The End Of Serendipity: What Happens When AI Predicts Every Choice?

The day starts like any other, not because of routine, but because everything has already been planned for you. The songs on your morning run playlist know just the right speed to keep you going. Your news feed shows you stories that match your beliefs, your shopping app fills up the things you didn’t know you needed, and your streaming service suggests the next show before you’ve even completed the one you’re watching. 

Every click, scroll, and look is silently read, recorded, and improved. The end effect is an environment of perfect forecasts, which makes life feel smooth but weirdly dead. What used to be a time of discovery is now a moment of confirmation. The algorithm knows you better than you know yourself, and it gives you that information back in a way that makes you feel better. 

But, that accuracy shows a big change in culture: the creeping death of the unexpected. The element of surprise, which used to be a big part of how people learn, grow, and interact, is now being taken away. This makes me think of a scary question: what happens when algorithms get too proficient at understanding us?

We live in the age of algorithmic prediction, when data decides not just what we see but also what we want. Behavioral models that predict our next choice with amazing precision impact everything about our decisions, from what we watch to what we eat. Netflix doesn’t only suggest movies; it also knows how you’re feeling. Spotify doesn’t just play tunes; it also keeps track of how you feel. Amazon doesn’t wait for you to need something; it knows what you want before you do. The algorithm has silently become the architect of our attention in its effort to make life easier.

This predictive efficiency is tempting, but it has an unseen cost. The randomness that used to lead to new ideas, unexpected friendships, or unplanned trips is being replaced by an endless cycle of familiarity. As the machine learns more, our options grow fewer. In a society where everything seems to be planned out, the excitement of finding a new artist, an unplanned book, or a lucky idea slips away. It turns out that convenience is a stealthy thief of curiosity.

This change is so sneaky because it looks like it gives people control when it really doesn’t. People tell us we’re getting “what we want,” but really, they’re teaching us what to desire. As computers learn not only our habits but also our weaknesses, the boundary between personalization and manipulation becomes less clear. This makes unpredictability a problem to be solved instead of a mystery to be cherished.

But unpredictability is more than just a nice thing about the past; it’s something we need for our mental health. Serendipity inspires new ideas, creativity, and empathy. We stop growing when everything we see reflects what we like. The algorithm’s ultimate success—total predictability—might also be humanity’s quietest defeat.

So, we come to a turning point in culture. Will we still have room for surprise in a world that is designed to be certain? Will we let randomness back into our lives, not as a weakness in the system, but as its most human trait?

Is it possible for humanity to flourish without the unforeseen?

Also Read: AiThority Interview with Jonathan Kershaw, Director of Product Management, Vonage

When Comfort Beats Curiosity

Every time you scroll, click, or stop, you add to a conversation between what people want and what algorithms can see coming. AI systems like Netflix’s recommendation engine, Spotify’s auto-curation, and TikTok’s For You page are all designed to guess what you’ll enjoy next, sometimes even before you know it yourself. And while this makes things feel safe and stable, it also silently stops people from becoming curious.

The brain loves new things. Studies in neuroscience have shown that surprise releases dopamine, which is the same neurotransmitter that makes people want to learn and achieve things. But AI takes away the excitement of discovering new things by getting rid of ambiguity. The end effect is a life that feels less vivid and more fluid, one that is optimized for pleasure rather than growth.

Curiosity, which used to be a natural instinct, is now something we had to strive for. We stop looking when everything we see fits with what we’ve enjoyed in the past. The playlist that used to introduce us to new sounds now plays the same melody over and over again. The news feed that used to give us new perspectives now shows us our biases. The irony of the paradox is painful: mechanisms that are supposed to help us end up helping us just as much.

• From Choice Fatigue to Choice Invisibility

Ten years ago, internet culture commonly talked about “choice fatigue,” which is the mental stress that comes from having too many alternatives. AI was supposed to fix that by smartly filtering information, which would save us time and mental energy. But it made another problem worse when it solved one.

We don’t see too many choices anymore; we just cease noticing the ones that count. This is choice invisibility: a world where the choices that may have changed us, challenged us, or made us happy never even make it to the screen. The algorithms choose which songs, articles, and people are worth your time and attention. You think you have power, but the freedom has changed in a subtle way, from your gut to an unseen, data-driven curation layer.

