Who Owns The Codes? The Sound and Ownership of Music in the AI Age

Posted on 25 February 2025 by ratpie

Preamble

Early in 2024 I was asked to contribute a chapter to the IAEL’s book on AI in music. It was an honour to be asked, and a significant effort to deliver something that I felt would not waste the readers’ time by adding to the torrent of ill-informed texts about this curious subject. Curious, because at no point have I heard anyone complain about the paucity of supply in music; quite the opposite. As a supremely human endeavour, music sits uneasily within the framework of capital and production for profit. AI promises to remove the human, and the endeavour, so that the gains can all accrue to capital. Why we should give this idea the time of day I find baffling, and the knowledge that our elected representatives here in the UK are seriously contemplating lifting one of the great achievements of the Enlightenment – copyright in creative work – to grease the extruders is profoundly depressing.

I set off at a clip, and by the third delayed deadline and some excellent editing had delivered a shortened version of this text. Some of the shortening took out references to interesting music produced along the timeline of music’s interaction with systems and ultimately with computing, of which generative AI is arguably a continuation. The IAEL is now no doubt working on another publication of equal importance to the music industry, and I can with confidence recommend whatever they produce as I know it is the product of a very thorough, well informed, and humanistic process. So with broader interest in music and AI probably peaking here in the UK I am taking the opportunity to evade my editors and publish my first version.

Introduction

AI and music is a big subject. I propose here to discuss some of the concepts and foundational methods behind using AI to generate music. I will ask how we can know about the sources that have been reworked into new music by generative AI. Finally I will consider some of the impacts we will very likely see on culture and the music industry, including how we might need to rethink some of our ideas about creativity, ownership, and attribution.

AI is already used widely in music for other things, some of which are certainly interesting and useful. Recently AI extracted a John Lennon vocal track from an old mono cassette demo, and gave us an unexpected and unprecedented new Beatles single. And AI is smoothing out human fallibilities in pitching and phrasing on many new recordings, and helping deliver industry standard mixing and mastering. Assistive technologies raise very different questions however; we see far greater cultural, legal, and economic challenges when AI itself makes the music.

First the usual warning! IANAL – I am not a lawyer, but I do have over 30 years broad and active experience in the digital music industry. And for another acronym – TLDR – here’s where I think we will land. I certainly don’t foresee wholesale destruction of either musicianship or the economics of making records; the value of these is not just an affective waveform, but rather a complex bundle of culture and identity alongside the traditional music making and recording skills. And that suggests that we will find ways to recognise and reward human creativity when it uses and is incorporated in new computer generated music.

Sonification of Code

To understand how generative AI can make music we need to start with an idea of how music can be understood in terms of codes and rules. People have been using codes to generate music probably for millennia. Anyone who been through a western music education will have learnt many sets of rules, such as those which underlie fugue and counterpoint. Some prominent composers played with their understanding of rules and chance. One thinks of Mozart’s ‘Musikalisches Würfelspiel’ (Musical Dice Game), where a roll of the dice selected precomposed bars. Outside of the western music, traditional forms such as raga and gamelan are essentially heuristics performed by musicians. 

Instruments such as the barrel organ and player piano, which performed mechanical representations of music, were transformative, helping sophisticated music reach further than could have happened solely with trained musicians. It was not just that the machines could be operated by an untrained musician; they could also play music that was beyond human capability.  Rendering the mysteries of music as a sequence of pins or holes in a roll of card must have suggested the possibilities inherent in applying maths and geometry to expand what could be created. This characteristic was exploited to the full by Conlon Nancarrow in his Studies for Player Piano. For a fun example, track down Study for Player Piano No. 3a, which is, to my ears, remarkably similar to early generative, or AI music, featuring manic boogie woogie motifs.

It was the introduction of the computer that blew the lid off what had until the mid 20th century seemed the rather constrained field of mechanised music. The digitisation of music in western culture, as notation, chiptunes, and later as MIDI data, made the rules inherent in music much more observable. Advances in digital signal processing during the 1990s showed that audio files containing music could be analysed and musical information extracted, starting out with pitch, onset, and duration of sounds. Early computers could be applied to algorithmic composition, picking up on the idea of music as the product of heuristic or stochastic processes.

So this approach treats music as essentially a sonification of codes, and there are some quite wild examples. The fruitful interchange between modern classical practice and the computer music labs that started to be built in the second half of the 20th century looks almost like a ‘scene’. Alvin Lucier for instance mapped brain impulses to electronic instruments, producing a percussive soundtrack to perception, thought, and feeling. Another academic composer, Xenakis, was among the most strongly identified with computer software as a compositional tool. It’s an important distinction that Xenakis was not looking for creativity in the algorithms that assisted in his composition. Instead he considered the computer as a way to express through the medium of orchestral music the mathematical concepts he was familiar with. Later he developed software that could turn his drawings into sound, enabling him to use in composing the skills he had developed as an architect.

Beyond the Code: How Generative AI Works For Music

Contemporary applications of AI in music are much more sophisticated in how closely they emulate what we think of as ‘human’ made music. And they generate music that aces the Turing test. Given how good humans are at making and following rules, that’s perhaps not so surprising. But when we can’t tell whether the pilot’s hands have ever touched the rudder, that seems to me a step change.

Many aspects of our cultural and economic life have been disrupted by the rapid global development of digital and communication technologies over the last 35 years, and the academic field known as MIR – Music Information Retrieval – is no exception. The ability to make huge collections of recorded music available digitally, to apply powerful computing resources to analysis, and quickly share the resulting codified data sets, catalysed the explosion of ‘generative’ AI that we are now in the middle of.

To understand better the impact AI will have on music, the music industry, and music culture, it helps to understand something of how those black box algorithms can churn out tunes.  With sincere apologies to the scientists and researchers whose work actually powers these extraordinary advances, here’s a very brief and sketchy overview of how generative AI works for music.

The sounds and patterns we perceive in music can be extracted from a corpus of compositions and recordings, and expressed as a set of probabilities. Some of the basics are easy to codify – pitch, basic timing blocks such as beats and bars. Different instruments can be separated out, and their roles understood, which enables the more properly ‘musical’ features such as melody, rhythm, and harmony to be delineated. Higher level structures such as verse, chorus, bridge, etc., emerge out of further analysis. Commonalities between different works and recordings can be clustered, creating sets which are analogous to genres, styles, and modes. Hierarchical sets of features further define the differing eras and traditions, enabling automated differentiation between similar sets of instruments used in different ways.

These models can be thought of as the vocabulary and grammar of the language of music, but of course without the meaning that natural human languages carry. In data terms the music has been separated out into tokens, the sequential relationships between the tokens have been inferred and stored, and the inference used to improve subsequent performance of the algorithms. The software machines that do this work are known as ‘transformers’. Recent advances have been driven by the discovery that adding more parameters and more transformers hugely improves the models that can then be used to generate output. In some ways this is similar to the way computers understand images, as a grid of pixels; the image gains fidelity the more gradations in colour and brightness there are, and the denser the pixels are packed. At a threshold human perception loses the ability to see the degradation, and just sees the image. Similarly with generative AI, adding vast complexity nudges the performance up to the point where we humans simply can’t differentiate between ourselves and robots.

