In this episode, we delve into the transformative power of AI and technology in the nonprofit sector with Jacek Siadkowsk, co-founder and CEO of Tech to the Rescue.
Our discussion explores how tech innovations are revolutionizing nonprofit operations, from enhancing efficiency and service delivery to driving social good through scalable solutions. Jacek's journey from running a digital agency to spearheading a global movement offers valuable insights into the mission and impact of Tech to the Rescue.
The episode kicks off with an exploration of how AI and automation are making significant strides in the nonprofit sector. Jacek highlights the role of Tech to the Rescue in bridging the gap between tech companies and nonprofits by facilitating pro bono collaborations.
These partnerships enable nonprofits to leverage advanced technologies to solve real-world problems, thereby amplifying their impact. The conversation underscores the critical role of AI in enhancing efficiency, fundraising, and service delivery for nonprofits, while also addressing the challenges of ensuring accurate and reliable AI applications.
A fascinating case study discussed in the episode is Bikara Udara's voter empowerment platform in Indonesia. This innovative tool is changing the political landscape by helping citizens elect candidates prioritising quality of life. The broader implications of such technology extend to various social issues like health, climate, and education.
Jacek also highlights exciting partnerships with tech giants like Google.org and AWS, which are propelling the AI for Changemakers program to support nonprofits globally.
Jacek's ambitious plans for Tech to the Rescue include facilitating tech services worth $1 billion by 2030 and popularizing the culture of tech for good.
This episode is a must-listen for anyone interested in harnessing technology to drive social good. From the transformative role of AI and automation in nonprofits to the ethical considerations of responsible AI development, the discussion offers valuable insights and inspiration.
More on Jacek
Jacek on LinkedIn
TTTR Website
Resources mentioned
Verner Vogels
Our discussion explores how tech innovations are revolutionizing nonprofit operations, from enhancing efficiency and service delivery to driving social good through scalable solutions. Jacek's journey from running a digital agency to spearheading a global movement offers valuable insights into the mission and impact of Tech to the Rescue.
The episode kicks off with an exploration of how AI and automation are making significant strides in the nonprofit sector. Jacek highlights the role of Tech to the Rescue in bridging the gap between tech companies and nonprofits by facilitating pro bono collaborations.
These partnerships enable nonprofits to leverage advanced technologies to solve real-world problems, thereby amplifying their impact. The conversation underscores the critical role of AI in enhancing efficiency, fundraising, and service delivery for nonprofits, while also addressing the challenges of ensuring accurate and reliable AI applications.
A fascinating case study discussed in the episode is Bikara Udara's voter empowerment platform in Indonesia. This innovative tool is changing the political landscape by helping citizens elect candidates prioritising quality of life. The broader implications of such technology extend to various social issues like health, climate, and education.
Jacek also highlights exciting partnerships with tech giants like Google.org and AWS, which are propelling the AI for Changemakers program to support nonprofits globally.
Jacek's ambitious plans for Tech to the Rescue include facilitating tech services worth $1 billion by 2030 and popularizing the culture of tech for good.
This episode is a must-listen for anyone interested in harnessing technology to drive social good. From the transformative role of AI and automation in nonprofits to the ethical considerations of responsible AI development, the discussion offers valuable insights and inspiration.
More on Jacek
Jacek on LinkedIn
TTTR Website
Resources mentioned
Verner Vogels
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Welcome to Digitally Curious, a podcast to help you
navigate the future of AI andbeyond.
Your host is world-renownedfuturist and author of Digitally
Curious, Andrew Grill.
Andrew's guests will help youbecome more curious about the
latest tech and what's justaround the corner.
Speaker 2 (00:22):
Today's guest is Jacek Czajkowski, the co-founder
and managing director of Techto the Rescue, who's at the
forefront of leveragingtechnology for social good.
Jacek has dedicated his careerto using technology to address
some of the world's mostpressing social and
environmental challenges.
At Tech to the Rescue, Jacekleads a global movement that
connects tech companies withnon-profits to solve real-world
(00:45):
problems using advancedtechnologies, including AI.
