As someone who has spent 28 years in music production — from recording studios in Seoul and New York to Hans Zimmer’s Remote Control Productions in LA — I’ve witnessed many technological shifts. The transition from analog to digital. The rise of DAWs. The democratization of home studios. But nothing compares to what AI is doing to our industry right now.
This isn’t hype. This isn’t speculation. I’m living it every day. At my company Montadecs, we’re actively building AI-powered audio tools that are changing how music gets created, tagged, licensed, and distributed. And I want to share what I’ve learned — both the incredible potential and the very real challenges.
The Old Way vs. The AI Way
Let me paint you a picture of traditional music production. You start with a concept. Maybe a brief from a client who needs 50 tracks for a meditation app. Each track needs to be unique, properly tagged with mood, tempo, genre, and instrumentation metadata. You need cover art. You need proper licensing documentation.
In the old world, this project would take a team of composers, producers, mixing engineers, and graphic designers several months. The cost? Easily tens of thousands of dollars. The timeline? Three to six months minimum.
Today, AI tools are compressing that timeline dramatically. At Greit Music Store, we’re producing approximately 50 tracks daily across 190 virtual artists and 38 genres. Not to replace human creativity — but to augment it at a scale that was previously impossible. Each track goes through AI-powered quality checks, automated tagging, and algorithmic cover art generation before being added to our ever-growing music library.
But here’s what most people miss: the AI doesn’t work alone. Every step still involves human oversight, creative direction, and quality control. The magic is in the collaboration between human intuition and machine efficiency.
Three Areas Where AI is Making the Biggest Impact
1. Music Generation — Beyond the Novelty Phase
AI-powered composition tools have moved well beyond the “toy” phase. In 2024, AI-generated music was mostly a curiosity — interesting but not commercially viable. In 2026, the gap between AI-generated and human-composed music has narrowed significantly, at least for certain use cases.
Where AI music generation truly shines is in the production of functional music — background tracks for videos, podcasts, games, and commercial content. These are tracks where the music serves a supporting role, setting mood and atmosphere without demanding the listener’s full attention.
Will AI replace the next Hans Zimmer or Ryuichi Sakamoto? Absolutely not. Film scoring, artistic albums, and emotionally complex compositions still require the depth of human experience and creative vision. But for the vast market of royalty-free, stock, and functional music? AI is a game-changer.
At Greit Music Store, we’ve found the sweet spot: AI generates the initial compositions, but human producers review, refine, and curate. The result is a catalog that grows at machine speed but maintains human quality standards. We currently maintain 190 virtual artists across 38 genres — from ambient electronica to cinematic orchestral, lo-fi hip hop to K-pop instrumentals.
2. Audio Tagging and Metadata — The Unsung Hero
If music generation gets all the headlines, audio tagging is the unsung hero of the AI music revolution. And honestly, this is where I see some of the most practical, immediate value for the industry.
Think about it: every track in a music library needs to be tagged with dozens of attributes. Tempo. Key. Mood (happy, melancholic, energetic, peaceful). Genre and sub-genre. Instrumentation. Energy level. Vocal presence. And these tags need to be accurate — because when a video editor searches for “upbeat corporate background music at 120 BPM,” they need to find exactly that.
Traditionally, this was done by human listeners. A skilled tagger might process 30-50 tracks per day. With AI models like Essentia ML — which we use extensively at Montadecs — we can tag thousands of tracks per hour with remarkable accuracy. In our testing, AI tagging achieves over 90% agreement with human taggers on most attributes, and for objective measurements like tempo and key detection, it’s essentially perfect.
But accuracy isn’t the only benefit. AI tagging brings consistency. Human taggers inevitably have subjective biases — what one person calls “melancholic” another might label “reflective.” AI models, once properly trained, apply the same standards uniformly across every track. This consistency is crucial for large-scale music libraries where searchability directly impacts revenue.
We’re currently working on what I call the “Greit AI Audio Suite” — a comprehensive toolset that includes an advanced Music Tagger, SFX Generator, and Voice Studio. The Music Tagger alone analyzes over 40 audio attributes per track and can process an entire library overnight.
3. Voice and Sound Design — The New Frontier
AI voice synthesis and sound effect generation are opening entirely new creative possibilities that we couldn’t have imagined even two years ago. The quality of AI-generated voices has reached a point where they’re commercially viable for narration, podcasts, and even some musical applications.
