The AI Mixing Revolution That Already Happened
If you’ve been getting ads for AI mixing and mastering services, you’re not alone. These platforms are everywhere, climbing to the top of Google search results and promising studio-quality mixes in minutes. But here’s what most people don’t realize: the AI mastering revolution already happened 10 years ago.
Companies like LANDR pioneered automated mastering services back in 2014, and while some mastering engineers panicked about being replaced, most of us are still here, still working. Why? Because mastering is fundamentally different from mixing.
As a professional mixing and mastering engineer, I recently decided to put these claims to the test. I uploaded actual tracks to one of the most popular AI mixing and mastering services to see if the technology has truly caught up to human expertise. What I discovered might surprise you.
What is an AI Mixing and Mastering Service?
Before diving into my experience, let’s clarify what we’re talking about.
Understanding AI Mixing vs. AI Mastering
AI Mastering uses algorithms to analyze your final stereo mix and apply processing like:
- EQ adjustments for tonal balance
- Compression for dynamic control
- Limiting to achieve commercial loudness standards
- Stereo enhancement for wider sound
Most AI mastering services have been around for years and work reasonably well for basic mastering tasks. Tools like LANDR, eMastered, CloudBounce, and Masterchannel have processed millions of tracks with generally acceptable results.
AI Mixing is far more complex. It attempts to:
- Balance levels across 20-100+ individual tracks
- Apply EQ to each instrument based on context
- Add compression that responds to musical dynamics
- Place elements in the stereo field with proper panning
- Apply reverb and effects that complement the arrangement
- Make every track work together as a cohesive whole
The challenge? Every decision affects five other decisions downstream. Change the vocal EQ, and suddenly the guitar needs adjustment. Boost the kick drum, and the bass might need re-thinking. This interconnected decision-making is incredibly difficult to automate.
How AI Mixing and Mastering Services Work
Most AI mixing platforms follow this workflow:
- Upload: You upload individual tracks (stems) or a stereo mix
- Analysis: The AI analyzes frequency content, dynamics, and perceived genre
- Processing: Algorithms apply learned patterns from thousands of reference tracks
- Adjustment: Some platforms offer controls to tweak the results
- Download: You receive the processed audio file
Popular services include:
- Cryo Mix – Vocal-focused mixing with chat interface
- RoEx Automix – Stem-based mixing with free trial
- Unchained Music – Quick mixing for distribution
- aimixingmastering.com – Hybrid AI + human engineer approach
- LANDR – Primarily mastering, with some mixing features
My Experience Testing an AI Mixing Service: The Reality
I went into this test genuinely wanting to give the platform a fair shot. I selected a song with approximately 30 tracks—a fairly standard rock/pop arrangement with vocals, guitars, bass, drums, and keyboards. Here’s what happened.
Technical Issues Right Out of the Gate
Problem #1: Failed Uploads
The first red flag appeared immediately. When I selected all 30 tracks for upload, only seven actually made it through. I tried uploading the missing tracks separately, and the program crashed completely.
This isn’t just inconvenient—it’s a dealbreaker. Professional mixing requires all elements to be present. You can’t properly balance vocals against guitars if half the guitar tracks are missing. I attempted the process two more times with identical results: crash, crash, crash.
Problem #2: Wrong Key Tuning
Eventually, I managed to get enough tracks uploaded to evaluate the mixing. The AI had automatically applied pitch correction to the vocals—but in the wrong key. This is a fundamental error that reveals a critical limitation: AI can detect that something is “out of tune,” but it lacks the contextual understanding to know what key the song is in.
For a human engineer, this takes seconds to identify. For AI, it’s a complex problem requiring understanding of harmonic context, chord progressions, and musical intent.
The Mixing Results: Where AI Falls Short
After correcting the pitch issue, I evaluated the actual mix. Here’s what I heard:
What It Sounded Like:
- Essentially the rough mix with added vocal reverb
- No meaningful balance changes between instruments
- Lack of depth and dimension
- Missing the “glue” that makes professional mixes cohesive
When I Requested Changes: I told the AI I wanted the drums to hit harder. Its interpretation? Add more low-end to the drums. But “hitting harder” in mixing terms usually means adjusting transients, parallel compression, or midrange punch—not just boosting bass frequencies. This literal interpretation shows the gap between AI pattern recognition and human musical understanding.
The Final Straw: When I finally reached the mastering stage, the platform crashed again. At that point, I abandoned the test. I couldn’t fairly evaluate a service when I couldn’t even complete the intended workflow.
