Prompt: The real flagship for Ethiopian tech startups is iceaddis. Like Nairobi’s iHub but about one-twentieth the size, it has an open workspace with young people working on laptops, whiteboards, a tiny kitchen, and a purposeful hum. When I was there a group of Seattle high-schoolers were visiting. I met with iceaddis’s energetic head, Markos Lemma, born and raised in Addis, who had learned his English, he said, “from Hollywood movies.” Lemma had just come from a long meeting with a government minister. “My argument is always that you can’t really have sustainable development without encouraging innovative and high-risk businesses, because those are how you compete in the future,” he told me. “How much capacity you have for producing technology is the best way to measure countries’ potential to grow. When I talk to ministers now they also understand there is a need for entrepreneurship.” And yet: “They don’t see the internet as the main accelerator of growth; it’s more like a threat, because it connects people.” Running With It All of Africa’s governments are facing a demographic explosion that they don’t know how to handle. “The internet” is a way to offer a bright future that would otherwise be hard to promise.

Response: At the same time, states find censorship and surveillance hard to forego. Ethiopia is a good example. Its new government under Abiy Ahmed has encouraged the tech sector but, faced in August 2018 with unrest in the east, resorted to blocking the internet. Such a move inflicts a high economic cost: the Brookings Institution estimates that internet shutdowns in Africa cost $7.4 million per day.  The bright side is that today’s tech entrepreneurs know that they have to engage in politics in order to not be destroyed by it. African tech entrepreneurs are tough. As Nanjala Nyabola says, “We’ve managed to build exciting lives despite the challenges, and when you look at the tech space it is really evidence of a society that refuses to give up on itself. Give us a little space and we will run with it. Because we are so used to having no space at all.” The Muzic Box was open on Tuesdays, Thursdays, and Saturdays, staying closed on Wednesdays and Fridays only to give Chicago’s competing disco club, the Power Plant, a chance to draw a crowd a couple nights a week. If you were a young, gay person of color in the Windy City during the early 1980s, there was no better place to be. The Muzic Box was a sanctuary, free from the judgment of a still-restrictive society that, in the days of panic over an increasingly visible queer culture, inhibited the sense of celebratory pride sorely needed by the local gay community.


Prompt: Between the Muzic Box’s four walls, dancers were welcomed in the embrace of an idea. As the attention of the mass audience shifted away from disco, and “Disco Sucks” rallies were held at Chicago’s Comiskey Park stadium, these clubs were the only place to hear the music of the future. The songs on rotation in the Muzic Box may not have been categorized in the same bin at the record store, but they all shared invigorating rhythms and futuristic tonalities that suggested a new genre cutting through those established categories. The fearless explorer breaking through the boundaries between them was Ron Hardy, a virtuoso who mixed records together in a spontaneous collage style inspired by New York disco DJs.

Response: Hardy had begun playing at the club in 1982, when it was called The Warehouse, and remained the resident DJ until it closed in 1987. His predecessor Frankie Knuckles first brought the disco DJing style, with its aim of creating a seamless landscape of rhythm, to Chicago from his hometown of New York. However, in spite of its East Coast origins, this approach to mixing music—and the approach to producing music that these DJs would eventually create—took flight in the Midwest. To this day, it bears the abbreviated name of the club where it began its international ascendance. The music had gotten so popular by the mid-1980s that Chicago record stores started giving the records that got played at The Warehouse their own bin, labeled “house music.” It was on a night in 1985—whether it was Tuesday, Thursday, or Saturday is lost to history—that Earl “Spanky” Smith and Nathaniel Pierre Jones entered the Muzic Box with a reel-to-reel tape, containing a track that they had just recorded in Smith’s bedroom. Hardy would sometimes switch from vinyl to a tape reel to play productions by local amateurs, in order to keep up with the developing sound of the genre. House DJs had already begun making inroads into production, thanks to the availability of cheap synthesizers.


Prompt: Smith and Jones recount the evening they debuted their new track in a documentary for the British broadcaster Channel 4 on house music called Pump Up the Volume: Pierre:	We thought, if anybody would be daring enough to play this, it would be Ron Hardy.Spanky:	He ended up playing it four times that night. First time he played it, people didn’t really know how to react to it.Pierre:	He played it again, people was looking like “Hm. It’s early, I guess he playing some crazy stuff.”Spanky:	The fourth time, they lost they mind. That was the birth of acid, right there.  A new era began that night. The track didn’t bother with song form, melodic development, harmony, or lyrical content; it was an investigation of the infinite permutations of sound itself. Winding through a pounding, jittery beat, an atonal three-note motif wriggled and thrashed, as though trying to escape, for more than twelve minutes.

Response: It did not sound like it had been produced by a musical instrument. Nor did it sound like a pop song, with its structure more akin to minimalist experimental music. Disco had already been moving in this direction, emphasizing texture over song. But this dispensed with the song altogether. It took a forward-thinking listener to see its promise, in 1985, as a potential dancefloor hit. Fortunately, Ron Hardy was precisely that kind of listener.


Prompt: The producers had called their composition “In Your Mind,” but that may have been too limiting—listeners had lost their minds upon hearing it. The track was not an object for contemplation, but a shockwave that affected the body. Either because of its fluid yet abrasive texture, or because of its tonal similarity to the guitars of psychedelic rock, or because some dancers at the Muzic Box were so blown away that they thought someone had spiked their drinks with LSD, the record became known as “Acid Trax.” The global phenomenon of rave music would eventually derive everything from “Acid Trax,” which was released as a 12-inch record in 1987 under the name Phuture. A subgenre of house music adopted its name: “acid house,” which would also become the label that rave music went by upon its earliest appearances in Europe. Acid’s abstract quality brought the futuristic nature of house music to the forefront, influencing the concurrent development of techno in Detroit and catalyzing the emergence of drum and bass in England. Today, aspects of EDM are uncannily similar to acid house as it was heard thirty years ago in Chicago.

Response: What made “Acid Trax” so revolutionary was not just its structure or its sound, but its use of the technology that produced it. Jazz, funk, disco, and even rock and roll had emphasized timbre and spontaneity as much as melody and narrative, in the struggle over centuries to adapt traditional musical practices to new languages and new tools. “Acid Trax,” however, did not derive its complex sound from overblown horns or bent strings—or even distorted amplifiers. It was made almost entirely with one instrument or, more accurately, one machine: the Roland TB-303, which generated sound waves from scratch. Then bought for next to nothing at a pawn shop, the 303 has since entered into legend, and now commands thousands of dollars on eBay. Even today, producers of underground dance music try to capture the sonic force it holds.


Prompt: In the writer Kodwo Eshun’s description, the machine itself shares authorship of “Acid Trax.” “Acid is an accident,” he suggests, “in which the TB-303 bass synthesizer uses Phuture to reproduce itself, to multiply the dimensions of electronic sound, to open up a nomadology of texturhythms, rhythmelodies.” The story of acid house begins in a factory.

Response: Transistor Rhythm While house music’s sonic textures drew from both African-American music’s corporeal funk and European synth pop’s electronic sheen, it only crystallized as a genre and production practice after some help from Japanese technology. The Roland Corporation, founded by Ikutaro Kakehashi in Osaka in 1972, unknowingly provided the final ingredients for the futuristic dance music that would emerge in America over the next few decades. In 1980, Roland’s TR-808 Rhythm Composer, the “TR” designating “Transistor Rhythm,” became one of the first synthesized drum machines to become available to consumers.


Prompt: Early synthesizers were as complicated—and as large—as electronic manufacturing technology. But Roland’s instruments were appearing in a new context. Before the widespread extension of electrical power to residential areas in the 1920s, machinery under capitalism had been an instrument of labor. In Marx’s “Fragment on Machines,” he remarked on the place of technology in the capitalist mode of production: Nature builds no machines, no locomotives, railways, electric telegraphs, self-acting mules etc. These are products of human industry: natural material transformed into organs of the human will over nature, or of human participation in nature. They are organs of the human brain, created by the human hand: the power of knowledge, objectified.  Over the course of the twentieth century, consumer electronics dispersed scientific knowledge further still. Beyond being a tool that workers used in the factory, electrical machinery became present in the home. Automation took on a presence in everyday life as well as within the means of production. The amplification of sound was inextricably linked to this process, with the development and distribution of electricity tied from its origin to telephone communication and radio broadcasting. By the mid-twentieth century, most homes had radios, making their operation familiar to a far broader population than expert telegraphers.

Response: In the mold of consumer devices like the portable stereo, the TR-808 was built for home use. Its target audience was musicians who needed accompaniment for practice sessions, and its interface more closely resembled a stereo than a circuit board. In the late 1970s, however, the TR-808 began to migrate from the home to the recording studio. It started to appear on pop records—Marvin Gaye’s “Sexual Healing” is a famous example—many of which may have been played by Knuckles or Hardy during their expansive DJ sets. Its rigid, aggressive drum sound may have driven professional musicians crazy, but its mathematically precise rhythmic divisions were perfect for DJ mixing, a process of matching up beats by adjusting the speed of rotation on turntables.


Prompt: In 1982, Roland engineer Tadao Kikumoto designed a new machine as a supplement to the TR-808: TB-303, the initials standing for “Transistor Bass.” For $400, the 303 could be used as a supplement to Roland’s drum machines, giving a solitary musician in a domestic setting the experience of playing with a band. “Electric” is not to be confused with “electronic”—while a bass guitar amplifies sound that is produced acoustically, the 303 was triggered by a voltage-controlled oscillator cued by a one-octave keyboard, with knobs and switches to manipulate the timbre.

