Women Defining AI for Architects

A Welcome to Practice Disrupted, a podcast where we find ways to create new solutions to current challenges while elevating the value of architects. I'm your host, Evelyn Lee, an architect who spans tech as an angel investor, startup advisor, and founder of Practice of Architecture. Whether you're a seasoned architect or just starting in the field, this podcast is your gateway to think differently about the role architects play within our global community. Hello, disruptors. Welcome back to this week's episode of Practice Disrupted. Today, we're bringing on 2 incredible human beings who are working to level the playing field for women and non binary individuals by closing the technology gap at work. They are Helen Lee Kupp and Nicole Sterling, the 2 cofounders of Woman Defining AI. Helen is no stranger to practice disrupted.

A She is a past coworker of mine at Slack and friend. She is also the coauthor of The Wall Street Journal best selling book, How the Future Works, Leading Flexible Teens to Do the Best Work of Their Lives. She believes in closing the gender technology gap starting with women at work to create a future of work that looks and feels fundamentally different for her 2 kids and the women she mentors. Nicole is a utility leader, blending strategy, marketing, HR, and finance in the tech industries. Her impact on company success ranges from returning 4,000,000 year over year to field operations teams through corporate learning initiatives, increasing sales pipeline activities by 10 to 30 times by establishing a rev ops philosophy, and even driving innovation within local municipal government. Nicole's current focus is on developing AI technologies, leading a stealth company specializing in digital twins, and perhaps there's more that we can explore there a little within this conversation and AI agents. A fervent advocate for women in tech, Nicole combines her passion for AI with a commitment to ethical and inclusive technology. I believe that the 2 of you are incredible representatives of what we have been calling portfolio career builders, and we could probably actually have another entire episode on that alone.

A But today, we're diving into women defining AI. So Nicole and Helen were just gonna break the ice and jump right in. Is there anything not I should say on your typical bio that you can share with our listeners?

B We like to have fun. Like, Helen and I got into this, you know, to just just to make a difference. And we, I think that's something that we want folks to always to always know is that this learning can be fun and we want it to be accessible. And, you know, that's what we're here for. So

C Yeah. I mean, aside from fun, Nicole is, like, one of the most fun cofounders to have to build something. I basically think a lot about working with Nicole as a parallel to building and teaching in AI where I'm just like one of the most important things with really ramping up on generative AI is both to think about how it can be useful and really be thoughtful about, you know, like, the work use cases. But equally as important is the personal use cases and the, like, the fun ones where you have these moments. Like today, in our community, they're in the part of the program where they're generating music and songs as part of their AI project. And I think it's just this pairing of not taking it too seriously as well as, you know, learning and building something useful. Doing those two things are both is is a definition of how Nicole and I work and build, you know, women defining AI, but also just us as people and our personalities.

A I want the 2 of you to actually talk about how you met. Because I think in today's distributed space, there's a lot of naysayers, you know, around work even when it comes to virtual environments. So could you guys talk a little bit about that?

B Well, so, yeah, I'll start off. So I saw Helen give a talk at a conference that we were on. It was a sort of conference for operators. And I remember here talking. It was something, you know, future work, blah blah blah. But mostly, it was like her focus on women and and AI and her wanting to, you know, she had talked to her a little bit about her reference and referencing the book that she had written. And it was just it was. It was about these, you know, how the future works.

B It was about having these remote instances, and it was okay, and you can still get work done because, really, you know, work or the way that work was designed was not for today's worker. Right? Women, people of color, that kind of thing. And, you know, all I and all I thought was, she's cool. I'm gonna connect with her on LinkedIn. And, like, that was it. And she was one of, like, 2 people who I connected with, and that was it after that conference. I was like, I don't really know what it'll turn into or what it's for, but I'll just connect and figure it out later. And then it was quite literally probably about a couple of months later.

B And honestly, at the conference, I don't even think we said hello to each other.

D I was

B just like, I'll just connect.

A No. I was gonna say, you didn't say hi. We did not meet in person. You just connected with me.

B Yeah. I was just like that. Just cool. I'll just connect with her. Figure it out later. And then it was a couple months later that I'd seen a post come through on my LinkedIn that she was, you know, thinking about starting this, like, women defining AI. You know, she had some kind of some rough ideas around it. I was like, oh, well, goodness.