For sound designers working in games and film, AI-powered SFX generation is becoming indispensable. Need a unique creature sound? A futuristic UI beep? An ambient soundscape for an alien planet? AI tools can generate dozens of variations in seconds, giving sound designers a rich palette to work from instead of starting from scratch or digging through sample libraries.
At our Dolby Atmos-certified facility, Greit Studios, we’re integrating these AI tools into professional workflows. The key insight we’ve discovered is that AI-generated audio elements work best as starting points — raw material that skilled engineers then shape, layer, and polish into final production-quality output.
The implications for localization are particularly exciting. Imagine producing a podcast in English and then using AI to generate natural-sounding versions in Korean, Japanese, Spanish, and Mandarin — all while preserving the original speaker’s vocal characteristics. This technology exists today, and it’s improving rapidly.
The Challenges Nobody Talks About
It’s easy to get caught up in the excitement, but there are real challenges that the AI music industry needs to address. As someone building these tools, I feel a responsibility to be honest about them.
Quality Quantification
How do you objectively measure the quality of AI-generated music? This is one of the hardest problems we face. A track can be technically perfect — correct tempo, clean mix, proper structure — and still feel lifeless. Conversely, some of the most beloved music in history is technically “imperfect” but emotionally resonant.
We’re developing evaluation frameworks that combine objective audio analysis with subjective quality scores, but this remains an active area of research. The industry desperately needs standardized quality metrics for AI-generated audio content.
Similarity Detection
When you’re generating thousands of tracks, how do you ensure each one is sufficiently unique? How do you prevent an AI from accidentally producing something too similar to an existing copyrighted work? These are critical questions for any AI music platform, and the current tools for audio similarity detection are still evolving.
Watermarking and Provenance
As AI-generated content becomes more prevalent, the need for reliable audio watermarking and content provenance tracking becomes urgent. Buyers need confidence that the music they’re licensing is original and properly attributed. Creators need protection against unauthorized use. The technology exists in various forms, but industry-wide standards are still lacking.
What This Means for Producers in 2026
If you’re a music producer reading this, here’s my honest, experience-based advice:
Don’t fear AI — learn to work with it. The producers who thrive will be those who use AI as a creative multiplier, not those who ignore it or try to compete against it on speed and volume. Your human creativity, emotional intelligence, and artistic vision are your superpowers. AI is the amplifier.
Focus on what AI can’t do. Emotional storytelling through music. Understanding a client’s unspoken needs. Making creative choices that surprise and delight. These are irreplaceably human skills. Sharpen them.
Learn the tools. Understanding how AI music generation, audio analysis, and voice synthesis work will make you more valuable, not less. The best audio engineers of the next decade will be those who can seamlessly blend traditional craft with AI capabilities.
Think about scale. AI enables individual creators to operate at a scale that previously required entire teams. This is an opportunity, not a threat. A solo producer with the right AI tools can now build and maintain a music library that generates passive income for years.
Stay ethical. As AI makes content creation easier, maintaining high ethical standards becomes even more important. Be transparent about AI’s role in your work. Respect copyright. Contribute to developing fair industry standards.
Looking Ahead
The future of music production isn’t human vs. AI. It’s human + AI. And that future is already here. Over the next year, I expect to see AI audio tools become as commonplace in studios as reverb plugins and compressors. They’ll be part of every producer’s toolkit — not replacing the creative process, but enhancing it in ways we’re only beginning to explore.
I’ll be sharing more about this journey here on this blog — including deep dives into specific AI audio tools, behind-the-scenes looks at our workflow at Montadecs, tutorials for producers looking to integrate AI into their process, and honest assessments of where the technology falls short.
If you’re a producer, audio engineer, composer, or anyone working in the audio industry, I’d love to hear your perspective. How are you using AI in your work? What excites you? What concerns you? Drop a comment below or connect with me on LinkedIn.
The conversation is just getting started.
🇰🇷 한국어 버전 (Korean Version)
2026년, AI가 음악 프로덕션을 바꾸는 이유 — 프로듀서가 알아야 할 것들
28년간 음악 프로덕션 현장에서 일해왔습니다. 서울과 뉴욕의 레코딩 스튜디오, LA에 있는 한스 짐머의 리모트 컨트롤 프로덕션까지 — 수많은 기술적 변화를 목격했지만, 지금 AI가 우리 산업에 가져오는 변화는 차원이 다릅니다.
