Navigating AI in the Music Industry

Today, we are going to talk about AI, but not in the way you expect. Today, I’m going to give creatives a solid three-point plan to beat AI in the marketplace. I’m going to explain how musicians, podcasters, authors and other artists can survive and even thrive amidst the unstoppable flood of AI generated slop we will all be forced to wade though for the foreseeable future,  And to maybe do some good in the process. It’s taken me over a year to write the script for this episode, and like so many of my solo episodes, I originally planned on it being very different from how it turned out. But sometimes the scripts takes on a life of its own, and I have to follow it to what always ends up being a far more interesting place. Those episodes tend to be my most popular I hope you enjoy it. Introduction This is a solid three-point plan for beating AI in the marketplace. I’m going to explain how musicians, podcasters, authors and other artists can survive and even thrive amidst the unstoppable flood of AI we will be forced to wade though for the foreseeable future. Artists, don’t kid yourself, generative AI is here to stay. There is no going back. But there is a way forward. This is a personal topic for me. I used to be a professional musician. I put myself though college playing in bars and clubs. I was Japan’s first professional podcaster. I also love generative AI and am excited about the amazing creative potential it promises. I want to see all of these things thrive. AI will be fine, of course. It’s supported with practically unlimited funds and by lawmakers and industry leaders around the world. Artists, however, could use a little help. What exactly does AI create? People asking if AI can create real art are asking the wrong question. Artists who need to put food on the table need to be asking what artistic needs AI meets in our economy. With those parameters, let’s look at what exactly AI is creating, using podcasts as an example. Google NotebookLM can take any textual input (your website’s FAQs, a press release, last quarter’s sales reports, anything) and create a convincing podcast from that input. A male and a female voice will smoothly and professionally banter about the topic and tease the listener that they won’t believe what’s coming up, and they express broadcast-caliber levels of surprise and admiration over the most trivial bits of information. It’s really good. NotebookLM has very high production standards. But there is nothing really inside. After a minute or two, it’s just not that interesting to listen to — even when the input information was interesting. This is because NotebookLM is incredibly good at imitating the structure and affect of a quality podcast. This is how all LLMs generate art, music, and video. They imitate a particular structure and affect, but the quality of the content is irrelevant. Structure and affect are the logical and emotional cues that let us classify a work as a particular type of art. The structure is the logical parameters; a pop song should be about three minutes long, it should have an identifiable melody. An image should be rectangular. An email should start with a greeting and end with a signature. Those kinds of things. The affect is the emotional parameters. It refers to the emotional reaction we have to a given work. It’s the vibe. Rock and country covers of the same song will have a different affect. They will feel different. Generative AI is successful today in those areas where structure and affect are important but quality is irrelevant. Saying “quality is irrelevant” is not an insult or a backhanded way of saying that quality is poor. The key fact is that AI-generated art (whether it is of high or low quality) excels in situations where quality is irrelevant, and human-generated art (whether it is of high or low quality) excels in situations where quality is relevant.