How Data and AI Are Revolutionizing Philanthropy for Nonprofits and Donors

If you can't completely articulate how you measure the success,

then what is the data that is supportive of that? Because data needs to be

actionable. It's not just collection of data. So that's one.

And then if that's the case, then the questions that there are people

asking, I mean, if many of the investors will probably, or

the donors, I should say, make decisions, especially more the individual ones

make decisions a lot around relationship, around trust, about

exposure, about a friend making a recommendation, hey, you should

put some money in the school or you should put some money there.

But they are not asking for the data as well because

it's relationship driven. Sometimes it's good feeling driven.

People want the good feeling of making a donation.

And if the investors are not asking, the investors are the

donors in this case, then the organization may not provide it. And then

that entire data driven approach is not developed.

Hello and welcome back to Data Driven, the podcast. We explore the emerging

merged industry of artificial intelligence, data science, and

of course, it's all powered by data engineering.

With that in mind, I have my favorite data engineer in the world, Andy Leonard.

How's it going, Andy? Frank. I am doing all right. How are

you? I'm doing all right. I like your background. It's very postmodern.

Cool, cool. We've had some interesting experiments with

teams today on our backgrounds, but without further

ado, I would like to introduce our guest guy, Miasnik, who

is CEO and chairman of Impala. And I am actually wearing my

Impala shirt, but I suspect it's a different type of Impala.

I'll take it. You'll take it? You'll take it? Yeah. You should totally

roll into it. Impala is the data intelligence platform

powering smarter decision making across the philanthropic sector.

He joined the CEO after two years of serving as chair of

Impala's board of directors. He brings a rare blend of

entrepreneurial and philanthropic experience. He's founded three

startups, including At Hoc, a mission driven

public safety company that secured 250 million

federal contacts before being acquired by BlackBerry for

alongside his business career, he spent decades committed to social

impact, founding two nonprofits in education and civic

engagement, and serving on the board of the Jewish Federation bay area, a

$2.4 billion foundation. He holds an MBA from

Harvard Business School, a BSc in electrical

engineering, and a law degree from Tel Aviv University. He's a

cyclist, photographer, traveler, and private pilot.

Wow. Your resume reads way cooler than

the average bears resume. Welcome to the show,

Brian Candy. Thank you very much. For having me. This is a pleasure and love

to have a discussion today with you about philanthropy and AI.

Awesome. I think that's a great conversation because

I think it's very topical. AI has gotten a lot of bad rep

in terms of social responsibility and things like that. I was

part of what eventually became Microsoft Philanthropy. The team I was on was

civic engagement or basically how to use technology to help cities

work better. So this, this, when this came up, I was like, this hits a

little warm spot for me. What led you to

Impala? Basically, aside from the love of the best car

Chevy's ever produced. And he's a mopar guy, so I didn't

want to say America ever produced. So I just am deferring. Respect

him. I'm going to get it and I'm going

to get that show of Frank. I mean, that's a beautiful show. Awesome.

So philanthropy and phase of life, let's

call it this way. So I've started actually and got engaged with Impala about two

years, two, almost three years ago at this point as a

user. And it's actually through my philanthropic work I found

myself looking for data, real data to make

decisions. I mean we are data people. I've been

in high tech entrepreneur, founder for years and always

was a consumer of data and user of data and

applies data in everything I do. And when I got involved more and

more in the social sector and doing some social activities at this phase of

my career, I noticed that there's a gap

and the utilization of data that we are used to in

high tech, in investment, et cetera, was not as

prolific as one would expect and one would want in the sector.

So I started to work with Impala as a user and found it

amazing. And then through some connection, the

founder of Impala invited me to join the board and their

investment group. So I said, you know what, I

appreciate that it's not my classical investment, but I said, let's go for it.

And I joined shortly after they

asked me to become the chair of the board. And through this

I had a good match between my startup life and experience

and my philanthropic activities.

And then last year through some opportunities, I was asked to

actually join and run the business. So I

joined because of. I found it valuable for me as a

user, but more important, I think it's actually a game changer for

the philanthropic sector. How?