Today, we'll delve into how AIis being harnessed to drive
social change, the challengesnon-profits face in adopting new
technologies, and the future ofAI in the social sector.
Welcome, Jacek.
It's a pleasure to be here.
For those who haven't heardabout Tech to the Rescue, could
you outline your mission andyour journey to date?
Speaker 3 (01:05):
Tech to the Rescue.
Could you outline your missionand your journey to date?
Tech to the Rescue was foundedbecause there's one big problem
in their world, which isnon-profits.
Globally, they lack technologyresources to invest in scalable
solutions that could make theirwork more accessible, more
adopted and more impactful.
So we work with non, withnonprofits, every single day.
(01:25):
We see that all around the worldthere are thousands of great
social entrepreneurs who havetested proven social impact
interventions.
Basically, they know how tosolve social problems.
They work with beneficiarieshand in hand.
Every single day they crack thecode on how to change people's
lives.
But their impact is limited toworking with the community or
(01:57):
with a small region, becausetechnology gives this
opportunity to copy pastesomething that works everywhere.
Right, and without access totechnology, they are limited in
impact to small communities andbecause of that, the whole
humanity and the civilizationmisses the chance to, you know,
improve social problems here andnow.
The problem is big, yeah,because you know it exists in
every single country and it'sdifficult to solve because
technology is expensive todayand it will be very, very
(02:21):
difficult to bring additionalbillions of dollars into the
non-profit market.
So the solution thatTech2TheRescue founded and
believes makes sense is acultural solution.
So basically, what we want todo is we want to make sure that
every single tech company in theworld will collaborate with
non-profits on a pro bono basis,and we mean that every single
(02:43):
company can afford sending atleast one qualified professional
team for a non-profit for two,three, maybe six months per year
to build a technical solutionthat would help this non-profit
scale up their work.
Speaker 2 (02:57):
So we're going to talk about AI, which is now
everywhere.
So what do you see as the roleof AI within nonprofits and
social impact organizations?
Speaker 3 (03:05):
Many nonprofits that we work with have some
bottlenecks that are solvablewith automation.
So you know, if you run anonprofit, your typical size is
less than 50 people, you don'thave hundreds of staff, you
don't have automated facilities,have hundreds of staff, you
(03:26):
don't have automated facilitiesand sometimes you are limited by
simple things like okay, wedon't have that many advisors or
consultants to help everysingle person with problems to
understand what to do next.
And when we work withnonprofits, we see that they
have limitations in theirinternal work, understood by how
well do they organize processes?
(03:47):
But also with external work,basically meaning that they are
limited with serving many peoplebecause they lack staff.
And AI, especially generativeAI, brings these new
capabilities to the market andwe believe that almost every
single nonprofit-profit that isbigger than you know, working
with the community, couldleverage those solutions to a
(04:08):
rise.
More funds, because we couldautomate relationships with
donors, and this is veryimportant because funds are
unlocking many different things.
It's the same thing as sales inbusinesses.
Second thing non-profits couldmake sure that their work is
more efficient, right, and it'svery important to offer the
(04:29):
world with a high-performingsolution which is cheap to
deliver impact, right.
So implementing AI-basedsolutions could reduce the cost
of serving one person or oneanimal or one social problem and
, of course, because we couldautomate the interface of
interacting with the directbeneficiary.
(04:50):
In the end we could make surethat one organization can serve
much more people.
So we believe a giganticpotential in almost every field
of work within nonprofit.
But there's one challenge,obviously, because AI is in a
very early stage right now, thatwe need to be sure that we
won't provide people with thewrong information or that
(05:11):
algorithm won't providemisguiding hint to a beneficiary
, because in some cases thatcould be very detrimental.
Speaker 2 (05:19):
So it sounds like you've got a really important
mission that you've got here.
So tell me about how you set itup, what was the journey to say
we need this, no one else isdoing it in the way that we can
do it, and how you actually thengot this off the ground.
