ai – The Implausi.blog

The Ditch-Digger’s Dilemma

“Hey bud, I hear you’re a pretty good at digging ditches, do you want some work?”

“Sure thing. Whadaya need?”

“Just need you to dig this ditch the length of my property here alongside this rode.”

“Sure thing, lemme go grab my excavator, and we can get it done this afternoon.”

“Whoa, no, can’t have you using that excavator.”

“Why? Too loud? Bad hydraulics? What’s up?”

“Naw you gotta use this.” Holds up a spoon.

“Excuse me? That doesn’t make any sense. Lemme grab the excavator and we can get the job done in an a couple hours. A spoon will take forever.”

Yeah, well ya see, ya gotta use the spoon. It doesn’t count otherwise.

What? It doesn’t count?

Doesn’t count. Use the spoon, or don’t get paid.

This is the Ditch Digger’s Dilemma.

In an era of high technology, of power tools and machinery, of extensions of man, of solutions to the problems that can amplify our capabilities a thousand-fold, the labourer laments being forced to use the low power, backbreaking way in order for the work to count, for it be accepted.

And the reason why is frustrating, it’s maddening.

The reason? Aesthetics and ideology.

Which are choices, or course, but it means we can also make different choices. We’ll recognize that it can be difficult to step outside an ideological frame we’re in and see what other options there are, but they do exist.

When the Studio Ghibli transformer on huggingface.co was intially released, it didn’t garner that much attention, but when OpenAI released a similar tool in March of 2025, the profile of the platform suddenly had everyone talking about it. Most notably, was the creator of Studio Ghibli, Hayao Miyazaki, who called it “an insult to life itself“. The transformer (a type of AI deep learning model) allows the creation of sequences of text and elements of image quickly, in ways that are recognizable to a human audience, and in so doing eliminate a lot of the work in the creation of those images.

And the Studio Ghibli model of animation is very labor intensive – hand-drawn frames labored over for days, weeks, months. A famous 4 second clip for the 2013 film The Wind Rises took a single animator over 15 months to create.

Assembling all the scenes into a full film requires a large number of employees working for years to bring it to fruition. It can be laborious, exacting and backbreaking. But animation isn’t the first industry where we’ve seen the impact of this type of automation. Take a look at engineering in the 20th century.

On the left we see engineers and draftsmen prior to the introduction of CAD (Computer Aided Design) in the 1960s. On the right, traditional animation, albeit from a Banksy-created opening montage to the Simpsons (“MoneyBART”, S21E03, 2010). While the introduction of CAD and other digital tools has radically transformed what modern engineering shops look like, engineering is still a viable career path. The total number of engineers employed worldwide has only grown. They can use the digital tools to engage in more projects, more quickly, across a broader spectrum of fields than existed previously.

This is the crossroads art is standing at with the AI tools as well. Rather than have one man labour for 15 months on a single 4 seconds of film for someone else in someone else’s style, these creators can create, develop, and release their own stories, their own art, to a broader part of the population than before.

If art only counts if its creator suffered, then that is what you’re consuming – it’s part of the aesthetic. Or rather, the suffering outweighs the other aesthetic concerns. Aesthetic elements are secondary. And in an era of Late Capitalism, you’re condemning someone to suffer for money, in order to live.

Rejection of the AI tools by traditional and/or trained artists leads to some sub-par works being put up. Pipeline tools choose the first available option, or are only superficially curated, and then posted automatically using workflow automation tools like Zapier, make.com and n8n. It’s given us a lot of “slop”.

This slop is what is getting used to pejoratively describe most AI art on the web. Much of the use of the term traces back to an article from The Atlantic in August 2024, by Charlie Warzel, but I’ve managed to find instances of the use of the term #aislop on Mastodon going back to as early as October of 2023 (possibly) and January 2024 (definitely). The use of the term may have originated in other public spheres and social media prior to that however.

The slop is the easily generated content that’s now flooding the web as more people now find creative tools accessible to them, even if they lack the skill or training to truly make them shine. Which is a shame. With a little bit of work, some knowledge and training of art theory, you get to something more usable. You can move away from the realm of “slop”, and into something more expressive.

This ties into the shift within the AI realm from prompt engineering to context engineering (and glimpsed faintly on the horizon: “Worldbuilding” (but we’re going to talk about that in an episode real soon). Context engineering is “the practice of designing systems that decide what information an AI model sees before it generates a response” (Datacamp, 2025).

What is context engineering for a movie but the script? Or is there more to it? Hmmm. Perhaps…

The tension with the AI tools is that at their core they are ultimately part of FALC – Fully Automated Luxury Communism (Bastani, 2020). The AI tools are a communistic technology – everything goes in (ref Soylent Culture), and ideally everyone can pull from them, but it is being pulled under tension by warring capitalists who seek to use it for their own ends. There are those in the tech sphere – the techno-capitalists (or cloudalists, vectoralists, “tech bros”; whatever philosophers are naming this group) building it and trying to capitalize on it in the usual ways: enforcing arbitrary tiers for uses, building hierarchy, installing friction, commodifying the users to sell to advertisers. You know, the usual. And they’re opposed on the other side by the rentiers seeking to extend their monopoly control on the IP, the intellectual property. They’re also capitalizing on the resource in the usual ways: gate-keeping access and distribution, locking in restrictive terms, installing friction via formats and region locking, commodifying the users to sell to advertisers. You know, the usual.

And like in any war, try not to get caught in the middle.

There is propaganda flying back and forth on both sides, of course, that you’re probably exposed to daily. This propaganda shapes and drives the discourse, leading to pilling and pipelines on both sides of the argument. With respect to AI, we’re starting to see a new pill arise, one that’s coloured Mauve.

Each of these groups of capitalists are facing challenges when dealing with the AI, and this much of this is inherent in the technology itself. For the techno-capitalists, trying to monetize it isn’t quite working – they’re losing a lot of money, and the traditional methods listed above aren’t recouping the investment. For the rentiers of the cultural industries, the paradox is that the AI tools do make the production of digital products easier, so they want to use them (at least at the C-level), but they’re facing pushback and resistance from the workers in those industries who view the tools (correctly) as a threat to their current employment.

So what’s a poor ditch digger to do? Pick sides in the ongoing War of Art? That’s one option, though the prospects if either side wins aren’t that great to be honest. Another would be STMOP. Rather, Seize the Means of Production. If the AI Tools are the new means of (digital) production, you need to grab that mop. And Sweep.

The Spirit of the AI-dio

(This was originally published as Implausipod Episode 49 on July 7th, 2025.)

https://www.implausipod.com/1935232/episodes/17441034-e0049-spirit-of-the-ai-dio

A look into the rise of ghost artists on Spotify, both AI generated and not, and what the history of Performer’s Rights Organizations mean for art and creativity in the 21st century, and how that may make us question the very nature of creativity itself.

Let me ask you a question. What do you do if you’re a musician working the mean streets of New York City trying to get paid for your work? You see, you’ve made some compositions, but thanks to some hot new tech, anybody can copy it and hear the songs, the music that you wrote, and you don’t get paid a single penny and New York City isn’t cheap.

It’s rough for a musician to make it, but this new tech, and you’ll admit it is a marvelous invention. Makes it hard for you to make a living. But the tech does have its limitations. It’s easier to copy and share your tunes for sure, but they still need to be copied by someone transferred to media. That limitation, that drawback gives you a crack or maybe just maybe you can get paid for your music.

This is the situation Victor Herbert found himself in a little over 110 years ago, and we’re going to look at exactly what the ramifications of his solution was. 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 this episode has some links, not just to stuff that we’ve been discussing here, but to some recent events in the news, and it’s gonna take some twists and turns. You see the solution that Victor Herbert and some of the other composers in and around New York City came up with in the early nineteen hundreds to help solve their problems has a lot to say about the current state of media in 2025.

You see in the development of a new technology, a lot rides on the physical limitations of the media. Often that could come down to logistical, practical concerns, the ease of duplicating something or transporting it. What Victor Herbert was dealing with was the rise of rolls of music for player pianos, the hot new tech at the time, tech that could be copied and shared and meant that he was losing opportunities to get paid for playing it.

So when Victor and a few of his fellow composers on Tin Pan Alley got together to perform the first. PRO or performance rights organization, one that would negotiate collectively on behalf of its member artists is ASCAP the American Society of Composers, authors and publishers. What they ended up doing, whether intentionally or not, for both ASCAP and the other PROs that follow, was providing a means for listeners to address some of the ethical concerns that they may have had when it comes to the content that they were consuming.

Hmm. It sounds a little weird when it’s phrased that way, talking about listening to music in 1914, in 21st century terms, but that’s basically what was going on, and that’s why the story of how the PROs came about is relevant to us today too. In one of our recent podcasts back in episode 42, where we talked about the incipient diaspora of the potential end of TikTok, we discussed how making informed choices and ethical consumption matters when it comes to media.

At the beginning of our episodes, we sometimes mention that we’re not on Spotify, and that this is an intentional act. I’m not a fan and I don’t like their business model, so I’m not using them. In late 2024 and 2025, some news came out about how Spotify was using AI generated content, algorithmically developed for easy listening and the reasons why we’re not on Spotify became crystal clear.

Now finding there’s a market for endlessly looping smooth jazz isn’t that surprising. It’s a concept that became so ubiquitous that a word was coined for it. Muzak. Invented in the 1920s by American George Owen Squier, Muzak was a non-radio form of music delivery that used the electrical wires to deliver songs directly to paid subscribers over the air. Systems like radio were inconsistent and spotty at the time, so there were takers for this new system. Think of it as an early version of broadband over electrical that you could set up in your own home today. Once radio started to catch on with the home market, Muzak shifted to business customers and as the company changed hands and ownership, it was used to regulate the mood in the environment where it was delivered.

A fast pace equals faster workers, or so the Taylorist line of reasoning went. Muzak was peak in the 1950s and sixties, but gradually became to be associated with bland corporate music, as competitors licensing more popular music came on board providing similar services. It would take a few years still for the popular music to also become bland and corporate.

But I digress. By the time the competitors started appearing, Muzak had become a genericized trademark like Jello, and it doesn’t really make a difference what version of elevator music you end up hearing, just that you’re hearing it. Which is where Spotify comes back into the story.

I said the endlessly looping background music isn’t that big of a surprise. How they are generating it as the use of AI for the delivery of muzak represents a sizable shift. And so in this episode of the ImplausiPod, we’re looking at the spirit in the machine, or in this case the spirit of the AI-dio. And here’s where we’d queue up that Rush song, if I had a budget for music licensing, or even for the muzak version. I’ll trust that you can hum along.

Now one sure thing about studying work in the AI space is that it moves incredibly quickly. It is acceleration made manifest, moving at a ridiculously quick speed. This velocity can be sensed, almost felt giving your eyes to the feeling of an ease many have when dealing with it. That and the killer robots, which we discussed earlier.

Of course, I say this as I started writing this episode back in December of 2024, based on a few articles that I had read, and a then forthcoming book, which came out back in January. Since then, the conditions being described progressed substantially in new stories were continually being added to the topic.

It turns out I have a bit of a halting problem when it comes to researching these episodes. Some of the things that we were planning on talking about have come to pass and we’ll. Still get to them, even though this episode will feel slightly less prescient now than it would’ve back in December. But cest La vie, it’s also a reminder that these things will always be like trying to hit a moving squirming target.

One of the ways to deal with the limit of this snowball sample that we’re working with is through a concept known as saturation. When new queries are not drawing in noticeably new or different information, you can stop the work and get to it. So now that we’ve drifted enough from the original topic, let’s do exactly that.

In December of 2024, the blogger Ted Gioia published a piece about The Ugly Truth of Spotify on his Honest Broker blog, and that he walked through the observations he was making about jazz playlists filled with artists he hadn’t heard before. They were also musically identical tracks published under different names. It would keep showing up.

It’s not a big deal if it’s in the background of an office or a retail outlet like this often was when no one is looking too hard at the playlist. This is something that Spotify called PFC or Perfect Fit Content, which had a royalty rating that was favorable to Spotify. This work by Gioia coincided and resonated with the work that was being done by Liz Pelly, and he mentions her in his blog post, in her book on Spotify titled Mood Machine.

She was talking about the rise of ghost artists, something she had been tracking since 2017. This is a rumor where Spotify was quote “filling its most popular playlists with stock music attributed to pseudonymous musicians” end quote, much like the Muzak corporation of 80 years earlier. The thought was that Spotify might be making the tracks in-house, all in an effort to lower royalties in a market where streams were already fractions of a cent. And perhaps this is the moment where a little background on Spotify is an order in 2025. It is a ubiquitous brand name for streaming music, but it had to start somewhere.

