Benn Jordan delivers a scathing, evidence-backed takedown of the booming AI mastering industry, arguing that most commercial services are merely repackaged open-source tools sold at a premium. Rather than accepting the hype, Jordan conducted a massive double-blind study pitting artificial intelligence against human experts, arriving at a conclusion that will save independent artists thousands of dollars: the technology is currently a financial trap, not a creative breakthrough.
The Loudness War and the Human Element
Jordan begins by demystifying the often-confusing hierarchy of music production, distinguishing between the producer, the mixing engineer, and the mastering engineer. He notes that while a producer builds the track and a mixer balances the levels, the mastering engineer provides the "final oomph" that determines how a song translates across different systems. However, he argues that this role has been distorted by decades of commercial pressure. "The problem with prioritizing loudness above all things is that you lose fidelity in the detail of the processes," Jordan writes, describing the destructive practice of "brick walling" where waveforms are smashed against a threshold to make tracks sound louder on the radio.
He illustrates this historical failure by pointing to the Red Hot Chili Peppers' Californication, an album he feels was ruined by aggressive mastering that stripped away the band's subtlety. This historical context is crucial; it frames the current AI boom not as a revolution, but as a continuation of a cycle where technology is used to sacrifice quality for volume. Critics might argue that modern streaming normalization has largely ended the loudness wars, yet Jordan's point remains valid: the industry's obsession with volume created a culture where subtlety is often viewed as a weakness, a mindset that cheap AI tools are eager to exploit.
"At least conservatively at least 90% of the time that you see the words AI in anything related to music production, what you're actually seeing is a subscription service that's charging you for repurposed open-source software without attribution."
The Open-Source Reality Check
The core of Jordan's argument rests on a technical revelation that few commentators have highlighted: the algorithms behind expensive AI mastering services are often freely available. He identifies a specific open-source repository called "Matching 2.0" as the engine driving many of these paid platforms. "If you are even remotely familiar with compiling and running a Python program this will be a piece of cake," he notes, explaining that artists can run this software locally on their own machines for free, with no internet connection required.
Jordan's analysis here is particularly sharp because it shifts the conversation from "can AI do it?" to "why are you paying for it?" He suggests that the value proposition of these services is largely marketing, wrapping accessible code in a user-friendly interface and charging a subscription fee. He envisions a future where tools allow users to "turn knobs and faders and instantly hear in real time all of those differences," rather than relying on a black-box algorithm that requires a credit purchase to describe a sound in English. This critique exposes the fragility of the business models built on the premise that AI mastering is a proprietary miracle.
"To me realistically using that to master music is kind of like putting on a pair of boxing gloves to build a Lego sculpture."
A Better Path: Tools Over Services
Instead of subscribing to opaque AI services, Jordan advocates for investing in transparent, one-time-purchase plugins that offer similar functionality without the recurring cost. He highlights specific tools like Native Instruments' Ozone and New Fangled Audio's Elevate, which use AI not to do the work for the user, but to suggest starting points. "Ozone standard has literally everything you need to learn the ins and outs of mastering a song," he writes, emphasizing that these tools serve as educational aids rather than replacements for human judgment.
His recommendation to spend money on learning the craft rather than outsourcing it to a server farm is a powerful counter-narrative to the current tech hype. He argues that the barrier to entry is lower than ever, provided artists are willing to trust their own ears. "Remember this is Art; there is no such thing as a perfect Master," Jordan asserts, urging producers to stop treating audio engineering as a mystery and start treating it as a skill. This approach empowers the artist, shifting the dynamic from consumer to creator.
"I promise you it is not rocket science and it is not all that complicated."
Bottom Line
Benn Jordan's piece is a vital corrective to the uncritical adoption of AI in creative fields, backed by a rare combination of technical transparency and empirical testing. While his dismissal of all AI mastering services may overlook niche use cases for rapid prototyping, his central thesis—that the industry is largely monetizing open-source code while selling a false promise of quality—is undeniable. The strongest takeaway is not that AI is useless, but that the current commercial model is a distraction from the real work of learning to shape sound with intention.