By Stuart Kerr, Technology Correspondent
Published: 17 July 2025Last Updated: 17 July 2025
In the age of infinite scrolling and predictive personalisation, a once-celebrated feature of the internet is quietly vanishing: serendipity. Once, online exploration was filled with unpredictable discoveries and accidental delights. Now, with algorithms designed to anticipate every click, the web has become eerily efficient — and startlingly repetitive.
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Is the algorithmic age erasing digital spontaneity? We explore how recommender systems are shaping, and potentially limiting, our online experiences.
The Algorithmic Cage
Every social media feed, video playlist, and e-commerce suggestion is now dictated by recommendation algorithms. These systems are fuelled by machine learning models that aim to maximise engagement by showing users what they are most likely to click next. But in doing so, they slowly restrict our exposure to novelty.
Platforms like TikTok, Netflix, and Spotify boast astonishing retention metrics. Yet the price we pay for this hyper-efficiency may be our ability to stumble upon something unexpected. According to the Pew Research Center, algorithmic sorting tends to reinforce existing preferences, creating filter bubbles and echo chambers that amplify predictability and suppress deviation.
Curated Comfort vs Chaotic Discovery
In the early 2000s, the internet was still a wild frontier. You might visit a blog about Romanian folklore and end up watching an obscure Canadian rock band's music video. This chaotic serendipity helped shape digital culture. Now, however, the internet is shaped more by what we "should" like than what we might like.
A recent Medium essay described this trend as "the domestication of curiosity". Recommender systems now privilege optimisation over exploration, efficiency over spontaneity. This shift has profound implications for everything from journalism to creativity, and even mental health.
In an article on LiveAIWire, we discussed how personalised mental health apps often fail to introduce users to broader insights outside their behavioural patterns. Instead of expanding horizons, these tools may reinforce narrow emotional narratives.
Designing for Serendipity
Researchers and ethicists are not blind to this algorithmic narrowing. In a widely discussed SSRN paper, legal scholar Joshua Krook proposed integrating engineered randomness into recommender systems. By building mechanisms that prioritise diversity and inject unexpected content, platforms could reclaim the element of surprise.
Similarly, in a Semantic Scholar PDF, Denis Kotkov and colleagues explored the technical difficulties of designing AI that values novelty. Their work highlights the challenge: the very models that optimise for engagement often penalise unpredictability, which they interpret as noise or irrelevance.
This echoes the concerns raised in another LiveAIWire article about AI's difficulties in mimicking human intuition. Where humans recognise the value of the unexpected, machines tend to discard it.
A Tipping Point in Design Philosophy
The movement to reintroduce randomness and diversity into algorithmic design is gaining momentum. Some developers are experimenting with "serendipity scores" and diversity indices within their models. Even Spotify's "Discover Weekly" — once a bastion of serendipitous music discovery — is facing scrutiny for becoming too predictable.
Ultimately, the problem may not be the technology itself but how we choose to wield it. As noted in LiveAIWire's piece on wellness tech, convenience can quickly become constraint. If we continue to favour efficiency over randomness, we risk creating a world in which the unfamiliar is algorithmically invisible.
About the Author
Stuart Kerr is the Technology Correspondent at LiveAIWire. He writes on the intersection of AI, ethics, and society.
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