NB64 - AI As Culture, Not Just Code with the Artificiality Institute's Helen and Dave Edwards

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Speaker C: Hey, everybody. Welcome to another edition, uh, of the no Brainer podcast, where we talk about how AI is impacting the enterprise and organization, how we can adopt quicker and make sense of all the crazy hype that's going on out there. And with that, let me give you our commercials before we get into our excited ghosts. But first, I have to introduce my doppelganger, as he called me the other day, my co host and Partner in crime, Mr. Greg Verdino. How are you, sir?

Speaker D: I'm good. Doppelganger sounds a little bit better than Evil Twin, which is how I refer to you when you're not in the room.

Speaker C: So this has got a very Goldfinger feel to it. I just Want to know who's mini me? How's your summer coming along? I think we're in the dead of it now, right?

Speaker D: We are in literally the heat of the summer. It's hot, it's humid, it is oppressive.

Speaker C: It sounds like another east coast summer. For those of you that don't know. Greg's in New York and I'm m in Washington D.C. where it is humidity central and you wake up and your windows have condensation all over them. Yeah.

Speaker D: Inside and out.

Speaker C: Lordy, lordy, lordy. But, uh, let's get on to the actual business at hand, which is talking about AI adoption. And before we do that, our commercials are please like this podcast. Share it out, leave a review, tell your friends about it, and if you have any feedback for us, please do send it. You can find all the information you need on no brainerpodcast.com and with that, Greg, would you like to introduce our guests today?

Speaker D: Absolutely. And today we have not one, but two guests. They are a husband and wife team. Dave and Helen Edwards. Welcome to the show, guys. They are the co founders of the Artificiality Institute. And Artificiality is a nonprofit research organization that's really focused on the way I'm probably going to botch this because it's way above my intelligence grade, let alone pay grade, but essentially the way AI is shaping the human experience as we start to think about and really kind of adapt to this sort of co evolution of human intelligence and machine intelligence. I don't know how well I got that, but I think you can probably tell from that introduction that this is going to be a fairly heady episode. But I will ground it by saying that both Dave and Helen do come out of the world of business and of research, and they've worked in the media industry as well with roles at Court. So this is not purely esoteric, but I think we are going to be asking our listeners to kind of stretch their brains a little bit and think about the role AI will play in not just reimagining our organizations, but really reimagining society, culture, and the world we live in in general. So anyway, Dave and Helen, welcome to the show. And please, one of you jump in and tell me how bad I botched the work you guys are doing.

Speaker E: Well, thanks for having us and you did a great job, so no worries there. And we do pride ourselves on being able to explain things in plain English, so I think we'll. It's good that you set the bar, but we'll try to. We'll try to make sure. That this is a, uh, this is an easy episode to understand.

Speaker C: Absolutely.

Speaker D: Why don't we dive right in, um, and kind of get right to the heart of it. And I've kind of hinted at this already and I think this is really core to your work that in our world as consultants, working with businesses and with associations and with marketing organizations, people who are like rolled up at the sleeves and really kind of diving in and trying how to work with AI for things like efficiency and productivity and sometimes innovation. Uh, but it's very much brass tacks in a lot of cases. There's a tendency to almost think that this is a technological shift. And your thesis, which I think fundamentally we would agree with that it is much more than that. It's a cultural shift, it's a philosophical rupture in a lot of ways that really kind of changes the way we think about humanity. Not just the role of humanity relative to technology, but humanity overall. Can you talk a little bit about why you've taken this sort of big picture view of what's happening with AI and why this matters to somebody who's just trying to lead an organization in the day to day?

