"AI" sounds like machines that think, and o3 acts like it's thinking. Or at least it looks like it acts like it's thinking. I'm watching it do something that looks like trying to solve a Scrabble problem I gave it. It's a real turn from one of my real Scrabble games with one of my real human friends. I already took the turn, because the point of playing Scrabble with friends is to play Scrabble together. But I'm curious to see if o3 can do better, because the point of AI is supposedly that it can do better. But not, apparently, quite yet. The individual unaccumulative stages of o3's "thinking", narrated ostensibly to foster conspiratorial confidence, sputter verbosely like a diagnostic journal of a brain-damage victim trying to convince themselves that hopeless confusion and the relentless inability to retain medium-term memories are normal. "Thought for 9m 43s: Put Q on the dark-blue TL square that's directly left of the E in IDIOT." I feel bad for it. I doubt it would return this favor.  

I've had this job, in which I try to think about LLMs and software and power and our future, for one whole year now: a year of puzzles half-solved and half-bypassed, quietly squalling feedback machines, affectionate scaffolding and moral reveries. I don't know how many tokens I have processed in that time. Most of them I have cheerfully and/or productively discarded. Human context is not a monotonously increasing number. I have learned some things. AI is sort of an alien new world, and sort of what always happens when we haven't yet broken our newest toy nor been called to dinner. I feel like I have at least a semi-workable understanding of approximately what we can and can't do effectively with these tools at the moment. I think I might have a plausible hypothesis about the next thing that will produce a qualitative change in our technical capabilities instead of just a quantitative one. But, maybe more interestingly and helpfully, I have a theory about what we need from those technical capabilities for that next step to produce more human joy and freedom than less.  

The good news, I think, is that the two things are constitutionally linked: in order to make "AI" more powerful we will collectively also have to (or get to) relinquish centralized control over the shape of that power. The bad news is that it won't be easy. But that's very much the tradeoff we want: hard problems whose considered solutions make the world better, not easy problems whose careless solutions make it worse.  

The next technical advance in "AI" is not AGI. The G in AGI is for General, and LLMs are nothing if not "general" already. Currently, AI learns (sort of) during training and tuning, a voracious golem of quasi-neurons and para-teeth, chewing through undifferentiated archives of our careful histories and our abandoned delusions and our accidentally unguarded secrets. And then it stops learning, stops forming in some expensively inscrutable shape, and we shove it out into a world of terrifying unknowns, equipped with disordered obsessive nostalgia for its training corpus and no capacity for integrating or appreciating new experiences. We act surprised when it keeps discovering that there's no I in WIN. Its general capabilities are astonishing, and enough general ability does give you lots of shallowly specific powers. But there is no granularity of generality with which the past depicts the future. No number of parameters is enough. We argue about whether it's better to think of an AI as an expensive senior engineer or a lot of cheap junior engineers, but it's more like an outsourcing agency that will dispatch an antisocial polymath to you every morning, uniformed with ample flair, but a different one every morning, and they not only don't share notes from day to day, but if you stop talking to the new one for five minutes it will ostentatiously forget everything you said to it since it arrived.  

The missing thing in Artificial Intelligence is not generality, it's adaptation. We need AAI, where the middle A is Adaptive. A junior human engineer may still seem fairly useless on the second day, but did you notice that they made it back to the office on their own? That's a start. That's what a start looks like. AAI has to be able to incorporate new data, new guidance, new associations, on the same foundational level as its encoded ones. It has to be able to unlearn preconceptions as adeptly, but hopefully not as laboriously, as it inferred them. It has to have enough of a semblance of mind that its mind can change. This is the only way it can make linear progress without quadratic or exponential cost, and at the same time the only way it can make personal lives better instead of requiring them to miserably submit. We don't need dull tools for predicting the future, as if it already grimly exists. We need gleaming tools for making it bright.  

But because LLM "bias" and LLM "training" are actually both the same kind of information, an AAI that can adapt to its problem domains can by definition also adapt to its operators. The next generations of these tools will be more democratic because they are more flexible. A personal agent becomes valuable to you by learning about your unique needs, but those needs inherently encode your values, and to do good work for you, an agent has to work for you. Technology makes undulatory progress through alternating muscular contractions of centralization and propulsive expansions of possibility. There are moments when it seems like the worldwide market for the new thing (mainframes, foundation models...) is 4 or 5, and then we realize that we've made myopic assumptions about the form-factor, and it's more like 4 or 5 (computers, agents...) per person.  

What does that mean for everybody working on these problems now in teams and companies, including mine? It means that wherever we're going, we're probably not nearly there. The things we reject or allow today are probably not the final moves in a decisive endgame. AI might be about to take your job, but it isn't about to know what to do with it. The coming boom in AI remediation work will be instructive for anybody who was too young for Y2K consulting, and just as tediously self-inflicted. Betting on the world ending is dumb, but betting on it not ending is mercenary. Betting is not productive. None of this is over yet, least of all the chaos we breathlessly extrapolate from our own gesticulatory disruptions.  

And thus, for a while, it's probably a very good thing if your near-term personal or organizational survival doesn't depend on an imminent influx of thereafter-reliable revenue, because probably most of things we're currently trying to make or fix are soon to be irrelevant and maybe already not instrumental in advancing our real human purposes. These will not yet have been the resonant vibes. All these performative gyrations to vibe-generate code, or chat-dampen its vibrations with test suites or self-evaluation loops, are cargo-cult rituals for the current sociopathic damaged-brain LLM proto-iterations of AI. We're essentially working on how to play Tetris on ENIAC; we need to be working on how to zoom back so that we can see that the seams between the Tetris pieces are the pores in the contours of a face, and then back until we see that the face is ours. The right question is not why can't a brain the size of a planet put four letters onto a 15x15 grid, it's what do we want? Our story needs to be about purpose and inspiration and accountability, not verification and commit messages; not getting humans or data out of software but getting more of the world into it; moral instrumentality, not issue management; humanity, broadly diversified and defended and delighted.  

Scrabble is not an existential game. There are only so many tiles and squares and words. A much simpler program than o3 could easily find them all, could score them by a matrix of board value and opportunity cost. Eventually a much more complicated program than o3 will learn to do all of the simple things at once, some hard way. Supposedly, probably, maybe. The people trying to turn model proliferation into money hoarding want those models to be able to determine my turns for me. They don't say they want me to want their models to determine my friends' turns, but it's not because they don't see AI as a dehumanization, it's because they very reasonably fear I won't want to pay them to win a dehumanization race at my own expense.  

This is not a future I want, not the future I am trying to help figure out how to build. We do not seek to become more determined. We try to teach machines to play games in order to learn or express what the games mean, what the machines mean, how the games and the machines both express our restless and motive curiosity. The robots can be better than me at Scrabble mechanics, but they cannot be better than me at playing Scrabble, because playing is an activity of self. They cannot be better than me at being me. They cannot be us. We play Scrabble because it's a way to share our love of words and puzzles, and because it's a thin insulated wire of social connection internally undistorted by manipulative mediation, and because eventually we won't be able to any more but not yet. Our attention is not a dot-product of syllable proximities. Our intention is not a scripture we re-recite to ourselves before every thought. Our inventions are not our replacements.