Response: A traditional instrument like a bass guitar makes waves. What fluctuates in these waves, in the case of acoustic instruments, is the air surrounding a vibration in or around part of the instrument’s material body. When two frequencies occur simultaneously, they affect the shape of the wave, creating the effect that musicians call timbre—the character of sound that differentiates separate instruments playing the same note. While a pure sine wave creates a tone so pure as to sound lifeless, the sound produced by any instrument is complex, with a fundamental tone altered by harmonic overtones.


Prompt: The early electronic synthesizer broke the relationship between the shape and substance of a physical object and the sound it produced, allowing for the formation of a waveform through a mathematical formula instead. There are two kinds of complex tones that formed the palette of electronic synthesizers like the 303: square and triangle. The square wave produces only odd-numbered harmonics of the fundamental tone, with a sound comparable to a clarinet. Though the wave looks like a square, it is in fact produced by a series of sine waves, embedded like a fractal set. The sawtooth wave produces both odd and even harmonics, making it sound something like a violin. The triangle wave resembles the square wave, but with less complexity.

Response: The 303 had six knobs, reading TUNING, CUT OFF FREQ, RESONANCE, ENV MOD, DECAY, and ACCENT, controlling the parameters of the sound’s tone and articulation. This was not a conventional instrument like a clarinet or a violin, but more like a manufacturing tool, the kind that was being used on the increasingly automated assembly lines of the industrial factories then dominating the economy of the American Midwest.


Prompt: As robotic instruments were incorporated into the manufacturing process, labor became oriented towards their operation and maintenance, rather than hand assembly. In a concert hall, an instrument is to be played; on a shop floor, it is to be worked on. The 303’s interface blurs the distinction: while it holds a portion of a piano’s keyboard, its primary mechanism is operated by means of knobs and buttons. Like a robotic arm, it is programmed as much as it is played.

Response: Machine Music The TB-303 didn’t sell. It may have been because the advent of sampling technology made it possible to reproduce recordings of other instruments directly. Or it may just have been, according to Peter Shapiro in The Wire, because Roland accidentally forgot to ship out manuals to English-speaking countries. Either way, the machine was devastatingly unpopular for its intended purpose.


Prompt: In the early 1980s, a handful of inventive pop musicians did begin to feature the 303’s unrealistic sound as a novelty. Still, the machine lapsed out of production in 1984—a year before the arrival of “Acid Trax” heralded the invention of acid house. Earl “Spanky” Smith bought one at a secondhand music store for $40—sans manual—knowing it was what Jesse Saunders had used for “On and On,” the earliest foray into house music production in Chicago. He programmed a beat into his Roland drum machine and called his friend Pierre: Spanky:	Well basically, we started using the 303 just to try to make bass lines, because when we first started making music, it sucked. So I made this rhythm…Pierre:	…and it’s just playing straight, and he said, “I can’t figure out how to work this thing,” and he’s still doing this weird sound. “I don’t know how to program it,” he said, “maybe you could figure out how to program it, ‘cause it ain’t come with a book.” So instead of trying to program it, I just started turning knobs. I was like, “woo-ooo-ooo.-ooo.” And he was like, “What you doing?” And I’m like, “I don’t know, I’m turning these knobs!” And he said, “Keep doing that!” So we was just sitting there for thirty, forty minutes, just turning knobs, going “I like that, I like that! In a 1988 interview, veteran Chicago house DJ Farley “Jackmaster” Funk explained  that before “Acid Trax,” the TB-303 was “an obsolete, old-fashioned piece of technology that no one had ever thought of using that way before.” House DJs had already begun to resist the conventional distinction between production and consumption in consumer society by turning the playing of records into the playing of music as a creative process. Pierre and Spanky extended this intervention in a way that called for a redefinition of musical sound.

Response: Since the TB-303 was built to emulate an electric bass, its real-world referent was specified. If you were to imagine a visual representation, you might picture someone playing the instrument, albeit one that looked a bit weird. But by playing the 303 itself, through manipulation of its electronic interface, Pierre transformed his virtual instrument as the track progressed. The image would have to exist in an amorphous dream logic, as though the instrument were a shape-shifting mutant: a violin, then a trumpet, then a flute, then a timpani, until it could no longer be associated with a recognizable correlate. As Pierre put it in The Fader, while the 303 “was supposed to be copying a bass guitar, [the way we used it] doesn’t sound like any previous sound you’ve ever heard before. And you can only describe it as being acid.” “Acid Trax,” with its extended texture, was truly a track rather than a song. In fact, it was more than one; trax, in the plural, its name also describing every succeeding installment in the genre it created. In the late 1980s and early 90s, acid house spread like a virus. New records proliferated—not just by Phuture, but by other local producers like Adonis, Lil Louis, Mike Dunn, and the teenage prodigy Armando. Armando’s 1987 classic “Land of Confusion” epitomizes Kodwo Eshun’s contention that the 303 was a collaborator in the production of acid house. Its composition was described by Mike Dunn in Chicago’s 5 Magazine as based on the machine’s settings. “If you take the batteries out for a minute and put them back in,” he recalled, “that’s the first bassline that will come up.” By the end of the 1980s, the style had become influential not only in Chicago but in Detroit, where a similar kind of dance music, called techno, had been emerging. Juan Atkins, the founder of techno, had begun to fuse the experimental qualities of funk and progressive rock with the rhythmic structure of contemporary dance music. His friend and collaborator Derrick May, after taking frequent trips across I-94 to visit his mother, had been “baptised” at The Warehouse. May established a link between the cities, even buying Frankie Knuckles’s TR-909, the successor to the TR-808, to produce his own records. Techno emphasized the space-age qualities of synthesized sound over the disco and gospel influences that characterized house. But if a line separates the sound of the two genres, acid house sits directly on it.


Prompt: Sonic Synthesis Acid house’s dissociation of sound from physical presence is now a fact of our sonic environment. For those who came of age after its emergence, acid has permanently altered our relationship to aural experience. Tones that might previously have been limited to sound effects in science-fiction cinema, intended as representation of alien objects, we now hear as musical in themselves. “For some, I guess, ‘synthesize’ means ‘duplicate,’” Juan Atkins told Wired. “But for me, ‘synthesize’ is synonymous with ‘create.’” Today, contemporary pop music has fully incorporated acid house’s sonic range, if not its production method. Producers used it as a starting point for the sound of R&B and hip-hop in the new millennium—in 2000, Timbaland’s backing track for Aaliyah’s “Try Again” used a TB-303 for its bass line, inspiring countless producers to imitate the sound on other synthesizers and computers. For his part, Pierre sees something prophetic in the name that he and Earl Smith chose for their work: Phuture. “Twenty-six years later and acid is still going strong,” he said in 2011. “You can see the proof of this when platinum-selling groups and artists like LMFAO and Skrillex are putting ‘acid’ in their songs.” But while the sound of acid has become incorporated into the output of what Adorno and Horkheimer called the “culture industry,” the act of its creation was a rupture of the equivalence that term assumes. Acid house was born in a space between the cultural and the industrial, between social life and the relations of production. It is here that we find not only art, but political action. Acid house relocates industrial sabotage from the factory to a domestic setting, allowing for creative acts that expanded the consumer sphere of the disco community to the practice of house music production.

Response: In this sense, acid house fits into the spectrum of an ongoing struggle. In his 1963 book Blues People, Amiri Baraka traced the origin of jazz to the use, by African-American musicians in the nineteenth century, of unfamiliar tools. These musicians, lacking formal training, “developed an instrumental technique and music of their own, a music that relied heavily on the non-European vocal tradition of blues.” The result was a musical practice that used European instruments to achieve “the altered timbral qualities and diverse vibrato effects of African music.” On a guitar or trumpet, a fretboard or valve system provides an interface for producing tones corresponding to the chromatic scale of Western harmony. But through physical intervention, they can be made to deviate from that limited use. Acid house directs this intervention towards consumer devices, drawing altered timbres and vibrato effects from machinery.


Prompt: This practice disrupts the presumption of autonomy between the spheres of art, consumer society, and industrial production—a presumption that was also investigated in Marx’s critique of capitalism. Italian political theorist Paolo Virno, in his essay “Virtuosity and Revolution,” shows that Marx had already addressed cultural production in his notes for Capital. Marx distinguished between two kinds of intellectual labor: “commodities which exist separately from the producer,” like paintings or books, and those in which “the product is not separable from the act of producing,” like the performing arts. As Virno argues, within the context of the service work that dominates advanced economies, “activity-without-a-finished-work moves from being a special and problematic case to becoming the prototype of waged labor in general.” The artist is in the same category as the service worker, in other words: “virtuosic activity comes across as universal servile labor.” In this light, an artistic practice in itself does not escape the boundaries of the capitalist mode of production, which has included service work and the dissemination of cultural phenomena since the days of the printing press. Speaking to The Fader, Pierre pointed out that the sound of the TB-303 itself has now become subject to imitation by an arm of the culture industry. “I think since people have made all these clones of the 303,” he says, “to me that’s just like other manufacturers making bass guitars or lead guitars, or pianos.” To his ears, much of the electronic music that has followed simply repeats an old formula: “Even dubstep, as crazy as that stuff sounds, has sounds that are connected to a previous instrument—it’s still copying something in some kind of way.” The real legacy of acid house may not reside in its sound, but in its method. Acid shows us a new way to relate to the machinery that increasingly populates our everyday lives, one that shifts our experience from the passive mode of consumerism into the realm of creative activity. The knowledge we share as operators of this machinery has the potential, as Virno puts it, to “affirm itself as an autonomous public sphere,” but only “if it cuts the linkage that binds it to the production of commodities and wage labor.” Acid house teaches us that the potential for a radical practice of culture may not lie in making a certain kind of sound, but in something more fundamental: not doing what it says in the manual.