Granted my knowledge of this space goes back maybe 10 plus years,

but I don't. A lot of the philanthropic organizations

weren't exactly tech savvy. Is that still true? And is like,

was that my experience or is that kind of

a thing? It's a great question. And

our data, you're comparing it, and we are coming from the heart

of technology, data, science, et cetera, AI.

Now, in general, the

philanthropic sector is slower to adapt in the technology space.

And again, in every generalization, there are except. And there are some

amazing groups, obviously, that are doing a great job. But as a whole, it's a.

Slow to adapt. But it's a huge market, and many

people don't really grasp the size of it. The

entire Nonprofit sector is 5%

of the GDP. That's actually the same size of the

software and data sector. Wow. Okay, so our

sector, software, data, 5%

philanthropic, or I should say nonprofit sector, is 5%.

Just the giving. The amount of donations that are running in the

sector per year is about 0.6 or

$600 billion a year. Okay. So that's a

huge amount of money. Just the giving element is 2%

of the GDP and the rest are other programmatic revenues, et cetera.

So we are talking a huge market, employs about 10%

of the workforce of America, and it

is not at the level that you and

I would have expected it to be. So what's way bigger?

Yeah, yeah. Interesting. One more question

that I'll let Andy ask questions. I don't want to hog the mic. Poor Andy.

What makes the challenge different for philanthropic

organizations than, say, regular? What's their challenge with the

data? Right. Obviously, enterprises have their own challenges that varies by

industry. What makes philanthropy

philanthropic industry? Time for more coffee. What

makes that different? What are the challenges unique to that

space? Fascinating question.

So I go a step back and look at how do you measure the

space and then figure out what's. How does data

apply when we look at business, the business side, we

measure through our return on investment. Right. How much dollars, how much

revenues, growth, things of that nature. It's a relatively very

easily quantified measurement of success.

And therefore, the data that is supporting that

is always in view of achieving

that. So if I am Pitchbook. Pitchbook, as you may know, is a

very known source of data for private companies.

They obviously look and analyze the financial aspects and the potential

investment outcome that will come. So the measurement is clear, and therefore

it directs the data people to know what to look at and how to

analyze the market. The philanthropic sector is not

driven by a simple roi. It's not a return on investment. It's not

dollars. I like to call it roi, but return on impact. Right.

What's the impact? That is Actually, it's making. But how do you measure impact?

What's the impact of a school and education versus the impact of a

healthcare organization versus the impact of an environmental organization?

Different methods, different approaches. And actually in most cases it's not as

mature as one would expect. So if the

measurement of your market is not as

clear and robust as it should be, then how do you

get data to play around and play a role here? And

that's, I think that's at the foundation of it, right. If you can't

completely articulate how you measure the success, then what is the

data that is supportive of that? Because data needs to be actionable. It's not just

collection of data. So that's one. And then

if that's the case, then the questions that there are people asking,

I mean if many of the investors will probably, or the

donors, I should say, make decisions, especially more the individual ones make

decisions a lot around relationship, around trust, about

exposure, about a friend making recommendation, hey, you should

put some money in the school or you should put some money there.

But they are not asking for the data as well because

it's relationship driven. Sometimes it's good feeling driven.

People want the good feeling of making a donation.

And if the investors are not asking the investors, that is the

donors in this case, then the organization may not provide it. And then

that entire data driven approach is not developed.

And I think that's what has been traditionally the

challenge. There is a layer now of professional

philanthropy that has evolved over the last years, and it's

in foundations or in some of the mega donors that

is bringing a much higher level of professionalism to the space.

And that's what we want to encourage, support and

democratize, meaning learning from their best practices

and make sure that the entire industry is learning and

adapting some of the tools and approaches and work processes for them.

Interesting. So it sounds like you're doing what

I, you know, in my field as a data engineer, we talk about life

cycle management, we talk about governance. And it sounds like

what's happening is that whole market, philanthropy as

a market is maturing into that space. Is that

accurate? I'm seeing that as well, yes.

Meaning I'm seeing a maturity process that is happening

and some of the dimension is actually driven by technology and

frankly by AI as well. There's a couple of interesting

trends that are happening. So one element is age of the

donors. And sometimes we, we don't think about it this way,

but traditionally in the past donors were on the

older side of the equation, et cetera. But There's a huge wealth transfer

process that is happening now and younger folks are coming in the

next generation. This next generation has

grown up in the world that we are playing in, right? Growing up

of the world of data connectivity, self serving people.

