The glum Digital Music News headline reads "Spotify Daylist is Blowing Up—Too Bad the Creator Was Laid Off", and although I haven't specifically talked to the person who came up with Daylist since the layoffs, I don't think they were affected in this round. The explanation in the body of the story is a little more specific:  

 

This is mostly true in what it actually says. I wasn't the only person working on the Spotify genre-categorization project, but I started it, I ran it, I wrote all of its tools and algorithms, and I worked on many applications of it to internal problems and app features. Without me it probably will not survive. And that genre system is one of the ingredients that feeds into Daylist.  

The DMN piece is derived from an earlier article at TechCrunch, where the assertion is more carefully phrased: "Spotify’s astrology-like Daylists go viral, but the company’s micro-genre mastermind was let go last month". And more carefully reported:  

 

...  

 

The "look no further" flourish is misguided, since I didn't curate every individual genre myself, and maybe didn't personally configure any of the ones they cite. We did not make up the name "egg punk", either.  

USA Today, drawing from both of these stories, kept the plot twist out of the headline ("How to find your Spotify Daylist: Changing playlists that capture 'every version of you'") and saved it for a rueful final paragraph:  

 

The judicious "help" there is fair enough. And as none of these say, in addition to working on genres I was also a prolific source of this kind of internal personalization experiment, and thus part of an environment that encouraged it.  

Daylist itself was absolutely not my doing, though. You'd have to ask its creator about their influences, but so far I haven't seen Spotify give public named credit for the feature, and in a period of sweeping layoffs, in particular, I encourage you to take note of the general corporate reluctance to acknowledge individual work. But while we're at it, I did not have anything to do with Discover Weekly, nor did anybody from the Echo Nest, which was the startup whose acquisition brought me to Spotify and which I did not found. These are not secret details, and a reporter could easily discover them by asking questions. None of three people who wrote those three articles about Daylist talked to me before publishing them.  
 

And although the Daylist feature itself is charming and viral, and I support its existence, it also demonstrates three recurring biases in music personalization that are worth noting for their wider implications.  

The most obvious one is that Daylist is based explicitly on the premise that listening is organized by, or at least varies according to, weekdays and dayparts. It is not the first Spotify feature to stipulate this idea, and clearly there are listeners for which it is relevant. But I think both schedule-driven and the similar activity-driven models of listening (workout music, study music, dinner music..) tend to encourage a functional disengagement from music itself. Daylist mitigates this by describing its daypart modes in mostly non-functional terms, including sometimes genres and other musical terminology, and of course you aren't required to listen to nothing but Daylist and thus it isn't obliged to provide all important cultural nutrients. But the eager every-few-hours updating does make a more active bid for constant attention than most other personalization features. Discover Weekly and Release Radar are only weekly, and short. Daily Mix is only (roughly) daily, although it's both endless and multiple. I don't think the cultural potential of having all the world's music online is exactly maximized by encouraging you to spend every Tuesday afternoon the same way you supposedly always have.  

The second common personalization bias in Daylist is that it manifestly draws from a large internal catalog of ideas, but you have no control over which subset you are allowed to see, and there is no way to explore the whole idea-space yourself. This parsimonious control-model is not at all unique to Spotify, but it's certainly pervasive in Spotify personalization features, from the type and details of recommendations you see on the Home page to the Mixes you get to the genre and mood filters in your Library. Daylist's decisions about your identity are friendly but unilateral. It's not a conversation. To its credit, Daylist is the first of these features that explains its judgments in interactive form, so you can tap a genre or adjective and see what that individual idea attempts to represent. But this enables only shallow exploration of the local neighborhood of the space. There's still no way to see a complete list of available terms or jump to a particular one even if you somehow know it exists. Obviously everynoise.com demonstrates my strong counterbias towards expansive openness and unrestricted exploration, but one might note that even after 10 years of me working on this genre project at Spotify, there's no place other than my own personal site to see the whole list of genres.  

