Ep 226: Beyond AI Ethics - Why nonprofits must focus on beneficial and responsible AI

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One group that I think can really benefit from Generative AI is non profits. But there's a lot to understand. There's a lot that you have to consider, not just on what are the best ways to use AI for nonprofits, but also how you can do it in an ethical manner and how you can do it responsibly. I have a little bit of a background there myself, but I'm no expert. But luckily, if you're tuning in today, we have an expert that we're going to be going over today and talking beyond AI ethics and why nonprofits must focus on beneficial and responsible AI. I'm super excited and thank you for joining Everyday AI. If you're new here, Everyday AI is a daily live stream, podcast and free daily newsletter helping everyday people like you and me learn and leverage Generative AI. So if you haven't already, make sure to go to your everydayai.com and sign up for that free daily newsletter each and every day. We recap our interview for the day, go into even more depth on the topics that we talk about as well, keep you updated on everything going on in in the news, fresh finds from across the Internet, etc, and this is one of those, you know, normally we go live every single day at 7:30am Central Time. That doesn't always work for every single guest and sometimes we do pre recorded ones. So today is pre recorded, but we are debuting this live and don't worry, I'll be in there in the comments. So still get your questions in. Maybe we'll just happen to answer them anyways on our conversation, but if not, I'll make sure to answer what I can and maybe we'll tap into our guests for the rest. So speaking of that, I'm very excited. Let's go ahead and bring on to the show. There we go. Got him. Nathan Chappelle, who is the Chief AI Officer at Donor Search AI. Nathan, thank you for joining the Everyday AI Show.

Yeah, thanks Jordan. It's great to be here. I've been looking forward to this.

Oh, me too. I love like I can talk nonprofits all day. More on that in a minute. But tell us, tell us a little bit about, you know, who you are and what you do at Donor Search AI.

Yeah, yeah. Well, I mean I probably had a kind of long winding career like in the nonprofit sector that a lot of people never intended to be in that space. And I was a technologist out of undergrad. Started one of the first. Well, the first.com to sell skis on the Internet. I just. Early adopter. I was focused on big data storage and that type of thing. Got into nonprofit on accident. Thought I'd be there for a few years. And I found myself there for 20 years, always a little bit of a fish out of water, thinking, like, why is the sector so unique in their resistance to adopting technology and why are they so slow? And back in 2017, frankly, I was just kind of dismayed at the lack of innovation in the sector. So I sat down and ended up creating the first algorithm that predicted gratitude that we spent about a year and a half, spent about a million and a half dollars to build. It worked really well. It's been totally disruptive to the sector and, and that's what I do. So my team at Donor Search AI, probably the, the. I mean, I, I'm very biased in this, but we have an incredible data science team that now does this with, I mean, hundreds. I mean, we have about 10,000 nonprofit clients and about over 100 of them where we build and operationalize custom machine learning models for. So that. And now, of course, I spent a lot of time both in the area of predictive and generative together and kind of what that means. And also on the responsible AI side, like you said in the introduction, which is definitely more my passion project that tends to be more my nonprofit work. Right now it's really on the responsible AI side.

Okay. I have so many questions now and I'm like, if you're listening on the podcast, I'm geeking out, smiling because yeah, I myself spent about nine and a half years working at a non profit. So what Nathan's talking about there, I feel it. Right. But you know, Nathan, I'm curious. So you've worked with, you know, you said more than 10,000 nonprofit clients. You know, when you're talking AI to the average, you know, nonprofit worker, is it, is it a lot? Because at least in my experience, I feel there's two types of nonprofits. I think there's like 2% that are, you know, so into data and, you know, donor data and technology. And then I feel everyone else can kind of be a dinosaur, unfortunately. And it's hard. I mean, what's your experience? Been working for so long with so many nonprofits when it comes to adapting to new technologies like artificial intelligence.

