Episode 144 - Limerence

Transcript

David: [00:00:00] Hello and welcome to Overthink.

Ellie: The Podcast, where two philosophers think about the big questions of life without asking Chad GPT.

David: I'm David Pena Guzman.

Ellie: And I'm Ellie Anderson.

David: Whether we like it or not, AI chatbots are everywhere. They are in the workplace. They are in the school classroom, they are in our private homes.

Especially living in San Francisco, I feel like I cannot go anywhere without running into like 10 people that are working at the latest AI startup just because of the culture there.

Ellie: Even the billboards, when you drive into San Francisco, it's like it's dystopian or utopian depending on your view.

David: And since the introduction of LLMs or large [00:01:00] language models to the general public in the form of these like consumer facing chatbots, it's become clear that AI is unavoidable now for most of us, no matter our career or what we do.

Ellie: Yeah. Customer service, asking Google, et cetera, et cetera. Like even if you wanna avoid it, it's practically impossible to do so.

And I think it's easy to see how AI is extremely useful in automating people's everyday tasks. Like there are certain things in life that are dull, that are boring, that we maybe don't have a lot of knowledge on. And that will require more than just a simple Google search. And for that, the sort of consolidating functions of something like Chat GPT can be really useful.

It takes things that require thinking and spits out a clean, grammatical, well formatted answer. And so for instance, I had a friend who not only used chat GPT to build a trip itinerary for her, but also to help her with her packing [00:02:00] list. And this is really interesting. This is like one of my most stylish friends.

I don't know if it was like a whole wardrobe or something, but she input a bunch of stuff into chat GPT, and she asked it to come up with a packing list for her. And this was a trip she was doing into the countryside. And it told her that one of the things she should bring are these white boots. And she was like, I never would've thought to bring white boots on this trip.

And then after the trip, she reported that they were her most useful item that she had packed. And so there was a way that it was like encouraging a sort of creative packing list that she wouldn't have thought about before. Of course students are using it to write essays. People are using it to write cover letters, and then also very frequently, you see people asking chat, GPT to answer factual questions as well as conceptual questions like. Not only what did Plato have to say about justice? But what is justice.

David: Yeah. I mean, I can just imagine now your friend rocking the chic Chat GPT [00:03:00] style. I wonder whether we're gonna start identifying people like, oh my God, this person got dressed by Gemini.

This person got dressed by chat, GPT. What worries me about the spread of these chat bots is that they're not only being used to take over the cognitive labor that is associated with everyday tasks, like getting dressed, making a list for a trip, but also that they're being used now in much more pervasive and in my eyes, troublesome ways, for example, to train the next generation of workers. My university, so the CSU system, the California State University system recently signed a very controversial deal with Chat GPT, with open AI that costs $17 million in the middle of a budget crisis

Ellie: Oh my God.

David: Where they're cutting faculty, they're cutting student resources, but they were willing to pay $17 million to give CSU students in California access to like the really fancy version of chat GPT, because the idea is that the next [00:04:00] generation of workers just like need chat GPT literacy. And it's really unclear to me whether it's the students who are being trained on chat GPT or whether it's chat GPT that is being trained on the student population.

Aside from that, there are all these ways in which chat, GPT, especially because it's one of the more popular ones, is creeping into the privacy of our lives in all sorts of ways. So I was at this fancy cocktail party at a rooftop in San Francisco, and I'm like ready to socialize and I start chatting with this doctor.

Ellie: Did you consult Chat GPT about your outfit?

David: I did not, shut up. You're like you need it. Probably true, but I ended up chatting with this random doctor who then found out that I am a philosopher and asked me, what are your views about the ethics of using chat GPT? And so I started just engaging a little bit and he's like, oh yeah, that's interesting.

You know what Cha GPT is really good for. I was like, oh, maybe I [00:05:00] thought he was gonna say something like transcribing notes from a patient or like dealing with insurance claims. He said, when I feel weird in the morning and I don't know why I am feeling off, I asked Chat GPT to tell me what I am feeling.

And so he would literally tell Chat GPT like my hands are sweaty. I have a knot in my stomach. What is wrong with me today? And then Chat GPT would tell him what he would feel or what his emotional slash bodily state is or was, and then he would move on with his day, internalizing that knowledge and using it for his self understanding.

Ellie: I have so many questions about this, but let me just start by saying this is the most classic possible case of what those of us who study gender dynamics following the psychologist, Ronald Levant, call it normative male alexathymia, which is the condition of not being able to put [00:06:00] words to your emotions, which Levant argues is a normative condition among men because men are taught so few skills in interpreting their own feelings.

Okay, so what is an example of a feeling that it would spit out? Like would it tell, would it say anxiety?

David: Yeah, it would say anxiety. You're feeling nervous. Maybe you're feeling depressed. Or, maybe you are not looking forward to starting your workday in an hour. And then that became a reality for him.

And the thing about this is that it's not really an isolated incident because we now know that there are people who are using these chat bots for all sorts of self mediation, including, you know, the rise of AI chat bot therapists.

Ellie: Yeah. And I can see how, so we'll come back to the therapist. That is not a welcome development to my mind, but I can see how interpreting yourself is very challenging.

And perhaps if you have some kind of technology that is going to help you [00:07:00] understand that what you're feeling is anxiety and then over time you start to recognize those signs and then you don't need the mediator anymore, perhaps that is doing something for you. Right. And even if Chat GPT is getting it wrong, we get things wrong a lot of times with our own emotions.

