My Complete Stance on AI-generated Music & My Ultimate Solution for Creatives.

May 27
6 min read

Based on my personal learning experiences, fulfillment in art comes from the work, not the shortcut. Art earns its meaning through struggle, patience, and growth. The struggle isn’t the cost of art, it’s part of the reward. The work is what gives art its weight and value.

Good art is never rushed; it’s never going to be 100% “perfect,” and that’s what keeps it humanly authentic. Sometimes, it’s best to abandon a project to allow it to breathe and realize its full potential. Creating patiently will age like fine wine.

Choosing not to incorporate AI into my music isn’t a lack of knowledge; it’s a deliberate decision to follow my personal code. I’d like to think many people would find this sentiment reasonable.

As long as humans exist, there will always be a demand for human-made music and art.

What's the problem with AI music?

Here's what's actually developing in the music industry currently:

Warner Music Group settled with Suno in November 2025, ending its claims through a multi-million dollar payment and a licensing partnership, while Universal Music Group settled with Udio in October 2025, launching a joint "walled garden" AI platform in 2026. Sony Music remains the only major label actively litigating against both Suno and Udio, with a pivotal fair-use ruling expected in Summer 2026 that could set legal precedent for the entire industry.

Key current developments include:

Sony’s Expansion: Sony has significantly expanded its lawsuits, adding tens of thousands of copyrighted recordings to its cases against Suno and Udio, with Udio recently admitting to scraping YouTube audio for training data.
Suno’s Model Shift: Following its settlement with Warner, Suno is transitioning to models trained only on licensed material and capping user downloads, though its pre-settlement models remain in legal limbo.
Independent Artist Actions: Class-action lawsuits filed by independent musicians against both platforms are ongoing, seeking compensation for artists whose work was allegedly used in training without consent.
Legal Stakes: The Sony cases directly address whether training generative AI on copyrighted recordings without a license constitutes fair use; a ruling against Suno/Udio would force widespread licensing, while a ruling for them would drastically lower licensing costs.

So in actuality, AI-generated music faces significant technical, ethical, and economic challenges that threaten artistic integrity and industry stability. The core issues revolve around the lack of human emotion, copyright ambiguity, and the devaluation of professional musicianship.

Technical and Artistic Limitations: Current AI systems struggle to maintain coherence in long compositions, often producing disjointed or repetitive structures that lack the dynamic variation of human creativity. AI models also fail to capture genuine emotional depth and nuance, resulting in music that is technically proficient but emotionally hollow because they lack personal life experiences. Furthermore, these systems are prone to algorithmic bias, potentially perpetuating racial or cultural stereotypes present in their training data, and they cannot truly innovate genres, relying instead on regurgitating existing patterns.

Ethical and Legal Concerns: A primary controversy is copyright infringement and intellectual property theft, as AI models are trained on vast datasets of copyrighted music without compensating the original artists. This raises complex questions about ownership and accountability for generated works, creating a legal gray area. Additionally, the lack of transparency in how these models operate and the potential for deepfake audio (such as unauthorized AI voice clones) erode consumer trust and violate artists' rights.

Economic Impact on the Industry: The proliferation of AI music threatens to displace session musicians, composers, and producers by offering a cheaper, faster alternative, particularly for background music and commercial jingles. This shift contributes to the homogenization of popular music, as algorithms prioritize safe, formulaic content that maximizes streaming engagement over artistic risk. Consequently, emerging artists face increased difficulty gaining exposure and fair compensation, as the market becomes flooded with low-effort, AI-generated tracks that dilute the value of human artistry.

But what if I just use it for getting a vibe, you ask?

Even when used strictly for internal "vibe checking" or preliminary sketching, relying on AI introduces significant ethical, legal, and cognitive risks that can compromise the final human-led project.

As someone who works in the music industry, allow me to explain a few things...

1. Ethical Complicity in Data Exploitation

The primary objection remains the origin of the training data. Most generative models are trained on billions of images scraped from the web without the consent, credit, or compensation of the original artists.