B This is this is something I've been thinking about is, like, how how do we get more women talking about AI? There's there's so much out there, but where are all the women voices? And so quite literally, I saw the post. I reached out to her. I said, hey. Do you need help with this? What are you doing? I wanna do it. I've been trying to you know, I've been thinking about doing something similar. What help do you need? And then that's when it got started.

C Yeah. Little did I know that Nicole would be cooler than me for 1. When you were like, oh, she's cool. I'm gonna connect. And I and I always say, like, if had Nicole not reached out to me, I think that women defining AI would have been kind of a fun experiment. What I had in my head was, hey, I kind of want to explore and learn AI and build some things And maybe it'll be useful to bring a small study group together of a handful of women, but it was an experiment. It was an experiment for me. It was an experiment for the both of us and if it was just me building this, I think I talk about how we've like dropped everyone into Slack and Slack gives you this sort of like 90 day free trial.

C And so at the end of 90 days, I was like, oh,

A well, maybe either this was the end of the experiment

C or we would have something here and we would need to figure out what we wanted to do in earnest and having Nicole around to be that sort of second half of the building team. Even though we had, we quite literally never met in person, even though she saw me at a conference, we had that first conversation over the phone where it just took like 30 minutes. And I was like, yes. Values aligned. This is great. Let's try it. Let's just try to build something together. And then over the course of those 3 months through our kind of conversations in Slack channels, the ways which we were sort of tackling different projects together and with the people who signed up, we knew, we knew that there was something here.

C We loved working together, you know, in that sort of like a sync and hybrid kind of mode. The best part was we really like we had not seen each other. We had not seen many of the women in the community ever and then last month actually we had an opportunity to partner with someone to host a small intimate dinner in the bay area and Nicole flew in and a handful of other people showed up from the community and you would think that like 8 months of being in a community, never seeing each other, only our Slack, you know, Slack photos wouldn't be enough. But we walked in and it just felt like a mini reunion. Like you would have never known that we had never met in person any of us. It was so it was so cool. We found out that we're all about the same height, basically. We're all really short people, which was amazing, except except for Shella.

C Right? Shella was much taller than everyone else. And it was just so fun. I feel like we were despite being 50% of the dinner, we sort of co opted it to be basically a mini women defining AI reunion and it was the most incredible thing. And and the thing that I often point people to is sometimes we forget that we we take it for granted that we need to be in person, we have to have these networking events to build relationships, but usually what happens is that it's it's really superficial. Right? It's inch deep. But when you have a community where you are work or you have opportunities to learn together, to collaborate on things, like we wrote a community perspective paper, we are building with different AI models and experimenting together. When you're getting something done, you're in the trenches together, whether that's in person or in this sort of async or hybrid mode, you get a chance to really get to know one another. And and that part of it, that's what builds those deep relationships.

C And so when we met in person, it was just I mean, that dynamic was obvious. Right? Even though it was the very first time.

B And I also think there was there was still kind of despite that we had come together, we hadn't known each other, we just started working with each other, trying to figure out what this thing was. I think there was a pivotal moment. And Helen always likes to tell the story of how we wanted to have our 1st study group. And it was like I think there was an article posted about building a chatbot, and I kinda looked into it. I was like

C Yeah. Someone was charging $2,000 to build a custom AI chatbot.

B That's right. Yeah. It was $2,000.

A And I'm you know, and I kinda

C took a look at it, and

B I was like, well, that's BS. I mean, why would you why would you charge that much? You know, Helen and I, we have this ethos of just, like, let's make it accessible. We we gotta get access, you know, to this AI and this technology. So, anyways, I took a weekend. I kinda hammered against it, and I was like and I got it working, a custom chatbot with code. And I'd never coded before, you know, at least not deliberately, and pulled it together. And I sent it to to Helen on Monday morning. I was like, hey.

