Summary of Key Points
The emergence of AI music tools, such as Suno V3 and Tencent's "Qimingxing," has completely rewritten the rules of music creation: anyone without musical skills or knowledge can generate commercial songs in just minutes, at a cost one-tenth of that of traditional methods. This has severely squeezed the space for traditional musicians to survive. Some have been forced to transition to the AI field (for example, Lu Xiaoxu laid off half of his staff to become an AI music blogger), others have ended up in low-wage, repetitive jobs (for instance, Zhou Zhou generates 20 songs a day for a monthly salary of 3,000 RMB), and some have simply given up creating music altogether (like Yao Lan, who hasn't written a single song this year). AI music, with its advantages of speed, low cost, and mass production, has taken over the market for "useful" music in short videos and dramas, while traditional, time-consuming methods are at risk of being marginalized.
1. AI Turns Music Creation from a Laborious Artform into a One-Click Process
Traditional music creation is like simmering soup: one must practice instruments from a young age and understand music theory; it takes months to develop a song, with dozens of revisions needed (for example, Yao Lan's "Heiman Guoji" went through 40 revisions). However, with AI tools, this process has become instant:
- Barriers Have Disappeared: With just a few prompts (e.g., "Linkin Park style"), Suno V3 can generate a commercial-quality song in three minutes, accessible to those without musical skills.
- Costs Have Plummeted: Tencent's "Qimingxing" can produce 5,000 songs per day at one-tenth of the cost of manual production.
- Production Has Explosively Increased: On streaming platforms like Deezer, AI-generated music accounted for 44% of daily uploads in 2026, with an average of 75,000 songs per day—n times the volume of traditional music creation. The path that once took Jay Chou seven years to achieve is now shortcutted by AI.
2. Three Fates for Traditional Musicians: Transition, Competition, or Giving Up
The three stories in the news reflect the overall situation of the industry:
- Transitons (Lu Xiaoxu): After running a music company for 20 years, Lu Xiaoxu panicked when Suno V3 was released. He laid off half of his staff and switched to becoming an AI music blogger, quickly gaining a lot of commercial work.
- Competition (Zhou Zhou): A third-year music student, Zhou Zhou used AI to generate 20 songs a day during his internship, modifying popular BGMs to avoid copyright issues. Despite his passion for creation, the rise of AI made him feel overwhelmed, leading him to resign.
- Giving Up (Yao Lan): An independent musician who once spent months on a single song has written none this year. Half of her peers have switched careers, and new artists from the 2000s generation are struggling to get commercial work. The path for traditional music creation is becoming increasingly narrow.
3. The Market Only Wants "Useful" Music, and AI Hits the Right Niche
AI music has quickly gained market traction because it meets current needs:
- Short Videos/BGM: Users watch videos for an average of 15 seconds and need catchy segments with hooks; AI can produce large quantities of BGMs that fit popular trends.
- Short Drama Soundtracks: Micro-dramas are produced in 7-15 days and require hundreds to thousands of tracks; AI can deliver them within two days (traditional soundtracks take 3-6 months).
- Pre-made Music: 80% of micro-dramas use pre-made sound effect packs; AI can quickly generate such music that doesn't need to be elaborate, as long as it fits the context.
The market no longer seeks "great" songs but only "useful" audio—AI can produce them in large quantities at low cost.
4. The Redefinition of Music Value: Carbon vs. Silicon
Traditional (human-made) music and AI-generated music represent different value paradigms:
- Carbon-based Music: A slow, artistic process that embodies youth (Jay Chou's "Blue and White Porcelain," Park Ju's "Orion Constellation") and emotional expression (Yao Lan's extensive revisions).
- Silicon-based Music: A fast, efficient tool that focuses on practicality, with no emotion or fatigue, and can be replicated indefinitely.
The question is: when users are accustomed to short, catchy music and the market only pays for "usefulness," can traditional music survive? Yao Lan believes that true music lovers will seek authentic expressions, but whether this will be enough to sustain traditional musicians?
5. The Future: AI Won't Replace All Musicians, But It Will Select the Irreplaceable
AI is not a threat; it will transform the industry:
- Those Who Embrace AI: Use it as a tool (like Lu Xiaoxu) to become AI music bloggers or train AI singers, finding new opportunities.
- Those Who Stick to Tradition: Believe in human creativity and uniqueness (AI can only imitate, not create new styles), but may face a niche market.
- Those Who Are Replaced: Those who follow routine methods without unique expression will be replaced by AI (e.g., the company where Zhou Zhou worked).
In the end, AI will distinguish between two types of musicians: those who integrate AI to improve efficiency and those with irreplaceable talent. The mediocre will likely be marginalized.
This news is not just about the music industry; it reflects a broader trend in all traditional industries. As machines can complete tasks quickly and cheaply, how must humans find their place? Perhaps by learning to collaborate with them or doing what machines cannot.