Artificial intelligence (AI) now plays a big role in entertainment, fashion, and art. Whether it’s Spotify making music playlists just for you or AI creating new outfit ideas, it’s clear that AI is shaping culture in big ways. But as we let algorithms guide what we watch, listen to, and wear, it’s worth asking: Does AI have its own “taste,” or is it just copying ours?
This isn’t just about AI recommending songs or curating your social media feed. It’s about how deeply AI is part of our culture, shaping our tastes in ways we might not notice. These algorithms don’t just reflect what we like; they help decide what we like. AI learns from us by studying vast amounts of human-made content, such as songs, movies, and books. It picks up on patterns and trends that show what people enjoy and what gets the most attention, then uses that to make future suggestions. For example, when Spotify or YouTube recommends a song, it’s not guessing. It uses data from millions of users to find what you’re likely to like. But what happens when AI starts to shape our tastes instead of just reflecting them? That’s where the idea of “taste” comes in. Even though AI can process lots of data to predict what we’ll enjoy, it’s still based on what’s already been popular or in demand. This can create a cycle where the same trends keep repeating, instead of new ones emerging.
As AI learns from our behaviors, it also begins to reinforce existing cultural patterns, often amplifying mainstream content while leaving behind niche or marginalized voices. This isn’t inherently a flaw of the system, but it reflects the biases embedded in the data it’s learning from. The deeper issue arises when AI, in curating culture, starts prioritizing what gets the most attention over what is necessarily the most original or meaningful. Algorithms are driven by engagement and clicks, not necessarily cultural depth or value.
While AI is generating music, art, and fashion designs, the recognition and accolades are typically given to the algorithm’s creators (usually tech companies) rather than the artists whose work might have inspired it. Take the example of AIVA, an AI system used to compose classical music. AIVA can generate symphonic compositions in the styles of legendary composers such as Beethoven and Mozart. While the AI may generate beautiful music, it raises the question: Who deserves credit for the creative output? Is it the algorithm that synthesized various compositions and styles? Or the composers whose works were used to train the algorithm? Credit, in this case, is diverted away from the true creators of the culture and instead goes to the machine that’s been trained on their work. This situation extends beyond just music. In fashion, algorithms are increasingly used to predict trends and even design collections. AI tools analyze what colors, patterns, and styles are popular in the market, then generate new designs based on these insights. However, the credit for these designs often goes to the tech companies behind the tools, not the designers or artists whose ideas were transformed into data for the machine to work with.
When algorithms help create culture, the people whose work trains the AI are often left out. The hidden effort of musicians, designers, and artists who shape these datasets rarely gets recognized. Instead, the companies that own the algorithms benefit, while the original creators are left behind.
As AI gets better at copying creative work, the problem of using someone’s influence without paying them becomes more common. For example, AI-generated music or fashion often borrows from existing styles without rewarding the original creators. AI tools are trained on vast amounts of human-generated content, but the people who create that content rarely get paid for what the AI produces. This is a big issue in music, where artists create new sounds that AI later copies. An AI might learn from thousands of hip hop tracks and make a similar song, but the original rapper or producer doesn’t get any credit or payment. AI can copy creativity, but it doesn’t recognize or reward the people who inspired it.
Not paying creators for their cultural contributions also raises concerns about exploitation. AI depends on data and often uses people’s work without their permission. As a result, the original creators get no money or recognition for their influence on AI-generated content. Big companies make a profit from this content, while the people who started the culture get nothing.
In the end, AI’s role in shaping culture brings up important questions about ethics, intellectual property, and who owns creativity. As AI continues to shape cultural content, it’s important to ensure the people whose work trains these systems are recognized, respected, and paid fairly. Whether taste comes from people or machines, it’s built on cultural contributions, and those who contribute deserve credit and reward.