Spotify is a Swedish online services company specializing in the delivery of streaming audio. This includes music as well as podcasts and audio books. Founded in 2006, it experienced rapid growth starting in 2011, and by 2015 had become the defacto streaming app on most platforms. With this growth, Spotify is now in position of being one of the key drivers of the music industry, setting rates in the business model that others must compete with.

And make no mistake, there are competitors. Tidal, the Swedish streaming service acquired by Super Bowl impresario, Jay-Z in 2015, and subsequently sold to ex-Twitter honcho Jack Dorsey currently has market share, and the now venerable iTunes from Apple still accounts from about 12.6% of the market share as well with Amazon and Google’s own YouTube music falling at 11.1 and 9.7% respectively.

So Spotify isn’t alone, but the scope of their business worldwide is staggering. They announced that the payouts they made to the music industry was in the neighborhood of $10 billion in 2024 alone, and that year was also the first year that it was profitable, providing those payouts from revenue of $15.7 billion.

But not all is rosy in Spotify land. Aside from the outsized influence they wield on the music industry, which would be bad enough in and of itself, Spotify has been the subject of controversy for almost its entire existence. Most prominently is the pay rate that they give out for artists, which can be about 0.0029 cents per stream. For your mega stars with millions or billions of streams, your Taylor Swifts and the like, this can still amount to a decent return, but it falls off rapidly. One would need about 1.7 billion streams if my trusty calculator is working correctly to earn the median income in the United States if one was being paid at that lowest rate. Though the rate does go up to an average of what Spotify states is about 0.70 cents per stream according to their press releases.

So over 10,000 artists make a hundred thousand dollars or more using their streaming services, but. Of course many artists earn much less than that. Spotify operates on the classic long tail model where a minority of artists make an outsize amount of the revenue, and most of the rest gets a tiny fraction of the sales. This business model can be seen in many cultural industries like the movies, book, sales, traditional music, and even things like OnlyFans. One or two big hits ends up funding the label or a platform, and the others break even if they’re lucky or more likely are a loss. This is ultimately a speculative enterprise, at least how it is constructive in the capitalist framework.

And this speculation preys on the artists as well, where dreams of quote, making it big” provide a constant stream of new entrants to the industry. This never-ending flood of new artists and content has been why the CEO of Spotify, Daniel Eck has said on record on Twitter in 2024 that quote “the cost of creating content was close to zero”.

Or sometimes less than zero, as much of the expenses of music production are born by the artists, and even after all that effort, they may not recoup anything if they list on Spotify. In November 2023, Spotify announced that they would no longer pay artists for less than a thousand streams, effectively cutting off many small artists from earning any income whatsoever from the platform.

And the list of Spotify’s misdeeds grows from there. While cutting off small artists from revenue, they turn around and take those funds to finance high profile artists like Joe Rogan and others. And recently Spotify CEO Daniel Eck made the headlines for a billion-dollar investment in drone warfare company Helsing, of vampire hunting fame. A German defense contractor, which uses AI for the control systems in its aerial and underwater swarm drone technologies.

They also create a virtual environment, which provides the drones with spatial awareness, and we’ll look into that in a future episode. Their technologies are currently being actively used in the Russo-Ukrainian War. Ek’s investment has caused an uproar among some Spotify users with cancellations being directly attributed to that connection and investment.

And of course, along with all that, there’s the aforementioned PFC. Depending on the extent of it, Spotify may be one of the few companies turning a profit on AI-fueled content. There’s no reliable measure on the extent of the issue, though it has been going on for years, and finally the amount of AI generated titles reached the point where it was noticeable to the keen observer, if not perhaps to the casual listening audience.

All of these reasons and a few more besides are why you can’t find the Implausipod on Spotify. Like we mentioned earlier, it’s an intentional act. When podcast creators say that they’re available everywhere or on all platforms, and they’re saying that the issues with the platform don’t matter to them. There’s a degree of what I like to call platform illiteracy going on, but we’ll save that topic for a later date. The end result of these developments with ai, music generation and algorithmic delivery is that we are now living in a world with endlessly available, unique instrumental music. So much of it is being created that you could listen for a lifetime and never hear the same song twice.

Now, this is also technically true under the current model with 120,000 new tracks hitting Spotify every day according to a 2023 article by Maurice Schon. But again, our focus here is on the AI generated music.

Hold that note in your head, that little snippet of the interstitial music we use for the show. We’ll get back to that in a hot minute. We need to address the question at hand. What’s the problem with AI generated music anyways, about six months ago, there’s a trend of AI style covers playing Metallica and the style of a fifties doo-wop band or whatever. And while that was an amusing exercise, the novelty soon wore off. There’s only so much of that kind of act that you can take as Richard Cheese and Me First and Gimme Gimmes can well attest. Clearly that kind of style cover or genre switch can be done without AI, but all the transformers are doing is accelerating the process, filling some niches that otherwise might never get explored.

If AI generated music is filling a need there, and otherwise it’s mostly supplanting the niche previously occupied by Muzak for inoffensive background noise, what’s the issue? Perhaps the issue is quote-unquote “authenticity”. I say that because literally, as I was in the middle of recording this, the news story came out about a hot new band on Spotify called Velvet Sundown.

They play a radio friendly mix of seventies rock and indie pop, and they had amassed over a million monthly listeners when people began looking to see if there’s more info, because it’s not like music fans are the ones to become obsessive about their favorite band. And what those music fans noticed was something that had a lot in common with the music noticed by Liz Pelly and Ted Gioia earlier.

Odd connections and inconsistencies and a lack of the data or digital footprint we’d expect to see of a band if they had been around for a while. It now looks like the band is a complete fabrication with AI generated art and music. A man operating under the pseudonym, Andrew Prelon, claimed responsibility saying that the music was generated with Suno AI and that the whole project was a quote unquote art hoax.

But even that might be in dispute as there’s more than one claimant that says they’re acting on behalf of the band. It may have been that there was another AI artist out there, and Prelon just decided to step in and act as the band’s publicist, and that little bit of the hoax was completely tangential to whatever was actually going on with Velvet Sundown.

What Prelon and the Velvet Sundown affair highlight is the question of whether a producer of an AI art is actually the artist. They’re the driving force, commissioning the various elements of the work. If so, do they occupy a similar role to managers of boy bands like Lou Pearlman and the Backstreet Boys and NSYNC, or Malcolm McClaren and the Sex Pistols?

At some level, these bands are still quote unquote authentic, even though they’re clearly manufactured in the same way that a chipboard table from IKEA is still a table in form and function, even if it’s not handcrafted from oak. This authenticity of art is one that has been under scrutiny since the dawn of the 20th century.

Walter Benjamin discussed how art loses its aura in an age of mechanical reproduction, where the aura is the very thing that cannot be reproduced. But maybe this whole Velvet Sundown thing highlights the way. If the music is replaceable, then maybe the art lies elsewhere.

When attempting to answer all these questions, much of it comes down to the position one takes on AI ethics. This is often driven by our feelings. The way AI ethics is framed in the media often leads one to believe that the only ethical stance is to oppose its use on all levels, and we see that cropping up more and more.

But this often feels like taking sides in a battle between billionaires, just as the image we have in our mind of the small independent farmers, often exploited by agribusiness concerns, The mental image of the struggling artist is often leveraged by billionaires and IP rights holders. If we recall that Robert Downey Jr. has a net worth of around $300 million. We can perhaps understand his stance when it comes to AI generated arc, but for others, the position is less clear. And as we’re talking about songs here, perhaps we could focus on the music industry. The history of the music industry is rife with abuse and exploitation where original artists have been tricked, coerced, or threatened into signing away the rights to the music that has gone on to make others millions.

By way of example about what copyright can mean for artists at the time of recording, the Verdict is being laid out in the trial of Sean Diddy Combs an artist who still pays Gordon Sumner AKA Sting, $2,000 a day every day, 365 days a year for the unauthorized use of a sample on “I’ll Be Missing You” in 1997.

At the time of his arrest, Combs had a net worth of $400 million. Sumner has a net worth of over $500 million and Combs’ former collaborator, Jimmy Page has an estimated net worth of $180 million. These artists have not done poorly, and granted these artists are household names with enduring legacies, but much like the farming analogy above, when looking at it from a distance, appears we are caught up in a proxy war between billionaires.

We may not want to be simp for either side in this fight. What confounds that ethical calculation when it comes to modern music is that much of the industry operates as a form of rentier capitalism. This is where property is held without new investment and used to extract rents. The intellectual property, the stuff under control of the rentier in this case is used for value extraction and they’re not really adding anything new to the system.

The near endless ownership of IP can be seen as the enclosure of the digital media commons, where the AI companies turn everything into soylent culture fighting against the enclosure of the analog media commons by the old guard media companies operating under the established paradigm. So what’s the solution to this entrenched warfare between media, titans, old and new?

We’re not trying to rehabilitate Spotify. Rather, we’re here to adapt the idea of an artist’s rights organization for use in an age of generative AI. If we accept that there are valid uses for AI, and there are, we talked about this in episode 38, then there needs to be a path forward to dealing with this.

And as we hinted at in the beginning of the show, our friend Victor Herbert and ASCAP show us one of the ways that this might be accomplished, and there’s been some very recent moves forward on this front. The Creative Commons Organization has recently announced CC Signals, a licensing framework that will quote “allow data set holders to signal their preferences for how their content can be reused by machines based on a set of limited but meaningful options”. In addition, recent court cases have found that some of the data gathering done by the AI companies falls under the provisions of fair use. Together, these don’t cover every instance – it’s still early days – but it does show that there is a path forward out of this to something that’s equitable to the parties involved.

Of course, here’s the big twist, which probably wasn’t much of a shock if you parsed the punny episode title. There’s more than just the ethical question behind AI generated music. One that the AI-PROs may help ameliorate, but cuts us all closer to the core. We are seeing a great deal of Echange, of technological replacement, come to the music industry.

For musicians finding themselves replaced or that an algorithmically generated smooth jazz music act is good enough in a lot of instances, does this call into question the very nature of art and creativity itself? This appeal to creativity, the ad creo or ad fascia, depending on how my Latin is working, is something that has been called for increasingly during the debates around AI and the cultural industries dotting YouTube thumbnails and memes on blue sky and everywhere in between.

The ad creo is the claim that using an AI is anathema to the creative act, as if using a tool to generate an image somehow negates the spark and inspiration that led to the creation of the piece. This leads us to the Ditch Digger Fallacy. The counter to the ad creo of course is that what do you think creativity actually is?

Let me illustrate that question by an example. It has long been observed in nature that crows are particularly clever, that given sufficient motivation, usually a treat, they can use sticks or bits of wire to fish out a treat from within a piper or other closed environment where one wouldn’t expect the crow to be able to navigate at all.

This anthropocentric conceit of them having a limited bird brain refuses to let us believe what we were witnessing before our eyes. But even more complex behavior has been observed in crows. They appear to hold grudges. Yes, the birds got beef. And these grudges can both persist for years and be shared amongst the group.

Observations of crows engaged in group attacks gaining up on smaller animals or humans who cross them has gotten so bad that trackers have been set up in cities like Vancouver and Seattle to show the incidences and locations where the attacks have been fiercest. And the research is growing. The field of ethology is the study of the behavior and communication of non-human animals and has been producing fascinating findings that challenge our anthropocentric view of the world.

Much like the one we just mentioned, we are constantly finding creativity, communication, and intellect within the natural world. The more we observe it, and just like in other natural sciences, as the tools of observation improve, the more we can witness within nature. What we are seeing – what the ethologists are guiding us to – is that the more we can observe nature without disturbing it in some Heisenberg manner, the more we can observe the intelligence of the other species of life with which we share the planet.

And it leads us to ask, are we going to continually redefine intelligence as the ethologists uncover more and more ways that animals are smarter than we think they are? Is intelligence something anthropocentric, something we can only think of in human terms? If intelligence abounds around us in nature, in ways that were previously reserved for us in terms of problem solving, communication, emotion, grief, and so on, perhaps we’re not as special as we like to think, and this potential fills us with existential dread.

When it comes to creativity, perhaps our role is much more limited. Perhaps our role is that of the watchmaker, not the machinist building the gears. Recall the concept of the Allographic art that we introduced back in episode 38. This is the creation of art by other hands. The artist as architect or programmer, as choreographer or composer, the kinds of artists who Victor Herbert brought together when founding the first performers rights organization.