Why AI Mixing is Incredibly Hard to Get Right
My experience isn’t just one platform’s failure—it highlights fundamental challenges in automating the mixing process.
The Context Problem: Every Song is Unique
Consider everything that varies from song to song:
Vocal Variables:
- Different singers have different tonal characteristics
- Recording environments vary (professional studio vs. bedroom)
- Microphone choices drastically affect sound
- Performance dynamics change song to song
- Emotional delivery requires different processing
Instrumental Variables:
- Genre expectations differ wildly
- Arrangement density affects processing needs
- Room acoustics impact recorded sound
- Instrument quality varies tremendously
- Playing styles require unique approaches
The Interconnected Nature of Mixing:
This is the real challenge. In mixing, everything affects everything else:
- You EQ the vocal because of how it sits against the music
- You compress differently because of the performance emotion
- You shape the low end because of how the kick and bass interact
- You adjust reverb based on the track’s density
- You set levels in context of the entire arrangement
AI excels at isolated tasks: “Make this brighter” or “Reduce this frequency.” But mixing requires understanding relationships between elements—a form of musical intelligence that’s difficult to reduce to algorithms.
The Input Quality Problem
Professional mixing engineers work with relatively controlled inputs. Bedroom producers upload:
- Tracks recorded with consumer microphones
- Unbalanced rough mixes
- Poorly recorded rooms with terrible acoustics
- Inconsistent gain staging
- Digital clipping and distortion
AI trained on professional studio recordings struggles with these real-world variables. The algorithm might “know” what a good vocal sounds like, but can it salvage a vocal recorded on a laptop microphone in a reflective room?
What Current AI Mixing and Mastering Services Do Well
Despite my negative experience, I want to be fair. AI mixing and mastering services have legitimate use cases:
Where AI Mixing Excels
1. Quick References and Demos Need to hear how your song might sound when mixed? AI can provide a rough approximation in minutes. This is valuable for:
- Sharing work-in-progress with collaborators
- Testing arrangement ideas
- Creating demos for pitching songs
- Getting a second opinion on balance
2. Learning Tool for Beginners Bedroom producers can learn from AI decisions:
- See which tracks got compression
- Observe EQ curves applied to different instruments
- Understand level balancing concepts
- Compare before/after processing
3. Consistent Starting Points Some producers use AI mixing as a starting point, then manually refine:
- Let AI set initial balances
- Use it for rough EQ suggestions
- Apply its compression settings as a foundation
- Build from there with human adjustment
Where AI Mastering Actually Works
AI mastering has come much further because it’s a simpler problem:
Why Mastering is Easier to Automate:
- Works with stereo files (not 50+ individual tracks)
- More standardized goals (loudness, tonal balance, format prep)
- Clearer success metrics
- Less contextual decision-making required
AI Mastering for Demos and References:
AI mastering can be valuable for:
- Quick demos to test how your mix translates
- Reference versions to share with collaborators
- Learning what mastering processing does to your tracks
- Budget practice projects while you’re developing skills
Popular AI Mastering Services:
- LANDR – Over 10 years of refinement, millions of tracks mastered
- iZotope Ozone – Professional-grade plugin with AI assistance
- eMastered – Grammy-winning engineer input in algorithm design
- Masterchannel – Free tier with solid results
- Cloudbounce – Quick turnaround for testing
For Your Actual Releases:
When it comes to singles, EPs, and albums you’re putting out into the world, you still want a professional mastering engineer with:
- Years of experience hearing how tracks translate across different playback systems
- Understanding of the current market and how your genre should sound to compete
- Critical listening skills developed over thousands of mastered tracks
- Specialized monitoring equipment in acoustically treated rooms
- Objective perspective on your music that AI algorithms can’t provide
- Quality control to catch issues before your music goes live
The cost difference ($5-20 for AI vs. $50-150+ for professional mastering) is minimal compared to what you’ve already invested in writing, recording, and mixing your music. Your release deserves that final professional polish.
The Bigger Picture: What Gets Lost with Full AI Automation
Beyond the technical limitations, there’s a philosophical concern about AI in music production.
The Craft Comes From the Process, Not the Results
My worry isn’t just about AI replacing jobs—it’s about what the next generation of music makers might lose:
The Value of Struggle:
- The hundreds of hours spent in studios trying things
- Making mistakes and learning why they didn’t work
- Developing instincts through repetition
- Building taste through experimentation
A guitarist sitting alone in their bedroom, playing the same part over and over, isn’t wasting time. They’re developing:
- Muscle memory
- Tonal awareness
- Dynamic control
- Musical judgment
If AI writes the song, mixes it, and masters it, where does personal growth happen?