B I got it to work. She's like, wait. What? And I think at at one point, yeah, you you like to tell the how like, you said, well, hey. Do you do you want us to find, like, engineering help or, you know, someone to to to do it, to help assist? And I was like, no. Let me let me just give it a go. Let me just try this thing and got it to work. And in a couple of weeks within a couple of weeks, we had held a lunch and learn and actually ran the community through in an hour and had them build their own custom shopper. And so I think it was like that was a very pivotal moment for both of us because we said, wait a second.

B We can do this. We can totally do this. And it was and this this idea of demystifying AI and, like, what's in the black box became a real kinda rallying call for us, and it's part of it's built into our our mission vision. And it was that moment where we demystified it demystified it for ourselves that we said,

E oh, okay. Yeah. We can go and do this for

D other folks as well. Yeah. I think what's really important for

F people to understand is women defining AI is actually filled

D with, I would say,

A a people to understand is women defining AI is actually filled with, I would say, a majority of nontechnical people who are working together trying to figure this out. People who don't typically code or people who don't even have, like, engineering or developer in their titles. Right? It's it's even a lot of people with we have attorneys in there, we have learning and development specialists in there, we have managers of both technical and nontechnical teams that are all just trying to figure out how to use this technology going forward. So before we jump into a little bit more, I know you guys have some statistics around adoption of technology and women in the field. Can you help me run through through some of those and that technology gap that we've referred to, but we haven't yet to call out in this conversation that we're trying to close?

C Yeah. There's a long standing gender tech gap overall, but when it comes to AI, part of, you know, the genesis of why bring women together to experiment with AI in the first place with us? There was this study done last year, right before we started the community that showed that for generative AI technology, less than a third of women were using AI in their work or personal lives compared to more than half of men. And that might not seem like a big deal at first blush, but it really becomes a big deal when you think about overall representation in STEM fields by women. It tends to be less than a third. It also matters when you think about who gets portrayed, who gets to speak about technology, especially the latest and greatest. And and I'll be honest, like, as a mom of a 2 year old daughter, like, I think about the role models that she has in terms of who is being, you know, the the voices that are defining this space. And it's just that that statistic made me and Nicole realize that we needed to change this. And if it continues from one tech innovation to the next, like, we'll never we'll never get enough people at the beginning of the pipeline, right, to solve this problem.

C And you see that all the time in the discussions that we have in the community, the types of use cases that come up with women trying to, like, figure out what this looks like in their work and personal lives. It's a very different conversation than the ones that we see external to the community that are that are highly male dominated. So, yeah, that's the big data point.

B Yeah. And, you know, and the reason why it's important that women are staying close to AI and and following the developments is that we're starting to see different behaviors from workplaces. More workplaces are looking for AI fluency. They're looking for AI skills. So as Helen mentioned, there's already a gap, right, just from a stem perspective. But if now workforces and workplaces are looking for AI fluency in their skill sets, women are just gonna continue to fall behind. And and there's lots of different reasons that we've been surfacing just within the community as to why that is in addition to some of the research. Right? So, typically, women are more hesitant of AI.

B They they consider that there's more perhaps, safety issues as they should. You know? They are are, you know, throwing out the yellow flags to say, hey. Is this a concern? Yes. It is a concern. This is why we need you as a part of the development and the deployment process. There's also some very interesting things that are popping up in in terms of of women in thinking that, this feels like cheating. I I I I can't engage with you know, if I use AI, then I fit then it's like I'm cheating at my job or or anything. And that just struck me as as wild.

B It was not it was not where I first initially, went to. But the more women that I talked to I just ran a a wine, women, and AI session last night in my community. And there were several hands that were raised of this feeling that I'm just I'm cheating. I'm I'm not being my authentic self. And and this is my response to to women who say that. For decades, CEOs have had an a personal assistant. They've had someone to bounce ideas off of. They've had somebody to give the first draft.

B They've had someone, you know, to do some of the work before it reached the final process or the final, you know, the final draft. I said, now that's your opportunity. You now have the opportunity to do that with AI. As Helen had mentioned, not just to help with the work use cases, but also the the use cases at home. One of our favorite projects that we like to do with the community is take chat gpt, take a picture of your refrigerator, and ask it to create a recipe for you at the end of the day. That's just helping you live. That is just helping you do the day to day. You know? So when I when I hear that as a reason why more women aren't getting involved, listen.