And the third common personalization bias demonstrated unapologetically by Daylist is the endemic tech-company fondness for unsupervised machine learning over explicit human curation. As you can see for yourself by comparing the "genre" mixes you find through Daylists with the corresponding genre pages on everynoise, the genre system is only one of Daylist's inputs. All the non-genre moods and vibes in Daylist obviously come from a different system, but even the genre terms are also filtered through other influences. I did help with those other systems, too, creditwise, but I didn't invent and wasn't running them.  

Nor, honestly, do I trust them. You will learn to trust or distrust your own Daylists, if you spend time listening to them or even just inspecting them, but if you follow conversations about them online to get a wider sample than just your own, you will quickly find that they do not always make sense. Mine, right now, claims to be giving me japanese metal and visual kei, but much of it is actually idol rock and a mysterious number of <100-listener Russian metalcore bands that I have never played and which have no evident connection to bands I have. The "Japanese Metal" mix is mostly Japanese, but only sporadically metal. The "Visual Kei" mix is mostly Japanese, and does contain some visual kei, but you'd have to already know what visual kei is to pick those songs out. The "Laptop" mix opens with Morbid Angel's "Visions from the Dark Side", a song that not only was not made on a laptop (to put it mildly), but which narrowly predates the commercial availability of laptops entirely.  

The genre system was not error-proof, either. But it was built on intelligible math, it was overseen by humans, and those humans had both the technical tools and moral motivation to fix errors. We did not have a "laptop" genre because "laptop" is not a community of artists or listeners or practice, but if we had, and the system had put Morbid Angel on it, I would have stopped all other work until I was 100% confident I understood why such an egregious error had happened and had taken actions to both prevent that error from recurring and improve the monitoring processes to instill programmatic vigilance against that kind of error.  

But once you commit to machine learning, instead of explicit math, you mostly give up on predictability. This doesn't prevent you from detecting errors, but it means you will generally find it hard to correct errors when you detect them, and even harder to prevent new ones from happening. The more complicated your systems, the weirder their failure modes, and the weirder the failures get, the harder it is to anticipate them or their consequences. If you delegate "learning" to machines, what you really mean is that you have given up on the humans learning. The real peril of LLM AI is not that ChatGPT hallucinates, it's that ChatGPT appears to be generating new ideas in such a way that it's tempting to think you don't need to pay people to do that any more. But people having written is why ChatGPT works at all. If generative AI arrives at human truths, sometimes or ever, it's because humans discovered those truths first, and wrote them down. Every problem you turn over to interpolative machines is a problem that will never thereafter be solved in a new way, that will never produce any new truths.  

The problem with a music service laying off its genre curator is not the pettiness of firing the person responsible for a shiny new brand-moment. I was responsible for some previous shiny new brand-moments, too, as recently as less than a week before the layoff, but not this one and mere ungratefulness is sad but not systemically destabilizing. Daylist was made by other people, and will be maintained by other people. The problem is that I insisted on putting human judgment and obstinate stewardship in the path of demand-generation, and if that isn't enough to keep you from getting laid off from a music-streaming company, it's hard to imagine anybody else having the idiotic courage to keep trying it.

Whatever my job is or, currently, isn't, I still really want to know about new music, and there were exactly two excellent methods for this and I made them both and I'm not allowed to use either one any more. So I keep poking at approaches to this problem using public tools, and the Spotify API is still the best of these. I've gone so far as to implement a version of the obviously-intractable brute-force approach of getting a genre's artists, expanding to all those artists' Fans Also Like artists (refcounting along the way), and then getting the catalog for each artist in the resulting longer list in order to filter their releases by release-date.  

This does sort of work, eventually. It's not especially convincing for coverage, because the public API only exposes 20 Fans Also Like artists, where the internal Spotify datasource behind that (at least as long as they keep maintaining the similar-artist system I wrote) had up to 250 for each seed artist. And it takes just about forever, because it requires thousands of individual queries, and even with only one instance of it running my API key quickly hits its rate-limit and gets throttled to wait in between calls.  