The paradox gets stronger because this lack of visibility doesn’t feel like a loss. You feel happy when the system gives you things you already like, even if you don’t know what’s missing. It’s not censorship; it’s curating so good that it seems like reality. The best thing about AI is not that it can guess what we want, but that it can make us believe that its guesses are what we want.

• The Illusion of Discovery

Spotify and Netflix, for example, characterize their suggestions as “discovery” by saying things like “new for you,” “just for you,” and “because you watched…” But there has always been some danger or chance involved in discovery. Real discovery transforms who we are, while algorithmic discovery validates who we are.

TikTok’s AI-powered The For You feed may seem like an infinite journey of discovery, but its popularity comes from its sameness: it gives you more of the same type of information, just with various tones. What seems like spontaneity is really the mechanical unfolding of what is known.

• The Human Cost of Perfect Prediction

This level of prediction accuracy may seem like progress, but it changes how we deal with uncertainty in small ways. Instead of being something fun, surprise becomes something to get rid of. The paradox of AI is that it tries to make our digital lives better, but it takes away the flaws that give them significance.

So, the predictive dilemma isn’t only about technology; it’s also about life. We’ve made machines that can predict what we’ll do next, but in doing so, we’ve put at danger the one thing that makes us human: our ability to be surprised.

Because nothing is found when everything is predicted.

The Power of the Unexpected

In a time when AI can guess what we want before we even realize it, surprise is hard to come by. But serendipity, those happy surprises that happen by chance, is not the same as meaningless randomness. It’s an important mental requirement that drives creativity, learning, and relationships with other people. AI curates and optimizes everything, which might mean that the world loses the friction that leads to growth. Randomness isn’t chaos; it’s what makes new ideas possible.

Our minds are wired to look for patterns, but they are also wired to want new things. Cognitive research demonstrates that unforeseen encounters induce dopamine release, the neurotransmitter associated with motivation and pleasure. This means that meeting new people not only feels wonderful, but it also helps you remember things and learn. In this way, serendipity isn’t a break in our thinking; it’s a necessary part of our mental diet.

• The Brain’s Reward System for Finding New Things

Neuroscientists have been looking into how the brain deals with things that are unpredictable for a long time. When someone hears a new sound, sees a new concept, or looks at a new piece of art, the brain’s reward regions light up. 

This reaction evolved to encourage exploration. Without unpredictability, neuronal engagement drops, which makes cognitive growth stop. AI’s ability to foresee things often takes away these little moments of surprise and replaces them with constant comfort. But when comfort is always there, it can make you numb.

This has big effects on digital ecosystems. Recommendation algorithms fine-tuned by AI aim to deliver what we “love,” but by doing so, they narrow our world. Instead of presenting us something new, our playlists, news feeds, and shopping carts become mirrors that show us what we already know. This is the paradox of personalization: AI can kill curiosity by knowing what you want before you do.

• The Function of Randomness in Creativity

For a long time, artists and thinkers have known that randomness may create new things. Brian Eno came up with the word “scenius,” which means “the collective form of genius,” to explain how creativity grows when people exchange ideas, make mistakes, and work together. 

Nassim Nicholas Taleb’s idea of antifragility says that systems get stronger when they are thrown into chaos and surprise. In this context, randomness is an essential structural component of evolution rather than a deficiency to be eliminated.

In enterprises, too, random meetings can lead to big changes. Conversations that happen by chance at the coffee machine can lead to new ideas that planned meetings never would. As more businesses use AI to schedule, analyze performance, and manage workflows, they risk ruining the very spontaneity that drives creativity. A culture that depends too much on AI forecasts might make things run more smoothly, but it might also kill creativity.

• The Empathy Multiplier

Serendipity is also very important for empathy. Meeting people, seeing things, or learning about cultures that are different from our own helps us comprehend what it means to be human. 

AI-curated digital environments, on the other hand, tend to make things more alike by giving consumers stuff that validates their perspective instead than challenging it. Empathy fades without chance meetings, and civilizations go toward polarization.

Think about the algorithms on social media: they are designed to get people to interact with each other more, but they often make popular viewpoints even more popular and limit cognitive diversity. But empathy rises when we come across something unexpected, like meeting someone who is different. Serendipity encourages us to transcend our habitual routines, reminding us that not all worth is found in foresight.