Yeah. I mean, and you know, to level the playing field, there's about 1.7, 1.8 million nonprofits in the US alone, around 10 million in the world. And so there is absolutely a digital divide. You know, there are those that have, you know, and in this case, we'll say, like, have, you know, a view on like, big data and data storage, data collection, and now AI and those that don't have. And there's a stark difference. So, you know, we, a vast majority of our clients buy data from us, and they've done that for 15 years. Those are the ones that at least see the value in big data. And, you know, for the most part, throughout my career, we always bought a lot of data. Didn't really know what to do with, Wasn't really until AI, you know, really truly more on the predictive side, like machine learning, deep learning allowed us to take all that enriched data, the data that we had as a nonprofit plus the data that we could buy, and then start making sense of it. That's still pretty new. I mean, there are a lot of reasons why we could spend a whole. We could spend an hour talking about why nonprofits are slow to adopt, but we don't have time for that. But I think what's more important is that the nonprofit sector is challenged right now. Less and less people are giving to charity. I wrote a book about it called the Generosity Crisis in November of whatever last year was or the year before last year. Time flies. But the reality is that less and less people like you and I are giving to charity because we're essentially disconnected. And so we see, like, the advent of AI and how AI can now essentially allow nonprofits to become more efficient, allow them to spend more time human to human. Like, this is a breakthrough and an advent of time that nonprofits have always been waiting for. And so I know you focus a lot on Gen AI and you know, from the Gen AI perspective, like, I started 20 years ago, like, when you were a nonprofit, could you imagine what life would have been like if you had tools like, you know, Perplexity or Cloud or ChatGPT at your disposal? Like, right, like, crushed it. Like, you would have crushed it, like. And so now we're in this time where, like, nonprofits can level up and compete at a level they've never been able to, yet they're still so slow to adopt. Only about 5% of nonprofits are actually fully deploying AI right now. So a lot of work to do. We made a lot of progress, but know, still definitely a lot of work to do.

So one thing that I wanted to pull out of that response There, Nathan, is, you know, how there is this, you know, like. Like the book behind you, you know, for those joining on the. On the live stream. You know, there is a generosity crisis, right? So I think that a lot of people have this thought of. Of generative AI or AI as, you know, people think, oh, it's robots, it's, you know, Terminator, it's Skynet, etc. But what I'm hearing from you is that nonprofits can actually use AI to make things more personal and to strengthen relationships. And it's not necessarily a very impersonal, you know, tool or technology. Can you talk a little bit more about that and how maybe nonprofits might want to change how they're viewing artificial intelligence to actually be something that can bolster relationships and drive positive change for this, you know, generosity crisis we may be facing?

Yeah, no, I mean, such a great question. Lots to unpack there. I mean, the reality is that for, you know, about 40 years now, nonprofits have been essentially using data largely based on, like, how wealthy people are. And they've been taking that data and prioritizing this idea of like, hey, you're a good donor, or you're a good prospect because you're wealthy and I'm going to spend more time with you. The reality is that wealth and generosity have very little to do with each other. Our very first data science project showed that wealth alone is about less than 10% predictive of whether or not you're likely to make a gift. So no matter how wealthy you are, there's only a 49.6% chance in America that you're going to give to any charity. Yet most nonprofits have just thought, oh, if I could, you know, get a dollar from everyone on the planet, you know, we. We'd be rich. So first is, I think, accepting this idea that not everyone is a good prospect. So predictive AI was really that area that we could really narrow in, that people that had the deepest connection to our organization were best prospects. That makes sense. So you're measuring connection, and then my connection today is different than my connection tomorrow, in two weeks, in two months, in two years, using AI to really harvest that connection. So the predictive side has been around for a while, and it's been actually pretty helpful at streamlining and identifying. Not all people are created equal. Some people care about you more, and those that are raising their hand will show up in data. Then what gets really exciting is advent of gen AI to basically take those predictions and then take action on them. Right? So predictive AI is about predicting things and making sense of all that data. But generative is about creating things so you match. And I think this is where our sector is going to go, where it's like, really understanding, just creating more for the sake of creating is not going to help anyone, but creating more and specific to an individual, where now we're talking about this idea of precision philanthropy, where it's like precision medicine, like, every person is unique to, like, how a therapy will help them or hurt them based on their DNA. Precision philanthropy is the same idea that there's no such thing as a good donor or bad prospect, that all people have a varying degree of connection to your organization. And then the tools that you use to essentially activate and spend more time with those individuals is really going to be, you know, what will make the big difference. And I'll say all that to say AI is not a silver bullet. It's not a magic wand. It should never replace a human. But what it should do is it should create a tremendous amount of efficiency where people in nonprofit who are the hardest working people, as you know, having been in that sector, like, it's a passion, not a job. You know, it's. It's a. It's a way of life, not a career. How do I use AI so that I can spend more time human to human? And that's true with AI in any domain, whether it's in clinicians wanting to use AI so they can spend more time with their patients or whatever the use case might be. That's where I see things get really exciting for the nonprofit sector.

