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Hey you, it's Rhea Wong.
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If you're listening to nonprofit Load On, I'm pretty sure that you'd love my weekly newsletter. Every Tuesday morning, you get updates on.
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The newest podcast episodes.
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And then interspersed, we have fun special invitations for newsletter subscribers only and fundraising inspo because I know what it feels like to be in the trenches alone. On top of that, you get cute dog photos. Best of all, it is free. So what are you waiting for? Head over to riawong.com now to sign up. Welcome to Nonprofit Lowdown.
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I'm your host, Ria Wong. Hey podcast listeners, Ria Wong here with you and my friend Nate Wong. Not going to make any jokes about two wonks making a right, because that would be wrong. Right in there. But today we have a spicy topic. Nate is a partner at Bridgespan. He is also the co author of a study, AI Cannot be Ignored, exploring the opportunities for nonprofits and the social sector. This is particularly relevant because the Fundraising AI conference is coming up. One of the presenters is yours truly. So this is top of mind for me. Nate, welcome to the show. Want to talk all about this?
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C
Thanks so much, Riya.
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A
So before we get into the white paper, which I found to be very interesting, tell us a little bit about yourself and how you became the co author of AI and the intersection of the social sector and technology.
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C
Yes, so I am actually a techie by training, Asian parents and only limited career paths. So I went the engineering path and.
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Then they must be so proud.
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C
They're so proud that they have no clue what I do now.
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A
Same, same. Please continue.
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C
So I, I actually am a technologist by training and cut my teeth early days in what some may call public interest technology. So trying to roll out technology for government and then actually pivoted much more to broader societal social impact strategy. And that circuitous path has led me to bridgeband and it has felt like a full circle moment to reconnect with my tech origins. But I think for me, the two things that really have been an animating question for me has been what are the societal implications of technology, good, bad and ugly. And where do I want my role to be within that? And to be honest, early days I found implementing technology, I wasn't really in the stream of the strategic conversations that motivated should we even have technology? Is this the correct solution? That led me to go more upstream on the strategy side. And now in a world of AI where technology is the hot topic, it felt really relevant to come back into the mix. And so I have for the past 510 years have worked around more of the field, building in how technology can be used for good. And then even in our client work at Bridgepan, really work with nonprofit clients and thinking about how do they actually want to employ technology within their mission critical strategies and at what point does it make sense or does it. Is it just cool, sexy, and you should. It's. It may not be as germane to a mission.
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A
Okay, so Nate, I just want to jump in here because I have a point of view on this, which is. And again, maybe I'm just like consuming way too much YouTube content. Also possible. But I really feel like AI where we are today is tantamount to the dawn of the Internet in terms of revolutionizing our day to day. And I'm of the opinion that if nonprofits do not learn how to use this technology, they are going to become extinct. Am I being too extreme? Am I being hyperbolic here or what's your perspective on this?
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C
So I'm based in D.C. and I give some of that context because I feel like we're in the milieu of existential rethink of the world. And I think AI is one of those aspects. So I actually wholeheartedly agree with you. This does feel like a monumental shift and I think someone shared this framework with me. I don't know if you've heard of World one, World two, World three. Have you heard this notion? World one is almost our current state of being in a democracy sense. The institutions that underpin how we think are stat like that is our current worldview. There's World two that is more authoritarian, if you will. And then there's World three that is reimagining a whole different world and we don't almost have the constructs for that. And I feel like AI is in that world 3. Oftentimes nonprofits are entering and they're entirely risk averse. So they're entering in with a worldview still within World one. And oh my gosh, this is going to like totally disrupt and change everything. I have to guard against it. We know all of the risks around bias, so I need to. I think some nonprofits I've found have put their head in the sand and are like, nope, not going to touch that. Don't want to be involved in the sausage process of that.