Here art is a question of control, and the skills in shaping art differ depending on the media. Within computing, one of the enduring tropes is that the users are like unto wizards and treating with demons in order to coax magic from the thinking sand. Here too, they must deal with the spirit of the AI-dio, the ghost in the machine.

Once again, thank you for joining us on the ImplausiPod. I’m your host, Dr. Implausible. You can reach me at drimplausible@implausipod.com, and you can also find the show archives and transcripts of all our previous shows at implausipod.com as well. I’m responsible for all elements of the show, including research, writing, mixing, mastering, and music, and the show is licensed under a Creative Commons 4.0 ShareAlike license.

You may have also noted that there was no advertising during the program and there’s no cost associated with the show, but it does grow from word of mouth of the community. So if you enjoy the show, please share it with a friend or two and pass it along. There’s also a buy me a coffee link on each show at implausipod.com, which will go to any hosting costs associated with the show.

AI Refractions

(this was originally published as Implausipod Episode 38 on October 5th, 2024)

https://www.implausipod.com/1935232/episodes/15804659-e0038-ai-refractions

Looking back in the year since the publication of our AI Reflections episode, we take a look at the state of the AI discourse at large, where recent controversies including those surrounding NaNoWriMo and whether AI counts as art, or can assist with science, bring the challenges of studying the new medium to the forefront.

In 2024, AI is still all the rage, but some are starting to question what it’s good for. There’s even a few that will claim that there’s no good use for AI whatsoever, though this denialist argument takes it a little bit too far. We took a look at some of the positive uses of AI a little over a year ago in an episode titled AI Reflections.

But it’s time to check out the current state of the art, take another look into the mirror and see if it’s cracked. So welcome to AI Refractions, this episode of ImplausiPod.

Welcome to The ImplausiPod, an academic podcast about the intersection of art, technology, and popular culture. I’m your host, Dr. Implausible. And in this episode, we’ve got a lot to catch up on with respect to AI. So we’re going to look at some of the positive uses that have come up and how AI relates to creativity and statements from NaNoWriMo caused a bit of controversy.

And how that leads into AI’s use in science. But it’s not all sunny over in AI land. We’ve looked at some of the concerns before with things like Echange, and we’ll look at some of the current critiques as well. And then look at the value proposition for AI, and how recent shakeups with open AI in September of 2024 might relate to that.

So we’ve got a lot to cover here on our near one year anniversary of that AI Reflections episode, so let’s get into it. We’ve mentioned AI a few other times since that episode aired in August of 2023. It came up in episode 28, our discussion on black boxes and the role of AI handhelds, as well as episode 31 when we looked at AI as a general purpose technology.

And it also came up a little bit in our discussion about the arts, things like Echanger and the Sphere, and how AI might be used to assist in higher fidelity productions. So it’s been an underlying theme about a lot of our episodes. And I think that’s just the nature of where we sit with relation to culture and technology.

When you spend your academic career studying the emergence of high technology and how it’s created and developed, when a new one comes on the scene, or at least becomes widely commercially available, you’re going to spend a lot of time talking about it. And we’ve been obviously talking about it for a while.

So if you’ve been with us for a while, first off, you’re Thank you, and this may be familiar to you, and if you just started listening recently, welcome, and feel free to check out those episodes that we mentioned earlier. I’ll put links to the specific ones in the text. And looking back at episode 12, we started by laying down a definition of technology.

We looked at how it functioned as an extension of man, to borrow from Marshall McLuhan, but the working definition of technology that I use, the one that I published in my PhD, is that “Technology is the material embodiment of an artifact and its associated systems, materials, and practices employed to achieve human ends.”

And this definition of technology covers everything from the sharp stick and sharp stick- related technologies like spears, pencils, and chopsticks, to our more advanced tech like satellites and AI and VR and robots and stuff. When you really think about it, it’s a very expansive definition, but that helps us in its utility in allowing us to recognize and identify things.

And by being able to cover everything from sharp sticks to satellites, from language to pharmaceuticals to games, it really covers the gamut of things that humans use technology for, and contributes to our view of technology as an emancipatory view. That technology is ultimately assistive and can aid us in issues that we’re struggling with.

We recognize that there’s other views and perspectives, but this is where we fall down on the spectrum. Returning back to episode 12, we showed how this emancipatory stance contributes to an empathetic view of technology, where we can step outside of our own frame of reference and think about how technology can be used by somebody who isn’t us.

Whether it’s a loved one, somebody close to us, or even a member of our community or collective, or you. More widely ranging, somebody that we’ll never come into contact with. How persons with different abilities and backgrounds will find different uses for the technology. Like the famous quote goes, “the street finds its own uses for things.”

Maybe we’ll return back to that in a sec. We finished off episode 12 looking at some of the positive uses of AI at that time that had been published just within a few weeks of us recording that episode. People were recounting how they were finding it as an aid or an enhancement to their creativity, and news stories were detailing how the predictive text abilities as well as generative AI facial animations could help stroke victims, as well as persons with ALS being able to converse at a regular tempo.

So by and large it could function as an assistive technology, and in recent weeks we have started trying to Catalogue all those stories. Back in July over on the blog we created the Positive AI Archive, a place where I could put those links to all the stories that I come across. Me being me, I forgot to update it since, but we’ll get those links up there and you should be able to follow along.

We’ll put the link to the archive in the show notes regardless. And, in the interest of positivity, that’s kinda where I wanted to start the show.

The street finds its own uses for things. It’s a great quote from Burning Chrome, a collection of short stories by William Gibson. It’s the one that held Johnny Mnemonic, which led to the film with Keanu Reeves, and then subsequently The Matrix and Cyberpunk 2077 and all those other derivative works. The street finds its own uses for things is a nuanced phrase and nuance can be required when we’re talking about things, especially online when everything gets reduced to a soundbite or a five second dance clip.

The street finds its own uses for things is a bit of a mantra and it’s one that I use when I’m studying the impacts of technology and what “the street finds its own uses for things” means is that the end users may put a given technology to tasks that its creators and developers never saw. Or even intended.

And what I’ve been preaching here, what I mentioned earlier, is the empathetic view of technology. And we look at who benefits from using that technology, and what we find with the AI tools is that there are benefits. The street is finding its own uses for AI. In August of 2024, a number of news reports talked about Casey Harrell, a 46 year old father suffering from ALS, amyotrophic lateral sclerosis, who was able to communicate with his daughter using a combination of brain implants and AI assisted text and speech generation.

Some of the work on these assistive technologies was done with grant money, and there’s more information about the details behind that work, and I’ll link to that article here. There’s multiple technologies that go into this, and we’re finding that with the AI tools, there’s very real benefits for persons with disabilities and their families.

Another thing we can do when we’re evaluating a technology is see where it’s actually used, where the street is located. And when it comes to assistive AI tools like ChatGPT, The street might not be where you think it is. In a recent survey published by Boston Consulting Group in August of 2024, they showed where the usage of ChatGPT was the highest.

It’s hard to visually describe a chart, obviously, but at the top of the scale, we saw countries like India, Morocco, Argentina, Brazil, Indonesia. English speaking countries like the US, Australia, and the UK were much further down on the chart. The country where ChatGPT is finding its most adoption are countries where English is not the primary language.

They’re in the global south, countries with large populations that have also had to deal with centuries of exploitation. And that isn’t to say that the citizens of these countries don’t have concerns, they do, but they’re using it as an assistive technology. They’re using it for translation, to remove barriers and to help reduce friction, and to customize their own experience. And these are just a fraction of the stories that are out there.

So there are positive use cases for AI, which may seem to directly contradict various denialist arguments that are trying to gaslight you into believing that there is no good use for AI. This is obviously false.

If the positive view, the use on the street, is being found by persons with disabilities, it follows that the denialist view is ableist. If the positive view, that use on the street, is being found by persons of color, non English speakers, persons in the global south, then the denialist view will carry all those elements of oppression, racism, and colonialism with it.

If the use on the street is by Those who find their creativity unlocked by the new tools and they’re finally able to express themselves where previously they may have struggled with a medium or been gatekept from having an arts education or poetry or English or what have you, only to now find themselves told that this isn’t art or this doesn’t count despite all evidence to the contrary, then there’s massive elements of class and bias that go into that as well.

So let’s be clear. An empathetic view of technology recognizes that there are positive use cases for AI. These are being found on the street by persons with disabilities, persons of the global south, non english speakers, and persons across the class spectrum. To deny this is to deny objective reality.

It’s to deny all these groups their actual uses of the technology. Are there problems? Yes, absolutely. Are there bad actors that may use the technology for nefarious means? Of course, this happens on a regular basis, and we’ll put a pin in that and return to that in a few moments, but to deny that there are no good uses is to deny the experience of all these groups that are finding uses for it, and we’re starting to see that when this denialism is pointed out, it’s causing a great degree of controversy.

In a statement made early in September of 2024, NaNoWriMo, the non profit organization behind National Novel Writing Month, it was acceptable to use AI as an assistive technology when writers were working on their pieces for NaNoWriMo, because this supports their mission, which is to quote, “provide the structure, community, and encouragement to help people use their voices, achieve creative goals, and build new worlds, on and off the page.” End quote.

But what drew the opprobrium of the online community is that they noted that some of the objections to the use of AI tools are classist and ableist. And, as we noted, they weren’t wrong. For all the reasons we just explained and more. But, due to the online uproar, they’ve walked that back somewhat.

I’ll link to the updated statement in the show. The thing is, if you believe that using AI for something like NaNoWriMo is against the spirit of things, that’s your decision. They’ve clearly stated that they feel that assistive technologies can help for people pursuing their dreams. And if you have concerns that they’re going to take stuff that’s put into the official app and sell it off to an LLM or AI company, well, that’s a discussion you need to have with NaNoWriMo, the nonprofit.

You’re still not held off from doing something like NaNoWriMo using notepad or obsidian or however else you take your notes, but that’s your call. I for one was glad to see that NaNoWriMo called it out. One of the things that I found both in my personal life, as well as in my research, when I was working on the PhD and looking at Tikkun Olam Makers is that it can be incredibly difficult and expensive for persons with disabilities to find a tool that can meet their needs, if it exists at all. So if you’re wondering where I come down on this, I’m on the side of the persons in need. We’re on the side of the streets. You might say we’re streets ahead.

Of course, one of the uses that the street finds for things has always been art. Or at least work that eventually gets recognized as art. It took a long time for the world to recognize that the graffiti of a street artist might count, but in 2024, if one was to argue that Banksy wasn’t an artist, you’d get some funny looks.

There are several threads of debates surrounding AI art, generative art, including the role of creativity, the provenance of the materials, the ethics of using the tools, but the primary question is what counts? What counts as art and who decides that it counts? That’s the point that we’re really raising with that question, and obviously it ties back to what we were talking about last episode when it comes to Soylent Culture, and before that when we were talking about the recently deceased Frederick Jameson as well.

In his work Nostalgia for the Present from 1989, Jameson mentioned this with respect to television. He said, Quote, “At the time, however, it was high culture in the 1950s who was authorized, as it still is, to pass judgment on reality, to say what real life is and what is mere appearance. And it is by leaving out, by ignoring, by passing over in silence and with the repugnance one may feel for the dreary stereotypes of television series, that high art palpably issues its judgments.” end quote.

Now, High Art in Bunny Quotes isn’t issuing anything, obviously, Jameson’s reifying the term, but what Jameson is getting at is that there’s stakes for those involved about what does and does not count. And we talked about this last episode, where it took a long time for various forms of new media to finally be accepted as art on its own terms.

For some, it takes longer than others. I mean, Jameson was talking about television in the 1980s, for something that had already existed for decades at that point. And even then, it wasn’t until the 90s and 2000s, to the eras of Oz and The Sopranos and Breaking Bad and Mad Men and the quote unquote “golden age of television” that it really began to be recognized and accepted as art on its own terms.

Television was seen as disposable ephemera for decades upon decades. There’s a lot of work that goes on on behalf of high art by those invested in it to valorize it and ensure that it maintains its position. This is why we see one of the critiques about A. I. art being that it lacks creativity, that it is simply theft.

As if the provenance of the materials that get used in the creation of art suddenly matter on whether it counts or not. It would be as if the conditions in the mines of Afghanistan for the lapis lazuli that was crushed to make the ultramarine used by Vermeer had a material impact on whether his painting counted as art. Or if the gold and jewels that went into the creation of the Fabergé eggs and were subsequently gifted to the Russian royal family mattered as to whether those count. It’s a nonsense argument. It makes no sense. And it’s completely orthogonal to the question of whether these works count as art.

