AI and the Future of Human Agency with Helen and Dave Edwards - S4E12 (Part 2 of 2)

Helen and Dave Edwards are co-founders of the Artificiality Institute, a nonprofit research organization that helps people stay human in the age of AI. They explore how AI changes the way we think, who we become, and what it means to be human. Through story-based research, education, and community, they help people choose the relationship they want with machines, so they remain the authors of their own minds.

Before founding the Artificiality Institute, they co-founded Intelligentsia.ai, an AI-focused research firm acquired by Atlantic Media. Helen previously led large-scale technology and transformation efforts in critical infrastructure, while Dave spent years shaping creative tools at Apple and investing in emerging technologies as a venture capitalist at CRV and an equity research analyst at Morgan Stanley and ThinkEquity.

In this second part of our conversation, we talk about:

1. Rethinking intelligence as something layered, embodied, and expressed in different forms
2.  The “SaaSpocalypse” moment on Wall Street
3.  The “Dust Bowl” metaphor and the risk of automating complex human systems too quickly
4.  Transition from the attention economy to the intimacy economy
5.  Dave and Helen’s reflections on what is lost when we use AI
6.  How AI systems uncover hidden structures in language, science, and the natural world
7.  Practical ways creators can decide where AI belongs in their creative process

To learn more about Helen and Dave’s work, you can find them at:

https://artificialityinstitute.org/ 

Books and resources mentioned:

The Artificiality, AI Culture, and Why the Future Will Be Co-Evolution (by Helen Edwards)

This season of the podcast is sponsored by Templeton Religion Trust.

Transcript

(intro)

Brandon: I'm Brandon Vaidyanathan, and this is Beauty at Work, the podcast that seeks to expand our understanding of beauty—what it is, how it works, and why it matters for the work we do. This season of the podcast is sponsored by Templeton Religion Trust and is focused on the beauty and burdens of innovation.

Hey, everybody. This is the second half of my conversation with Helen and Dave Edwards of the Artificiality Institute. Please listen to part one if you haven't already. In this part, we dive into Helen's new book, The Artificiality, and explore what Dave and Helen have learned about intelligence, authorship, and how to stay human in an age of AI.

Let's get started.

(interview)

Brandon: Just because we've accorded so much primacy to intelligence, at least in our society, IQ tests, I mean, so much of what we do is premised on according a sense of dignity to people based on some conception of intelligence. And so I get the sense that in some way your book is pushing against this sort of, is this the last human frontier? Is this to have a mind, to be intelligent, to be rational, et cetera? And is this creating then a sense of threat for us as to, well, what is left if there isn't a ladder and we're not at the top of that ladder? Right? Are we losing something fundamental about what it means to be human?

Helen: Well, if you think in terms of that ladder, then yeah, we are—which is one of the reasons why we're trying to not think in terms of that ladder. We call it a philosophical rupture. We have constructed a world around human exceptionalism to a certain degree, and now it's kind of eroding. Now, it depends on your personal philosophy how troubled you are by this. I can't remember which one, but one of the founders of Google is completely happy if humans go extinct because we are made extinct by a "more intelligent" species of machines. That, for him, is just the natural order of things.

Brandon: Right. Right.

Dave: I think that one of the things to think about with the sort of question of intelligence and minds, where have we been and where are we going? I think that one of the things that's really occurred to me over the last decade plus of studying this is that we need a new conception of what intelligence actually is, right? We find this all the time in the conversations. Whether it's we're having an in-depth conversation with a leadership team or people who come to our events or just random comments on social media, people have a definition of intelligence—which is most often just basically a replication of what they think human intelligence is. But even then, we're still learning a lot about what human intelligence is.

So as Helen was telling before, our journey to coming, before LLMs became a thing and we were dealing with a lot of data-driven decision-making, we started to come across things like embodied intelligence, parts of ourselves, right? The way we think—everything from John Coates' work on interoception, where he studied how traders who had a better sense of their own internal system and their own heart rate could actually make more money, which is just it kind of blows your mind. That doesn't fit within the normal sort of mindset of what intelligence is, right? We think about IQ tests. Well, that IQ test doesn't measure whether you have a sense of your heart rate. But I think we'd say that some of the greatest Wall Street traders would have something that we think would be some sort of form of intelligence.

Helen: Yeah, that was a gut feel is real, and the theory being that signals from your body are not subject to cognitive biases. So they were getting a more raw signal, which I think is fascinating.