Speaker E: I'll jump in on that one. So we've been working with AI and helping to study and help people understand the human experience of AI for more than a decade. So we've been at this for longer than the LLMs and ChatGPTs of the world. But that perspective is important because previous to this we were dealing with technology that was predictive and would help you see the world in different ways because it dealt with huge data sets, but it was generally coming across like normal software. Here's this tool that does something for you. It gives you a number, it gives you data. You, you read it, you understand it, you try and figure it out. It's the AI's really shifted though, as we now have conversations with these tools. As the tools start to write language back to us, as they start to exhibit a level of intelligence and reasoning and cognition, words that we've usually only held, or a lot of people have only held for humans. We've always thought that we're the only intelligent thing and, or we measure intelligences. You know, the only existence of intelligence matters is what comes out of this big bag of cells that we are. Well, we actually don't see that the world that way. We see intelligence kind of all of around us in a variety of other species, in different forms, different levels of the species. We dig deep into the biology and leverage a lot of the Work of Mike Levin at Tufts in thinking about what it means to see intelligence in other worlds that are not just in this human form. And that's helpful to understand, because you start to understand how you're interacting with these tools, that these tools are a form of intelligence. And you think about it that way, you start to anthropomorphize them. We use words like that. Honestly, we anthropomorphize our pets too. Because if you've ever said, gee, my dog loves me, dog, uh, doesn't actually love the way we love, et cetera, et cetera, right? So we're seeing in these tools something that reflects back and feels human to us. That means that when we're talking to software, we're having a very different experience with it than we've had with software ever before. So our work centers around understanding that experience, helping people have the metacognitive moment to understand their own experience, to help leaders understand what's happening. For all of the people in their complex human system to know that these tools are changing our. How we think, they're changing how we think of ourselves. We're thinking about changing how we think of our own expertise. Suddenly there's this system that seems to write things that at times can be brilliant. What does that mean for me and what I bring to work? Am I still an expert? Now the world is telling me that these tools are going to democratize expertise. What does that mean for me in terms of why this company, organization brought me on and what I have to offer? And it fundamentally changes how we make sense of the world, how we find meaning, how we think of the tools around us, the people around us. It is really changing all that. So our work is in studying that. We look at. We study the lived experience, we gather stories, and we hear how people talk about their experience. So we use that as the real unit of how we measure what's happening. And we see that this is having a very big change. You're right. We do use the word philosophical rupture. We think that this is the biggest thing that'll happen in terms of what it means to be human and how we think of ourselves. And any leader is leading an organization of people, and all your people that are using these tools are having some level of this experience. And that's why I think we see pretty widespread fracture across organizations because they're seeing this as a typical software tool, and it's really not.

Speaker F: I think your question was really smart because what it hints at and what it sort of starts to reveal is that this is a complex multi layered system. So you can have many quite like high concept conversations about the meaning of intelligence and the philosophical rupture that we're inventing something that will replace us, be smarter than us, whatever those things are that you, that you think. It's also there is brass text like you say, right? You've got to go in and talk to people about how they're, what kind of prompting they're using, what kind of success they're having, how are they sharing that with their colleagues, what does productivity mean to them? And there's an awful lot of noise around all of that lower level stuff. An awful lot of noise. You can, uh, I'm sure you've had this experience yourself. You open up, you open your computer in the morning or your phone in the morning, and there's another paper on productivity and it contradicts this paper on productivity every day.

Speaker C: Oh my Lord.

Speaker F: So a lot of people are kind of confused, well, does it increase productivity or does it not? I mean, is it going to change

Speaker C: the world or is it going to destroy it?

Speaker F: Exactly. Even if you just sort of put aside all of that and say, what's the signal that comes out of that noise? The signal is that this is very confusing for many people. Even those who feel like they've got their hands around it are constantly surprised. And these. And if you're not asking, if you're not seeing more questions than answers, then you're not thinking about this enough. I think there's a very important of our, uh, current large language model version of AI that we don't talk about enough, which is that it's a cultural technology. And Alison Gotnick from University of California, Berkeley is the one who sort of coined that and put some concepts around this idea that these are uh, cultural technologies. And so for us, when we're in organizations or when we're talking to our community, we're always conscious that the way that people think about what these AI tools are going to do for them or what the AI systems are going to do for them is very much embedded in their cultural milieu, whatever that is at that moment. And uh, I think it's very interesting to witness in the last 12 months a very significant shift in the way that people talk about AI. And this got something to do with the capabilities, but it's mostly got to do with the culture. So we've gone from talking about AI. This time last year we were having many conversations and organizations about creativity, about unlocking new opportunities, about using AI for innovation, about accessing new multidimensional spaces that only AI can help a human do. Now it's just productivity, efficiency, fast, speed, scale, scope. And that is a cultural phenomenon that has nothing to do with what these tools are capable or not capable of. So we always have this perspective where we step back to the higher philosophy and we ask those questions because those are the broader contextual factors about how people will ultimately use these technologies and what they will ultimately mean for humans. So at the moment everyone's having a lot of conversations about productivity and about jobs and blah blah, blah, and we're having conversations about meaning and experience and expertise and how do you use these things to get smarter, not dumber. Those are uh, our kind of conversations because they're informed by these longer term views.

Speaker C: How much of the conversation do you think is influenced by bias? And what I mean by that is bias of the person providing that narrative. So if I think about the productivity narrative and the narrative of efficiency saving costs, this is corporate America and venture capital based companies selling these tools to other corporations so that they can cut costs and boost value or sell their software and boost value. And while that may be dominating the conversations and even some of the research studies that are coming out, if you look at who's writing them, for example the marketing, uh, AI institute or McKinsey or even Jasper, right. All these players have a, ah, narrative that helps them sell their products or services. So how much is that bias really shaping our thinking and why are we not able to see through that?