Something that you mentioned, there is kind of this intersection of predictive AI and generative AI. So, Nathan, maybe for those people in the nonprofit sector out there, and they're hearing this and, you know, you hear all these buzzwords, right? And for those people that maybe aren't, you know, lifelong technologists or have a background in machine learning like you do, can you briefly explain, you know, kind of what that means, you know, bringing predictive AI and generative AI together and specifically, you know, how, you know, nonprofit people that maybe aren't even super technical can take advantage of that intersection?

Yeah, no, that's a great point. I actually did a poll recently of, like, how if someone asked you, could you explain the differences between predictive and generative AI and, you know, at least fumble your way through it? So it's, you know, we're getting there, you know. So I think what resonates for most people, you know, this idea, especially in the nonprofit sector, are questions like how Much will someone give or when will they give or if they will give? So all those things where predictive AI, which, you know, predicts things. So it's about precision. It's about creating precision based on lots of data. Those questions are not new questions. In fact, like, I. I started talking about this back in 2017, and I stumbled upon this quote by Aristotle. Like, this is, like 2,300 years ago. Aristotle said, you know, the idea of giving away money is easy, but how much to give and when to give and how to give it in what way, that's hard. Right? Those are essentially barriers to generosity that have existed, like, since the beginning of, like, you know, Western civilization. But the idea of so, like, predicting who might do those things or how much is one thing, but the idea of, like, what's the best way to reach out to them in a way that, say, personalized to them based on their own unique characteristics, that's generative. So generative, again, creates things where predictive AI predicts things. If you mat. If you match those things together now you have precision and personalization at scale. And, you know, I think one of the best examples that I've seen recently is, like, Carvana. Like, Carvana is, like, as a company, you know, struggling a bit. Like, the stock market's got. Not. Not been super great. They jumped into this, you know, predictive gen area because they're like, okay, we have lots of people who bought cars. Those people have stories about their cars. And I don't know if you've seen this example, so if others haven't, it's a really great example. They produce, like, I think, 1.3 million custom videos. Basically, it's a picture of the car that I bought, like, two years ago, and it's the car talking to me. So everything is generated through, you know, gen AI, but they use precision to determine. Okay, not every customer is going to get one of these videos. Only certain customers are going to get these videos. But they were able to offer that precision at scale in a really profound way. And I see that blend of precision and personalization supporting the nonprofit sector. So, well, being able to say, you are important to us because of these reasons, and this is a journey we've been on together, and our journey's just begun. How about we change the world together and be able to do that at scale, on the fly, in real time, would be just absolutely amazing. So we're already seeing these technologies emerge really prolifically in the private sector. So now in the nonprofit sector, which lags, it's like, they become more affordable and they become scalable. Where nonprofits now, for many years didn't have access to machine learning, now they do. They didn't access a generative AI. Now everyone has access to generative AI. So it's again, I'll leave with the optimistic side of like, it's a really cool time to be alive. Nonprofits really need to like, double down to get out of their comfort zone and learn something new every day about AI and probably starts with listening to your podcast every day. If you do that, you're probably like 95% ahead of the, you know, the rest of the crowd.