Response: How did you first get involved with Computer People for Peace (CPP)? In the 1960s, I was working at IBM, which was a marvelous job for me because I was a single mother with a kid. Programming was the only thing that paid a woman a living wage. And they trained me, so that was wonderful.  Around 1968, I joined CPP. We had a steering committee of six to eight people, which I served on. We were working for different companies, mostly doing programming. It was mainframe-based. We had a lot of demonstrations in New York because it was the 1960s. It was the war that started us out—that’s why we put peace in our name.  I remember you saying something at one point about a commune. Were you all living together? It was only a small part of CPP, but yes. In 1971, seven of us who met through the collective started a commune in Brooklyn. Our commune was in an amazing old brownstone with original oak panelling and a big kitchen on the lower level.  We reasoned that it would only take three people working at any one time to support the house and buy all the food and everything, and then the others could be doing movement work—anti-war work or what I got into, which was trying to organize a computer workers’ union.  You tried to organize a computer workers’ union at IBM? No, this was during the year I took off. We each took six months to a year off to do movement work.  CPP had the idea that if we could organize mainframe programmers and mainframe operators, then we could shut down everything. I worked on implementing that vision, but it was very difficult. The only inroads I remember making were at NYU and a city agency. It was difficult because the workers were well-paid and thought of themselves as professionals.  One of the things that the mainframe era did was to enable women and working-class people to walk into a professional job and earn a decent living. It just required a college education. My division of IBM had a lot of working-class young men who got draft deferments for working there. It was a leg on the rung of the middle class.


Prompt: Were these programmers receptive to the idea of unionizing? Programmers were not really interested, but the operators were. Operators were the guys—and 99.9% of them were guys—who worked in the machine room, the mainframe room. They could shut anything down. And being an operator didn’t require a college education. There were a lot of African-Americans who hadn’t been to college and whose consciousness had been raised in the 1960s and 1970s.

Response: But we had this idealistic vision of programmers and operators, together. Today it’d be like a union of… I don’t know what the job titles are now. Software engineers? I’m kind of confused by everyone being called an engineer now. In my day, an engineer was somebody who had an engineering degree, like a civil or mechanical engineer. But programmers or systems analysts weren’t that. Do software engineers have engineering degrees? They might have computer science degrees. My sense is that “engineer” is an umbrella word for people who may be doing a bunch of things: developing front-end applications, programming how interfaces work and connect to a database, people working on servers and maintaining infrastructure… Yeah, but there are a lot of class divisions within that work, and those are really important for organizing. I’ve been trying to follow the changes and crunch the numbers that the Bureau of Labor Statistics puts out to understand what’s increasing and what’s decreasing. What I’m looking for is the numbers of people in the field—and what is the field?  In 1973, when I started teaching, young people were told to go study data processing. “That’s the wave of the future!” And, of course, it wasn’t. It never was. For a while, you could get jobs in it just walking in the door if you were white. But there were never that many jobs.


Prompt: Tell me more about your work with CPP.  I edited and wrote for the CPP newsletter, Interrupt. “Interrupt” was a signal. You could program an interrupt to the mainframe. We meant it in a political way.  A glimpse of Interrupt, as digitized by Eli Naeher and Jen Kagan. Several issues of Interrupt are available at Naeher's website.

Response: The CPP steering committee had a lot of interesting tactics. We would meet regularly and come up with ideas like going to an Association for Computing Machinery (ACM) convention to distribute antiwar literature. The people at ACM were just dumbfounded by us. But there were others who came out of the woodwork and said, “I really think the war is a bad thing. What do you have?” And they’d get on our mailing list. Back then there was no email.  We had what we thought of as privileged jobs because we were making decent income and we had Xerox machines. That was the key to the movement: Xerox machines. In those days, most people had to crank out leaflets on mimeograph machines with this blue ink. It was a big drum and you had to roll it around. Xerox machines were much better. We would sneak in when no one was looking and make copies.


Prompt: You had access to Xerox machines through the computing jobs you had? Yeah, we thought that was the main criteria for a job. Earn a decent wage and have a Xerox machine. We also raised bail for one of the Panther 21. His name was Clark Squire and he’s still in prison. Do you know about the Panther 21? No.

Response: The Panther 21 was a group of Black Panthers arrested in New York in 1969. The trial went on for a long time and they were all acquitted. One of them was a programmer named Clark Squire. One of our members said: “Look, Clark Squire is a programmer. If we raise bail, it will raise consciousness among programmers not just about the war but about what’s happening to black people in America today.” So we went to ACM one year and raised a lot of money. People were aware that something really bad was happening to black people in America. Did we raise consciousness among a lot of computer workers? No. But enough of them ponied up money.


Prompt: We got the money together but the judge wouldn’t take it. They kept all the Panthers in jail. They got out in 1971. I’m not sure what we did with the money, except then I went to work for the Women’s Bail Fund, which was an organization that included some Panther women. So we applied some of that money to women who were in prison.  Clark Squire is now known as Sundiata Acoli. He’s still in prison, on a different charge. He’s eighty-one years old. I write to him sometimes. He remembers everything about those days, things that I forget.

Response: I’m curious about what you said about raising money but not consciousness. Sometimes I wonder whether tech workers who are speaking out now see themselves as part of larger movements and whether they’ll stick around for longer-term organizing that’s not directly tech-related. Did you ever worry about that? I believe everything starts with a single issue. You start with a single issue, and my issue was working conditions. You start with a single issue and people start to say, “Oh, it’s not me. It’s not my fault. I didn’t do that. It’s happening to other people.” And then it can go anyplace.


Prompt: The people I worked with then and now came in at different points and on different issues, but could all see the larger picture.  So when you think about your work today, it’s not as if Interrupt stopped and now you’re doing other things. No, it’s a total continuum.  When did Interrupt stop publishing? 1973, I think. By then the energy wasn’t all in one basket anymore. The Panther trial made me very aware. It was also partly because we each took turns leaving our jobs to do movement work, and coming back was never easy.  In my case, coming back was particularly hard. In 1970, protesters had occupied the  Courant Institute, the computer center at NYU where I had tried to organize a union, and threatened to destroy some very expensive equipment there. So I had gone back to look for work and a woman who was a headhunter—this was totally new in the industry, that there were headhunters and that it would be a woman—sent me out on one or two interviews. Then—this is a wacky story but it’s true—she said, “Meet me at the public library on 42nd Street and don’t look like you know me. I’ll be wearing a big hat.”  She was dressed amazingly. She saw me and said, “Walk this way.” She said, “The FBI is after you. I can’t send you on any other interviews.” I was wondering because the two I’d gone on, I knew the people who were interviewing me. And they were chilly. They did not move or change expression.

Response: She said, “You can’t get a job. You’ve got to change your name.” It was scary. I straightened my hair. I changed my name. I wore contact lenses. The job I ended up getting in 1973 was at LaGuardia Community College, which was just starting. They were mainly hiring political activists. It was an absolutely wonderful environment for cooperative learning and working. The two guys who hired me, one had been associated with the Panthers and knew my work with Clark Squire and the other had been my manager at IBM. He had later left to start a small consulting firm—we would now call it a “startup”—and hired me away from IBM. Then I tried to organize a union there and he fired me!  But anyway, I was hired to teach. I was very lucky. LaGuardia Community College was a tribe that I loved.


Prompt: In one of the issues of Interrupt, there were transcripts of “security hearings” in which workers who were being interviewed for government security clearances were asked about their associations with CPP. That must’ve scared people in the collective. I was freaked.  We always knew that there was an informer in the group. All of our meetings were announced and anyone could come. There was one rather heavyset man who never said anything and turned up most of the time.  When I got married, he was the first one to come to my wedding party at the commune. Then, when I put in a Freedom of Information Act request several years later, the files showed our menu for the wedding, but almost everything else was redacted! COINTELPRO was very active then. It could have been our work with the Panthers. It could have been my work with the union. I don’t know, but it was scary.

Response: And you continued doing technical work? Yeah. In those days it was called the data processing department. I taught programming and systems analysis and design for thirty-five years. That’s what I really liked: looking at the big picture. That’s what I got into at IBM and the skills were useful for CPP.  How do you connect the dots? How do you find the information? It was exciting. I think it was the beginning of my love of research and going on to get a PhD.  Is there anything else I didn’t ask that you wanted to talk about? We didn’t have a model, we didn’t know what we were doing, but we became a collective that, well, we had a lot of arguments—if you look at any organization, you’ll find arguments—but we’d reach a consensus on the issues that we worked on in Interrupt.  I think it takes a physical presence. I was just on a Skype call with collaborators in Belgium for a participatory design conference. You can do certain things that way, of course. But I think you need the tribe.  I’ve been organizing in different groups for fifty or sixty years. Whether it was CPP or the CUNY union, it’s been a lot of work. But you have an evolving belief system together. And some naivete! I mean, I thought in 1971, “Organizing computer workers, oh yeah, this needs to be done! Operators and programmers together, yeah!” I had no idea what the structure of unions was. I had no idea National Labor Relations Board classifications can keep workers at the same workplace from forming a union together.   If you slice and dice workers, you don’t have a union. You have mashed potatoes. Some battles we fought. Some we lost.


