Category: Transcript

Échanger

(This was originally released as Implausipod Episode 25, on January 2, 2024)

https://www.implausipod.com/1935232/14232183-implausipod-e0025-echanger

[buzzsprout episode=’14232183′ player=’true’]

Échanger

Bonjour. J’ai une question à vous poser. Voulez vous échanger avec moi? Really? Are you sure? That’s fantastic! Because sometimes the English language doesn’t have the right word that does exactly what you need it to do, that expresses the entirety of what you’re looking for. And in this case, that word, échanger, is what we’re going to use when we’re talking about automation.

I’ll explain more in this episode of The Implausipod.

Welcome to The Implausipod, a podcast about the intersection of art, technology, and popular culture. I’m your host, Dr. Implausible. And in this episode, we’re going to take a look at part three of our two part series on the sphere in Las Vegas. Yeah, things got out of hand. And follow through on an observation that dominated the discourse in 2023 and serves to be at the forefront of our discussion about technology in 2024 and beyond.

And that concept is échanger.

So I mentioned this the other episode when we were looking at the Sphere in Las Vegas and how it had a lot of workers that were doing fairly regular rote tasks, like holding up signs and directing traffic. And as they funneled everybody into the entrance of the Sphere, into the first floor of that massive auditorium, We met the robots, the auras, that were doing almost exactly the same thing:

responding to the crowd, answering questions of the audience, and directing them. But responding to them personally. And it struck me at the time, especially as we were kind of going through and looking at five different Auras, the sisters, that were explaining what we saw in each of these stations, that each of them could do the job of the others, their human chaperones, without too much more training.

It was job replacement made real. And this is where I started to look for a term that can kind of encompass that. Now, it’s something that’s been discussed a whole lot, that idea of job loss through automation, and it’s accelerated in the last year since the release of ChatGPT and the other AI assisted art tools or large language models, as people are worried that that’s going to directly lead to job loss.

But that’s only one part of the story, as there’s also things like the development of the Boston Dynamics robots, and other robotic assisted tools that are taking the roles of persons, and dogs, and mules within various environments. And so we have this assemblage of different things that are all connected to this job loss.

And in order to encompass these factors, I found myself stumbling for a word. I recalled back to some of my training in grad school where we were looking at the idea of actor network theory and the author Michael Callon. In 1986, he came up with the idea of interessement, And obviously he was French, but in his work titled Some Elements of the Sociology of Translation, he was talking about that shift that took place, and he was using the French language to describe it, a specific instance.

So I thought I’d reach out and draw on that inspiration, and see if perhaps a verb in French could encompass what we are seeing within the world at large. Hence, Échanger. And I like it. It works. I know there’s been some other authors who have used other verbs to describe different processes within the tech sphere lately, and sometimes those will get caught by language filters and sometimes they won’t, but I think Échanger, with all its multiplicity of meanings, adequately captures the breadth of what we’re looking for here when we’re talking about automation, agentrification via AI tools, and virtualization,

and what they might mean for workers that are working alongside machines that will replace them. That’s what the term means, or what it means now in the context of this episode, and in my reference to technological replacement. And speaking from a personal perspective, I have more than just an academic interest in echange.

I’ve been automated out of jobs on at least a couple different occasions over the last 30 years, and I’ve experienced outsourcing from a worker perspective on a couple occasions as well. And in some cases, both at the same time. For example, in one of those instances, I was working for a local tech company that was manufacturing phone handsets.

And there was seven people working on the assembly line, and after a few months, they brought in one machine that could replace the role of one of the persons on the line. And our duty was to feed material into the machine. And then after that was tested and worked out, within a month, they brought in another one.

And slowly, that team of seven was whittled down to two, as we’d just really need somebody at the front end to load the parts, and at the back end to take out the manufactured ones and test them. And it ran pretty much 24 7. And after they had fine tuned that, they packed up the whole factory and shipped it down to Mexico.

So we had both replacement, échanger, and outsourcing happening within the same instance. Now, obviously, this isn’t anything new, it’s been happening for years. The term technological unemployment was originally proposed by Keynes and included in his Essays in Persuasion from 1931, and has been returned to many times since, including by Nobel Prize winner Wassily Leontief in his paper titled Is Technological Unemployment Inevitable?

