices are not only more successful but also more motivated. Why? Because nothing kills motivation like feeling stuck in a loop of ineffective study methods. Metacognition helps you nip that in the bud.

growth mindset

Growth mindset has been talked around too much it’s cliché. But it is necessary to talk about, regardless.

Dweck’s research distinguishes between two mindsets: fixed and growth.

A person with a fixed mindset believes abilities are set in stone—either you’re good at math or you’re not, full stop. On the other hand, someone with a growth mindset understands that even if you bomb that algebra test, it doesn’t mean you’re “bad at math” forever. It just means you haven’t nailed it yet. This seemingly small shift in thinking has a massive impact on how we approach challenges and respond to failure.

Studies show that people with a growth mindset are more likely to embrace obstacles, learn from criticism, and persist even when things get tough. In one of Dweck’s famous experiments, students who were taught to see failure as part of the learning process showed greater academic improvement than those who were not. It’s like turning every setback into a stepping stone. Or at least that’s the goal—realistically, some days setbacks just feel like faceplants.

Why does the growth mindset work so well? It comes down to how we interpret failure. People with a growth mindset view mistakes as valuable data points rather than personal shortcomings.

Neuroscience backs this up: studies by Moser et al. (2011) found that brain activity spikes when individuals with a growth mindset encounter errors, meaning their brains are more engaged in the learning process.

This mindset also nurtures grit, which psychologist Angela Duckworth defines as the ability to persevere toward long-term goals despite difficulties.

Duckworth’s research shows that grit, combined with a growth mindset, predicts success more accurately than talent or IQ. So, if you’ve ever felt like an imposter for struggling while others seem naturally gifted, take heart: persistence beats innate ability in the long run.

Cultivating resilience and the desire to keep improving are essential in an ever-changing landscape. Dweck’s research shows that individuals with a growth mindset are more likely to embrace challenges and persist in the face of setbacks. So, the next time you find yourself failing spectacularly at something new (hello, sourdough bread-making), remind yourself that it’s not a disaster; it’s just another opportunity for growth. Plus, isn’t it comforting to know that failing is just a part of the learning process? It’s like having a safety net, but instead of catching you, it just lets you bounce back up.

jack of all trades, master of a lot: cross-disciplinary skills

The world’s hyperconnected, and sticking to one field of expertise is like only using a hammer when you’ve got a whole toolbox available. Sure, you might become the world’s foremost expert on 13th-century Norwegian knitting techniques, but wouldn’t it be more useful to also know a bit about psychology, technology, or even why goldfish probably won’t learn to fetch?

Research published in the Journal of Educational Psychology shows that interdisciplinary education enhances both critical thinking and problem-solving abilities, giving you the mental flexibility to tackle complex issues from multiple perspectives. In short, being a jack-of-all-trades isn’t such a bad thing after all—it’s how the best innovations happen.

So, while your friend is busy diving deep into the latest niche topic (how to train a goldfish to play fetch, perhaps), you can be the one connecting the dots between psychology, technology, and whatever else sparks your interest. Think of it as creating your own unique cocktail of knowledge—one that’s both refreshing and slightly unpredictable.

At its core, interdisciplinary learning is about breaking down the silos between fields.

A 2017 study by Repko et al. found that combining insights from multiple disciplines fosters cognitive flexibility—the ability to switch between different ways of thinking based on the problem at hand. This is critical because many real-world challenges don’t come neatly packaged in one discipline.

Climate change? That’s a chemistry, economics, and sociology problem. Artificial intelligence ethics? You’ll need computer science, philosophy, and maybe a bit of law to make sense of that.

The magic happens in the overlap.

Research by Newell (2007) shows that when students engage in interdisciplinary learning, they become better at identifying patterns and drawing connections between seemingly unrelated ideas. It’s like constructing your own mental web—each strand is knowledge from a different field, and the more connections you weave, the stronger your understanding becomes.

Specialization has its place, but here’s the catch: industries and fields evolve quickly. What’s in demand today might be irrelevant tomorrow. A report by the World Economic Forum (WEF) predicts that future job markets will increasingly require a blend of skills—technical know-how plus creativity, emotional intelligence, and problem-solving abilities. Sticking too narrowly to one area can leave you vulnerable in a world that prizes adaptability.

Think of it like this: if your skillset is a one-trick pony, that pony better be very good at what it does. But if it can also do some juggling, maybe tap-dance a little and charm an audience?

Now that’s the kind of pony people want around. In fact, Nobel laureates often credit their most creative ideas to their hobbies and side interests—like Richard Feynman, whose significant contributions to quantum mechanics, quantum electrodynamics (QED), and particle physics is partly attributed to his passion of doodling and playing the bongos.

After all, the future belongs to those who can pivot, adapt, and thrive in the face of change. And remember, while the machines may be programmed to evolve, you have the creative power to reinvent yourself time and time again—without needing a software update.

As we navigate the rapidly evolving landscape shaped by artificial intelligence, it’s crucial to reassess what skills and knowledge are truly worth our time and effort in this new era.

Consider this a rallying cry to pivot your learning journey: rather than investing your energy in areas that AI can easily automate, channel your efforts into cultivating your unique human qualities.

In summary, let this be an empowering call to action: embrace change, challenge yourself to explore new realms of creativity, and invest in the development of skills that will set you apart in a world increasingly dominated by technology.

By doing so, you will not only future-proof your career but also contribute to a vibrant, human-centered workforce that celebrates the essence of what it means to be human. In the age of AI, it is our creativity, emotional depth, and ability to connect with one another that will truly define our impact and legacy.

A few other things I don't mention:
- Sports: Apart from being exclusively designed to test human's physiology, the reason sports is popular because it is deeply tied to emotions, which machine performance cannot replicate
- Comedy: AI is phenomenal at doing things that are expected. But comedy depends on shared experience, timing, and understanding subtle differences in meaning, which confuses AI.

This post is dedicated to the community of HCH.