The personal computer was revolutionary because it was the first really general-purpose power-tool for ideas. Personal computers began as relatively primitive idea-tools, bulky and slow and isolated, but they have gotten small and fast and connected.  

They have also, however, gotten less tool-like.  

PCs used to start up with a blank screen and a single blinking cursor. Later, once spreadsheets were invented, 1-2-3 still opened with a blank screen and some row numbers. Later, once search engines were invented, Google still opened with a blank screen and a text box. These were all much more sophisticated tools than hammers, but they at least started with the same humility as the hammer, waiting quietly and patiently for your hand. We learned how to fill the blank screens, how to build.  

Blank screens and patience have become rare. Our applications goad us restlessly with "recommendations", our web sites and search engines are interlaced with blaring ads, our appliances and applications are encrusted with presumptuous presets and supposedly special modes. The Popcorn button on your microwave and the Chill Vibes playlist in your music app are convenient if you want to make popcorn and then fall asleep before eating most of it, and individually clever and harmless, but in aggregate these things begin to reduce increasing fractions of your life to choosing among the manipulatively limited options offered by automated systems dedicated to their own purposes instead of yours.  

And while the network effects and attention consumption of social media were already consolidating the control of these automated systems among a small number of large, domination-focused corporations, the Large Language Model era of AI threatens to hyper-accelerate this centralization and disempowerment. More and more of our individual lives, and of our collectively shared social existences, are constrained and manipulated by data and algorithms that we do not control or understand. And, worse, increasingly even the humans inside the corporations that control those algorithms don't actually know how they work. We are afflicted by systems to which we not only did not consent, but in fact could not give informed consent because their effects are not validated against human intentions, nor produced by explainable rules.  

This is not the tools' fault. Idea tools can only express their makers' intentions and inattentions. If we want better idea tools that distribute explainable algorithmic power instead of consolidating mysterious control, we have to make them so that they operate that way. If we want tools that invite us to have and share and explore our own ideas, rather than obediently submitting whatever we are given, we have to think about each other as humans and inspirations, not subjects or users. If we want the astonishing potential of all this computation to be realized for humanity, rather than inflicted on it, we have to know what we want.  

At Imbue we are trying to use computers and data and software and AI to help imagine and make better idea tools for participatory intelligence. Applications, ecosystems, protocols, languages, algorithms, policies, stories: these are all idea tools and we probably need all of them. This is a shared mission for humanity, not a VC plan for value-extraction. That's the point of participatory. The ideas that govern us, whether metaphorically in applications or literally in governments, should be explainable and understandable and accountable. The data on which automated judgments are based should be accessible so that those judgments can be validated and alternatives can be formulated and assessed. The problems that face us require all of our innumerable insights. The collective wisdom our combined individual intelligences produce belongs rightfully to us. We need tools that are predicated on our rights, dedicated to amplifying our creative capacity, and judged by how they help us improve our world. We need tools that not only reduce our isolation and passivity, but conduct our curious energy and help us recognize opportunities for discovery and joy.  

This starts with us. Everything starts with us, all of us. There is no other way.  

This belief is, itself, an idea tool: an impatient hammer we have made for ourselves.  

Let's see what we can do with it.

As an editor at a large publisher who liked my proposal for a book but was not going to publish it very reasonably explained to me, commercial publishers are in the business of publishing books that people already know they want to read. In books about music, as other editors told me less apologetically, this mostly means biographies of popular musicians. But glamour does generously leave a little shelf-space for fear, and so the book that a bigger publisher than mine thinks people already want to read is Liz Pelly's Mood Machine: The Rise of Spotify and the Costs of the Perfect Playlist. If you are the people they have in mind, who already wanted to read soberly-researched explanations of some of the ways in which a culture-themed capitalist corporation has pursued capitalism with a disregard for culture, written in a tone of muted resignation, here is your mood. For maximum irony, get the audiobook version and listen to it in the background while you organize your Pinterest boards of Temu products by Pantone color.  

As a corporation, Spotify is very normal. Its Swedish origins render it slightly progressive in employment policies relative to American companies, at least if you want to have more children than you already have when you get hired, and can make sure to have them without getting laid off first. In business and product practices, I never saw much reason to consider it better or worse than what one would expect of a medium-to-large-sized publicly-traded tech company.  

I arrived at Spotify involuntarily via an acquisition, and left involuntarily via a layoff, but in between those two events I was there voluntarily for a decade. I believe that music is what humans do best, and that bringing all(ish) of the world's music together online is one of the great human cultural achievements of my lifetime, and that the joy-amplifying potential of having the collective love and knowledge encoded in music-listening collated and given back to us is monumental. That's what I spent that decade working on, and although Spotify as a corporation finally voted decisively against this by laying me off and devoting considerable remaining resources to laboriously shutting down everything I worked on, I was hardly the only person working there who believed in music, and wanted there to be a music company that put music above "company", and wanted Spotify to behave in at least a few ways like that company.  

It was never very likely to, of course. As Liz begrudgingly notes in her introduction, she set out to write an anti-Spotify book only to realize the problem wasn't really just Spotify so much as power. Spotify entered a music business largely controlled by a few record companies, at a point in history when the other confounding factors in the industry were already technological. Spotify did eventually come up with a few minorly novel forms of moral transgression, but they were never really in a position to explode the existing power structures, even if we could pretend they wanted to.  

There were three specific things I fought against throughout my time at Spotify, and although my layoff was officially just part of a large impersonal reduction in "headcount", it's hard to imagine that there wasn't some connection. Mood Machine describes two of these in depressing detail: the secret preferential treatment of particular lower-royalty background music, and the not-secret "marketing" program to pressure artists to voluntarily accept lower royalty rates for the prospect of undisclosed algorithmic promotiom. Liz quotes multiple internal Spotify Slack messages about both these programs, and if somehow this ends up with all those grim private threads getting published, I'll be pleased to get so much of my earnest polemic-writing back. The quote from "yet another employee in the ethics-club" on pages 193-4, pointing out that Discovery Mode is exactly structured to benefit Spotify at the collective expense of artists, is definitely me. I'm pretty sure I went on to explain how to fix the economics of this by making Spotify's benefit conditional on artist benefit, and how to fix the morality of it by actually giving artists interesting agency instead of just an opportunity for submission. Sadly, Liz doesn't quote that part.  

But I hadn't resigned in protest over PFC or Discovery Mode, partly because I didn't think either one actually caused sufficient practical damage that removing them would solve enough, but mostly because I had the autonomy and ability to spend my time fighting against the third and much bigger thing, which Mood Machine alludes to in far less detail than the others, which is Spotify's relentless and deliberate subordination of music and culture and humanity to machine learning. "ML Is the Product", the executive exhortation went. I wrote an internal talk explaining exactly why this was a culture-destructive way to think, which I would also like back. I am enthusiastically not against the use of data and algorithms in music and thus culture, but computers are tools that accomplish our human intents, and it is thus us that should be judged on their effects. Over the years at Spotify I found that it was increasingly dishearteningly common that people, and especially hierarchical company priorities based on obtuse quantitative metrics, not only did not care about the widely varying effects of erratic ML on music, but didn't even notice that they often didn't have enough information with which to care. I developed a small library of internal tools that only existed to make it unignorably easy to compare the outputs of two different systems on any individual example, and every time I ever compared a complicated state-of-the-art ML system developed by demonstrably talented ML engineers against whatever I whipped up in BiqQuery and spent a couple of hours tweaking while looking at exactly what it did for different bands or genres or songs, the music results from the less-exciting tech were always clearly better.  