In AI music analysis and generation, as in other applications, this phase is known as ‘training’. Any corpus of music, which in the AI field is known as a ‘training set’, can thus be rendered as a set of descriptors, along with the probability that each musical idea or motif will happen at a given point in a piece. The result in AI terminology is called a ‘foundation model’. Prompt the foundation model with some terms that are strongly associated with a particular form or style of music and out comes what you might call a statistically probable fugue, or rockabilly song, or eurodisco track. Or a guitar part, or drum track, or string section to supply what would otherwise be provided by a musician.

All very interesting, but if audiences don’t rate the results the whole enterprise remains, literally, academic. Early computer music did indeed sound pretty inhuman. The micro-timings and weights in human performance, and the infinite timbral spectrum of the voice and acoustic instruments were not easy to emulate. Perhaps encouraged by the market’s reaction to his Ambient series of recordings, in the mid 1990s Brian Eno produced and released his ‘Generative Music 1’, using software created by Tim Cole & Pete Cole at their company SSEYO, and which they called ‘Koan Music Engine’. Their software emulated stochastic processes, such as the path a ball bearing takes down a chute. It lent itself naturally to contemplative, slowly developing soundscapes. Eno himself made the connection with the minimalist composers of the 1960s.

The huge scale of today’s foundation models, well beyond what even the most powerful computers could process in the 1990s, has opened up many more styles and genres of music to convincing generative production.

Owning the Codes

This asks some very fundamental questions about whether the creators of the music in the training sets have an attribution or ownership interest in the output of generative AI. It is as well to know what we could be able to do about that before considering what we should do. Tracing the output back to the inputs in the sets is technically difficult, but by no means impossible. Any piece of music in a training corpus can be uniquely identified using methods we have developed and applied in the digital music industry. A combination of technological tools such as file hashes and audio fingerprints can be used to build strong associations between the music and copyright holders. The nature of the analysis required to produce a foundation model maps very well onto a requirement to store and manage the provenance of each inference stored as a token in the model. And when the model is used to generate a new piece of music, the tokens invoked, and therefore the provenance of each element, can be logged.

This describes a situation where we could have visibility and control over each step in the process, from raw material to finished product. There are two aspects to this which would present a challenge in the real world. Firstly it is data heavy, and very dependent on trust in the parties and the systems involved. When very few entities have the resources to manage the processes involved in training foundation models this might not be insurmountably costly. As this capability spreads however it will quickly become impossible to identify who has done what with which music. And that’s the second challenge; we wouldn’t necessarily think it desirable to embed the kind of external accountability and surveillance into private computing systems that would be required to make an auditable supply chain.

One response to this could be to decide just to select reputable sources and rely on what they tell us. That is the approach Google is taking with its SynthID product. When its own music generator, Lyria, has output a new piece of music, an identifier is also generated and embedded in the waveform. When the audio is rendered for playback this identifier can be extracted. It’s robust enough to withstand compression and transcoding. But it clearly has no meaning outside of a Google world, so doesn’t help us much when we leave the ecosystem. Auditing Google seems tough too, one of those ‘quis custodiet’ type of questions.

Where we have no visibility or trust, either because we can’t or don’t want to embed traceability in our music production and distribution systems, there is another approach which might yield at least a reasonable degree of confidence that a source was in a training set. Just as the characteristics of sets of music are codified and stored by the foundation models in the generative AI process, the same process can be applied to the results. If the resulting models display a high degree of similarity it’s possible that the generated music was based on a highly correlated training set. That’s not quite the same as fingerprints on the murder weapon, but close.

Given the complexity and disproportionate cost of tracking, and some of the uncertainties, it doesn’t seem viable to me to use the same private licensing and accounting for AI generated music that we have developed for sampling, for example. And with complete supply chain tracking unlikely to be achieved, we would naturally look for some kind of collectivisation of ownership, with distribution rules to deliver fairness rather than absolute accuracy. This comes with note of caution, as this is a rapidly developing field of study and much remains uncertain or unknown. But if we can train the transformers to deliver an opinion much like a trained musicologist might, the same technology that generated the music could be used to apportion revenue to the creators of the training set. We can see from this how we can maintain a set of sufficiently identified works and recordings in a repertoire pool, and a set of usage data and statistical processes with which to share out revenue pools derived from AI generated works and recordings.

Speed and Unflagging Energy

If AI music makes some people nervous, it’s easy to see why. We’re very attached to our Romantic notions of creativity, and the myth of the inspired and lonely genius. Indeed, artistic copyright has its foundation in individualism, and the inalienable identification of the work with its author and of the performance with the artist. Mozart’s appreciation of his own genius was ahead of its time, as his letters painfully show. His miserable daily life was that of a court servant who refused to please his master.

It seems possible that our Romantic notions of creativity follow, rather than lead our investment of copyright in the results of creative efforts. The myth of the lonely genius seems to emerge alongside the introduction of copyright, perhaps to manage the risk of being accused of plagiarism. In an inversion of the political economy that made a pauper of Mozart, the most successful copyright creators have ended up with the wealth and status of their erstwhile aristocratic patrons, while somehow preserving their bohemian freedoms. Exclusive ownership in music turned out to be very much worth having.

Even the loneliest and most inspired Romantic composer leaned on formal systems, such as scales, harmony, regular rhythms and time structures, and larger musical forms such as the symphony. Most have studied music theory, covering systems and rules as well as precedent. Innovators such as Satie and Debussy were contemporaries at the Paris Conservatoire, and today there is a Conservatoire named after Debussy, which tells you which of the two was more of an iconoclast!

Contrast the most formal approach to composing and performing music with the speed and unflagging energy of AI, processing tune after tune in seconds. Then add the notion that the AI could at some point be able to call on an almost perfect and complete set of music theory, and millions of previous examples of human music. It’s a classic B movie plot – the machines are taking over, and all we can do is build more electricity generating plants and data centres to feed their insatiable maws. And, thanks to the combination of cloud computing and the internet, the music machines are universally accessible, at low cost, and already connected to global music distribution platforms.

A Synthetic Economy of Music?

It’s here, at the intersection of culture and the economy of music, that some anticipate an AI-induced tectonic shift. There’s a dystopian way of looking at this; synthetic artists flooding synthetic markets with synthetic music. To me this seems to misunderstand the nature and meaning of value in music, severing it from its cultural and human context. That today’s markets might not be great at rewarding the value we find in music is not an argument for abandoning them!

Digital music platforms have a history of intervening in the supply side of the market; indeed the digital market was kickstarted by two big interventions, unbundling the album, and enabling mass ripping of CDs to fill iPods. Platforms have encouraged mass piracy, and more recently incubated and supported DIY music upload businesses. AI is the latest intervention and is already fueling a massive increase in supply. Just one company, Boomy, claims to have generated nearly 20m songs.

Success in the industry equates, broadly, to high popularity over time. Music is a cultural good with very low barriers to consumption, and a typical consumer’s usage remains strongly influenced by her awareness that others like her are also enjoying the same music. Such a competitive dynamic fuels a very concentrated market; a few tracks earn almost all the revenue, while most get little or none. Even the crudest division of the market into winners and losers asks the pertinent question: which cohort is most vulnerable to competition from AI-generated music?