B Now is your time. You just never had the opportunity before.

A So what I love about this community too is, for me, it is so hard to keep up with the various different aspects of AI as it's gaining momentum and changing over time. But it's been a great collection of people and subject matter experts in their own industry verticals that are posting to the channel what is most or posting to the Slack workspace what is most relevant to them. And then I can kinda pick and choose, oh, but this is also relevant to to me, and I need to make sure that I'm looking at and following that for my own job. The other great thing that is happening in the community is that you guys are coming out with with these papers, and you're posting them to the public. And the latest one was focused on well, the latest 3, I should say. Right? It's in 3 parts. It is responsible AI in action, balancing regulation, ethics, and the future. So, Nicole, can you talk I know you've been editing these papers.

A Can you talk a little bit about this latest series and anything related to data privacy, which is something that architects are really struggling with as more of our output and what we consider the work that we do against our contracts is purely digital.

B Yes. This was our first series, and we decided to launch this as our first because there was just just so much happening from a regulation standpoint. You know, if any of your data touches the European Union, you've got to follow the EU AI Act. And this was some of the things that we we we parsed out in the 3 part series. It was meant to be 1, but we had so much that we decided, oh, we've gotta make 3. And so that first one is all around the regulatory landscape. You know, what do you listen to if you are an AI business? You know, while we know a lot of folks that are reading those papers are kind of from an enterprise standpoint, we also tend to also wanna help out the small businesses, right, who may not have the resources and may not know what to what to really listen in on. And so those papers, especially in part 1, when we were looking at the regulatory landscape, it's like, here's what you really have to know.

B Like, just just get through all the hype and get through all the information that's there and really just focus on these things. And then here over here are some nice to knows. Right? We really try to distill it down, well for folks. And then in part 2, it was really all around, you know, what businesses can do if they have an AI product. What what are the protocols that they need to put into place? For instance, you know, first up was talking about taking an inventory of all your AI tools. Like, what exactly are you using? What is considered high risk? And so, you know, Evelyn, when we talk about data privacy and making sure and knowing whether or not data has, you know, PII, you know, the protected the personal information on it. Like, those are big deals. And especially in some of the regulated industries where states are starting to come forward and say no to things like automated decision making.

B Right? Meaning that I can't make a decision necessarily or my company can't make a decision on whether or not you get a loan just based off of AI. So there's lots of regulations that are coming down regarding that. But, yeah, data privacy is is a big deal, and especially if your data is touching the European Union, you're gonna have even more regulation on you. Here in the United States, we're still figuring that out. We have Biden's executive order, but nothing really is is sticking at this point. There's a lot of things that are being said that's gonna happen, but a lot of folks, even here in the United States, they're really following what's happening with the EU AI Act. And so, you know, then going back to data privacy, whether, you know, you're scoping out your AI tools, you're identifying what's high risk, what's low risk. You know, in part 3, what we talk about is, okay.

B Well, what what's gonna happen in the future? What should you start doing in order to prepare for all this regulation and all these things that you should be doing for your AI tools and your AI business. And one of the things that I'm actually kind of very interested in and I'm following really closely is this these creations of cross functional teams cross functional AI committees. I currently sit on ForHumanities. ForHumanity is an organization. It's a nonprofit that's working to help create the auditing criteria for these new AI tools. And so I sit on the large language model, the large multimodal model, and digital worker committee that actually creates the auditing criteria for auditors and so and for businesses. And, you know, one of the things that just is is hard for small businesses is like, okay. I'm supposed to have an ethics committee.

B Okay. I'm supposed to have an algorithmic risk committee. You know, how how, as a small business, am I gonna stay on par and keep up with enterprises that have the resources, that have the money? And, you know, For Humanity is one of the organizations that I got involved with because they'll help you get that talent. Right? So when we're talking about ethics committees, you gotta have somebody who's actually really well versed in ethics and and the state and the statutes around it and everything. And so, you know, whether that and so one of our predictions is you're gonna see a lot of that more of that come up, those cross functional teams, whether that's having DEI professionals. You know, our authors really hit on this point and that DEI has been like a really diversity, equity, and inclusion has been a buzzword and has been kind of a nice to have. But boy, oh, boy, do we see an opportunity where those folks can be front and center, especially on these cross functional teams, and to dig into products and assess what are, you know, the the impacts from a diversity, equity, and inclusion standpoint, that's really exciting too. And then, of course, like, some of the rise of these folks that are just focusing like a chief AI officer pulling all these folks together in order to make sure that the data privacy is locked in.