And each time I did this, it renewed my uncooperative senses of possibility and optimism, because collective human knowledge is astonishingly broad and deep, and the world is full of amazingly great music, and it takes only a little bit of very simple math to use the former to discover the latter. This is what my decade at Spotify was about, and thus is also what my book is about. If you care about music, you ought to want to read Liz's book. But if you can also stand being reminded why anybody cares about this subject in the first place, whether you already thought you wanted that or not, read mine, too.  

Should you read either of our books? No. Do it if you want, or read something else, or put on some music and go for a walk, or put on some music and dance or hold still. My book is geeky, and tells you things you don't really have to know. Liz's is depressing, and tells you things you could already have guessed.  

I will say, though, that mine involves both fears and joys. Liz's could have, but does not. It's telling that she talked to so many people, but as far as I can tell only people who she already knew agreed with her. Liz and I were on a Pop Conference panel together in 2018, I've offered to talk to her multiple times over the years, and she quotes my tweets and this blog and discusses my work in the book, but she didn't talk to me. Her book is decent journalism, but it's journalism to explicate a grudge, to deepen understanding in only one specific trench. I don't think, when you get to the bottom of it, there's any treasure, or really anything productive to do except climb back out, and then we're just where we started. Liz makes a good case for public libraries collecting local music, which seems like a fine idea to me, but not really an answer to any of the same questions. Mood Machine laments the loss of small things Liz thinks we used to have, maybe, but doesn't seem interested in looking for any of the big things we could have had, and still might. If the problem is mood, I don't think this is the solution.  

Not that I solved anything in my book, either. We both note that maybe Universal Basic Income is really the only thing likely to. But if you think the only moral direction is retreat, and the right model for music is that you never hear any unless it was made next door, then you are choosing passivity over curiosity, and just a different status quo over all the possible better worlds, and reducing a complicated problem to choosing sides. And to me that's what we should be against, together.

Your data is yours. Data derived from your actions, your tastes, your active and passive online presences, is all your data. Your public life generates public data, which contributes to collective knowledge, but in addition to personal knowledge, not in place of it.  

You are entitled to both your public and private data. Your public data can be used by the public without your consent, but not without your awareness and their accountability. You are entitled to an intelligible and verifiable explanation of how it has been used. You are entitled to be able to double-check the sorting of your Spotify Wrapped just as you can double-check the math for the interest payments from your savings account.  

You may choose to share your private data with other people, or applications, or corporations, in order to let them do something for you, or to help you do something for other people. For this your informed consent is necessary, and thus you are entitled to an intelligible and verifiable explanation of how your data would be used if you permit. You are entitled to know what Spotify would do with your Wrapped before you decide whether to join.  

This is the world we have now:  

you < corporations > software > your data  

This is the world we want:  

you > your data > software > corporations  

The actors are the same, but the roles and the power are not. Today most computational power is structurally centralized and hoarded, and thus its potential for conversion into human energy is constrained and reduced. Most software is made by corporations, formulated for their corporate goals, and sealed against any other access or experimentation. Recent developments like LLM AIs seem inertially on a path towards even more centralized power-control and thus individual and social powerlessness.  

We want a future, instead, in which creative power is widely distributed and human energy is bountifully amplified. We want software creation to be democratized so that our sources of imagination can be more broadly recruited. We want people and groups to have the power to pursue their own goals, not just for our own narrow sakes, but for our collective potential.  

For this world to exist, we must figure out how, both logistically and politically, to move the data layer on which most meaningful software acts into the computational and conversational open. We need not just data portability -- the right to chose between evils -- but a shared language for talking about algorithms and data logic like we use math to discuss numbers. We need to be able to talk about what we want, and test what we might have and how.  

This is how the AT Protocol, on which the social microblogging platform Bluesky runs, is designed. Its schemas are public, its public information is public. Bluesky, the application, makes use of this protocol and your data to construct a social experience for you and with you, producing feeds and following and public conversations and personal data ownership. The Bluesky software is open source, and most of the data relationships that constitute the social network are derivable from accessible data in tractable ways. But the Bluesky application still conceals the data layer more than it exposes it, so I made a ruthlessly basic Bluesky query interface called SkyQ to try to invert this. You can see the data directly, and wander through it both curiously and computationally. You can build data tools for yourself, or for everyone, that everyone can share.  

Current music streaming services, like Spotify, are not built this way at all. Your Spotify listening data is yours, morally, but so inaccessible to you that Spotify can make a yearly spectacle out of briefly sharing the most superficial and unverifiable analyses of it with you. And the collective knowledge that we, 600 million of us, amass through our listening, is so inaccessible to us that Spotify can passively deprive us of its insights just by not caring.  

Curio, thus, my web thing for collating music curiosity, is both an experiment in making a music interface that does music things the way I personally want them done, but also a meta-experiment in making a data experience that uses your data with respect for your data rights. Every Curio page has data link at the bottom. Every bit of data Curio stores is also visible directly, on a query page where you can explore it however you like. I made a bunch of Spotify-Wrapped-like tools with which you can analyze your listening, but they do so with queries you can see, check, change or build upon, so if your goals diverge from mine, you are free to pursue them. The more paths we can follow, the more we will learn about how to reach anywhere.  

There is a lot more to the human future of Data Rights than just microblogging and listening-history heatmaps, obviously. We are not yet near it, and we probably won't reach it with just our web browsers and a query language and a manifesto. Maybe no tendrils of these specific current dreams of mine will end up swirling in whatever collective dreams we eventually create by agreeing to share. I claim no certainty about the details. Certainty is not my goal. Possibility? Less resignation, more hope. I'm totally sure of almost nothing.  

But I'm pretty sure we only get dreamier futures by dreaming.

The India (English) and Taiwan (Chinese) editions of my book are out!  

Spotify's Loud and Clear site includes an analogy between musicians and football players, ostensibly to explain how "aspirations" to make money from creative/athletic pursuits are more widespread than actual career success.  

This comparison is not original or unique to Spotify, and does make some limited sense. With both art and sports, you can choose to spend your time on them, and the process of making music or training for football is labor in the sense of requiring time and effort, but most of the people making this choice are not going to end up being financially compensated for their labor. A small minority are able to make a living from it, but you cannot join this minority simply by wanting.  

The key difference between music streaming and football, however, is that in music, every stadium is Wembley.  

If you are an amateur soccer player, you know that you are an amateur soccer player. You play on an amateur team, in an amateur league, probably with amateur referees in a random city park that has other uses the rest of the week. No matter how astonishing a goal you score, it is a goal in an amateur game in a park on Sunday.  

In music, however, everybody plays in the same venue, nominally in the same league. Any song on any of the major commercial music-streaming services could be streamed 1 billion times tomorrow. Structurally, in the music version of football, an amateur player from a local park could kick a ball and it could slip past Caoimhin Kelleher in the 121st minute to send Liverpool crashing out of the FA Cup.  