I suggested above that current AI-generated music aces the Turing test; this assertion needs heavy qualification. There is a big difference between ambient trance music, and a complex piece involving traditional acoustic instruments, singers, and sophisticated production techniques. AI is much better at trance. A big movie soundtrack remains a big beast of a creative challenge (although one can prompt an AI music generator with ‘epic cinematic soundtrack’ – the result sounds like the band Dragonforce but without the humour). So the strength of AI music generators is their effectively infinite capacity to increase the supply of generic music.

But our collective preference is demonstrably for hits and innovation over time, rather than just more generic music with less of the cultural and human value in the bundle. Even the recommendation and curation interventions struggle to compete with audience preference. The prized Spotify editorial playlist placements have a follower to playcount ratio of between 1% and 5%. Unless we get to the point where we prefer AI generated music, its impact on success for artists will be minimal.

Assimilation

Like so often in culture and markets, the infiltration of generative AI into music turns out to be a question of values as much as of technology. A dystopian outlook for a human economy of music making seems to me to be founded on a view that the value in music is solely in any anonymous affect it has when heard. This is ahistorical. Music might not have the cosmological significance it once had, but it remains an experience and activity loaded with personal and societal significance. We might find comfort in familiar or unchallenging music, but the norms expressed in our copyright laws incentivise innovation and originality, and markets agree. It’s the Queen catalogue that is the Koh-i-Noor diamond for the music rights hoarders, not the tens of thousands of contemporaneous rock plodders.

Here then is a positive view of a future in which generative AI makes music. We will surely find ways to harness AI in generating therapeutic or performance enhancing music for health and sport. Generative AI can iterate and shorten the feedback loop in ways that humans just can’t. The value of this music will be immense, and there is already a lot of literature supporting its effectiveness, even before we could adapt and optimise the music itself. 

Alongside this the processes involved in generative AI are producing new insights into what music is, leading to an expanded commons of ideas and understanding available to the foundation models as well as to human artists and composers. Our copyright framework recognises the expression of an idea as worthy of protection, not the idea itself. This common stock from which musicians draw is an important cultural seedbank. We use tunings and structures in contemporary music that were fresh in the Baroque period; generative AI will perhaps help keep this stock fresh so that future musicians have their own go at making the perfect break up song or pastoral tone poem.

Our veneration for artists and creators is unlikely to be eroded by the presence of artificially produced music in our public spaces and on music platforms. We will continue to want to see the artist’s presence in and behind the music, and to do so we will surely need to strengthen the way we manage identity and authenticity in creation and performance. So with an awareness that I am writing for an audience of entertainment lawyers, here is my best guess for what the near future holds for music in the age of generative AI. We will add an AI value for the use of music and recordings to our collecting society mandates, and find inexpensive ways to distribute revenue, probably using similar technology to that which made the AI music in the first place. And we will support the deep cultural and social value our artists represent with stronger attribution rights, and technology to identify and authenticate their work.

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A Pigouvian Pollution Tax on ChatGPT!

Posted on 13 January 2023 by

If one had to characterise the state of mind among knowledge and creative workers, musicians included, in 2022, worries about meeting demand for quantity or variety of content would not figure high. Misinformation, provenance, and the difficulties of making a living, yes. Being short a few million blog posts, product reviews, or new music tracks, no.

So it was perhaps a bit surprising when the appearance of a flock of AI engines, and their ubermensch ChatGPT, an easy to use interface to a new text AI, generated (pun intended) wild enthusiasm, along with some doomsterism, and of course digital reams of new content as the commentariat contemplated their own industry’s potential demise. Perhaps the boosters thought that with AI doing the creative stuff they would have more time for the real work; self-promotion.

There were some practical responses too. Stack Overflow, the techie Q&A site, moved quickly last month to ban answers generated by pasting questions into ChatGPT. Their reasoning? “…while the answers which ChatGPT produces have a high rate of being incorrect, they typically look like they might be good and the answers are very easy to produce”. New York City schools followed with another swift ban, adding concern for safety and appropriateness.

Behind ChatGPT is a language model developed by OpenAI. It can output school essays, reviews on Amazon, answers on Stack Overflow, even rather lame sonnets (try it!). Other AI models produce images, music, and video, churning out in seconds what would take humans hours or days.

AI output is being widely monetised in copyright markets intended to nurture human understanding, research skills, and creativity, over which it has unfair cost and quantity advantages. Here’s California’s Boomy Corporation, an AI music generator, boasting about its productivity: “Boomy users have created 10,900,994 songs, around 10.8% of the world’s recorded music.” Of course the bit about Boomy users creating songs is a lie – the users filled in a short form and tapped some buttons.

Anything that can be consumed as news, opinion, entertainment, or art, can be over-produced by these generative AI systems, making them part of what I term ‘hypercompetition’. This is a seemingly ineluctable rule of the classical platform business model, which bundles distribution with curation, and follows up with interventions to increase supply. The aim is to heighten competition with and between human practitioners in the monthly zero sum subscription share-out games.

AI could make light of much drudgework and formal, logical writing, freeing time for more human creativity. It will almost certainly accelerate knowledge extraction and cataloguing in some specialist fields. But almost all ChatGPT output is spam, low quality stuff we really don’t need more of. However I’d argue we should not push string by trying to ban AI. Instead we should treat it like the pollution it is, and slap a large Pigouvian tax on it. 

Some activities create costs that are borne by people who don’t share in the gains of those undertaking them. Congested roads, smoke from factory chimneys, and unusual demands on publicly funded health services are examples. Economists think of these as costs external to the economic activity associated with producing things, driving somewhere, or smoking tobacco. There are rare cases of external gains too; think of how good state-funded schools inflate property prices in their catchment areas.

The external costs from ChatGPT are the teachers’ time and effort, and pupils’ lack of learning caused by cheating at school, incorrect Stack Overflow advice followed, as well as revenue extracted from copyright markets and the extra efforts human creators need to put in to compete. Arthur Pigou himself, Cambridge Professor of Economics in the first half of the C20th, was deeply interested in welfare. He argued that tax was the simplest and most effective way to deal with gains and losses that fell to those outside of a transaction.

As well as being a redress for harmful activity, Pigouvian taxes can provide funding for more of what we do want. This is important – interventions need to have broad popular understanding and support. In this case the harms fall on education and the arts, two  significant absorbers of taxpayers money which could be supplemented or offset.

I asked ChatGPT if it thought it should be taxed; it denied any knowledge of itself. It might not yet write a decent poem, but AI has clearly got its head around tax evasion.

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Fair Shares, Fair Markets. Can Music Have Both?

Posted on 27 October 2022 by

The music we listen to on music services has many creators, facilitators, and distributors. Everyone wants to be paid fairly for their work, and get a fair share of any future revenue. But are any of the different types of contributors – artists, songwriters, etc., – treated so unfairly, or do any of them so systematically disadvantage other groups, that we need systemic change?

The way music and recordings get made and experienced might seem complex to an outsider or a neophyte. In simple terms, composers and songwriters write the music, artists perform it, and if anyone records the performance the person or company who organised it is deemed by law to own the copyright in the recording. Copyright laws make it possible for composers and performers to form contracts with investors, and with people who can turn the music and recordings into revenue. So, for instance, what is commonly called a record deal is an exclusive right to record future performances – the right to make records.

In music’s case this moderate degree of complexity delivers the great benefit of flexibility which allows the same contractual and back end processes to accommodate a very wide range of musical practices with only moderate stresses and strains. Someone who produces electronic dance music entirely in software and at their own behest and risk ends up with copyrights and remuneration rights that they can chance in the market if they wish, and no obligations to anyone else. At the other end of the scale, anyone with the money can commission new compositions, agree terms with a group of musicians, and hire a producer to make recordings.