Daniel Suskind writes in his 2020 book, A World Without Work, that there can be two kinds of technological unemployment, frictional and structural. Frictional tech unemployment is that kind that is imposed by switching costs and not all workers being able to transition to the new jobs available in the changed economy.

The friction prevents the workers from moving as freely as needed. And this is what was happening in my experience with the jobs where échanger occurred. I want to be clear, a lot of those jobs that I was automated out of were not great. It was hard, demanding work, or physical work that was replaced by labor saving devices, in this case, machines.

But it still meant a job loss, and there was one less role, or entry level role, for a high school student, or college student, or casual worker, or whatever I was at the time.

Échanger. (part 2)

And that’s part of the problem. On March 27th, 2023, the Economics Research Department at Goldman Sachs released a report titled The Potentially Large Effects of Artificial Intelligence on Economic Growth, otherwise known as the Briggs-Kodnani Report. The report was published several months after the release of ChatGPT4 to the general public and captures the fear that was seen during its initial wave of use.

The report focuses on the economic impacts of generative AI and its ability to create content that is, quote, indistinguishable from human created outputs and breaks down communication barriers, end quote, and speculates what the macroeconomic effects of a large scale rollout of such technology would be.

Now, the authors state that this large scale introduction of AI tools would be, or Could be a significant disruption to the labor market. The authors take a look at occupational tasks on jobs, and using standard industry classifications, they find that approximately two thirds of current jobs are exposed to some degree of AI automation.

And the generated AI could, quote, substitute up to one fourth of current work. Now, if you take those estimates, like they did, it means it could expose something like 300 million full time jobs to automation through AI, or what I like to call agentrification. And that’s over a 10 year period. This would create an incredible amount of churn in the workforce, and whenever we hear about churn, we need to consider the human costs behind those terms.

A lot of people will lose their jobs, and well, the Schumpeterian creative destruction generally means that people get new jobs, or that old workers that haven’t moved become more productive, as a study by David Autor and others from 2022 found when they looked at U. S. census data from 1940 to 2018. and found that 60 percent of workers in 2018 were working at jobs that did not exist in 1940, and that most of this growth is fueled by technology driven job creation.

But there’s usually a lag between the two, between losing one job and having tech create new positions, the frictional tech unemployment we mentioned earlier. But there could also be more, the second kind mentioned above, structural technological unemployment. As stated by Briggs and Kodnani, there could very well be just some permanent job losses, and that can be a challenge for us to address as a society.

Now, with the productivity growth, Briggs and Kodnani argue we could see a 1. 5 percent growth over a 10 year period following widespread adoption, so the timing for all of this is actually quite distant. Everybody’s thinking everything’s going to end immediately, and that’s not necessarily the case. But it sure can feel like it’s coming around the corner right away.

The authors also estimated that GDP globally could increase by 7%, but that would depend on a whole lot of factors, so I’d like to bracket off that prediction, as there’s too many variables involved. The two things I really found interesting about their report was a, the timescale that they’re looking at this and B, the specific jobs that they’re looking at.

So, as I said, the ability to predict the specific GDP on something as large scale as this across the economy on a 10 year timeframe is just like, let’s not do that. It’s just. There, you can put numbers into it, but I think there’s just far too much speculation involved in actually being able to get to that level of precision with anything.

The interesting thing in the paper was their estimate of the work tasks that could be automated in the industries that could be more significantly affected. There’s two key charts for this. It’s Exhibit 5, which is the share of industry employment exposed to automation, and Exhibit 8, which is the share of industry employment by relative exposure to automation by AI.

And there’s some of these that are, you’re not going to see any automation improvements in. Some industries are just not really going to take a hit. But some of them could have AI as a complement, and some of them will have AI as a replacement. And this is in Exhibit 8, and I think this is probably the most interesting thing in the whole article.

The thing the Briggs and Kodnani report captures is a lot of the public’s initial impressions of OpenAI, and of ChatGPT as well. This drove some of the furor because as people were able to access the tool and use it, one of the things they’d naturally do is go, Well, does this help me? Can I use this for my own job?