B That bias is being put into check. Right? Helen and I have talked in the past about that stable diffusion study that was done, where, oh my goodness. You know, this stable diffusion being a text to to image large language models. So you enter in your text, and then it spits you out an image. And, wow, did it feel like we were going backward here in our progress, in the representation of women. Because when you would type in the word judge into stable diffusion, it would only about 7% of the images came back as women, when 34% of US judges are women. Or you take the fact that you type in fast food worker, and 70% of the time darker skin tones came back in the images, when 70% of all fast food workers in the United States are white. And so there is a real there is a, unfortunately, where if those images and that bias continues to get baked into our large language models and people aren't educated on it.

B Right? I've there's there's a feeling sometimes like, some of that progress that we've gained over the last several decades just feels erased very quickly. And so that's why these cross functional teams need to come together in our organizations. Data privacy, in addition to bias, need to be addressed by these teams to make sure we're creating ethical, responsible, and safe AI. That was a really long answer.

C It's a long answer because I think it's a really important one, and it's nuanced enough that it's one of the reasons why we started this initiative around writing community perspective papers is because, 1, like so much of what's happening in the media with AI is inch deep. Right? It feels very hypey. It feels very short form and there's not enough out there. You sort of piece together a lot of different things to say, okay, so what what do I do with this information? And how do I integrate that with the series of, you know, news and updates and information that's coming through things in the AI field because we're so early still is evolving so fast that it feels like you're you're just like drinking from the fire hose of AI news. Right? And then the second is because there are these, like, topics that are evergreen that it is about just continuing to integrate and learn what's the right path here because there is no, like, right answer. There's just sort of, here's what we know. Here's what we don't know. Here's what we're doing.

C And I think the that's a big reason why we started to write these communities perspective papers is to bring multiple voices together to dissect a really needy and important topic. But I will also emphasize that so much of this is about getting, like, hands on experience with some of these tools and technologies. I think the what I'm seeing enterprises and and and people in corporations get wrong is to be too fearful of all the data security conversations and ethical conversations that they're like, well, we're just not gonna do it. We're not gonna try. And the problem with doing that is that AI is being baked into all of our tools and software, whether we like it or not. And if it is behind a feature and you don't know what are the potential edges to this technology and you don't know to ask those questions, then then you are inherently using something that might be biased, might be you know, might have, issues and risk, and you won't know. You won't know. And a lot of this a lot of the reasons why we're like, just experiment with it in small ways, it doesn't even have to be that serious, is really to build that intuition around, like, what are the questions that you should be asking? What are some of those edges? Like, how is that changing? And how do you integrate all of the things that sound really hypey right now and, like, what's real and what's not.

C Right? A really great example is there was a lot of news about, like, video generation and how amazing you have these, like, Pixar, like, quality video generation type of things. Oh my goodness. It's gonna be it's gonna put tons of creatives out of work. And, like, yes, it's gonna change the industry for sure. But if you play around with the tool, one, if if you know nothing about creating art and video like myself, I'm a Excel person. So, like, the farthest you could get from being creative. You don't know the right terms, the right things to and it just you you can only generate a few seconds of video at a time, so it's not a full, like, movie. And you get some really funky things if you don't get it right.

C And that's, like, a fun experiment, but I think that it takes it takes really just trying it to see, like, oh, it's not it's not perfect. It can feel like magic sometimes, and it can feel like absolute crap other times. And and getting hands on is the only way that you know that. And so, yeah, I would say, like, the biggest thing is don't avoid the ethical and data security conversations. It really just takes diving in and and navigating that.

A What do you say so, you know, I've I've found this, Helen, you and I have kind of talked extensively about this, though, is people jump into AI not necessarily with this experimental mindset while they're doing it, and they have a bad first impression, and that becomes our overall first impression. So what is your response to individuals to kind of encourage them to keep experimenting?