As I have noted, Spotify could mostly fix this problem by enabling genre: filtering in the /search API when searching for "albums" (which actually means releases despite my 10 years of trying to persuade a nominal music service to take the difference between singles and albums seriously), since this API already has a tag:new filter for getting new releases (from the last 2 weeks, which is also kind of arbitrary but at least means the last full release-week is always completely covered). There's already internal data for artists' "extended" genres, which is the extrapolated version using collective artist similarity. Or at least there is if they keep maintaining the genre system I wrote.  

You can see exactly how viable this is, if you're curious and not unmanageably triggered by a thing that takes the shape of our loss without salving it, because the search API does already allow release searches to be filtered by label: the same way it could allow searching by genre. Any API app could do this, there's nothing special about my access or techniques here. But I looked and didn't find one, so I made it. This is what my job was like, too, and apparently I was literally correct when I used to say that I'd be doing it even if they weren't paying me.  

Thus: New Releases by Label for, e.g., a list of 58 metal-related labels.  

 

The chances are decent that if too many people try this at once it will slow down or die, too, but for each label it requires as few as two queries: one to get that label's new releases (in pages of 50 if a single label has more than 50 new releases from the last two weeks), and then at least one follow-up query (in pages of 20) to get those albums' tracks. This is reasonable overhead.  

Labels are no direct substitute for genres, obviously, not least because if you care about music you need not also care about labels or whether artists are even on one.  

And even if you do care about labels, label data is messy. It's something of a stretch to call it "label data", in the current music-distribution ecosystem. There's a text field for "label", and humans type stuff into it. If the humans doing a given bit of typing are diligent, and none of the other undiligent humans stray into the diligent namespaces by accident or nefarity, then you can kind of pretend it's data. I spent a while in my former job trying to do slightly better than this by aggressively normalizing name-variations and algorithmically distinguishing between actual labels and whatever words people who aren't on labels would type into that box, with some success:  

 

I see there that my past-job self could have combined "Hell's Headbangers" and "Hells Headbangers Records" by removing apostrophes, which either didn't occur to me or caused more problems than it solved, and I no longer remember which and can't check.  

There are, though, many labels that exist to release a certain kind of music according to some kind of unifying principle, and those principles tend to align with genres, or more accurately tend to be part of the social structure that builds music-based communities, which are what I usually mean when I talk about genres. So this approach is wildly incomplete, but seems at least potentially helpful to me. You can try it with some labels you like, and see if it helps you, too.  

The one small catch with this is that the API label filter is very literal. You have to know the exact way the label you're looking for is typed in the "label" fields. And, inconveniently, that label field does not actually appear in the Spotify app.** What you see instead are the copyright and publishing credits, at the bottom of an album's tracklist like this:  

 

You might guess that the "label" here could be either "AFM Records" or "Soulfood Music Distribution GmbH", and as it turns out "AFM Records" is right, but it didn't have to be either of those and guessing is tedious anyway.  

But I have a thing for exercising my petty annoyances about how to display albums, already, so if you look up an artist in the everynoise research tools, you can now see each release's label next to its release date.  

 

But looking labels up album by album is tedious, too. The one automated tool left in my new-release workflow is Release Radar, which provides a subsistence level of new-release awareness if you take the time to follow all the artists you know you care about. And I have a thing for exercising my petty annoyances about how to display playlists, too, so I added a label column to it, which you can even click on to sort or group a whole playlist by label:  

 

And if your playlist happens to represent a subset of new releases you know you care about, look at the bottom of that page for a little helpful link to feed all the labels from a given playlist back into New Releases by Label.  

 

Obviously it would be better if there was also a link to find new releases from this playlist's genres, instead of just its labels, and of course that's what this link used to do. And could do, again, in a better future.  

We will get better futures. That is, we'll get them if we build them, and we will build them, one way or another, because it's too annoying not having them.  
 

** 1/30: An alert reader notes that the label actually is available in the Spotify app, not on the release itself but in the Song Credits dialog for any of its songs, at the bottom labeled "Source".

I did my job with love and belief. This was always obviously risky. I had no illusions that the love was returned, or that it is even possible for a public corporation of non-trivial scale to behave in human ways at all.  