Speaker 3 (05:32):
Tech to the Rescue.
It's a movement story.
So prior to launching Tech tothe Rescue, I used to own my own
digital company.
It was basically a digitalagency that was building
software solutions for othernon-profits with a very special
twist because my company wasfocused on building gamified
solutions.
So basically, we weretransforming boring life into
(05:54):
interesting games that helppeople change habits and improve
behaviors.
The work was very impactful.
Everyone who worked with usobserved improvements in how
people behave in their own lives.
But every single time we wereapproached by a client meaning a
non-profit we saw a potential.
But we also saw that they couldafford hiring us for one month
(06:16):
or two months, because biddingtechnology is very, very
expensive, and how do you scaleup such a company if your
clients don't have budgets toactually afford any margin?
Right, you cannot hire morepeople, you cannot invest.
So you know, being in this typeof company was very fulfilling
in a social impact sense.
It was very difficult in abusiness sense.
(06:38):
So I was observing thesituation.
I saw clearly that so manygreat nonprofits lack resources
and, on the other hand, I was aparticipant of the digital
solutions market.
I was hiring the same people asthe commercial companies are
hiring and so on.
So I had this insight that whenyou run a digital services
(06:58):
company, sometimes you have thesituation where at least one of
your teams is in betweenprojects, right?
So, for example, they finishsome multi-year project with one
big client and before the nextclient buys their services, they
wait for two, three, sometimesfour months until something new
comes in.
And these people are waiting.
(07:19):
You won't fire them becauseit's too expensive to hire new
staff.
You want to have them availablebecause sometimes clients need
them right away and you knowthey're essentially you know
getting bored, frustratedbecause they don't develop
themselves as engineers.
So I had this insight that mostprobably it is possible to
encourage thousands of companiesworldwide to dedicate these
(07:42):
people waiting for the nextproject for a non-profit project
, because that could bewin-win-win for everyone and for
a company.
It's a way to utilize andmobilize staff for those people
who are waiting for the nextproject, an opportunity to learn
new skills, to do somethingvery fulfilling and very
purposeful and, for a non-profit, the only way sometimes to
actually invest in technologyand build something which is not
(08:04):
doable using only the grantmoney, which is very little.
So I had this idea in my drawerbecause I thought well, you
know, building a movement ofthousands of companies is not
easy.
Would I have to attendthousands of meetings to
actually build this criticalmass that needs to be there?
But then something happened,something that everyone felt at
(08:26):
one moment, and that thing wasCOVID.
When that big crisis hit, likeCOVID, like war in Ukraine,
everyone in the society ismobilized and people want to be
useful, and people from thetechnology sector also want to
be useful.
It was, I think, april 2020,when one of the tech
entrepreneurs in Poland had thisinsight and he wanted to do
(08:49):
something.
So he published a LinkedIn postthat, basically, he will donate
his team to a non-profit thatknows what to do to stop the
pandemic.
He didn't feel as an epidemicexpert, he didn't know what to
do, but if there is anynonprofit that has a good recipe
for stopping this or forhelping people, then take my
(09:10):
people, use them for nine monthsand do whatever is needed.
So he published this post.
It got viral very quickly, sohe saw that people are picking
up the idea, so he included avery simple Google spreadsheet
link in the comment and he wroteif you like the idea and he
included a very simple Googlespreadsheet link in the comment
and he wrote if you like theidea and if you want to donate
your tech company stuff, justsign up here.
(09:31):
After the weekend there waslike 35 companies signed up.
So he felt like, wow, I didn'texpect it, maybe there's
something we should invest in.
So he asked his team that hewanted to donate to the
non-profit to actually promotethe idea.
So they built a website, theylaunched a product hand campaign
, they did everything thatstartups do and after six weeks
(09:52):
there were like 150 companiesalready.
So I was observing this and Iwas like, okay, this could be
the movement I was hoping for.
So I basically picked up thephone.
I called the person.
I offered him this insight.
I told him, you know, like thisinitiative will die if we don't
transform this into somethingbigger than the pandemic.