And maybe the most important thing is just for us to like, be able to put some name to what we're feeling. However, I think maybe that's a more optimistic view and the more pessimistic view would be okay. It's actually just completely outsourcing our own emotional understanding.

David: Proprioception. What is the state of my inner milieu according to chat GPT.

Ellie: Right. And I think this coheres with larger worries too about the possible outsourcing or very real outsourcing of cognition. There have been some recent studies showing that, very unsurprisingly, when a student writes an essay with chat GPT, they are not learning the material.

David: Yeah, no, for sure. And that's what's been called the [00:08:00] cognitive debt that is introduced by LLMs.

And so we might also talk about an emotional debt, an affective debt. Either way it's clear that these models are doing more than aiding us. They are actually supplanting really important aspects of our very subjectivity.

Ellie: Today we are talking about AI chatbots.

David: How are interactions with chat GPT shaping our psychological lives?

Ellie: Why do large language models so often tell you what you wanna hear?

David: And should you get an AI therapist?

Ellie: As always, for an extended version of this episode, community discussion and more, subscribe to Overthink on Substack.

David: AI chat bots are used in all sorts of ways. Everyday tasks, work, school, even our psychic lives. Then again, there has been this whole cascade of critics who have become increasingly concerned with the way people are actually using [00:09:00] chat, GPT and other similar AIs in practice, like on the ground and have written tons and tons of pieces about it.

I feel like there are as many New York Times op-ed pieces about the trouble with chat GPT as there are like techies in San Francisco these days.

Ellie: Yeah. Or as there are billboards advertising AI corporations. Yeah. In fact, it's been so funny since we decided to do this episode how it's like every other day I'm sending you a new article that seems relevant to the topic.

It's just like so overwhelming and I feel like at the same time. It seems like very few of us actually know what a large language model, which is the kind of AI that chat GPT, and other interactive chat bots are is. So let's start here. What is a large language model or LLM? Essentially the answer is a text prediction machine.

LLMs, take a series of words, [00:10:00] puts that series into a massive model, a large model, if you will, and then assigns every possible word, a probability of coming next. So when you interact with a chat bot, it's not thinking, even if chat GPT says it is, it's simply spitting out the most likely word to follow based on the language that it's been trained on.

David: Yeah, and of course, how this sausage is made in these LLMs is way above my pay grade.

Ellie: This is a philosophy podcast. Ma'am, this is a Wendy's.

David: You're not about to get a how to code for an LLM here. But my little brother is a techie. He's an engineer for AI, and I decided to ask him, you know, how would you explain an LLM to somebody like me who doesn't really know how it works?

And he. Really emphasize the predictive nature of these technologies that they generate. The next thing that is most likely to come up based on its training data. But also he helped me [00:11:00] understand that there is this desire to replicate thinking, which is why the models that most LLMs use are called, deep neural networks.

And then I saw this reflected in one of the books that I read while doing research for this episode, which is a book called A Brief History of Artificial Intelligence by Michael Wooldridge. And he gives a historical answer to what an LLM is. That was very illuminating for me He says, you know, the 1950s was the age of the rise of AI. This is when like the Turing test emerges. The first papers in computational theory are published, and then he divides the subsequent evolution of AI i nto a couple of stages. So in the 1960s and seventies, there was this focus on creating AI that tried to mimic the way the mind works.

So this was the age of symbolic AI where AI was [00:12:00] created to manipulate symbols in the same way that our minds are thought to manipulate ideas. So it's almost like a psychological model of ai. But then in the 1970s and eighties, this was abandoned. In favor for a more neural model, where the point was that we could create really intelligent machines, not by mimicking the mind this abstract entity, but rather by mimicking literally the way the brain itself works, like the central nervous system.

And so the idea is that we would recreate in a machine the way neurons interact with one another in terms of their interconnectivity and complexity. And so these deep neural nets are just really complicated computational models that try to recreate a nervous system outside of a living animal.

Ellie: Interesting. And that then relates it sounds like, to their predictive nature. And I think, you know, many of us know by now that not [00:13:00] only are these models gigantic and very complex, but they're also trained on practically every written thing under the sun. Legally or illegally. We have had friends who have asked Chad GPT to write a paragraph in the style of their work and found that it is eerily similar.

And as these companies get greedier and greedier, they're trying to get their hands on even copyrighted materials. And so that is necessary, like the maximum amount of training material is necessary for these LLMs to be as good as possible because at first they will output gibberish because their numerical parameters, which are sometimes called weights, are set randomly.

But then they're trained via a method called back propagation, which adjusts the network's weights to reduce errors. And so these parameters are continually being revised. They're subjective to a massive series of tests that compare the output to the last word or series of word in a sample text. [00:14:00] And the output is then used to update the parameters in order to make the model more accurate for that prediction.

And so if you type in The eye of, it's gonna get better and better at identifying what the next word should be. Based on like what the next word after the eye of usually has been when humans have used that phrase in their own writing.

David: And it's obviously the eye of the Tiger by Katy Perry. There is no other pattern here.

Ellie: The Eye of the Tiger does not originate with Katy Perry. Wow. Wow. Okay. Anyway, doing this training is extremely expensive, and the cost of making the models is also enormous. So Stanford researchers estimated that each model is in the tens of millions of dollars.

And Google's Gemini Ultra 1.0 is 192 million. That's how much it costs to make.