It's easy to find cogent advice for how to modulate your emotional attachment to your job, so that losing it is not like losing a part of yourself. I cheerfully recommend ignoring this. The world is better if everybody does their jobs with love and commitment. You definitely want everybody who does a job that affects you to do it with love, so lead by example. This method will result in sporadic stabs of excruciating pain, but the parts of you you lose to jobs regrow quickly, even if you sometimes have to rub some numbing ointment on the wounds for a while.  

The advice industry, like most modern industries, encodes a sneaky bias to sustain corporatism by implicitly casting individual adaptation as the only medium for change. Good luck finding books that somberly advise corporations on how to encourage a dangerous reliance on unsupervised individual inspiration. I have to get a new job now, or something of the sort, but that's only one small problem to solve. The company I no longer work at has to sort through a thousand things I used to do, almost all of which I did because I thought they ought to get done, and then other people came to depend on them because I was right. A large company would probably never hire someone into a role with this little structure, but the startup where I began was inherently based on the individual efforts of its founders and other early employees, and once we were acquired I just kept doing the job my way and it took 10 years for somebody to stop me.  

This is problematic, clearly. A company needs to be able to treat its employees as interchangeable and expendable, both individually and collectively. It needs to be able to periodically layoff 17% of its workforce to cut its margin overhead by 1% and temporarily boost its stock price by 5%, without having to endure existential upheaval to its ongoing business processes. It needs to be able to double and redouble recklessly in size for the same dubious market reasons, without those people all piling up in the lobby where their chaos is visible from the street. Both expansions and contractions are actions of corporate musculature, flexed as much for show as for motive.  

The key to these flexibilities, as we have understood at least since Henry Ford, is to formalize the operational roles so that their function in the overall system is symbolic and anonymous. As long as people are just units inserted into well-defined slots, the machinery doesn't need to care who they are.  

But the resulting machine, because it operates symbolically on abstract definitions, cannot readily adapt. It needs to "innovate", we know, but if you bring a new idea to a well-organized unit in a corporate machine, it will efficiently reroute you into a well-defined pipeline for queuing up potential future input for consideration two quarters from now, because the defining quality of a well-organized unit in a well-organized organization is that it already has its next six months of work fully prioritized in alignment with established corporate goals.  

Whereas if you came to me with a new idea, or an unanswered question whose answer might suggest new ideas, or a problem that might be solved by something I already figured out, I would listen to you, and ask some questions, and gradually pivot my body towards my keyboard as we talked until eventually I started typing. Sometimes, after a minute of this, I would say "Sorry, I'm still listening, but give me five minutes and let me see what I can figure out." Occasionally I'd have to say "This is interesting, but it's kind of complicated. Can I poke at it a bit and get back to you tomorrow?" I could do new things because my inquiries weren't prescribed. I was prepared to solve unexpected problems because I spent most of my time unexpecting things and seeing where that took me.  

There are, of course, books about corporate agility. There are ways to keep the latency for change to smaller increments than quarters or even months. But none of them advise you to find individual people who happen to be able to do pertinent unique work on the fly, just because they have the right combination of skills and knowledge and stubbornness. You can't sell a book of methodology in which a crucial step is "Luck into anomalous contributors". Anomalies are exactly what prudent processes attempt to preclude.  

But everybody is better off if companies ignore this caution with the same exuberant disregard as people doing their jobs with inadvisable devotion. The most transformational human ideas begin in individual hearts, whatever gantlets of brainstorming and strategic opportunity-analysis they subsequently have to run. Spotify was more right, I think, to tolerate my curiosities and experiments for 10 years than they were to finally give up on them out of exasperation or ignorance. Spotify, like probably every other interesting company, only exists because a few people once had unruly unsupervised impulses that the better-organized status quo couldn't accommodate. The secret truth of business advice is that it's mostly about how to grimly extract residual value from the luck you already had, and the unearned love you were already unguardedly given, because there's really no method for making more of it.

There were a lot of things on Every Noise at Once that updated daily or weekly, and some of them will lose value only slowly now that they can't be updated. The one that loses almost all of its value at once is the weekly New Releases by Genre.  