People at the creative end of the music industry have traditionally made a calculated trade off, between money now, and money in the future if the music should prove successful in the market. If their need for money now is greater, to support a bigger team and a better recording studio or to bring in the services of songwriters with a track record of hits, they can seek investors, who, given the specialist nature of the music industry, are likely to have a track record themselves.

Some of the public arguments over fair pay and fair markets that surface in the music industry seem either wilfully ignorant or cynical. As always it helps to know who has work and investment at risk, and who has already been paid. Some terms used are unhelpful – ‘artist’ for instance, which often conflates the role of composer, performer, and producer of the sound recording. The basis on which each person’s work is contributed is often obfuscated. An instrumental performer should have been paid for work done, while a record company might have made an un-repayable but recoupable advance to members of a band for a recording. Some parties end up with copyrights, some with remuneration rights, and some with a pay cheque.

The music industry – notoriously – doesn’t have a perfect record in attributing work correctly and fairly, and has been know to take advantage of the ignorance of musicians about copyright and contracts. Neither law nor standard industry practice protects this kind of behaviour, and when it surfaces there is justified public outrage. When anyone with an interest says ‘fair’ they invariably mean more; no interest group would get support if they argued for less. Music is no exception. Lobbyists, trade associations, grassroots organisation, unions, politicians; all are engaged in a constant round of appeals to public and regulators to change the terms on which they trade.

A growing market delivers more to most participants of course. And a changing market often re-orders the way it rewards the different contributors. But it is hard to argue that anyone is deterred from entering this complex market, or that the cost of doing so is prohibitively high in all but the least developed economies. So perhaps, while always being alert to the signs of exploitation and foreclosure, we should have a little more trust in the market to decide what is fair.

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AI Will Help and Hurt Music. Here’s How.

Posted on 16 October 2022 by

Computers have been helping us make and store music for many years. Music AI is not so new either; already by the early 1990s there was widely available software runing on consumer grade desktop computers which could generate and regenerate music-like patterns of sound. The last 30 years have seen incremental improvements in the way computers analyse, categorise, and parameterise music.

This body of knowledge is increasingly used to make new music, but it’s hard to find a basis on which to judge the output of these robot composers. Should we fully decontextualise the results, risking the rather nihilistic conclusion that everything produced is a perfect version of itself? Or should we continue to base our judgements on our own human history, achieving perfection only when we can’t tell the fuguebot from the Bach? Or perhaps we should be simple hedonists, and ask the artificial intelligence to iterate for our greater pleasure.

The digital music market gives us another new way to evaluate the robots – commercial productivity. Is their output merchantable alongside music made by humans, and at a competitive cost to produce and supply? Early indications suggest that it is, and that in many ways robots are a much better fit than musicians with the market conditions created by music subscription services.

So what is it that makes a robot the perfect modern artist? Consider the contours of the digital market: each month is a contest for the plays that are claims on a finite pot of money, paid in by subscribers who can only play one track at a time. Increasing the play count across the whole service does not increase the money available, so one track’s success comes at the expense of all the rest. If instead of being an artist a supplier could be the owner of a fleet of them, each turning out the optimal amount of the right kind of music at the right time, sheer bulk in each monthly contest would tilt the odds slightly, even without any algorithmically refined fitness.

An entirely digital supply chain means that new tracks can go from AI algo to audience without being touched by human hands, and on an unprecedented scale. Here is Boomy, a fairly recent start-up taking advantage of these capabilities:

How does Boomy work?
Boomy uses music automation technology powered by artificial intelligence, which you can use to create and save original songs in seconds for free. You can also create Releases and distribute them to all major streaming services and digital music retailers worldwide, and earn a share of royalties when your songs are played on networks like Spotify, Apple Music, TikTok, and YouTube.

https://boomy.com/about

Choose a style and a few options, click a button, and it will make a track. Type in a title and ‘artist’ name, and it is ready to be delivered. Boomy retains the ownership ‘for convenience’, but pays whatever pennies the music generates on Spotify and many other music services. Somewhat disingenuously Boomy claims, “Boomy users have created 9,007,441 songs, around 9.1% of the world’s recorded music”, all without a single shake of a tambourine. They are conveniently short too, ideal for increasing the collective playcount when playlisted.

It’s surely not controversial to suggest that capital likes indefatigable and fatigue-free robots. So to be able to piggyback on 300 years of hard won creators’ copyright law and soak up some music subscription money seems like an offshorer’s dream scenario. They should enjoy it while it lasts, because there are many reasons why that won’t be very long.

Music seems to be fundamental to who and what we are as humans. Musical instruments are among the earliest artefacts that we recognise as setting us apart from our fellow primates. We know that flutes have a continuous history of more than 40,000 years; it would not be surprising that tuned percussion has been even longer with us. And of course our voices and bodies precede anything we made in prehistory. Mechanisation came to music surprisingly early too. There are references to instruments resembling the hurdy-gurdy from very early mediaeval times.

With its brief periods of fashionability, the hurdy gurdy is an illuminating glass through which to see AI in music. For a while it was an innovation, playing a role in churches before the mighty organs were developed. Then a smaller version brought more music to more people as a reliable way to get a dance going at fairs and gatherings. Rediscovered by the fops of the French court it made an arch appearance in high society for a while, before resuming its place with the peasants and itinerant beggars who were music’s first democratic professionals. It is now perpetuated as a heritage instrument, maintained as a reflection of our inability to give up our cultural past.

Despite its presence at so many events over the ages there is not one single piece of hurdy gurdy music among the highest achievements of our musical traditions. Contrast the piano, also an innovation in the mechanics of musical performance, but not designed for efficient production or portability. Instead the hammered strings brought the potential of infinite subtlety to the rather limited prior pluckers of the keyboard family. Initially a sceptic, the great Bach was later so impressed he became a sales agent for the second wave of pianofortes.

So there are two contrasting innovations, one to increase the productivity of less skilled music workers, and one to extend the expressiveness of the best musicians of the age. And perhaps that is how we can understand today’s emergence of AI in mass production of music, while keeping open our hope for new possibilities in human creativity.

The value of music is the humanity we invest in it, and find in it. As an unashamed elitist I see no value in the millions of hours of audio generated from the parameterisation of musical concepts; and no inherent additional value in the particular instruments and tools that musicians deploy. But I anticipate with joy the new worlds that will be created when the best musicians harness the generative power of AI.

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Hypercompetition. How Platforms Destroy Markets.

Posted on 21 May 2021 by

The first rule of platform business success: increase supply as far beyond the market’s ability to sustain the suppliers as possible. It doesn’t really matter how – and many tactics have been tried. It doesn’t matter what it costs either – the prize is worth it. And it seems structural, by which I mean that this is an imperative for platforms if they are to succeed, imposed on them by their mode of business rather than an option they can choose or reject.

It’s easy to look across many fields of endeavour and see this processes happening. There are more taxi drivers per customer than ever; more restaurants delivering for each eater; more brands and more retailers for each product, and for each buyer of a product; more writers for every reader of the news, etc.

This idea, I call ‘hypercompetion’.