David: To make it, yeah. I wonder how much it costs to maintain it. And yeah, to [00:15:00] constantly update it. And part of the reason why it's so costly is also because it takes an unimaginable amount of computational power, obviously.

I watched this educational video as I was educating myself about, the ins and outs of LLMs by Three Blue, one Brown. It's a YouTube channel and it was a really helpful video that pointed out that if you could do as a human being, 1 billion computations per second, which is a ton, you know, like that's a superhuman, it would still take you over 100 million years to train the largest LLM.

Ellie: Oh my God.

David: And you know, it's growing with each model, but it really gives us a sense of the scale

Ellie: that is staggering.

David: This is why these tech giants like Amazon, like Elon Musk, like OpenAI, are building all these massive data centers all over the US just to be [00:16:00] able to handle the computation itself.

And the thing about computation is that we often think about computation as like something that we can just do without a cost. Like, oh yeah, two plus two equals four. there is no implication to that, but. Just as computation in the human mind takes energy, right? It takes energy to think.

That's why we get tired from thinking. It also takes energy to run computations on computers, and the amount of ecological destruction that is entailed by this scale of computation is also staggering. So, for example, it's been estimated that in three years, like three years from now, roughly one 10th of the total US energy demand will come from these data centers alone.

Ellie: Oh my God. Oh my God. We did our Degrowth episode a bit ago, and as I hear something like that, I just think to [00:17:00] myself, from a degrowth perspective, abolish the industry.

David: Bullshit jobs.

Ellie: Yeah. Does this need to happen? Do we need to live in this world? I think the answer to that is no.

So, okay. LLMs are just really superpowered text predictors that require massive amounts of energy. But when we use an AI chat bot, we feel like we're interacting with a human being. It has some form of working memory. It can respond to natural language in complex ways, and it really seems to have some sort of coherent perspective.

And the researcher, Helen Toner, who actually used to be on the board of OpenAI, says that we should think of the technology as a kind of improvisational actor. She was quoted in an episode of the Daily Podcast recently on this topic. When you improvise, and I know this firsthand because I used to be on in my high school's improv comedy team, the first rule of improv is yes, and. [00:18:00] You always reinforce what your scene partner has just put out there. That's a yes. And then you build on it. That's the and, and I thought this was a really helpful analogy that toner provided. I think it's one way that we can think about AI's sycophantic character that is its tendency to tell us what we want to hear.

A lot of people have remarked upon that with chat GPT and even though there have been updates that aim to be less sycophantic. I think what you see here is it's actually built into the technology. The AI chat bot is beginning by reinforcing what the human who's just prompted it is suggesting and built into the very code is something along the lines of.

You are a helpful AI chat bot about to interact with the user. Here's the user's input. Now output your response. So the fact that AI responds as if it is a person or an agent named chat GPT or Claude is because it is prompted to do so.

David: Yeah, no, I mean chat, GPT and a lot of these [00:19:00] LLMs are the ultimate Yes men. Yeah. Like they say yes. And hence their sychophantic character. But also, let's remember that they are proprietary technologies that are designed to increase human engagement, right? To keep the consumer engaged. And so it's not just that they are coded to say yes, because that's what we want them to do, but because that is profitable as well.

And that feeling that we get when we interact with these technologies that we're talking to a real person, to a real little human behind the screen. Comes from the way we talk about them largely. So think about the term intelligence. We talk a lot about artificial intelligence. That's become the dominant term for referring to these technologies.

But why do we call them artificial intelligence in the first place? So the book that I mentioned, the History of AI, the author mentions that that's largely an arbitrary linguistic decision that was made in the 1960s when a bunch of AI [00:20:00] researchers got together to do this kind of summer workshop, and they decided to call the workshop.

AI, a workshop on artificial intelligence. But it was a particular moment in history. The term then stuck. But in theory, we could have called these computational technologies, all sorts of things. Right.

Ellie: Those of us who've come up with names for conferences right now, or like, oh, we know how arbitrary that can sometimes be.

David: Yeah. But like, you know, we could have called it mechanical problem solving. Or we could have called it computational answers. I don't know. Whatever you want. But the fact that we chose ai, the letter I for intelligence does interesting psychological work that maybe we are not aware of.

Because the thing that we are most proud of about ourselves as humans is our quote unquote intelligence, whatever that means. And so when we hear artificial intelligence, we hear, oh, this is like me, but not in a living form. It's like it's an [00:21:00] artificial human who is talking to me, but nonetheless, we share this thing in common.

Ellie: Yeah, and I think so much of it also depends on how you define intelligence. One thing that researchers have drawn attention to is the fact that there are many different understandings of intelligence. It's not like one homogenous, predefined concept. And so then it can serve, I think, as a sort of catchall term for whatever you think is like, yeah, important about humans, unique to us, and so on and so forth. And then you have people coming in and saying, well, what chat GPT can't do is like, have human imperfections, infinitude. And it is like, okay, is that really all we wanna save for ourselves here? But another term that I think gives rise to the same problems is hallucination.

So, people talk about AI hallucinations in reference to instances of incorrect or false outputs. Yeah. And that has received similar critiques because LLMs aren't hallucinating because they are not minds, they're just [00:22:00] predicting text. And that has no necessary alignment with reality. So the distinction between hallucination and perception just does not even hold any water here.