Prompt: Can you start by telling us a little bit about your area of expertise? I work on behavioral economics and game theory, so I study insights from psychology and build them into mathematical models of human behavior. In particular, I’m interested in what we call market design.  For a lot of the history of economics, we've taken as given the way that market institutions work. Market design turns that on its head. It says that instead of taking the rules of the market as given, we should look at how those rules are written.

Response: There are any number of times where the government or a private company needs to set up a market. For example, the Federal Communications Commission (FCC) might be selling the rights for companies to broadcast over a certain band of the wireless spectrum. The agency needs to come up with a set of rules that determines who pays them what for which portion of the spectrum. That's a market design question.  How are big tech companies engaged in market design? Google and Facebook get most of their revenue from selling advertising. So they’ve had to design a system that enables advertisers to transact with them.  The evolution of that system has been very ad hoc. It started at Google. Early on, the company realized that it needed a way to allocate the different ad slots that appear next to its search results to different advertisers. So it started looking at something called a “second-price auction.” This is a kind of auction that gets invented by William Vickrey in the 1960s.  At the time, there were two standard auction formats: the first-price auction and the ascending auction. The first-price auction is where we all simultaneously submit bids and the highest bidder wins. The ascending auction is the one we generally think of when we picture an auction: there's somebody with a gavel and the price keeps going up and everybody drops out except the highest bidder and that person wins.


Prompt: Vickrey recognized that both types of auctions have their advantages. One obvious virtue of the first-price auction is that everybody can participate asynchronously at a distance. We don’t all have to be in the room bidding at the same time. Logistically, it’s very efficient.  On the other hand, one virtue of the ascending auction is that it’s easy to know how to bid. You watch the price go up and once it's above your value, you quit. It’s simple: you keep bidding at all prices below your value, and you quit at all prices above your value.  Whereas in a first-price auction, you really need to strategize. You need to say, “Okay, I guess this object is worth $1000 to me, but if I get it for $1000 then I walk away no better off than I was before, so I should put in a bid somewhere below $1000.” But how much below $1000? That depends on what I think the other bidders are going to do—and what the other bidders do depend on what they think I’m going to do. It’s potentially a very complicated calculation.  So in 1961 Vickrey writes a paper where he proposes a new kind of auction that combines the benefits of both formats. In this new kind of auction, we can all submit bids asynchronously and we don’t need to strategize. Here’s how it works: everybody bids, the highest bidder wins, and then the winner pays the second-highest bid. Thus the second-price auction was born.

Response: Why does that solve the problem of strategy? Why don't you have to strategize in a second-price auction? The trick is to see that there is a neat isomorphism between the price you choose to quit at in an ascending auction and a bid that you place in a second-price auction. Instead of running an ascending auction and having everybody decide dynamically at the time when to quit, you can just ask everybody when they are going to quit in advance and call that their bid. The highest bidder will win and then pay the second-highest bid.  This encourages you to submit a bid at however much you value the object. So if you value the object at $1000, it is better to bid $1000 than to bid any other amount. And that's true regardless of the other players’ behavior.


Prompt: What happens to Vickrey’s idea? Mostly it gets ignored. No real auctioneers adopt this way of selling things. Then, about a half-century later, in the early 2000s, Google picks it up and dusts it off as they’re figuring out how to sell ads. If you’re searching for car insurance on Google, a bunch of companies that sell car insurance want to place their ads next to those search results. Google needs to figure out a way to determine which companies get to put their ads where. Let's say there are three slots for ads on the page with the search results. There is the top slot, which is the most valuable because people see it first. Then there is the second slot below it, which is less valuable, and the third slot even further down, which is the least valuable.  Google decides it’s going to run an auction to determine which advertiser gets which slot. Google knows roughly how many clicks each slot will get. For example, they may know that the top slot gets clicked 300 times for every 10,000 views, the second slot 200, the third slot 100.  When advertisers submit their bids in the auction, they’re submitting their bid per click. They’re saying how much they’re willing to pay Google for each click they get. The highest bidder gets the top slot, the second-highest bidder gets the second, and the third-highest bidder gets the third.  The payment works in the following way. The top slot is worth 300 clicks. If you bid ten and I bid nine, you win the top slot and you’re going to pay my bid—the second-highest bid—times 300 clicks. And if I bid nine and somebody else bids eight, I'm going to pay eight, the third-highest bid, times 200 clicks, which is how many clicks the second slot is worth. And then let's say somebody else bid five. That person, the third-highest bidder, pays the fourth-highest bid times 100 clicks, which is how many clicks the third slot is worth.

Response: So Google implements something that looks a lot like Vickrey's idea.  Do they cite Vickrey?  Their advertising materials for what they called the “generalized second-price auction” claimed that it used “Nobel Prize-winning economic theory to eliminate... that feeling that you’ve paid too much.” So they directly draw a line to Vickrey and his academic credentials as a justification for the format that they're using. Except they get it wrong.  How so? A defining feature of a Vickrey's auction is the “dominant strategy property.” This means there is a strategy that will always perform the best no matter what any of the other players do. In the case of Vickrey’s auction, the dominant strategy is to bid your value.


Prompt: Now, there is a correct way to generalize Vickrey's auction to preserve this property. But Google did not do it correctly, at least not at first. The way Google did it, there were times when you should strategize. Sometimes you can benefit by misrepresenting your value per click.  When is that? Let's say there are a bunch of advertisers who are already in the three slots. You come in as a new advertiser, you submit the highest bid per click, and you take the top slot. Now, Google's auction made you pay for displacing the previous top bidder down to the second slot. But that's not the full effect of your participation. What you've really done is knock everybody below you down one rung of the ladder, but Google’s auction instead charged you as though you knocked the top bidder out entirely. It doesn’t account for this waterfall effect.  The point of Vickrey's auction is that it charges you your externality. It charges you an amount equal to what all the other bidders lose due to your participation. It turns out that there were certain circumstances where Google wasn’t calculating that externality correctly—and advertisers could benefit from bidding slightly less than their value.

Response: Was that miscalculation good for Google? It's certainly possible that if people misunderstand the auction this way, Google might make more money. But this also gets to an important issue: whether or not people are willing to play in your auction depends on how user-friendly it is. If Google's auction requires players to strategize in order to bid well, it's entirely possible that fewer bidders will be willing to participate.  It's one thing if your only job in Google's auction is to figure out how much a click is worth to you. That's difficult enough because you need to rule out the bots, you have to think about your sales model, and so on. But if on top of all of that you also have to think about how everybody else is bidding—and come up with an optimal strategy given the possible strategies of other bidders—the ensuing complexity will deter a lot of participation.  Anyway, the format that Google starts using in the early 2000s has this problem. And it remains for almost a decade. It's only fairly recently that Google has adopted the correct generalization of Vickrey's auction that accounts for the waterfall effect.  What about Facebook? How do they sell advertising? In Google you're mostly bidding on keywords. You're placing ads around certain search terms, like “car insurance.”  Facebook is selling a more personal product because Facebook knows a lot about you. It also has expert data scientists who can take everything Facebook knows about you and infer clever things from it.  The result is that advertisers can’t bid on Facebook the way they bid on Google because they don’t know what an ad is worth. They can’t take all that data that Facebook has—your gender, race, age, interests, social graph, online history, and so on—and turn it into, "Here's how much we think it's worth to show this person an ad for a ski holiday." Moreover, there are black-box machine learning algorithms that Facebook uses internally to help calibrate each campaign. It would be difficult to explain them to potential advertisers—and it's not even clear that Facebook can explain them to itself.


Prompt: So Facebook adopted a different solution to selling ads. Instead of placing a bid, you tell Facebook what kind of ad campaign you want to run and they bid on your behalf. And then there's something like Vickrey's auction running in the background deciding who bids what and at what price.  That doesn’t really sound like a market anymore.

Response: That's the oddness of it. It’s as if you went to a supermarket and rather than the owner saying, “Here are all the prices, please buy what you want,” the owner says, “Why don't you tell me how you're feeling this week and what you have a taste for, and I’ll find the optimal bundle and tell you how much it costs. Don't worry, I know my warehouse much better than you do.” One of the weaknesses of game theory as a way of thinking about the world is that it assumes we all know the rules of the game we're playing. But when some players have the lion’s share of the information, and when there are black-box algorithms in the middle, it becomes impossible for all of the players to understand the game they’re playing.  Facebook isn’t telling advertisers, “Here's the price you would need to pay to buy Mary’s eyeballs versus Adam’s eyeballs and here's all of the information you would need to make the decision about whose eyeballs you should purchase.” Instead, Facebook is saying, “Tell us about your advertising campaign, and we will figure out what you would have rationally done if you had access to our troves of information.” Do advertisers not care about that lack of transparency? Whenever there is a black-box element to a system, that system has to run on trust. When Facebook says they know better than you what kind of advertising you want to be buying, you really have to believe that Facebook has your interests at heart. And in some cases it’s not that Facebook doesn’t want to explain their reasoning—it’s that they can’t explain it, because the machine-learning models they’re using aren’t explainable. So it’s up to Facebook to convince advertisers that they’re not taking advantage of them. That may be difficult to prove when everything is so opaque.