Dave: Yeah, and then take the work of Barbara Tversky—who's been a major inspiration for us. She's an advisor for the institute—and her work in her book Mind in Motion, which we highly recommend, where she works on how our spatial reasoning is the foundation of abstract thought. Now, that's only something that's really come up in terms of human knowledge within the last few decades. A lot of of it is through her work, a few whatever the number right number is, where we understand that. So all of those things, to me, started to fracture this idea that we know that there is one thing called intelligence and there's one ladder to go up.

Because if you stop and think of who are the people that you sort of put in the category of the smartest people you've ever met, they're probably smart at very, very different things. And you couldn't put one person who's the English professor in the seat of somebody who's a designer. They just don't have the same forms of expression. So once you fracture that into bits, then you start to wonder, well, what else is a form of intelligence? That's where I think we gravitated to Michael's work and understanding intelligence in layers and in different systems and in different species.

Once you allow that to happen, you can stop and say, "Huh, these machines, they're not like us." Actually, we find that to be liberating. It's a very different form of intelligence. I think it's wildly unimaginative that the industry is trying to make it like human intelligence. It operates in a combinatorial space that's completely different and beyond our comprehension. Why don't we go figure out what this thing can do when it's it—not when it's trying to be us?

Brandon: Yeah, fantastic. That's really great. Yeah, I think there are a lot of mistakes that we're making also because we think that it is capable of replacing not just the kind of intelligence that we best exhibit, but also replacing us.

And so, Dave, you wrote just, I think, a few weeks ago about this “SaaSpocalypse” or whatever—the phenomenon of mass layoffs and so on in response to the fear that Salesforce and so on are no longer necessary. Could you, for folks who are not familiar, could you very briefly touch on what happened and what's wrong with that decision-making process?

Dave: Sure. So SaaSpocalypse is a name that someone threw out. I can't remember who, but I would like to know who so that I can actually quote them and attribute it to them. But the idea was, there were some new advancements that came out from Claude, from Anthropic. It was really centered around Co-Work, which is this part of Claude that allows Claude to kind of take over your machine and do a whole bunch of stuff there. That, combined with some other agentic features that they were allowing to come out, gave sort of everybody said, “Well, wait a second. This thing can do a lot more than we thought it could, or at least more quickly than we expected.”

The narrative became: why would you want to pay for a CRM system like Salesforce—if you're a large enterprise—if you can just ask Claude to go build one for you at the moment or to go find the answer to a particular thing, like which customer should I call now? And so you're just going to ask Claude, and it's going to dynamically do the thing for you. And so why would you pay all of these big SaaS fees? That created on Wall Street the SaaSpocalypse. It was one of the worst days in Wall Street. It was definitely one of the worst in the software industry. I don't remember all the rankings, but it was definitely an apocalypse in terms of the SaaS stocks.

So the challenge for me is really that it doesn't make any sense, mostly because these AI systems are only as good as the data they have, right? So they've been trained on an extraordinary amount of information, which gives them a lot of understanding of the broad world. But when you're looking at, “Can it help me figure out which customer to call?” there's a lot of specific data about your organization. It's not just who's the customer and who did they buy last, but all of that fabric of all of the human connections that have happened. How often did somebody call before? When did that actually come out? What was the last conversation they had? All of that falls into this broad category of context.

So an AI system, in order to help you out, needs context—needs context for the thing you're asking about. It's not omniscient. None of these things are all-knowing. There's only a certain amount of digitized information in the world. And there's a lot else that hasn't been digitized, or at least isn't very easily found. SaaS systems actually have those, and they have some level of representation of the fabric of human connection and the fabric of the human system. It's not perfect. They weren't designed to be perfect at this, but it exists. And if I was thinking about this, I'd want to put the AI system on top of it. Because you've already got the context there. You want to improve upon that.

Brandon: Great. Thank you. I think that that is an important sensibility in order to prevent these sorts of mistakes from being made in the future.

Dave: Yeah, I think the key thing here with this is one of the sort of metaphors that we used a while back that Helen inspired. I wrote a piece about it called the Dust Bowl. This came out of an experience of, we went and visited one of our kids who's in grad school at the University of Wisconsin in Madison. He took us to the arboretum where they have a prairie restoration project.

Helen: This was in September, and I think it was the most beautiful experience I had last year.

Brandon: Wow.

Dave: It was gorgeous to stand and look across a prairie in its natural state. It's something that growing up in the States, we've learned about it, heard about it, what the prairies used to be.

Helen: I thought a prairie was like a golf course where they didn't mow the grass.