Today, when someone buys, they first of all checks online, everything

studies online, what's going on and then makes a decision. As this

generation becomes the new age philanthropist, they are expecting the same

experience as well. I'll give you a story here from a

foundation that we were working with and it's a large

foundation that provides DAFs. DAFs are donor advisor

funds. These are effectively accounts, think about

it as charitable accounts for people, for donors to manage.

And what they were saying here is they were already seeing and they wanted

to encourage younger generation to come in and

open these charitable accounts in their foundation.

But these younger generation were asking them or looking at what information

they provide and it was basically nothing, just the name

of the organization and their mission. So very

quickly to adapt that to that they requested more data, data

about the impact, about the financials, data about the

governance so the individual can make actually more robust

decisions on their own. And so they took Impala, in

our case, integrated into the backend using API

and now when they offer the information to their donors,

it's a much more robust experience and much more self serving

experience from a data perspective. And, and that's part of

addressing the next generation. So it sounds like

what Impala is providing is perhaps a

schema, a way of organizing the data and

perhaps flattening that data so that you can

apply this to many different not

for profits. Is that. So you kind of organize the data that's

common across the board to all of the

philanthropic organizations. Is that an accurate statement?

Yes, thank you, Andy. It's a very good statement. And I

explain the process here. So on one side, we are starting

with information about each organization so you can get information

about the organization in a very consistent, credible way

and integrated way. But the next leap is actually

very interesting. And that's providing data

not just about a single organization, but about an

entire sector. Sector that is of interest to you. So Andy, for example,

you may be interested in education in

New York City, for example. Okay, so Impala is

able to look at everything that is education in New York City

and provide you who are all the players, who are the nonprofits

that are active, who are the funders, how money flows, what are the

sources of funding that is coming in. So in one

click and this is where AI comes in and where the smart of Data science

comes in is in one click you are getting a market view

and you're getting basically the forest and not just each one of the trees.

And that's a very robust way and very

smart way to look at markets

as investors. Think about it from investors, from tech. It's very natural for us. And

I've been investing for a while. You are not just analyzing a specific

company. You are analyzing always the market. You're always asking yourself what's the

dynamics of the markets, etc. Who are the players, competitors, et cetera. What

Impala does is answers that question. Give me the market in the philanthropic

space and position the specific

organization within that market to understand where it

stands and is it something worthwhile investing in?

Interesting. What's your take on the rise

of philanthropy among the younger generations?

Right. You mentioned that the donors are getting

younger. There's also other options like public benefit corporations.

Are we heading into a golden age of philanthropy?

So it's interesting if you look at the dynamics actually the number

of donors is declining so

meaning the percentage of households that are actually

participating in philanthropy is going down over

time, but philanthropy is going up. Meaning that

more philanthropy is done by less people.

And that's an interesting dynamics that is happening right now or happening in the

last years in the market. Saying that there is a

different dynamics that is happening is that the younger generation is actually much

more impact driven than what

I to say it. But my generation was. And then

I'm sort of. I'm aging. I'm placing myself right now

on the timeline. But and that's actually I enjoy that. I

mean I work in Impala and in other companies that I involved in. And

I see the younger generation and I see the spark in their eyes about

mission oriented and changing the world.

And that translates into. Into

we're not necessarily selling to always philanthropy, but into social impact of different

sort. And that's exciting. I

will say that AI specifically is fascinating in this

respect because AI at the

end and we you spoke about it in some of your other

sessions does a very interesting element here in which it.

It at the end increases innovation. If

you are as an innovator in whatever field it is

and social sector is not different. Now using

AI you are able to actually implement your ideas in

a much faster, easier way without the need to assemble

a large tech organization, et cetera. So one of the things that we are

seeing is actually increased innovation. So the younger generation

may not go to philanthropy per se, but may go into

innovation and effectively social entrepreneurial approach

using AI and that's, that's another dynamics that we are seeing

here. Vibe coding hits the philanthropy world.