Yeah. And one thing again, like when you keep saying these things, right, I'm like, man, what a time to be alive, right? Like, like, if you're a non profit right now, like, because I remember personally, you know, I, I would spend, you know, 10, 15, 20 hours on these, on these projects that now take 10, 15, 20 seconds, right? So even this example, right, of Carvana kind of operating with this, you know, precision and personalization at scale. What do you think is maybe some examples, you know, what is that, that example for the average nonprofit that they can use, you know, precision and personalization at scale because it's, it's available now, right? Like, you have the data, the data's always been out there for nonprofits, but the data has usually led to a lot of manual work. Right. So what is that kind of sweet spot between precision and personalization that your average nonprofit can go take advantage of right now?

Yeah, and you can start small. I mean, it's all about baby steps, right? Not everyone's going to, you know, build and operationalize custom machine learning models and then, you know, build out the curve on a type thing. But the reality is that, you know, almost any nonprofit can use. They, they have some basic information. So say like a university or a K12 school or whatever. Be like, hey, this person went here around these, the, this year, you know, this is the year they graduated. This is the degree they got. And you know, or like they volunteered at our food bank or whatever, they, they can track, they absolutely can track some of that data that shows like, this person cares about us. And at the very, very least, and it's super cheap to do, and not many do is like, do a survey, like, find out, you know, like start out with building a Google survey to find out, like, this is what people like about us. This is what this person cares about us. We know that they're one of their top charities or they're not Take any of that type of data, which we would consider, like experiential data. It's like, represents, like, how they care, do just a little bit of segmentation around that. Then use generative AI essentially to then build, say, an engagement story about your organization that's going to speak to the reason why the person answered that survey that way anyway. And so, like this, like that, like understanding someone from a survey response, building an engagement strategy with a person that represents their sentiment of what they were feeling in that survey response, that is like super scalable. Like, that is like every. That literally costs no money. So, like, when I'm talking about the digital divide and nonprofits that don't have budget for this could do things like that right away. And not to say that, you know, they don't have to learn something new. Of course you've got to learn something new. You've got to get out of your comfort zone. But start small, take baby steps, and just start doing it. And you're going to figure out how to automate those types of things, how to deploy a GPT so that you can have people engage with your nonprofit and learn more about the history and what it means and how it impacts society. So, I mean, think about things like communication thing, you know, from a generative perspective, like analysis, creative ideas on thanking people. Like, what a novel concept. Like, people get bored of thanking people the same way. Like, ask GPT what are some creative ways to thank someone? So, I mean, I could probably rattle off 20 use cases right now that would cost a nonprofit $0. That would actually make them feel and appear more modern, like Starbucks, who's like, hey, it's your birthday month. Like, we've missed you. Like, where you could come in and see us and the coffee on us. Like, those things don't. You don't have to be Starbucks to do that. Like, you literally could do that right now with like, $0. Yeah.

And I don't. I don't know if there's ever been a time, and I'd love to get your thoughts on this, Nathan, is. Has there ever been a time in your experience, you know, working in this field, you know, you obviously have a much deeper and, you know, more tenured background in machine learning and AI than the average person out there, but has there ever been a time before that you've seen where the digital divide between nonprofits and everyone else shrinks this quickly? Because, you know, even my own personal experience is, you know, we would sometimes get, you know, support or grants, you know, from these big Tech companies, and they were great by, you know, supporting nonprofits. But then you go see, you know, how they operate, you see the technology that they're using. And, you know, from the nonprofit perspective, it's like, wow, that seems like a different world now. I, I personally don't know if it's like that. You know, it seems like it's a level playing field. Can you speak to that a little bit and talk. Has there been a time before where the divide seemingly is shrinking that quickly?