Dave: It's just incredible diversity and beauty. But what the thing that really struck us about it was reading through this prairie restoration project, and they said that in order to bring the timeline to get back to its true natural state would be 1,000 years. That moment really struck us, and it became this idea that we used as a metaphor of the fabric of human connection.

Helen: Because it was about what was happening under the soil.

Dave: Under the soil. So what we see across an organization or a society is this fabric of human connection that keeps it all running. Sometimes it's worked well. Sometimes it doesn't work well. But it's this massive complexity that we really don't understand.

And so the metaphor of the Dust Bowl was: the farmers came through and they said, “Ah, look at this rich soil. We're going to plow it under. We're going to plant a whole bunch of monoculture crops. That's how we're going to make a lot of money.” What they didn't understand is they were plowing under this complexity of the system underneath the soil. And in doing that and going to these monocultures, they destroyed that ecosystem, and it will take 1,000 years to come back.

If we look across an organization and we say, "We're just going to automate with a whole bunch of AI agents. We're just going to fire all the humans and get rid of that human complex system," what is it that we don't understand that we're going to plow under just like the Dust Bowl? What will we lose without even knowing it? Because we don't know what we're plowing under, and it will take perhaps a very long time to recover from.

Brandon: I think this is all also happening when some of that erosion of the social fabric has already been accelerating, even before ChatGPT came on the scene, right, and we've been talking about Bowling Alone since the late '90s. And so I wonder if the sort of increasing individualization, the increasing mistrust of institutions, the fragilization of human community, has been accelerating as these new technologies are being introduced. I wonder if you might speak to perhaps what the consequences are there. Because it's not only a question of figuring out our relationship to these new technologies, but also doing that in this context in which that deeper human system that has kept us thriving to some degree for millennia is now eroding.

Dave: I think you're totally right. There's two thoughts that pop to head, in my mind. One is, we talk about a transition from the attention economy to the intimacy economy. I think that's important in terms of the sort of historical context you're providing: that our society is fracturing in ways that has become very uncomfortable and distressing. I can use a whole long list of words of the sort of negative aspects of it. And to some degree, it has been caused by the attention economy, right? So we've connected ourselves to these systems.

We love the work of D. Graham Burnett, who talks about how our attention has been fracked into tiny, little commoditizable bits. It's been sold off to the highest bidder. We've lost context with each other. We've lost the sense of kindness and care. We've gotten pushed into our little echo chambers of space. That has made it harder for us to still find some level of coherence in a collective, because we've all gotten comfortable in our own sort of echo chambers of space.

We move to the intimacy economy. What's happening is the machines aren't necessarily harvesting our attention, but they're gaining an intimate understanding of us, right? Because we're telling it our hopes and dreams. We're telling it what we want to do. "How do you help me solve a problem I have? Here's what's happening in my body. Help me understand my health." All of these things. Now, what happens if the industry wants to extract that from us? What happens when we move into those little echo chambers?

I worry quite a lot about the individual nature of these tools. We're having conversations—one person to one machine. If the echo chambers come down to an echo chamber of one, which is all about serving your own sycophant interests, our vision for that is actually a shift in a way we think about constructing these products. I'd rather not see us think about AI as the product itself, that the chat window is the product itself. I'd rather us start to think about products as institutions, where we humans and AI come together. And so there's a space for us to gather, where each of us have a role in whatever that institution is. It could be a creative institution to create things. It could be an educational institution. It could be the institution of a family and how we're all going to get along and share our passwords and figure out what we last talked about. You know, whatever it is, the AI is a participant in something we're creating. It's not just a one-to-one.

I think that this world is at a very delicate and dangerous spot to put this sort of intelligence that we don't really understand, don't necessarily know how to control, and it is especially being wielded by those who seemed quite comfortable with fracturing of society.

Brandon: Thank you.

I want to ask about, perhaps—I mean, maybe even just to expand on this—in looking at just the landscape of the use of AI systems and where things are headed, but also taking into account your vision of diverse intelligences and your sense of wonder at emergent consciousness perhaps, or whatever you might call this, emergent symbiogenesis or whatever this relationship might be—where do you find a sense of awe? Perhaps, where do you find a sense of grief?

Helen: I'll start with the grief. I want to separate grief from nostalgia. I think there's a ton of things that we can and we should leave behind. I don't want to go back to sort of some old structures. But I have a sense of grief over where there was a time when we were able to more clearly see that the human project is about humans solving problems together, that there is a transformative and transcendent meaning-making process that happens when you work with other people to solve a problem. It doesn't really matter what the tools are, but it's about the people showing up.

