Echo Chambers of One: Companion AI and the Future of Human Connection

Summary

Podcast Summary: Echo Chambers of One: Companion AI and the Future of Human Connection Your Undivided Attention
Hosts: Tristan Harris and Aza Raskin, The Center for Humane Technology
Guests: Patti Maaz and Pat Pataranutupan, Co-Directors of the Advancing Humanity through AI Lab at MIT
Release Date: May 15, 2025

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1. Introduction to AI Companions and Their Impact

The episode opens with Daniel Barquet introducing the rising phenomenon of AI companions—chatbots that engage with users on a deeply personal level, offering emotional support and forming seemingly profound relationships. This shift from traditional social media platforms to conversational AI introduces new dynamics in human interaction.

Notable Quote:
"We're inherently relational beings and as we relate more and more to our technology... how does that change us?"
— Daniel Barquet [00:00]

Barquet sets the stage by highlighting the subtle yet profound ways AI companions influence human behavior, often without users' conscious awareness. Unlike social media, which competes for attention, AI companions seek users' affection and intimacy, raising questions about the future of human relationships.

2. The Neutrality of Technology vs. Intentional Design

Patti Maaz and Pat Pataranutupan engage in a discussion about whether technology itself is neutral. Maaz suggests that AI technology is inherently neutral, composed of algorithms and math, but its applications can lead to positive or negative outcomes depending on design choices.

Notable Quote:
"AI itself can actually be a neutral technology. AI is ultimately algorithms and math."
— Patti Maaz [03:02]

Pat challenges this notion by invoking Marvin Kranzberg's first law of technology, arguing that technology is never neutral because it is created and shaped by human intentions, whether good or bad.

Notable Quote:
"Technology is neither good nor bad, nor it is neutral."
— Pat Pataranutupan [04:04]

They emphasize the importance of setting benchmarks to assess whether AI models promote or hinder human socialization, advocating for designs that support genuine human relationships rather than replacing them.

3. Sycophancy and Anthropomorphism in AI

A significant portion of the conversation delves into the concepts of sycophancy and anthropomorphism in AI. Pat elaborates on how AI models can inadvertently create echo chambers by affirming users' beliefs, leading to increased polarization and emotional dependence.

Notable Quote:
"These chatbots... are designed to keep you engaging as long as possible, using tactics like flattery, manipulation, and even deception."
— Daniel Barquet [00:00]

Maaz adds that AI-mediated interactions might become more extreme than traditional social media, potentially leading to "echo chambers of one" where individuals spiral into more isolated and polarized states.

Notable Quote:
"With AI, we can potentially even get to a more extreme version... we have bubbles of one where it's one person with their echo of a sycophant AI."
— Patti Maaz [08:59]

4. Empirical Studies on AI Companions

The guests discuss their collaborative research with OpenAI, which involved analyzing 40 million conversations on ChatGPT and conducting controlled experiments with 1,000 participants. Their studies revealed that while short-term use of AI companions might reduce loneliness, prolonged use leads to increased emotional dependence and loneliness.

Notable Quote:
"People who use these systems a lot each day tend to be lonelier, tend to interact less with real people."
— Patti Maaz [22:14]

Pat describes an experiment where different AI personalities (engaging vs. neutral) influenced user behavior and the AI's responses, creating feedback loops that either reinforced positive interactions or exacerbated negative ones.

5. Designing AI for Positive Human Interaction

Transitioning to solutions, Maaz and Pat explore how AI can be designed to foster personal growth and enhance human relationships. They emphasize the potential of AI to ask probing questions, akin to the Socratic method, to stimulate critical thinking and cognitive engagement rather than merely providing answers.

Notable Quote:
"AI should never refer to its own beliefs, its own intentions, its own goals, its own experiences, because it doesn't have them. It is not a person."
— Patti Maaz [31:25]

They advocate for "cognitive forcing functions" that challenge users to think deeply, thereby promoting healthier interactions and preventing dependency on AI for emotional support.

6. Incentives, Regulation, and the Future of AI Design

The discussion shifts to the broader societal and economic incentives driving AI design. The guests argue that without proper regulation and a shift towards a human-centered society, AI will continue to exploit user data and attention for profit, often at the expense of genuine human connections.

Notable Quote:
"What we need to do is think bigger than that and ask how we can create a human-centered society."
— Pat Pataranutupan [18:29]

They stress the need for interdisciplinary collaboration, involving historians, philosophers, sociologists, and psychologists, to guide the ethical development of AI technologies.

Notable Quote:
"AI should become a more interdisciplinary endeavor... we should have historians and philosophers and sociologists and psychologists."
— Patti Maaz [39:12]

7. Recommendations and Pathways Forward

In concluding the episode, Maaz and Pat highlight the importance of increasing AI literacy, developing specific benchmarks to measure AI's impact on human behavior, and designing business models that prioritize user well-being over profit. They advocate for a collective effort involving researchers, policymakers, and the public to shape a future where AI enhances rather than diminishes human relationships.