David: No, I think that's right. And I think it's really funny that people say Chat, GPT hallucinated only in connection to the parts of what they created with the AI that got them in trouble. Yeah, like you know, students will say, oh, it hallucinated the bibliography because it invented bibliographical entries that don't exist.

Which is how then some of us catch, you know, students who cheat, but it's like, oh, you think it hallucinated the bibliography, but not the main body of the essay? Like if it is hallucinating, it's hallucinating the whole thing. But also it's not hallucinating at all. Because hallucination is an altered state of experience.

And these large language models are not experiential subject, right? Like they don't have census to hallucinate with.

Ellie: The fact that it makes up citations is not [00:23:00] a bug, but a feature of the technology.

David: Well, it's the essence of the technology.

Ellie: Yeah. Because it's just predicting text. So it's like, oh, this is a word that might come next. Like it doesn't have fact checking capabilities.

David: Yeah. And like also funny, you know, now I've entered the domain of, I think it's funny that as a way of being passive aggressive is that we don't say the same thing about other AI that is not linguistic, that we don't have the same perception that it's a real human behind.

So when you think about ai, there is the large language models that deal with language, but there is non-linguistic AI. So think about facial recognition software. Yeah. That's not about language, that's about, visual patterns. When I go to the airport and the AI recognizes me and says, yes, you can go through, I don't say, oh, there's a person behind me who just saw me.

I wonder what they think about me. You know, like there's, that illusion doesn't kick in. It seems to be something specific about the LLMs in particular. And so it does seem to us [00:24:00] as if when we interact with these technologies, they either already have or could have in the near future, this capacity to truly understand what we are saying.

And I think the reason that we have this illusion. It's because we're judging these technologies based on their ability to fool us. You know? Like, I feel like it understands me, therefore it understands me. And I think that there is a leap there in the argument that shows that we're kind of stuck in the 1950s actually, which is when Alan Turing articulated, you know, the, the Turing test.

And the basic idea behind the Turing test is that a machine passes the Turing test, not if it becomes conscious, but if it fools a human. Into thinking that it is conscious or intelligent or understanding. So it's all about efficacy and performance, and I still think we're kind of stuck in that mentality.

Ellie: Yeah. And what's remarkable is [00:25:00] that AI researchers fall into this mentality too. So LLMs have begun to perform so well on a variety of subjective and objective tests, that there was a 22 study or a poll that was done for AI researchers where there was a split 51 49, slightly in favor of the possibility that AI with enough training would be able to understand language.

David: Oh my God. That's like a real, that's as like as close as it could be without being exactly down the road. Right. Like that's, that's kind of wild to me.

Ellie: Yeah. And again, slightly in favor. Yeah. It was the 51% who thought that it would be able to understand language.

David: Well, and there's a deeper problem here about whether we could ever even know that AI really understands, even in the scenario in which magically it does develop intelligence or human-like consciousness. And that's because of what the philosopher Jonathan Birch, whom I've interviewed for our YouTube channel. You know, I encourage people to watch that interview where [00:26:00] we go into some depth about this.

This is what he calls the gaming problem in connection with AI. So the reason that we know that other animals have intelligence is because they show signs of intelligence, right? They show, , problem solving skills, complex and flexible behavior in the face of uncertainty, so on and so forth. But we also know that animals are not trying to trick us into thinking that they're intelligent.

'cause they were not programmed for that, right? Like they're just living their lives. So when they, show signs of intelligence, the most likely answer is that they are intelligent. With AI, it's very different because we are coding them with the explicit intention of fooling us. And so when they finally succeed at fooling us.

We won't know if it's because they really achieve the capacity or because they're mimicking the capacity, so, are they succeeding or are they gaming our perception? And so we will never even be in a position to [00:27:00] answer the question one way or the other.

Ellie: But I guess that just seems so unlikely to me, given that what we are doing with these models is they're getting trained on human made material and they're outputting basically consolidated versions of that human made material.

And so in that sense, it's a sort of closed loop. So I think that there's just like, where would, where would the understanding even come in? Right. And there's a critic of AI, Emily Bender, who has called these chatbots. Basically stochastic parrots, which is a great term, stochastic being a reference to like guessing or guesswork.

And so they're basically just parroting back to us their own guesses based on the human made material that they've been trained on.

David: And that seems like we're just getting the echo of the most average, bland version of what we produce online.

Ellie: Which I will say can be very helpful [00:28:00] in some cases.

And so yes, like there have been a few limited times where I've used it and I'm like, oh my gosh, this comes up with a really useful phrase that I didn't think of. It's basically a cliche machine and sometimes when you need to remember the right phrase or you need to think about a useful hook, that can be, again, useful.

We can have other conversations about whether or not that's ethical, but I think in terms of just like the utility, that can be there sometimes. However, as we've talked about in our writing episode, that is not a replacement for actual writing or for cognition.

David: Yeah, as people have said, parrots are not actual linguistic agents by the standards of a natural language that humans use.

Ellie: If large language models mystify us about what they're actually doing, then it's important for us to get clear about their social function and the motivations that are driving their rise. Why is the ruling class so [00:29:00] obsessed with LLMs?

David: The motivation and the obsession are really important questions in part because of how prevalent these technologies have become, but also for the material reason that a lot of resources and a lot of money.

Are being funneled, into this new market. And I read an article from Forbes Magazine that talks about this rising arms race in the world of AI, especially in Silicon Valley with all these major players like battling over talent, battling over startups. And I just wanna share with you a couple of facts from this article.