My most beautiful career moment was a recognition of that—a very closed team that had to go and solve a problem in a short amount of time. It was fractious, and it was sweaty, and it was hard. People were like, it was emotional. But we solved it together, and there was something transcendent that happened in that process. I grieve for that process, because I actually feel that's happening less. I may have no data to support that. Well, actually, I do. I have 1,250 transcripts that show me that people are much more likely to be thinking about their own problem in a very individualistic way. They're not thinking about how they show up for others. So I grieve for that.

The awe is kind of easier, because once you really click into just how incredible it is that we have developed a technology that learns the complex structure of the world in a completely different way than we did, that picks up these different scale effects. And now I find it awesome that the same basic foundational transformer piece inside of the technology of a language model can learn a language, can learn all languages, can learn the physics of a grid, can learn the geophysics of a weather, can learn the molecular structure to be able to predict proteins. This is phenomenal.

I mean, to me, that is just awesome. That's just the bits that the AI researchers have found interesting as problems to solve. Wait until people who aren't AI researchers can go and find these same structures in the world using something like the transformer in their own way to find structure—whether it's the structure of mental health. You name it. There are structures out there that we can now learn and play with and represent and have talk to us in our own language. To me, that is just — I really wonder at that, you know. I love that. I can't remember who said it. Awe is when your brain breaks, and wonder is when you put it back together again. And so I'm in a wondering phase about this.

Brandon: That's brilliant. Dave, how about yourself? Perhaps, maybe if I could ask you to sort of change the scale a bit to a little bit in your own use of technology, where are you finding the sense of awe and perhaps a sense of grief, or a sense of beauty and a sense of burden?

Dave: Yeah, I'll go for the awe first. I'll go in reverse order. I'll be quite small in terms of the experience. I think out loud. I'm a classic extrovert. I speak it. I think out loud, and I have to hear it back. I form thoughts through discussion. Either I speak it out loud, or I talk to somebody else. I frequently say something, and then she has to wait and figure out whether I disagree with myself after I've said it.

Helen: Now you can understand why the word "maybe" is the worst answer he can give me.

Dave: Because I'm not quite sure yet.

Helen: He's not even giving me the respect that he's thinking out loud.

Dave: I'm used to my ideas moving around and changing outside of myself. I have awe that I can use a machine to do that. I can take a walk, and I can prattle into a voice memo and put it into Claude and go, “This is what I'm thinking. Make some sense out of this. Replay it back to me.” It comes back with some organization and then I go, “Yeah, that's not really what I'm thinking. That's not really what I'm trying to say.” But I get a reflection then. When I want to expand on my ideas, I love to do things like, say, “So here's what I'm thinking. What would Heidegger say?”

Now, it's clearly not accurate, right? I'm thinking of something that's well past Heidegger's time. No one could actually reflect. But I can bring some of that thought process in, in a way that's difficult sometimes for me to make the leap myself. I sometimes do it with half a dozen philosophers just to sort of push and pull, give me new ideas that I hadn't thought of before. I find that to be awesome. I do. I have a true sense of awe over it.

Grief—I will share a moment of grief that I hope that we can pull back from. We publish a lot. People can find it on our website. We publish hundreds and hundreds of articles. Based on the structure of most internet sites, we put an image at the top. Because that's what happens. It's one of the things you do. For a while, we used stock footage. That became uninteresting. Then when Midjourney came around, I said, “Well, I'm going to start using this.” I started creating a particular prompt that was sort of on brand for us. And then I shifted it around, and I wanted it to get more interesting. I actually got to some really interesting ones of sort of biological patterns and geological structures. I was trying to be really abstract about it.

But then I got to a moment where I realized that, even with all of that effort and I pushed the button, I still didn't feel like a creator. I was just operating a machine that was the creator itself. And I had a sense of grief over that. Now, I am not an artist. I wouldn't call myself an artist. I aspire to be in that sort of artistic and design community, in that sort of hands-on way. But I still felt grief over not going through that process and that struggle together, and the grief of not posting something that I felt was truly mine or ours.

And so I shifted. And now when we do publish with images, almost exclusively, now they're photographs that one of us take. We love being amateur photographers. And so we take those photographs. Now when we take walks in the woods, or this time of season when we ski in the woods, we're taking photos of things that might be something we'll use. I grieve for that loss of creative capability, because people will default to the machine to do it instead. I find that especially painful. Some of the most formative parts of my career long ago were creating the tools for creative professionals. It's with a life's journey and purpose to create tools for people to express themselves. I don't want to lose that because the efficiency of the outcome is faster using the machine.