Normalization of Theft: Using these tools, even for private sketches, validates a business model built on the unauthorized exploitation of human labor. It creates demand for systems that actively harm the livelihoods of the very artists whose styles are being mimicked for the "vibe."

Style Laundering: When you prompt for a specific aesthetic (the style of the artist), the AI is effectively remixing that artist's life work without permission. Using this as a starting point treats their unique visual language as a free resource rather than protected intellectual property.

2. Legal and Professional Liability

Even if the AI sketch is never shown to a client or vise versa, it can contaminate the final deliverable with legal risks.

Unintentional Infringement: AI models often reproduce recognizable elements of copyrighted works in their outputs. If a human artist later refines an AI sketch that contains subtle, protected elements (like a specific character design or proprietary texture), the final work could be deemed a derivative work, exposing the artist and their client to lawsuits.

Discovery Risks: In professional settings, internal communications and files (including AI prompt logs and generated sketches) can be discoverable in litigation. If a dispute arises over the originality of a final design, the existence of AI-generated preliminary work can be used as evidence that the final product was not wholly human-authored, potentially voiding contracts or insurance coverage that requires human authorship.

3. Cognitive Atrophy and "Premature Convergence"

From a workflow perspective, using AI for the "vibe" stage can actively degrade the quality of the human creative process.

Loss of "Physical Thinking": Neuroscience suggests that the act of sketching by hand activates different neural pathways involved in spatial reasoning and problem-solving than typing prompts. Hand-sketching allows artists to "think through their pencil," discovering problems and solutions organically. AI sketching bypasses this critical cognitive engagement, offering a polished but superficial solution before the core problem is understood.

Premature Convergence: AI generates images that look "finished" very quickly. This can trap the creative team into anchoring bias, where they subconsciously limit their exploration to variations of the AI's initial suggestion rather than exploring the full solution space. The "vibe" becomes a constraint rather than an inspiration, stifling true innovation.

4. The "Human-in-the-Loop" Fallacy

Proponents argue that human refinement makes the process ethical. However, if the foundational concept, composition, and lighting are dictated by an AI trained on stolen data, the human artist is reduced to a high-paid editor rather than a creator. This devalues the role of the concept artist, shifting the core creative intelligence (something only a human can do) to a machine that has no understanding of the narrative or emotional context it is depicting.

In fact, don’t just take my word for it. I conducted my research and created a helpful visualization to illustrate my all my points.

So how do we solve these issues? It turns out we did!

I'm proud to present: Signature Sound Engine.

I’ve been a composer for over 12 years. I’ve worked on various projects, including film and theater, scoring and crafting stories that resonate with audiences on a human level.

In today’s era of music generation predominantly driven by AI, my mission is to reverse this trend.

I introduce you to, Signature Sound Engine, a solution built upon the very foundations of authentic music.

I help independent, human creators with little to no musical knowledge build a recognizable sonic identity across every platform in 30 days, with clear usage rights and zero licensing confusion.

With SSE, you have the power to translate any idea into your unique Sonic DNA for social media content and other media-based projects, including short-form, long-form, livestreams, and campaigns/trailers.

I am so committed to originality that paying close attention to detail is essential. Even the audio waveform on the SSE branded service mark was intentionally designed from my actual voice recording, where I spoke the words, “Signature Sound Engine.”

Here's how SSE works at a glance

Signature Sound Engine begins by clarifying your Sonic DNA, then together we build your Sonic Asset Kit, without AI, that carries that identity consistently across formats, and finally helps integrate those assets into your real publishing workflow so your sound becomes repeatable, recognizable, and effortless to use.

A Sonic Asset Kit (SAK) is a curated set of repeatable, platform-ready sound elements that function like audio branding building blocks. Think signature motifs, hooks, stingers, transitions, intros, outros, and loopable beds that all share the same “Sonic DNA,” so your sound stays consistent across every format and channel. Instead of choosing random tracks each time you post, a Sonic Asset Kit gives you a unified sound system you can reuse, remix, and deploy on purpose, so recognition compounds with every upload.