Well, I mean, I, if, you know, because I'm old and I, you know, in high school I was learning DOS. So like, when I, you know, in 90, you know, probably 95, 96, you know, the Internet was a great equalizer between those that have and those that didn't have. So like, you know, the only firms that afforded compute power were those that, like, were. Were buying, like, big servers, right? So, like, so. So that was a time where the digital divide shrank very quickly. It's like, you know, it's. It's insane to think now it's like, hey, nonprofit, like, are you using the Internet? Well, of course you are. So, like, so I think this is where a lot of people equate, like, generative AI to like the Internet, because it is a great equalizer in the sense of, like, you don't have to, like, build a big server farm and you don't have to even pay AWS to have like a big. Or Microsoft, a big cl. You just like, go on your Internet browser and now you have like, all the stuff at your disposal. But I think the scary part with this is that nonprofits, again, being so slow that the key difference between, like, generative AI or just AI in general compared to like, the Internet is that AI is an exponential technology, meaning that it doesn't say the same. It gets better. And so progress in exponential technology is not measured in time. It's not measured by like, oh, I'm not ready for this, and I'm a little scared of AI, so I'm going to do it in a year. You're not one year behind. You're like 365 AI cycles behind, right? And so it's like this idea. Harvard wrote this article on this, like, years ago, and it stuck in my brain of like, those that failed to deploy AI may never catch up because it's that exponential technology. And so the digital divide, seemingly, to your point, because you're, you're looking at this objectively, it was like, oh, it's like the, the great equalizer Everyone has access to it. Everything should be great. The reality is if the nonprofit sector does not get out of their comfort zone and they are like, oh, I don't trust this. I'm not going to do it until everybody else is doing it, that digital divide will grow exponentially. And while at the same time there's lots of reports out there from whoever McKenzie and the IMF who is saying AI can be can help bridge the digital divide, it actually can Also, you know, 60% of jobs are going to be impacted by AI in some regard, and by the way inequality might increase. So this is, again, I think this is a need and opportunity for the nonprofit sector to rise. The occasion, since the nonprofit sector focuses on inequality at large, that's kind of what they're built to do, is that waiting and seeing is contrary to like, what this, what society needs right now. And so this is why, again, it's like, it's really great time to do live, really great opportunity. But it's also like a moral imperative that the nonprofit sector rise to the occasion. Be like, okay, we're in this. Like, let's, let's help steer this technology into responsible ways.

Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on gen AI? Hey, this is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft and Nvidia have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use Gen AI. So whether you're looking for ChatGPT training for thousands or just need help building your front end AI strategy, you, you can partner with us too, just like some of the biggest companies in the world do. Go to your everydayai.com partner to get in contact with our team. Or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on gen. Such. You know, my, my brain right now is, is rattling because, you know, I'm, I'm trying to process something here, Nathan, because what you just said right there, right, like my way of thinking has always been, yes, you know, generative AI can be something that helps bridge the gap, but it makes sense. It makes sense what you're saying it can bridge the gap if you're, you know, putting through or putting in the work, I guess, to actually use and understand generative AI. But if you're not, if you're falling behind, you know, that 365, you know, light, light years in AI, you know, behind, then. Then the divide actually becomes much, much greater. So how, how can nonprofits keep up? Right? Because I think the biggest thing is, is there's always, you know, having to, to win that trust of a nonprofit. Right. Before using a new technology, a lot of times from personal experience. So hey, if, if you're a nonprofit leader out there, don't get mad at me. But you know, there is more of a distrust with new technology.

Oh, for sure.

Because people say, oh, you know, I want to protect my, my donors, my volunteers, which I, which I get and I understand. So how can they balance all of that?

Yeah, and I, I think some of that's warranted, right? Because, you know, if you compare and contrast the nonprofit sector, the private sector. So let's use. We could pick on Twitter. I don't know. Hopefully they're not a sponsor of yours and this will piss them off. Right?

Okay.