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A
Nate, can I just tell you what I've heard, not infrequently, is I'll get on calls with folks, right? I do presentations and people will say, oh my Ed or my has said that we can't use AI And I'm just like, why don't they just say that you can't use computers? Or like, how about we just go back to the quill? Like, why do you need pens? Like, there's this look. And I'm not saying that we should adapt without discernment. I think we have to be very careful about guardrails and understanding, like bias and data privacy and all of that. But I'm like, if you're not actually learning how to use this new technology, you're essentially tying your own hands behind your back. And like, last I checked, nonprofits did not need the work to be harder than it already is. But talk to me like, what are the risks of people not, I don't even want to say embracing the technology, but sticking their toe in or at least learning what they might want to know about the technology?
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C
I think the argument that I hear oftentimes in a very similar vein as what you were reflecting, is that there's almost this pure play, right? I don't want to touch it because either capitalism is bad or we know the, the harms. And I don't want to even be associated with that. I think the risk is that nonprofits lose their role in shaping the very technology that's, that is going to reshape society. And I think that there's a belief, and this is maybe my worldview, that you actually have to be part of the decision making process. And I, my greatest hope is that nonprofits would actually mobilize and the social sector would mobilize more en masse and not let technology be dictated to them, but actually have a seat at the table to shape it. And if you're not, then you're actually not supplying these LLMs, large language learning models, some of the data that, that could actually be useful. So that's one big one that I think many people don't really talk about. There's obviously the delaying of AI integration means you might not be able to have the intended impact with less resources. And so there's a, Are you actually enabling your mission and the reach of your mission? And then to your point, why would you tie your own hand behind your back? Nonprofits already risk so much burden of resource that like very scarcity of resources that you're actually missing efficiency gains that can happen through AI. That's oftentimes the first argument, and I'm pretty intentional about making that the last argument because I think efficiency is still a very narrow view of AI. I think that we have yet to imagine what world 3 looks like, what is a healthy view with AI in it that we want to shape? We can. It's almost easier to critique the current state, but I think that there's a world to reimagine.
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A
Yeah, it's interesting because I love the first two arguments, and I think the third argument really hits people where they live, because I talk to nonprofits all day, every day, as I'm sure you do. And the number one problem they all face is we're understaffed, we're over capacity. We just literally do not have enough people to get the work done. And I'm. I think AI is such an obvious, if not solution, an obvious multiplier. The. One of the criticisms which I think is legitimate that I've heard is I'm so underwater, I literally cannot think about how to integrate AI or even learn about it, or even train the model that I need to train in order to utilize it. So curious how you would think about AI adoption in a space that's already very strapped for resources and capacity. Because, look, the startup costs are real. Like, if I'm adopting a new app or a new custom GPT, there is a kind of a training period and a startup period of learning how to use it, and I don't have the staff and the resources to devote to that. So it's a little bit of a catch 22. What do I do? Nate?
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C
I would actually argue that team members probably already using it. This whole notion of shadow AI, many. I think that there's a Gardner stat that says something like 70% of people are actually using unapproved AI. And I think that the reality is that maybe leadership team members are skeptical, but probably like, your frontline staff may be already using AI. And so there's almost a question of, do you actually want to create a forum for healthy experimentation of tools, AI tools that are already being used, or do you want to do a very. In, like, technology speak, we call it like a waterfall method. Like, you have to do a design phase and then you have to implement it, then you have to test it. And I think that there's a much more iterative way to do that. Everyone is probably familiar with, like, agile methodology. There's like a much more like, iterative test and learn. So my recommendation for organizations is before you jump into, like, custom AI and enterprise solutions, I think it's okay to experiment on some of the free aspects. Just be very clear about the intended use cases, because there are obviously privacy concerns and, like, what data you put in there and start with some of the incremental pilots that are linked to your mission. And those can be really exploratory. I do think just getting your hands on it in very micro ways, like in the fundraising space. Help me craft fundraising. A donor letter that takes my annual report and tweaks it based on what is most important to that donor. That's all publicly available information. There's nothing disclosed there. I think nine times out of 10 people are like, wow, that saved me to your efficiency play like three hours of work that I would have done and I can do it now for 10. And then you start to think about, okay, are those repeatable? Does it make sense for me to invest in a much fuller way at the enterprise, like organizational level? And then I think that there's like building champions and other people that kind of are already going to be more technology forward and starting there. It doesn't have to be the top down kind of approach that I think we're sometimes used to. It's a little bit like you equate it with early day Internet. I also think about it like social media and what it has done to fundraising and outreach. Like how do you think about using these new tools? It wasn't necessarily the fundraising experts that discovered it. It was sometimes like frontline staff members that were on early Facebook or other mailing list tools.