That's exactly. I'm going to quote you on that. Sure. Please

do, please do. Man, you have great taste in shirts,

T shirts and cars. But what,

what do you think? Because this is a big topic in enterprise world, right. I

can understand why the philanthropy world be really into vibe coding, right?

Because historically building in software has been

a large undertaking. With vibe coding, it's

still a large undertaking if you want to do it right, but it's less. The

barrier is way less. So do you think that

my concern with this. I think one, I think it's awesome because more people can

innovate, but the long term unintended consequences. One of my

favorite mentors once told me, there's really kind of

three things that really make up the universe. Energy, matter, and

unintended consequences. And what really

concerns me is what's going to happen when the vibe coding

apps turn out to be a little

more hairy to maintain and run than we thought. Now enterprises, they have

money, they have roi, they can kind of manage this risk. They

can. Whether or not they will, I think remains to be seen. But what do

you think the longer term impact to philanthropy could be like if vibe coding

turns out to not be as wonderful as we say it is?

So I'm actually somewhat, I'm optimistic in this respect. I mean,

maybe because I'm entrepreneurs, I'm an inegonity or over optimistic.

Right. But the,

I mean the vibe coding will not be a complete replacement of tech people

and coders, et cetera, but it will make people

who have some technical skills much more self sustaining

and innovative. Now when you look at the philanthropic

sector, there's about

one and a half million nonprofits out there. But out of

all this one and a half million, just over a million are

tiny Nonprofits, less than 50k in budget. Not

everybody's. The Gates foundation or whatever Warren Buffett's doing.

Yeah, exactly. And here I'm talking about also the

nonprofits, the people who are actually doing this stuff. Now. What does that mean? It

means that a lot of what's happening in this sector is actually done

by small groups into and very niche

use cases. And that's where

vibe coding can make a huge difference because that's where

relatively, with small amount of resources and

dedicated scoped activities, you can make an

impact. Now do you need for that a mega application

that supports millions of people? No, because the scope of that organization is

by definition very small. So I Think the

AI and Vibe coding in general will empower these

smaller organizations to achieve much more than what they are

able to do right now with the resources that they have.

And that's for me, an exciting outcome

and an exciting future for the philanthropic sector. So the

opportunity is there, but the blast radius of destruction is not quite the

same as it would be if it was a major bank doing five coding. Got

it. Exactly. That's exactly right. By the way, the major ones, the ones that are

really doing the bigger players in the nonprofit sector, education, healthcare,

etc. They do have a more robust and they really function like

normal enterprises that you're familiar with. So they will need to deal

with this in the same way as the large enterprises will do.

Okay, characterization. I'm sorry, Frank. No, go ahead, go

ahead. I love your characterization of it that

the majority of the organizations, if you're just

counting organizations, are the small, the very much smaller ones.

And I see this a lot and just in data in

general, in dealing with data in general. If you look

at the market cap, then it's very pyramid

shaped. At the very top it's a small number of them that are

controlling more than half of the market cap easily.

But at the bottom you've got these SMBs and that smaller

organizations. And I see the hit there.

Not that Frank's pessimistic. I don't think he is. I think Frank's a realist. I'm

more optimistic, I think, on AI than many and

I may very well get burned by it. Frank's my witness. It's happened before,

but I agree with you. I see AI as a force multiplier,

especially when leveraged in a small team

setting. Yeah, absolutely, Andy.

And that's exciting because a lot of the activities is happening in that small.

I'll connect it a bit into what Impala is and the strategy

of Impala in the AI space because we recognize this

dynamics. What we are doing now is

essentially creating a platform for these small

organizations to make it easy for them to

do Vibe coding and address their specific

workflows, but with the auspices and

the support of a company that specializes in data and

AI. So obviously MCP that is coming out

and very robust way to host and support

as well as education and services to create

AI based applications for these smaller organizations.

And this is a platform that will be coming out soon. Soon

and really becoming part of the core element of how we address this market.

That's nice. Interesting.

So tell me about the. What does the platform do? What does Impala do?

Right. Like what if you were talking

to a small nonprofit, what would

you say to them? Right. I'm not asking to a sales pitch, but I guess

I kind of am. But I mean like, because like I. Look, the

website's cool. It looks like it's from just a

cursory look at it. It looks like it's a data visualization kind of

platform where people could then once they get their data in there,

can make better decisions based on visualizations.