And B, how well does this do my own job? So a lot of the initial uproar and the impacts from ChatGPT was people using it to see how it would do their job and being concerned with what they saw. So I think a lot of their concerns and fears are well founded. If you’re doing basic coding tasks, and the tool is able to replicate some of those tasks fairly simply, you’re like, oh my god, what’s going on?

If you’re doing copywriting or any of those roles that receive a significant amount of replacement, as in the Table 8 on the Report, like office and administrative support, and legal, you know, traditionally one of those things we didn’t really think would be automated, you’re going to have some serious concerns.

Martin Ford’s book, The Rise of the Robot, talks about that white collar replacement, where we’re seeing job loss and automation in roles that traditionally hadn’t seen it before. When we think of échanger. When we think of automation, we think of it as, like, large industrial machinery. We’re thinking of things like factory machines, being able to produce something that a craftsman might have had to work at for long hours, but able to do that at an industrial scale

or rapid scale. And this change has us going all the way back to the era of the Luddites in the early industrial revolution in England. Now, when ChatGPT launched, we’re starting to see the process of what I like to call agentrification, tech replacement through AI tools. And basically, we’re having automation of white collar work in things like the legal field.

I mean, this might fly under the radar for a lot of academic analysis, but if you’re paying attention to what gets advertised, there were signs. Tools like LegalZoom were continually advertised on the Jim Rome sports talk show over a decade ago, and we note that being able to be centralized and outsourcing that work would indicate that there’s, you know, some risks of échanger involved in those particular fields.

Now, there’s other fields where this white collar work is at the risk of echangér as well. The Hollywood Strikes of 2023 had similar motivations. Though their industries were moving quicker to roll out the tools, being on the forefront of their use, the Actors Guild and the Writers Guild were much more proactive against the tools because they saw the role that would take place in their replacement.

Given the role of the cultural industries, like movie production, being at the leading edge of soft innovation, we were already seeing digital de-aging tech and reinsertion in major motion pictures, notably from Disney properties like Star Wars with both Peter Cushing and Carrie Fisher, whose likenesses were used in films after they had passed away, and the de aging of Harrison Ford in Indiana Jones 5.

This leads to an interesting question. Can Échanger lead to a replacement of you with your younger self? I don’t know. Let’s explore that a bit more, next.

Échanger (part 3)

On December 2nd, 2023, the rock band KISS played their final show at Madison Square Gardens. Now, this may have not been newsworthy, as they had been doing Last show ever since late last century, but as the members were now in their 70s, there was a feeling that they really meant it this time. However, at the end of the show, they revealed that they weren’t quite done just yet, and they unveiled their quote unquote immortal digital avatars that will represent the band on stage in the future.

Now, KISS aren’t the first in doing this by any means. The Swedish pop band ABBA has been doing this for a while, and Kiss contacted the same company, Pop House Entertainment, to work on their avatars. Now, Bloomberg News reports that the ABBA shows are pulling in 2 million a week. Yes, you heard that correctly.

Clearly, I’m in the wrong business. But this trend to virtual entertainers has been happening for a while. When a hologram Tupac appeared with Snoop Dogg and Dr. Dre at Coachella in 2012, it was something that had already been in the works. Bands like Gorillaz and Death Clock had long used virtual or animated avatars, and within countries like South Korea, virtual avatars are growing in popularity as well, like M.A.V.E., the four member virtual K pop group that’s been moving up the charts in 2023. We noted a few episodes ago that one of the challenges for 21st century entertainment complexes like the Sphere is providing enough continuous content, and virtualized groups like this may well be able to fill that role and allow the Sphere to provide content worldwide by having virtual avatars that can fill the entire space in ways that Bono and the Edge on a small stage in front of a massive screen can’t quite do. And more than just this, the shift to remote that’s happened as part of the pandemic response could mean this technology could be rolled out in education and other fields as well.

So we’re just seeing the thin edge of the wedge of this virtualization component of Échanger. With large companies like Apple and Meta continually pushing the Metaverse, we’re going to see more and more of it in the coming years. So 2024 may well be the year of virtualization. We’ll dive further into virtualization and the Metaverse in upcoming weeks here on the Implausipod.