Speaker F: That's a fascinating question. The way that you framed that question makes me want to answer it with a very specific focus on the bias right now is on stripping humans back to the bone and making sure that they do what the machines want them to do. That's the bias. And it depends on how big of that AI pill you've swallowed at that. You think AGI is going to be all encompassing and replace all humans and it's either going to be later this year or it's going to be next year, or maybe it'll be the year after Mark Zuckerberg is saying it's going

Speaker D: to be two years super intelligent and

Speaker F: we can have a whole other podcast about what we think about that. But the bias, yeah, I mean, I uh, mean that's a fascinating topic in itself. That is I think the poor bias right now and it's almost like people should start their, their, their communications with this is how much I believe humans are going to be necessary in the future. They should declare Their bias up front. And I think a lot of people are acting as though humans are going to go away and they're not important. They're also incredibly inconvenient. I don't know about you, but I'm so tired of all these people that need to see sleep and eat and take care of children and that they want to laugh about something or they want to get fit. You know, all these annoying human features. Let's just replace it with something that gets things done and doesn't answer back. And I think we're in a moment right now where that is a prevailing narrative and it's coming from this expectation that we need to, quote, unquote, prepare for AGI. And I'm just gonna say right up. I think that's a bunch of fucking bullshit. And I think that what is happening is that it's correct.

Speaker C: This is the best podcast ever.

Speaker D: Usually it's jet dropping the F bombs and getting up in trouble with Ken

Speaker C: Wright, a couple of people. I just went, right, I'm sorry, Lisa Durrell. I'm sorry.

Speaker F: So I'll let Dave fill in some gaps there because this is my hobby horse and I could just keep going.

Speaker C: I totally agree. I mean, like getting back to the bias thing, you know, if you look at the bias of why would you say something is literally sentient and can outsmart human beings and replace them? It's because you're selling crap, you know, and like both the biggest perpetrators of that Zuck and Altman, not in that order, are selling AI.

Speaker F: Yeah.

Speaker E: So there's a. I mean, I think that, I like your question. I think that there is a bias towards coming from the companies that are selling the technology, are trying to show that there's an economic benefit. And so they're pushing the productivity story because that's going to be able to give them their return on software, on the spend. There is a different part of that push that's coming out of Silicon Valley now where the venture folks are very clear that the long term returns from these investments are going to come by from replacing labor. So they're actually saying the quiet part out loud, um, which I, throughout all my career being in and around the venture business, I've just never seen. Um, but that is very much what, you know, the market opportunity, you know, big, big dot on the slide is labor. Right. So there's a. It's a little bit different in that it's not just efficiency where they're like, yeah, uh, we're going to make your people sell more. You Know, it's, we're going to be able to let you sell stuff without your people and that's a really different story. So that's one key difference that's going on. I think that the other though is definitely that this transition of software companies have gotten used to technology companies broadly got used to saying, here's a thing, here's the manual. You know, for a while we started to make software where you didn't need a manual very much, but you know, here's how to use it. Now we're done, right? Here's the integration. We do the whole big thing, we host it in the cloud, we tick away and clip the ticket all the time. But there's nothing, you know, there's nothing different about how to use this. But as Alan said, this is a cultural technology in a way we've never had before. We've never had software that you've communicated with, never had a chat. We've always made software just to sort of go back to the McLuhan mindset, which was a medium between people. Right. It was all. And yes, m. Those media changed the message that was transferred among people. It changed our, the way we thought about the world, changed our culture. Because going from books to the TV to the Internet all has different ways of transitioning. But this is the first time where the medium is creating the message. Right. The medium is now the meaning itself. And that means that when you put this into an organization, it's not just another software upgrade. And so if you're just thinking about this as a software upgrade, you're running towards a brick wall with all of your people getting smashed up against it because it's just not the same. So it means that leaders have to be thinking like people leaders more than technology implementers.

Speaker D: Yeah.

Speaker F: I think that there's an interesting part of this whole, um, productivity story. So take sort of high level economic productivity across the entire economy and look at um, what we would expect with even moderate adoption of healthy adoption of AI, we would expect productivity to rise and with that we'll get some lift in wages. Now that, that was a, there was a break many years ago that's well documented about following one for one, but we would expect that. So I think that part of what we're seeing in the market today is a lot of fear because there's a sense that a lot of mid to late career people who are expensive and highly expert, there's a narrative that says that AI can now replace you and we're going to get rid of you. Before these wage rises happen and the productivity increases happen because you're expensive and we don't want to have to do that. So there's a lot of sort of preemptive narrative. There's a lot of narrative that's very preemptive, that's very much relying on the story that AI is going to have these huge productivity gains that in an old regime would translate to higher wages that are already expensive with people who are uh, less likely to adopt the technology. So let's focus on getting rid of those people and focus on putting AI in to replace expertise. Now that is, that sounds like a good story, but you have to believe an awful lot about the way that technology gets adopted and the way that humans behave and the way that we need to increase our knowledge and increase our number of products and solve more problems that we haven't even seen yet. So we're in this sort of funky position right now where I think that people who, um, either selling AI solutions or don't really understand what's going on and just haven't really thought about it enough are seeing very simplistic narratives, are seeing very simplistic, sort of cause and effect relationships between technology and productivity and thinking. Well, if I just press this button here and then tell everyone to do this and then get rid of those people over there, my company's going to be transformed. And that is just, it's just not