Prompt: Thinking more broadly about this new kind of advertising, what do you think is most distinctive about it? How does it differ from what came before? One difference that springs to mind is the sheer individualization of it. There are some auctions where you can even bid for an individual human impression. For example, there’s a startup that will let you target a particular person with an ad campaign.

Response: How does that work? Maybe you want your partner to stop smoking. This startup will generate a special link for you that looks like it’s an e-commerce site. You send it to your partner and when they click it, they get a cookie secretly loaded into their browser. This cookie enables the company to track your partner across the web. You write up an anti-smoking ad, and the company will ensure that your partner sees that ad everywhere. Now your partner’s entire internet experience is permeated with pressures to stop smoking.  You can design a similar campaign for a coworker you don't like. You can show them ads for job-hunting websites, to encourage them to get another job.


Prompt: That's pretty funny. I knew about the extent to which Facebook and Google track you around the web, but I hadn’t realized that smaller companies could do it as well. Most people don't realize how many companies have access to the cookies that are in your browser, and how much information those companies can learn about you from those cookies.  Let's say you go to the New York Times website. Now, at various points in your browsing history, all sorts of cookies have attached to your browser. When you click through to the New York Times, as the page is loading, there is an instantaneous algorithmic auction for the right to show you an ad impression. That auction takes about a tenth of a second. In that tenth of a second, the New York Times passes on all of your cookies to an online auctioneer called a supply-side platform. These auctioneers then send out a query to a whole bunch of bidding companies that exist to help advertisers run online ad campaigns. They'll say, essentially, "At 4:01 pm, from the following IP address in Cambridge, Massachusetts, somebody we think is female, twenty-five to thirty-five years old, is looking at an article about Trump."  Presumably the time and the IP address are logged by the New York Times, but how do they know I’m female and twenty-five to thirty-five years old? They’re guessing that based on your cookies, which are just strings of text that contain information about your online activity. But the New York Times may not be able to interpret your cookies. So they’ll pass the cookies on to the auctioneer, who passes them on to the bidders, and the bidders will interpret them.  Now maybe the bidders can understand your cookies better than the New York Times. Maybe they can make a pretty good guess that you’ve been looking for a ski holiday or that you've been looking for a divorce lawyer. And from that they'll compute how much they think your eyeballs are worth to them. They'll send their bid back to the auctioneer, who determines the winner and then sends the winning impression back to the New York Times. That's how you get an ad for a ski holiday or a divorce lawyer one tenth of a second later.

Response: And so every time I go to the New York Times it is taking all this information and sending it to third parties who send it along to fourth parties?  Yes. Whenever you're accessing one of the many websites that sells advertising in this way, all of your cookies are being made public in this fashion. I think people don't realize that this is part of their everyday internet experience.  Is there any government oversight of this process? Who's even supposed to be regulating that? No, there's essentially no oversight. It's the Wild West out there.


Prompt: Can you tell us a bit about your background? I work for one of the biggest AAA developers. We have studios internationally, and our games are worked on by teams that range from 400 to over 1,000 people—at least our big marquee titles.  I've worked there for about six years. I came to work at a game development company because I had a passion for games—I was excited to be part of a company whose products I really enjoyed.

Response: The game development industry is unique because it's multidisciplinary in a way that I can't really think of having analogues in any other industry. Game development is this complicated intersection of software, tech, media, and Hollywood. There are so many different kinds of skills that come into making a game. And it can often feel like it’s just a lot of pieces that are hastily thrown together. The development cycle is very, very hectic—a game never feels actualized until the last few months.


Prompt: In terms of revenue, the game development industry makes more than twice what Hollywood does any given year. It’s been ahead for almost a decade now. But that's not going to the creators—not the programmers, not the QA folks, not the artists. Game development is centered in metropolitan areas like San Francisco, London, Tokyo, Paris. These are places where the rents are going up, but the pay for game workers isn’t. So more and more, we're seeing people struggling to make it in this industry.

Response: How have the jobs changed within the gaming industry over time? Similar to other parts of the tech sector, the game development industry is moving away from hiring people full-time and towards a gig model. It’s becoming commonplace to hire contractors and freelancers, instead of hiring full-time visual artists, sound designers, or writers for games. Or having fans submit speculative work to be used in games, paying them only a pittance.


Prompt: We're also seeing executives increasingly try to pay people in exposure, particularly in the promotion of games. While most companies have dedicated marketing and community teams, there is also a trend towards trying to promote through “influencers”—often younger gamers with a sizable social media presence that will spend hours streaming their game sessions. If someone has a lot of views on their channels, the company will pitch a partnership, offering free games or flights out to flashy events—a moment in the sun. In reality, these people should be hired and paid a living wage to do this sort of promotion.

Response: Twitch is another way that companies are exploiting players. A company will say, “Oh, your Twitch channel gets millions of views every month, why don’t you sign a deal with us?” And for a lot of these young players, maybe they've never signed a contract before. They have no idea whether what they are signing is a fair contract or if they are going to be compensated fairly. And unfortunately, with thousands of Twitch and YouTube video game personalities, they have to make hours and hours of content every week to stay relevant or they lose their viewership. Game companies will tease people with the possibility of full-time employment in one of the few cherished on-staff social media positions, in order to get them to keep grinding to stay in the good graces of the company.  In terms of the labor of game-making, if people have heard of anything, it’s often the idea of “crunch.” Could you talk a bit about that? What is your experience with crunch? Every developer has a story involving crunch—it’s existed as long as the industry has. It's generally a three-month-per-year period of mandatory unpaid overtime, sometimes up to twenty hours a day. Three to six months is average. I've heard people say that there are crunch periods that have lasted over a year, which is extreme. I’ll say, crunch time doesn’t exist solely in the game development industry. Tech companies definitely have crunch periods, as does Hollywood. But it is particularly acute in game development.


Prompt: Crunch periods have led to things like people working so long that they suffer from memory loss and anxiety attacks. There was one developer who described working for so long that he had an attack of paralysis — he went out to his car and was literally unable to move for an hour. All sorts of health problems are attributed to crunch periods.  Anyone knows that working a sixteen-hour work day for three months will fuck with your health. And obviously those sorts of work hours are also detrimental to your relationships.

Response: Crunch, in my experience, is almost entirely due to bad management practices. There have been plenty of studies that show that crunch is not only bad for your health, it’s actually ineffective and wastes money. It's not profitable. In every way, shape, and form, it is a bad labor practice that has no practical benefit. But it’s the thing that management has always done.


Prompt: Crunch is contributing to a brain drain in the industry. The average burnout rate is around five or six years, after which people just quit. They can't take it anymore. They want to raise a family, but they're not making the money they need to be able to. They're too exhausted and don’t have time to do the things they want to do in life outside of work. So they leave the industry, and then we have to reinvent the wheel with a new batch of folks fresh out of college.

Response: It's not a problem as far as management is concerned, because there are always plenty of new people that they can pay even less, who are told they should be grateful to work in this industry that they have idolized for their whole lives, who are willing to put in the same long hours. Until they burn out, and then the cycle repeats.  Now that most consoles are connected to the internet, do technological changes like digital patches and updates impact crunch time? You might expect that since we now live in a world where you can always patch a game after it is released, the pressure to crunch to get it perfect the first time might be lessened.


Prompt: You would think so. Before we could patch games, you would finish the game, send it out, and then that’s the end of the project. But the reality is that this just means you get an extra three more months to continue the crunch cycle. What ends up happening is you produce an unfinished or under-polished game that will get patched later—and creating the patch then becomes even more urgent because the game has already "gone Gold" (industry parlance for "finished and ready to ship"). There are games that come out where if you don't have internet access, you are getting a markedly different product. It’s a playable product, because first party publishers (i.e. console makers) have thresholds that games are required to meet—but barely.

Response: The pressure this creates just makes it more intense, because when an unfinished game is going to come out, you know that if you don’t continue to work on it you will have released a shoddy product. You feel like your future is riding on fixing it with a day-one patch, because otherwise the game you’ve worked on for two or three years will review poorly, which will affect sales, and as a result you’ll get laid off. And having a game that is critically panned on your resume doesn’t look great when you go looking for your next job.


Prompt: Do conditions differ between indie vs. AAA studios? It really depends. There are some smaller independent companies which are co-ops, which is super cool. Motion Twin, who recently released a very popular game called Dead Cells, is a company of about twenty people in France who are all paid the same wage, and all have a say in everything about how the company is run. And there are definitely some smaller studios who are open to the idea of unionization.

Response: But the finances are very different. It’s not like there is a culture of venture funding in game development, and a lot of smaller studios are folks who are doing it either on the side of another job or in their free time. So their development cycle is much, much different than for AAA. Most AAA games have a very strict two- to-three-year development cycle, whereas indie games can take up to five to ten years. And as far as compensation, working conditions, and stuff like that, it varies. It’s not as simple as, smaller studios treat people better because it’s a more familial setting or whatever. I have heard stories of small studios that are nightmares, and I've heard from people who work in AAA studios who really like their experience there. So it just depends on who you ask.


