Should I Build It Should I Build It
11 May 2025
Artificial Intelligence Music

AI wrapper that curates new music based on a Gen Xer’s peak-era tastes

Confidence
Engagement
Net use signal
Net buy signal

Idea type: Swamp

The market has seen several mediocre solutions that nobody loves. Unless you can offer something fundamentally different, you’ll likely struggle to stand out or make money.

Should You Build It?

Don't build it.

Your are here

Based on similar product launches, your AI music curation idea falls into a 'Swamp' category, meaning several similar solutions exist, but none have truly broken through. Five similar products suggest moderate competition. The average engagement is low, with just 2 comments per product, which means that similar products didn't generate much buzz and the will to use or buy them is neutral. This indicates that the market might not be easily impressed, or that there is some sort of structural problem with these products. Existing solutions may be perceived as mediocre, making it difficult to stand out without a fundamentally different approach. A key element that your competitors struggled with was the lack of language options and visible genre indicators for playlists.

Recommendations

  1. Thoroughly research why existing music curation solutions haven’t achieved widespread success. Identify the pain points and unmet needs in the market. Try to find out if the problem is the execution, the marketing, or the fundamental idea of the product itself.
  2. If you decide to proceed, deeply analyze the target demographic (Gen Xers). Understand their specific musical tastes, preferences, and the frustrations they experience with current music platforms. Find a niche within the Gen X demographic that is currently underserved by the market.
  3. Instead of directly competing with established music platforms, explore building complementary tools or features for them. Focus on enhancing the user experience within existing ecosystems, such as creating AI-powered playlist generators for Spotify tailored to specific Gen X subgenres.
  4. Consider exploring adjacent problems that may have a higher chance of success. For example, you could focus on creating AI tools for music discovery specifically for independent artists, or develop a platform that connects Gen X musicians with their fans.
  5. Given the crowded and challenging market, realistically assess the resources and effort required for this project. Focus on saving your energy and time for more promising opportunities that align with your skills and interests. Perhaps there is some other user group that your tech can better serve, or a category you could combine it with to build a novel product.
  6. Address the common criticisms from your competitors. Implement visible genre indicators for playlists and ensure users can easily understand the rationale behind the music selection. Consider using familiar genre categories that resonate with Gen X listeners.
  7. Streamline the user onboarding process to reduce friction and build trust. Instead of requesting full authorization to user's accounts, focus on a more privacy-friendly approach. For example, allow users to manually input their favorite bands, albums or songs, or genres, to generate a personalized playlist.
  8. Prioritize performance and responsiveness to provide a smooth and seamless user experience. Optimize rendering speed to ensure playlists load quickly and music plays without interruptions. Also consider adding an audio preview feature

Questions

  1. What are the core reasons why existing music curation services haven't resonated with Gen Xers specifically? Are there any unmet needs or pain points that your AI wrapper can uniquely address?
  2. How can you differentiate your AI-powered music curation from existing algorithms, and what specific data points or musical features will it analyze to capture the unique tastes of Gen X listeners?
  3. What specific monetization strategies can you implement to generate revenue from your AI music curation service, and how will you ensure that it aligns with the values and expectations of your target audience (Gen Xers)?

Your are here

Based on similar product launches, your AI music curation idea falls into a 'Swamp' category, meaning several similar solutions exist, but none have truly broken through. Five similar products suggest moderate competition. The average engagement is low, with just 2 comments per product, which means that similar products didn't generate much buzz and the will to use or buy them is neutral. This indicates that the market might not be easily impressed, or that there is some sort of structural problem with these products. Existing solutions may be perceived as mediocre, making it difficult to stand out without a fundamentally different approach. A key element that your competitors struggled with was the lack of language options and visible genre indicators for playlists.