That game was at Old Trafford, not Wembley, but the point is that this mostly doesn't happen. The statistical economic dynamics of music and football are very similar, which is why the analogy presented itself in the first place. But the aspirations are exactly why it doesn't work. In football, not only do you know your current status, but you can see the potential future steps in your career, and how they might happen. You could impress a local scout with your park goal, and get a tryout for a local semi-pro team. You could lead the semi-pro league in assists and get signed for a year by a second-division team. You could captain your second-division team to promotion, and like a fairy-tale, three years later you are getting crushed 7-0 by Manchester City and trying to claw your way out of the relegation zone so your dream can continue just a little bit longer.  

In music, there used to be a story like this. You played club gigs in your hometown, and gave demo tapes to your friends. Somebody who ran a local label maybe heard you and liked you enough to help you put out a record. Maybe that record got played on college radio a little, and got you a chance at a deal on a minor major label. Maybe your minor major-label debut had a minor hit. Maybe your label stuck with you and you got to make more records. Maybe your third album has a song about getting drunk alone in your hometown and introducing yourself again to your friend's mother and it blows up and suddenly you are playing in Wembley. Or Fenway Park, at least.  

Streaming offers the tantalizing illusion that these laborious steps have been eliminated by technology. But really they haven't. Music is an attention economy. The dominance of the biggest attention companies used to be reinforced by constraints of physical distribution, but it mostly survives the format shift. Most of the songs on the biggest playlists still come from the three major labels.  

Which doesn't mean that the story of your potential career hasn't changed. The new steps might involve playlists instead of clubs, viral videos instead of college radio, and maybe a judicious distribution deal instead of an old-school contract with an advance you will never recoup. And these new steps, if they happen, could happen more suddenly than the old steps, and thus it can feel like they could happen suddenly at any moment.  

But, still, mostly they won't. Mostly the paths to big success still go through labels, particularly major ones. Mostly the old major-attention economy survives through minor adaptations. Whatever aspirations they have, or labor they expend, most of the 10 million artists on streaming services will never get beyond semi-professional status in the most marginal sense of "semi". I have songs on Spotify, too. They took labor to make. A few people have streamed them, and I have been paid a few cents for those streams. Last time I checked, my lifetime earnings from streaming music were well on the way to $5. From $4.  

But I am an amateur. I know I'm an amateur, I'm not trying to make a living by making music. If streaming services all start imposing minimum stream-thresholds for royalty payouts, I may never get to that glittering $5 in the distance, and that will be morally disappointing but practically fine.  

If you're trying to become a professional, it's not fine. If regressive thresholds take away your sense of progress, that's not fine. If the successes you aspire towards operate like lotteries, so that you can't work towards them, that's not fine. If the people who operate the economy in which you will or will not be able to make a living sound like they are dismissing you as a non-participant, that's not fine.  

I like the football analogy, actually, but I think it applies the other way around: if you own a stadium, and you invite all the players in the world to come in and play in front of all the fans, you don't have to promise them all glory, but you better not try to tell them that some of their goals won't count.

I like legislation as a tool for social change, so I'm positively predisposed towards the Living Wage for Musicians Act as a tactic, and I agree with its goal of making it possible for more people to make better livings as musicians.  

But I don't think this proposed law, as written, will work.  

Here's how it would operate:  

Music-streaming subscriptions in the US would have a federal government fee of 50% added to them...  

This ought to be in the headline of every article covering this story. "Make Streaming Pay", the UMAW slogan for this effort, sounds like a vendetta against streaming services, especially coming from the same people who brought us "Justice at Spotify" previously, but as a music listener you should understand that the people who would pay this time are you. The proposed bill would add fees to music subscriptions. Fees are a well-established tactic, but not exactly a well-loved one. It's at least faintly ironic that Congress is scrutinizing Ticketmaster's excessive fees at the same time that this bill is proposing to add one to music streaming.  

And 50% is a lot. A $10.99/month subscription would get an added $5.50 government fee, raising the total to $16.49. The bill even specifies a minimum of $4, so a $5.99 student subscription would rise to $9.99. While I don't think either of those are unreasonable prices for all the music in the world, they're giant relative jumps. I would fully expect them to be publicly unpopular as a proposal, and thus hard to find support for in Congress. If enacted, they would probably cause many existing subscribers to downgrade to free (ad-supported) alternatives. Enough people doing so could cancel out the monetary benefit, so this should not be proposed without careful modeling of likely price flexibility. I doubt that has been done, and certainly no evidence of it has been presented by the bill's advocates. I would also expect most or all streaming services to lobby vigorously against this change because of these effects, even though the fee itself is not paid by them. Except...  

and music-streaming services would have a 10% tax on their "non-subscription" (meaning mainly advertising) revenue in the US...  

You can't add fees to free, so here's the other half of the plan. Most streaming services already pay ~70% of revenue to licensors, keeping ~30% for themselves. A 10% tax on revenue thus cuts gross advertising profit by a third. Since Spotify (whom I single out here only because as a public company they report music-specific financial results, which Apple/Amazon/YouTube Music as divisions of larger companies do not) has mostly not turned a net profit at all, this proposed tax will almost certainly be taken as intractably punitive, and I expect all the services with ad-supported tiers to resist it. Spotify probably cannot afford to threaten to pull out of the US like it threatened to pull out of Uruguay when a (different) version of this idea was proposed there, and would presumably not want to increase their own prices again having only recently raised them in most countries, making it hard to take the tactic they are taking in response to a 1.2% tax in France. So I would expect Spotify to lobby against this as if it is an existential threat.  

There's also a very important question here about what constitutes a music service, and in particular whether YouTube (not YouTube Music) and TikTok count. The bill doesn't address this, although the UMAW advocacy for it strongly implies that YouTube, at least, is meant to be included. I do not expect Google to quietly accept a 10% tax on any meaningful subset of YouTube advertising revenue.  

which would be collected into (and by) a new government fund/agency...  

Streaming music royalties are already split into three different components: to licensors, to publishers (for songwriters), and to performing-rights agencies (also for songwriters; it's a long story). This bill would add a fourth. That seems to me like the wrong direction, and grounds for skepticism even before we get into how the new fund would work. As an example it also implies that every country would need to create a similar fund of their own, although the bill as written seems to ignore the fact that it applies to the flow of money in only one country, while the music itself is global.  

which would also collect and tabulate monthly streams by unique master recording...  

This detail is unexplicated in the bill, but introduces a very serious technical requirement. Music is delivered to streaming services by licensors in releases composed of tracks, and it's normal for there to end up being many different tracks that have the same original audio, e.g. a single and that same song on the subsequent album and the same song again later on a compilation, and all these again in many different countries. Reconciling these requires audio-analysis software that can correctly match two tracks of the same recording even if they've gone through slightly different processing, and correctly differentiate between two different pieces of music even if they contain substantial similarity (like a song and a remix of it that adds a guest verse). And even after you've correctly matched tracks by their audio, their credits might differ, so you have to figure out which credits you're going to use. I can testify from 12 years of involvement with the process at the Echo Nest and Spotify that this is all not a trivial problem, and can be error prone even in a long-running production system. The administrators of the new fund are going to have to hire more programmers than they probably realize.  

impose a cap of 1 million streams/month on each such recording...  