And similarly, where people say that good artists borrow, great artists steal, well, we’ll concede that Picasso might have known a thing or two about art, but Where exactly are they stealing it from? The artists aren’t exactly tippy toeing into the art gallery and yoinking it off the walls now, are they?

No, they’re stealing it from memory, from their experience of that thing, and the memory is the key. Here, I’ll share a quote. “Art consists in bringing the memory of things past to the surface. But the author is not a Paessiest. He is a link to history, to memory, which is linked to the common dream.” This is of course a quote by Saul Bellow, talking about his field, literature, and while I know nowadays not as many people are as familiar with his work, if you’re at a computer while you’re listening to this, it might be worth to just look him up.

Are we back? Awesome. Alright, so what the Nobel Prize Laureate and Pulitzer Prize winner Saul Bellow was getting at is that art is an act of memory, and we’ve been going in depth into memory in the last three episodes. And the artist can only work with what they have access to, what they’ve experienced during the course of their lifetime.

The more they’ve experienced, the more they can draw on and put into their art. And this is where the AI art tools come in as an assistive technology, because they would have access to much, much more than a human being can experience, right? Possibly anything that has been stored and put into the database and the creator accessing that tool will have access to everything, all the memory scanned and stored within it as well.

And so then the act of art becomes one of curation of deciding what to put forth. AI art is a digital art form, or at least everything that’s been produced to date. So how does that differ? Right? Well, let me give you an example. If I reach over to my paint shelf and grab an ultramarine paint, right, a cheap Daler Rowney acrylic ink, it’s right there with all the other colors that might be available to me on my paint shelf.

But, back in the day, if we were looking for a specific blue paint, an ultramarine, it would be made with lapis lazuli, like the stuff that Vermeer was looking for. It would be incredibly expensive, and so the artist would be limited in their selection to the paints that they had available to them, or be limited in the amount that they could actually paint within a given year.

And sometimes the cost would be exorbitant. For some paints, it still actually is, but a digital artist working on an iPad or a Wacom tablet or whatever would have access to a nigh unlimited range of colors. And so the only choice and selection for that artist is by deciding what’s right for the piece that they’re doing.

The digital artist is not working with a limited palette of, you know, a dozen paints or whatever they happen to have on hand. It’s a different kind of thing entirely. The digital artist has a much wider range of things to choose from, but it still requires skill. You know, conceptualization, composition, planning, visualization.

There’s still artistry involved. It’s no less art, but it’s a different kind of art. But one that already exists today and one that’s already existed for hundreds of years. And because of a banger that just got dropped in the last couple of weeks, it might be eligible for a Grammy next year. It’s an allographic art.

And if you’re going to try and tell me that Mozart isn’t an artist, I’m going to have a hard time believing you.

Allographic art is a type of art that was originally introduced by Nelson Goodman back in the 60s and 70s. Goodman is kind of like Gordon Freeman, except, you know, not a particle physicist. He was a mathematician and aesthetician, or sorry, philosopher interested in aesthetics, not esthetician as we normally call them now, which has a bit of a different meaning and is a reminder that I probably need to book a pedicure.

Nelson was interested in the question of what’s the difference between a painting and a symphony, and it rests on the idea of like uniqueness versus forgery. A painting, especially an oil painting, can be forged, but it relies on the strokes and the process and the materials that went into it, so you need to basically replicate the entire thing while doing it in order to make an accurate forgery, much like Pierre Menard trying to reproduce Cervantes ‘Quixote’ in the Jorge Luis Borges short story.

Whereas a symphony, or any song really, that is performed based off of a score, a notational system, is simply going to be a reproduction of that thing. And this is basically what Walter Benjamin was getting at when he was talking about art in the age of mechanical reproduction, too, right? So, a work that’s based off of a notational system can still count as a work of art.

Like, no one’s going to argue that a symphony doesn’t count as art, or that Mozart wasn’t an artist. And we can extend that to other forms of art that use a notational system as well. Like, I don’t know, architecture. Frank Lloyd Wright didn’t personally build Falling Water or the Guggenheim, but he created the plans for it, right?

And those were enacted. He did. We can say that, yeah, there’s artistic value there. So these things, composition, architecture, et cetera, are allographic arts, as opposed to autographic arts, things like painting or sculpture, or in some instances, the performance of an allographic work. If I go to see an orchestra playing a symphony, a work based off of a score, I’m not saying that I’m not engaged with art.

And this brings us back to the AI Art question, because one of the arguments you often see against it is that it’s just, you know, typing in some prompts to a computer and then poof, getting some results back. At a very high level, this is an approximation of what’s going on, but it kind of misses some of the finer points, right?

When we look at notational systems, we could have a very, you know, simple set of notes that are there, or we could have a very complex one. We could be looking at the score for Chopsticks or Twinkle Twinkle Little Star, or a long lost piece by Mozart called Serenade in C Major that he wrote when he was a teenager and has finally come to light.

This is an allographic art, and the fact that it can be produced and played 250 years later kind of proves the point. But that difference between simplicity and complexity is part of the key. When we look at the prompts that are input into a computer, we rarely see something with the complexity of say a Mozart.

As we increase the complexity of what we’re putting into one of the generative AI tools, we increase the complexity of what we get back as well. And this is not to suggest that the current AI artists are operating at the level of Mozart either. Some of the earliest notational music we have is found on ancient cuneiform tablets called the Hurrian Hymns, dating back to about 1400 BCE, so it took us a little over 3000 years to get to the level of Mozart in the 1700s.

We can give the AI artists a little bit of time to practice. The generative AI art tools, which are very much in their infancy, appear to be allographic arts, and they’re following in their lineage from procedurally generated art has been around for a little while longer. And as an art form in its infancy, there’s still a lot of contested areas.

Whether it counts, the provenance of materials, ethics of where it’s used, all of those things are coming into question. But we’re not going to say that it’s not art, right? And as an art, as work conducted in a new medium, we have certain responsibilities for documenting its use, its procedures, how it’s created.

In the introduction to 2001’s The Language of New Media, Lev Manovich, in talking about the creation of a new media, digital media in this case, noted how there was a lost opportunity in the late 19th and early 20th century with the creation of cinema. Quote, “I wish that someone in 1895, 1897, or at least 1903 had realized the fundamental significance of the emergence of the new medium of cinema and produced a comprehensive record.

Interviews with audiences, systematic account of narrative strategies, scenography, and camera positions as they developed year by year. An analysis of the connections between the emerging language of cinema and different forms of popular entertainment that coexisted with it. Unfortunately, such records do not exist.

Instead, we are left with newspaper reports, diaries of cinema’s inventors, programs of film showings, and other bits and pieces. A set of random and unevenly distributed historical samples. Today, we are witnessing the emergence of a new medium, the meta medium of the digital computer. In contrast to a hundred years ago, when cinema was coming into being, We are fully aware of the significance of this new media revolution.

Yet I am afraid that future theorists and historians of computer media will be left with not much more than the equivalence of the newspaper reports and film programs from cinema’s first decades.” End quote.

Manovich goes on to note that a lot of the work that was being done on computers, especially in the 90s, was stuff prognosticating about its future uses, rather than documenting what was actually going on.

And this is the risk that the denialist framing of AI art puts us in. By not recognizing that something new is going on, that art is being created, and allographic art, we lose the opportunity to document it for the future. And

And as with art, so too with science. We’ve long noted that there’s an incredible amount of creativity that goes into scientific research, that the STEM fields, science, technology, engineering, and mathematics, require and benefit so much from the arts that they’d be better classified as STEAM, and a small side effect of that may mean that we see better funding for the arts at the university level.

But I digress. In the examples I gave earlier of medical research, of AI being used as an assistive technology, we were seeing some real groundbreaking developments of the boundaries being pushed, and we’re seeing that throughout the science fields. Part of this is because of what AI does well with things like pattern recognition, allowing weather forecasts, for example, to be predicted more quickly and accurately.

It’s also been able to provide more assistance with medical diagnostics and imaging as well. The massive growth in the number of AI related projects in recent years is often due to the fact that a number of these projects are just rebranded machine learning or deep learning. In a report released by the Royal Society in England in May of 2024 as part of their Disruptive Technology for Research project, they note how, quote, “AI is a broad term covering all efforts aiming to replicate and extend human capabilities for intelligence and reasoning in machines.”

End quote. They go on further to state that, quote, “Since the founding of the AI field at the 1956 Dartmouth Summer Research Project on Artificial Intelligence, Many different techniques have been invented and studied in pursuit of this goal. Many of these techniques have developed into their own sub fields within computer science, such as expert systems and symbolic reasoning.” end quote.

And they note how the rise of the big data paradigm has made machine learning and deep learning techniques a lot more affordable and accessible, and scalable too. And all of this has contributed to the amount of stuff that’s floating around out there that’s branded as AI. Despite this confusion in branding and nomenclature, AI is starting to contribute to basic science.

A New York Times article published July by Siobhan Roberts talked about how a couple AI models were able to compete at the level of a silver medalist at the recent International Mathematical Olympiad. And this is the first time that the AI model has medaled at that competition. So there may be a role for AI to assist even high level mathematicians to function as collaborators and, again, assistive technologies there.

And we can see this in science more broadly. In a paper submitted to arxiv. org in August of 2024, titled, The AI Scientist Towards Fully Automated Open Ended Scientific Discovery, authors Liu et al. use a frontier large language model to perform research independently. Quote, “We introduce the AI scientist, which generates novel research ideas, writes code, executes experiments, visualizes results, describes its findings by writing a scientific paper, And then runs the simulated review process for evaluation” end quote.

So, a lot of this is scripts and bots and hooking into other AI tools in order to simulate the entire scientific process. And I can’t speak to the veracity of the results that they’re producing in the fields that they’ve chosen. They state that their paper can, quote, “Produce papers that exceed the acceptance threshold at a top machine learning conference as judged by our automated reviewer,” end quote.

And that’s Fine, but it shows that the process of doing the science can be assisted in various realms as well. And in one of those areas of assistance, it’s in providing help for stuff outside the scope of knowledge of a given researcher. AI as an aid in creativity can help explore the design space and allow for the combination of new ideas outside of everything we know.

As science is increasingly interdisciplinary. We need to be able to bring in more material, more knowledge, and that can be done through collaboration, but here we have a tool that can assist us as well. As we talked about with Nessience and Excession a few episodes ago, we don’t know everything. There’s more than we can possibly know, so the AI tools help expand the field of what’s available to us.

We don’t necessarily know where new ideas are going to come from. And if you don’t believe me on this, let me reach out to another scientist who said some words on this back in 1980. Quote, “We do not know beforehand where fundamental insights will arise from about our mysterious and lovely solar system.

And the history of our study of the solar system shows clearly that accepted and conventional ideas are often wrong, and that fundamental insights can arise from the most unexpected sources.” End quote. That, of course, is Carl Sagan. From an October 1980 episode of Cosmos A Personal Journey, titled Heaven and Hell, where he talks about the Velkovsky Affair.

I haven’t spliced in the original audio because I’m not looking to grab a copyright strike, but it’s out there if you want to look for it. And what Sagan is describing there is basically the process by which a Kuhnian paradigm shift takes place. Sagan is speaking to the need to reach beyond ourselves, especially in the fields of science, and the AI assisted research tools can help us with that.

And not just in the conduction of the research, but also in the writing and dissemination of that. Not all scientists are strong or comfortable writers or speakers, and many of them come to English as a second, third, or even fourth language. And the role of AI tools as translation devices means we have more people able to communicate and share their ideas and participate in the pursuit of knowledge.

This is not to say that everything is rosy. Are there valid concerns when it comes to AI? Absolutely. Yes. We talked about a few at the outset and we’ve documented a number of them throughout the run of this podcast. One of our primary concerns is the role of the AI tools in échanger, that replacement effect that happens that leads to technological unemployment.

Much of the initial hype and furor around the AI tools was people recognizing that potential for échanger following the initial public release of ChatGPT. There’s also concerns about the degree to which the AI tools may be used as instruments of control, and how they can contribute to what Gilles Deleuze calls a control society, which we talked about in our Reflections episode last year.

And related to that is the lack of transparency, the degree to which the AI tools are black boxes, where based on a given set of inputs, we’re not necessarily sure about how it came up with the outputs. And this is a challenge regardless of whether it’s a hardware device or a software tool.