• Serendipity as a Survival Mechanism

A balance between order and surprise has always been important for human growth. If there isn’t enough structure, things go out of hand; if there is too much prediction, we can’t change. The future of AI should not be about getting rid of uncertainty, but about managing it so that unpredictability can continue to play its part in evolution in art, thinking, and empathy.

So, the issue is not to fight AI, but to build it with humility, making sure that algorithms provide room for the unexpected. What makes us human isn’t being able to predict things, but how we react to things that surprise us. Serendipity not only makes life fascinating, but it also keeps the mind and the species alive.

The Promise and the Trap of Personalization

In the digital age, personalization has become a kind of invisible comfort blanket — a promise that technology will anticipate our needs, smooth our experiences, and save us from irrelevant noise. 

Yet, beneath that promise lies a quiet danger: the algorithmic cage. Predictive systems, powered by artificial intelligence (AI), have grown so adept at curating our choices that they’ve begun to confine them. The same mechanisms that once made discovery effortless are now narrowing it.

From music recommendations to news feeds and product suggestions, every tap, scroll, and pause feeds an immense feedback system designed to know us better than we know ourselves. But the more accurately AI predicts, the less space it leaves for unpredictability — and the more our digital lives become repetitions of what we already like. This is not just convenience; it’s conditioning.

• The Age of the Filter Bubble

The concept of the “filter bubble,” introduced by Eli Pariser, captures this phenomenon perfectly: algorithms tailor information to our preferences, shielding us from content that might challenge or surprise us. AI systems create personalized realities — not maliciously, but mathematically — by reinforcing what data suggests we want to see. As a result, two people can live in entirely different informational worlds, even when consuming the same platforms.

These bubbles extend far beyond social media. Streaming services, retail platforms, and even education apps rely on predictive modeling to keep users engaged. But what they optimize for — attention and satisfaction — often undermines what makes human experience meaningful: contrast, complexity, and the unexpected.

The outcome is an ecosystem where our options shrink while our sense of control expands — an illusion of freedom that conceals a system of quiet determinism.

• Echo Chambers and Algorithmic Determinism

“Echo chambers” amplify this narrowing effect. Inside these digital enclosures, AI learns that reinforcement equals retention. Every click becomes an endorsement, every scroll a signal. Over time, algorithms trained to maximize engagement start feeding us more of what keeps us predictable — outrage, affirmation, or comfort. 

The cycle becomes self-sustaining: humans feed the algorithm, and the algorithm feeds the human back a reflection of their most consistent impulses. 

This is algorithmic determinism — the notion that our future choices are increasingly shaped, if not decided, by our past ones. The spontaneity of decision-making erodes when AI already knows what we’ll choose next. What was once discovery becomes automation. In this way, AI doesn’t just anticipate behavior; it scripts it.

• The Homogenization of Culture

The consequences ripple beyond individual psychology into culture itself. When algorithms optimize for popularity, sameness rises to the surface. Music begins to sound alike because recommendation systems push the most-streamed songs. Film and television follow formulaic patterns because data shows what “works.” Even memes — once symbols of collective creativity — now follow algorithm-friendly formats designed to maximize virality.

The result is the decline of subculture discovery. In the analog era, subcultures thrived in obscurity, discovered by those curious enough to seek them out. Today, AI homogenizes taste by prioritizing patterns over anomalies. The underground becomes invisible, drowned out by the algorithmic mainstream.

Equally troubling is the loss of shared cultural moments — those collective experiences that once united diverse audiences around common stories or events. With AI fragmenting attention into hyper-personalized niches, culture becomes less communal and more compartmentalized. The world no longer gathers around the same songs, shows, or news — it gathers around its own reflections.

• Feedback Loops and the Commodification of Identity

At the heart of the algorithmic cage lies the feedback loop — a cycle where data about our behavior becomes the raw material for shaping future behavior. This turns individuals into predictable commodities. Every action is tracked, analyzed, and repackaged into a forecast that determines what we see next.

The irony is profound: the more unique we believe our digital experiences to be, the more standardized they actually are. We become data profiles optimized for engagement rather than exploration. And in that optimization, a quiet uniformity takes root — not just in what we consume, but in how we think, feel, and imagine.

The Cost of Predictable Living

The algorithmic cage isn’t built with malice; it’s built with math. AI systems are not evil architects but obedient mirrors of human preference. Yet their success — measured in precision and prediction — may be the very thing that erodes the texture of life. When algorithms learn too much about us, they don’t just narrow our choices; they flatten our humanity.