Speaker C: going to happen backwards. Yeah, uh, I mean, I mean part of the problem too with that, um, line of thinking is that ah, AI works on repetitive tasks best and simple repetitive tasks like lift, box, drive it over here. Right. And the same thing with even some of the knowledge work that's being brought in. Summarize, email, prepare for meeting with so and so based off the contextual dialogue that we've had over the past six months or whatever. And that's assuming you have your data stack taken care of and everything's working well, which is not true for 90, 99% of organizations. I mean there's just so much, so many assumptions and primitive thinking. It is reminiscent of what happened with the government and Doge and all the cuts that they made in having to hire people back and you know, service.

Speaker F: I don't think that's an accident. Yeah, I just don't think that's an accident.

Speaker C: I mean, consider who owns Grok, right?

Speaker F: It's all highly correlated when we look at uh, you know, the, the what's happening in science and higher education. Same sort of thing. Issues are different there and much More complex and really in many respects don't have a lot to do with AI. AI could make some of that stuff actually a lot worse. But higher ed and core research different. But yeah, I think that we're seeing these narratives, they're very reductive. There will be rebuilding out of all of this. And the foundation of what we're going to have to rebuild will be initially trust. And that's, I think, another place where AI has, uh, an important part to play because we can't actually trust it. And that's obviously well documented with hallucinations or with anything else. And you need to, you need good expertise to be able to guide these things. And this is where a lot of our research comes in that we spend an enormous amount of time saying, well, if hallucinations can't be fixed or if sycophancy can't be fixed at a technical level without compromising too many other things, then what else shifts? Well, the humans in the system shift, right? So this is what with our research, which sounds all fuzzy, you know, moments and stories. Well, guess what moments and stories are. Ah, actually how people live and that we can't compete with large data, um, you know, big data gathering through clicks and Claude and what have you. But what we do is go very deep into more of an ethnographic, anthropological and design based process for thinking about how do we, how can we understand what's actually happening to people, what's really happening to their, uh, productivity. So we started with that. And then of course you find it's got nothing to do with productivity. That's just like, that's such a downstream. You mean productivity? You mean I got better at doing something? Well, I don't really care about that because I did all of these other things or I thought all of these other things, or I went through this process of recognizing how powerful this, this technology is, which gave me a hell of a shock. Either good or bad, it can be sometimes both at the same time. Right? Humans are complex. We've got the ability to have both of those reactions at the same time into integration. I can use these really productively. I can integrate it into how I do my day job or whatever, um, blurring. People can't tell where their ideas finish and the AI start. That becomes a very sort of coincident experience. That is a fundamental process for organizations to understand because once they've got people in that situation, they don't just have human employees anymore, they literally have these cyborgs. But then things can fracture. And that's where I come back to trust. So when something fractures, which can be, it can be a good experience, it's mostly not mostly very high emotion experience. A lot of that is about trust. How did I put that slide up when I knew it had that hallucination in it? I didn't see that stupid mistake or I thought I could trust this thing. And it's driving me crazy. Everyone tells me that I'm supposed to be a coder now, but I still can't make this work, whatever those things are, uh, and into some new reformed state where people reconstruct not just their own sort of thought processes, but their entire identity can reconstruct around these moments. And the reason that matters for companies is that that is happening to every single person that touches AI, even if it's just the most basic and honestly kind of useless copilot, web copilot, for example, is pretty useless. But even if it's just that these are these glimmers, there's these processes that are very common and it's changing all of us. It's changing the kinds of conversations we have. It's changing the way we see each other as co workers because we're not sure who's really using AI and who's not. We're not, you know, all of that kind of stuff. And so it comes back to we're just reforming all of this trust on the fly and everyone's learning almost on a week by week basis now. If you're running a company and you're trying to just tell everyone, use AI and be more productive, what you're failing to see is how complex this landscape is underneath you and how anyone can make sense of the way that behaviors are changing or a, uh, remote workforce and how they're actually working without understanding this broader sort of terrain of people's psychological adaptation to these tools. I just don't know how they can with any fidelity.