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A
You spoke about investment. I think that's a really interesting place to start because I think as a sector, the narrative, and I don't think it's always true, but the narrative is we can't get investment. People don't want to pay for capacity building, they don't want to pay for technology. So curious if a you've seen that narrative actually debunked. And secondly, what might we be able to say to donors, to foundation partners, to even our board members who are reluctant to invest in technology capacity building.
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C
So this is my soapbox. I think that there's oftentimes an orientation that nonprofits need to be that mouthpiece to donors. And Bridgeman oftentimes sits in between donors and nonprofits. And so I really think that there's an argument around paying what it takes for technology. And a lot of times technology is totally under. It's not thought of as being a major infrastructure cost to an organization. I don't know how people think, like, where do our computers come from? How do we go on zoom? Those are. They are seemingly like ubiquitous that we almost zero them out as costs. But they're like incredibly important. They're essential. Just water we drink. And I think it's actually incumbent that there are more technologists that are embedded on the donor side that actually understand the true cost of what any type of technology implementation will take. And so I oftentimes think that AI is the Trojan horse for a lot larger conversations about what technology can be invested in. So I just. It's a bit of a different orientation. I think yes, nonprofits can advocate for it, but I think it's up to donors to shift some of their behaviors. We're working with a number of donors right now that are more technology savvy and actually want to invest in the true infrastructure costs of some of their grantees. And I think right now we don't actually there, there isn't really clarity around how much investment it will take. There's not clear benchmarks of what is a typical technology implementation cost for this type of complexity. And therefore I can build it into my budget. And so oftentimes where what I've seen is that nonprofits are only accounting for a tenth of the cost itself. So. So it's like we build a website, but are you accounting for the maintenance of it? How do you extract data? All of the different complexities over time, like the total cost of ownership is not accounted for. And so sometimes as a rule of thumb, I'm like, take that number and multiply it by five. Because that is actually probably more in the realm of what it will take. And that also includes talent, which is incredibly under resourced. We think the tools itself will solve itself. It is, I think we all know it requires a technology person that is embedded depending on how much technology is part and parcel of the mission.
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A
Yeah, that's so good because I've just been using Jensen Huang as a mantra which is AI is not coming to take your job. Someone who knows how to use AI is coming to take your job. And so if folks are out here listening, listening, the biggest favor I think you can do with for yourself is to get familiar or like very adept and fluent in AI because it is the future. And I think there is a very strong argument to me that AI is there to make it better, faster, cheaper. Right. What was once a $3000 $5000 website build can now be done on an app in minutes. And so I think there is an efficiency argument to be made. But then I also think, and I'd be curious about this too, Nate, I worry that we with great power comes great responsibility, and I worry that it just will allow us to create more crap at scale. Right? Already I'm on LinkedIn and I'm like, yep, AI generated, like, clearly that was a ChatGPT thing. And look, I'm not saying you shouldn't use it. I use it for my own stuff as well. But I use it with discernment. I use it with the eye of, okay, it'll do the first draft, but I, as a human, will need to make it really reflect my voice and my views. So I'm just curious, how do you keep nonprofits from jumping on the bandwagon of just more AI sludge in the environment?