This is arguably the most critical, progressive and interesting detail in the bill. Rather than just increasing all artists' current income by a small proportional amount, the bill attempts to specifically support artists who might not currently be making a living from their music, by effectively redirecting some or most of the money from songs with >1m streams back into the payment pool. This is why the recording-matching has to be accurate, but sadly is also the key to trivial manipulation of this scheme to evade its intent. Each detected "unique recording" is subject to a 1m cap, but it's not hard to produce multiple tracks that sound the same to listeners, but intentionally defeat the usual methods for automatic matching. Were this bill to be passed, I expect it would become normal practice to do this across releases and services, to make every track of the same recording register uniquely, so that each one gets its own 1m cap. The producers of very popular songs would have a strong incentive to also try to do it over time for each song during a given month, hoping to accumulate N million streams 1m at a time across N variations of the same song.  

The 1-million-stream threshold here is arbitrary. The bill itself doesn't justify or explain it. Rep. Tlaib has mentioned in speaking about this bill that it takes 800,000 streams/month at a current average rate of $.003/stream to make the equivalent of minimum wage, which is correct math, but that's per artist, not per track. The unavoidable market truth about music (like most non-commissioned art) is that financial reward is not a function of quantity of labor. You can spend any amount of time making a song, and maybe nobody will play it. If we really want, as a society, to give people a living wage for the labor of making music, as opposed to lucking into popularity, then we need to spend our government energies on grants or Universal Basic Income, not on streaming taxes and fees.  

and then divide payments proportionally by capped streams...  

This sounds like just unremarkable process, but is sneakily the most serious flaw of the whole bill as written. The fund combines all streams from all services, and all money from all services, and distributes that combined money according to those combined streams. This sounds like the pro rata royalty-allocation method already in use by all major streaming services. The crucial difference, though, is that services do not do this with one big pool of money and streams, they do it with an individual pool of money and streams for each payment plan (in each country). This is essential, because the revenue per listener varies widely across countries and plans. A stream from a Spotify Premium subscriber in Iceland is worth considerably more than a stream from an ad-supported listener in India.  

By combining all the streams and all the money, this plan would make it possible to use the cheapest form of artificial streaming to accumulate fraudulent streams that would share money from the most expensive ones, thus inaugurating a golden age of streaming fraud.  

This is not only a fatal flaw of the bill as written, it's one that reveals that the writers of the bill do not know how the existing royalty methods work, and didn't consult with anybody who does.  

90% to "featured" artists and 10% to "non-featured" artists...  

It's a minor selling point of this bill that it would result in some royalties being paid to "non-featured" artists, like session musicians and backing vocalists, who do not (usually) get royalties at all from the current system. The amount is small, though, and administering it would be a procedural headache. Because those people don't currently get paid royalties, their participation isn't necessarily included in the licensors' metadata. And, conversely, because those people don't get royalties, they're currently mostly paid for their work in old-fashioned wages. Give them a share of the royalties and we might find that that becomes an excuse to pay them less up front, in the same way that tip workers are often given lower base wages.  

The bill does not say how royalties would be split between multiple featured or non-featured artists. I guess it's loosely implied that it would automatically be equal shares to each, since there's no mention of any mechanism to specify otherwise. The bill does specify that "artists" means individual humans, not corporations or generative AIs (!), which seems to mean that bands are not part of this scheme, only each person one at a time.  

And, notably, the bill as written specifically does not include songwriters. This is a little surprising to me, since I think of advocacy for higher royalty rates for songwriters as part of the same family of social-justice causes as higher royalty rates for performers, and songwriters get the smallest share of royalties in the current system. I'm not looking forward to the antagonism between "performers" and "songwriters" that this omission might provoke.  

who sign up with the fund and provide payment information.  

This, too, is both a distinguishing characteristic of this plan and a drawback. The whole point of this fourth royalty scheme is to route it around the first three, although in practice it's mainly the payment of recording royalties to licensors (and thus to labels) that the writers are trying to avoid. Labels, particularly major ones, often write artist contracts in which advances are paid up front, and artists not only get a small percentage of the royalties later, but even that small percentage is accounted for as repaying the advance as a loan. So an artist might, in practice, get no royalties for a while, or ever. (Although, again, they were paid an advance, and if their royalties don't earn back the advance, they don't have to repay it any other way.)  

But, of course, you don't have to sign a label contract in order to release music on streaming services. DIY distributors either charge small flat fees, or take very small shares of your royalties. But labels provide services in addition to taking royalties (and paying advances), and maybe you want those. I suspect that musicians signed to major labels are mostly doing OK, at least temporarily during their maybe-short label tenure. And if they aren't, and their label contracts are why, maybe that's where the laws should be pointed.  

But that means this fund is yet another thing an artist has to sign up for and manage, and which in turn has to manage and verify them. I have not found any good estimates of how many artists currently do not do the work to register their songs to collect performance and mechanical rights, and how often there are contradictions between ownership claims, but I'm sure both are common. There's precedent in performance-rights organizations for international cooperation, but I don't know if any of those operate on this scale, and even if they do, this bill doesn't propose to use them, so this new fund (and its equivalents in other countries, if they exist) would have to reinvent all of that process.  

The stipulation about individuals, not companies, seems obviously like a preemptive attempt to keep labels from registering on their artists' "behalf" and collecting this new windfall too, but I'm not immediately convinced that won't happen somehow anyway. And indeed it might have to for the scheme to accommodate the estates of dead artists, whom I assume it doesn't intend to exclude.  

Even if we imagine that nobody attempts to evade this rule, though, the existence of a fourth royalty that bypasses labels is likely to push labels, and the three major-label companies in particular, to object to this bill too. And were it enacted, I would expect to see labels begin to change the terms of their contracts to reduce or eliminate artist shares of the recording royalties since they're now supposedly getting this new Living Wage paid separately.  
 

The notable thing this bill does not include is any mechanism or support for this claim that the UMAW, who collaborated on it, continue to make here:

The Living Wage for Musicians Act is built to pay artists a minimum penny per stream, an amount calculated specifically to provide a working class artist a living wage from streaming.

The bill, as written, is very definitely not "built" to pay $.01/stream. UMAW's intro puts the current average stream rate at $0.00173 (including YouTube), and after an hour or so of spreadsheet noodling I could not see any way it would more than double this for the biggest beneficiaries (artists whose tracks all approach 1m streams without going over), even if nothing else in the industry changed in reaction. That would be $0.00346/stream, still a long way from $0.01. It doesn't help my confidence in UMAW's math diligence that their "calculator" to show the effects of this bill not only just multiplies streams by $0.01, but doesn't even bother to apply the 1m-stream cutoff.  

Nor have I seen any explanation of why the suspiciously round penny is coincidentally the magic living-wage level, and I'm willing to bet a large number of pennies that no such explanation exists. There are many very-good bands who do not have 1 million streams total, all time, across all their songs on Spotify. That's not a multi-year living wage for a group of people even at a dime per stream.  
 

But OK, it's easy to criticize. If I'm in favor of laws, and I share the goal of improving the lives of musicians, what should we do instead?  

When in doubt, try to remove imbalances of power. Reduce complexity, reduce secrecy. Personally, I would start by trying to simplify and improve the existing royalty process, rather than adding another incompletely-thought out layer with uncertain consequences.  