There's probably more to it, but is that a good.

Yes, that's the beginning of it. And what we do is really we take

data. I mean data. We are in the world of data. Data by itself is

not important. The question is, what do you do with the data? Right. How do

you make data actionable to improve your operation? That's what we are

focusing on in Impala is how to make data actionable

and allows you to make better decision. And this can be done for the

nonprofit side, meaning those that actually received philanthropic money

in order to execute, and to the philanthropic

philanthropists, meaning to the donors who need to

make decisions about where to invest and where to put their money and

capability. So what Impala does is we provide

each one of these organizations, each part of this sector,

think about it as two sided investors and portfolios,

the data and the process by which

to improve what they do. So for example, if I'm an investor, a

donor, I do due diligence,

I go and check for the validity of the organization

that they want to put money in. Impala takes the data,

takes additional information that the organization can provide

and manages for you. The due diligence process, analyzes the

information, identifies governance issue, financial issue,

integrates news, integrates information from all sources,

and provides you a due diligence report so you can make your

decision much faster, smarter and easier,

looks at the ecosystem that the organization belongs to and

explains to you where does it fit and how does it compare to

colleagues in the space. So all of these are elements that are being done through

robust data. Now, what we are doing with the

AI here. So first of all, AI plays a role in actually analyzing all of

that information and providing you the capability

to review that. But the future of it is

now embedded into your own workflow. What

we're seeing is that organizations are asking us to create

mini applications that take information from their internal sources,

external sources, and really create agents of due

diligence. These agents communicate and collect information,

but they also interact with the grantees, ask for more

data and create out of it to Finalize

recommendation we are seeing that's really what we

do today. And some of it is also where we are taking the company

at the next level with the AI platform.

Interesting. I

just noticed that you have Impalas Suite 2.0,

which looks like it was recently launched.

What's different in 2.0?

If 1.0 was really a large data platform, 2.0 is really

articulating the examples I just provided, meaning it's focused on

specific workflows for

different audiences, for foundations, for nonprofits

and for philanthropic advisors. So we basically mapped the

market, mapped the use cases and provided

data and applications that support their specific

workflows. And that's the

generation that we are providing right now.

Very cool. So you mentioned a little

bit some of the features that, that you guys are planning to implement. You

mentioned that more support for, I believe you said

the MCP servers. I believe you mentioned that as a new place.

What else is on the plate that you can talk about? I know you can't

give everything away, but what are you planning maybe next

or some long, long term vision or maybe even it's

an issue that you guys hope to solve later.

So you know, there's always a question on a,

with data platform, the intelligence platform, how do they play

in the world of AI? Because AI and at the

end for data can be a major disintermediator, right? I

mean you can ask AI and they can collect the information, give you the answers.

So that's a key element of the strategy of

what's the role of a data layer, an intelligence layer in the world

of AI. And that's really where we are focusing on right now, our energy.

So we came up with a couple of, with several elements. So first of all,

the mcp, as I mentioned, the MCP allows us to take our

robust, credible, integrated data and embed it

into AI based processes of the organization. So make

sure that the data is there wherever you need it as an organization.

That's the first element of our approach.

The second is using AI to really

collect and integrate many more sources of

data into our platform. AI is a

great place where you can actually collect, robust, connect with

additional information, information from web, from private sources,

et cetera, in a much more robust way and create a

system, create a more, let's call it data

with higher level of integrity and credibility. So we use.

That's a second element of our strategy. The third one is

really agent development. So we believe that in the world of

AI actually there's going to be a stronger emphasis on the

services side on providing services to

organizations and therefore we are developing

and building a platform. This goes back, Andy, to what you

correctfully identified. Right. For the smaller organizations, how do we

give them a credible and ability to create

those agents or those applications in a way that is sustainable? We want to

do that and support them with that. So our platform becomes more

supportive of customized applications using

AI, using the credible data that we have for the

organization. So three layers, integration of

vast amount of data provided via MCP

where you need it, and creating a services platform that allows

creation of agents and AI applications for any organization.