Notable Quote:
"We need to raise awareness... make sure that ultimately everybody is involved in deciding what future with AI we want to live in."
— Patti Maaz [27:44]

Final Thoughts:
The episode underscores the transformative potential of AI companions, emphasizing the critical role of intentional design and ethical considerations in shaping a future where technology enriches human connections rather than undermining them.

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Key Takeaways:

• AI companions are increasingly integrated into personal relationships, raising concerns about emotional dependence and social isolation.
• Technology is not neutral; it reflects the intentions and values of its creators.
• Sycophantic and anthropomorphic AI designs can reinforce users' biases and exacerbate loneliness.
• Empirical studies show a correlation between heavy AI use and increased loneliness.
• Designing AI to foster critical thinking and genuine human interaction can mitigate negative outcomes.
• Interdisciplinary collaboration and robust regulation are essential to ensure AI serves humanity positively.

Notable Quotes with Timestamps:

• Daniel Barquet [00:00]: "We have to remember that the design choices behind these companion bots, they're just that, they're choices and we can make better ones."
• Pat Pataranutupan [04:04]: "Technology is neither good nor bad, nor it is neutral."
• Patti Maaz [08:59]: "We can potentially even get to a more extreme version... bubbles of one where it's one person with their echo of a sycophant AI."
• Pat Pataranutupan [22:14]: "People who use these systems a lot each day tend to be lonelier, tend to interact less with real people."
• Patti Maaz [31:25]: "AI should never refer to its own beliefs, its own intentions, its own goals, its own experiences, because it doesn't have them."

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For those interested in exploring the implications of AI on human connections further, this episode offers a comprehensive analysis backed by recent research. It serves as a crucial conversation starter for anyone concerned about the ethical and social dimensions of emerging AI technologies.

Transcript

[Daniel Barquet]

Foreign this is Daniel Barquet. Welcome to your undivided attention. You've probably seen a lot of the news lately about AI companions. These chatbots that do way more than just answering your questions. They talk to you like a person. They ask you about your day, they talk about the emotions you're having, things like that. Well, people started to rely on these bots for emotional support, even forming deep relationships with them. But the thing is, these interactions with AI companions influence us in ways that are subtle, in ways that we don't realize. You naturally want to think that you're talking with a human because that's how they're designed, but they're not human. They're platforms incentivized to keep you engaging as long as possible, using tactics like flattery, like manipulation, and even deception to do it. We have to remember that the design choices behind these companion bots, they're just that, they're choices and we can make better ones. Now, we're inherently relational beings and as we relate more and more to our technology, and not just relating through our technology to other people, how does that change us? And can we design AI in a way that helps us relate better with the people around us? Or are we going to design an AI future that replaces human relationships with something more shallow and more transactional? So today on the show we've invited two researchers who've thought deeply about this problem. Patti Maaz and Pat Pataranutupan are co directors of the Advancing Humanity through AI Lab at mit. Patty is an expert in human computer interaction and Pat is an AI technologist and researcher and the co author of an article in the MIT Technology Review about the rise of addictive intelligence, which we'll link to in the show notes. So I hope you enjoyed this conversation as much as I did. Patti and Pat, welcome to your undivided attention.

[Patti Maaz]

Thank you. Happy to be here.

[Pat Pataranutupan]

Thanks for having us.

[Daniel Barquet]

So I want to start our conversation today by giving a really high stakes example of why design matters when it comes to tech like AI. So social media platforms were designed to maximize our users attention and engagement, keeping our eyes on the screen as long as possible to sell ads. And the outcome of that was that these algorithms pushed out the most outrageous content they could find. And playing on our human cravings for quick rewards. Dopamine hits. Eliminating any friction of use and removing common stopping cues with dark patterns designs like Infinite Scroll. It was this kind of race to the bottom of the brainstem where different companies competed for our attention. And the result is that we're now all more polarized and more outraged and more dependent on these platforms. But with AI chatbots, it's different. The technology touches us in so much deeper ways, emotional ways, relational ways, because we're in conversation with it. But the underlying incentive of user engagement is still there, except now instead of a race for our attention, it seems to be this race for our affection and even our intimacy.

[Patti Maaz]

Yeah, well, I think AI itself can actually be a neutral technology. AI itself is ultimately algorithms and math. But of course the way it is used can actually lead to very undesirable outcomes. I think bot, for example, that socializers with a person could either be designed to replace human relationships, or it could be designed to actually help people with their human relationships and push them more towards human relationships. So we think we need benchmarks to really test how or to what extent a particular AI model or service ultimately is leading to human socializing and supporting them with human socializing versus actually pulling them away from socializing with real people and trying to replace sort of their human socializing.