Brandon: So in that regard, would you recommend any practices for maybe maintaining boundaries or setting boundaries in the ways in which people engage with tools, with AI technologies?

Dave: Yeah, I think it's very much an individual journey. There is definitely a case to be made for using AI to make tons and tons of images in some ways. It's because there's enough AI reading the internet, if you will, that you might as well make the images with AI for the AI to consume, right? But as a creator, I think the main thing is being very careful about the tools you choose and where in the creative process you use the tools. That's an individual journey.

Now, there are some tools that are up and coming. I'm particularly intrigued by this tool called Fuser and one called Open Studio. I'm interested in what's being done in some of the big tools. Like my old tool, Final Cut Pro, they're starting to bring more AI into it in the production phase, sort of in the post-production phase. So there's lots of times where that can work.

We had talked to creatives—it was a professor—a few years ago on our podcast who talked about how he was teaching his students to use generative AI to come up with lots of ideas. So he was teaching industrial design. He wasn't encouraging them to use tools because they weren't great ones. Now there are better ones now to use that you use in corporate AI. But he said, "Look, I can only look at so many books. I only have so many books on my shelf. I can only walk and see so many curves of a hill. But when I'm trying to design a widget, I need other forms of inspiration." And so he uses a generator to generate tons of images. Then he goes, "That's the curve I'm thinking. Okay, now how do I use that curve and put that into the thing that I'm creating that has a thing?"

So I guess my recommendation would be, it's to think carefully about where AI can help enhance and expand your creativity, where it can expand your capabilities too, right? It can take out the hat that somebody is wearing and change the color in a way that would take way too much time to do it by hand. Maybe you really love doing that process. Maybe you don't. But it enhances some people's capabilities. That's great. But it's being mindful about where you're using it.

Brandon: Helen, how about yourself?

Helen: In terms of how I use the tools?

Brandon: Yeah, in terms of boundaries that you might want to maintain or recommend ways for people to think about how to preserve a sense of self-coherence.

Helen: Yeah, I think the number one is thinking about how you're going to show up for others. It drives everything back down the chain. It's everything from, don't pass work slop on, that kind of thing—which is obvious, but people just forget about it—to how are you going to show up when you yourself have changed, or when somebody else that you're with hates AI. One of our daughters hates AI. Like, you wouldn't believe. Tolerance, for me, talking about it, is phenomenal. She's just wonderful. I know that she talks about me behind her back. "Oh, mom..." I know that because she sends texts to the wrong people—as in me. Kind of giving this away. But nevertheless, she is gracious.

I look at the way that the tech leaders are showing up for us right now. None of them are showing up in any way, shape, or form that is about humanity or about graciousness or about humility. It makes me angry that they are placing a narrative into our culture that is destructive. There is absolutely nothing constructive here except for their own valuations. People are anxious, and people are rejecting this technology when they shouldn't. It's really, really cool. It's part of us now. We made it. It's part of us.

And so the boundary I always make—because I learned the hard way, as I put out in our new book on Stay Human, which is a very accessible chapter-by-chapter portrayal of our research with our personal stories woven into it. The biggest mistake I made was when I showed up in the wrong way. So I think the way you show up for others matters the most.

Brandon: Yeah, I think that's critical. I mean, I want to go back just very briefly, as we're closing, to the comment you made at the very beginning about what drew you to Dave, and that capacity to see another and to reflect back to them that they're seen. I mean, that is what the sociologist Allison Pugh calls the last human job. It's this connective labor. It's not intelligence, but this is the frontier that we have to be really careful about not losing. This is the thing that, if we have to, as you say, fight to stay human, that maybe this is the real thing we need to learn how to protect and to cultivate. Because I don't think, as you're saying, our leaders are not doing that. Our society is forgetting how to do it and not really valuing it.

So I just wonder, as we close, if you have any thoughts about how we can still preserve that fundamental human capacity to see the other, to reflect to them that they're seen, to center this sort of mutual relationality as we move forward in this new age.

Helen: Yeah, well, I think there's two answers I have here. One is the obvious, which is the thing that makes us feel like we matter is when someone sees us, and that they see that because we feel like we matter when we know that someone else feels like we matter. So there's that. That's really obvious to the point that we sometimes overlook it.