Prompt: I will say that AAA studios are experts at giving the appearance that they are taking care of you. My company is constantly sending us emails emphasizing the amenities they are offering. There are rewards for working there for certain periods of time that are ludicrous when you look at them. Like if you’ve worked there for five years, you get to drive a sports car for a day. If you’ve worked there for ten years, you’ll get a paid weekend away somewhere.

Response: And it’s like, ten years? If I’ve worked there for that long, I better be making a good living. My partner and I have plans for raising a family one day. In ten years, I don’t want to go on a weekend excursion, I want to make sure I can put my kids through college. There is this very paternalistic way that all the game development companies treat their employees. We’re often told, “We’re one big family.” And the workers are the children.


Prompt: Yeah, exactly. It’s a way to manipulate you into feeling guilt for demanding more, for expecting a reasonable work-life balance.  Looking For Group You’re currently involved in organizing within the game industry. How did you get involved with that? When I started at my company six years ago, I had no nuanced political analysis, and no real idea that I could be bargaining for anything. I was just drinking the company Kool-Aid, and accepting everything they said. I really believed that the company was taking care of me. Only in the last three years have I started to piece things together through my own experiences and reading stories of labor abuses through the industry, which go back thirty to forty years—pretty much since the industry started.

Response: I first got involved with GWU after the March 2018 GDC, which is the major annual industry conference for game developers. During GDC, there was what was effectively an anti-union panel, which galvanized people to join the organization. After I read about that, I inquired a bit more and reached out to folks on Twitter.


Prompt: And immediately I was like, this is the intersection of the two things I’m most passionate about: the organizing work I’ve been doing for the last two and a half years with the Democratic Socialists of America (DSA), and game development, the industry I have been in for the last six years. I had been in the same position for a long time and felt like I was stagnating. I was weighing whether it was worth staying in the industry. But after hearing about GWU I thought, here’s the opportunity to organize the game development industry and make it something that that lives up to the expectations that people have for it. I definitely wanted to be a part of that movement.  So after the GDC action in march I reached out to Emma, one of the founders of GWU. I learned there was no chapter where I was, and I was encouraged to start one.  Can you give a bit more background on what you do within Game Workers Unite? GWU is a union advocacy group. Our job is to advocate for unionization in the games industry, generally advocate for game workers, and spread a grassroots movement internationally. If anyone feels alone, or that unionization is something they want to see but they don't know how to achieve it, they can reach out to us. We give training and education to workers so they can start organizing in their studios and communities.

Response: We are a completely horizontal organization. There are no leaders, no one is “in charge.” To make things manageable and to be able to have a grassroots, bottom-up movement, we organize around local chapters so people have a place to plug into. But everyone has an equal say in the business of any chapter.  The organization has scaled up massively since starting. We went from essentially a Facebook group of frustrated developers to over twenty-five chapters internationally with thousands of supporters. We now have hundreds of members from all areas of the industry, from folks who are freelance or contractors to people who have been in the industry for years to students who are just getting interested in game development. Some have years of organizing experience, while for others this is their first foray into organizing.


Prompt: I’ve never seen a movement grow so fast and so organically and remain so positive. In contrast to other groups I have been involved with, there’s far less worry about the politics of how we organize, and much more focus on making sure that people get the support they need to build the kind of industry they want to see.  We are involved with the effort to unionize, but I would say that the final goal of becoming an industrial union is many years away. It's thousands of steps away. We're on like step five. So right now we're focusing less on formally becoming a union, and more on just helping people start to organize their workplaces. And that begins with establishing some rapport with your coworkers, learning how to talk to them and identify their self-interest, seeing what they need that their company is not providing them, and seeing if you can organize around that.

Response: Locally , we’ve started our education efforts with some basic initiatives, like giving people workplace organizing training and working with unions and organizations like Labor Notes and Tech Workers Coalition. We’ve also been learning about the history of the labor movement, reading about things like organizing in the steel industry.  We’ve started to form really good partnerships with existing unions. The stagehands union (IATSE), which is responsible for building all of the booths at GDC, has sent union organizers to our meetings to help guide us through things. Everyone I’ve spoken to in a union is very excited to see the industry unionize, which is really great.  So yeah, right now it’s still this massive scaffolding of an organization, but with a lot of exciting plans down the line.  Have there been any successful labor actions within the industry? The only labor action I’m aware of in the game development industry has been the voice actors strike of 2016. During that strike, basically all unionized voice actors stopped voice acting for games. The strike lasted for almost a year. They won—but honestly, it wasn’t as big of a victory as their union, SAG-AFTRA, played it up to be.  The voice actors were fighting for royalties on their games. They will work on these huge products like Grand Theft Auto that make over a billion dollars, but only get paid a few hundred dollars per session. In response, CEOs tried to pit workers in the industry against each other. They would say, “Well, it wouldn't be fair to everyone else if we gave you royalties—the programmers and the designers and the artists aren’t getting royalties.” And it’s like, “No, you’re absolutely right, they all should get royalties!” They try to play us all against each other, but really we should all be in this together.


Prompt: Gamer Solidarity What has the reception been like with the player and fan community? People are really interested in what we're doing. There's been a lot of media attention. Prominent game developers and respected journalists have been writing about us and what we are up to, which has been extremely good for us.  Because that's the big challenge: any unionization movement needs a big solidarity effort from the community. We need players to be on board with our efforts to unionize and understand why we're doing it, and that it is not going to detract their experiences. It’s not going to make games worse, it’s not going to delay games—all these myths that are popping up.  GWU is not just against shitty labor practices. We’re also against shady business practices, and those impact players. A good example is the recent controversy around “loot boxes.” People will design systems that require a ton of grinding and say, you could toil through this game for 100 hours in order to get all the content, or you pay us five dollars and we'll unlock the Darth Vader mask or whatever it happens to be that you want to get.

Response: That's an exploitative business practice, and it's shitty because game workers didn’t come into this industry to make little tchotchkes to sell to people. They came into this industry because they loved the great experiences they had playing games growing up, and they wanted to contribute to that and tell their own stories. And instead they're forced into making this cheap, exploitative content.


Prompt: In building an anti-capitalist framework to fight back against shitty labor practices, there is a natural alliance with players who are exploited by shitty business practices—not that they always appreciate that. It sounds like the relationship between developers and players is pretty complicated. It’s tough, because to some people outside of the industry there is a perception that working in games is a dream job. That making games is so fun and easy because you get to play games all day. And it’s not like that. Sometimes it’s hell, all day, and then you go home and people troll you on social media for making something that didn't live up to their standards.  It’s not just frustrating, it's something that hurts the organizing, because it makes people not empathize with your situation. People think you’re doing fine for yourself because you’re working in the games industry—you're not working in service or whatever.  And there are a lot of examples where particularly toxic fan reactions have had a real impact on people’s lives in the industry. We saw this with GamerGate, where women in game development and games media were made targets of online harassment, including death threats, and in some cases companies folded under the pressure and fired workers for standing up for themselves.  In the last few months, ArenaNet, a company that makes an MMO called Guild Wars, fired two of their workers for defending themselves on social media. One of the narrative designers wrote a long Twitter thread about a complicated narrative puzzle that affects her as an MMO narrative designer. To which some dude with no actual experience making games responded with some incredibly basic, obvious response. And so she got frustrated and tweeted back the equivalent of: “I don’t need you to mansplaining my job to me.”  That spawned this tornado of vitriol, where people were like, how dare you treat one of your customers like that. And one of her co-workers defended her by observing that as a man, surprise surprise, he’d never received this sort of treatment from the community.

Response: The two were unceremoniously fired the next day for “failing to uphold standards of communicating with players” —basically proving to the trolls that they have power. The bosses value their purchasing power more than the well-being of their own employees. This is absolutely a labor issue, because if these companies were unionized, there would be a defense against this. We shouldn't take this—we should have protection. We should at the very least not get fired for defending ourselves online.


Prompt: Recently there was a huge exposé of the sexism that happens at Riot Games, the company that makes League of Legends. I have a friend who works at Riot who told me things that weren’t in the stories that were published—it’s just been toxic for years and years, and only has come to light recently when workers tried to defend them themselves publicly, and were fired.

Response: This is unfortunately very common. In the game development industry, if you are a woman, or if you are trans or nonbinary or intersex, you've always had to deal with sexism or workplace discrimination, whether in the form of outright harassment or lower pay. I am hard-pressed to think of anyone that I know personally in the industry who hasn't.


Prompt: And that's something you organize around.  We met with the Anonymous Game Worker in September 2018. In the days and months after our conversation, a number of new high-profile cases illustrated the volatility of the video game industry. Two days after we spoke, Telltale Games, the creator of the critically-acclaimed narrative game The Walking Dead, laid off 250 employees with no severance. A few weeks later, another 150 workers were laid off at Trion Worlds. Meanwhile, leaders at Rockstar games have been called out for speaking proudly of their companies’ exhaustive crunch periods for Red Dead Redemption 2, describing several “100-hour” weeks.

Response: How did Project Cybersyn begin? After Salvador Allende was elected in 1970, a friend of mine, Fernando Flores, was appointed technical director of the State Development Corporation (CORFO). CORFO is a Chilean government agency that was created in 1939 to promote economic growth, and was responsible for many nationalized companies.