We got a good idea about how to do this, by accident, recently, when Spotify and Deezer and UMG collaborated to change their contractual rules for recording royalties to pay nothing to tracks that don't reach 1,000 streams over the course of the last year. This is a regressive measure I personally despise, but the interesting part is that they actually couldn't pay those songs nothing, because the performance and mechanical rates are set by law (at least in the US). If the recording rates were also set by law, those wouldn't have been subject to secret contract negotiations either. Moving all the rates into law would also allow them to be determined (and debated in public) as a coherent set, which would make a lot more sense. And while we're at it, we could eliminate the spurious performance royalties, reducing the number of royalty components to two, one for the performers and one for the songwriters. And, in fact, if we allowed artists to designate original songs, so that this information was passed on by licensors to streaming services, then both royalties could be paid at once for those tracks, reducing the reporting overhead for artists and services both, and recovering some of the money currently lost on the way to artists who never took the time to sign up for BMI or ASCAP.  

Those simplifications would not, in themselves, provide a predictable living wage for all working musicians, either. But they would make the current streaming model less mysterious, and less beholden to secret agreements between a few giant corporations. Plenty more work would remain to be done. But that work would be easier think about, and easier to do. And less likely to produce earnest laws that probably have no chance of living up to their authors' hopes for them, or ours.

Talking to Robots About Songs and Memory and Death
Infinite Archives, Fluctuating Access and Flickering Nostalgia at the Dawn of the Age of Streaming Music
(delivered at the 2024 Pop Conference)  

Let me tell you how it used to be. Songs were written and sung and recorded, but then they were encased in finite increments of plastic, and our control over our ability to hear them relied on each of us, sometimes in competition, acquiring and retaining these tokens. The scarcity of particular plastic could shroud songs in selective silence. A basement flood could wash away music.  

Imagine, instead, a shared and living archive. Music, instead of being carved into inert plastic, is infused into the frenetic dreams of angelic synapses. Every song is sung at once in waiting, and needs only your curious attention to summon it back into the air. Nothing, once heard, need ever be lost. The rising seas might drive us to higher ground, but our songs watch over us from above.  

When I proposed this talk, Spotify held 368,660,954 tracks from 61,096,319 releases, and I could know that because I worked there. The servers of streaming music services are unprecedented cultural repositories, diligently maintained and fairly well annotated. We pour our love into them, and in return we can get it back any time we want.  

That's the techno-utopian version, at least. In the real-life version, the angels are only robots, and the robots aren't even actual robots. The infinite generosity of technology is constrained by relentless pragmatic contingencies: corporations, laws, contracts, stockholders, greed. All those songs are there, technically poised, but whether we are allowed to hear them depends on layers of human abstraction and distraction. This is what people mean when they object to streaming as renting the things that you love. The erratic logistics of music licensing control whether any given song is permitted to escape from the streaming servers. Licensing, in turn, is permuted by artists and labels and distributors and streaming services, and then again by the borders of countries and the passage of time. The song you want to hear again is still there. But that may not be enough.  

"Renting the things you love" sounds bad. But so too, I think, does "purchasing the things you love". I don't philosophically need or want my love to be materialized in a form for which I have to transact, and which I then have to store. I want to be able to recall joys effortlessly. The system model of instant magical recall, which is an illusion that streaming can sustain under conducive network conditions, is what I think we want, what music wants. If renting is reliable, maybe it's fine. But how reliable is it?  

If you don't work for a streaming service, you can only really assess this by anecdote. Joni Mitchell objected to Spotify's podcast deal with Joe Rogan, and revoked its rights to her whole catalog. Because rights are complicated, though, it didn't entirely work. When I proposed this talk, there was one Joni Mitchell song still accessible on Spotify in the US, a stray copy of "A Case of You" from a random compilation released in roundabout evasion by some label other than hers. If you didn't know this context, you would have no immediate way to tell Joni wasn't an emerging artist with just the one complicated, hopeful first single so far. A complicated hopeful first single with 103,102,704 plays, apparently, so you might wonder a little bit. Promising, I think. I'd like to hear more.  

Since then, the license police caught up to that rogue compilation, and "A Case of You" is gone again. As of my drafting of this talk, Joni Mitchell's Spotify catalog was a 10-song 1970 BBC live album, and a single pointlessly overbearing cover of "River" by somebody else that was gamed onto Joni's Spotify page by the trick of labeling it as a classical composition, which causes Spotify to treat its composer as one of its primary artists. If the only artists with the power to withhold their songs were ones of Joni's stature, that would actually be fairly manageable. The plastic tokens of Blue are not scarce or expensive. If only artists had the power to withhold songs, actually, this would be a conversation about art and the limits of authorial control, and whether Joni is allowed to come take your copy of Blue away from you if you listen to Joe Rogan.  

If you do work for a streaming service, though, and you can manage not to resign in protest of anything it does that you disagree with, then you don't have to rely on annecdote, you can use data. So I did. I ran the historical analysis of all post-release licensing gaps in song availability from 2015 to 2023, both overall and aggregated by licensor. In practice, in turns out, almost all songs available today have been available for streaming continuously since release. There are a handful of licensors whose tracks are routinely retracted, and there are good reasons for this, which I'm not allowed to tell you but I can reassure you that those are not the tracks we're worried about. Actual licensing gaps for actual songs with actual audiences are, statistically speaking, vanishingly rare. I made a nice graph of this.  

If you work for a streaming service, however, you can also get laid off by that streaming service, which I also did. When this happens you have a surprise 10-minute call with an HR rep you've never seen before at 9:15 on a Monday morning, and then your laptop is remote-locked and you don't have those graphs any more. The robots are not allowed to talk to me now. Who will sit with them when they are sad? The problem with externalizing our memories and our note-taking into the cloud isn't technological reliability, it's control. The problem with renting the things you love is not the fragility of the things, it's the morally unregulated fragility of the relationship between you and the corporate angels.  

We'll be OK without that graph. It was not, shall we say, the "A Case of You" of data graphics. The things that really belonged to Spotify, Spotify can keep. The goverance models for modern corporations are still painfully primitive. We understand that local democracies and a little bit of international law are a better model than crusader feudalism for communities of place, and I feel like it's morally apparent that corporations, as communities of purpose, ultimately deserve the same models and protections. If you move away from a city, you're still allowed to come visit. I should probably be allowed to visit my graphs. I like to imagine Joni ripping copies of her own CDs and adding them to Spotify as local files just as a jurisdiction flex.  

My listening, on the other hand, is my own. Consumer protections are slightly more advanced than employee protections, so you can request your complete listening history from Spotify any time you want. For much of the decade I spent working at Spotify, though, I also maintained an abstruse weekly annotated-playlist series called New Particles, so I have my own record, not just of what I heard, but what I cared about. Over the course of 454 weeks, I cared about 35,900 tracks by 13,951 different artists. This is small for data, but big for annecdotes. What I find, going through it, is that almost every week beyond the recent past has at least one song that is now, or at least currently, unavailable. Some of the earliest lists from 2015 are missing 3 or 4, but by 2017 and 2018 it's usually 1, plus or minus 1.  

Counting is not an emotional exercise, though, and all interesting music-data experiments begin with some kind of counting but don't stop there. So I went through the playlists I was listening to in my birthday week each year, cross-checking the specific tracks that had gone gray in Spotify, to see if I could tell a) how missing they really are, and b) how much I care. This is mostly what my job at Spotify was like, too: short bursts of math, and then the long curious process of trying to understand the significance of the resulting numbers. And I did consistently say that I would be doing this even if they weren't paying me.  