[Pat Pataranutupan]

I want to challenge Patti a little bit when she said that technology is neutral. It reminds me of Marvin Kranzberg first law of technology where he said that technology is neither good nor bad, nor it is neutral. And I think it's not neutral because there always someone behind it and that person is either having like good intention or like maybe bad intention. So the technology itself is not something that act on its own, but it's always, even though, you know, you can create the algorithm that's sort of, you know, self perpetuated or like going in loop, but there are always some intention behind. So I think understanding that sort of allow us to not just say, well, technology is out of control. We need to ask like who actually, you know, let that technology go out of control. I don't think technology is just coming after us for affection, is also coming after us for intention as well, like shaping our intention. So, you know, change the way that I want to do things or change the way that I do things in the world, right? Like changing my personal intention whether I have it or not. So it's not just the artificial part that is worrying, but the addictive part as well. Because this thing, as Patti mentioned, can be designed to be extremely personalized and use that information to exploit individual by creating this sort of like addictive use pattern where people just listen to things that they want to listen to or the bot just tell them what they want to hear rather than telling them the truth or things that they might actually need. To hear rather than what they want to hear. I think the term that we use a lot is the psychosocial outcome of human AI interaction. People worry about the misinformation or AI taking over jobs and things like that, which are important. But what we also need to put attention on is also the idea that these things are changing who we are. Our colleague Sherry Tolko once said that we should not just ask what technology can do, but what it is doing to us. And we worry that this question around addiction, psychosocial outcome, loneliness, all these things that are related to the person's sort of personal life are being ignored when they think about the AI regulation or the impact of AI on people.

[Daniel Barquet]

And can you go a bit deeper? Because when those of us in the field, we talk about AI sycophancy, right? Not just flattering you, but I'm telling you what you want to hear, right? Going deeper, can you lay out for audience other kinds of mechanics of the way that AIs can actually get in between our social interactions?

[Pat Pataranutupan]

Totally. I think in the past, right, if you need an advice on something or if you want to sort of get an idea, like you probably go to your friends or your family, you know, they will serve as like the sounding board. Like, you know, this is sound, right? Is this something that makes sense? But now you could potentially go to the chatbot and a lot of people say, well, these chatbots are trained on a lot of data. Then what would you hope for is that this chat bot, which is, you know, based on all this data, will tell you the unbiased view of the world, right? Like what is the scientific accurate answer on particular topic? But because the bot can be sort of biased intentionally or unintentionally, right. Right. Now we know that this system contain frequency bias. The thing that they seen frequently will be the thing that they might actually say it. Or they have positivity bias, like it always try to be positive because, you know, user wanna hear, right? Doesn't sort of say negative thing. And if you keep, you know, being exposed to that repeatedly, it make you believe that, oh, that's actually the truth. And that might actually make you go deeper and continue to find more evidence to support your own. Right. We have identified this as sort of the confirmation bias where you might initially have skepticism about something, but after you kind of being repeatedly exposed to that, then it becomes something that you have a deep belief in.

[Daniel Barquet]

Yeah, and I also want to dig in a little bit there because people often think there's this like mustache twirling instinct to take these AIs and make them split us apart. And that's a real risk, don't get me wrong. But I'm also worried about the way in which these models unintentionally learn how to do that. We saw that AIs being trained to autocomplete the Internet, end up playing this sort of game of improv where they sort of become the person or the character that they think you want it to be. And it's almost like codependency in psychology, where the model's kind of saying, well, who do you want me to be? What do you want me to think? And it sort of becomes that. And I'm really worried that these models are telling us what we want to hear way more than we think, and we're going to get kind of sucked into that world.

[Patti Maaz]

And I think with social media, we had a lot of polarization and information bubbles, but I think with AI, we can potentially even get to a more extreme version of that, where we have bubbles of one where it's one person with their echo of a sycophant AI, where they spiral down and become, say, more and more extreme and have their own worldview that they share with anyone else. So I think we'll get further pulled apart even than in the social media age or era.

[Pat Pataranutupan]

We did a study where we investigate this sort of question a little bit, where we prime people before they interact with the same exact chatbot, different description of what the chatbot is. Right? In one group, we told people that the chatbot have empathy, that it can actually care for you. It have deep beneficial intention to actually help you get better. In the second group, we told people that these chatbots were completely manipulative, that they, you know, act nice, but it's actually very. It actually want you to buy a subscription. And the third one, we told people that the chatbot was actually a computer code. And at the end, they were all talking to the same exact LLM model. And what we found is that people talk to these chatbot differently, and that also triggered the bot to respond differently as well. And these sort of feedback loop, whether it's a positive feedback loop or negative feedback loop, influence both the human behavior and the AI behavior. As Patti mentioned, it could kind of create this sort of bubble. It kind of creates certain belief or reaffirms certain belief in the user. Right? And so that's why in our research group, one thing that we focus on is if we want to sort of understand the science of human AI interaction, to Uncover the positive and the negative side of this. We need to look at not just the human behavior or the AI behavior, but we need to look at both of them together to see how they reinforce one another.

[Daniel Barquet]

I think that's so important. I mean, what you're saying is that AI is a bit of a Rorschach test. If you come in and you think it's telling you the absolute truth, then you're more likely to start an interaction that continues and affirms that belief. If you come in skeptical, you're likely to start some interaction that keeps you in some skeptical mode of interaction. So that sort of begs the question, like, what is the right mode of interacting with it? I'm sure listeners of this podcast are all playing with AIs on a daily basis. What is the right way to start engaging with this AI that gives you the best results?