There were no new-release features in Every Noise at Once at its literal outset, pre-Spotify when it was powered by second-hand Echo Nest data and the Rdio API. I found a 2014-07-30 message from me on I Love Music sharing the URL of the first single-page version of a Spotify new-release list, but the earliest capture in the Internet Archive is from 2014-09-24, when Maroon 5's "Maps" was the top single of the week, and the righthand column listed "all 3304 releases this week". I called this version the Spotify Sorting Hat, because certain things hadn't happened yet.  

By 2019 that version became untenable, and I rewrote it from scratch to separate the genre collation from the raw list, and introduce more control so you had a prayer of finding the subset of music you actually cared about. That version is dead now without the internal Spotify feeds that provided its data.  

You might imagine that there would be alternatives by now, but there aren't any on the same scale. Spotify has by far the best API for this kind of idea, but it isn't quite set up to provide any of the three things that a serious new-release tool should offer:  

- "all" new releases: in truth I started imposing thresholds on what the Sorting Hat would include before switching to NRbG, and NRbG never showed literally everything, either. But it showed a lot. The Spotify API for searching allows you to specify "tag:new" and get only things released in the last two weeks, but you can only get 50 of them at a time, and only 1000 total, sorted by popularity. Most weeks there are more than 100,000.  

- new releases by genre: you can filter by genre in the Spotify API, but only in artist searches. And you can only use "tag:new" in album searches. So currently they can't be combined. Unlike the all-release issue, this one would be a reasonable feature request for the API, as it fits the existing usage-models and wouldn't be particularly onerous to support. Assigning artist-level genres to albums can get existential if you think too hard about it, but if you stick to the idea that genres are communities, then calling an album "atmospheric black metal" is shorthand for saying that it's an album made by a band that is part of the atmospheric black metal community, which makes sense even if the particular album is acoustic folk pastiches, and in the new-release case gets it to the audience that wants to know about that album, so it's fine.  

- discovery: the more complicated thing NRbG did was to try to distribute new releases by bands who aren't really the canonical representatives of any genre to the genres whose fans would be their most likely audiences. This absolutely can't be done using the existing API, for the same reasons that you can't extract a "full" list. You can get the 20 most similar artists for any artist, but for matching unknown artists to genres you need to go in the other direction, finding the 100s of artists whose Fans Also Like lists includes the known artists in a genre. But adding this feature to the API wouldn't be much harder than adding the genre filter itself.  

Absent those features, the Spotify API can't be used to build this, and so far all the other services are even farther from being able to provide the tools for it. NRbG worked because the end results weren't confidential, just inaccessible, and I could solve the inaccessibility by running a set of carefully interlocking internal queries. Now I can't.  

I don't really know how we can do new-release discovery now, without this. We can go back to the human, community-based modes of knowledge we used to use, of course: mailing lists, discussion forums, blogs, playlists maintained by individual experts. One genre at a time, these ways are usually better than queries, detailed and contextual and exultory. But they can't be aggregated the way data can. You can keep track of a genre or two or five this way, but not 20. Not 100. I've been monitoring hundreds of genres every week, for years. Now I am as lost as you.

Between the Echo Nest and then, via acquisition, Spotify, I spent 12 years doing a slowly mutating job of trying to use data and math and computers to help all the world's music self-organize. It seems to be the unanimous opinion of people who send me nice notes on email and Twitter and LinkedIn that I did valuable things at Spotify and from Spotify, and that laying me off was some combination of corporate error and public tragedy. I don't think this is merely kindness. Over that time I created or improved a lot of things by direct individual effort, including Daily Mix, This Is artist playlists, Fans Also Like, a genre system, fraud and abuse detection, many pieces of Spotify Wrapped, more internal tools and analytics and prototypes than you can probably imagine, and a public web-temple to music exploration and the discovery of joy.  

I am aware, of course, that people telling me they appreciate what I did is a clear and heartening demonstration of empathetic selection bias. If you didn't care about my work, then it isn't news that I'm not going to be doing it, and doesn't require your comment. It's tempting to imagine that there's somebody at Spotify who actually disagrees with this, and has been waiting for years for an opportunity to replace my uncooperative insistence on using math to make musical sense with something more acquiescent, willing to say "content" instead of "music" and celebrate 0.05% average-metric nudges without asking to see the distributions under the averages and stop posing moral objections to profit-margin KPIs.  