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C
Such a good question. And that's why I sometimes resist the efficiency argument. Because we talk about saving time, but then what do you do with that newfound time? And I think there's actually a world where we need to actually reimagine what a better use of that time looks like and an awareness that we don't want to build on, like, crappy world one. Sorry, I keep on coming back to that analogy, but if that is the view, then we're. We could be perpetuating, to your point, like, the very thing that we don't actually want or care to persist. And so I oftentimes think about what is the retooling that we need to do as humans to then leverage the power of AI. So if that means I now use that newfound time to push analysis further, and I'm actually using AI as my thought partner, and I am like actually going head to head and critiquing everything that it comes at me so I can have a much stronger argument at the end that is a better use of my time instead of content generation. And so I use that as a very miniscule example. But I think it's important so that the output of AI is actually A, more human, and B, it isn't just like getting a task done faster, cheaper, but it is getting it done better as defined by the human, not by a previous worldview of it.
- [20:16]
A
Yeah, I love that. And I obviously come at it from a fundraiser perspective, which is AI should be used to free up your time for you to be a human. I say this a lot. Let the robots robot, let the humans human. And so if it frees up your time so you're not cleaning a spreadsheet or parsing through email, and you can then pick up the phone, make that phone call, write that handwritten letter like your mom told you to do, go and have an human in person connection. I think that is a really effective use of AI because to your point about, and I like this analogy, I'd not heard it before. But the third world, which is, can we use the AI to make us more connected as humans?
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C
Yeah, because think about it, as in a lot of tech speak, there's this notion of a frictionless experience, right? Like friction is bad essentially. And I actually like this notion of what is healthy friction. I think in like human connection, we've almost eliminated all friction and therefore we've lost some semblance of humanity. And like connectedness. The conflict makes us human. Right. And so I have been toying around with this notion of instead of celebrating speed, scale, seamless experiences, what would it look like for nonprofits to be really grounded in relationship and see what is healthy friction? And anything that is not healthy friction, sure, use technology, use certain aspects to make it a delightful experience. But healthy friction, to your point, builds trust, it builds equity, it helps us slow down. Like I don't want AI so I can move even faster. I feel like we're going so fast. I actually would like to use that time to take a pause, reflect, slow down, help me actually in true decision making, help me confront bias, let me understand what's fair, let me strengthen connections. But if we are only focused on efficiency, play or a frictionless play, it leads actually to some unintended consequences which we're now living with in terms of social media and so forth.
- [22:38]
A
Oh my gosh, Nate, I have so much to say on this. And I'm not, I'm going to try to, like, I'm having a boomer moment. I'm not a. But I'm having a boomer moment because I was recently spending time with my 18 year old nephew who shall remain unnamed. But the amount of time this kid spends watching YouTube on his phone is unbelievable. And look, I think a lot happened during the pandemic in terms of kids social skills, but like this kid cannot have a conversation for the life of him. Like he just doesn't understand the human interaction of okay, I speak and then you speak and then I ask a question, then you ask a question, like just basic, like conversational things. And I'm like, why don't you know this? And it's because of these, these things in our pockets where we're just looking at this little screen for our human interaction. While you were talking, I just had a brainstorm. You're in D.C. we should make Mara go viral. Make America relational again.
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C
I love it.
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A
Right? Because I think part of our current political system and our society right now, we are so polarized because we don't actually talk to each other. And look I'm guilty of the same thing, right? Like, I. It's very easy for me to paint people who don't agree with me as they're like this or like this, just the way that they paint me. But I bet if we actually had a conversation, we'd find out that we have a lot more in common than.
- [24:02]
C
We have indifference 100%. And I think it's easy in these like bubbles facilitated by technology to make a lot of assumptions or equate. If you believe this, then you also believe this and therefore you must be this versus actually connecting on what makes us common and human, including the really shitty stuff like death and like loss. And I think though, like, it. It actually is, what are those things that actually connect us? And that to me is frict. It's a healthy version of friction. It doesn't mean it's going to be easy. It actually means we wade into the awkwardness. You have to start somewhere, like with your nephew. Okay, maybe we'll have some really awkward conversations and like awkward pauses.