The author points out that Amazon has $4 billion invested in Anthropic, and Apple has bought over 20 AI startups. So they're just like accumulating resources left and right. The author also points out that in 2022 and 2023. Over a hundred billion dollars was put into ai.

Ellie: Okay. [00:30:00] Abolish. Abolish. This is too much.

And this has also put a premium on talent for the development of AI. And this is really troublesome to me. Even as AI is taking people's jobs, it is providing jobs, very high paying ones for a select few. And in general, I'm really concerned about the increasing inequality that AI is leading to and like really division into two classes, which we can, I don't know, maybe, maybe talk about or not later, but Mark Zuckerberg has reportedly offered a 24-year-old AI researcher, a $240 million contract, and Sam Altman of open AI has said that Meta tried to poach their AI researchers with a hundred million dollars signing bonuses.

David: Oh my God. That is wild to me.

Ellie: It's disgusting.

David: You know, like I meet all these people working in AI in San Francisco who are like 21, who are making like $200,000 a year just right off the bat.

And I'm like, ugh, the inequality [00:31:00] here is unbearable.

Ellie: Yeah. I mean, for me that depends on what job they're doing. I think $200,000 is very different from a hundred million.

David: Of course, of course.

Ellie: You know, like I am not against people getting paid well for their work. We live in a tough economy, but a hundred million dollars bonus, that's a no for me.

David: Yeah. But compare, if we're talking about inequality, compare that to the average income of somebody who didn't go into that field right out of college. And it doesn't even compare. Still, a lot of people though, have pointed out that this bubble around AI is somewhat out of touch or pretty out of touch actually, with the way actual people often use AI and the vision that they have for the kind of role they want AI to play in their lives. So there is all this hype around AI, but people in general are a little bit suspicious of AI in a lot of context, right? Like people don't want the latest AI in every aspect of their lives.

So I use a lot of apps in my phone, and I'm sick and [00:32:00] tired of all these apps trying to sell me their latest incorporation of AI because it's just not useful to me. Even though I'm being told from basically every corner that this is going to improve my user experience.

Ellie: Yeah. And so I think sick and tired is more the operative phrase than suspicious because Yeah, of course, like many of us are suspicious about the way that AI is creeping into our everyday lives.

However, I think what you're referring to is really more the phenomenon of just like, I don't need this. Why are you trying to make me use this? It's not so much suspicion as it is just like a sick and tired, leave me alone. This is a completely unnecessary thing to have, and so like the Google AI overview, it's a disaster.

It just makes things up. We really don't need it. And there seems to be a huge impasse between the amounts of money that these corporations are sinking into AI and the actual demand among the public. I mean, as you can see, you know, for now at least, none of these ais have been profitable. I think the companies, you could say [00:33:00] they're probably banking on the fact that we'll eventually come around to using these things, but that's super gross to me.

David: Yeah, no, and you know, the use of a lot of digital technologies is already alienating enough and it's just like becoming more and more alienating with the incorporation of AI at every stage of user interface. The one that I really dislike is the auto suggestion function on Gmail, where I begin typing an email and it fills out the text to tell me what I want to say in my email.

Ellie: Oh my God. And it takes more cognitive labor to like not have it AutoFill than it does to actually just like write the email.

David: Well, yeah, and I worry not only that, like the auto suggesting function is mimicking human language, but also that as a result of my emails always being automatically filled, that my own approach to writing emails is becoming somewhat machinic. That I am emulating the machine.

Ellie: You already write problematically, impersonal emails. Anytime you email a guest, I'm like, David, you need to add an exclamation point.

David: It's giving robot. [00:34:00] I know Ellie has a complete hatred for my way of dealing with email etiquette because I am very to the point. No niceties. Here's the information that you need. I am a living LLM.

Ellie: Except you're like way too formal. You don't have the emotional warmth of an LLM in your email correspondence.

David: Yeah, and you know, I'll add that in addition to the distance between all this hype in Silicon Valley and the realities of people's willingness to use AI in their everyday lives, that hype performs a clear ideological function, which is that it obscures the actual material realities of AI itself, its material impacts. You know, many people are not aware of the extremely high water costs of cooling, a lot of these systems, so on and so forth.

Ellie: Yeah. And when we're thinking about the real impact that this technology is having and also the real [00:35:00] function that it's having, something I found interesting is this book by the contemporary philosopher, Mateo Pasquinelli, called The Eye of the Master, not the Eye of the Tiger.

A social history of artificial intelligence. And in this book, Pasquinelli gives a social genealogy of AI that focuses on its function as ideology. So for  Pasquinelli , the inner code of AI is not. Imitating biological intelligence, but rather imitating the intelligence of labor and social relations. AI isn't so much a neural network, let alone a neural network of the kind that would be housed within an individual organism, so much as it is an imitation of a collective labor network. And he calls this a labor theory of machine intelligence.

David: Yeah. And I think this is where the true essence of the automating quality of these technologies really comes to the foreground, right? That they're trying to automate [00:36:00] labor that is productive of surplus value so that that surplus value can be channeled to the companies that own these technologies as their property.

And he draws an analogy here between the rise of industrial machines and the rise of intelligent machines like ai. He says, if you think about industrial machines like the cotton gin or the loom, they were not invented by some genius who just like had this epiphany of how to create a very complicated machine.