The challenge ahead is not to reject AI but to reimagine it — to design systems that celebrate difference, preserve surprise, and keep the edges of experience alive. Because when prediction replaces possibility, culture loses its pulse — and the cage, no matter how comfortable, becomes a quiet end to the unexpected.

The Quest to Recode the Unexpected

In an era where algorithms predict nearly every move, the question arises: can AI learn to surprise us again? Engineers and designers are now experimenting with ways to reintroduce randomness into systems built for precision. 

From “serendipity sliders” in recommendation engines to stochastic models that inject uncertainty into outputs, the effort represents a paradox — programming unpredictability into predictability itself.

AI has long been trained to optimize: for accuracy, engagement, and personalization. But as digital life becomes smoother and more frictionless, many are beginning to sense what’s missing — the spark of surprise, the joy of stumbling onto something we didn’t know we needed. The challenge is profound: can something fundamentally logical and data-driven ever recreate the chaotic magic of discovery?

Engineering the Unpredictable

To simulate surprise, technologists have begun exploring stochastic design — systems that intentionally introduce randomness into AI outputs. This can take many forms: probabilistic algorithms that sometimes choose less-likely options, adversarial models that challenge an AI’s assumptions, or “controlled chaos” mechanisms that ensure not every decision is optimized.

For instance, some recommendation engines now feature “random discovery” modes or “serendipity sliders” that users can adjust to receive less predictable results. Spotify’s “Discover Weekly” playlist occasionally mixes in tracks from unrelated genres; Netflix occasionally surfaces obscure titles to test engagement. These small deviations from strict personalization are not errors but design choices — calculated injections of novelty meant to mimic the thrill of the unexpected.

Even large language models, the foundation of modern AI, use stochastic processes to generate text. Every word chosen by a model depends on probability — what might come next — allowing for variance between outputs. This randomness helps maintain creativity, but it is still bounded by data: the model can surprise only within the limits of what it already knows.

The Paradox of Artificial Serendipity

Simulating surprise exposes a deeper philosophical tension. True serendipity is not just randomness — it’s relevance emerging by accident. It’s the bookstore discovery, the unplanned conversation, the movie we never meant to watch that somehow changes us. AI can mimic the mechanics of surprise but struggles to recreate its meaning.

Artificial randomness, no matter how cleverly engineered, lacks the emotional resonance of real discovery because it isn’t anchored in intent or context. Human surprise carries a spark of wonder precisely because it violates our expectations in meaningful ways — it challenges our assumptions, not just our patterns. When AI tries to do this, it often feels hollow: a synthetic simulation of awe, generated without understanding what awe truly is.

Moreover, randomness itself is not enough. Too much and users lose trust; too little and they lose curiosity. The balance between predictability and novelty requires not just mathematical tuning but psychological empathy — a sensitivity that remains difficult for AI to replicate.

• Efforts Toward Meaningful Surprise

That said, efforts to restore surprise are evolving. Some researchers are experimenting with adversarial generation — training one AI model to produce outputs specifically designed to challenge another. This creates an ecosystem of digital creativity where one algorithm’s disruption forces the other to adapt, yielding unexpected outcomes.

Others are exploring “co-creative” systems where humans and AI collaborate — with the human setting broad goals and the AI introducing unpredictable variations. Artists and designers have begun to embrace this as a kind of digital improvisation, where surprise emerges not from the machine alone but from the tension between intention and accident.

Recommendation platforms are also beginning to measure serendipity as a user experience metric, not a statistical outlier. Companies recognize that delight — not just efficiency — sustains engagement. The rise of these “serendipity-aware” systems suggests that even within automation, we crave moments that feel unscripted.

The Limits of Simulated Wonder

Yet, despite these advances, there remains a qualitative gap between algorithmic surprise and human serendipity. Real surprise often carries a story — a sense of significance discovered through chance. AI, however, cannot yet feel or interpret meaning; it generates variation without genuine curiosity. The randomness it offers is procedural, not poetic.

We might therefore see artificial surprise as a form of mimicry — useful, stimulating, but ultimately performative. It can reignite engagement and diversify experiences, but it cannot replicate the existential spark that makes human discovery transformative.