The Death of Happy Accidents
Some of the most iconic sounds in music history came from:
- Equipment malfunctions
- Processing mistakes
- Experimental mic placements
- “Wrong” techniques that worked
Examples:
- The Beatles’ “Tomorrow Never Knows” – Backward tape loops
- Led Zeppelin’s “When the Levee Breaks” – Distant room mics on drums
- T. Rex’s “Bang a Gong” – Overdriven console creating distortion
- Miles Davis’ “Kind of Blue” – First-take improvisation
These moments require human curiosity, risk-taking, and creative problem-solving—qualities AI doesn’t possess.
Building Musical Instinct Takes Time
Much of my knowledge as a mixing engineer comes from trial and error:
- Hearing how different compressors respond to different material
- Learning which EQ moves work for which problems
- Understanding how reverb interacts with tempo
- Knowing when to break the “rules”
This tacit knowledge—the kind you can’t easily explain but have internalized through experience—is what separates competent engineers from great ones. If everything is automated, this knowledge never develops.
Comparing AI Mixing Services: What to Look For
If you’re considering using an AI mixing and mastering service despite my reservations, here’s what to evaluate:
Key Features to Compare
1. Upload Reliability
- Can the service handle 30+ tracks?
- What file formats are supported?
- Are there file size or project limitations?
- Does it preserve audio quality (24-bit/48kHz minimum)?
2. Genre Compatibility
- Does it offer genre-specific processing?
- Can it handle electronic, rock, hip-hop, classical equally well?
- Are there user examples in your style?
3. Revision Options
- Can you adjust the AI’s decisions?
- How many revision iterations are allowed?
- Is there an A/B comparison feature?
- Can you save different versions?
4. Integration Options
- DAW plugin available (LANDR offers this)?
- Stem vs. stereo mix processing?
- Batch processing for albums/EPs?
- Export format options?
5. Cost Structure
- Per-track pricing vs. subscription?
- Unlimited revisions included?
- Free trial or preview available?
- Refund policy if unsatisfied?
Red Flags to Watch For
Based on my experience and research:
⚠️ Services that crash during basic operations
⚠️ No way to preview before paying
⚠️ Vague or missing technical specifications
⚠️ No real user examples (only company demos)
⚠️ Overpromising “radio-ready” or “Grammy-quality” results
⚠️ No human support or revision options
Alternatives to Fully Automated AI Mixing
If you’re not ready to hire a professional mixing engineer but want better results than pure AI, consider these hybrid approaches:
Option 1: AI-Assisted Plugins in Your DAW
Rather than uploading to a web service, use AI plugins within your mix:
iZotope Neutron ($129-399)
- Mix Assistant analyzes tracks and suggests starting points
- You maintain control over every adjustment
- Visual feedback helps you learn
- Works within your existing workflow
Sonible smart:EQ/Compressor ($149-299 each)
- Track-aware processing adapts to your audio
- Learns from your preferences
- Offers suggestions, not dictates
- Professional results with minimal experience
Waves StudioVerse ($9.99/month)
- AI-recommended plugin chains
- Thousands of starting presets
- Full customization after AI suggestion
Option 2: Hire a Human Engineer
When you’re ready for professional results, working with a real mixing engineer is the gold standard:
- Professional mixing and mastering from an experienced engineer
- Direct communication about your artistic vision
- Unlimited revisions until you’re satisfied
- Personal attention to every detail of your track
- Transparent pricing and quick turnaround
- Marketplace of mixing engineers at various price points
- Read reviews and hear samples before hiring
- Browse engineers by genre specialty and experience level
- Direct communication with engineer
- Range from emerging talent ($100-200) to industry professionals ($500+)
Option 3: Learn Mixing Yourself
Why This Might Be Your Best Option:
The skills you build will serve you forever:
- Understand your own sound better
- Make informed decisions about hiring help later
- Develop a marketable skill
- Maintain creative control throughout
Recommended Learning Path:
- Start with Free Resources:
- YouTube channels (Produce Like A Pro, MixBusTV)
- Free trial versions of major plugins
- Practice with free multitracks online
- Invest in Core Tools:
- A good DAW (Reaper is $60, incredibly powerful)
- Basic plugin bundle (Waves Horizon, FabFilter)
- Reference headphones ($100-300)
- Study Systematically:
- Take one course deeply (Mixing Breakthroughs, Recording Revolution)
- Practice on multiple genres
- Reference professional mixes constantly
- Get Feedback:
- Post mixes to forums (Gearspace, Reddit WeAreTheMusicMakers)
- Compare your mixes to references using SPAN or similar
- Iterate based on critique
The Future of AI in Mixing and Mastering
Do I think AI will keep improving? Absolutely. We’re going to see incredible advances. But I also think we’re asking the wrong question.