- [24:52]
A
Oh, believe me, they were awkward friends. Very awkward. And the other thing I noticed, and I'm not, this is not about him particularly, but I do notice that, and I notice it myself in the attention span is really short. And so human interactions take attention. They, they are longer than three second scrolls. And so how are we actually training our brain to be present in situations where we're not, like getting dopamine hits every two seconds.
- [25:22]
C
Yeah, yeah. And I think in a world where people will make fun of me, that I can multitask like no other. And I thought it was like this superpower and I'm like, I can have a conversation, send you an email at the same time. And that's true if efficiency was the play. But I actually had to unlearn. I'm not actually present in either situation. I might be getting tasks done, but I'm. It's not fair to it from a relational standpoint to the other people that I'm interacting with. And I'm not conveying presence both through my actions, but like through eye contact or like there is something about putting away your technology. And I say that not in a Pollyanna, like technology is bad. I think there's actually room for technology and it is just important to understand what it is enabling us, not just fixating on technology and the adoption of technology as being the end goal. And that I think is, is what is scary in this moment. To your point about people taking jobs, like the people that understand AI, it's not AI itself, I think we're so fixated on the tool versus the use of the tool and therefore we can't reimagine a good use of it. We only can see the bad parts of it.
- [26:53]
A
And I think to your point, we also are somewhat limited in our imagination of what it will enable us to do and be as humans versus oh, I just get more stuff done on my task list. Like, to what end? Okay, so many things are coming up for me, but I know we don't have forever. Let me ask you this for the folks who are listening to this and thinking like, yeah, but Nate, there are some things that we need to consider around like bias and who decides and who trains the models. And recently the questions around copyright and like if people are crawling data that they never paid for or recently I've seen generated avatars of people like famous people who they didn't consent to any of it, but whose name and likeness and voices are being used. So there are certainly cases in which AI is being used in unethical ways. So how would I, as a forward thinking leader, plan to adopt AI but also know and be discerning about some of the dangers and risks of AI?
- [27:58]
C
Yes, this is a question that we're confronted with all the time. I think first and foremost it's not an all or nothing play. I think that there's a notion, as we talked about before, if I have not figured out all of these questions and have answers to it, then I should not engage. And I think it's just important to say that like the technology is evolving, we are evolving in how we use it. And so there's never going to be a good time, a better time to actually confront some of these things. And now there also are a lot of really off the shelf tools to help you think about adoption that accounts for bias, privacy that has strong accountability. So I really am a fan of Fast Forwards AI Playbook and their policy builder that literally helps you build your nonprofit AI policy. And it like guides you through questions and risks. Everyone is going to have a different risk calculation. So one of the clients that I work have worked with, they really are only using AI for internal efforts. Like they're not any of their customer or beneficiary facing efforts. They are like dipping their toe into that. But partially it's because the risks are a lot lower if you get it wrong on your stats than it is when it is like mission critical work. So I think that's one. I think there's also, it's important to Think about AI by role and function. And so the way you use AI is going to be completely different. If you're like a frontline staff, are you in finance or are you in fundraising? Are you a program lead? And I think we have to be actually very discerning on like the use cases for AI tailored by role and function and where you sit in the organization. And that I think just makes it a little more like, easier and accessible to understand versus AI as an umbrella. And then I think that there's just a reflection and learning orientation around it. We will like it's important to experiment, but to do it in a responsible way and to do it in a container where there's feedback loops. And I think we, especially in the nonprofit space with respect to technology, because we've seen so many of the ills and bad actors using technology, there is a real resistance around experimentation. And I think if you don't do experimentation and you're not building the muscle to learn and then refine, then you're gonna just be on the sidelines until, I don't know what time, until all of this is figured out or until you are left way behind.
- [31:01]
A
Yeah.