I love that vision and it makes sense, right? Because

I mean ultimately data is going to connect and do that.

But now that we have data, are there certain metrics? Because again this,

you mentioned that the non pro. I'm sorry, the coffee is

kicking in now. What would you say

is a good metric for.

To track? Right, there's obviously the feel good metric. It's also a very relationship

heavy thing. It sounds to me like human in the loop here in

terms of decision making is going to be critically important. Am I on base?

Am I off base? What's your take on that? Yeah, no, that's. That's a

great question. That's really a big discussion in the philanthropic space and in general, I

mean, what's the role of AI? All of human in the

space.

The human aspect in the social sector is

extremely important and robust. The

elements of relationship is core to actually how philanthropic

decisions are being made. The elements of trust is core to how

philanthropic decisions are made. AI cannot replace

that. AI is not there to replace those elements. But

AI can make everything else much more efficient.

Okay, so if I am a philanthropist, I'm a donor or in my

foundation that is working on making decisions, AI can help me see

the bigger picture, can look at many more options and places where I

deploy my money so I can achieve my mission in a more effective

way and extract to me where

are the places where I need to focus my own personal time. And that

personal time will always go into knowing the people,

understanding the impact that they are making, creating the relationship,

creating the trust. Because the ROI is not

as easily measured as it is in the business side.

The trust element is even more so, very

important. Business as well, of course, but even more so important here because that's

really what sits at the heart of my decisions. So I think

the human side will always be extremely important. And if we can make the

humans get more information and

save them time on the stuff that can, AI can do,

they can do their job better on the trust and the relationship aspects.

Interesting. What would

you say? So it looks like one of the things you have

is you have a. And I could be miscategorizing, but

you have philanthropy database, which it looks like you can.

What's really cool about this is it looks like you could do a search until

you see what the. Like, if I want to start a new nonprofit to do

something, I can look and see who has donated to several other.

I mean, is that kind of the, you know, say I want to come up

with a. I don't know, a research fund to help dingoes in.

In Maryland. Right. Granted, there's not a lot of dingoes in Maryland, but.

But like what? So, but it looks like I can go and I can take

a look at a state by state, at least in the US presumably elsewhere too,

that you can kind of go state by state. I like the fact that New

Jersey was your sample state. That was cool. I spent my formative years there.

But if you kind of. It seems like you

can get a pretty good view of the industry, if you can call this an

industry, I think that's really cool. Like you're basically. It sounds like you're

collating all this data and packaging it up as a service. Is that

correct? As at least one of the product lines you have? Yes.

So a big part of what we identified is the

notion that providing just a general database for

everybody is complex for people. People need to be

focused on their area. So if I am a New Jersey guy,

I want to know what's going on in New Jersey. I need to understand the

New Jersey market and I need to go and drill down to different aspects. If

I'm interested in democracy issues,

then I need. I need a place where I can study

philanthropy in the democracy space, et cetera, et cetera. So we took

that notion and created what we call hubs,

philanthropic hubs. And those philanthropic hubs are effectively our

database, but analyzed and presented

with a specific target audience in mind.

Whether it's New Jersey or whether it's South California, we

have that. Whether New York or whether it's a cause based

element could be the Jewish people, could be

Christian faith related aspects, or could be democracy, or

could be environmental element as well.

So the view is first of all a sector based view

and then providing and providing information for people to make

decision there. Now it lends

itself, frank, to what you were talking about, which is collaborative giving. So the

idea of knowing who else is giving in this area and who else is

interested in this space.

Notion of collaborative giving is another major trend that is happening in

philanthropy where more and more

funders want to see who else is playing and sometimes come together

as a group of funders to support a cause. So our

hubs allows you to identify those

collaborative giving opportunities, maps different sectors and

subsectors who are all the existing giving there. You can compare

notes and you can make decisions together. And

that's a big part of using data to make

more actionable and more accessible philanthropy and smarter philanthropy.

It sounds like that philanthropy is going to be

radically transformed by not just AI, but data as

well. Right. It seems like you're

really at the cusp of a brand new age route for this.