And regardless of how the AI tool is deployed, the increased prevalence of it means we’re leading to a soylent culture. With an increased amount of data smog, or bitslop, or however you want to refer to the digital pollution that takes place with the increased amount of AI content in our channels and For-You-Feeds, and this is likely to become even more heightened as Facebook moves to pushing AI generated posts into the timelines.

Many are speculating that this is becoming so prevalent that the internet is largely bots pushing out AI generated content, what’s called the “Dead Internet Theory”, which we’ll definitely have to take a look at it in a future episode. Hint, the internet is alive and well, it’s just not necessarily where you think it is.

And with all this AI generated content, we’re still facing the risk of the hallucinations, which we talked about, holy moly, over two years ago when we discussed the LOAB, that brief little bit of creepypasta that was making the rounds as people were trying out the new digital tools. But the hallucinations still highlight one of the primary issues with the AI tools, and that’s the errors in the results.

In order to document and collate these issues, a research team over at MIT has created the AI Risk Repository. It’s available at airisk. mit. edu. Here they have created taxonomies of the causes and domains where the risks may take place. However, not all of these risks are equal. One of the primary ones that gets mentioned is the energy usage for AI.

And while it’s not insignificant, I think it needs to be looked at in context. One estimate of global data center usage was between 240 and 340 terawatt hours, which is a lot of energy, and it might be rising as data center usage for the big players like Microsoft and Google has gone up by like 30 percent since 2022.

And that still might be too low, as one report noted that the actual estimate could be as much as 600 percent higher. So when you put that all together, that initial estimate could be anywhere between a thousand and 2000 terawatts. But the AI tools are only a fraction of what goes on at the data centers, which include cloud storage and services, streaming video, gaming, social media, and other high volume activities.

So you bring that number right back down. And AI is using? The thing is, whatever that number is, 300 terawatts times 1. 3 times six divided by five. Whatever that result ends up being doesn’t even chart when looking at global energy usage. Looking at a recent chart on global primary energy consumption by source over at Our World in Data, we see that the worldwide consumption in 2023 was 180, 000 terawatt hours.

The amount of energy potentially used by AI hardly registers as a pixel on the screen compared to worldwide energy usage that were presented with the picture in the media where AI is burning up the planet. I’m not saying AI energy usage isn’t a concern. It should be green and renewable. And it needs to be verifiable, this energy usage of the AI companies, as there is the risk of greenwashing the work that is done, of painting over their activities true energy costs by highlighting their positive impacts for the environment.

And the energy usage may be far exceeded by the water usage that’s used for the cooling of the data centers. And as with the energy usage, the amount of water that’s actually going to AI is incredibly hard to dissociate from all the other activities that are taking place in these data centers. And this greenwashing, which various industries have long been accused of, might show up in another form as well.

There is always the possibility that the helpful stories that are presented, AI tools have provided for various at risk and minority populations, are presented as a form of “aidwashing”. And this is something we have to evaluate for each of the stories posted in the AI Positivity Archive. Now I can’t say for sure that “aidwashing” specifically as a term exists.

A couple searches didn’t return any hits, so you may have heard it here first. However, while positive stories about AI often do get touted, do we think this is the driving motivation for the massive investment we’re seeing in the AI technologies? No, not even for a second. These assistive uses of AI don’t really work with the value proposition for the industry, even though those street uses of technology may point the way forward in resolving some of the larger issues for AI tools with respect to resource consumption and energy usage.

The AI tools used to assist Casey Harrell, the ALS patient mentioned near the beginning of the show, use a significantly smaller model than one’s conventionally available, like those found in ChatGPT. The future of AI may be small, personalized, and local, but again, that doesn’t fit with the value proposition.

And that value proposition is coming under increased scrutiny. In a report published by Goldman Sachs on June 25th, 2024, they question if there’s enough benefit for all the money that’s being poured into the field. In a series of interviews with a number of experts in the field, they note how initial estimates about both the cost savings, the complexity of tasks that AI is available to do, and the productivity gains that would derive from it, are all much lower than initially proposed or happening on a much longer time frame.

In it, MIT professor Daron Acemoglu forecasts minimal productivity and GDP growths, around 0. 5 percent or 1%, whereas Goldman Sachs predictions were closer to 9 percent and 6 percent increase in GDP. With such varying degrees of estimates, what the actual impact of AI in the next 10 years is, is anybody’s guess.

It could be at either extreme or somewhere in between. But the main takeaway from this is that even Goldman Sachs is starting to look at the balance sheet and question the amount of money that’s being invested in AI. And that amount of money is quite large indeed.

In between starting recording this podcast episode and finishing it, OpenAI raised 6. 6 billion dollars in a funding round from its investors, including Microsoft and Nvidia, which is the largest ever recorded. As reported by Reuters, this could value the company at 157 billion dollars and make it one of the the world. valuable private companies in the world. And this coincides with the recent restructuring from a week earlier which would remove the non profit control and see it move to a for profit business model.

But my final question is, would this even work? Because it seems diametrically opposed to what AI might actually bring about. If assistive technology focused on automation and Echange, then the end result may be something closer to what Aaron Bastani calls “fully automated luxury communism”, where the future is a post-scarcity environment that’s much closer to Star Trek than it is to Snow Crash.

How do you make that work when you’re focused on a for profit model? The tool that you’re using is not designed to do what you’re trying to make it do. Remember, “The street finds its own uses for things”, though in this case that street might be Wall Street. The investors and forecasters at Goldman Sachs are recognizing that disconnect by looking at the charts and tables in the balance sheet.

But their disconnect, the part that they’re missing, is that the driving force towards AI may be one more of ideology. And that ideology is the California ideology, a term that’s been floating around since at least the mid 1990s. And we’ll take a look at it next episode and return to the works of Lev Manovich, as well as Richard Barbrook, Andy Cameron, and Adrian Daub, as well as a recent post by Sam Altman titled ‘The Intelligence Age’.

There’s definitely a lot more going on behind the scenes.

Once again, thank you for joining us on the Implausipod. I’m your host, Dr. Implausible. You can reach me at drimplausible at implausipod. com. And you can also find the show archives and transcripts of all our previous shows at implausipod. com as well. I’m responsible for all elements of the show, including research, writing, mixing, mastering, and music.

And perhaps somewhat surprisingly, given the topic of our episode, no AI is used in the production of this podcast. Though I think some machine learning goes into the transcription service that we use. And the show is licensed under Creative Commons 4. 0 share alike license. You may have noticed at the beginning of the show that we described the show as an academic podcast and you should be able to find us on the Academic Podcast Network when that gets updated.

You may have also noted that there was no advertising during the program and there’s no cost associated with the show. But it does grow from word of mouth of the community. So if you enjoy the show, please share it with a friend or two, and pass it along. There’s also a buy me a coffee link on each show at implausopod.

com, which will go to any hosting costs associated with the show. I’ve put a bit of a hold on the blog and the newsletter, as WordPress is turning into a bit of a dumpster fire, and I need to figure out how to re host it. But the material is still up there, I own the domain. It’ll just probably look a little bit more basic soon.

Join us next time as we explore that Californian ideology, and then we’ll be asking, who are Roads for? And do a deeper dive into how we model the world. Until next time, take care and have fun.

Bibliography

A bottle of water per email: The hidden environmental costs of using AI chatbots. (2024, September 18). Washington Post. https://www.washingtonpost.com/technology/2024/09/18/energy-ai-use-electricity-water-data-centers/

A Note to Our Community About our Comments on AI – September 2024 | NaNoWriMo. (n.d.). Retrieved October 5, 2024, from https://nanowrimo.org/a-note-to-our-community-about-our-comments-on-ai-september-2024/

Advances in Brain-Computer Interface Technology Help One Man Find His Voice | The ALS Association. (n.d.). Retrieved October 5, 2024, from https://www.als.org/blog/advances-brain-computer-interface-technology-help-one-man-find-his-voice

Balevic, K. (n.d.). Goldman Sachs says the return on investment for AI might be disappointing. Business Insider. Retrieved October 5, 2024, from https://www.businessinsider.com/ai-return-investment-disappointing-goldman-sachs-report-2024-6

Broad, W. J. (2024, July 29). Artificial Intelligence Gives Weather Forecasters a New Edge. The New York Times. https://www.nytimes.com/interactive/2024/07/29/science/ai-weather-forecast-hurricane.html

Card, N. S., Wairagkar, M., Iacobacci, C., Hou, X., Singer-Clark, T., Willett, F. R., Kunz, E. M., Fan, C., Nia, M. V., Deo, D. R., Srinivasan, A., Choi, E. Y., Glasser, M. F., Hochberg, L. R., Henderson, J. M., Shahlaie, K., Stavisky, S. D., & Brandman, D. M. (2024). An Accurate and Rapidly Calibrating Speech Neuroprosthesis. New England Journal of Medicine, 391(7), 609–618. https://doi.org/10.1056/NEJMoa2314132

Consumers Know More About AI Than Business Leaders Think. (2024, April 8). BCG Global. https://www.bcg.com/publications/2024/consumers-know-more-about-ai-than-businesses-think

Cosmos. (1980, September 28). [Documentary]. KCET, Carl Sagan Productions, British Broadcasting Corporation (BBC).

Donna. (2023, October 9). Banksy Replaced by a Robot: A Thought-Provoking Commentary on the Role of Technology in our World, London 2023. GraffitiStreet. https://www.graffitistreet.com/banksy-replaced-by-a-robot-a-thought-provoking-commentary-on-the-role-of-technology-in-our-world-london-2023/

Gen AI: Too much spend, too little benefit? (n.d.). Retrieved October 5, 2024, from https://www.goldmansachs.com/insights/top-of-mind/gen-ai-too-much-spend-too-little-benefit

Goodman, N. (1976). Languages of Art (2 edition). Hackett Publishing Company, Inc.

Goodman, N. (1978). Ways Of Worldmaking. http://archive.org/details/GoodmanWaysOfWorldmaking

Hill, L. W. (2024, September 11). Inside the Heated Controversy That’s Tearing a Writing Community Apart. Slate. https://slate.com/technology/2024/09/national-novel-writing-month-ai-bots-controversy.html

Hu, K. (2024, October 3). OpenAI closes $6.6 billion funding haul with investment from Microsoft and Nvidia. Reuters. https://www.reuters.com/technology/artificial-intelligence/openai-closes-66-billion-funding-haul-valuation-157-billion-with-investment-2024-10-02/

Hu, K., & Cai, K. (2024, September 26). Exclusive: OpenAI to remove non-profit control and give Sam Altman equity. Reuters. https://www.reuters.com/technology/artificial-intelligence/openai-remove-non-profit-control-give-sam-altman-equity-sources-say-2024-09-25/

Knight, W. (n.d.). An ‘AI Scientist’ Is Inventing and Running Its Own Experiments. Wired. Retrieved September 9, 2024, from https://www.wired.com/story/ai-scientist-ubc-lab/

LaBossiere, M. (n.d.). AI: I Want a Banksy vs I Want a Picture of a Dragon. Retrieved October 5, 2024, from https://aphilosopher.drmcl.com/2024/04/01/ai-i-want-a-banksy-vs-i-want-a-picture-of-a-dragon/

Lu, C., Lu, C., Lange, R. T., Foerster, J., Clune, J., & Ha, D. (2024, August 12). The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery. arXiv.Org. https://arxiv.org/abs/2408.06292v3

Manovich, L. (2001). The language of new media. MIT Press.

McLuhan, M. (1964). Understanding Media: The Extensions of Man. The New American Library.