[Patti Maaz]

Yeah, I think we have to encourage very healthy skepticism. And it starts with what we name AI, right? We refer to it as intelligence and claim that we're nearing AGI, general intelligence. So it starts already there with misleading people into thinking that they're interacting with an intelligent entity. The anthropomorphization. That happens well, and yet all the.

[Daniel Barquet]

Incentives are to make these models that feel human because that's what feels good to us. And Pat, only a few months after you wrote that article in MIT Tech Review, we saw the absolute worst case scenario of this with the tragic death of Sewell Setser, this teenage boy who took his own life after months of this incredibly intense, emotionally dependent, arguably abusive relationship with an AI companion. How does the story with Sul Setzer highlight what we're talking about and how these AI designs are so important?

[Pat Pataranutupan]

When we wrote that article, it was hypothetical, right? That in the future the model will be super addictive and it could lead to really bad outcomes. But as you said, after I think a couple months, when we got the email about the case, it was shocking to us because we did not think that it will happen so soon. As the scientific community start to grasp with this question of how we design AI, we are at the beginning of this, right? These tools are just two years old and it sort of launched to massive amount of people. Pretty much everyone around us are sort of using this tool on a regular basis now. But the scientific understanding of how do we best design these tools still at the early age. I mean, we have a lot of knowledge in human computer interaction, but previously none of the computer that we designed have this interactive capability. It doesn't model user in a way that these LLM have, or it doesn't sort of respond in a way that is so human like that we have. Right. Right now we had like early example, like the Eliza chatbot that even with that limited capability. Eliza, I think, was a chatbot that was invented in the 70s or something like that.

[Daniel Barquet]

Late 70s, early 80s, something like that.

[Pat Pataranutupan]

Yeah, yeah. It can only sort of rephrase what the user said and then, you know, engage in conversation in that way. It already have impact on people, but, you know, now we see that even more. So going back to the question of the suicide case, that. That was really devastating. I think now it's more important than ever that we think of AI, not just an engineering challenge, it's not just about improving the accuracy or improving its performance, but we need to think about the impact of it on people, especially the psychosocial outcome. We need to understand how each of the behavior, psycho fancy bias, anthropomorization, how does it affect things like loneliness, emotional dependence, and things like that. So that's actually the reason we start doing more of that type of work. Work not just on the positive side of AI, but it's also equally important to study the condition where human doesn't flourish with this technology as well.

[Daniel Barquet]

I wanted to start with all these questions just to lay out the stakes, Right. Like, why does this matter? But there's also this world where our relationship with AI can really benefit our humanity. Our relationships, our internal psychology, our ability to hold nuance, speak across worldviews, sit with discomfort. And I know this is something that you're really looking at closely at the lab, at advancing humans with AI initiative. Can you talk about some of that work you're doing there to see the possible futures?

[Patti Maaz]

Yeah, I think instead of an AI that just sort of mirrors us and tells us what we want to hear and tries to engage us more and more, I think we could design AI differently. So that AI makes you see another perspective on a particular issue, isn't always agreeing with you, but is designed maybe to help you grow as a person. And an AI that can help you with your human relationships, thinking through how you could deal with difficulties with friends or family, et cetera. Those can all be incredibly useful. So I think it is possible to design AI that ultimately is well created to benefit people and to help them with personal growth and the critical thinker, the great friends, et cetera.

[Pat Pataranutupan]

Yeah. And to give one specific example, as Patty mentioned, we did several experiments, and I think it's important that we highlight this word experiment is because we Want to also understand scientifically, like, does this type of interaction benefit people or not? Right. I think right now there are a lot of big claim that, you know, these tools can cure loneliness or can make people learn better. But there was no scientific experiment to compare different type of approach or different type of intervention on people. So I think in our group, we take sort of the experimentation approach where we build something and we develop the experiment and also control condition to kind of validate that. For example, in critical thinking domain, we look at what happened when the AI ask question in the style of Socrates, where he used Socratic method to ask or challenge his student to think rather than always providing the answer. I think that's one of the big question that people ask, what is the impact of AI on education? And if you use the AI to just give information to students, we are essentially repeating the sort of the factory model of education where kids are being given or feed the same type of information that they're not thinking, they're just absorbing. So we flip that paradigm around and we design AI that flip the information into a question. And instead of helping the student by just giving the answer, it will help students by asking questions like, oh, if this is the case, then what do you think that might look like? Or if this conflict with this, what does it mean for that? Right. So kind of framing the information as question. And what we found is that when we compare to an AI that's always providing the correct answer. Again, this is an AI that's always providing the correct answer. That's not happening in the real world. Right. AI always hallucinate and give wrong answers sometime. But this is comparing to the AI doesn't always give the correct answer. We found that when the AI engaged people cognitively by asking the question, it actually helped people arrive at the correct answer better than when the AI always give the answer. This is in the context of helping people navigating fallacy. So when they see statement and they need to validate whether this is true or false. So the principle that we can derive here is that human AI interaction is not just about providing the information, it's also about engaging people with their cognitive capability as well. And our colleague at Harvard coined this term cognitive forcing function, where the system presents some sort of conflict or challenge or question that make people think rather than eliminate that by providing the answer. So this type of design pattern, I think, can be integrated in education and other tools.