Hypercompetition is far from a worked up and formal concept; it started as some observations that seemed to me to be connected to each other or related somehow, and has been forming gradually into a model. It’s an attempt to make sense of how some areas of our platform-mediated lives are evolving.

In music, streaming services have had a few years of fast growth in subscriber numbers and revenue, but faster growth in the number of artists active in the market. Music’s hypercompetition looks something like this:

Digital tools have made it quicker and cheaper to produce and distribute new tracks. Hobbyist and semi-professional music producers have moved to an assumption of distribution, rather than an assumption that they will negotiate with an intermediary. With a bigger market of music producers who have less of a focus on a return on their investment, more digital tools arrive making it even quicker and even cheaper to produce and distribute. Some producers have visible success without engaging with an old intermediary.

This phase in the process could be called a competitive acceleration perhaps, and as it took its course observers tended to describe music making as ‘democratised’, although many areas of the music market were already very democratic. Perversely the new mode seems to create more opportunities for those with private incomes and a capital cushion to fund their projects.

Whatever you call it, previous market barriers have been broken. And it could be argued this is good for creators and intermediaries – the intermediaries that used to do very little for their rents will struggle, and others will work harder to prove their worth. Creators too find themselves working harder, making and releasing more music, and logging in daily to social media and dashboards.

The next phase moves beyond normal competition. This is what I have started calling ‘hyercompetition’. One or more interventions bring about an influx of new participants in music production and distribution. These have included include software that effectively makes music for you; marketplaces for services that help put together releases; and quick, cheap, ’no questions asked’ distribution services. It’s very hard to say that the innovations and interventions are meeting a latent demand – they seem to create demand that would not otherwise have existed.

Hridayeshwar Singh Bhati, with his 6 and more dimensional chess variants.

At this point one can see a few characteristic signs that the nature of competition has changed in three important ways.

First it is no longer reasonable for creators to expect any meaningful payment, or more than a token audience beyond their friendship circle. Second, intermediaries know that they need to charge for what they do, as there is no longer a reasonable expectation that revenue from their successes will pay for the failures. Third, withholding the music becomes meaningless to the consumer music services, so producers and intermediaries lose any value in exclusivity, or any negotiating or pricing power when selling to consumer services.

When withholding is meaningless, and when no reward can reasonably be expected for the inventory (rather than the services around its production and distribution), that seems to me to indicate the destruction of the market for recorded music.

With the recorded music market effectively dead it’s likely that consumer music services will increasingly see their ’suppliers’ – music producers and intermediaries – as customers. They are in a position to define entirely new categories of goods and services simply by changing the rules of visibility and success. They need to be careful; there are plenty of examples where consumer services, two sided markets, and platforms, have replaced high quality goods and sophisticated supply chains with aggregations of undifferentiated low quality goods.

There is of course a big impact on service providers to the markets that are being disrupted, whose customers previously were a smaller number of professional producers and intermediaries. As consumer services and intermediaries are selling services to producers, the old service providers end up with a massive increase in the number of potential customers; but challenged, on price and service and in the sophistication and competence of their customers. It is almost as though hypercompetion is recursively imported into each community it touches.

So here’s an attempt to summarise a few characteristics of hypercompetition:

  1. Unreasonably high contention for success – not just 1:10, or 1:20 as in the old music industry. Now more like 1:100,000.
  2. Close to cost-free and barrier-free entry to the market for a new participant; certainly no qualification or quality requirement.
  3. Inversion of supplier/customer categories – suppliers become customers.
  4. Intermediaries become service providers rather than sharing revenue/success.
  5. Service providers import competition from their previous customers, the intermediaries and producers.

This is a small window into a very big and complex idea. I called it hypercompetition to capture the sense of a competition which is all against all, and in which what you win is simply the right to continue to compete.

Importantly, platforms seem to succeed to the extent they can generate hypercompetition. If you are a supplier, platforms are effectively casinos. In music you often see service providers advertising ‘keep 100% of your copyright’. It’s not valuable; it has become a lottery ticket.

What comes next after this kind of mass meltdown?

I have been interested in chess for a long time – not as a player, but as an observer. Cheap computers have been beating the best players for decades. It’s just as difficult to play as it ever was. There’s a small but lively market for learning aids of all kinds. Chess as a spectator sport continues to develop, and has generated the biggest aggregate prize fund of all e-sports due to the pandemic migration online. My guess is we like watching humans attempt very difficult things, so my long term bet on music is that it becomes more focused around human activity, and the value in the created audio object drains away over time.

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Platformed! Music’s New ‘Disempowerment’ Business Model

Posted on 8 March 2021 by

In his book, Noise, Jacques Attali makes a startling assertion – that music is the vanguard of revolution. Changes, he says, in the relationship between music and the economic and social structures that govern its position and remuneration prefigure fundamental changes in those structures themselves.

He’s probably too busy to publish an update, but with file sharing, unbundling, streaming, and UGC, the quarter century since the last edition has been a carousel of economic and social beasts for music to try to ride. In the music industry itself it became a truism that what happens first to music happens next to other industries. Now, in 2021, it’s worth asking, at whose volition do such revolutions arrive? And is it possible to make a collective choice, based on some ideas of rights and values, rather than concede to the brute force of wealth and power?

Attali’s Noise is a complex and dense work. I’d been struggling through it with the effort of an untrained reader, dealing as well as I could with its formal language and concepts, when the UK’s Supreme Court handed down a judgement in favour of Uber drivers. The drivers had challenged Uber’s view that each of them was an entrepreneur, a sole trader, a business, and that they therefore had none of the rights and protections of employees.

Uber itself had been founded by someone known to the music industry, Travis Kalanick, whose ‘Scour’ peer to peer filesharing application was part of that music industry carousel. Fairwork, a project based at Oxford University’s Internet Institute, tells the story with  Yaseem Aslam, one of the drivers.

You can find the podcast here: https://shows.acast.com/fairwork-podcast/episodes/003-contracts

https://play.acast.com/s/3173f311-cf17-5fa4-a6ee-ebf36adabc6e/603e41fb89244d1f6098bdc2

What comes across strongly as Yaseem speaks is his sense of entrapment. What seemed at first like a liberation from the constraints and sometimes unfairness of an industry based on human decisions turns, ride by ride, into a world of harm and unaccountability, an active rejection by Uber of responsibility towards the drivers.

It’s possible to see this simply as regulatory arbitrage, and an application of Clay Christiansen’s ‘disruptive innovation’, with Uber unlocking assets that had previously been under-utilised – the drivers’ cars and in many cases the time they would have spent with families or sleeping. But, listening, I couldn’t help thinking of Uber’s share price, and it seemed to me that Uber had managed to convert not just the surplus from their labour, but also the safety, freedom, self respect, and the human rights of the drivers, into wealth for its shareholders.

Attali makes a distinction in his book between singing for money, and being paid to sing by an employer who makes a profit from it, the same distinction that the Supreme Court had been asked to decide for Uber’s drivers. Only paid employment is ‘productive’, he says, by which he means that the profit can be used to fund production of more things that can be sold for profit. In Attali’s terms, artists active in the music market have a better and stronger position than drivers hailable from the Uber app, because they have copyrights that they own and on which they collect rents. He looks ahead to other workers having a similar relationship with their output.