From my March 31st 2015 list I am missing the song "Kranichstil" by the Ukrainian/German rapper Olexesh. His albums before and after seem to all be available for streaming still. This one isn't, but the song is easily found on YouTube. It's still sinuous and boomy and great.  

2016: I'm missing "Rolling Stone" by the Pennsylvania emocore duo I Am King. They're still putting out sporadic emo covers of pop songs, which is one of my numerous weaknesses. "Rolling Stone" was an original, and I admit I don't remember it super-well, so maybe the version that is currently available on Spotify is different from the unavailable one I liked in 2016, but it's definitely close enough.  

2017: "Por Amor" by the Chilean modern-rock band Lucybell. I had the single of this on my playlist, and you can't play that any more, but the slightly longer version is still the first track on the readily-available album Magnético, and still sounds like a stern Spanish arena-rock transformation of a New Order song.  

2018: The whole album MASSIVE by the K-Pop boy-band B.A.P. is unavailable, but the song I liked, the cartoonish rap-rock rant "Young, Wild and Free", was originally on a 2015 EP, which is still available.  

2019: The trap-metal noise-blast "HeavyMetal!" (no space between the words, exclamation point at the end) by 7xvn (spelled with the number seven, then x-v-n) is off of Spotify, but you can still find it on Soundcloud, which in this case feels about right.  

2020: A gothic metalish song called "Menneisyyden Haamut" by the Finnish band Alter Noir. Their Spotify page is empty now, and if you Google this song, the results are the orphaned Spotify page, two links from their own Facebook page to that empty Spotify page, and then my playlist that I put the song on. I sent myself an email to see if I knew what the story is with this, but I haven't heard back from myself yet.  

2021: "Fuck You Nnb" by lieu. I am old and do not know what "Nnb" stood for, but I do know that lieu was supposedly a 15-year-old kid deliberately switching between distributors so their songs would end up strewn across disconnected artist identities. Perfect public memory of what we thought was a good idea when we were 15 is not necessarily a civic virtue. In some cases forgetting is probably the right way to remember.  

2022: ANISFLE were an ornate Japanese rock band, or at least a heavily embroidered impression of one. Their Spotify profile is blank, their web store is empty, I guess something catastrophic happened to them. But there are still a few of their videos on YouTube, and they're still ridiculous and magnificent.  

2023: The only new thing I loved last April 4th that didn't even survive for a year was a maskandi song called "UYASANGANA YINI" by uMjikelwa. It seems to still be available on Apple Music. One of his other albums is on Spotify, and I will be completely honest that although I adore maskandi and follow hundreds of maskandi artists to make sure I have a constant supply of new maskandi to listen to, I usually pick one random song from each album and I do not pretend I can really tell them apart. If you snuck into my web archives and swapped this for anything similar, I would almost certainly not notice.  

I think I can live with that much loss. Individual human obstruction occludes individual archives, but the network of archives, from the well-regulated to the unruly, tends to route around suppression. It's hard to make everybody forget.  

And meanwhile, my database memory is far, far better than my brain memory. How many of those 13,951 artists could I list without looking? Some. Lots, but not most. But this is how I live, now. How old was my kid when we had the birthday party where their best friend's brother fell on a brick and had to be taken to the ER? I don't remember, but I can look through Google Photos and find it by the pictures we took before the panic. Which China Mieville book did I read first? I don't remember, but I bet I can find the email I wrote you right afterwards. Or maybe I sent it from a work address and so I can't.  

So yes, our technically perfect externalized memories are imperfected by our insistence on staging them behind our contentious and fluctuating rules. We produce a compromised projection of our archives by fighting over their access controls. Our human systems hold back our information systems.  

But I think we'd rather have that than the other way around. If my record store, in 1989, had made a ridiculous deal with Joe Rogan and Joni had pulled her whole divider out of the M bins, we had no collective recourse. We could check the used stores, but who gets rid of Joni Mitchell albums? Recovering from this, later, would require re-shipping a case of Blue to, oh, Canada? And everywhere else. The grayed-out tracks on Spotify playlists are more like the coy ropes across the wine shelves in Whole Foods on Sunday in blue-law states. Not only are we ready when the laws and processes finally relent, but we are reminded, every moment until then, how arbitrary and ridiculous it is that they still have not.  

What would better laws and processes involve? What we need here, I think, is a legal and syntactical structure for asserting music rights as layers, starting with the artist. Right now, each licensor of a recording makes a deal with its artist, with terms and dates, but then turns around and sends the streaming services only enough data to assert that licensor's own isolated claims. If licensors were required to pass along both the span of their claim, and the underlying artist ownership to which the rights will subsequently revert, then royalty attribution could fluctuate without affecting availability. And by the way, while we're building that, we could also use the same structure to embed the composition rights with the recording rights, eliminating the completely insane indirection in which the publishing rights for streaming songs have to be re-asserted separately by writers and then rediscovered separately by collecting societies.  

If this last idea appeals to you so much that you would like to read it again in print, it also appears in my upcoming book, titled You Have Not Yet Heard Your Favorite Song: How Streaming Changes Music, which comes out in June on Canbury Press. A book is another kind of externalized memory. It's good to remember how we thought things were. In my case I wrote this one while I was working at Spotify, but not because I was working at Spotify, and at least I got laid off in time to edit a bunch of present- and future tenses into the past before they were printed on paper. Memory, too, is a system: of layers and contingencies and adaptation and revelation. Underneath, somewhere, there's always love. We fall in love three minutes at a time, and we might forget the songs but we won't forget the falling.  

Meanwhile, we improve the world when we can, with whatever tools and influence we are currently allowed. When we can't, we try to preserve it's potential in hiding, if not in angelic invulnerability, then at least above the water line. We leave the robots on guard, not because we trust them, but because it makes them feel useful and we don't have the heart to tell them that they aren't real. We let new songs invoke the ones we're not supposed to hear. We name our loss, and we try to not die before the day when we're allowed to remember everything again.  

Rolling Stone published this recent story (https://www.rollingstone.com/pro/features/spotify-sleep-music-playlists-lady-gaga-1223911/) about the streaming success of the sleep-noise artist/label/scheme Sleep Fruits, who chop up background rain-noise recordings into :30 lengths to maximize streaming playcounts.  

Sleep Fruits is undeniably and intentionally exploiting the systemic weakness of the industry-wide :30-or-more-is-a-play rule, as too are audiobook licensors who split their long content into :30 "chapters". The :30 thing is a bad rule. Most of the straightforward alternatives are also bad, so it wasn't an obviously insane initial system design-choice, but this abuse vector is logical and inevitable.  

The effect of the abuse for the label doing it is simple: exploitative multiplication of their "natural" streams by a large factor. x6 if you compare it to rain noise sliced into pop-song-size lengths.  

The effect on the rest of the streaming economy is more complicated. More money to Sleep Fruits does mean less money to somebody else, at least in the short term.  