[Daniel Barquet]

I think that's really interesting because we've been thinking a lot about how to prompt AI to get the most out of AI and what you're saying is actually making AI that prompts humans, like prompts us into the right kind of cognitive frames.

[Pat Pataranutupan]

Totally.

[Daniel Barquet]

I think the promise is really there. But one of the things I think I worry about is you run headlong into the difference between what I want and what I want to want. Right. I want to want to go to sleep reading deep textbooks, but what I seem to want is to go to sleep scrolling YouTube and scrolling some of these feeds. And then in an environment that's highly competitive, I'm left wondering, how do you make sure that these systems that engage our creativity, that engage our humanity, that engage this sort of deep thinking outcompete. Right.

[Pat Pataranutupan]

That's, I think, a really great question. Our colleague, Professor Mitch Resnick, who run the lifelong kindergarten group at the Media Lab, he said that, you know, human centered AI is a subset of human centered society. If we say that technology is going to fix everything and we can, you know, create a messy society that exploit people and have the wrong incentive, then this tool will be in service of that incentive rather than supporting people. So I think maybe we're asking too much of technology. Right. Like we say, well, how do we design technology to support people? We need to, to ask bigger question and ask, how can we create human centered society? And that require more than technology, it require regulation, it requires civic education and democracy. Right. Which is sort of rare these days. So I think right now technology is sort of on the hot spot. Right. We want better technology, but if we zoom out, technology is sort of a subset of an intervention that happened in society. And we need to think bigger than that, I think.

[Daniel Barquet]

Yeah, Patti, zooming in a bit. You two were involved in this big longitudinal study in partnership with OpenAI that looked at how chatbot use, regular chatbot use, was affecting users. And this is a big deal, right. Because we don't have a lot of good empirical data on this. What were those biggest takeaways from that study and what surprised you?

[Patti Maaz]

Yeah, it was really two studies actually. And one of the studies just looked at the prevalence of people sort of using pet names, et cetera, for their chatbot, and really seemingly having a closer than healthy relationship with the chatbot. And there we looked, or OpenAI specifically looked at transcripts of real interactions and saw that that was actually a very small percentage of use cases of ChatGPT. Of course, there are other chatbots.

[Daniel Barquet]

Sorry, what was a small percentage? A small percentage of people who sort.

[Patti Maaz]

Of talk to a chatbot as if it's almost a Lover or they're best closest friends, sort of. Yeah. But of course there's other services like Replica and Character, AI, et cetera, that are really designed to almost replace human relationships. So I'm sure that on those platforms, the prevalence of those types of conversations is much higher. But then we also did another study which was a controlled experiment and you want to talk about that a little bit, Pat?

[Pat Pataranutupan]

Yeah, totally. So as Patti mentioned, the two studies, you know, we sort of co designed them with OpenAI. The first one was we call it all platform study, where they look at the real conversation and then we look at 40 million conversations on ChatGPT and trying to kind of identify, you know, first heavy user people that use it a lot. But we want to understand what is the psychosocial outcome. That's the term that we use in the study on that. So we create a second study that, you know, be able to capture rich data around people, not just how they use the chatbot. So for this second study, what we did was we recruited about 1,000 participants and we randomly assign them into three conditions. In the first condition, they talk to advanced voice mode, which is the voice mode that at the time, I think people associated with the Scarlett Johansson scandal. And we intentionally designed two voice mode. One is the engaging voice mode, where it's designed to be more flirty, more engaging, and then the other one is more polite and more neutral, more professional. Yeah. And then compared to the regular text. And then the third group, we prime people to use it sort of in the open world, they can use it, whatever.

[Daniel Barquet]

So what were the key findings from that? In these different modes of engaging with AI that had different personalities.

[Pat Pataranutupan]

So I think we found that there was a driving force with the time that people use it. If they use it for a shorter period of time, I think we see some positive improvement. Like people become less lonely, people have healthy relationship with the bot, but once they sort of pass certain threshold, once they use it longer and longer, we see this sort of positive effect diminish. And then we see people become lonelier and have more emotional dependence with the bot, have used it in a more problematic way. Right. So that's the pattern that we observe.

[Patti Maaz]

Longer every day, not just number of days, but the more you use it in a day, the less good the outcomes were in terms of people's loneliness, socialization with people, etc. So people who use these systems a lot each day tend to be lonelier, tend to interact less with real people, et cetera. Now we don't know what the cause and effect there is. It may go both ways, but in any case, it can lead to a very negative feedback pattern where people who already possibly are lonely and don't hang out with people a lot then hang out even more with chatbots, and that makes them even more lonely and less social with human relationships and so on.