But probably it's far worse than that: There was no enemy, there was no purpose. I didn't lose a heroic battle, I lost a meaningless lottery. A no-warning 1500-person layoff probably cannot be done "well". I see co-workers who were also laid off that had been at Spotify for 12, 13, 14 years, and who thus must have been there in the basement with Daniel and Martin at the beginning. If there is anybody who can take a big company back to its resourceful small-company past-life, it's the people who were literally part of it. Surely you don't lay off the people with the very qualities you're supposedly trying to recapture unless you genuinely can't help it. I did a lot more things inside Spotify than things you could see from outside, and the pragmatic corporate arguments against laying me off needn't have invoked the public good at all. Public loss is collateral damage from capitalism operating for capital's sake.  

Meanwhile, here is the situation: everynoise.com is cut off from data updates, and I expect this will not change. The processes I left running are still running, so the missing data is probably all waiting in dark staging servers, wondering when it will finally be summoned into the light. It won't. The Approaching Worms of Xmas will never reach it this year. 2023 Around the World, my deliberate celebration of full calendar years, will have to be gallingly content with 11-month provisional results. Anything static will remain, in its current state.  

My automated playlists, on the other hand, get updated through Spotify-internal systems, and are still operating. I think it's likely that they'll be spared for the holidays, but if you care about any of those, you should take any further updates as gifts. At best, nobody at Spotify will bother to figure out how my automation actually functions, and everything will be left running until they're ready to turn the whole system off again with one big switch. At worst, tomorrow something will break that nobody knows how to fix or even debug, and that will be it. I don't normally claim that fault-tolerant engineering is one of my core competencies, so it will be a minor triumph if my automation survives long enough to get killed.  

I have some time to find a new job, or at least a plan for ongoing health-insurance coverage. My belief in the promise of streaming music is a function of music and humanity, not of Spotify, so certainly my first inclination is to find another way of contributing to its expanding fulfillment of that promise. But of course there's also a part of my brain that occasionally mutters "Um, climate change?" I also have an idea for a second book, which I was going to work on over the holidays, except that I didn't anticipate having to spend some of that time changing present tenses to past in my first book, which still has to survive the next six months of routine cosmic weirdness before it finally exists.  

The job I've been doing, because I did it with personal goals, affected a lot more than my nominal work-hours, and getting myself to stop trying to do it is harder than remote-locking my work laptop, and a lot more complicated. Urges will have to be channeled somewhere. I will probably need a new way to think about my music-listening, and maybe new tools to replace the ones I lost, and I've never been able to listen to music without also writing about it for very long, so I imagine there might be a new form of that, too. But probably not this week. For now I'm going to put Hitsujibungaku on repeat, and try to let the blurry futures resolve a little. I feel basically OK about the last 12 years, I think. They are not invalidated by their sudden end. But I want the next 12 to be better.  

---

A few of those nice notes that were written (or ranted) in public:
Every Noise at Once Shuts Down? at Kill the DJ
The Day Music Neutrality Died (a bit) at flyctory.
The 6000 Musical Tribes at The Limited Times, which is a translation of Las 6.000 tribus musicales at El País.
Spotify Fired the Wrong Person at Venture Music.
Continue Everynoise at community.spotify.com.

The job I used to do at Spotify was to experiment with what could be made out of listening data, but the experiments that worked were then automated to run on appropriate schedules. These stopped working temporarily when my employee account was deactivated, but were revived, and some data-driven daily playlists are updating again. Currently no data updates are making it to everynoise.com, though, and all the big weekly jobs run on Fridays, so it remains to be seen what my ghosts there are still allowed to sing.

everynoise.com is and was a projection of things I worked on at the Echo Nest, and then Spotify. As of this morning, I'm not working there, and I expect most of the things I did will no longer be done. The domain is mine, but the processes that updated the site have already stopped running, and the current server may stop serving at any moment. Once that happens, I will try to bring back at least a wistful memorial to what it was.  