Why échanger? (part 4)

Well, basically it covers three things. We’ve kind of discovered it covers automation, which is the industrial process that we’ve been seeing for centuries now. It covers virtualization, the shift to digital in entertainment, education, conferences, and distribution. And the third thing it covers is agentrification, the replacement of workers or roles or jobs by AI.

So, this is different than outsourcing, as outsourcing may work in conjunction with some of the above, as noted in my own personal experience earlier, and these are all metaprocesses of the trends towards technological unemployment. If we look at any of these, automation, Virtualization and agentification, they’re all metaprocesses of translation.

Now, the work I mentioned earlier by Michel Callon, in Some Elements Towards the Sociology of Translation from 1986, is basically talking about that, describing what we call a flat ontology. An ontology, in this case, is a way of describing the world. And what a flat ontology does is it treats the actors in the world as similar.

So, normally, when we talk about an ontology, we’re talking about like with like, right? We’re talking about people, or we’re talking about things, or we’re talking about institutions, firms, we’re looking at things on the same level. When we flatten the ontology, we treat all the actors or agents in the system equally, and we can look at the power relations between them.

We use the same terms for the actors, so in this case, it would mean human and non human actors are treated in the same way. We treat the things the same as the people. That doesn’t necessarily mean we treat the people as things, but we say that everything here has to be described with the same terms when it comes to their agency.

This is what interessment means. That’s the agency. In between state, the interposition, when Michel Callon is talking about translation between asymmetrical actors, it’s that moment where we connect dissimilar things. And so this is where we come into the idea of échanger as a metaprocess for these three trends of replacement.

And that’s why we chose échanger for this process of translation as well. Échanger is a process of translation of a different kind. Échanger is the metaprocess of having something different do the job or being a replacement for the task. So if échanger means in French, literally a trade and exchange, a swap, then we’re extending or exapting the term a little bit in this case, where to us échanger means replacement in place.

So if we return to our example from the Sphere in Las Vegas, we can see this happening with the Auras and the workers. The role is similar, but it’s a different agent, different actor that is taking that place. This is what we see with virtualization as well, or automation, the agentrification that’s taking place due to AI.

And sometimes those machines, those tools, those devices, means the job of many can be done by one. But it also means that the one still occupies the same place within the network of tasks and associations within the process around it. Think of those machines embedded in the assembly line I mentioned earlier.

Where the staff went down from 7 to 2 and the production line was turned into a black box with inputs and outputs. But what’s actually going on in that black box? We can have some questions. With some automated processes, we can tell. But with AI tools, we don’t necessarily know. And that can be a significant problem. Especially when we’re facing Échanger.

Bibliography:

Autor, D., Chin, C., Salomons, A. M., & Seegmiller, B. (2022). New Frontiers: The Origins and Content of New Work, 1940–2018 (Working Paper 30389). National Bureau of Economic Research. https://doi.org/10.3386/w30389

Hatzius, J. et al. (2023)The Potentially Large Effects of Artificial Intelligence on Economic Growth . (Briggs/Kodnani). Retrieved December 5, 2023, 

Ford, M. (2016). The Rise of the Robots: Technology and the Threat of Mass Unemployment. Oneworld Publications.

Leontief, W. (1979). Is Technological Unemployment Inevitable? Challenge, 22(4), 48–50.

Susskind, D. (2020). A World Without Work: Technology, Automation, and How We Should Respond. Metropolitan Books.

They’re not human? AI-powered K-pop girl group Mave: eye global success. (2023, March 17). South China Morning Post.

Tupac Coachella hologram: Behind the technology – CBS News. (2012, November 9). 

Implausipod EP008: Audience Commodity

(Editor’s note: this is part 2 of the previous post on the audience commodity, which was drawn from a discussion thread on Mastodon. Much of that made it into the transcript of both the Youtube episode and the Podcast (both embedded below). This post will include the full transcript of the audio (and video), so there may be some duplication with the previous post, in the interest of completeness.

If this format of posting works out, then they should be better aligned in the future. Still working on the basics of the POSSE system. Better life through Additive Manufacturing though; iterate and improve. In the meantime, enjoy!)