Recommendations

  1. Thoroughly research why existing music curation solutions haven’t achieved widespread success. Identify the pain points and unmet needs in the market. Try to find out if the problem is the execution, the marketing, or the fundamental idea of the product itself.
  2. If you decide to proceed, deeply analyze the target demographic (Gen Xers). Understand their specific musical tastes, preferences, and the frustrations they experience with current music platforms. Find a niche within the Gen X demographic that is currently underserved by the market.
  3. Instead of directly competing with established music platforms, explore building complementary tools or features for them. Focus on enhancing the user experience within existing ecosystems, such as creating AI-powered playlist generators for Spotify tailored to specific Gen X subgenres.
  4. Consider exploring adjacent problems that may have a higher chance of success. For example, you could focus on creating AI tools for music discovery specifically for independent artists, or develop a platform that connects Gen X musicians with their fans.
  5. Given the crowded and challenging market, realistically assess the resources and effort required for this project. Focus on saving your energy and time for more promising opportunities that align with your skills and interests. Perhaps there is some other user group that your tech can better serve, or a category you could combine it with to build a novel product.
  6. Address the common criticisms from your competitors. Implement visible genre indicators for playlists and ensure users can easily understand the rationale behind the music selection. Consider using familiar genre categories that resonate with Gen X listeners.
  7. Streamline the user onboarding process to reduce friction and build trust. Instead of requesting full authorization to user's accounts, focus on a more privacy-friendly approach. For example, allow users to manually input their favorite bands, albums or songs, or genres, to generate a personalized playlist.
  8. Prioritize performance and responsiveness to provide a smooth and seamless user experience. Optimize rendering speed to ensure playlists load quickly and music plays without interruptions. Also consider adding an audio preview feature

Questions

  1. What are the core reasons why existing music curation services haven't resonated with Gen Xers specifically? Are there any unmet needs or pain points that your AI wrapper can uniquely address?
  2. How can you differentiate your AI-powered music curation from existing algorithms, and what specific data points or musical features will it analyze to capture the unique tastes of Gen X listeners?
  3. What specific monetization strategies can you implement to generate revenue from your AI music curation service, and how will you ensure that it aligns with the values and expectations of your target audience (Gen Xers)?

  • Confidence: Medium
    • Number of similar products: 5
  • Engagement: Low
    • Average number of comments: 2
  • Net use signal: 7.3%
    • Positive use signal: 7.3%
    • Negative use signal: 0.0%
  • Net buy signal: 0.0%
    • Positive buy signal: 0.0%
    • Negative buy signal: 0.0%

This chart summarizes all the similar products we found for your idea in a single plot.

The x-axis represents the overall feedback each product received. This is calculated from the net use and buy signals that were expressed in the comments. The maximum is +1, which means all comments (across all similar products) were positive, expressed a willingness to use & buy said product. The minimum is -1 and it means the exact opposite.

The y-axis captures the strength of the signal, i.e. how many people commented and how does this rank against other products in this category. The maximum is +1, which means these products were the most liked, upvoted and talked about launches recently. The minimum is 0, meaning zero engagement or feedback was received.

The sizes of the product dots are determined by the relevance to your idea, where 10 is the maximum.

Your idea is the big blueish dot, which should lie somewhere in the polygon defined by these products. It can be off-center because we use custom weighting to summarize these metrics.

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Hi HN!We’re a group of electronic music artists and PhD researchers working in AI music.We've been working on a project to help us discover new music in a more musically objective way, called Speak Music:https://speakmusic.sonophase.com/We recently presented at Sonar+D in Barcelona.We’ve trained an AI model to understand the correspondence between music and language, combining a machine listening system for audio signal processing with transformers for text embeddings. Once trained, we index a huge catalogue of unseen audio, so that the search system can efficiently scale to millions of tracks.At the moment, our model is optimised for our preferred music; electronic, techno, ambient and relaxing music. We’re currently fine-tuning to handle all kinds of genres and moods.Our model handles two types of discovery: (a) natural language search and (b) similarity search.(a) Speak: search for music using freeform natural language prompts, describing the mood, aesthetic, texture, setting and context of a track.(b) Music: discover tracks that are acoustically similar to ANY reference track from Apple Music.We hope you like it. Let us know what you think!

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Users appreciate the idea and find the website appealing. There are multiple requests for additional language options, suggesting a demand for broader accessibility. An audio feature addition is also suggested. One user inquired about the integration with the YouTube API, indicating interest in expanded functionality.

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