Thorstein Veblen, the thinker who gave the world the term ‘conspicuous consumption’, also gave us ‘conspicuous leisure’, – activity with no purpose beyond the honour it conferred. And he counted music as one of the most typical examples. He seems to have been a dour character, railing against decorative brickwork for instance in his most well known work, The Theory of the Leisure Class.

In a Veblenite world music’s job is to remind the rest of us that some of us are rich, – rich enough to do, or pay someone to do, something he thought had no ‘serviceability’, – an analytical framework that seems as impoverished as many musicians are today. But given that many musicians choose to make music without being asked it would be just as unreasonable to insist on minimum wage or sick pay as it would to allow a taxi driver to force unwanted trips on us in order to get paid.

So, in a culture that defines excellence as much in market terms as in empirical or hedonic, copyright seems a reasonable instrument by which to encourage and reward the better music makers, with little harm to the other music makers or to the public. Better, I suspect, than expanding the patronage power of government officials; we probably need less rather than more government approved music and art. And it is surely better than a return to the pre-copyright model of private patronage; why in the 21st century should the rich define our musical culture?

Copyright however requires a market. And a market is not just supply and a reciprocal flow of money; it also needs some evidence of selling and buying, and of prices being set, investments being made, evidence of entrepreneurial activity. Otherwise it is a simulacrum of a market, not the real thing. But the new terms of engagement between music and the social and economic structures in which it operates are now defined by platforms, just as Uber defines the terms of work for its drivers. Platforms are not markets; rather, platforms have strong incentives to destroy markets, just as they try to destroy the rights and protections of labour.

The words of the UK Supreme Court in the Uber case help, by analogy, to illuminate this point:

“Drivers are in a position of subordination and dependency in relation to Uber such that they have little or no ability to improve their economic position through professional or entrepreneurial skill. In practice the only way in which they can increase their earnings is by working longer hours while constantly meeting Uber’s measures of performance.”

UK Supreme Court Press Statement, 19 February 2021

This in a context where the court found that despite all the contracts and legal texts Uber put forward, really it was Uber and not the driver who sold the customer a ride and Uber’s employee who drove the car. 

How similar this looks to the position of a musician, who cannot bargain with Spotify, Apple, YouTube, Amazon, or Facebook, but must accept the terms offered, spend more time promoting, make more music, and hope to be selected for a playlist. This was in fact explicitly the advice from Spotify’s founder in an interview in the Summer of 2020. ‘Work harder’ he said, because the new model is effectively ‘always on’, so if you are a driver – sorry, a musician – you should be either releasing or promoting non-stop.

This new model sees copyright as an administrative nicety with no marketability. A routing key for content in and money out. The incentives for creators which previously had justified copyright’s place in law and society, incentives to invest time, effort, and imagination in new music for the benefit of the public, vanish, set aside by the imperatives of platform life. And, far from destroying the value in those copyrights, music platforms convert that copyright value into wealth for shareholders, just as Uber does with drivers’ rights.

And yet I suspect that few music makers would readily exchange copyright for zero hours employment contracts, particularly those on the losing side of the zero sum game which is the monthly subscription revenue pool share-out. And this presents us with a dilemma, because alongside the millions of musicians who spend more on their music making than they earn from their music there are thousands for whom music is work and sustenance. Minimum wage, income tax and national insurance contributions, sick pay, lack of insurance against copyright theft, all fall on the musician, and particularly onerously on those in the twilight zone between the start of a decent part time income and being popular enough to hire an assistant or two.

So on the one hand society tells musicians, ‘you are not workers, you are creators, and the gift of copyright allows you to make fair and rewarding bargains in the marketplace’, while on the other hand it removes any practical ability to strike that bargain or to protect those property rights from encroachment, counterfeiting, and appropriation.

If, as Attali suggests, music prefigures revolution, it would be nice to think that it will be one in which the dignity and rights of musicians are protected, just as Uber’s UK drivers’ rights should be following the court ruling. The music industry, I suspect, would prefer to do the minimum necessary to avert a revolution, while musicians and audiences would, given a choice, have more ambition. There is at present however no obvious path to free music from its ongoing ‘platformisation’, and with it the disempowerment of copyright.

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A Truth-Based Alternative to Spotify’s Danceability Index

Posted on 25 February 2021 by

When Spotify bought Echonest, several years ago, they added a set of skills and knowledge in how computing can be used to analyse music. This field of work is fundamental to recommendation systems.

Conventionally researchers had taken one of two approaches; either try to understand how listeners express previous preferences, individually and collectively, and project that historical data into the future, or, try to find characteristics in the audio that cluster with previous preferences, and find new music that seems similar.

The Echonest team had added a new element, which seemed to me at the time (late 1990s) very insightful. Noticing how groups of listeners bent and stretched the definitions of musical genre they started looking at human evaluations as a filter through which to see what the computers were saying about the audio.

The humans in the mix might be considered ‘experts’; after all we would surely not want to cede our cultural definitions to computer science too completely. Now, after a few years of being the biggest music recommendation platform in the world, Spotify employs experts to add tracks to playlists, and thereby not only decides which artists get paid, but also which music is considered typical of most of the genres in global pop music.

In a music market that has, with a lot of help from Spotify, generated huge amounts more new music than its subscribers want to listen to, the more mechanical parts of the recommendation toolkit have gained new importance, for musicians and for culture. For artists new to the system then, the audio analysis is acting partly as a gatekeeper while the other signals are built up (or not, as the case may be).

So it’s worth trying to understand what’s going on in the datacentres, as new audio files hit algorithms and spit out analysis data that will decide which musicians are going to fail fast and which might go on to bigger things. And one of the parameters of a track that Spotify probably uses is what they call ‘danceability’.

Spotify exposes its danceability index as a floating point number between 0 and 1 via their developer API. What do we know about it, and how is it used? Here’s how they describe it:

Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.

https://developer.spotify.com/documentation/web-api/reference/#endpoint-get-audio-features

This metric seems to have some importance in the ecosystem. In a publicly shared Tableau visualisation, it’s the measurement most strongly correlated with success across all of Spotify’s vast catalogue of music.

https://public.tableau.com/profile/greice7948#!/vizhome/50spotify/top50spotify

It is undiscoverable whether music that scores highly on this index is inherently more popular with consumers, or whether it accumulates higher play counts because it is more recommended by Spotify. And we need to remember that familiarity drives preference in music more than preference driving playcounts, so it is vitally important to understand what music any consumer service pushes to the top.

It is also an unproven hypothesis that music scored by Spotify as more danceable is more danced to by consumers. Paradoxically one of history’s most famous pieces written for dancing to, Strauss’s Blue Danube Waltz, scores very low on danceability.

Due perhaps to high variability between bars and other temporal measures Spotify’s audio feature analyser seems to struggle to categorise some aspects of orchestral tracks. This version has a danceability score of 0.216.

Nobody rational would attempt to dance to The Blue Danube Waltz if they had no information about it other than Spotify’s danceability number.

So this opaque metric on close inspection looks really quite dysfunctional. But can it be done better, and if so how? Here’s one idea. With some simple experiments to measure how enthusiastically groups of people dance to different tracks this Spotify metric could be compared to a truth-based danceability index.

Groups of dancers could be asked to wear small unobtrusive monitors while they dance, with data collected over pervasive networks, such as 5G, and collated to capture the movements they make; whether they are more or less energetic, or rhythmic, and how closely they align with patterns in the music.