The link to audio version, from Implausipod Episode 008 is here: https://www.implausipod.com/1935232/13185814-implausipod-e0008-audience-commodity

Introduction

Getting started with a brief rundown of an old article that details the rise of the Audience Commodity: Smythe (1977) “Communications: Blindspot of Western Marxism”, we use that to explain the recent events of the internet of the last month or so, including the Twitter-pocalypse, the Reddit Meltdown, the rise of ChatGPT, and some general media theory too.

Transcript

 Welcome to the Implausipod, a podcast about the intersection of art, technology, and popular culture. I’m your host, Dr. Implausible, and as we return to a regular recording schedule, I’m going to introduce you to the audience commodity, an old idea from economics tat goes a long way to explain some of the current events we’re seeing in the social media spaces.

What exactly is the audience commodity? Well, that’s a fantastic question. With the recent introduction of Threads a little bit ahead of schedule because of the Twitter apocalypse, I thought it’d be worth going into the background of it because it’s really got some relevance for the current events that are happening today. Because it was published in a relatively obscure Canadian academic journal back in the seventies, it hasn’t seen that much adoption by mainstream economics, but we’ll get into it. If that the kind of thing is your bag, then by all means, stick around.

In short, the audience commodity is all about how you and I and all of us really are turned into products by the cultural industries, whether it’s media or advertisements or websites.

I’ll put the citation on the screen (see below) for those that are interested. The author, Dallas W. Smythe was writing it as a bit of a challenge to traditional Marxist economic thinking at the time in the seventies. He said they were getting it wrong when it came to the cultural industries and the impact that they actually had, what they were doing.

Now Dallas Smythe was a former economist at the FCC, and he was blacklisted due to McCarthyism. I mean, Hoover had a file on him, for reasons, and he is drawing heavily on a book called Monopoly Capital that was put out in the sixties by Baran and Sweezy. We should probably do a whole episode on that at some point in time, but we’ll see how this goes.

Now for Smythe, the main argument speaks directly to Facebook or Meta’s business model. This goes the same for like Google and everything else too. And what is their business model? Websites? No. Apps? No. Advertising? Close, but still not the whole picture. Their business model is the production of the audience commodity. Advertisers buy audiences and those audiences. Time is their labor. And how did Smythe come to this conclusion? Well, he’s asking a simple economic question. Basically, what economic functions for capital do mass communication systems serve? And in this case, both Google and Facebook, Meta and Alphabet, whatever, both fit in the same “mass” of mass communication. They have a huge reach. So in order to figure out the economic function, you need to figure out what the commodity those companies produce actually is. And you might think you know what this is, it’s the whole: “if you’re not paying, you’re the product” line. And this is a part of that, only in a lot more detail.

A part of Smythe’s argument is that traditional economics was getting it wrong. If you asked “what does the media produce?”, you might answer something like content or information or messages or entertainment or shows or something like that. And that’s understandable. It’s what it looks like they do. So you’d be forgiven if you thought That’s how it worked, because that was the traditional orthodox idealist point of view.  It was held by everybody from Marx to Galbraith to Veblen to McCluhan. There’s a lot of academic writing on this idea and non-academic writing too. Everybody thought that’s what was going on. Smythe’s argument is that it misses the point. If the trad orthodox view of economics is getting it wrong, what do the media companies actually produce?

What is the commodity form of advertising sponsored communications under “late capitalism”, or “monopoly capitalism” as Baran and Sweezy would say? The answer is audiences and readerships, or just the audience. The audience commodity here, the labor power of the workers, is resold to the advertisers. This is normally in the parlance of the time called the Consciousness industry.

So remember this: TV stations and walled platforms on the internet are factories that produce audiences for advertisers. That’s what’s coming outta the end of the factory. So that’s a lot of the overarching stuff. Let’s get into some of the specifics. Smythe has eight main points, and we’re gonna cover these quickly and then move on to how it connects to the social media platforms: Threads, Facebook, Twitter, TikTok, AOL, Reddit, whatever.  

So Smythe’s questions are in order. Here we go. Question one, what did the advertisers buy with their money? Answer: the services of audiences in predictable numbers. It’s a service economy and we are the ones providing the service.  We’re also ones being a served up, which is, I guess, ironic. The commodity is the collective.