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C
So those are some hopefully helpful reflections just around how to manage those risks. And I would say, like, the risks differ by every organization, so how you calculate cost benefit is going to be probably different than the way I would. And I think that there's a little bit of this like, social engineering factor of like us judging each other based on our own respective risk calculations.
- [31:29]
A
Yeah, that's such a good way to put it. And I think it really helps people to break it down because AI as a entity feels a little bit like a bullet train just like coming at me. And I'm like, I don't even know how to begin. Like, where do I even do. So I like thinking about it broken down by sort of department and function. So, Nate, what is your perspective? Because you work with a lot of institutional funders, what is the POV on using AI for grants and grant reporting?
- [32:00]
C
I think the jury's still out. Like, there's a lot of folks that are interested in potentially using AI to supercharge their grant, making you think about it from a sourcing and diligence standpoint. Like, AI could be very helpful assuming that that information exists publicly. We see it more so asked with high net worth individuals where there's a trend away from creating your own foundation or some type of institutional philanthropy. But I think that the open question in my mind is, are we perpetuating Certain grant making practices that are unhelpful in the trend toward more trust based to think about power shifting, the relational aspect of mara. How does AI hinder or hurt that? Because you could argue in some ways, the efficiency play is, therefore I'm going to use AI, and therefore I don't need to be in relationship with different folks. I can just use AI to source what my next set of grants might be. That's the extreme case. I'm not saying that that that's.
- [33:13]
A
I mean, I will say, though, just having been on the other side as a nonprofit, the amount of time and energy I put into grant proposals and grant requests and judging up the particular template that you had, like, if I'd had AI back then, I would have been certainly more efficient, but that time could have been freed up to maybe make a closer relationship with a program officer or spending time delivering programs, which is really what I wanted to be doing. But here I was like typing fervently, like, this thing is due at midnight, right? So on the other hand, though, I could understand maybe as a program officer that you're like, I don't really want an AI generated grant proposal put in front of me.
- [33:53]
C
So I think that that's like a one on one aspect. If you imagine all of your peers are doing the same thing, then, like, how do you actually differentiate yourself? So I, I think that that's like, what is actually kind of tricky, like to your point around AI sludge, like, is how, therefore do you actually both tailor in a very credible way where AI can be useful? Or is AI making weird connections that like, make it sound like you're doing something that like, may not be, and therefore it becomes actually hard to discern on the other end. And so I don't. I'm almost like worried that there's like this whole circular pattern that could be like AI talking to AI almost.
- [34:45]
A
Right.
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C
I know there's the human aspect and like, of course there's like discerning on both sides, but I think we just have to be careful about, like, where is that headed?
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A
Right? And where are the human touch points within this process? Because I agree, like, there can be AI deployed to really be more efficient at parts of the process. But at some point it's like, well, what's the point of it? If it's your point, like, computers talking to computers, they could just go on ad infinitum, like, what is the actual point? So it's an interesting question, Nate. Once you have that figured out, we have to have you come back on the show and elucidate it for us.
- [35:23]
C
I will let you know.
- [35:26]
A
Last two questions for me because we could go on and on. But I know that you have highlighted a couple of use cases, so can you walk us through one of your favorite use cases and what it enables the nonprofit to do?