What AI Will Get Better At
Near-Term Improvements (1-3 years):
- More reliable handling of varied input quality
- Better genre recognition and appropriate processing
- Faster processing with lower latency
- More nuanced control over AI decisions
- Integration with major DAWs
Medium-Term Possibilities (3-7 years):
- Understanding musical context (verse vs. chorus treatment)
- Style transfer from reference tracks
- Vocal rider technology that sounds natural
- Adaptive processing that responds to arrangement changes
What AI May Never Fully Replace:
- Subjective artistic decisions
- Communication with artists about their vision
- Understanding cultural context and trends
- Creative problem-solving for unique challenges
- The intangible “vibe” that makes mixes special
The Likely Reality: Coexistence, Not Replacement
Much like photography, where phone cameras are incredible but professional photographers still thrive, mixing will likely follow a tiered model:
Tier 1: DIY/AI – Bedroom producers, demos, quick content
Tier 2: AI-Assisted – Mid-level productions, educated producers using tools
Tier 3: Professional Human – Serious releases, albums, major label work
Each tier serves different needs and budgets. AI won’t eliminate the top tier—it will make the bottom tier more accessible.
Frequently Asked Questions About AI Mixing and Mastering
Q: Can AI mixing really sound as good as a professional engineer?
A: For simple projects with clean recordings, AI mixing can produce acceptable results. For complex productions, nuanced genres, or tracks that need creative problem-solving, human engineers consistently deliver superior results. The gap is still significant.
Q: How much does AI mixing and mastering cost compared to human services?
A: AI services typically cost $10-50 per track for mixing, $5-20 for mastering, often with subscription options. Human mixing ranges from $100-500+ per song, mastering from $50-150+. The price difference is substantial, but so is the quality difference for serious releases.
Q: What’s the biggest limitation of current AI mixing technology?
A: Context and interconnected decision-making. AI struggles to understand how changing one element affects others throughout a mix, and lacks the musical intuition to make creative choices that serve the song’s emotional intent.
Q: Can I use AI mixing for my album or EP?
A: You can, but I’d recommend against it for your serious releases. If budget is truly zero, AI mixing is better than no mixing. But if you can afford it, hire a human for at least your singles or lead tracks. Your release deserves the best you can provide.
Q: Which AI mixing service is the best?
A: Based on user reviews and industry reputation: Cryo Mix offers the most control, LANDR is most established (though primarily for mastering), and RoEx Automix has good results for stem-based mixing. But “best” depends on your specific needs, genre, and expectations.
Q: Will AI replace mixing engineers?
A: Not likely in the foreseeable future. AI will handle more of the routine work, but creative mixing that serves artistic vision requires human judgment, communication, and musical understanding. The role will evolve, not disappear.
Final Thoughts: Where AI Fits in Your Music Production
Here’s my bottom line after testing an AI mixing and mastering service:
The technology is impressive in theory but still unreliable in practice. My experience—crashes, wrong key detection, superficial mixing results—revealed that we’re not yet at a point where AI can fully replace human judgment in complex audio work.
That doesn’t mean AI has no place. For references, demos, learning, and even acceptable-quality releases when budget is zero, these services fill a need. AI mastering specifically has matured to the point where many users won’t notice the difference from human mastering on consumer playback systems.
But for your important releases—the tracks you’ve poured your heart into—I still believe in human expertise. The craft of mixing involves too many subjective, contextual, and interconnected decisions for current AI to handle at a professional level.
My concern extends beyond quality to creativity itself. If we automate away the struggle of learning music production, we risk losing the instinct-building, taste-developing, problem-solving process that creates true artistry.
So by all means, experiment with AI mixing and mastering services. See what they can do. Learn from their decisions. But don’t abandon the pursuit of understanding the craft yourself. The journey is what makes you an artist, not just someone with AI-generated tracks.
Want a Human to Mix and Master Your Song?
If you’ve decided you want professional human expertise on your music, I’d love to help. Visit mixandmastermysong.comto learn more about my services, hear examples of my work, and get started on making your tracks sound their absolute best.
Because at the end of the day, your music deserves more than an algorithm—it deserves someone who cares about your artistic vision as much as you do.