Question two, how do advertisers know what they’re getting what they’re paid for? Well, various rating agencies back in the day, like the Nielsen’s and whatever, and the analysis, which has largely moved in-house for streaming and internet platforms.  There’s a whole host of stuff that falls under the umbrella of market research.

Question three, what institutions produce the commodity that advertisers want? Well, we’ve hinted at this, but it’s principally and traditionally the owners of TV and radio stations and newspapers and magazine publishers, and we can add most web platforms to this nowadays ’cause they all work on the same model.  Of course there’s a host of secondary producers in industries that provide content for the principal market, obviously, but this is the main outlet.

Question four, and what is the nature of that content in economic terms under monopoly capitalism or late capitalism? Well, it’s an inducement. It’s the free lunch that attracts the audience to the saloon.  It gets ’em in the door and encourages them to stay. Now this speaks nothing to the cost, the quality, the format. In fact, the cheaper that this can be procured, the better. A free lunch isn’t free, obviously, but someone is providing the bread and the meat, and if the users bring their own, it’s the case of social media then even better.  And what are those users doing?

Question five, what is the nature of the servers performed for the advertiser by members of the purchased audiences? Well, the audience commodity is in economic terms, a non-durable producer’s good bought and used in the marketing of the advertiser’s product. The work that the audience is doing is to learn to buy and consume various brands of products and spend their income accordingly.

If they can develop brand loyalty while doing this, then that’s fantastic. Now, there’s a whole lot of work that goes into that learning. It’s like the reproduction of ideology and Ian terms and a whole lot more going on. But we will again, delve into this and either later in this episode or in future episodes as we keep this going on, but for Smythe, question five is all about the management of demand.

And question six is the big one: How does the management of that demand relate to the notion of free or leisure time under the labor theory of value? And for Smythe the answer is: the goal under monopoly capitalism is for all non-sleeping time to be work time for most of the population. I’ll let you do the math on the missing percentage yourself, but basically free time and leisure time are all turned into work time and in the 21st century, even work time can do double duty as branded elements take place within work.

Now Smythe goes on for about four pages in answering number six. It’s this key point and there’s a lot to unpack there. So again, we’re gonna circle back, but in the interest of brevity:

question seven, does the audience commodity perform an essential economic function? Well, the answer there is “it’s complicated”.  As noted above, Orthodox theories didn’t really go into this, and mass media and brands were before Marx’s time, so he didn’t have much to say about them either. Smythe turns to Marx’s Grundrisse to tease out an answer where production produces consumption, which is, I think page 91 and 92. There’s a whole paragraph on it.  So yes, there’s an essential economic function that’s taking place, but again, it isn’t what we think it is.

Question eight addresses some of that, what we touched on earlier, which is why have the traditional Marxist economists been indifferent to the role of advertising? They were focused on content instead.  Again, this is in the seventies, and it was obviously shiny things. The content was front and center, so people thought that that was what was going on. Remember, this is 1977, a full decade before authors Edward Herman and Noam Chomsky were publishing “Manufacturing Consent”, even though this was contemporaneous with some of Edward Herman’s earlier writings.

Now Smythe had actually published two versions of this paper. The peer reviewed article from 1977 that we’ve been using, and again, it came up as a chapter in 1981’s Dependency Road. These are again, foundational, critical for understanding what’s going on, but what does it mean for right now? Now as I’m recording this, on the evening of July 6th, 2023, Facebook has just launched Threads their Twitter competitor within the last 24 hours.

Earlier this week when I was writing it, I thought the main argument would be the Reddit implosion and Twitter’s issues, which were leading to a mass exodus of users looking for an alternative and heading towards the Fediverse, including Mastodon, which is an ActivityPub protocol tool that’s very similar in some ways to early Twitter.

Earlier, back in June or a thousand years ago, it seems, there was a lot of discussion on the Fediverse because there was news that Facebook was using the ActivityPub protocol for their Threads tools. All of this has gone by in like, you know, Lightspeed, where weeks, sometimes decades happen, right.

Anyways, when I started drafting this in response to those particular events and the general bad idea of engaging with Facebook on anything, (we’ll get into what Triple E means, probably in a future episode too), the online universe was vastly different. The Reddit moderator strike wasn’t even a thing that had happened yet, and even though there was problems at Twitter, it didn’t seem to be the mass expulsion that happened on July 1st.