Okay, so. And now it's not even Twitter anyway. So we'll just, you know, at, at one point, years ago, Twitter created an algorithm that was like, I don't know if you remember this, but like racist ageist, Islamophobic and ableist and all this thing every is that you can imagine. And it got out and this algorithm was predicting all these really kind of bad things. And of course, like their stock price goes down. But did that affect like Facebook stock or Google stock or Microsoft? Not at all. Right. Because that was one single player. That was fate. That was whatever. We were just talking with Twitter that did those bad things, right? And then they had to rebuild trust with their, their stakeholders and shareholders to rebuild that. But if the nonprofit sector did the same thing, and especially if like say a large nonprofit that's trusted, so like Red Cross or somebody like that created an Aegis Islamophobic ableist algorithm, it would impact trust in all nonprofits. So the reality is that the nonprofit sector has nothing to provide back to individuals in terms of like a service or a product. So like if Nike, you know, did something really bad, but I still really like Nike because they fit my feet better, I'm going to still buy Nike. But a non profit sector, essentially you're exchanging money for trust, you know, so the nonprofit sector is in the trust business. So I do think it's warranted to some extent that the nonprofit sector needs to prioritize trust at the very highest level and ensure everything they do essentially does not diminish trust. And with that said, advances in technology and AI has been so fast and there are no real universal standards for what responsible AI is. So of course, like in the uk, there's some in the White House has very loose guidelines on like responsible AI. And, but so many corporations have created their own to try to instill trust. The nonprofit sector saying, well, wait, like let's, let's wait and see what this means until we're going to go full bore. That's where fundraising AI stepped in. And back in November of a year ago, we created the first framework for responsible AI for the nonprofit sector. And that specifically was to highlight the need for things like transparency and beyond ethics, but like transparency, accountability, explainability, and not to only look at is this ethical, but is it also beneficial? So like, we might say that like using, you know, Instagram is not unethical, but is it beneficial? And we've seen lots of downside from that. And so those are the things that I think the nonprofit sector has to wrestle with. Not just things that are ethical, but are they also beneficial. And so that's kind of where we're at today. And I think we made a lot of progress in that space, but there's, you know, there's a lot of room to grow. And I, but I do think that this is a time where the nonprofit sector has to lean in and they have to take a strategic and measured approach to using AI to remain relevant and to address inequality as, as it is also created by AI.

So we'll definitely link to that, the framework for responsible AI in the newsletter. But Nathan, if you could maybe just give us a super high level overview of what nonprofits should be looking at. Right. Because I feel those that quote, unquote, get generative AI and they can work at that intersection that we were talking about earlier, you know, with, with precision and personalization. They're, they're going to be, you know, have abilities, you know, when it comes to, you know, outreach or fundraising or data collection and what you can use the data for, maybe new capabilities or powers that they didn't have before. So, you know, what are some of those things that, that they should be focusing on once they do get it to make sure that they're still using it in a responsible way that benefits others.

Yeah, you know, and I don't think it's that hard. I would Say when you're, when you're thinking about the litmus test for a nonprofit is that, you know, is this not only beneficial for my short term goals, but also is it beneficial to our sector as a whole? So I think that's something that the nonprofit sector kind of uniquely has, is this kind of protectionism of the sector as a whole, of society as a whole, and as they should. Right? Because we, not nonprofits and nonprofit employees are kind of, they're grounded by this idea of like everything that we do and it has, you know, an impact. And so I think for the most part, nonprofits, when they lean into this, have to ask themselves that question. Like, is sounding authentic the same as being authentic? Right. So prioritizing authenticity, transparency, vulnerability is something that we have to go a step beyond. Whereas, like, most frameworks that exist in the world will focus on like robots serving humanity's best interest. And so that's like White House lingo. The White House lingo is like, we need to build AI that serves humanity's best interest. That literally translates to like robots not killing people. That's not that. I mean, of course that's important, right? We don't want robots going down the street. We've seen Chappie, you know, and like bad things happen. But the reality is that nonprofit sectors, the nonprofit sector needs to really focus on, on AI that is going to preserve and instill trust. If there was just one question, like if I'm using generative AI, predictive AI, and however I'm using generative AI, so whether it's communications or analysis or HR or legal support or creating images, if I was to ask myself one question, does this action preserve and protect trust? Not rocket science at all. So that's just one question. Everything I do in generative AI, which is a lot, I could do so much with generative AI. Does this activity preserve and protect trust? And what that means in the nonprofit sector is that when you're using AI, essentially there should be the call to question, like, how do I trust a black box? Like, I need to make sure that to the best of my knowledge and gen is very hard in this case because like predictive AI, you can actually see mathematically, like how a prediction is made. Like, it's actually fairly easy to see, like prediction is made by the combination of these data points, you know, that, that say, this person's going to do this thing. Generative is a little harder. So I think you've got to work with and use generative. That is taking ample activity to make sure. That they're being responsible in their building of AI. So like an open AI or a cloud or perplexity, which like essentially, you know, will give you footnotes of everything that it's doing. I think that's almost a requirement in the nonprofit sector, where it's a nice to have in the private sector that to really understand how the AI is actually making decisions, what it's telling you to do, and to ask the question, does this activity preserve and protect trust, like go from there, you know, and so, you know, demand kind of those things. Transparency, accountability, explainability.