C I would say that it never is, like, a good response the first time. This is true. Partly one because usually when you're trying things out the very first time, you're probably trying a free tool. That's 1. And free tools are using models that are lower performing. I mean, they have 2 because it's free, and so the output doesn't feel as magical. I think the other thing is also we hear so much hype in the news that it's it feels like it should feel like magic from the beginning. But actually, AI is software, and and if if you've ever been, you know, adjacent to, you know, technical roles, you'll know that building with software, there's a lot of just, like, iterations involved.

C And that's really why they call it prompt engineering. It's not because it's a technical thing. It's because it's this emphasis on, like, getting good output from AI just like you do from code is it takes iterations. It takes practice. It takes you know, honestly, with just, like, a chat gbt and a text based model, It's just about asking follow-up questions or showing examples of good, like, what do you actually want? And and you'll learn pretty quickly, but it's it's really I encourage you to try even just asking for additional follow-up questions and see where that gets you. It's as simple as that. Right? It's not about taking a course to be the best prompt engineer possible. It's about just nudging the AI to do a bit better.

C And I kid you not, I have a lot of chat logs that I've shared with people where I'm doing data analysis with chat2bt and trying to get it to create more, you know, charts and and data visuals. And, and my follow-up statement is just make it more beautiful. More beautiful, please. And it's shocking. It works because generative AI is weird. It's really weird, and it do it does stuff like that. The other thing that I I usually coach people on is, like, think of these AI models as kind of a like an entry level intern and how you might work with an intern. Right? You don't expect magic right away, but if you coach it enough, if you scope it well enough, you give it specific enough instructions, then the output you get is higher quality.

C And thinking about that way, rather than thinking about it as a magic wand, often helps in terms of how you you work with it and get the thing that you want on the other end.

B You probably have just as many examples as I do, Helen, where there was this idea that I had to take the prompt engineering course. I had to, you know, download and buy the prompt engineering library or whatnot. And I think you and I are are both on the same page when we say, no. Just just just get in there. Just just ask it a question. And like you said, you know, you kinda treat it like the junior intern or whatnot. You know, how would you guide someone through what it is that you're asking for? Right? Some people have even said, it's like asking my my my kid to do something. Well, yeah.

B You know, you there's usually a set of instructions. You can't just say, you know, do this and then expect the output that's in your head. It's not that magical as you said.

A So we've I feel like we've gone a little bit on the doom and gloom, and we're on the up swing with some kind of encouragement. You know, a lot of people talk about how many jobs that are going to be lost due to AI. I wanna look at the potential opportunity that new technology usually creates on the other side. So there's a lot there. What do you see on the horizon in terms of new opportunities and new potential job roles coming out of this technology?

B Like, what I kind of alluded to before with even within our businesses and folks who are trying to build AI products, that DEI might have a new role. Like, they might shift a bit and be more within the product scene. We're gonna see chief AI officers or folks who are actually overseeing, you know, the production and the implementation of AI. Just like any technology that has been disruptive in our past, there's always been, you know, lifts, shifts, and maybe drifts. You know? Yes. As Helen had mentioned before, there is going to be some disruption. The chatbot, you know, the new story that came out where the chatbot replaced 700 employees. Yeah.

B That's that's pretty significant. Then, like, what I wanna think about is, okay. And or and then there's also always about the software developers. You know, there's no software developers. My eldest son called me and said, mom, I'm not gonna have a job after I graduate. And they said, no. No. No.

B No. You'll still have a job. But, yes, will there be less of something needed? Will there be less of software developers? Will there be less of potential customer, you know, engagement specialists? Yeah. And yet there will be new opportunities into the future. Well, I don't think that it'll really obliterate any type of job function, even our artists. Right? There's a lot of there's a lot of concern that it'll just completely, you know, obliterate certain professions. I and I'm open to see what it creates that we don't know yet. There could be some really cool things and jobs that are created that we just we're we're just not we're just not aware of at this point.

C There's a discussion around, like, is AI gonna generate the next single employee $1,000,000,000 business?

A The CEO of OpenAI kind of said, like, AI is gonna generate the next unicorn, single person unicorn or something like that.