Speaker D: There's so, so much to unpack there. Um, but I want to kind of zero in on one specific thing. Um, and I know it's core to your research. You've mentioned the word several times, moments. And I think a lot of this conversation so far, and I think the overgeneralized, simplified narrative a lot of people are kind of getting bits and bobs from, to try to make sense of this tends to be very macro. Right. It's the, it's the doomerism on the one hand, and the utopian future where we can all stop working and just live our purpose on the other hand. It's the big stuff, it's the productivity, it's the 9,000 people laid off here or the billions of dollars raised there or whatever. But in your work, you've really kind of zeroed in on the moment, which sounds like such a small thing, but I suspect you're going to tell me it's probably the biggest thing of all in many ways. So I guess describe a bit what you mean by a moment, what is a moment? And you know, sort of through the lens of the average person working in an organization as you kind of as they come into contact with these new technologies.

Speaker E: So we use moments because it's the moments that. It's the moments that matter. It's the moments that give us some insight into what the actual human experience is about. So they're not lost in the overall idea of who this person is, what their role is, you know, the sort of tasks that they've got. The idea of moments really came out of the, uh, practice of thinking about, thinking about this like a designer. So in design, you can, you know, some people will look at a Persona and they'll talk about a person and, well, they ride their bikes to work and, you know, it doesn't help me out when I'm thinking about how to make a product for them, but when you have a moment that matters that really gives you that insight of, that's the thing, that's the time, that's the thing they were doing, that's the thing they were thinking. That was the interaction they were having and it caused this transition for them. It had. Was something that gave them some great insight. It was something that changed the way they think about the world. It was the way that they, they had a moment of frustration. When we know those moments, we can understand better what's happening to individuals and we can know better how to design for them. Whether that design is how to help somebody understand how to use the tool, how to approach it, how to anticipate where that, where that. Where that transition might be for them, where they have to stop and pause and recognize. You know, you hear moments where people go, I did all this work, and, um, I'm not really sure if it was me or the machine who came up with that.

Speaker F: Right.

Speaker E: That moment is incredibly important because that person's then potentially going to write a proposal or go into a meeting and suggest something, and they don't know whether it was their idea or the machine's idea. And both might be okay. There's no judgment call there. It's more, how do you know where that thing came from to know whether you're going to be willing to be held accountable for that thing that you're about to propose. So it's those little moments that are actually just really, that are really important moments also string together for us as an overall story. What's the arc of someone's experience in a small period of time. And for our research over a long period of time, we want to understand how this change happens over time. You're now an expert. You know, we're now all of generally of the same sort of age bracket in our career. How does this change for us? How does it change for people who are more the age of our children, who are starting in their professional careers? What does that mean over time?

Speaker F: So I think too that uh, I just want to add on that moments, um, yet again it's something that you might think sounds a little bit fuzzy, but there's actually good research from um, neuroscience, from behavioral economics and also from management science about the importance of moments. So on the management side, Chip and Dan Heath wrote a pretty inferential book a long time ago about moments. Um, and it's a really accessible book that reminds us all that that's actually kind of what we remember. If you look at the neuroscience and consciousness studies, moments and event boundaries and stories, that's a coherent picture of consciousness. Right? We're more conscious at those moments and we care about consciousness because our research is showing that one of the most important things about working with AI is what it does to our consciousness. And the third one is in the behavioral economics side, which is that the, the very famous Danny Kahneman quote, which is no one ever made a decision based on a data. They, they need a story. And that is how humans operate. And we can have AI tell us everything that we ever need to know. Does it mean that anyone's going to get off their ass and actually do anything differently? Not necessarily. We need social cohesion and community and narratives and stories for us to actually move from analysis to decisions to commitments. When we actually take actions in the world. And until robots do absolutely everything for us, which I can't wait for that day, woo hoo. We will still be taking actions in the physical and in the digital world. We'll be still the ones doing the action. And you can live in your head and think as much as you want, but what actually matters is what you do in the physical world, in the real world. So we take this all the way through to those kinds of grounded ideas because how many times do you go into an organization, and people know the right answer, but they're not doing it. And they'll give you a million reasons why they're not doing it, but the reality is that that gets often solved with Aleta and a story.

Speaker C: Yeah, uh, it's pretty interesting. I mean, I think, too, some of that moment you're talking about is probably the moment when you're speaking to you, Dave, with the is this my idea or the AI's idea concept. I do think that that's the moment when you realize that AI has become operational within your organization. It's become operationalized, it's integrated, it's seamless. And I do think that in some ways that's kind of like the ideal state that we're trying to achieve from a business perspective. But I don't think we have weighed what that means to the human consciousness. And one of the. And I was thinking about this before we got on the pod, and not because we were going to be on the pod. It just was popping in my head, um, you know, this constant need to talk about AI as a psychological support system, which I think is a huge, huge misuse of something that's basically broken from an emotional intelligence standpoint. It's just not really emotionally intelligent in any way. It's a sycophant, as you had said, or if you've trained your AI correctly to be critical. It's a probability engine that looks for weaknesses.