Yeah, I think, I think the explainability, especially of generative AI is crucial for nonprofits, right. Because, yeah, they have to be able to communicate it not only internally, but also with external stakeholders, you know, donors, volunteers, et cetera. You know, what they're, what they're doing, you know, how they're using this data that they do have. So, so Nathan, we've, we've talked about so much in this episode, right? We've talked about machine learning and donor data, predictive AI, generative AI, you know, and then how we can also, you know, how nonprofits can, can use this all responsibly and use it transparently to benefit others. So normally I don't ask people, you know, to, to predict the future in their space because it's hard. Right. Like we kind of talked about, every day is almost like, you know, can almost feel like a year of development. But you know, with, with someone such as yourself who spends so much time and has such a deep background for maybe those nonprofits right now that are hearing this and they haven't fully adapted, I feel if, if, if you kind of go to where we're at now, you're going to be behind maybe, right? So how can they, you know, sort of skate to where the puck is going when it comes to, you know, generative AI and taking advantage of the data and the, you know, technology that's out there.

Yeah, I, I mean, it's such a good question. I get this question a lot. You know, I mean, the reality is I don't believe that AI will replace fundraisers, replace nonprofits, but non profits or fundraisers that use AI will replace those that don't. And so I, I mean, fear, you know, the fear is that it's actually just not like fear mongering. Like that's true. Like you will not be in a place where you, as an individual will compete or as an organization will compete in the future if you're not using AI. And so now is the time you have to AI proof your career and your organization. So with that said, you know, start where you start. But to your point, like, generative is so. It's a great equalizer. It's so accessible that there's no reason not to. So, I mean, I think about that movie, what About Bob, which is like Bill Murray and I forget. Paul, whoever it was, I forget it was. Whoever was with Bill Murray was like, this idea of, like, baby steps. Like, you just got to take baby steps and, like, every day, learn something new about AI, try something new. Always asking the question, does this preserve and protect trust, but just start small? I think when most people don't start working in AI, it's because they're just afraid of starting. And, you know, the kind of common kind of philosophy in AI is that 70% of AI has nothing to do with data or models. It has to do with people. So take away that idea of, like, AI is all about data and models, because it's not. It's about you as an individual and the people around you and how you will either level up your work, you know, and see, you know, because you spend a lot of time in generative AI, it's like, you know, producing more work faster, you know, more accurately and with less burnout. Like, who doesn't want those things? Like, that literally is the definition of every nonprofit worker I've ever met, you know, so, like, let me do something faster with a higher, you know, quality of work and be less burned out, like, all day long. So jump in baby steps. Learn something new every day. I always tell people, start with podcasts, like, listen to one new thing every day and, you know, and it doesn't have to be rocket science. Just, like, get comfortable with the lingo. And then, you know, log into ChatGPT kind of starts there, or cloud or Perplexity or Copilot or whatever your flavor of choice is, and just start, you know, start asking it questions that solve, you know, some of your more immediate business needs.

So good. And I think, Nathan, I love the. The analogy of baby steps, but I think that you helped all of us and not just those who are in the nonprofit sector. I think you helped all of us take a little bit more than baby steps with today's conversation. So thank you so much for joining the Everyday AI Show. We really appreciate your insights.

Absolutely. Pleasure. Thanks so much, Jordan.

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