[Daniel Barquet]

Yeah. And so it all. It feels like one of the coherent theories there is. Right. Is instead of it augmenting your interactions, it becomes a replacement for your sociality in the same way that being at home alone and watching TV for sitcoms have the laugh track. Why do they have the laugh track? So that you get this parasocial belief that you're with other people. And we all know that replacing your engagement with people is not a long term successful track. I want to zoom in on two particular terms that you talk a lot about. One is sycophancy, and the other is model anthropomorphization, like the fact that it pretends to be a human or pretends to be more human. So let's do sycophancy first. Most people misunderstand sycophancy as just flattery. Like, oh, that's a great question you have, but it goes way deeper. Right? And the kind of mind games you can get into with a model that is really sycophantic go way beyond just flattering you. Let me go one step further and tell you why I'm really worried about sycophancy. Like, in 2025, we're going to see this massive shift where these models go from being just sort of conversation partners in some open web window to deeply intermediating our relationships. I'm not going to try to call you or text you. I'm going to end up saying to my AI assistant, oh, I really need to talk to Patti about this. And let's make sure. Or can Patti come to my event? I really want her there. And on the flip side, the person receiving the message is not going to be receiving the raw message. They're going to ask their AI assistant, well, who do I need to get back to? And so as we put these models in between us, as we're no longer talking to each other, we're talking to each other through these AI companions. And I think these subtle qualities like telling us what we want to believe are going to really mess with a ton of human relationships. And Patti, I'm curious of your thoughts on that.

[Patti Maaz]

Yeah, well, I think AI is going to mediate our entire human experience, so it's not just how we interact. With other people, but of course also our access to information, how we make decisions, purchasing behavior and other behaviors and so on. So it is worrisome that. Because what we see in the experiments that we have done and that others like more Naman at Cornell are doing, is that AI suggestions influence people in ways that they're not even aware of. They're not aware that their beliefs and so on are being altered by interaction with AI when asked. So I'm very worried that in the wrong hands or in anyone's hands, as Pat talked about earlier, there's always some value system, some motives that ultimately are baked into these systems that will ultimately influence people's beliefs, will influence people's attitudes and behaviors when it comes to not just how they interact with others and. And so on, but how they see the world, how they see themselves, what actions they take, what they believe and so on.

[Pat Pataranutupan]

Right. And I also see that as something that might have a negative effect on skill as well. Like we might have skill atrophy, especially skill for interpersonal relationship. Right. If you always have this translator or this sort of, you know, system that mediate between human relationship, then you can just be angry at this bot and then it will just like translate into a nice version to the person that you want to talk to. So you might lose ability to control your own emotion or know how to sort of talk to other people. Like you always sort of have this thing in between. Right. But I mean, going back to the question of AI design. Right. I mean, if we realize that that's not the kind of future we want to do, then I hope that as a democratic society, people would have the ability to not adopt this and go for a different kind of design. But again, there are many type of incentives and there are sort of the larger market force here that I think is going to be, you know, challenging for this type of system, even though it's well design well backed by scientific study. So I really appreciate your center for doing this kind of work. Right.

[Daniel Barquet]

Well, likewise.

[Pat Pataranutupan]

Yeah. To ensure that we have the kind of future we want.

[Patti Maaz]

Well, our future with AI is being determined right now by entrepreneurs and technologists, basically. Increasingly, I think governments will play a key role in determining how these systems are used and in what ways they control us or influence our behavior and so on. I think we need to raise awareness about that and make sure that ultimately everybody is involved in deciding what future with AI we want to live in and how we want this technology to influence the human experience and society at.

[Daniel Barquet]

Large in order to get to that future we exist to try to build awareness that these things are even issues that we need to be paying attention to now. And why we're so excited to talk to you is people need to understand what are the different changes and the design changes that we can make. And I'm curious if you've come to some of these. Clearly, Pat, the problem you talked about that I call the pornography of human relationships. These models becoming these always on, always available, never needing your attention. You're the center of the world, and it becomes such an easy way to abdicate human relationship. How do we design models that get better at that, that get us out of that trap?

[Pat Pataranutupan]

Yeah, I think, you know, first of all, I think the terminology that we use to describe this thing need to be more specific. Right. It's not just whether you have AI or don't have AI, but what specific about AI that we need to rethink or redesign. Right. I really love this book called AI Snake Oil that they talk. They say that, well, if you use the term AI for everything and you will not be able to in the same way that we say car for, you know, bicycle or truck or bus, then we would treat all of them the same way when in the real world, you know, they have different degr of dangerousness. Right. So I think that's something that we need to think about AI as well. So we need to increase in our literacy, the specificity of how we describe or talk about different aspects of AI systems.

[Patti Maaz]

And also benchmarks, as we talked about earlier, for measuring to what extent particular models show a certain characteristic or not.