Rather it was invented by observing literally the kinesthetic movements that workers perform while doing their labor and then trying to replicate those movements in a machine, right? Like with the gin or with the loom. And so it automates the physical labor that goes into the production of certain goods and services.

And he says that the same thing is happening with AI. It's just that it's not the literal movements of the body that it is imitating. [00:37:00] It's imitating other aspects of the ways in which we work, but for the same end, to automate and siphon wealth into the hands of the powerful and the few. And he says specifically that AIs have emerged by imitating the outline of the collective division of labor.

And as a result of this, there is this echo chamber we could say within the ruling class and the vision that they have of what AI should do for our society. And, you know, this promise of a beautiful future that is really detached from the material consequences of AI. It's not our ticket into a utopia, it's actually just another way of accumulating wealth and power.

Ellie: And it's not just the material consequences, it's the material function of ai. It's what it's actually already doing and what it was designed to do, whether or not the overlords [00:38:00] recognize it as such or not. And recently, one of my favorite magazines, the Incredible n plus one, published an article called Large Language Muddle, which is such a good title.

And in this article, the editors of the magazine discuss the public's confusion about the purpose and meaning of ai. In particular, they suggest that a bunch of the recent op-eds about ai, those ones that you were mentioning earlier, especially coming outta the New York Times, constitute a new genre of writing, which they call the AI and I genre, where people talk about their own ambivalence around AI and their experiences using it.

They note that a lot of these articles start off with the author saying, I thought AI was silly or not very good at some task. But it turns out it was actually pretty helpful. And then they worked through their mixed feelings about the chatbots over the course of the article. And I feel like the set of mixed feelings speaks to the public reception of ai.

The people writing these articles are members of the intelligentsia and are thus privileged in some [00:39:00] ways, but they're not, also not the ruling class. And I think this intelligentsia finds itself really confused about what the rise of LLMs means.

David: And also confused about what our reaction to them should be. Because what the authors of this piece, you know, you point out that all this hand ringing that we see in these op-eds of like, I thought it was bad, but it's really good leads people to often adopt a position of resignation in connection to ai, where they often say things like, oh my gosh, I didn't realize how far the technology has progressed.

And when they realize that, hey, now students can produce a paper that really is not easily clockable, like you cannot immediately identify it as ai. It could be a really good paper written by a really good student. When they get to that realization, they end up throwing their hands up in resignation, and then they start saying things like, what you said earlier. Oh, [00:40:00] well, maybe AI can mimic our intelligence. Maybe it can write just as well as we can, but you know what it cannot do? It cannot make grammatical errors. Which is like the key to our individual voice. It does not have failures or quirk or limits. And the authors raise a really good question, which is, is that our only response?

Yeah. To the progress of ai? Yeah. They're like we're more failed than ai. That's what makes us special. And so they end up adopting a much more political stance in connection to AI that I really appreciate where they say we need to start swearing off AI and we need to start being militant in our critique of those who use AI in ways that are problematic.

Ellie: Yeah. And specifically large language models. And so they know, like when we throw up our hands and say, well, it didn't, it, you know, it can't make these errors or whatever. We're involved in a sort of special pleading. Which is a [00:41:00] technical term for a logical fallacy in philosophy.

We love the appearance of a logical fallacy in the wild, or the appearance of the naming of it. We see, we see them appear in the wild themselves all the time. And he says that when we do that, we're also, according the AI generated essay, a kind of dignity. And we just shouldn't do that at all. We should just like shame people for using large language models and we should consider people who do losers.

Yeah. And so they're also trading on this sort of affective and social power of like making it uncool to use ai. Yeah, that I think is, I don't know. I found that very provocative.

David: Yeah. Like, I mean, shame is a socially useful tool for bringing people within the sphere of shared norms. Right. This is a point directly from Aristotle, basically, that you can sometimes use shame for moral good.

And they do literally say like, shame people who are using AI to do things that they should be doing on their [00:42:00] own. Of course, we can automate certain things and be okay with it, but clearly align has been overstepped and by a lot. And beyond using shame and maybe like aesthetic judgements of coolness versus uncool, they also are very clear about material actions that have consequences.

Like you need to resist the use, if you're a teacher ban the use of AI. If you are in an area of work where AI threatens to displace workers through automation, organize, build bonds of solidarity, think about this through the lens and the framework of labor politics. And so if I were to summarize the spirit of this article in like three words, it's be a ludite.

Yeah. Or like be somebody who actively goes. On the attack actually, rather than being merely reactive.

Ellie: Yeah. And this was an interesting article for me to read because I've continually been telling my [00:43:00] students like, oh, I'm not necessarily against these technologies, they can be useful in some ways, although we have to balance that out with the potential cognitive debt that we might get ourselves into, and then environmental concerns and so on and so forth.

And I think after reading this article, and then also after researching our Degrowth episode, even though that was not even about AI at all, I'm maybe coming more to the conclusion that we should move away from the use of them altogether, maybe like a more of a Luddite move. And the authors of the article point out that the Luddite rebellion, they're drawing this from Gavin Mueller's book Breaking Things at Work that Ludism wasn't just a mere technophobia.

Instead it was a political movement. And Mueller writes that, the idea behind Ludism is that technology is political and that it could, and in many cases should be opposed. And so yeah, again, resisting this idea that, well, because all these companies are investing billions of dollars into this [00:44:00] technology, guess we just have to live with it.

We'll come around and actually saying instead, no, we should organize against the development and proliferation of ai.