Mickle, T. (2024, September 23). Will A.I. Be a Bust? A Wall Street Skeptic Rings the Alarm. The New York Times. https://www.nytimes.com/2024/09/23/technology/ai-jim-covello-goldman-sachs.html

Milman, O. (2024, March 7). AI likely to increase energy use and accelerate climate misinformation – report. The Guardian. https://www.theguardian.com/technology/2024/mar/07/ai-climate-change-energy-disinformation-report

Mueller, B. (2024, August 14). A.L.S. Stole His Voice. A.I. Retrieved It. The New York Times. https://www.nytimes.com/2024/08/14/health/als-ai-brain-implants.html

Overview and key findings – World Energy Investment 2024 – Analysis. (n.d.). IEA. Retrieved October 5, 2024, from https://www.iea.org/reports/world-energy-investment-2024/overview-and-key-findings

Roberts, S. (2024, July 25). Move Over, Mathematicians, Here Comes AlphaProof. The New York Times. https://www.nytimes.com/2024/07/25/science/ai-math-alphaproof-deepmind.html

Schacter, R. (2024, August 18). How does Banksy feel about the destruction of his art? He may well be cheering. The Guardian. https://www.theguardian.com/commentisfree/article/2024/aug/18/banksy-art-destruction-graffiti-street-art

Science in the age of AI | Royal Society. (n.d.). Retrieved October 2, 2024, from https://royalsociety.org/news-resources/projects/science-in-the-age-of-ai/

Sullivan, S. (2024, September 25). New Mozart Song Released 200 Years Later—How It Was Found. Woman’s World. https://www.womansworld.com/entertainment/music/new-mozart-song-released-200-yaers-later-how-it-was-found

Taylor, C. (2024, September 3). How much is AI hurting the planet? Big tech won’t tell us. Mashable. https://mashable.com/article/ai-environment-energy

The AI Risk Repository. (n.d.). Retrieved October 5, 2024, from https://airisk.mit.edu/

The Intelligence Age. (2024, September 23). https://ia.samaltman.com/

What is NaNoWriMo’s position on Artificial Intelligence (AI)? (2024, September 2). National Novel Writing Month. https://nanowrimo.zendesk.com/hc/en-us/articles/29933455931412-What-is-NaNoWriMo-s-position-on-Artificial-Intelligence-AI

Wickelgren, I. (n.d.). Brain-to-Speech Tech Good Enough for Everyday Use Debuts in a Man with ALS. Scientific American. Retrieved October 5, 2024, from https://www.scientificamerican.com/article/brain-to-speech-tech-good-enough-for-everyday-use-debuts-in-a-man-with-als/

Soylent Culture

(this was originally published as Implausipod Episode 37 on September 22nd, 2024)

https://www.implausipod.com/1935232/episodes/15791252-e0037-soylent-culture

What is Soylent Culture? Whether it is in the mass media, the new media, or the media consumed by the current crop of generative AI tools, it is culture that has been fed on itself. But of course, there’s more. Have a listen to find out how Soylent Culture is driving the potential for “Model Collapse” with our AI tools, and what that might mean.

In 1964, Canadian media theorist Marshall McLuhan published his work Understanding Media, The Extensions of Man. In it, he described how the content of any new medium is that of an older medium. This can help make it stronger and more intense. Quote, “The content of a movie is a novel, or a play, or an opera.

The effect of the movie form is not related to its programmed content. The content of writing or print is speech, but the reader is almost entirely unaware either of print or of speech.” End quote.

60 years later, in 2024, this is the promise of the generative AI tools that are spreading rapidly throughout society, and has been the end result of 30 years of new media, which has seen the digitalization of anything and everything that provides some form of content on the internet.

Our culture has been built on these successive waves of media, but what happens when there’s nothing left to feed the next wave? It begins to feed on itself, which is why we live now in an era of soylent culture.

Welcome to the Implausipod, an academic podcast about the intersection of art, technology, and popular culture. I’m your host, Dr. Implausible, and in this episode, we’re going to draw together some threads we’ve been collecting for over a year and weave them together into a tapestry that describes our current age, an era of soylent culture.

And way back in episode 8, when we introduced you to the idea of the audience commodity, where media companies real product isn’t the shiny stuff on screen, but rather the audiences that they can serve up to the advertisers, we noted how Reddit and Twitter were in a bit of a bind because other companies had come in and slurped up all the user generated content that was so fundamental to Web 2. 0 and fundamental to their business model as well, as they were still in that old model of courting the business of advertisers.

And all that UGC – the useless byproduct of having people chat online in a community that serve up to those advertisers – got tossed into the wood chipper, added a little bit of glue and paint, and then sold back to you as shiny new furniture, just like IKEA.

And this is what the AI companies are doing. We’ve been talking about this a little bit off and on, and since then, Reddit and Twitter have both gone all in on leveraging their own resources, and either creating their own AI models, like the Grok model, or at least licensing and selling it to other LLMs.

In episode 16, we looked a little bit more at that Web 2. 0 idea of spreadable media and how the atomization of culture actually took place. How the encouragement of that user generated content by the developers and platform owners is now the very material that’s feeding the AI models. And finally, our look at nostalgia over the past two episodes, starting with our look at the Dial-up Pastorale and that wistful approach to an earlier internet, one that never actually existed.

All of these point towards the existence of Soylent Culture. What I’m saying is is that it’s been a long time coming. The atomization of culture into its component parts, the reduction and eclipsed of soundbites to TikToks to Vines, the meme-ification of culture in general were all evidence of this happening.

This isn’t inherently a bad thing. We’re not ascribing some kind of value to this. We’re just describing how culture was reduced to its bare essentials as even smaller bits were carved off of the mass audience to draw the attention of even smaller and smaller niche audiences that could be catered to.

And a lot of this is because culture is inherently memetic. That’s memetic as in memes, not memetic as in mimesis, though the latter applies as well. But when we say that culture is memetic, I want to build on it more than just Dawkins’s original formulation of the idea of a meme to describe a unit of cultural transmission.

Because, honestly, the whole field of anthropology was sitting right over there when he came up with it. A memetic form of culture allows for the combination and recombination of various cultural components in the pursuit of novelty, and this can lead to innovation in the arts and the aesthetic dimension.

In the digital era, we’ve been presented with a new medium. Well, several perhaps, but the underlying logic of the digital media – the reduction of everything to bits, to ones and zeros that allow for the mass storage and fast transmission of everything anywhere, where the limiting factors are starting to boil down to fundamental laws of physics –

this commonality can be found across all the digital arts, whether it’s in images, audio, video, gaming. Anything that’s appearing on your computer or on your phone has this underlying logic to it. And when a new medium presents itself due to changing technology, the first forays into that new medium will often be adaptations or translations of work done in an earlier form.

As noted by Marshall McLuhan at the beginning of this episode, it can take a while for new media to come into its own. It’ll be grasped by the masses as popular entertainment and derided by the high arts, or at least those who are fans of it. Frederick Jameson, who we talked about a whole lot last episode on nostalgia noted, quote, “it was high culture in the fifties that was authorized as it still is to pass judgment on reality.

to say what real life is and what is mere appearance. And it is by leaving out, by ignoring, by passing over in silence and with the repugnance one may feel for the dreary stereotypes of television series that high art palpably issues its judgment.” End quote.

So, the new medium, or works that are done in the new medium, can often feel derivative as it copies stories of old, retelling them in a new way.

But over time, what we see happen again and again and again are that fresh stories start to be told by those familiar with the medium that have and can leverage the strengths and weaknesses of the medium, telling tales that reflect their own experiences, their own lives, and the lives of people living in the current age, not just reflections of earlier tales.

And eventually, the new medium finds acceptance, but it can take a little while.

So let me ask you, how long does it take for a new medium to be accepted as art? First they said radio wasn’t art, and then we got War of the Worlds. They said comic books weren’t art, and then we got Maus, and Watchmen, and Dark Knight Returns. They said rock and roll wasn’t art, and we got Dark Side of the Moon and Pet Sounds, Sgt.

Pepper’s and many, many others. They said films weren’t art, and we got Citizen Kane. They said video games weren’t art, and we got Final Fantasy VII and Myst and Breath of the Wild. They said TV wasn’t art, and we got Oz and Breaking Bad and Hannibal and The Wire. And now they’re telling us that AI generated art isn’t art, and I’m wondering how long it will take until they admit that they were wrong here, too.

Because even though it’s early days, I’ve seen and heard some AI generated art pieces that would absolutely count as art. There are pieces that produce an emotional effect, they evoke a response, whether it’s whimsy or wonder or sublime awe, and for all of these reasons, I think the AI generated art that I’ve seen or experienced counts.

And the point at which creators in a new medium produce something that counts as art often happens relatively early in the life cycle of that new media. In all of the examples I gave, things like War of the Worlds, Citizen Kane, Final Fantasy VII, these weren’t the first titles produced in that medium, but they did come about relatively early, once creators became accustomed to the cultural form.

As newer creators began working with the media, they can take it further, but there’s a risk. Creators that have grown up with the media may become too familiar with the source material, drawing on the representations from within itself. And we can all think of examples of this, where writers on police procedurals or action movies have grown up watching police procedurals and action movies and they simply endlessly repeat the tropes that are foundational to the genre.

The works become pastiches, parodies of themselves, often unintentionally, and they’re unable to escape from the weight of the tropes that they carry. This is especially evident in long running shows and franchises. Think of later seasons of The Simpsons, if you’ve actually watched recent seasons of The Simpsons, compared to the earlier ones.

Or recent seasons of Saturday Night Live, with the endlessly recycled bits, because we really needed another game show knock off, or a cringy community access parody. We can see it in later seasons of Doctor Who, and Star Trek, and Star Wars, and Pro Wrestling as well, and the granddaddy of them all, the soap opera.

This is what happens with normal culture when it is trained on itself. You get Soylent Culture.

Soylent Culture is this, the self referential culture that is fed on itself, an ouroboros of references that always point at something else. It is culture comprised of rapid fire clips coming at the audience faster than a Dennis Miller era Saturday Night Live weekend update. Or the speed of a Weird Al Yankovic polka medley.

It is 30 years of Simpsons Halloween episodes referring to the first 10 years of Simpsons Halloween episodes. It is the hyper referential titles like The Family Guy and Deadpool, whether in print or film, throwing references at the audience rapid fire with rhyme and reason but so little of it, that works like Ready Player One start to seem like the inevitable result of the form.

And I’m not suggesting that the above works aren’t creative. They’re high examples of this cultural form; of soylent culture. But the endless demand for fresh material in an era of consumption culture means that the hyper-referentiality will soon exhaust itself and turn inward. This is where the nostalgia that we’ve been discussing for the previous couple episodes comes into play.

It’s a resource for mining, providing variations of previous works to spark a glimmer in the audience’s eyes of, hey, I recognize that. But even though these works are creative, they’re limited, they’re bound to previous, more popular titles, referring to art that was more widely accessible, more widely known.

They’re derivative works and they can’t come up with anything new, perhaps.

And I say perhaps because there’s more out there than we can know. There’s more art that’s been created that we can possibly experience in a lifetime. There’s more stuff posted to YouTube in a minute than you’ll ever see in your 80 years on the planet.

And the rate at which that is happening is increasing. So, for anybody watching these hyper referential titles, if their first exposure to Faulkner is through Family Guy, or to Diogenes is through Deadpool, then so be it. Maybe their curiosity will inspire them to track that down, to check out the originals, to get a broader sense of the culture that they’re immersed in.

If they don’t get the joke and look around and wonder why the rest of the audience is laughing at this and say, you know, maybe it’s a me thing. Maybe I need to learn more. And that’s all right. It can lead to an act of discovery; of somebody looking at other titles and curating them, bringing them together and developing their own sense of style and working on that to create an aesthetic.

And that’s ultimately what it comes down to. Is art an act of learning and discovery and curation? Or is it an act of invention and generation and creation, or these all components of it? If an artist’s aesthetic is reliant on what they’ve experienced, well, then, as I’ve said, we’re finite, tiny creatures.

How many books or TV shows can you watch in a lifetime to incorporate into your experience? And if you repeatedly watch something, the same thing, are you limiting yourself from exposure to something new? And this is where the generative art tools come back into play. The AI tools that have been facilitated by the digitalization of everything during web 1. 0 and the subsequent slurping up of everything into feeding the models.

Because the AI tools expand the realm of what we have access to. They can draw from every movie ever made, or at least digitalized. Not just the two dozen titles that the video store clerked happened to watch on repeat while they were working on their script, before finally following through and getting it made.

In theory, the AI tools can aid the creativity of those engaging with it, and in practice we’re starting to see that as well. It comes back to that question of whether art is generative or whether it’s an act of discovery and curation. But there’s a catch. Like we said, Soylent cultures existed long before the AI art tools arrived on the scene.

The derivative stories of soap operas and police procedurals and comic books and pulp sci-fi. But it has become increasingly obvious that the AI tools facilitate Soylent culture, drive it forward, and feed off of it even more. The A. I. tools are voracious, continually wanting more, needing fresh new stuff in order to increase the fidelity of the model.

That hallowed heart that drives the beast that continually hungers. But you see, the model is weak. It is Vulnerable like the phylactery of a lich hidden away somewhere deep.

The one thing the model can’t take too much of is itself: model collapse is the very real risk of a GPT being trained on text generated by a large language model identified by Shumailov, et al, and “ubiquitous among all learned generative models” end quote. Model collapse is a risk that creators of AI tools face in further developing those tools.

Quoting again from Shumailov: “model collapse is a degenerative process affecting generations of learned generative models in which the data they generate end up polluting the training set. of the next generation. Being trained on polluted data, they then misperceive reality.” End quote. This model collapse can result in the models ‘forgetting’ or ‘hallucinating’.

