4 Essential thoughts for surviving the AI transformation in Music
- Develop a critical mindset - Balance innovation and authenticity - Think ethically and ecosystematically - New artist models
When we began exploring “AI+MUSIC - CONTEXT+USES+ETHICS: Artificial Intelligence in Music Ecosystems”, we asked ourselves: What do future creators need to navigate the intersection of technology and culture? Today, anyone involved in a cultural ecosystem must update and develop new skills to confront the changes technology brings.
What mindset shifts must we adopt to thrive in this new context? While we can't cover everything, we'll delve into four crucial keys for successfully navigating this landscape:
Cultivate a critical mindset, both in creation and consumption.
Face the tensions that technology presents.
Build bridges, create legal frameworks, and promote transparency.
Envision and shape the future of artists.
We’ve put together a brief summary for those looking to tackle these challenges, along with some links to help understand practical applications of these points:
1. Develop a Critical Mindset: Beyond the Technical
AI as a collaborator, not a replacement: AI isn't here to displace human creativity but to enhance it. The challenge lies in understanding its capabilities and limitations to strategically integrate it into the creative process.
The art of prompting as creative dialogue: Mastering prompting means learning to converse with AI. It's not just about giving precise instructions but guiding the process to achieve results that enrich your creative vision.
Embracing co-creation and its implications: AI redefines our concepts of authorship and originality. It's essential to reflect on these issues and adapt ethical and legal frameworks to this new creative paradigm.
Example: Many boundaries are delicate: last year, together with Pol Lain and Berenice Llorences, we created "Voz Contora Latam," where we used the voices of some of the most important female singers from Latin America to generate something entirely new. The experiment was worthwhile, but it raises many questions.
2. Balance Innovation and Authenticity: Navigating Key Tensions
Balancing the organic and the synthetic: AI introduces new creative possibilities but also risks standardization. Creators must find a balance between exploring new technologies and preserving artistic authenticity.
Rethinking originality in the AI era: With AI generating content from massive datasets, questions about originality and authorship arise. We need new ways to value creativity when AI plays an active role in the process.
Addressing biases and promoting diversity: AI can perpetuate biases present in training data. It's crucial to use technology ethically, promoting diversity and inclusion in music.
Example: It may seem new, but on Spotify, there have been many instances of identical songs with different titles trying to occupy our algorithms. This isn’t something unique to AI, but now it’s becoming more evident. We are facing a massive layer of content that is simply digital noise.
3. Think Ethically and Ecosystemically: Fostering Sustainable Practices
Fostering collaboration between artists and AI developers: Ethical AI development in music requires close collaboration among artists, programmers, legislators, and industry leaders. Dialogue and joint experimentation are key to ensuring AI benefits the entire musical ecosystem.
Establishing clear legal and ethical frameworks: The rapid evolution of AI demands legal structures that address challenges like intellectual property and controversial technological uses. These frameworks must adapt to ongoing technological advances.
Educating the audience and promoting transparency: Public trust in AI hinges on transparency and education. Artists should inform audiences about AI's role in music creation and encourage open discussions about its implications.
Example: Stable Audio, a generative audio model, is trained on Freesound's specific library, ensuring transparency in its dataset. This is in contrast to other models like SUNO, where the data the model works on remains less clear.
4. Understand New Artist Models in the AI Era
Artists integrating AI into their projects: Using AI as a tool to expand creative possibilities and explore new forms of audience interaction, redefining the creative process.
Artists with their AI versions: More artists are creating digital "alter egos" that interact with fans and generate personalized content, opening new avenues for collaboration and engagement.
Artists entirely generated by AI: Fully AI-created musical projects raise questions about authorship, originality, and artistic value in the digital age.
Example: As mentioned before, Grimes' AI has already generated over 300 songs. While it’s unclear whether this model is scalable to smaller artists, it remains a great example of how AI can expand an artist's creative output.
LINKS
🎧 triniti: your musical collaborator (TRINITI)
TRINITI gives you new ways to create and express yourself through music. You can think of TRINITI as a collaborator, here to help you — not replace you — in the creative process.
✘ TRINITI aims to create comprehensive tools that serve creators in multiple ways. This platform is a key advancement in how artists can collaborate with AI without losing their creative autonomy.
🎶 the best ai tools for music creators (AI Musicpreneur)
Every music artist and creator should know these: the best AI tools in music.
✘ It’s incredible to find such a specific and up-to-date resource. This collection of AI tools is essential for music creators looking to optimize their workflow while maintaining their artistic vision.
📚 notebooklm: your virtual research assistant (NotebookLM)
When you upload the documents that are central to your projects, NotebookLM instantly becomes an expert in the information that matters most to you.
✘ This tool is very powerful and was incredibly helpful for this article! Its ability to organize and analyze information makes research work more efficient and effective.
🛠 the ai disruption in music creation (Water & Music)
Creative AI may be the most disruptive technology for the music business since the Napster era of piracy. Already in 2023, over 10 different music AI models have been released by independent researchers and big-tech companies like Google and ByteDance, allowing users to generate custom tracks in seconds using a simple text prompt.
✘ Water & Music creates systematizations that serve as beacons for those of us working in this field. Their segmentation of the topic greatly helped us structure our report, providing clarity in such a complex and ever-changing subject.
🎮 onchain computation: beyond finance (Autonomous Worlds N1)
Autonomous Worlds N1 is an anthology of essays by engineers, game designers, artists, and writers exploring onchain computation as a novel medium for virtual worlds. These essays go beyond the limited view of blockchain as purely a financial tool and engage with new design patterns, game mechanics, and narrative possibilities enabled by the technology.
✘ This new era requires a new vocabulary. It may seem abstract at times, but I believe it will be necessary to navigate the complexities that blockchain and virtual worlds will present.