But even failing that, its 10-year history lives on in the Wayback Machine. And music, of course, survives our fragile and temporary machines for organizing it.  

More information when I have it.

I read a lot of other people's writing about streaming and music, in the press and in books and on Twitter (or its scattered mirror-shards), and I almost always end up annoyed that the story is most consistently told from the point of view of an outsider with speculative information and dour grudges. There are occasional exceptions, like Nick Seaver's Computing Taste, but that's a work of anthropological scholarship: a story about stories about our future, not the story itself.  

The story, I really believe, is that having all the world's music online together is one of the greatest cultural developments of the internet age. Somebody who both fully understands and emotionally believes in music streaming, I kept muttering, should write that book.  

I've been arguing this idea in scattered blog posts and comment threads and talks, already, but I had more spare time than usual during the pandemic, so I started trying to organize the whole story, not the specific business of Spotify or any one company, but the underlying argument for why streaming is good for music and streaming music is good for humanity. I both know and believe enough to explain how the fears it provokes are mostly less scary than they seem, although in some cases also more scary than you might realize, but that either way the joys are even more transformational. I haven't been talking about this during the process, because I wasn't sure how far I'd get, but I wrote it, and an agent found me, and the agent found a publisher, and today the book was announced to the UK trade press, so it's officially unsecret.  

It's called You Have Not Yet Heard Your Favorite Song: How Streaming Changes Music. It's not coming out until June 2024, because paper is a slow liquid. I have been blithely accustomed to writing about music online and hitting OK for my entire adult writing life, so this is a series of weird new old experiences for me: writing things and then scrutinizing them repeatedly; having an actual professional editor badger me (kindly) into taking out half of the adverbs and a 20,000-word digression about E.F. Codd; the idea that I still have to wait nine more months before people can read a thing I finished writing months ago.  

But, on the other hand, I still like reading books myself, and a 2024 publication date is a heartening gesture of faith that the AI apocalypse will hold off at least that long. So far that faith is scheduled to be expressed in English (complete with "Favourite" in the UK) by Canbury Press, translated into French by Hachette, and into Chinese by ECUS Publishing House. If you have, or are, contacts at publishers of books in other languages, or especially any US publisher interested in handling the American edition, get in touch.  

Otherwise, it is not necessary to take any action at this time. Continue with your lives. Listen to music. Read Jon Alexander's excellent Citizens, about the historical shifts in social narratives from subject/rule to consumer/vendor to citizen/community, which was also published by Canbury and isn't about music but isn't entirely unrelated to mine. If you think of ways to share or experience more joy, or less fear, you needn't wait to see if I covered them.  

But if you haven't thought of any by next June, maybe my book will be able to help.  
 
 

Some links as I spot them:
- The announcement was also covered on BookBrunch, but you can't read that without a subscription.
- Amazon UK lists both the ebook and the UK paperback.
- WHSmith lists the UK paperback.
- Amazon US and Barnes & Noble have only the ebook version so far.
- I see the (English) ebook in Austrian, Italian, Japanese, 1 2 Portuguese and 1 2 3 German shops.

In a 2009 post about music-data modeling, I posed the reasonable human question "What Beatles album is 'Day Tripper' on?" and bemoaned the various ways in which all available music-data sources failed to effectively answer it.  

My human answer was this:  

This is partially a trick question, of course, as "Day Tripper" was originally a non-album single, but it has been on several Beatles compilations over the years, including the red 1962-1966 best-of, and in the remastered 2009 catalog it lands on both the mono and stereo versions of Past Masters.

 

As of today, ChatGPT's (GPT-3) answer was this:  

"Day Tripper" is a single released by The Beatles in 1965, along with the song "We Can Work It Out". Although it was not included on any of The Beatles' original studio albums, it was later released on several compilation albums, including the "1962-1966" (also known as the "Red Album") and "Past Masters" compilations.

 

That's pretty good. Of course, the Wikipedia page for the song has this information, and my original question was about structured data-querying, not summarization of pre-written human text.  