Prompt: I joined CORFO as an operations research scientist. We were extremely young, and this was an extraordinary opportunity to do things that people who were much older than us would find difficult. Fernando and I started to talk about how to proceed with the policies of Allende's government, which included a plan to expand the nationalization of Chile's industries with the aim of creating a strategy of economic development that prioritized the production of affordable goods. We wanted to create simple, accessible, and efficient industries that would produce products that everyone could afford.

Response: One way to do that was to centralize the economy. Now, Fernando and I didn’t want centralized planning. But, for at least the first year and a half of Allende's government, that's what CORFO was doing. Still, we thought there were more imaginative ways to bring together government policy with current developments in knowledge. It was in this period that we started to work with Stafford Beer, a British management consultant known for his work on cybernetics. Through Beer, we found how to achieve decentralization through devolving power. We needed to create the conditions where people could express their wishes and interests.


Prompt: How did you first come across Beer? It was quite serendipitous that Stafford Beer had published his book Decision and Control in 1966, and that Fernando found it in a bookshop while he was visiting New York City. He brought it back to Chile, and a group of us read and discussed the book. At that time, I was a student of engineering.  The cybernetician W. Ross Ashby’s “law of requisite variety” is central to Beer’s method for controlling complex systems. According to Ashby, variety is the only thing that can control variety. To control a complex system like an industrial economy in order to get it to perform a certain way, you must ensure that the relevant people, such as workers and managers, can respond to all of the possible scenarios that might prevent the system from performing that way.

Response: With great elegance, Beer developed these ideas about how to manage complexity. They appealed to us, because we were not intending to have a centrally planned economy. We were intending to develop organizations that had the capacity to make decisions within themselves to support the development of the economy.


Prompt: In 1971, Flores wrote to Beer and requested his help to apply cybernetics to the management of Chile’s increasingly nationalized economy. A few months later, Beer arrived in Chile. What were some of Beer’s specific recommendations as you began developing Cybersyn? Beer said that we needed to move from a model of running the economy that was based on information to a model that was based on communications. That meant that you weren’t going to ask companies to send reports and have them sit on desk trays until someone had the time to read them. That isn't the way the world works. The world works through communication and on-the-spot interactions.

Response: In one of the earliest ideas he produced for us, he proposed having Telex machines distributed throughout the country. Telex machines were teleprinters that sent text-based messages over a network. The previous government in Chile had bought about 500 Telex machines and didn't know what to do with them. They were in a warehouse without any purpose. It was very serendipitous that someone in the engineering team informed us that we had Telex machines available and he knew how to install them. What would have taken years ended up taking four months to put into place.


Prompt: At the beginning, the idea was to transmit real-time figures from the factories to CORFO through the network of Telex machines, which we called “Cybernet.” Then the figures would be sent to a computer that ran a software program called "Cyberstride" that analyzed the data. The outcome of this analysis was displayed in the Cybersyn “operations room.” We worked with industrial designers at CORFO’s Committee for Technological Research (INTEC) to create a room conducive to non-hierarchical management. The chairs, which each had slide-control panels, were placed in a circular arrangement. The room did not have tables; data and graphics were displayed on a panel in the main wall. Everything was designed to facilitate a relaxed environment to produce ideas for the future of Chilean socialism.  The operations room epitomized the Cybersyn project. It was an extraordinary design produced by a transnational team. Many people thought it was too flashy, too technological, and not something that was connected to the workers. I think it was the opposite—the room was designed to facilitate creativity and imagination. It was offering something that couldn't be obtained by doing something trivial like sitting in front of a computer. Today, after all these years, I am amazed. I really think Beer was an imaginative man.  In hindsight, what do you think were some of the limitations of your team? We had more to learn about the cybernetics of organizations. Stafford Beer knew that subject well, but we were only learning. And when you’re only learning, your bias is towards the technology. So our approach tended to be technocratic. We wanted to design good indices of economic performance. And those indices would be designed by specialists, experts in operational research who could do all the technical aspects of economic modeling in mathematical terms.

Response: However, while that work is necessary and useful, it is not sufficient in the social sense. We needed to get workers much more involved in the meaning of these indices. We needed to give them far more opportunities to influence the design of Cybersyn. To do that, we had to build up rapport with workers—but this wasn’t always possible.


Prompt: Second, we had a limited understanding of Beer’s concept of the Viable System Model (VSM). The VSM is a management model that works on the premise that managing the complexity of factories, enterprises, sectors, and even whole industries is isomorphic to the way complexity is managed by the human nervous system. Autonomy was the essence of the model. What we struggled with was, how do you recognize autonomous systems within autonomous systems? How do you recognize the capacity of the government to organize the activities of state-owned industry and implement its policies while connecting these activities and policies to the specific dynamics of various factories? Today, we would do it with more sophisticated methodologies. We would have a better understanding of how to promote autonomy. That is very important, because if you are going to devolve power, then workers, managers, and administrators must understand how to close the loops locally. They must understand how to produce feedback locally. We shouldn’t insist on each unit sending information to the higher echelons and receiving decisions in return from above. Rather, we should explore ways of providing each unit with enough resources to match the complexity of its environment through local decisions.  What are the lessons of Project Cybersyn for today, in the era of Google and Facebook? Perhaps the most crucial thing that has changed is the abundance of data, and the possibilities for the peer-to-peer coordination of activities. Data is overwhelming us. Companies like Google and Facebook are greatly increasing the variety and the complexity of networks. But we hear very little about how to bring the data they are producing to a manageable level, one that can be managed directly by the people.  The VSM suggested the need to chop up a situational complexity into different autonomous units at a whole range of levels, each with their own decision-making capacity.  A simple system can proliferate a huge number of possible states. We need to learn how to "chunk" those possible states. The more we recognize constraint in the world in which we function, the more we can produce this chunking, and the more we can manage situations that appear to be totally out of hand. Constraint is where we need to focus our attention, because it increases our adaptability to complex situations.

Response: And we need to carry out this chunking and adapt to those situations in real time. One of the most interesting aspects of Project Cybersyn was its attempt to perform economic management in real time. We weren't interested so much in the past as we were interested in the current situation. How do we recognize what needs to be changed today? We need to do cybernetics in time zero rather than trying to respond to past situations.   What is the role of artificial intelligence in a cybernetic socialist project? One thing that we need to understand with artificial intelligence is that, in one form or another, a person is responsible for the code. Whose values are then embedded in the code? Developers of artificial intelligence must communicate with citizens to understand their values, so that we are not living with technology created to support a set of values that only meet the needs of a small fraction of society. To build a system that benefits all people, experts must communicate with all different levels of society.


Prompt: Let’s start by talking about your background. How did you get involved in finance? I was always interested in economics and had a quantitative background. Anyone who succeeds academically where I grew up ends up being very quantitatively oriented. After school, as I was trying to find a profession that would be financially rewarding but would also allow me to use what I studied, I started looking at the financial industry. I ended up taking a job on a trading floor in an investment bank.  Most large banks have at least one, typically several trading floors. It’s an actual floor, about the size of a football field, filled with traders who do business with large investors looking to trade stocks, bonds, or futures, or to borrow money. The bank makes money by taking a commission, or by “market-making”—intermediating between buyers and sellers, taking some risk with its own money while it waits for the two sides to match up.

Response: When I think of a trading floor, I think of a bunch of guys screaming into the phone, Wolf of Wall Street-style. It’s not so much people yelling into the phones anymore. The trading floor has evolved quite a bit over time. It used to be more about being alive to the transactional flow of global markets. It’s increasingly about the operations that enable that flow, and the intellectual property that allows people to make money off that flow.  I liked it. The trading floor is still where a lot of the actual design and transactions of global markets take place. And it’s stimulating. If you want to use your intellectual muscles, you can do so pretty quickly. You’re not just sitting at a desk somewhere out of the way, or trying to pitch corporate titans with some arbitrary analysis to back you up —which can be more of a salesmanship game and less of an intellectual exercise.  Anyway, over time I migrated to the investment strategy part of the financial world. I started helping large asset owners—entities like pension funds and sovereign wealth funds—allocate their money to systematic investment programs. That’s where I migrated to because that’s where most of the financial world was migrating to after the 2008 financial crisis, as everyone realized that the old ways of investing were not really doing what they wanted them to do.  Portfolios had been too exposed to the same underlying risks. Technology was now enabling investors to understand their risks better, and to take more direct control over their investments. Part of the shift involved removing human decision-making when it wasn’t perceived as adding any value.


Prompt: What do those new ways of investing look like? What are “systematic investment programs,” and how do they fit into the field of algorithmic finance as a whole?  There are many ways that algorithms are actually used in finance, so the term algorithmic finance gets used more loosely than it should.  There are at least two major domains in which algorithms dominate. The first is what’s frequently called algorithmic trading, which focuses on market microstructures. It programs computers to make split-second automated decisions on how stocks are bought and sold. Should you buy a whole bunch of shares at once? Or should you split up your purchases over time? How do you more intelligently execute trades? Algorithmic trading uses algorithms to help answer these questions—and it’s an enormous industry. There are a lot of hedge funds and traditional investment banks that try to make money there.  The other domain, which is the one that I’m more focused on, is sometimes called systematic investing and sometimes called quantitative investing. It’s also very much algorithmic investing. It involves using algorithms to allocate money systematically based on data.  An early version of quantitative investing—starting roughly in the 1950s, with the birth of Modern Portfolio Theory—was designed to create rules to save for retirement. These rules were supposed to help people decide how much of their money to put into stocks and how much of it to put into bonds. Once you’ve made that decision, you have a rule that lets you allocate money across stocks and bonds at some defined frequency automatically, without a human being going in and having to make any qualitative decision. This basic framework was rapidly adopted across investment portfolios at every scale, from mutual funds for individual investors to asset allocation decisions by the largest funds in the world.