이것은 과대광고가 아닙니다. 저는 매일 이 변화를 직접 경험하고 있습니다. 제 회사 몬타덱스(Montadecs)에서는 음악이 만들어지고, 태깅되고, 라이선싱되고, 배포되는 방식을 바꾸는 AI 기반 오디오 도구를 개발하고 있습니다.
기존 방식 vs. AI 방식
전통적인 음악 프로덕션을 생각해보세요. 명상 앱을 위한 50곡이 필요한 클라이언트가 있다고 합시다. 각 곡은 고유해야 하고, 분위기·템포·장르·악기 메타데이터로 정확하게 태깅되어야 합니다. 커버 아트도 필요하고, 라이선싱 문서도 필요합니다.
과거에는 이 프로젝트에 작곡가, 프로듀서, 믹싱 엔지니어, 그래픽 디자이너 팀이 수개월에 걸쳐 수천만 원을 들여야 했습니다.
오늘날 Greit Music Store에서는 190명의 가상 아티스트, 38개 장르에 걸쳐 매일 약 50곡을 생산하고 있습니다. 인간의 창의성을 대체하는 것이 아니라, 이전에는 불가능했던 규모로 증폭시키는 것입니다.
AI가 가장 큰 영향을 미치는 3가지 영역
1. 음악 생성: AI 작곡 도구는 이제 상업적으로 활용 가능한 수준에 도달했습니다. 영화 스코어링이나 예술적 앨범을 대체할 수는 없지만, 로열티 프리 음악과 기능적 음악 시장에서는 혁명적입니다.
2. 오디오 태깅과 메타데이터: Essentia ML과 같은 AI 모델로 시간당 수천 곡을 태깅할 수 있습니다. 인간 태거의 하루 처리량(30-50곡)과 비교하면 엄청난 효율성입니다. 정확도도 대부분의 속성에서 인간 태거와 90% 이상 일치합니다.
3. 음성 및 사운드 디자인: AI 음성 합성과 효과음 생성은 게임, 영화, 팟캐스트 분야에서 새로운 창작 가능성을 열고 있습니다. 특히 다국어 현지화 분야에서의 잠재력이 매우 큽니다.
아무도 말하지 않는 과제들
흥분에 빠지기 쉽지만, 해결해야 할 진짜 과제들이 있습니다. 품질 정량화 — AI 생성 음악의 품질을 어떻게 객관적으로 측정할 것인가? 유사도 탐지 — 수천 곡을 생성할 때 각 곡의 고유성을 어떻게 보장할 것인가? 워터마킹과 출처 추적 — 구매자와 창작자를 어떻게 보호할 것인가? 이러한 문제들은 업계 전체가 함께 해결해야 합니다.
프로듀서를 위한 조언
AI를 두려워하지 마세요 — 함께 일하는 법을 배우세요. 성공하는 프로듀서는 AI를 창의적 증폭기로 사용하는 사람들입니다. AI가 할 수 없는 것 — 감정적 스토리텔링, 클라이언트의 숨은 니즈 이해, 놀랍고 기쁨을 주는 창의적 선택 — 에 집중하세요. 이것이 대체 불가능한 인간의 능력입니다.
음악 프로덕션의 미래는 인간 vs. AI가 아닙니다. 인간 + AI입니다. 그리고 그 미래는 이미 여기에 있습니다.
Sean Kim is the CEO of Montadecs (주식회사 몬타덱스), an AI audio technology company based in Seoul. He operates Greit Studios, a Dolby Atmos-certified production facility, and Greit Music Store, an AI-generated music licensing platform. With 28 years of experience spanning Broadway, Netflix, KBS, and Hans Zimmer’s Remote Control Productions, Sean brings a unique perspective to the intersection of AI and professional audio production. He holds degrees from Hanyang University and NYU, and is a member of ASCAP and SCL.
김선규(Sean Kim)는 서울에 본사를 둔 AI 오디오 기술 기업 주식회사 몬타덱스의 대표입니다. 돌비 애트모스 인증 프로덕션 시설인 Greit Studios와 AI 생성 음악 라이선싱 플랫폼 Greit Music Store를 운영하고 있습니다. 브로드웨이, 넷플릭스, KBS, 한스 짐머의 리모트 컨트롤 프로덕션 등에서 28년간의 경험을 보유하고 있습니다.