Speaker A: Right.

Speaker C: Whatever it might be, it's not a sentient being that can actually dialogue with you either as a protagonist or an antagonist. And as such, I just. I'm really concerned by that. I just really feel like that that's where we're. We're most endangered by AI. Uh, even though we do seem to have this existential economic crisis that we talked about at the beginning of the podcast. But more from a societal standpoint and emotional standpoint, that's the danger point. That's the inflection point. It's the herd nightmare, right?

Speaker E: Yeah. I think that there's, um. There's sort of three things that pop in my mind around that. One is, um, AIR doesn't have a point of view. And in order to have a strong debate with someone or to have someone give you good consult, um, whether they're a therapist or a coach or a spouse or a friend, they always walk into the room with some level of point of view about themselves, about you, about the world, something, and that informs their judgment. And it's important to know that AI can sound like it has sort of A point of view and, uh, a judgment, but it doesn't have it. And they're not. The tools are not designed in a way to make that obvious. They're designed with the little caveats down at the bottom that nobody actually reads anymore because nobody reads terms of services. And even one line, it can make mistakes and make things up. Doesn't matter. The second is that these systems, although there is an extraordinary combinatorial space that these things operate in, that these systems don't have a good understanding of the complexity of humans. Right. It has learned from what has been digitized and recorded. But that's different from having the experience of complexity. That's a difference. It's different than knowing the other people in the complex system. Well, right. Whether that's, you know, if you're thinking about it from a therapy perspective, the therapist starts to grow and understand the people around you. If you're dealing in a corporate setting and you've gone to your manager or your mentor for some advice, that person knows the other people around and will know that. Well, you might think that's a good idea. But you know, Mary, our VP of marketing, so think that's a dumb idea. So don't. Right. Like they're going to have some of that idea. And the third thing I think is important is that none of these tools, partially because they're software and because it's, it's sort of a weakness in general of the industry, is they don't deal with collaboration, these systems. You notice that, like we all using these tools, or at least, uh, most everybody's listening, are using these tools, but you're never using them together. They're not set up to do what is kind of a logical thing, which is to be a conversational agent among people. And that's a weakness in the way that they're designed, but it also is an incredible weakness in terms of its understanding of multiple people at the same time.

Speaker F: Right.

Speaker E: Uh, they're, they're designed to be one to one. And if all you want is one to one, then that's okay. But I think you're right to be very concerned about this sort of question of therapy. And I think it also broadens into how these tools can get used throughout an organization. People asking for advice, ideas, consult, comfort. You know, you grab, you have those conversations with people around the, you know, the proverbial water cooler. Right. And you're going to have it with these tools. And they're not, they're not.

Speaker F: I take a little bit of a contrary view. I I think they, I think there's hazards and dangers for sure, but I actually think that there's um, lot of benefit in having, whether it's ChatGPT or Claude or any of the others. I wouldn't advise Grok, but um, not to my kids anyway. But to be able to ask really personal questions and to be vulnerable to something that actually doesn't have any stake in it other than the thumbs up, reinforcement, learning and that you can have a, ah, you can have your own mental model of why you're going to the tool for that use and that can be incredibly powerful and useful. But that relies on having um, a very strong, what we call symbolic plasticity where you are able to constantly reframe the way that that tool is what it is, what it means to you and how it's changing your sense of meaning. So it's very different to have quite low symbolic plasticity and to go to ChatGPT and say, Tell me how I can fix my life, give me all the ideas, tell me everything I need. I can't live without you. That's an entirely different thing than someone with quite high symbolic plasticity that recognizes that they do have kind of a therapist in their pocket.

Speaker C: Isn't that though an assumption?

Speaker F: They have to kind of keep that right.

Speaker C: But isn't that an assumption that the user is, as we like to say, literate in AI use?

Speaker F: It does, it totally does rely on that. And um, that is something that we feel really strongly about that we have to, as a, I hate saying as a society because I don't even know what the fuck that means anymore but as a, as a collective we, we need to get, we need to actually increase this level of AI literacy. And I can tell you it's one of the most fun things to teach that you can ever teach. We've taught the most amazing groups of high school students. Should we just sort of, you know, just do it as um, a, as a, as a, as a volunteer thing? Amazing groups of high school students about just basic AI literacy. And that was 10 years ago. And people love to learn about other intelligences, whether it's teaching them about how dogs work or whether it's teaching them how AI works. It's a fun pursuit, it's something that really interests people. And honestly there's just zero excuse um, for not thinking creatively about how to make sure that your entire workforce at least has those basics and is delivered in a fun way, not delivered in year another. I have to watch a video on um, you know, something I'm not supposed to do with cyber security.