The "Day Tripper" thing was also a deliberately simplified test-question, too, because when I tried to write that post using my actual question for testing the existential level of musical question-answering, the narration was unhelpfully complicated. That question was this:  

"Who are all the producers who produced non-Big-Country albums that members of Big Country played on?"  

I have never had the patience to assemble a decent human answer to that, but I'd still like to read one. So today I asked ChatGPT. Here's what it said:  

Here are some producers who worked on non-Big Country albums that featured members of the band:  

Alan Shacklock: Produced the album "The Raphaels" (1993), which featured Tony Butler on bass guitar.  

Robin Millar: Produced the album "Hope and Despair" (1994) by Justin Currie, which featured Mark Brzezicki on drums.  

Mike Thorne: Produced the album "Bare" (1995) by the band Iona, which featured Dave Fielding (who had briefly played guitar for Big Country) on guitar.  

Steve Brown: Produced the album "Westworld" (1987) by the band Westworld, which featured Mark Brzezicki on drums.  

Brian Eno: Produced the album "Apollo: Atmospheres and Soundtracks" (1983) by Brian Eno with Daniel Lanois and Roger Eno, which featured Stuart Adamson on guitar.  

Again, this is not an exhaustive list, but it includes some notable examples of producers who worked on albums that featured members of Big Country in a different musical context.

 

Points awarded for clarifying that this is not an exhaustive list. Points deducted for failing to clarify that it is not a factual list, either. That is, it does involve or at any rate implicate some facts, but those facts mostly do not connect in the ways described here.  

Alan Shacklock was an English producer who moved to Nashville. Big Country singer Stuart Adamson also moved to Nashville at one point, and formed a country duo called The Raphaels. Their one album came out in 2001, not 1993, and was called Supernatural, not The Raphaels. As best I can tell, Shacklock did not produce it, nor any other album named The Raphaels. Tony Butler was Big Country's bassist, but didn't play on Supernatural, and would be the less-good example to pick even if he had, since it was Stuart's band.  

Robin Millar produced the Big Country album The Seer, so including him in this list via some other album is not really what I meant, but I didn't clarify that, so I'll allow it. Or I would allow it, except that Justin Currie (better known as the singer in fellow-Scots Del Amitri) has no solo album called Hope and Despair, nor indeed any solo album earlier than 2007, nor any solo album produced by Robin Millar or played on by Big Country drummer Mark Brzezicki, although with Mark I guess it's always possible he participated in disguise or by accident.  

Iona is another Scottish band, so one can easily imagine that some connection exists, but Iona's albums are all self-produced by co-founder Dave Bainbridge, none of them are called Bare, none of them came out in 1995, and none of them feature Dave Fielding, who was in The Chameleons but not Big Country for any non-quantum sense of "briefly".  

Westworld did release their debut album in 1987, but it wasn't self-titled, wasn't produced by Steve Brown, and probably didn't feature Brzezicki.  

The Brian Eno line is the best one in this answer, since it describes an actual album with its actual year and artists, and one of its actual producers. Sadly, Stuart didn't play on it, which is the only reason it's supposed to be in this list, so giving it credit for not introducing ancillary errors is like praising a cook for making cauliflower gratin with only organic red grapes and fresh herring.  

I admit that I did ask it for 10 more examples, and if you willingly eat red-grape herrings you can't really complain, so I won't explain all the errors in those. Except to note that Mark Knopfler of Dire Straits definitely did not "co-found" Big Country. I never regarded this question as unanswerable, and having it misanswered doesn't change my feelings. I've always assumed that answering it would be an eventual triumph of conscious data-modeling, not massive ML overtraining, and I think ChatGPT wants, if it "wants" anything, me to at least doubt that now.  

Maybe? A little bit? The answers exist, spread out among our minds. Maybe brute-force retro-structuring can eventually extract them from chaos without requiring the chaos to be improved first. I guess ChatGPT wins while losing by the mere fact that it has learned to answer questions "well" enough that we keep asking it things even though the answers are bad. If we are eradicated by this wave of AI, it will certainly be our own fault.  

But that was always going to be true. Our self-created dooms always have the same ingredient.

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.