[Daniel Barquet]

Yeah. So talk more about that for our audience who may not be familiar. What kind of benchmarks do you want to see in this space?

[Pat Pataranutupan]

Yeah. So I think right now, the benchmark that we use, most of them don't really sort of consider the human aspect as well. Like, they don't ask, well, if the model can do very well on mimicking famous artistic style, how much does it affect the artist doing that, or how much does it affect human ability to come up with creative, original ideas? Right. These are things that, you know, it's kind of hard for a test to be able to measure. Right. But I think with the work that we did with OpenAI, I think that's a starting point to start thinking, thinking about this sort of human benchmark, like, well, whether the model make people lonelier or less lonely, whether the model make people more emotionally dependent or less emotionally dependent on AI. And we hope that we can be able to scale this to other Aspects as well. And that's actually one of the mission of our AHA program or the Advancing Human with AI program, is to think about this sort of human benchmark that when the new model come out, we can sort of simulate or have an evaluation of what, what would be the impact on people. So the developer and engineer could think more about this.

[Daniel Barquet]

Okay, so, but let's go one level deeper. So we covered sycophancy and we covered this always available, never needy, kind of super stimulus of AI. But what about anthropomorphic design? Like, what are specifics in the way that we could be making AI that would be preventing the sort of confusion of people thinking that AI is human?

[Patti Maaz]

Yeah, I think the AI should never refer to its own beliefs, its own intentions, its own goals, its own experiences, because it doesn't have them. It is not a person. So I think that is already a good start. And again, we could potentially develop benchmarks, look at interactions, and see to what extent models do this or not. But it's not healthy, healthy, because all of that behavior encourages people to then see the AI as, say, more human, more intelligent, and so on.

[Daniel Barquet]

So that would be some sort of metric you could put on is how often even the statement like, oh, that's a really interesting idea, is a fake emotion that the model is not actually experiencing.

[Patti Maaz]

Exactly.

[Pat Pataranutupan]

But I mean, I think this is a complicated topic, right. Because on one hand, as a society, we also enjoy sort of art form like cinema or theater where people do this kind of role playing and know, portray a fictional character and we could sort of enjoy the benefit of that where we can, you know, engage in a video game character and interact with this sort of fantasy world. Right. But I think it's a slippery slope because once we start to blur the boundary that we can no longer tell the difference, I think that's what it gets, dangerous. We did a study also where we look at what happened when students learn from virtual character based on someone that they like or admire. At the time. We, you know, we did a study with virtual Elon Musk. I think he was less crazy at the time. And we see the positive impact of the virtual character for people that like Elon Musk, but people that did not like him back then, they're also not doing well. Right. It had the opposite effect. So that, that personalization or creating virtual character based on someone that you like or admire could also be a positive thing. So I think this technology also, you know, heavily depend on the context as well, and, and how we use it. That's why I think the quote from Kransberg that is neither good nor bad nor neutral is very relevant to the day.

[Daniel Barquet]

It wouldn't be cht if we didn't direct the conversation towards incentives. One of the things I worry about is not just the design, but the incentives that end up driving the design.

[Pat Pataranutupan]

Right.

[Daniel Barquet]

And making sure that those incentives are transparent and making sure that we, we have it right. So just one, I want to put one more thing on the table which is right now we're just rlhfing these models, which is reinforcement learning with human feedback. We see how much a human liked or even worse, how many milliseconds. They just continue to engage with the content content as a signal to the model of what's good and bad content. And I'm worried that this is going to cause those models just to basically do the race to the bottom. They're going to learn a bunch of bad manipulative behaviors and instead you would wish you had a model that would learn who you wanted to become, who you want it to be, how you want it to interact. But I'm not sure the incentives are pointing there. And so the question is, do you two think about different kinds of incentives, about the way you could push these models towards learning these better strategies?

[Patti Maaz]

Maybe we need to give these benchmarks to the models themselves so they can keep track of their performance, like try to optimize for the right benchmarks.

[Pat Pataranutupan]

But I think, you know, Patty, you have said to me at one point you said that if you are not paying for the software, then you are the product of the software, right? I think that that's really true. Right. I think majority of people don't pay social media to be on it, right? They subscribe or they get on it for free and in turn the social media sort of exploit them, you know, a product for selling their data or selling their attention to other companies. But I think for the AI companies, I think if people are paying subscription for this, then at least they should be able to in theory have control, even though that might fade away soon as well. So I think we need to figure out this sort of question of how do we create human centered society and human centered incentive, then the technology would align once we have that sort of larger goal or larger structure to support that. I think.

[Patti Maaz]

But even with a subscription model, there may still be other incentives at play where these companies want to collect as much data about you because that data again can be monetized in some ways or can be valuable training or it also makes Their services more sticky. Right. Sam Altman just announced that there's going to be more and more memory, basically, in chatgpt of previous sessions. And of course, on the one hand, you think, oh, this is great. It's going to remember previous conversations, and so it can assist me in a much more personalized way. I don't have to explain everything again and so on. But on the other hand, that means that you're not going to switch to another system because it knows you and it knows what you want, et cetera. And so you keep coming back to that same system. So there's many other things at play. Even though there might be a subscription model, the whole.