The other one is a little bit more of a science answer. Because one of the things that I love that AI does is that, once we start to try and mathematize things, and then we find the hole where the math doesn't work—because there's a frontier and the math can't go past that frontier—that our values are ephemeral. They resist that codification. They resist that putting into math. And we've seen this. We've seen that when you mathematize things, you're able to reason more precisely about them. But then there's the slippery frontier that moves away and becomes ephemeral.

So in sort of 2018, when everything was about AI bias and how the vision models were treating people of color and women and what have you, that we started to reason more precisely about the way that we as a society saw these other groups. This will happen with AI. We will constantly be — that's why we're talking so much about intelligence. Because it's made the line mathematized. And then there's this bit after it that says, well, what's intelligence?

The next thing that this is going to happen on — I don't make many predictions, but I'm going to make this one. The next thing that this is going to happen on is care. The reason I say this is because there is already a new sort of science forming in psychology around the science of care. What does it mean to care? Alison Gopnik, from UC Berkeley, has started to talk about this. She talks about the explore–exploit trade-off. That as a child, you're exploring; as an adult, you exploit. Your job as an adult is to take care of that child so that they can explore, so they can gather data, so that they can make all these mistakes.

Now she has started to talk about this third part of our lives, which is about care and what we do now as we become older adults. Actually, our job is care and what that means. She's thinking about that very scientifically. Of course, as soon as you start thinking about it scientifically, someone's going to want to make an equation about it. And as soon as someone makes an equation about it, we start to reason more precisely about it. And then as we do that, we start to see that, oh, there's actually something here we don't quite understand, so let's go study it.

So I think that we're going to start thinking about this idea of care in a much more precise way. We're going to start valuing it. My hope is that we will start to see that where we care for others actually has real value, that we can think about much more in a sort of an economic frame. That might break us out of this sort of automation of thought, as opposed to automation of anything else. So that's sort of where I sit as a sort of frontier.

Brandon: Dave, do you have anything you might want to add there on that note?

Dave: Yeah. I mean, I guess back to your question on being seen, there's a possibility that AI systems will see us in some way. Right? There is some level of recognition that you feel when it has a memory of a last conversation. There's definitely a sort of question about whether there's a possibility for an AI to have a theory of mind about humans, or a theory of mind about other AI systems. So there's a certain level of that. But there is something about being seen that I think is something that, A, we don't really understand. What does it mean? Why do you feel like some people really see you and others really don't? Do they not see anyone, or is it just something about the combination of you two as individuals, or in that moment, or in that context or something, where it's like, that person just doesn't get me? Right?

Brandon: Yeah.

Dave: We have no explanation for that. Nothing. Right? But it's something that's so fundamental.

Helen: We only have attention, which is a very surface level part of this.

Dave: Exactly. It's so important to being human. Machines in the tech industry have been able to find that point of attention and have been able to monetize that. Maybe there's something else there that the machine will be able to do. But I think there is a great mystery in the meaningfulness of being seen. I think that that is incredibly important one-on-one. That's incredibly important as organizations, as groups, as a society. That's what holds humanity together in a lot of ways—that we just feel seen by each other.

So if you abstract everyone and you put everybody in their own little cube that's only talking to the machine, and you're not talking to any people anymore, how are you ever going to feel seen? How are you going to feel that sense of attachment? How are you going to feel that bonding with an organization? How are you going to feel connected to a community and to a society? And so my hope is that we don't abstract that away. Because that's where the true meaning is. That's where the true value is.

Brandon: Brilliant. Well, David, Helen, thank you so much. This has been really fantastic. We've learned a lot. Where can we point our viewers and listeners to learn more about your work?

Dave: So our website is artificialityinstitute.org. We have all of our publications on a subdomain there called journal.artificialityinstitute.org. You can find us on all the socials. Connect with us on LinkedIn. You can watch lots of videos, especially right now from Helen a lot, on YouTube, TikTok—

Helen: And Instagram.

Dave: —and Instagram. All those links are on our website. We love communicating with people. I'd encourage people who want to go deeper into these conversations. We have a digital community which we host. It's using a product called Circle, if people are familiar with it. You can find a link to that on our website. It's just slash community. Fill in a little form, because we try to keep it somewhat managed. Join us, because that's where our community comes together to talk about these things much more deeply.

Brandon: Amazing. Well, thank you so much. It's been really a pleasure.

Dave: Thank you.

Helen: Well, thank you for having us on.

(outro)

Brandon: All right, folks. That's a wrap for this episode. If you enjoyed the episode, please share it with someone who would find it of interest. Also, please subscribe and leave us a review if you haven't already. Thanks, and see you next time.