C Yes. And and that speaks to both the doom and gloom and the opportunity. Right? Because, yes, I think that with AI, we will be able to do a whole lot more as an individual, but it also opens up this opportunity that I'm really excited about, which is to create more builders and creators in this world. I think that if you aren't stuck doing this sort of like, you know, bottom level things, you have more and more opportunity to think big about, like, what's the thing you're building? How big could this be? What are the really big opportunities? And I use that as a as an anchor. Right? Like, the single person $1,000,000,000 business because versions of that are very true even just like within our jobs. Right? There's a lot of cool stuff that you can do with AI in terms of what it generates. Right? Images, sound, etcetera. But I actually think the power is in thinking through the shit work that you have every day that you just feel like this is so manual.

C I hate this part of my job. How can you use AI to really reduce the load on the things that, like, you don't wanna spend time on, that are kind of repetitive and not interesting, that are not, you know, creative, and and to use the technology to to say, okay. How can I simplify that, automate that, whatever that looks like, and then really focus your energy and time on the the craft? Right? I think that that is really powerful. And the more that we do that, the more we will actually see jobs and the shape of jobs really change and shift. And I think that is for the better. I think that gives people more freedom to step into more of that higher level thinking, creative work, and and building, like, net new types of things that we we didn't have time and space to do before. So that part is really cool.

B I was just gonna add with the shifts that's gonna happen with work, one of the things that we also talk about a lot are what does that new employee or that what are what are the skill sets the employee needs to have in the AI era? And we talk about the power skills. You know, typically, they use they used to be called traditionally the soft skills, you know, nice to haves, but we really think that in this new AI era, the power skills of problem solving, critical thinking, adaptability, those are gonna be crucial because if AI allows you to sort of level the playing field from a technical standpoint or for, you know, from the domain experience than what's left. Well, you've got to be able to have the ability to critically think about what the outputs are. You still you know, to be in this era, to be human is ever more important because you still have to test. You still have to interact with other humans. Right? And so to those power skills, and we talk about the shifting that's gonna happen with work, I bet you're gonna see a lot more companies focusing on those things because AI just becomes an equalizer from domain experience.

A So architects, in general, focusing back on the field and the industry that I am in and this podcast is about, are really late at adopting technology. For instance, I know a lot of architects have moved into this building and modeling 3 d space, but they're still using it as, like, as for 2 dimensional drawings. They've purchased the software. The software has the power to do new things, and they're not using it to its full extent. So what words of advice or encouragement do you have for an industry that is typically late at adopting adopting these type of technologies, whether it's to kind of slowly ease them into it or even kind of light the fire to get them going in this space.

C Why do you think that's happening, Evelyn?

A So this is a huge generalization. I do think there's there's absolutely, as with anything, a lot of outliers that are and architects that are doing an incredible award winning work and writing their own scripts and writing their own code and and algorithms on top of other tools. But I think a part of it is because there's this artist mindset or modality with architects that they if they are not the ones creating, like, the tool just becomes that. It becomes it's not it's not necessarily a partnership in their creation, as much as it is a means to the end to I have to produce these drawings to to implement my creation. I also think, you know, the architecture is really interesting. People tend to retire really late in architecture, which means that the leaders that are making decisions or the ones that are making decisions about how things happen within organizations are are on the older side of things. In fact, I think there's a lot of conversations right now that's happening with the lack of firm leadership transition as we're looking at how the baby boomers in that generation will begin to age out. But I also think that those individuals, tends to gravitate towards the way they've always done things.

A So there's that. And then finally, I think there is this gatekeeping mentality that we've had in the industry, which means that like, you don't know enough for us to allow you to show up on-site with us. So heavens forbid you you suggest a new technology that, like, has us jump over these other hoops because you're not you're not there. We need you to pass through these other gates to get there. So it's it's all encompassing, and I think it's really culturally rooted, unfortunately. But it's something that I'm trying to change.