Two terms drawn not just from psychology, but from our own long history of engaging with and thinking about our own minds and the minds of others. And we’re exacting them here to apply to our AI tools, which – I want to be clear – aren’t thinking, but are the results of generative processes of taking lots of things and putting them together in new ways, which is honestly what we do for art too.

But this ‘forgetting’ can be toxic to the models. It’s like a cybernetic prion disease, like the cattle that developed BSE by being fed feed that contained parts of other ground up cows that were sick with the disease. The burgeoning electronic minds of our AI tools cannot digest other generated content.

And in an era of Soylent Culture, where there’s a risk of model collapse, where these incredibly expensive AI tools that require mothballed nuclear reactors to be brought online to provide enough power to service them, that thirst for fresh water like a marathon runner in the desert, In this era, then the human generated content of the earlier pre AI web becomes a much more valuable resource, the digital equivalent of the low background steel that was sought after for the creation of precision instruments following the era of atmospheric nuclear testing, where all the above ground and newly mined ore was too irradiated for use in precision instruments.

And it should be noted that we’re no longer living in that era because we stopped doing atmospheric nuclear testing. And for some, the takeaway for that may be that to stop an era of Soylent culture, we may need to stop using these AI tools completely. But I think that would be the wrong takeaway because the Soylent culture existed long before the AI tools existed, long before new media, as shown by the soap operas and the like.

And it’s something that’s more tied to mass culture in general, though. New media and the AI tools can make Soylent Culture much, much worse, let me be clear. Despite this, despite the speed with which all this is happening, the research on model collapse is still in its early days. The long term ramifications of model collapse and its consequences will only be learned through time.

In the meantime, we can discuss some possible solutions to dealing with Soylent Culture. Both AI generated and otherwise. If Soylent Culture is art that’s fed on itself, then the most effective way to combat it would be to find new stuff. To find new things to tell stories about. To create new art about.

Historically, how has this happened with traditional art? Well, we’ve hinted at a few ways throughout this episode, even though, as we noted, in an era of mass culture, even traditional arts are not immune from becoming soylent culture as well. One of the ways we get those new artistic ideas is through mimesis, the observation of the world around us, and imitating that, putting it into artistic forms.

Another way we get new art is through soft innovation when technologies enhance or change the way that we can produce media and art, or where art inspires the development of new technology as they feed back and forth between each other, trading ideas. And as we’ve seen throughout this episode and throughout the podcast in general, new media and new modes of production can encourage new stories to be told as artists are dealing with their surroundings and whatever the current zeitgeist is and putting that into production with whatever media that they have available.

As our world and society and culture changes, we’re going to reflect upon our current condition and tell tales about that to share with those around us. And as we noted much. Earlier in this particular episode, that familiarity with a form, a technical form, allows those who are using it to innovate within that form, creating new, more complex, better produced and higher fidelity works in whatever medium they happen to be choosing to work in.

And ultimately that comes down to choice. By the artists and the audience and the associated industries that allow the audience to experience those works, whether they are audio, visual, tactile, experiential, like games, any version of art that we might come in contact with. The generation and invention in the process is important to be sure, but the curation and discovery is no less important within this process.

And this is where humans with an a sense for aesthetic and style will still be able to tell. How would an AI tool discover or create? How could it test something that’s in the loop? The generative AI tools can’t tell. They have no sense. They can provide output, but no aura, no discernment. Could an AI run a script that does A-B testing on an audience for each new generated piece of art to see how they react, and the most popular one gets put forward?

I guess so, it’s not outside the realm of possibility, but that isn’t really something that they’re able to do on their own, or at least I hope not.

Would programming in some variance and randomness in the AI tools allow for them to avoid the model collapse that comes with ingesting soylent culture in much the same way that we saw with the reveries for the hosts in the Westworld TV series?

Well, the research by Shumailov et al that we mentioned earlier suggests that that’s possibly not the case. I mean, it might help with the variation, perhaps, but that doesn’t help with the selection mechanisms, the discernment.

AI is a blind watch, trying to become a watchmaker, making new watches. The question might be, what would an AI even want with a watch anyways?

Thank you for joining us on the Implausipod. I’m your host Dr. Implausible. We’ll explore more on the current state of AI art tools and their role as assistive technologies in our next episode. called AI Refractions. But before we get there, we need to return to our last episode, episode 36, and offer a postscript on that one.

Even though it’s been only a week, as of the recording of this episode, September 22nd, 2024, we regret to inform you of the passing of Professor Frederick Jameson, who was the subject of episode 36. As we noted in that episode, he was a giant in the field of literary criticism and philosophy, and a long time professor at Duke University.

Our condolences go out to his family and friends. Rest in peace. If you’d like to contact the show, you can reach me at drimplausible at implausipod. com, and you can also find the show archives and transcripts of all our previous shows at implausipod. com as well. I’m responsible for all elements of the show, including research, writing, mixing, mastering, and music, and the show is licensed under a Creative Commons 4. 0 share alike license.

You may have noticed at the beginning of the show that we described the show as an academic podcast, and you should be able to find us on the Academic Podcast Network when that gets updated. You may have also noted that there was no advertising during the program, and there is no cost associated with the show, but it does grow from word of mouth of the community, so if you enjoy the show, please share it with a friend or two.

and pass it along. There’s also a buy me a coffee link on each show at implausipod. com which will go to any hosting costs associated with the show. Over on the blog, we’ve started up a monthly newsletter. There will likely be some overlap with future podcast episodes, and newsletter subscribers can get a hint of what’s to come ahead of time, so consider signing up and I’ll leave a link in the show notes.

Until then, take care and have fun.

Bibliography

McLuhan, M. (1964). Understanding Media: The Extensions of Man. The New American Library.

Shumailov, I., Shumaylov, Z., Zhao, Y., Gal, Y., Papernot, N., & Anderson, R. (2024). The Curse of Recursion: Training on Generated Data Makes Models Forget (No. arXiv:2305.17493). arXiv. https://doi.org/10.48550/arXiv.2305.17493

Shumailov, I., Shumaylov, Z., Zhao, Y., Papernot, N., Anderson, R., & Gal, Y. (2024). AI models collapse when trained on recursively generated data. Nature, 631(8022), 755–759. https://doi.org/10.1038/s41586-024-07566-y

Snoswell, A. J. (2024, August 19). What is ‘model collapse’? An expert explains the rumours about an impending AI doom. The Conversation. http://theconversation.com/what-is-model-collapse-an-expert-explains-the-rumours-about-an-impending-ai-doom-236415

GPT Squared

(this was originally published as Implausipod E0031 on March 31, 2024)

https://www.implausipod.com/1935232/episodes/14799673-e0031-gpt-squared

Are the new GPTs – Generative Pre-trained Transformers – powering the current wave of AI tools actually the emergence of a new GPT – General Purpose Technology – that we will soon find embedded into every aspect of our lives? Earlier examples of GPTs include tech like steam power, electricity, radio, and computing, all tech that is foundational to our modern way of life. Will our AI tools soon join this pantheon as another long wave of technological progress begins?

Let’s start with the question. Do you remember a time before electricity? Unless this show is vastly more popular with time travelers and certain vampires than I thought, the answer is probably not. But now, in 2024, it’s literally everywhere. It’s sublimated into the background. It’s become part of the infrastructure, and we no longer really think about it.

We flip a switch and the lights go on, and we can find a plug in almost anywhere to recharge our devices and take that electricity with us on the go. But how long did it take to get to that point? And the answer is longer than you think. The production of electricity was invented in 1832, but it took half a century for it to become commercially viable, and then from there another 70 years to effectively transform our lives with everything from lights and appliances to communication devices like radios and television.

And even now we’re still feeling the effects of that transformation as we move to electric powered vehicles for personal use. So across all those decades, it took a long time for electricity to come from concept to application to becoming a general purpose technology, or GPT. And in 2024, we’re just starting to feel the impacts of another GPT, degenerative pre trained transformers that are powering the current wave of AI tools.

So the question we’re really trying to find out is, are these current GPTs a new GPT, or what we might call GPT squared, in this week’s episode of The Implosipod.

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’ll be exploring exactly what a GPT is, a general purpose technology, that is, and how they have had a massive impact on society. By looking at the definition and some commonalities amongst them, we’ll be able to evaluate whether the current GPT, the generative pre-trained transformers, are going to have the same impact, or whether they qualify as a GPT at all.

As always, I’m using a couple of references for this, and I’ll put the bibliography in the show notes so you can track back the people we’re citing here. For us, the two main sources are going to be Engines of Growth by Bresnahan and Trattenberg from 1992 1995, and Mordecai Kurz’s The Market Power of Technology, Understanding the Second Gilded Age, a book he published in 2023.

Kurz is a professor of economics at Stanford University, and his first book was published back in 1970, so he’s literally been doing this longer than I’ve been alive. And in addition to those, I’m sure we’ll fold in a few more references as required. Now, for the first half of this episode, whenever I mention GPT, we’re going to be explicitly talking about general purpose technology, so I’ll call out the AI tools when mentioned.

And we’ll get to the discussion of those in the second half after we talk about the cyclical nature of technological development. But for the moment, we should get right down to business and find out exactly what is the GPT. A general purpose technology is basically that, a technology that can apply broadly to virtually all sectors of the economy.

And by doing so, it can change the way that society functions. They do this by being pervasive, in that they can be used in a wide variety of functions. And they also do this by sublimating into the background, as David E. Nye notes in his history of the Electrification of America, that once they’re part of the infrastructure, we can stop thinking about it and use them in almost any function.

Now, it took a little while for electricity to get to that point, but that’s part of their nature, that the general purpose technology will evolve in advance and spread throughout the economy. For previous instances of GPTs, that led to productivity gains in a wide number of areas, but even if we’re not specifically looking at Productivity growth, we can still see how they have beneficial impacts.

Now in the original study on GPTs, Bresnahan and Trattenberg’s engines of growth from 1992, they looked at three particular case studies, steam power, the electric motor, and the integrated circuit. And by studying these GPTs, they were able to come up with some basic characteristics. The first and most obvious is that their general purpose, that the function they provide is generic, and because of that generic nature, they’re able to apply it in a lot of different contexts.

If we think of all the ways that the continuous rotary motion that was provided by steam power and then electric engines has been adapted and serves throughout our economy, it’s massive, it’s fascinating. And once the production of the integrated circuit really started taking off in the 60s and 70s, it became a product that could be embedded in almost anything, and very nearly has.

This is obviously scaled over time as integrated circuits have followed Moore’s Law, providing exponential growth in the amount of circuitry that can provide it in the same space, and complementary technologies like batteries have also improved and shrunk and been able to service the chips that have gotten more and more power efficient over time, leading to even more widespread adoption.

And this brief description hints at the second and third characteristics. The second one is that they have technological dynamism. That continuous work is done to innovate and improve the basic technology that makes it more efficient over time. This is why you often see that costs to use that GPT drop over time.

And that’s why it shows up in more and more parts of our society. And the third characteristic of GPTs that Bresnahan and Trattenberg talk about are the innovational complementarities that technical advances in the GPT make it more profitable for its users to innovate and vice versa. And we can see hints of that with how the improving battery technologies went hand in hand with the development of integrated circuits.

One of the things that B& T note, especially in their example of the steam engine electric motor, is that the function that they provide isn’t necessarily obvious with respect to some of the jobs. That the continuous rotary motion that is now used in a lot of things, everything from sewing, polishing, cutting, wasn’t necessarily seen as something that could be adapted to those skills.

So the people that were doing them were surprised when there was a technological replacement for the things that they were doing. Let’s put a pin in that idea and we’ll come back to it in about 10 or 15 minutes. Sometimes the way that the GPT is applied is inefficient initially, but as price and performance ratios improve, as the technology and the complementary technologies around it improve, then it becomes more feasible.

Sometimes those payoffs come quickly, but Often it takes a long time for it to get distributed throughout the economy. In the case of electric motors, they note that it took about three decades to go from 5 percent of the installed horsepower in the U. S. to over 80 percent by 1930. And those productivity gains came because everything was getting electrified at the same time.

The infrastructure was there. And these all go hand in hand. They’re complementary. B& T quote at length from Rosenberg from 1982. Quote, the social payoff to electricity would have to include not only lower energy and capital costs, but also the benefits flowing from the newfound freedom to redesign factories with a far more flexible power source.