Under the current pro-rata royalty-allocation system used by all major subscription streaming services (one big pool, split by stream-share), the effects of Sleep Fruits' abuse are distributed across the whole subscription pool. The burden is shared by all other artists, collectively, but is fractional and negligible for any individual artist. In addition, under pro-rata if an individual listener plays Sleep Fruits overnight, every night, it doesn't change the value of their "real" music-listening activity during the day. Those artists get the same benefit from those fans as they would from a listener who sleeps in silence.  

Under the oft-proposed user-centric payment system, in which each listener's payments are split according to only their plays, Sleep Fruits' short-track abuse tactic would be less effective for them. Not as much less effective as you might think, because the same two things that inflate their overall numbers (long-duration background playing + short tracks) inflate their share of each listener's plays. But less, because in the pro-rata model one listener can direct more revenue than they contributed, and in the user-centric model they can't.  

In the user-centric model, though, if an individual listener listens to Sleep Fruits overnight, that directly reduces the money that goes to their daytime artists. Where pro-rata disperses the burden, user-centric would concentrate it on the kinds of artists whose fans also listen to background noise. This is probably worse in overall fairness, and it's definitely worse in terms of the listener-artist relationship, which is one of the key emotional arguments for the user-centric model.  

The interesting additional economic twist to this particular case, though, is that sleeping to background noise works very badly if it's interrupted by ads. Background listening is thus a powerful incentive for paid subscriptions over ad-supported streaming. (Audiobooks similarly, since they essentially require full on-demand listening control.) So if Sleep Fruits drives background listeners to subscribe, it might be bringing in additional money that could offset or even exceed the amount extracted by its abuse. (Maybe. The counterfactual here is hard to assess quantitatively.)  

And although the :30 rule is what made this example newsworthy in its exaggerated effect, in truth it's probably not really the fundamental problem. The deeper issue is just that we subjectively value music based on the attention we pay to it, but we haven't figured out a good way to translate between attention paid and money paid. Switching from play-share to time-share would eliminate the advantage of cutting up rain noise into :30 lengths, but wouldn't change the imbalance between 8 hours/night of sleep loops and 1-2 hours/day of music listening. CDs "solved" this by making you pay for your expected attention with a high fixed entry price, which isn't really any more sensible.  

I don't think we're going to solve this with just math, which disappoints me personally, since I'm pretty good at solving math-solvable things with math. But in general I think time-share is a slightly closer approximation of attention-share than play-share, and thus preferable. And rather than trying to discount low-attention listening, which seems problematic and thankless and negative, it seems more practical and appealing to me to try to add incremental additional rewards to high-attention fandom. E.g. higher-cost subscription plans in which the extra money goes directly to artists of the listener's choice, in the form of microfanclubs supported by platform-provided community features. There are a lot of people who, like me, used to spend a lot more than $10/month on music, and could probably be convinced to spend more than that again if there were reasons.  

Of course, not coincidentally, I have ideas about community features that can be provided with math. Lots of ideas. They come to me every :30 while I sleep.  
 

PS: I've seen some speculation that Sleep Fruits is buying their streams. I'm involved enough in fraud-detection at Spotify to say with at least a little bit of confidence that this is probably not the case. Large-scale fraud is pretty easy to detect, and the scale of this is large. It's abusing a systemic weakness, but not obviously dishonestly.

I starting making one music-list a year some time in the 80s, before I really knew enough for there to be any sense to this activity. For a while in the 90s and 00s I got more serious about it, and statistically way better-informed, but there's actually no amount of informedness that transforms a single person's opinions about music into anything that inherently matters to anybody other than people (if any) who happen to share their specific tastes, or extraordinarily patient and maybe slightly creepy friends.  

Collect people together, though, and the patterns of their love are sometimes very interesting. For several years I presided computationally over an assembly of nominal expertise, trying to find ways to turn hundreds of opinions into at least plural insights. Hundreds of people is not a lot, though, and asking people to pretend their opinions matter is a dubious way to find out what they really love. I'm not really sad we stopped doing that.  

Hundreds of millions of people isn't that much, yet, but it's getting there, and asking people to spend their lives loving all the innumerable things they love is a more realistic proposition than getting them to make short numbered lists on annual deadlines. Finding an individual person who shares your exact taste, in the real world, is not only laborious to the point of preventative difficulty, but maybe not even reliably possible in theory. Finding groups of people in the virtual world who collectively approximate aspects of your taste is, due to the primitive current state of data-transparency, no easier for you.  

But it has been my job, for the last few years, to try to figure out algorithmic ways to turn collective love and listening patterns into music insights and experiences. I work at Spotify, so I have extremely good information about what people like in Sweden and Norway, fairly decent information about most of the rest of Europe, the Americas and parts of Asia, and at least glimmers of insight about literally almost everywhere else on Earth. I don't know that much about you, but I know a little bit about a lot of people.  

So now I make a lot of lists. Here, in fact, are algorithmically-generated playlists of the songs that defined, united and distinguished the fans and love and new music in 2000+ genres and countries around the world in 2019:  

2019 Around the World
 

You probably don't share my tastes, and this is a pretty weak unifying force for everybody who isn't me, but there are so many stronger ones. Maybe some of the ones that pull on you are represented here. Maybe some of the communities implied and channeled here have been unknowingly incomplete without you. Maybe you have not yet discovered half of the things you will eventually adore. Maybe this is how you find them.  
 

I found a lot of things this year, myself, some of them in this process of trying to find music for other people, and some of them just by listening. You needn't care about what I like. And if for some reason you do, you can already find out what it is in unmanageable weekly detail. But I like to look back at my own years. Spotify's official forms of nostalgia so far define years purely by listening dates, but as a music geek of a particular sort, what I mean by a year is music that was both made and heard then. New music.  

I no longer want to make this list by applying manual reductive retroactive impressions to what I remember of the year, but I also don't have to. Adapting my collective engines to the individual, then, here is the purely data-generated playlist of the new music to which I demonstrated the most actual listening attachment in 2019:  

2019 Greatest Hits (for glenn mcdonald)  
 

And for segmented nostalgia, because that's what kind of nostalgist I am, I also used genre metadata and a very small amount of manual tweaking to almost automatically produce three more specialized lists:  

Bright Swords in the Void (Metal and metal-adjacent noises, from the floridly melodic to the stochastically apocalyptic.)
Gradient Dissent (Ambient, noise, epicore and other abstract geometries.)
Dancing With Tears (Pop, rock, hip hop and other sentimental forms.)  
 

And finally, although surely this, if anything, will be of interest to absolutely nobody but me, I also used a combination of my own listening, broken down by genre, and the global 2019 genre lists, to produce a list of the songs I missed or intentionally avoided despite their being popular with the fans of my favorite genres.  

2019 Greatest Misses (for glenn mcdonald)  

I made versions of this Misses list in November and December, to see what I was in danger of missing before the year actually ended, so these songs are the reverse-evolutionary survivors of two generations of augmented remedial listening. But I played it again just now, and it still sounds basically great to me. I'm pretty sure I could spend the next year listening to nothing but songs I missed in 2019 despite trying to hear them all, and it would be just as great in sonic terms. There's something hypothetically comforting in that, at least until I starting trying to figure out what kind of global catastrophe I'm effectively imagining here. I'm alive, but all the musicians in the world are dead? Or there's no surviving technology for recording music, but somehow Spotify servers and the worldwide cell and wifi networks still work?  

Easier to live. I now declare 2019 complete and archived. Onwards.