Response: Then people took that framework and applied it to an increasing number of underlying assets, with a much finer degree of granularity. So now you’re not just making rules that determine the overall mix of stocks and bonds in a portfolio but which stocks, which bonds, which commodities, which corn futures, and so on. And the rules used to distribute assets become far more complex.


Prompt: When you say “making rules,” what exactly are we talking about here?  The simplest rules can be run on a basic spreadsheet. For instance, classic pension portfolios used to allocate 60% of the portfolio to large-cap stocks and 40% to bonds. Then Modern Portfolio Theory started to allocate assets accounting for mathematically measured risk and return. You run a giant optimization that promotes diversification—across stocks within your stock portfolio, across asset classes—to maximize the return you make per unit of risk.

Response: These rules didn’t need any further human intervention, in the sense that they completely defined a portfolio. But in practice, investment wasn’t completely rule-based: investors used the model outputs as a baseline, and then tweaked it with their own decisions. Human insight could further improve the asset mix, in a variety of ways. Investors might want to buy cheaper stocks, for instance, or “time” the market by getting in or out at the right time. Or they might want to find new assets like commodities and mortgage securities, or improve the measurement of risk.


Prompt: More recently, however, advances in computing power and financial engineering have vastly expanded the universe of analytical tools that can be applied to investing. The latest “rules” involve developing machine learning models that train on large amounts of data. It could be data from the financial statements of publicly traded companies. It could be macroeconomic data. It could be the price history of certain financial instruments. It could also be more esoteric data like satellite imagery.

Response: What’s a concrete example of an investment decision driven by a machine learning model? You could purchase a “sentiment” score developed by a firm that trawls Twitter on a continuous basis to understand changes in the mood of the market, or around a specific company. You could use that data to train your model, which could then determine whether to buy or sell certain shares. Usually signals like a sentiment score decay pretty quickly though, so you would want to be able to make that trade fast.


Prompt: How automated would that process be? Are we talking about software making recommendations to human traders, or actually executing trades itself? The level of human oversight varies. Among sophisticated quantitative investors, the process is fairly automatic. The models are being researched and refined almost constantly, but you would rarely intervene in the trading decisions of a live model. A number of hedge funds, mutual funds, and exchange-traded funds (ETFs) run on auto-pilot.  By contrast, most traditional investors use models to provide guidance rather than to generate automated trading decisions, since its unlikely that they could operationalize a complex trading strategy.

Response: One of the challenges with machine learning is explainability. As the model becomes more complex, it can become harder, even impossible, to explain the results that it generates. This has become a source of concern as public scrutiny of the tech industry has increased, because you have algorithms making decisions that affect people’s lives in all sorts of ways while the reasoning for those decisions remains completely opaque.


Prompt: When the financial industry plugs a bunch of data into a model in order to make an investment decision, how important is the explainability of the result? I think the result should be very explainable. But that’s not a universal view. In fact, there’s a fairly big split between people who have concluded that explainability is holding back the advancement of the use of these techniques, and the people who hold on to the rather quaint notion that explainability is important.  But to some extent, explainability was already an issue well before we started using machine learning, because even traditional models of investing were hampered by some of these same issues. Finance is not like physics. You have a lot of feedback loop mechanisms impacting how participants interact with financial markets.  To give you a simple example, you might look at the price data of a stock and conclude that because that stock went up last month, it’s a good idea to buy that stock today. And if you do that systematically, you might expect to make some money. But if everybody else comes to the same conclusion, then the stock could get overbought today based on the movement of the stock over the past month. And if it’s overbought, you might actually expect to lose money on it over the next month.

Response: Looking at historical data to figure out where your investment is going to go is useless if you haven’t thought about the mechanism by which it’s going to do that. In the example I gave, if you didn’t have an explanation for why the stock was moving the way it was moving, you might have missed the fact that the underlying mechanism didn’t really exist, or that it wasn’t robust enough to weather a whole lot of market participants looking to take advantage of that phenomenon.


Prompt: So explainability has been an issue for a while. Everyone is always looking for a story for why they’re doing what they’re doing. And many of those stories aren’t that robust.  But isn’t there a strong financial incentive to try to understand why you’re doing what you’re doing, whether it’s an algorithm or a human executing the trades? Otherwise it seems very easy to lose a lot of money.   Sure. But the market structure of investing dilutes that incentive.  The people who are developing the most sophisticated quantitative techniques work for hedge funds and investment banks. For them, there are two ways to make money. You make money by charging fees on the assets you manage, and you make money on the performance of the fund. That split will give you a sense of why there’s a dilution of the incentive. Because even if your assets don’t perform well, you can still make money on the fees that you’re charging to manage those assets.

Response: The rewards from those fees are so large that if you can sustain a story for why your technique is superior, you can manage assets for a long time and make a ton of money without having to perform well. And, to be fair, sometimes it takes a number of years before you know whether the quantitative technique you tried actually works or not. So even if you aren’t making money in the short term, you could have a reasonable story for why you aren’t.  At the end of the day, for the manager, it’s as important to gather a lot of assets as it is to run a successful strategy. And gathering assets can be largely a marketing game.  And you play that marketing game by talking about your algorithms and machine learning models and artificial intelligence techniques and so on.


Prompt: That’s right. Let’s look at hedge funds in particular. Hedge funds are a very expensive form of investment management. So they need to justify why they’re getting paid as much as they’re getting paid.  There’s a large amount of data that suggests that the average hedge fund, after you’ve paid all the fees that they charge, is not doing much for you as an investor. The last several years in particular have not been very kind to the hedge fund industry in terms of the returns they’ve produced. So hedge funds have a strong incentive for differentiation in their marketing story. The first marketing question for a hedge fund is always, “Why are you not the average hedge fund?”  Investors want to know how a hedge fund is going to make money, given the poor performance of the hedge fund industry as a whole. These days, investors are excited by an orientation towards technology and big data and machine learning and artificial intelligence. These tools offer the promise of untapped returns, unlike older strategies that may have competed away the returns they were chasing. Regardless of whether you’re actually good at technology as a hedge fund, you want to have a story for why you might be.  Some of the most prominent hedge fund managers of the last few decades—Steve Cohen, Paul Tudor Jones—are going against type and launching technology-driven quantitative investment funds. They employ physicists and computer scientists to write algorithms to invest money, because that’s what investors want. You’re seeing a massive arms race across hedge funds to rebrand themselves in that direction.

Response: It reminds me a bit of startup founders marketing themselves to Silicon Valley venture capitalists by peppering their pitch decks with buzzwords related to artificial intelligence or some other hot field. The startups might get funded, but the technology might not really work—or it might not even exist. What the startup is calling artificial intelligence could be a bunch of workers in the Philippines doing manual data entry.


Prompt: In the financial industry, investors want firms that use big data and machine learning and artificial intelligence—but do those new tools actually generate better results?  That’s a good question. The best way to explore it might be to talk about the role of data. There’s a lot of excitement in the financial industry about the amount of new data that’s being made available. Think about what kind of data might be useful for predicting the price of an oil future. It might be a piece of political news, public announcements from regulators, satellite images of oil refineries to calculate oil reserves. There are tons of different kinds of data out there—pretty much anything you can think of.

Response: Along with new forms of data, there are also new forms of data analysis. The early versions of complex data analysis included looking at the financial statements of publicly traded companies. But now you can parse through the data in those statements in more interesting ways. Back in the day, you might care about how much debt the company has or what its earnings are relative to its price, and you might compare those figures to the broader market. But you were ultimately limited by your capacity to source and process this data.  Now you can analyze more variables more systematically across thousands of stocks. You can also do more exotic things like use natural language processing techniques to figure out what the company is saying in its statement that isn’t reflected in its numbers. How did the commentary change from previous earnings reports? What is the tone of the words they use to describe the underlying business? How does this tone compare to words used by its competitors? Even though it’s the same data you had access to before, you have more processing power and better techniques to understand that data.  The challenge is that not all of these sources of data and ways to analyze them will be useful for predicting the prices of financial instruments. Many of the new data sets, like satellite imagery, tend to be quite expensive. And they may not add any information more useful than what is already available to market participants from the vast streams of data on prices, companies, employees, and so on. We’re still in the phase where we’re trying to figure out what to do with all the data that’s coming in. And one of the answers might be that most of it is simply not that valuable.


Prompt: The Big Data Gold Rush Let’s say it does turn out to be valuable. What does the financial industry look like then? Everyone is competing against everyone else. If one firm succeeds in making the market more efficient through quantitative techniques, then there’s less money left over for other people to make exceptional investment returns. There will be one or two firms that are good at innovation and recognizing things that other people haven’t recognized. But everyone else will be fighting over scraps.

Response: One of the fallacies that people have is the assumption that because the people who are working at certain firms are smart, they must be successful. But the fact that they understand artificial intelligence or machine learning or big data is somewhat useless as a competitive advantage if everyone else understands it as well.