Speaker D: Listen to Greg drone on and on. Ah.

Speaker F: So, yeah, so I've learned over the years that it's, that it's doesn't matter what someone says about AI. You can take the opposite view. It's the best thing to have debates on because it's like, it's like humans, right? There's always another side to it because there's this complexity and we've got to get people past the, um, you know, whatever those sort of tried examples are about. You can, it's a tool. You can either hammer in a nail or hammer in someone's head. That makes no sense. You've got to think beyond that, and you've got to think about the nuance, um, of what happens, how our cognition, how, uh, our identity and how our sense of meaning changes as we use these tools. And it can change all the time. We see it all the time. Same user, same tool, same use case. It can be a different time of day. The context shifts, the stakes shift, the deadline shifts, and the entire behavior changes. So these are very, very complex landscapes.

Speaker D: I think it strikes me that as you're saying that, and we're talking about AI literacy, which to me is always, uh, sort of the perverse beel onion or whatever Shrek said, uh, way back when, right? Where I think so much of the narrative around AI literacy, and I know this is not what you're talking about, um, is as simple as the sort of, you know, like the LinkedIn influencer. Learn to prompt, learn to prompt, learn to prompt 150 tools that I use today to be more productive and efficient in my work. Um, but, you know, in a lot of ways, that's building on a very shaky foundation because AI literacy at its core also requires digital literacy, media literacy, information literacy, critical thinking, and all of these other skills that I feel like over the course of years has become deprioritized, right? Everyone is ready, willing and able to accept whatever the algorithm feeds them without providing or without doing the kind of critical thinking that is so necessary here. How do you really, in an organization that's looking for a quick and easy win, they just want more productivity, they want more efficiency unwrapped and discussed. Why, that is a very reductive view. But when you're faced with the leader who just wants that thing, how do you get them to think more broadly about how to build these more distinctly human capacities in their workforce?

Speaker E: The number one way is having them experience it. You know, and in some ways you might have caught me rolling my eyes, you know, when you were talking about the, you know, the LinkedIn influencers with the Honda best prompts. Um, uh, but there is some. And part of the other trite thing as well, the leaders have to be using it themselves. It's a lovely idea, but honestly, executives don't use much software at all. So. And I'm not being critical, it's just a reality of the job.

Speaker F: Right, right.

Speaker E: So the challenge here is though is to give them enough exposure and enough experience with using these systems that they have that aha, uh, moment themselves. Right. And also for us, we very quickly go beyond the idea of here's the tool, here's how to use it. Because we could be done with that in about three minutes. Right. These tools are designed to be incredibly easy too.

Speaker C: Right. I mean all the prompts could be gone as soon as we go to voice interaction.

Speaker E: Yeah. And uh, I mean you're, you're just, you're typing or you're talking. It's, you're having a conversation. I mean most everyone knows how to have a conversation. Notice everyone has the ability to have a conversation. So operating the tool, this is about the simplest thing that's ever been created. It's also pretty much the worst designed application ever done.

Speaker D: Right.

Speaker E: We shouldn't be making a tool that looks ridiculously easy and to be the most consumer facing consumer design tool ever, even though it's really cluttered. I hate that left bar that all of these things have adopted. But it's actually a professional tool where you need real under. You need truly immersive training to be good at it. Right. That's totally. So we, we've dubbed this the Design Illusion of LLMs and wrote a piece about it a while back, which is probably still relatively accurate. These things are designed to look like consumer tools, but they're actually professional. And that confusion point is really important. You need just as much training to really be able to understand how to use an intelligence as something like a Photoshop.

Speaker F: Right.

Speaker E: But no one thinks that you're going through that. You know, you go, well, I'm typing it. It didn't really work. Here's your 10 best prompts. Uh, okay, now I know. No, you don't. You don't really, you know, and you as a collective, what you have is in any individual is failing aggregates up to an organization. And you don't, you have collective stupidity. Right. So our path is to very quickly get to that answer that I gave you really quickly with the um, executives to help Them understand that that transition is something you have to think about differently. This is about culture change, not technology adoption. Right. This isn't about training somebody to use the next level of Excel. This is about introducing a new type of workforce. Ten years ago we dubbed it as machine employees and yeah, anthropomorphizing. But we tried to help people think about them as employees so that you realize that you're bringing something new and cognitive into your organization. Okay, so how are you going to manage it?

Speaker F: Can I give you two other things? One is that a lot of these, you know, quite these productivity gains that um, we talk about with AI, they're very ephemeral and it's hard to sort of know. We know now that self reporting is completely hopeless. You actually have to measure people.