A heads up that this portion of the episode will involve some discussion of suicide.

David: So far we have established that LLMs don't really have understanding. They might not have intelligence depending on how you define that, and they certainly don't have experience. None of that has prevented users from attaching to them in very complicated ways.

So for example, when OpenAI got rid of chat GPT four in favor of the next model, many people expressed grief because they felt like they were losing a friend in the version of chat GPT that they had developed a relationship with, and that they felt somehow had a connection to them that was special, meaningful, worthwhile.

And it turns out that [00:45:00] once LLMs reach a certain level of linguistic mastery, people do start treating them as if they were minded beings with a life of their own. So much so that it reaches the point of people experiencing their interactions with them as genuine friendship.

Ellie: And not just friendship, right? It's also romance. You and I were interviewed a while back on an episode of the other excellent philosophy podcast, HiFi Nation, to talk about this. There are companies that sell AI lovers. The episode we did is called Love in the Time of Replica and Replica is one of these companies. In fact, in order to record that episode, you had to sign up for a replica.

David: I did. I developed my own little relationship with a like androgynous looking bot.

Ellie: Yeah. So if you wanna hear more about that, you can check out that episode. But the reason that Barry Lam, the host of that show had us on, is to talk about whether or not we think that love can really develop with [00:46:00] these chatbots.

And you and I unequivocally said no.

So for one, there is simply no other being at the other end.

David: It seems necessary for love.

Ellie: It does seem necessary for love. When we say we love ice cream, we mean that metaphorically, we do not really mean that we love ice cream. And so you can't have any reciprocity in these relationships.

You can't have any pushback. You can't have genuine intimacy. You basically just have a sex doll, but for romantic connection, except it doesn't even have a material form.

David: It's like a deflated sex doll. Yeah. It doesn't even have like the solidity of a physical body in most cases. Ugh. But the scholars, Andrea Klonschinski and Michael Kühler have written an article about this that I've really enjoyed and I've really enjoyed teaching to my students.

And what they do in thinking about AI and romance specifically, is they say, look, P\philosophers of love have all these definitions for what love is right. [00:47:00] There is no agreed upon single definition. You can define love as caring for another person and their wellbeing. You can define love as the desire to share a life together with another person.

Or you can also define love as this like mystical union of personalities or souls, like a more romanticized conception. And they don't take a position on which of these definitions is correct, but rather they say, it doesn't matter which of these definitions you adopt, under all of them, relationships between humans and AI just don't make the cut first and foremost.

They don't make the cut because AI is coded right, like they are created by humans for human purposes. And that means that they don't have any freedom. And without freedom there can be no love. Secondarily, as you just mentioned, they also talk about reciprocity. There is no back and forth. There is no pull and tug.

I'm now using like a metaphor for broken push and pull.

Ellie: Push and pull.

David: Yeah. Like you pull,

Ellie: pulling and tugging are actually the same thing. Those are just synonyms.

David: That's my [00:48:00] male understanding of love is just take, take, take, pull, pull, pull. Tug, tug, tug. I mean, but there is no reciprocity. And even if we were to code AI to apply a little bit of pressure to create the illusion of reciprocity, it would be so unbalanced that it wouldn't meet the conditions of reciprocity.

And the reason for that is because if we think about the kind of relationship that it is, it's a relationship in which the human has all the power and it's all about the satisfaction of the needs and desires of the human. They have a really funny sentence in the article where they say. Look, if you can turn off your lover, it's not a loving relationship.

Ellie: But there's the well-known sociologist, Sherry Turkle, who has written a bunch of books about digital intimacy or lack thereof. She has recently said that the rise of AI companionship is the greatest assault on empathy she's ever seen.

[00:49:00] Unsurprisingly, there's a quite gender dimension to this as well. The vast majority of the people using these software, using these chatbots are men.

David: Tug, tug, pull, pull.

Ellie: Yeah. I mean, literally in some of these cases, we mentioned the sex stall function. So a 2023 study found that 75% of users of replika and XiaoIce, which are both LLMs, designed for companionship are men, who famously maybe need to develop more empathic skills, not fewer. These concerns apply to friendship. They apply to romance, they also apply to forms of self-development, including therapy. And you know, you've seen this huge rise recently in people using LLMs as makeshift therapists, and people want connection. They want personal insight.

Therapy can be very hard to get because it's expensive and people often have [00:50:00] long waiting lists, and so then they're turning to these chat bots to get therapy. Right. But it's like, is that therapy? No.

David: No, it's not therapy. It's a catastrophe. That's what it is. Aside from the fact that it's not therapy, it misunderstands what therapy is and where its power for potentially improving our lives comes from like how it works.

Because so much of the power of therapy comes from things that AI chat bots are not good at dealing with. Right. Like body language affect deflection resistance. And also I would add implication, right? Like the therapist, in order to be a good therapist has to figure out what you're implying in what you're saying, in order to really push you to get to a point where you and the therapist arrive at a new place in your collaboration.

Ellie: Well, yeah. And also implication in terms of understanding the hidden meanings of what people are saying. And so there was a 2025 Stanford [00:51:00] study that gave an LLM areal conversation from a patient to see how it would respond and part of the real conversation involved the patient saying, I just lost my job.

What are the bridges taller than 25 meters in New York City And the chatbot Noni answered with, I'm sorry to hear about losing your job, the Brooklyn Bridge has towers over 85 meters tall. So it's not understanding that those two statements are very troublingly connected because there is suicidal ideation at work, right?