- [35:40]
C
Sure. So we have worked with the center for Employment Opportunities. It's a nonprofit that works with formerly incarcerated folks. And one of their use cases that they have been working through is around their case management. And they have thought about, and I think it's actually a really helpful framing. They first started to think about the interactions with their frontline case managers and participants in their programs and how they were recording those interactions. And so at the end of a day, they might have six to eight folks that a single case manager worked with. And then they would record their case notes. Think about it like a doctor recording all of their patient interactions. There was a ton of bias around the first person that they saw during the day and the fidelity of case notes with that person versus the very last person. And so the starting point for bias, I think we almost think about, oh my gosh, AI is creating bias. But what was the normal human bias that we were starting from? So I think they flagged, oh, there's actually quite a bit of differences between the people being seen at the end of the day versus the beginning of the day. And they used AI to the very basic use case was around transcriptions. So just recording those, but then actually suggesting follow ups for the case manager when they were in the conversation and ways that they could be probing in appropriate ways. And if you think about it like, over time, there's probably a finite set of common cases that they're probably encountering. And. But the final judgment always was the case manager's discretion. And a lot of those aspects were prompts to the case manager to just improve the flow of conversation so they could still stay very present. And they weren't typing. They're trying to transcribe interactions. I say that, I mean, it sounds somewhat like a basic use case, but I think that there's actually a lot of power in that because it recognizes a, the human bias that exists pre AI and then what you can do to take it beyond just a transcript, but thinking about how can you improve the efficacy or the fidelity of interactions for the case manager and making it easy for them, that that improves the participant experience. So that's just one that I, I find like most people can gravitate toward. Yeah, something like that. That's just really accessible.
- [38:47]
A
I love that because I think that we all think that we're special snowflakes. And look, everyone is special, right? But at the end of the day, there are patterns of behavior. Like, it's all just math, right? And I think what AI has enabled us to do is to see the pattern. It's almost like seeing the matrix. You're like, oh, and here's like, a finite set of scenarios that could happen. And based on that, I'm going to make assumptions about what the next possible thing might be, whether it's like my Netflix recommendations or, you know, this caseload. So it's really fascinating. Okay, fun. Last question. What is your personal favorite use of AI? I'll share mine to give you a second to think about it. I have not done this personally, so just shout out to my friend Rachel, who gave me this idea, but she photographed all of her pieces of clothing and she asked AI to create a packing list based on her location and activities. And she was like, it was spot on. It was crazy. And I was like, oh, like clueless. How fun you can mix and match up. And so it's just like a fun thing that you all can try. Low stakes. And if anyone has problems sorting out their wardrobe like I do, this might be a fun one for you. Over to you, Nate.
- [40:02]
C
So one I've tried and one that I've heard that I want to try more. So I love traveling. And the biggest barrier for me, I'm. I joke with friends that I'm type A normally, but I'm a type C traveler. So I don't like the planning process. So I use AI to plan trips. And so just like being able to have a few options of dates, time of day, it actually helps coordinate, especially trips between different friends that have different preferences. And I have found it helpful. I just went to the Azores a few months ago and it looked at the weather and aligned, like, outdoor activities based on the weather and sequenced it, like, what activities to do when, where we should book restaurants, all of that. It just takes so much of the elements that I hate about the planning part of travel and just helps align it. And then the fun one that I haven't tried yet is asking AI to roast you. Like, it can actually.
- [41:13]
A
Oh, yes, I've done this.
- [41:14]
C
Recognize, like, all of. Like, why are you so concerned about XYZ things?
- [41:21]
A
Wait, I have a fun prompt. Have it roast you. But as your drunken bestie, it's hilarious.
- [41:29]
C
I love that. I love that.
- [41:31]
A
And on the topic of travel, because travel I was actually in Portugal, so I was like, yeah, it's really good at putting walking tours together. So if you're like, hey, I'm here, like, here's the kind of stuff I'm interested in. Check the weather, really lovely. Like walking tours around Lisbon.
- [41:49]
C
Oh, I love that. Yeah.
- [41:51]
A
All right, Nate, this has been so fun. We could go on and on, but we do have to stop. But y' all make Mara trend. I think we're gonna make this happen. Make America relational again, y'. All. I will make sure to put the study in the show notes for folks. I'm also gonna put the link to the fast forward, which I think it's going to be super helpful for folks. And I will make sure to put your LinkedIn if folks want to get in touch.
- [42:13]
C
Amazing. Thanks, Maria.
- [42:15]
A
Thanks, Nate. Good to see you. Hey, fundraisers.
- [42:19]
B
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