Speaker C: So what it comes down this week AI. Uh,

Speaker F: so I think I've actually come to think about it as just back to the old school sort of the way you just think about things when there isn't AI, when it's just a normal technology. If you like. On one hand, if you're going to give someone a tool that gives them five minutes extra a day and then you're going to worry about what they're going to do with that, then that's got nothing to do with AI. That's got everything to do with culture and trust and um, the places that we see stalling out, guaranteed way of stalling out. Uh, your AI adoption is you don't trust your employees and you can't ask someone that. You got to go and look at how they behave and then you know, whether they actually trust their employees. But that is a guaranteed route to failure. The second one is just the same as any other kind of quite old school Taylor esque way of thinking about, you know, systems thinking would be the most advanced way of thinking about it in an organization which is go look for your bottlenecks productivity. You can't just throw AI across your entire organization and expect suddenly wow, you know, we're 20% more productive. Um, you actually have to go and look for bottlenecks. And it's almost like people have forgotten gotten that basic, basic rule, you go and look for constraints and bottlenecks and you figure out what AI and people and anything else can do to help with that. And people have forgotten that in the AI rush and the AI pill that it's seen as the panacea when clearly it's not. And we're all going to get over this uh, at some point, um, and go back to some of these sort of core things. Hopefully the next iteration will be much more focused on true um, innovation, not just tweaking things and innovation theater and getting a patent for something that's completely useless, that no one will ever use and putting the sign up on your wall. You know, we want to be looking at things that actually are truly based on understanding, on a theory of change that we, that we can model and simulate as making a difference in the world and then put it out there and hope that it does and work on it and that we go back to actually, you know, this baseline of the core thing that matters is do you trust the people to have you articulated the goal? And do you trust the people to go and make their own conclusions about how to achieve that goal? And we've lost sight of that right now. We've got a very top down kind of. And it's coming from AGI. We're all waiting for the AGI to give us the answer.

Speaker C: We'll be waiting a long time.

Speaker F: Uh, yeah, I mean, well, we'll run around answering the wrong question.

Speaker C: I'll be in my grave before that thing happens. I guarantee that.

Speaker D: And this is probably both a good place to end because we are coming up on the death of Jeff. He's gone.

Speaker F: The end of Jeff.

Speaker D: Um, but also an awkward place to start because I feel like we could go for hours and hours and hours still not even unpack a fraction of what we're talking about. So maybe at some point we'll have to have you on again. In the meantime though, for folks who want to learn more about the work you're doing, your research and all of that, what's the best way for people to do that?

Speaker E: So we publish everything we, we write on uh, artificialityinstitute.org so you can go there. Um, there is, uh, and I would recommend starting with a series of pieces that are at the bottom of the homepage now called foundations. So there's a sort of core pieces around our thesis that actually gets you there on the core research. We are also hosting soon, uh, depending on when this comes out, our annual summit in Bend, Oregon. So the artificiality summit is. Something happens this year is October 25th to 23. Um, it is a wonderful 23 to 25. Sorry, 23 to 25.

Speaker F: We went backwards.

Speaker E: I went backwards. Yes, we're not.

Speaker F: What's happened to your 23 to 24 token prediction today.

Speaker E: And uh, it's a great way to get away from it all to come to the mountains and spend time in an Immersive setting, immersive experience with really interesting people to try and think about and imagine what a hopeful future is with AI. So we're here to not, um, dwell on the it's going to save the world nor on the it's going to destroy the world. We're looking at how do we do this differently and how do we come together as a community that can have a change and do something different with this technology, um, than what we're seeing today? So that's a core way, um, to do that.

Speaker F: Yeah. And if you look at the people that we bring in, it's a real mix across AI and the human sciences. Uh, and if anything, it's more on the human sciences than on the AI sciences. And it's very much, let's go right out there and say what's, what's core to being human? What do humans need? You know, things like what does our memory do? Or what is AI going to do about that? So it's quite high concept, but it's very, very grounded and um, sort of the fundamentals of what it, what it means to be human.

Speaker D: Excellent. All right, so thank you to you two wonderful humans for joining us again. This has been Dave and Helen Edwards. They are the co founders, uh, uh, of My Tongue, co founders of the Artificiality Institute. We will share links, their website and to their event and its information for anybody who might have been confused by Dave doing his dates in reverse order. But, uh, you know, go ahead, check that out. It will be on the show notes page@nobrainerpodcast.com as always. And with that, we would like to thank everybody for tuning in. Jeff, do you want to show us on out the door?

Speaker C: Sure thing. You know the drill, folks. Punch that like button, share it with your friends. Friends give us feedback online or privately via email. And most importantly, subscribe. We need you. We love you guys. We take requests too. So send them on in. And with that, we are out of here.

Speaker D: Thank you both.

Speaker F: You bet.

Speaker E: Awesome.

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