In the end, AI may succeed in reintroducing variety, but not true wonder. It can shuffle the cards, but not rewrite the rules of awe. The challenge for the future is to build systems that don’t just generate novelty but enable genuine encounter — spaces where technology leaves room for the unplanned. Because if everything we see, read, or hear is the product of perfect foresight, even the best-designed surprise will always feel like something the algorithm saw coming.

The Human Hack: Reclaiming Serendipity

In an age where algorithms guide everything from our playlists to our partners, reclaiming the art of surprise has become a radical act. The endless optimization of experience — powered by data-driven systems that know what we want before we do — has dulled the edges of human curiosity. Serendipity, once the lifeblood of creativity and discovery, is being quietly coded out of our daily lives. But there’s a growing cultural movement pushing back — a human hack against predictability.

The goal isn’t to reject technology, but to rediscover unpredictability within it. The question isn’t how to turn off the algorithm forever, but how to step outside its predictive reach long enough to let randomness — and possibility — back in.

Taking Algorithmic Fasts

The first step toward reclaiming serendipity is awareness — noticing how often our choices are preselected for us. Every “recommended for you,” every auto-generated playlist, every trending feed slowly erodes the joy of searching, of stumbling upon something uncurated. Taking “algorithmic fasts” — short breaks from personalized feeds — is one way to reset.

An algorithmic fast doesn’t require digital asceticism. It can be as simple as disabling YouTube’s autoplay, avoiding Netflix’s top picks, or browsing music manually instead of relying on algorithmic mixes. Some people set aside one day a week as a “random scroll” day — diving into unexplored corners of the internet without following suggestions.

These small acts reintroduce friction — the kind that algorithms work to eliminate. Yet friction is where attention sharpens. It reminds us that exploration is not just about efficiency but about agency. By occasionally unplugging from predictive systems, we recover the muscle memory of curiosity — the willingness to wander without a map.

Analog Hobbies and Chance Encounters

If digital life narrows our horizons, the analog world widens them again. Offline pursuits — from browsing secondhand bookstores to attending community events — reintroduce the chaos of the unfiltered. A record store visit might lead to a conversation that changes your taste; a missed train could lead to a new friendship. These moments, impossible to algorithmically optimize, are where spontaneity thrives.

Cognitive scientists have long shown that the brain’s reward systems are highly sensitive to novelty. We are wired to seek the new, the uncertain, the serendipitous. Analog environments provide that unpredictability in abundance — without data-driven scaffolding. In this sense, engaging with the real world is not nostalgic escapism; it’s neurological self-care.

Some artists and writers deliberately design for serendipity through “creative accidents.” Brian Eno’s Oblique Strategies cards — prompts meant to disrupt habitual thinking — are a perfect example. So are surrealist practices like “exquisite corpse” drawing, where each contributor adds to a composition without knowing the rest. These exercises mirror what algorithms remove: randomness, disruption, and the productive discomfort of not knowing.

• Designing for Discovery Moments

Organizations, too, can intentionally design for serendipity. Workplaces optimized for productivity often sacrifice the unplanned — those hallway chats or cross-team collisions that spark ideas. Some companies are now rethinking their digital and physical environments to foster these discovery moments.

In design and media, platforms are experimenting with features that reintroduce controlled unpredictability. Streaming services might include a “wild card” recommendation; learning platforms could suggest a course outside a user’s domain; news feeds could surface articles from unrelated perspectives. This approach — “engineered randomness” — doesn’t abandon data but uses it to stretch rather than shrink the user’s worldview.

The creative industries, in particular, depend on this dynamic. True innovation often emerges at the intersection of unrelated ideas — what Eno calls “scenius,” or collective genius born from serendipitous collaboration. For creativity to flourish, systems must allow for collision, contradiction, and chance. That means leaders and designers alike must value discovery as much as efficiency.

Tools for Controlled Unpredictability

There’s a quiet rise of tools built to fight algorithmic determinism. Apps like StumbleUpon (recently revived as Mix) once championed random web exploration. Modern versions of this philosophy include Glimpse — which shows unexpected cultural artifacts — or Radio Garden, which lets users spin a globe to hear live radio from anywhere in the world.

These platforms embody a new design ethic: randomness as a feature, not a flaw. By integrating unpredictability into digital systems, they encourage users to step outside comfort zones while still providing meaningful context. This is serendipity by design — structured enough to be engaging, open enough to be surprising.