5G, wearables, and dancers could thus provide a truth-based counterpoint to Spotify’s opaque danceability metric. If anyone is in a position to collaborate on making this happen let me know!

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The Public Interest in Music

Posted on 26 January 2021 by

The UK music industry is going through a period of internal arguments, and as sometimes happens some of us are reaching out for an adjudicator, or referee.

January 2021 saw the UK’s law makers brought into the ring in a big way. With Members of Parliament it’s always hard to know whether they see themselves as referees or as protagonists. In a series of streamed Q&A sessions a parade of music industry executives and participants – artists, former artists, accountants, etc – made sure there were plenty of fists flying; in a biting winter sleet of subsequent private and public debate the commentariat tried to decide among themselves who had landed a blow on what or on whom.

The background to this bitterness is the impact streaming has had as it became the dominant revenue source for recorded music, and therefore for some, but not all, of the UK’s artists. In these pandemic years the other leg of the stool, live performance, has disappeared, so some artists are suffering badly. And gallingly the biggest owners of catalogues of recorded music are enjoying growth and profitability, much of which comes from recordings they funded or bought decades ago.

When the big catalogue owners controlled distribution getting an exclusive recording contract with a recoupable advance made a lot of sense to musicians. The label was on the hook while the account was in the red: advances are recoupable from record sales, not recoverable or repayable. Trading as much as 80-90% of future revenue for present funding worked while a band built a following, with the intention to inflate future advances, and to cash in on touring and merchandise, which traditionally were out of the label’s grasp.

Different markets make commercial sense of different deal structures, and to many people those recording contracts now seem unfair. The asset value of the copyright in the recording sits on the label’s balance sheet, and the biggest labels move into profit long before the artists do, as well as enjoying a far greater share of the future revenue. Streaming seems to have woken up a lot of older recordings that wouldn’t have made it onto the shelves in record shops. With such a strong grip on the top of the market the biggest labels have parlayed control of distribution into a reasonable position in the control of exposure and discovery, which is now most of the value creation game in music.

I think it would be fair to ask what the biggest labels are doing with the windfall profits from back catalogue that streaming has rained on them. But that doesn’t address the question of whether they have structural advantage, and if so whether it is earned. And it doesn’t start to consider whether such a structure is good for music in the UK or anywhere else.

This very live and somewhat acrimonious debate popped up again in a surprising context in January 2021. On a another streamed aural enquiry, this time into the problems the ICO, the UK’s information commission, is facing, lawmaker Damien Green asked about the use of data in the context of music services such as Spotify, Apple Music, YouTube and others. He asked, – are musicians adversely affected? The ICO punted. The question was very pertinent, in my view, but perhaps in ways that might not have seemed immediately obvious.

My professional life has been dedicated to the music industry, and it coincides with the disruption and regeneration that the connected digital world has brought. Green’s question, seemingly simple, brought many of the structural problems music faces right to the surface.

There are, as I see it, three distinct facets to problematic use of data in music:

  1. Music services use music listening data, as well as social media and other tracking data, to give themselves a critical advantage over musicians and record labels when either predicting, or increasingly deciding, which music will give its creators and investors a return on their investment.
  2. Music services track listeners in many different environments on social media and the web, and participate in the grey personal data economy, in ways that are almost entirely invisible to the music industry and to music fans, and almost certainly often illegal.
  3. The music industry as an ecosystem has many databases but has been slow to move towards interoperability and transparency. This makes it hard for many of the people who contribute to the creation of music to know who is storing information about them and their work, and impossible sometimes to supply missing data or correct errors.

Many people in the music industry have been working diligently to overcome these problems, with some success. In fact, the UK provides a very outsized proportion of the global music industry’s critical shared infrastructure, and hosts important centres of excellence and development.

For the UK there are opportunities for some relatively small interventions from Government which could make a dramatic difference to the healthy flow of music and data in the music industry and in the public realm. I suspect that other regulatory regimes could find similar benefits from similar approaches.

Two suggestions from me would be:

  1. A single centre of research and data that would help inform the music industry and policy makers on all issues related to the creation and enjoyment of music. This might be hosted at a University, or perhaps the British Library. It should be tasked with supplying facts rather than opinions or speculative applications.
  2. An annual ‘state of the music nation’ report which recognises all the interests in music and performance, commissioned by the government as the representative of the public, and holding copyright owners, music services, and others to account. Over time this could surface internal music industry tensions earlier, and provide a fascinating history of the music industry in the UK.

Between them these two interventions would hugely improve the quality of the debate within the music industry, and between the industry and external stakeholders of all kinds, including the public.

Of course none of this by itself answers the increasingly important questions about whether the people who create and perform the music are fairly treated, and whether the industry seems sustainable as we all consider how we will emerge from the pandemic. What does ‘build back better’ mean for music?

On yet another UK Parliament enquiry, Jeremy Silver, who runs one of the UK’s critical agencies promoting and catalysing innovation, and who himself has a music industry background, made what seemed to me an essential point. The creative industries generally, he suggested, lack a formal approach to innovation. There’s a lot of new and interesting work going on, he was quick to point out, but perhaps (was the implication) it translated into less new economic value than it could if it were better structured.

In the UK’s music industry, and the increasingly connected global markets, we find it hard to cooperate with each other, and the result can be seen in the bitter and factionalised arguments that sometimes spill over into the public sphere. Perhaps the UK could find a role for itself in the future music industry as a centre for coordinated research and strategic thinking, to everyone’s benefit.

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Music’s Dissonant Value Gap

Posted on 30 November 2018 by

At the time of writing, a Google video search for ‘crushed by the wheels of industry’ delivers as its first result a YouTube upload with the plea, ‘NO COPYRIGHT INFRINGEMENT INTENDED’ from a user which calls itself ‘mrrockwithmebaby’.

Try it here: https://www.google.com/search?q=crushed+by+the+wheels+of+industry&num=50&tbm=vid0

Here’s the description in full:

NO COPYRIGHT INFRINGEMENT INTENDED. COPYRIGHTS RESERVED BY COPYRIGHT OWNER.This video is used only for non-profit usage and publishing.

https://www.youtube.com/watch?v=lE9glw_M8sw
NO COPYRIGHT INFRINGEMENT INTENDED by Heaven 17

As one of 69,600 results for that search, many of them straight rips of the Heaven 17 hit from the 1980s with nothing more than a photo as video content, it’s hard to argue that the upload adds to the sum of human knowledge. It’s also a flat lie that the video is used only for non-profit usage. It has the usual advertisements.

The recording in mrrockwithmebaby’s video has been claimed by the copyright representatives through YouTube’s ContentID tool; but it is one of the millions of videos that the music industry is claiming are remunerated below market price, due to the way that YouTube manages competition between owners and unauthorised uploaders so that no music is ever missing for long.

At the same time as the music industry protests the ‘value gap’ another debate is happening about the nature of the business that generates the revenue in the first place.

A Google search for ‘profit from the proceeds of crime‘ is many times more helpful than the one above, with links to Wikipedia and the sorts of neat summaries lawyers do to show off their expertise, helpfully pointing out that this is the activity more commonly known as money laundering, and that being any part of the chain is considered a crime.