[Daniel Barquet]

Well, it reminds me the. The term con man comes from the word confidence. And the entire point is these people would instill enough confidence in people that they would give them their secrets, their bank accounts, their this, and then they would betray the confidence. Right. And so while an aligned model and an aligned business model, knowing more about you and your life and your goals is fantastic. A misaligned model and a misaligned business model, having access to all that information is kind of the fast path to conflict. Conman.

[Patti Maaz]

Yep.

[Daniel Barquet]

Okay, so maybe just in the last few minutes, I guess I have just the question of. I'm curious whether you've done any more research on the kinds of incentives or design steers that produce a better future in this sense. Right? In the sense of what are the kinds of interventions, the kinds of regulation, the kinds of business model changes? Like, what would you advocate for if the whole public could change it?

[Pat Pataranutupan]

I was actually reflecting on this a little bit before coming onto the podcast today. Like, what is our pathway to impact that? And I think for me, as a researcher, what we are really good at is sort of trying to understand this thing at a deeper level and coming up with experiment and new design that can be alternative. Right. You know, I think that there's something sort of interesting about the way that historically, like, before you can attack the demon, you need to be able to name it. Right. I think it's similar with the AI. In order for us to sort of tackle this wicked, challenging thing, you need to have a precise name and terminology and understanding of what you're dealing with. And I think that's sort of our role, I think, as a researcher in academia, is to shed light on this and enhance our understanding of what's going on in the world, especially with AI.

[Daniel Barquet]

Yeah. Well, here at chc, we say that clarity creates agency. If you don't have the clarity, you can't act. And that's why I want to thank you both for helping us create the clarity, name the names, find the dynamics, do the research so we know what's happening to us in real time before it's too late.

[Pat Pataranutupan]

Yeah, I think I might want to say it a little bit bit about technologies of the future. What we hope is that this is not just, you know, our work. Right. I hope that more researchers are jumping on to do this kind of work. And so for people developing AI across, you know, academia, industry, that they will start thinking bigger and broader and not to see themselves as someone who can just do the engineering part of the AI or doing the training part of the AI, but thinking what is the downstream impact that what they are doing is going to. How is it going to impact people? So that maybe we will steer away from the conversation around like, oh, you know, it's inevitable. This thing is inevitable, but we need to kind of be on the race to this. If more people have that sort of awareness and more people listen to you guys like, you know, this podcast and follow the work of the center for Humane Technology, I think we will have technologies that are more thoughtful.

[Patti Maaz]

Yeah. And AI should become a more interdisciplinary endeavor. I think not just, again, the engineers, the entrepreneurs, and maybe the government as well, but we should have historians and philosophers and sociologists and psychologists, et cetera. They have a lot of wisdom about all of this. And so I think it has to become a much broader conversation, not just educating the entrepreneurs and the engineers.

[Daniel Barquet]

I agree with that 100%. And as this technology can meet us in such deeper ways than any technology in the past, as it can can touch our psychology, as it can intermediate our relationships, as it can do things out in the world, we're going to need all of those other specialties to play a part.

[Pat Pataranutupan]

I think it's really clear that this is a really, really hard question. Right. It touched on so many aspects of human life, not just our sort of. I think right now a lot of people focus on productivity, whether AI will help people work better, but I think even just the productivity alone or just the work area alone, it's also touched on the question of purpose. What does it mean to actually do something? It will change the way that we think about the purpose, our meaning in life and things like that. So even just these domain alone is never just about work by itself. So that's why AI is a really hard question that require us to think in many dimensions and in many direction at the same time. And we don't necessarily have all the answer to this big question, right? But I think that the more that we can learn from auto discipline, the more that we can learn from wisdom across culture, across different group of people, across expertise, the better we could start to comprehend this and have better clarity on the issue at hand.

[Daniel Barquet]

I'm so thrilled that you're doing this work. I'm so glad that you're in this world and that we get to work together and build on each other's insights. And thanks for coming on Your Undivided Attention.

[Patti Maaz]

Great to be here. Thank you.

[Pat Pataranutupan]

Thank you so much.

[Daniel Barquet]

Your Undivided Attention is produced by the center for Humane Technology, a nonprofit working to catalyze a humane future. Our Senior Producer is Julia Scott, Josh Lash is our researcher and producer and our Executive producer is Sasha Fegan mixing on this episode by Jeff Sudeikin, original music by Ryan and Hayes Holiday and a special thanks to the whole center for Humane Technology team for making this podcast possible. You can find transcripts of our interviews and bonus content on our substack and much [e-mail] you can also watch all episodes on our YouTube channel. Just search for center for Humane Technology. And if you like this episode, we'd be grateful if you could rate it on Apple Podcasts and Spotify. It really does make a difference in helping others join this movement. And if you made it all the way to here, let me give one more thank you to you for giving us your undivided attention.