I believe so, because I think the AI amount, if anything, it's going to

do to this sector, it makes it top of mind. Today

when you speak with any philanthropy. How do

I use AI to improve my work? Is a top question. It's a top

priority for every foundation, for every organization, as it is in the

business sector. If you get to any board, AI and the use cases

of AI is top of mind. Now the moment

you speak about AI, you speak about data. Titles

connected, of course. So suddenly the

dialogue of thinking about AI, technology,

use of data, it becomes a primary dialogue in

the organization because of the age that we are at right now.

And that's a big deal that's changing. It's going to be changing the industry.

And that's not something I could have said a few years back before AI

became a thing.

Right. So yes, I think we are on the cusp of a big

change. And it comes

from the mindset of AI. It comes from a

generational aspect. It also comes from money. By the way,

AI money is going to be changing philanthropy. If you look at

even the commitment by OpenAI,

Sam Altman and Dalio Amodi from philanthropic,

basically dedicating 50 to 80% of their money and

of their ecosystem to philanthropy. That's huge amount of money that also comes

in. So suddenly you have very sophisticated money

that is coming in, that is looking for robust uses of data and

AI and influencing in a material way the industry.

Same thing as Gates, as the foundation has done and influenced the industry in a

big way when Bill Gates and Bill and Melinda

Gates came in with their investment. So the fact

that these kind of mega players are coming in is also going to and

will be influencing the sector. And this is part of the dialogue

right now in the sectors how to get ourselves ready

for such a change and such an impact. Interesting,

Interesting. Andy, do you have any questions?

No, I'm just fascinated. It is really fascinating because you don't really

think about philanthropy and you think about donating

to your local goodwill or you think about donating to the

soup kitchen or something like that. But you're right, there's

any number of things. And the fact that you can explore and see

what programs are available for what types of

constituents, because they're not really customers, people you want to serve,

that's interesting. Anytime you can foster human

flourishing, you know, and, and

where that intersects with technology, I'm always fascinated by it.

So kudos, kudos to you guy, and, and

the groups that you work with and, and to Impala

for facilitating this, especially for the, those

smaller markets. Because it's like you said, the larger companies,

they operate just like for profit enterprises in many

ways. And the smaller companies, they, they also operate

like other smaller for profit companies as well. But you know, being the

owner operator of one of those smaller companies, I can tell you there's a gulf

between me and the big enterprises.

I sympathize and empathize with them. So it's super cool

to see somebody not just, you know, deploying

technology to help out the smaller companies, but also

having that. It's almost like a step beyond that when it's

not for profit and hopefully encouraging human

flourishing and helping people lead better

lives, that's just awesome. Thank you,

Andy. And you're touching one of the reasons I got

into the space. And I think in general

there's a phase in life where sort of you want to figure out

ways to combine what you, your knowledge and your experience with

making some level of social impact. And for me, Impala is that is

a way to take sort of my background in

entrepreneurship and tech and data and transform

it into an operation and an initiative that can make some

social impact through these areas, through data, through

AI in the space. So yes, it's exciting and

it's exciting things. That's a great feeling when you can have

those intersect. Yeah, absolutely.

So where can folks find out more about you about

Impala? If people wanted to know more,

where would they go? First of all, definitely to our website,

Impala Digital, that's always available

and can get from there. Soon

end of July, you will have an ability to do it

directly from cloud or others ask for information through

our mcp. You'll get it. So that's another place I'm giving you an

introduction. But also approach us directly. I mean, we

are very approachable, we are very open. And if you have and you're active

in the social sector whether on the philanthropic side or whether on

the nonprofit side, reach out

through our website, contact or get my information and

speak with us on how we can help you take your social course to the

next level. Very cool. Any

final questions, Andy? No. That's great interview.

Thank you. Yeah, it was great. Yeah. I could talk to you for another hour

or two but we won't be respectful of your time. But no

thanks. Thanks for joining and thanks for doing the great work to help

people do great work. I think that's awesome. Thank Andy.

Thank you very much and thank you for what you're doing as well and hopefully

this will help and support many people who have who want to make

some social impact out there and leverage data to make that

smarter decisions and more impactful efforts.

Excellent. Thanks. And we'll let the outro music play. That's great

man. I really this is a great interview. Agreed.

Yeah, I'm going to stop. Yeah, I love that.