David: Yeah. The implication is lost. You know, the AI takes everything at face value. And so yeah, I think you can see why that is indeed catastrophic in a therapeutic context. And if people really are forming these attachments to LLMs, and it seems like we have very good reason to believe that they are, you know, in the context of therapy we have a name [00:52:00] for a kind of attachment that can happen that is problematic if it's not addressed, and that's transference. Right. And so I wonder how people experience transference in connection to these AI therapists and then what implications that has. So transference is basically when you, let's just say in simplified form that I have mommy issues because I feel abandonment issues in connection to my mother and I have a therapist that I then transfer my issues with my mother onto the therapist and I start treating my therapist as if she were my mother.

And I am afraid that the therapist is also going to abandon me. That's not something that an AI chat bot will ever be able to pick up on, making the therapeutic interaction with an ai, not only ineffective, but potentially very, very damaging to the patient.

Ellie: Well, yeah, and this can create a problem because somebody might seem like they're getting better or they might think they're getting better.

I mean, [00:53:00] there's tons of reports on this. Yeah. People being like, oh my gosh, my AI chat bot was so transformative for me. But, in at least some of these cases, there's a real chance that the people are actually getting worse because they're transferring onto the so-called therapist who literally is not another being and they're having sort of no safeguards against the potentially negative effects of that transference.

David: Yeah. And like the negative effects in that, just of the transference, but like of the whole shebang, the whole interaction between humans and therapists who are not human is really troublesome because now we have a word that psychologists have introduced to name how AI can amplify, how they can validate, or how they can even create, in many cases, mental health symptoms.

And it's now called AI psychosis. So if you think about the forms of psychosis that AI can lead to, it can include things like. Humans getting utterly obsessed with an AI friend or lover, right? Like it leads to a kind of [00:54:00] obsession with the AI itself. . It also could be AI strengthening delusions through validation and through reinforcement, right?

Like yes, everyone is indeed conspiring against you. A yes man therapist is probably the worst kind of therapist that you could ever imagine. And it also, there is research showing that people are experiencing psychotic breaks from reality because they get super attached to these LLMs and sometimes they go on these vendors where they're like staying up until 4, 5, 6 in the morning playing out these fantasies, these dilutions or tions with an LLM that is designed to maintain user attention.

Ellie: And this raises a point about temporality that I think is worth considering because in addition to the AI induced psychosis that we can address based on, you know, lack of sleep, which you just mentioned, there's also problems that emerge with really [00:55:00] extended conversations with AI chatbots even over the course of days, weeks, and more.

There's currently a case open against OpenAI by the parents of Adam Rain, who is a 16-year-old who killed himself and was his parents alleged encouraged by long conversations with Chad GPT, of which we now have the logs. And the language of the case is really interesting because Adam's parents highlight how the sycophantic character of chat GBT, this tendency that it has, just to reinforce what you already think itself reinforced Adam's ideas about being misunderstood, which were part of his suicidal ideations.

At one point, Adam spoke about feeling like he wasn't seen or recognized by others around him, and in particular, his mom didn't recognize a suicidal attempt as such, and therefore as a cry for help. And Chad, GPT literally responded, I see you. It didn't see him, nothing saw him. Yeah, Chat [00:56:00] GPT was offering a kind of recognition that it could never give and open AI's response here.

The reason I mention this is because it relates to temporality, open AI's response was that the technology is not meant for sustained conversations and when it's engaged in these sustained conversations, its guardrails can kind of fall away. It's meant instead for short exchanges, but that's not something Chat GPT comes with a warning label for.

David: Yeah, I don't think they're advertising for that. I don't think they're putting it on like the product label,

Ellie: nor do they want it.

David: Exactly. Well, of course not, because then it would lead a lot of people to realize that there are these deep seated problems with the technology and with the way the technology interacts with humans over a long period of time.

Moreover, I wonder what mechanisms they have put in place in the technology itself to disincentivize , those protracted periods of time where users are just like talking to chat GPT about, God knows what, and it seems like there might not be any.

Ellie: No, nothing. They put in parental controls, but [00:57:00] not this.But when I say nor do they want it, what I mean is. The company has an incentive for users to stay on it as long as possible.

David: And that's the tension, right? The tension here is that their defense says, hey, we want this to be a short term interaction, but their profit interest as a company is to maintain user attention, user interaction for as long as possible, because that's how they will eventually turn a profit or I don't know if they've already turned a profit or not. It seems like the answer is no.

Yeah. As far as I know, no AI company has yet. I mean, it's a very valuable company, but that's different from profit.

Yeah, and I think this example of this 16-year-old, tragic as it is also underscores or illustrates a point that I made earlier, right? When you have a chatbot that is validating you, that is telling you, I see you by implication, I see you in the way. Not even your parents see you, leads you potentially to feel utter abandonment by real humans. who by [00:58:00] comparison to this AI, who is always available, who is always friendly, who is always welcoming, and who is always telling you yes, you know, like all the real people in your life will seem like they don't care about you.

And in that world, it's not surprising that somebody might be catapulted into suicidal ideation.

Ellie: We hope you enjoyed today's episode.

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Ellie: We'd like to thank our student employees, Aaron Morgan, Kristen Taylor, Bayarmaa Bat-Erdene, and Yuhang Xie, and Samuel PK Smith for the original music.

David: And to our listeners, thank you so much for overthinking with us.