A new campaign from Privacy International could, if successful, make much of online advertising look indistinguishable from money laundering. PI has filed complaints against a string of businesses that they argue flout regulation in the way they collect and use data about us. Here’s the summary: 

A new campaign from Privacy International could, if successful, make much of online advertising look indistinguishable from money laundering. PI has filed complaints against a string of businesses that they argue flout regulation in the way they collect and use data about us. Here’s the summary: https://privacyinternational.org/legal-action/challenge-hidden-data-ecosystem. The submission to the ICO is worth a quick read too: https://privacyinternational.org/sites/default/files/2018-11/08.11.18%20Final%20Complaint%20Acxiom%20%26%20Oracle.pdf

Advertising supported services sounds benign; our mental models have not moved on from ads in a magazine article or in the commercial breaks on TV and radio. But the reach, scope, and nature of the data business is largely hidden from us in daily life; even if we are interested there is not much we can do to see and control who knows what about us. And this blows a big hole in the intent of data and privacy regulation, the foundation of which is, explicitly, permission, visibility, and control.

How does this relate to copyright and the ‘value gap’? With online advertising, companies are stealing our data and selling it to advertisers. The ‘value gap’ is those same companies stealing our music and selling the attention it gets to advertisers.

Here’s the common foundation both forms of abuse rest on. Once the individual has been disenfranchised from everything they do and everything they create they can be coopted into a business model that sees them as fuel, rather than as parties to meaningful and wilful transactions.

Don Quixote versus a windmill

And when we have lost control of our data and our privacy we lose control over democratic processes too. It’s worth considering how data about ‘susceptibility to conspiracy theories’ was sold to political advertisers recently, gleaned from responses to online material about vaccination. Most of us are not equipped to read the science about vaccines, so we have to trust the judgement of people who are highly trained. The insight that mistrust of experts can be used to vaccinate voters against moderate and reasonable choices is both brilliant and very disturbing.

So here are two strong points which I think we in the music industry should consider as we campaign for more of the advertising money that these pernicious practices generate: First, even if we get our own assets recognised and compensated, do we really want to be making part of our living from the proceeds of crime? Second, does music have a role in driving change in business and society, and if so what do we do with our power?

Because surely the worst position for music to take would be to turn a blind eye to the privacy and data rights of our audience. And to spell it out, the erosion of both copyright and privacy come from the same political philosophy that put the rights of the individual below those of state and corporation. So we should probably take a moment to stand up for privacy and make common cause with those campaigning to protect it, in between our whining about not getting paid a fair share of the cash generated by our collective descent into data serfdom.

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An Act for the Encouragement of…

Posted on 18 February 2018 by

The Statute of Anne, enacted in England in 1710, opens with a statement of intent:

An Act for the Encouragement of Learning

I have argued elsewhere that it succeeded. Whether by the mechanisms intended, essentially the first modern copyright, or by serendipity as some would have it, Learning was Encouraged, universal literacy and scientific advance were achieved, and the profession of writing threw off the shackles of patronage to find an honourable place in the market. Printers moaned of course as writers got new pricing power, but made little impact on what was seen as a just and fair settlement.

Queen Anne making laws

All in all it was a very busy Act of Parliament. And worth reading from time to time in an age when many people think that copyright is a tool to impede learning by preventing writing, withholding access, and inflating prices. Today we benefit from the public domain, and lively and diverse markets for old and new books, plus a public library system (albeit one under stress), and for those who can afford it digital access to writing of all kinds.

But today’s faultlines are very visible. Instead of printers laboriously setting type, the new publishers are massive and almost instant copying machines, whose advertising engines are making unaccountable and unattributable money from the efforts of writers and creators. This widespread abuse is protected by a kind of enforced pseudonymity, which weakens not only the bonds between identity and authorship, but also our ability to hold each other accountable for our words and actions. And if creativity is flourishing on the horizontal axis, on the vertical, among the undoubted gems there is a viral outbreak of plagiarism and shallow re-use.

So where the deficit at the start of the 18th century was a self-regenerating engine for learning, at the start of the 21st century we are looking at a deficit of civil discourse and authentic creativity.

The Lord Keeper of the Great Seal.

It takes an historical imagination to understand why a late 17th scholar or parliamentarian would think that an Act to create copyright would encourage learning. The argument presented runs thus: Printers had been taking liberties, not asking permission before printing and reprinting, nor sharing the money they were making with writers. The lawmakers wanted to give writers a tool to negotiate a better deal with printers, and redress against infringements of their economic and reputational interests in their writings.

Alongside the private interests of writers, the public interest was clearly represented in the text and new rights and obligations the Act created. No protection for either the writer’s or the printer’s property could be obtained without a gift to the public of three copies each to the most established libraries of the day. The Act also formalised the public domain, by setting limits on the term of protection. The public interest was further advanced by the establishment of a register of works, under rules which allowed open inspection by anyone who cared to look.

The Act also added price control; if you thought a book was too expensive you could complain to any of a long list of authorities, including the Lord Keeper of the Great Seal and the Archbishop of Canterbury, who could decide what the fair price should be.

So, here’s an attempt to frame a Statute for the 21st century. Excuse me while I adjust my wig…

An Act for the Encouragement of Civil Discourse and Creativity by Vesting the Control of any Representation of Identity; and of the Use or Reuse of any Texts Images Sound Recordings Videos or any other Materials that may be Captured Copied and Transferred on Digital Services and Platforms; in the Person Represented by that Identity, and in the Original Creator of those Materials, during the times therein mentioned.

Whereas Digital Services and Platforms have of late frequently taken the Liberty of enabling and encouraging the Original Creations of one Person to be copied by another Person without Notice or Permission from the Creator; and in Flagrant Disregard of Common Courtesy allowed one Person to masquerade as Another while denying any Person the Ability to be identified as the Authentic Creator of what they have made; and of allowing and enabling Anonymous Messages to be sent in both Public and Private preventing any Redress for Abuse or Plagiarism of the Counterfeiting of Works of Art and Literature and Music; to the Detriment of Writers, Artists, Musicians, and Others who live by selling Copies of their Creations; and to the Great Detriment of Civil Discourse between People; May it be Enacted that the True Identity of each Person as they are Represented on such Digital Services and Platforms, and the Authentic Origination of any of Such Materials as they Create or Make, and any Permissions and Notifications about uses of Such Materials shall be solely under the Control and Right of those Persons; and further that any Assignment of those Rights and Permissions to the Owners and Operators of Digital Services and Platforms shall be subject to Valuable Consideration and shall be Accurately Recorded, such Records being freely available to any Interested Person on Demand.

If the sceptics are right we might well be coming to the end of the efficacy of copyright in the encouragement of learning. Times have after all changed, and printing presses are daily becoming less essential to the dissemination of knowledge and the exchange of ideas. But even if they are right, three hundred years is a pretty good run for an idea of how law and markets needed to be shaped, and we can apply the same approach and sound thinking to the deficits we see in today’s lives as they are lived on digital platforms.

Today we need better ways to talk to each other in public, so that divisions move towards compromise rather than conflict. And we need support for creativity that is less at the whim of the owners of digital platforms, who can tweak whole classes of creators out of a living by tuning an advertising algorithm or changing some monetisation rules; and whose incentives are diametrically opposed to authenticity and originality of content if it gives the creator stronger pricing power. We should recognise a common interest in getting this right for the next three hundred years. Those 17th century printers moaned and whined about the enfranchisement of writers, but profited handsomely from the encouragement of learning that copyright brought.

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