Where Prediction Ends, Creativity Begins

Ultimately, reclaiming serendipity is not just a lifestyle choice; it’s a creative necessity. Every major leap in art, science, or philosophy has emerged from the unplanned — from accidents, errors, and detours. The painter discovering a new technique by mistake, the scientist noticing an anomaly in data, the writer overhearing a stranger’s conversation — these moments cannot be optimized or automated.

When AI and algorithms strip away unpredictability, they also sanitize creativity. To thrive, humanity must preserve the messy, nonlinear paths that fuel imagination. This doesn’t mean rejecting predictive technology altogether, but rather designing a future where it coexists with the unexpected.

The human hack, then, is not to outsmart the algorithm, but to out-feel it — to seek wonder in imperfection and surprise in the unplanned. Because where prediction ends, creativity begins.

The next era of digital life may depend not on how well machines can anticipate us, but on how boldly we can wander beyond what they predict. In doing so, we won’t just reclaim randomness — we’ll reclaim the essence of being human: curiosity uncontained, discovery unplanned, and meaning born from the beautiful chaos of chance.

Conclusion – The Future of Discovery

We are at a peculiar and contradictory point in time when prediction is becoming more and more important. The mechanisms we developed to make life easier by predicting our wants and making our choices easier could now dull the joy of discovery, which is what makes life worth living. Every suggestion that suits us too well and every algorithm that “knows” us too well has a hidden cost. We lose the unexpected slowly and without realizing it.

The promise of AI has always been to give us insight—to show us patterns we can’t see and make things clearer. But when predictive technologies become a part of every element of our lives, from love to education to entertainment, the question becomes louder: What happens when prediction takes the place of participation? When AI tells us what to do and we become passive characters in its flawlessly designed story?

How we address that question will affect the future of discovery. Prediction gives us peace of mind. It makes things easier, safer, and more efficient. But discovery brings wonder, and wonder is what makes life rich. People have always had a conversation between what they expect and what surprises them. Without the latter, curiosity dies, innovation stops, and even fun becomes boring.

If we let AI make all of our choices, we might mistake being comfortable for being happy. A feed that never surprises us, a playlist that never pushes us, and a perspective that never goes against us are not indicators of concord; they are signs of shrinking. The greatest brains throughout history, including artists and scientists, have thrived on change and the tension between what is known and what is not. Taking something away flattens the landscape of imagination itself.

The next step for AI shouldn’t be to get rid of uncertainty, but to guide it by making systems that provide room for chance. This is what you might call “guided unpredictability”: algorithms that on purpose include things that are new and unexpected to keep people interested.

Think about an AI that sometimes suggests a book, movie, or news story that you wouldn’t ordinarily read, watch, or read about in a different culture. Imagine a learning system that randomly mixes up subjects, like history with design or physics with philosophy, to make people think beyond the box. These kinds of tools wouldn’t merely guess what we like; they would also help us find new things we like.

In this future, AI is less of an oracle and more of a partner in discovery. It doesn’t tell you what to do; it makes your experience richer. Discovery isn’t really a data function; it’s more of an emotional one. It’s the gasp of recognition when something unexpected suddenly makes sense, and it’s the spark when two ideas that don’t seem to go together suddenly do. No matter how advanced an algorithm is, it can’t make that feeling happen; it can only set the stage for it to happen.

That’s why AI’s future should be based on how unpredictable people are, not how predictable they are. Systems should be made to learn from our creativity, not limit it. They should ask questions instead of merely giving responses. And most significantly, they should teach us that we don’t find meaning in perfect predictions; we find it in the moments that startle us and make us alert.

We need to remember that the most important part of intelligence, whether it’s human or artificial, is not simply being able to see patterns, but also being able to be amazed. The technologies that can find a balance between structure and spontaneity, knowledge and mystery, and efficiency and exploration will be the ones that last.

Every great narrative, every invention, and every love starts with not knowing what will happen. Taking it away would mean taking away the heartbeat of progress. Not being surprised by a prediction is not wisdom; it’s stasis.

We need to keep our connection to the unpredictable if the digital era is going to become more than just automation. We have to protect our right to be shocked by other people, the world, and even ourselves. When discovery goes away, so does the feeling of becoming, which is what being alive entails.

When AI forecasts every option, we lose more than simply spontaneity; we also lose our sense of self in the process of discovery.

Also Read: What is Shadow AI? A Quick Breakdown

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