AI is a Force Multiplier like we've Never Seen

Photo by Nik

I’ve come across the same idea in 3 different framings this last week- and it got me thinking about how AI is impacting productivity:

1. I watched Casey Neistat’s video on Sora (an AI video generation platform)- “Will Slop End Creativity?”

   
   

2. I read an allegory about a subordinate who used AI to develop 5 bullet points into a full 12 page report for their boss. The superior had no time to read it… so they used AI to distill the 12 page report down into 5 bullet points.

   
   

3. Our team had to wade through a huge pile of metadata JUNK. It struck me just how critical years of contextual knowledge are to decipher what is what (and in particular, where to focus attention to get the most reward).

The modern day philosopher/investor Naval Ravikant has a simple but deeply insightful axiom- “leverage is a force multiplier for your judgment”. His argument is that leverage comes in many forms- capital, human, software- but how and where to apply that leverage is what creates outsized returns. As Archimedes put it - “Give me a lever long enough and a fulcrum on which to place it, and I shall move the world”.

AI is leverage like humanity has never seen before- but it is amplifying both signal and noise. AI isn’t creating wisdom- it doesn’t make us smarter, or more insightful, or even necessarily more effective. It is simply scaling what already exists.

The AI wave sweeping across every industry has the same seductive promise: more output for less effort, with Music being no exception. From songwriting to administration, we’re seeing a surge in automation and algorithmic productivity- and with it, a growing tension between speed and understanding.

Technology hasn’t (and arguably won’t) replace humans. But it is extending them.

The Productivity Mirage

In the creative domain AI has made it possible for anyone to “make music” in seconds. This democratisation has unleashed an avalanche of generative output- songs, stems, and soundscapes that didn’t exist yesterday and are unlikely to be remembered tomorrow.

It’s a repeated pattern- samplers, synthesisers and DAWs have each in turn unlocked creativity in new ways and been heralded as the end of human creativity. In each wave productivity exploded, but there has always been an adjacent element of collapsing discernment. We produced more content, but not always more value.

The same dynamic is now at play in with AI, but on a scale not previously imaginable:

• Generative models produce more music than humans can possibly consume.

• Recommendation engines are amplifying the flood rather than filtering it.

• The perceived productivity gain is often disguising the absence of genuine innovation or utility (= value).

This dynamic isn’t limited to music, or even creativity at large. We can certainly see the same phenomenon emerging within the less glamorous layers of music administration- where Big Data is now needed to track, match, and monetise rights.

AI’s Potential in Rights Management

Publishing rights administration is the connective tissue of the music industry- the invisible machinery that ensures creators and rights holders are remunerated quickly and accurately. It’s a system built on matching: usage to recordings, recordings to compositions, compositions to rights holders.

In that sense, it’s a perfect candidate for automation. AI can parse enormous volumes of data, detect patterns, and reconcile inconsistencies faster than any human team.

Applied intelligently, it can:

• Identify unclaimed or mismatched works across datasets

• Predict income flows and usage trends

• Surface potential duplicates or registration conflicts

• Automate responses to society/rights holder queries and claims

When deployed with domain understanding, this technology is transformational. Catalogs that once took months to clean can be reconciled in weeks. Collections that once depended on human resources can now be modelled with precision. The threshold for economic recovery of revenue can be significantly lowered.

But without that domain expertise- without human knowledge of copyright, licensing frameworks and the historic quirks of data provenance- the same tools can create chaos (or 'slop' to use the more colloquial term on the creator side).

As with previous technology, AI will happily match two works that share a title but nothing else, merge duplicates that aren’t, and confidently generate false metadata with absolute authority. The result isn’t efficiency but it’s accelerated disorder.

Humans Will Remain Essential

Music rights data is complex, inconsistent, and steeped in decades of territorial nuance. A single composition might have multiple co-writers, sub-publishers, alternative titles and localised derivatives. Rights might revert in one country, remain active in another, or differ across income streams.

AI can identify anomalies, but it can’t interpret them. It doesn’t know why a match is wrong, which dataset to trust, or how to interpret the context around adjacent metadata to understand if the surfaced error has material value or not. That requires understanding- the kind that comes from years working within publishing, from seeing how catalogs behave in the real world.

Human-in-the-loop systems blend both strengths. AI handles the heavy lifting: scanning, sorting, suggesting. Humans handle the interpretation: confirming, contextualising, correcting (from which AI can subsequently learn). The result is a system that is both faster and more accurate than either could achieve alone. Automation without expertise is simply error at scale.

In music publishing, as within other sectors, AI is a powerful new form of leverage- one that scales the expertise of those who understand the vertical’s nuances- the architecture of rights, data, and royalties. For experienced administrators, this makes AI a superpower. For those without that context, the same tools produce surface-level activity that feels productive but generates no true value.

This distinction will become more pronounced as AI continues to proliferate. The professionals who thrive won’t be those who use AI to replace thinking, but those who use it to extend their current operations and existing, well developed processes- to multiply their reach and accelerate the impact of their expertise.

A New Definition of Value

In a world where anyone can automate tasks, the differentiator isn’t speed or volume but discernment. The ability to understand which data matters, why a pattern exists, or how a rights chain operates has always defined the best-in-class publishing administrators. Tech (and now AI) are just dramatically amplifying those skillsets.

AI will continue to become more capable- it will clean data, flag inconsistencies, and surface opportunities, but humans will still determine what “good” looks like. The future of rights management will be defined not by competition between humans and machines, but by collaboration between them.

The most successful publishing operations will empower their in-house expertise with tooling to successfully manage copyrights on an unprecedented scale- and in particular to an unprecedented depth. As mentioned above, the key to unlocking the ‘micro-penny’ business of publishing is being able to economically collect every cent- delivering teams and structures that pick up revenue in an equation that is viable. There is no point spending a dollar to retrieve 50 cents. The use of human “judgement” with the force multiplier of AI means that dollar cost can come down, until it is a profitable activity to chase smaller amounts.

There is significant value in the music publishing long tail- so finely spread that only scalable tooling can efficiently collect it. My main point here is that AI is making that threshold fall dramatically- but it still needs human judgement as the core ingredient. As in the past, the real competitive advantage won’t be access to the best tools alone, but access to the best people using them.

May the Force (multiplier) Be With You

AI will not replace rights professionals.... but rights professionals who understand how to use AI will replace those who don’t.

In a world where machines can process everything but understand nothing, the most valuable skill of all is still judgment- and it just got a lot more leverage.