The steam engine required clumsy belting and shafting techniques for the transmission of power within the plant. These methods imposed serious constraints upon the organization and flow of work. which had to be grouped according to their power requirements close to the energy source. With the advent of fractionalized power made possible by electricity in the electric motor, it now became possible to provide power in very small, less costly units.

This flexibility made possible a wholesale reorganization of work arrangements and in this way, made a wide and pervasive contribution to productivity growth throughout manufacturing. Machines and tools could now be put anywhere efficiency dictated, not where belts and shafts could most easily reach them.

Now, I want to state that I’m not a member of the cult of efficiency by any means, and that Rosenzberg’s claim that quote, it’s not some contradiction to that Foucauldian argument that the architecture of society is shaped by the architecture of our factories and our other buildings. That we have some Deleuzian form of control society because the very hierarchy of the way our power is distributed within our factories lends itself to certain forms of social organization.

Far be it. I think these are saying exactly the same thing. Different perspectives. What the subtext of all these articles is, is that to get past those hierarchical forms, united find different ways to distribute the power. And by doing so, you can have very liberating effects on society as a whole. And.

Ultimately, this is what a GPT is. It’s what it provides. As Kurz notes, GPTs reflect fundamental changes in the state of human knowledge that occur maybe once in a generation or once in a century. They are technologies that enable up to Paradigm Shift, and as Kurz notes,

we need to distinguish between small changes within a given technological paradigm and revolutionary technologies that change everything.

End quote. A GPT serves as a founding technology or platform for Further technological innovation. And because of that, it’s really important to note something on the work that goes into the development of a GPT as both B&T and Kurz note, quote, it is vital to keep in mind the distinction between innovations within the paradigm of a GPT.

And innovation of a new technological paradigm, or a new GPT. Some GPTs, like electricity or IT, change everything and ultimately transform the entire economy. Others, like the discovery of DNA and genetic sequencing, change completely only a segment of the economy, like we’ve seen with CRISPR and genetic engineering.

And this idea of a paradigm shift is perhaps one of the most central features of the introduction of a new GPT, especially if you’re a large incumbent firm well established within the Current dominant technological paradigm for you see a paradigm shift threatens to upset the natural order of things where the large Incumbent firms exercise their market power and use small firms operating within that paradigm effectively as research labs acquiring them if they happen to develop a patent or an innovation that would prove it to be useful or would Threaten their own dominance within the marketplace These patterns have been well observed historically within the development of electricity, with the rollout of radio and television, with the early computing industry, and can be even seen within 21st century industries, where a dominant player like Facebook will acquire Instagram or WhatsApp.

that may threaten their dominance. And if they’re unable to acquire those competitors outright, they may exert their market power through lobbying or other efforts in order to challenge them, as we’re seeing currently in the United States with the proposed TikTok ban of March 2024. This is all standard operating procedure.

It’s the way these things seem to work. But when a new technology comes around, when the paradigm shifts, that’s when things get interesting. As Kurz notes, it’s a period where Quote, the most intense technological competition arises when a new GPT is invented. This leads to the eruption of economy wide technological competition in which winners begin the long journey to consolidate market power.

During that period, we’ll either see new players rise to the level of the incumbents, pushing out the old dominant players that can’t adapt. Or we’ll see those dominant players do everything they can to try and keep their hand in the game. Which is what we’re starting to see already within the field of AI, which is one of the reasons we suggest it might be a new GPT, a General Purpose Technology.

But as we’ve hinted at, these things go in cycles, so let’s look at what some of the earlier ones were.

The idea that the economy behaves in a cyclical manner was first introduced almost 100 years ago, in the 1920s, by Nikolai Kondratiev. They’ve been subsequently named in his honor. Kondratiev hypothesized that these cycles were due to the underlying technological basis of society, the technological paradigms that we’ve been discussing in the first half of this episode, where we see rising boom and bust cycles that take place over a period of roughly 50 to 60 years.

Now, the Kondratiev waves, or what are sometimes called long waves or Carrier waves are only one of the various economic waves or cycles that have been observed. Others, including those proposed by Kuznets or Jugler or Kitchen, look at things like infrastructure or investment or even inventory for various products, and development time frames can have a major impact for all of this as well.

When you map these all out on a timeline, the various economic waves can all seem to interact, much like overlapping sine waves in a synthesizer, where the sum of the smaller waves occasionally comes together in a much larger peak, or ocean waves come together out of nowhere and suddenly form a rogue wave big enough to sink a ship.

When Kondratiev was originally observing that a long 60 year period, he said that there was three phases to the cycle, a period of expansion, stagnation, recession, and nowadays we’ve added collapse into that as well. When Kondratiev was originally writing in the 1920s, he identified a number of periods that as it’s originally taken place with, starting with the Industrial Revolution, followed by the Age of Steam and the expansion of the railways, and then the subsequent rise of electric power that took place, as noted, between the 1890s and 1930s in North America.

Since then, we’ve seen the cycle continue in two other long waves, the rise of the internal combustion engine, the associated technologies that that facilitated, like the automobile and air flight, and then the rise of the microchip and the transformation that the computing technologies and communication technologies had across the face of a modern world.

Now, the idea of an economic long wave has had an enduring appeal. People have taken the theory and have Cast it back earlier in time and a lot of predictions have come about trying to guess what the sixth long wave would be. Again, five that we’ve had so far if we started at the industrial revolution.

Some of the possible contenders as a driver for the sixth Kondratiev wave include that of renewable energy and green technologies as proposed by Moody and Nagredi, or that of biotechnology as proposed by Leo Neffiato. Back in 1996. And while those are strong contenders, they haven’t necessarily turned into the drivers of economic change that we might have expected.

They may still yet, but in some ways they lack the general purpose nature of the technologies that we’ve seen as drivers of previous long waves. In 2024, it looks like another contender has emerged. A GPT build out of GPTs, the generative pre-trained transformers that power our AI tools. So based on our three characteristics of those GPTs that we mentioned earlier, we’ll take a closer look and see if those AI tools might qualify.

As we said earlier, with Bresnahan and Trattenberg’s definition of a GPT, the three characteristics were general purposeness, technological dynamism, and innovational complementarities. Within their paper from 1992, they use the case study of semiconductor technology, which is the dominant GPT at the time of the 1990s.

At the time they were writing, the pervasiveness of computing had already been assumed, but initially that assumption wasn’t the case. Hence early prognostications like Thomas Watson’s from IBM famously saying that I think there is a world market for maybe five computers, a prediction that turned out to be drastically wrong.

By the 1970s, the integrated circuit was well developed and its use in the computing mainframes of large banks was already well underway. What allowed electronic circuits to become a general purpose technology was that they could work inside virtually any system. Those systems can be rationalized and broken down into their component activities, and each of those activities could be replaced with a integrated circuit or transformer at certain stages.

And if you can break the steps down to something that can be replicated by binary logic, like ones and zeros with gates opening or switches turning on and off, then you can apply it anywhere within a production process. It meant that there was a wide range of. technological processes that at its root were pretty simple operations.

But as B& T note here, even though substituting binary logic for a mechanical part was often very inefficient, because you might have to increase the number of steps in order to accomplish something with binary logic, as the price dropped on the circuits and more and more processes could be included within one circuit, it became much easier to actually implement the technology.

electronic circuits within the system. And as the costs came down and the processes were improved, they became more widely implemented within a lot more sectors of the economy, to now that they’re basically everywhere. So, do our current GPTs, the current crop of AI tools, exhibit these same characteristics?

Is there a general purpose-ness to them? Well, qualified yes. I think when it comes to the current AI tools, we need to recognize a few things. The first is that they’re part of a much longer process that a lot of the tools that we’re seeing right now were two years ago called machine learning tools, and they’ve just been rebranded as a I tools with the popularity of Chat GPT and some of the AI art tools like stable diffusion and mid journey.

So both the history of the technology and its implications go back much further. And it’s actually uses are much broader than we’re currently seeing and thinking about the range of industries where I’ve seen AI tools adopted far exceed just the large language models popularized by Chat GPT, or the art tools that were seen online increasingly.

We’re seeing machine learning algorithms deployed in everything from photography to astronomy, to health, to production, to robotics, to website design, to audio engineering, and a whole host of industries. And this explains partially why we’re seeing so many companies involved, which feeds directly into the second characteristics of GPTs, the dynamism, the continuous innovation that’s being brought forth by companies that currently developing those AI tools.

Now, is everyone going to be a hit? No, there’s a lot of them that are absolutely not places where AI should be involved. But some of them are going to be creating tools that are well suited to the application of AI. And just as the early days of electricity and radio and television all saw a lot of different ways that people were trying to apply the new technology to their particular field or product or problem.

We’re seeing a lot of that with AI right now, just as any company that has a machine learning model is either rebranding it or adapting it to the use of AI. I think a lot of people are recognizing that. AI tools could be that general purpose technology that would be applicable to whatever their given field is.

There’s definitely a speculative resource rush component that’s driving some of this growth. There’s a lot of people are getting into the market, but, but as Mordecai Kurz points out, there’s a difference between working within the new paradigm created by a GPT, which a lot of these companies are doing, and on working Directly on the GPT itself, those working directly on the AI tools like OpenAI are the ones that are looking to become the new incumbents, which goes a long way in explaining why Microsoft has reached out and partnered with OpenAI in the development of their tools.

Incumbents that are lagging behind in the development of the tools may soon find themselves locked out, so a company that was dominant within the previous paradigm, like Apple, that currently doesn’t have much in the way of AI development, could be in a precarious position as things change and the cycle of technology continues.

Now, the last characteristics of a GPT was the complementarity that it had that allowed for a Other innovations to take place. And I think at this point, it’s still too soon to tell. We can speculate about how AI may interface with other technologies, but for now, the most interesting ones look to be things like robotics and drones.

Seeing how a tool like OpenAI can integrate with the robots from Boston Dynamics, or the recent announcement of the Fusion AI model that can provide robotic workers for Amazon’s warehouses. Both hinted where some of this may be going. It may seem like the science fiction of 30 or 40 or 50 years ago, but as it was written back then, the future is already here, it’s just not widely distributed yet.

Ultimately, the labeling of a technological era or a GPT or a Kondratiev wave is something that’s done historically. Looking back from a vantage point, it’s confirmed yet, yes, this is what took place and this was the dominant paradigm. But from our vantage point right now, there’s definitely signs and it looks like the gts, maybe the GPT we need to deal with as the wave rises and falls.

Once again, thanks for joining us on this episode of the Implausipod. I’ve been your host Dr. Implausible, responsible for the research, writing, editing, mixing, and mastering. You can reach me at drimplausible at implausipod. com and check out our episode archive at implausipod. com as well. I have a few quick announcements.

Depending on scheduling, I should have another tech based episode about the architecture of our internet coming out in the next few weeks. And then around the middle of April, we’ll invite some guests to discuss the first episode of the Fallout series airing on Amazon Prime. Or streaming on Amazon Prime, I guess.

Nothing’s really broadcast anymore. Following that, Tie in with another Jonathan Nolan series and also its linkages to AI, we’re going to take a look at Westworld season one. And if you’ve been following our Appendix W series on the sci fi prehistory of Warhammer 40, 000, we’re going to spin off the next episode into its own podcast.

Starting on April 15th, we’re currently looking at the Joe Haldeman’s 1974 novel, The Forever War. So if you’d like to read ahead and send us any questions you might have about the text, you can send them to Dr. implausible@implausiblepod.com. We will keep the same address, but the website for Appendix W should now be available.

Check it out@appendixw.com and we’ll start moving those episodes over to there. You can also find the transcript only version of those episodes up on YouTube. Just look for Appendix W in your search bar. We’ve made the first few available, and as I finish off the transcription, I’ll move more and more over.

And just a reminder that both the Implausipod and the Appendix W podcast are licensed under Creative Commons Share A Like 4. 0 license. And we look forward to having you join us with the upcoming episodes soon. Take care, have fun.

Bibliography
Bresnahan, T. F., & Trajtenberg, M. (1995). General purpose technologies “Engines of growth”? Journal of Econometrics, 65(1), 83–108.

Kurz, M. (2023). The Market Power of Technology: Understanding the Second Gilded Age. Columbia University Press.

Nye, D. E. (1990). Electrifying America: Social meanings of a new technology, 1880-1940. MIT Press.

Rosenberg, N. (1982). Inside the black box: Technology and economics. Cambridge University Press.

Winner, L. (1993). Upon Opening the Black Box and Finding it Empty: Social Constructivism and the Philosophy of Technology. Science Technology & Human Values, 18(3), 362–378.