C Architects are not the only one, for what it's worth. I I feel like I've had a similar conversation with, like, the legal profession, for example. Mhmm. And I hear you on all of those points. What I would say in case this does help light the fire is like, look, we've had a lot of major technical innovations over the last couple of decades, but this is one that is, like, absolutely not going away. It is not only not going away. It is accelerating in the, like, amount of innovation, what's possible, that's really changing fundamental paradigms in the technology space and the creation space, that ignoring it would be like ignoring the fact that Internet was coming. Right? It's huge.

C And you get that sense when you start to experiment with it about how much of a paradigm shift it truly, truly is. But I will also say I sent this to a bunch of lawyers once when they were talking about how, you know, like being a lawyer is about, like, doing your diligence. Right? Law school is about, like, memorizing lots and lots and lots of, like, cases and and learning to, like, absorb, like, data really fast. And the funny thing is, like, it's a ton of work and you move to this model of creating a law firm that it's all about billable hours. And it is, like, a very classic innovator's dilemma where you're like, yes. This is how we've always done it. But, like, someone is going to come by and create a completely new type of law firm that doesn't require all of these hours to scour books and legal cases, and is gonna win. And there's just and not only will people be happier because it's faster and cheaper, but the people running the business and doing law in that way are gonna be happier because they don't have all this, like, crazy research work and, like, looking through stacks and stacks and stacks of, like, paper and discovery and stuff.

C And so it's almost like there is a better for both sides of the equation and it just takes some very innovative people to be like, you know what? This can be different in big ways and it's coming. There's always those people and it's coming whether or not you want it to come. And so my advice is just lean into it. There's some parts of it that are incredible and you don't have to use AI in the parts of your work that are very core to, like, your creation process. That's fine. Right? This is why Nicole and I talk a lot about, like, let's think more expansively about how you experiment with these tools and see what's possible. Think about the things that you don't wanna do. Think about the meal planning stuff.

C Right? Like, get versed on what's possible before you start to think about, does this replace the thing that I do that is very authentically me? You may just be like, you know what? That's like I have my process for doing, you know, architecture in this way. Just like I I could use AI to write my damn LinkedIn post, but I don't because it's just me. And I just I wanna write it my way. And it takes me a little bit longer, but that is just that is me, and I'm I'm fine with that. I've tried it multiple versions now, multiple ways. And that's okay. That's okay. No one's saying that, like, you need to change a piece of it that you don't wanna change.

C What we're saying is, like, it's really worth experimenting with it at least in parts of your work and personal life so that you get a sense for what's possible and not just ignore, you know, this innovation that's coming for for all industries.

A So as you all are looking towards the future of WDAI and women defining AI, can you share any upcoming projects, courses, collaborations? Where are you guys focused relative to the the community for the remainder of at least 2024?

B So we still have some community perspective papers that we're going to be releasing. Our next one will come up in q 2 that's focused on AI and strategy. And then throughout the year, we're going to release subsequent ones such as, and even have some industry specifics. So AI and education, AI and health care. So we're still going to make sure we're getting our community members to write those and to just do the deep dives and, you know, cut through all the hype. And then what we have are just various community classes, lunch and learns. Of course, we have the 30 days of gen AI. That is our sort of our flagship course where every day, you're given a project, like a 5 minute project to do.

B And we started to we've broken those up into 3 different modules between beginning and advanced use cases. But those folks who are looking for a cost effective way to just start doing AI every day and understand where the guardrails are and what can it do really great and what does it do really crappy that that's free with our membership. And so that's definitely something that we're gonna continue to to build out. And then, of course, like I said, we have just our various lunch and learns, and and we really do pull from our community members. We have folks who are gonna be talking about, you know, how to get your first job with AI and product. We have someone coming in who's going to be talking about how do you actually get into the upper executive levels when you're in a minority woman. Right? How do you how do you traverse that narrative? And then just and then again, where we can spread the word from an external standpoint and get more women supporting women, that'll continue to be our focus.

A Hi, disruptors. Thank you for joining us today on an episode of Practice Disrupted. If you like the content for today's show, you can find all of our past episodes over on practice of architecture.com/podcast. Be a part of the conversation by joining me, our speakers, and other disruptors in our community at practice of architecture.com/community. Our social media handle is practice of arch. That's practice of a r c h. We'd love to hear from you, so feel free to drop us a DM and say hello. Tune in next week for a new conversation on change in the profession.