So let’s tie it back to our main characters. Both Meta and Alphabet, Facebook and Google are well entrenched as advertising companies at this point. There’s no surprises going on there, and it’s also, it’s reasonably well known what’s going on when the auction service is used, being detailed in this explainer from the markup (see below).  I’ll put the link up in the show notes here. I.

They also have a wonderful explainer article going into the breakdown of market segmentation that’s done by, in this case, Microsoft and their Xandr platform, but actually takes place behind the scenes by all of these major social media companies. And these major companies know exactly what they’re doing, or they get into troubles when they lose sight of exactly what their core business model is serving up an audience to their customers, the advertisers.

Often they get themselves distracted by thinking themselves of content providers, and really that’s not the case. The most famous example of this would be like AOL. When they bought Time Warner and moved into providing content on a more regular basis, they kind of lost track of what they’re doing. Their subsequent failure and being overtaken by like everything else on the internet really speaks to them losing sight of that fact and investing in areas where they shouldn’t have. If AOL had focused on either infrastructure or their core business model, the audience, they would’ve weathered the dot-com bust significantly better than all the other companies out there.

But they got distracted by the shininess of Hollywood and thought that they were in the content business. So too, for Reddit and Twitter is some of the problems that they’ve had or because of moves that they’ve made to protect that content. But they can be forgiven slightly because there’s something that changed, something that Smythe didn’t foresee back in 1977.

And that’s AI. See AI flipped the equation around a little bit and turned all that user generated content stuff provided by the labor of the audience for free into something useful data for their large language models. You can understand why Elon Musk and Steve Huffman are a little bit miffed. Imagine you had a lumber mill and someone came in and took a look around and said, “Hey, you’re doing anything with all that sawdust?” and he said “No, take it”. And then they took that useless byproduct and added a little bit of glue to it, and all of a sudden turned it into, I don’t know, designer Swedish furniture and made a mint. You’d be like, what’s going on here? And try and stop them from taking the sawdust and figure out how to use it yourself, because all of a sudden, that stuff’s gold.

Jerry Gold. Because they didn’t know it or didn’t understand the process, both read it and Twitter in the process of lighting a fire in their factory and burning it to the ground. And meanwhile, the users, the audience commodity that was driving their business are all exiting stage left. And that pretty much gets us up to now.

Now we haven’t even gone into some of the other events like TikTok and the proposed ban that seems to be continually ongoing or some of the other social media networks or television, broadcast tv, what’s happening over there. And we also haven’t really gone into Threads and their use of the ActivityPub protocol that we kind of hinted at it.

But we need to get into something else related to that. And that’s a philosophy called Triple E or Embrace Extend Extinguish, but I think that’s gonna be a whole other video. Things are moving pretty fast and I’m just one guy. So for now, we’ll just wrap this up and try and catch the next one. I’m Dr. Implausible. The audio will be available over on the Implausible Pod and the text of the show should be available on the blog or in the comments sometime soon. The whole show is produced under the Creative Comments Attribution Sharealike 4.0 International Public License. We’ll try and make this one look prettier as I figure out how this whole video thing works.

But in the meantime, the world’s moving pretty fast, so we’ll see what it looks like in a week or so. I’m Dr. Implausible. Have fun.

Other links and references:

Baran, P. A., & Sweezy, P. M. (1966). Monopoly Capital. Monthly Review Press.

Smythe, D. W. (1981). Dependency Road: Communications, Capitalism, Consciousness, and Canada (Revised ed. edition). Praeger.

Eastwood, J., Hongsdusit, G., & Keegan, J. (2023, June 23). How Your Attention Is Auctioned Off to Advertisers – The Markup. https://themarkup.org/privacy/2023/06/23/how-your-attention-is-auctioned-off-to-advertisers

Keegan, J., & Eastwood, J. (2023, June 8). From “Heavy Purchasers” of Pregnancy Tests to the Depression-Prone: We Found 650,000 Ways Advertisers Label You – The Markup. https://themarkup.org/privacy/2023/06/08/from-heavy-purchasers-of-pregnancy-tests-to-the-depression-prone-we-found-650000-ways-advertisers-label-you