Then we started workingtogether.
(10:12):
The first 10 sponsors fromCentral Europe joined us like in
two weeks and then we startedbuilding this.
Right now, the community isbigger than 1500 tech companies
from over 60 countries and wesee every single day that this
idea, it makes sense.
People in technology arecreative, they want to do
something impactful and in theregular business sometimes they
(10:35):
don't have this opportunity.
So if some wonderful nonprofitfrom other parts of the world is
coming, they see those people,their motivation, they see how,
every day, they are changingpeople's lives.
They feel it.
They just want to do something,they just want to join.
This way it's scaling quicklyand this way it brings impact
every single day.
Speaker 2 (10:55):
Yeah, just to sort of reflect on what you said, when
I was at IBM and I was running aconsulting practice, often
people in other teams had peopleon the bench.
People listening to this willbe aware of that.
They haven't got a project atthe moment, they're in between
projects.
They're still keeping the benchwarm.
What a great idea to havepeople on the bench doing
something for good.
(11:15):
Tech for good.
It's an amazing initiative.
Covid has come and gone.
We've now got this new amazingtool called AI.
I mean, it's been around for 74years, but I think now
everyone's talking about it.
So maybe you could talk aboutsome examples of how AI has been
used in projects facilitated byTech to the Rescue.
Speaker 3 (11:33):
Let me start with saying that Tech to the Rescue
supports nonprofits working inany field.
We are not limiting ourselvesto climate or to health.
We try to be helpful foreveryone because we know that
tech companies worldwide havedifferent preferences and we
just want to make sure that anycompany that comes to Tech to
the Rescue, they will find theproject they will love.
But sometimes we launchcampaigns, so bigger efforts to
(11:59):
support organizations in a givenfield, and one example of such
a campaign is the campaign thatwe launched last year with the
support of AWS, where we wantedto move the needle of the air
quality movement worldwide.
So first we decided toprioritize two regions in the
world, which is Southeast Asiaand Sub-Saharan Africa, because
(12:21):
the air quality problem isgetting worse and worse in those
areas and there are not manynon-profits that are working on
this.
And then we scouted excellentnon-profits in almost all of the
countries in those regions andthen we basically asked those
organizations what kind oftechnology do you need to make
your work more impactful?
And I will tell you about threeexamples of solutions that we
(12:46):
built, I think, in quiteinteresting cases.
So first organization is calledSensors Africa.
The organization's mission isto make sure that the data about
air pollution in whole Africais publicly available so that
people could raise theirawareness and they could design
better public policies, knowingwhat actually the problem is,
(13:08):
right.
So they are building the globalnetwork of air quality sensors,
but those sensors are usuallylow-cost sensors and they
sometimes break.
So what organization asked usto do is to create AI-based
solution that will simulate theair quality sensor in a
situation where it has adowntime right.
(13:30):
So when it breaks, it's brokenfor one or two weeks and we miss
the data.
So we built a machine learningalgorithm that is estimating
what would be the air qualitylevel in a specific area if the
sensor was working.
So this is one of the cases.
Second case, a very interestingone, comes from Thailand.
(13:53):
So there's an organizationcalled Clean Air Thailand and
they designed a new air qualitybill that they want to take into
the parliament, but to be ableto do it, they need to collect
100,000 signatures from thecitizens, and it's not easy
because the bill is quitecomplicated and people don't
(14:13):
understand what it means forthem, especially that air
quality problem is not verypopular in Thailand.
So what they did is they builta generative AI-based solution
that is translating the bill toany person.
That is speaking to the bot,right?
So let's assume you are afarmer, andrew, and you want to
understand, okay?
(14:34):
So, if the bill is passed, howthis impacts my life, is my work
more difficult?
Is it easier, what it means,what it means for my children,
for my family, for my farm.
So, basically, they built asimple solution that is
explaining the impact of thebill and helps collect new
signatures, which I believe is avery interesting way to explain
(14:55):
politics and explain the impactof different policies on
everyday lives.
And the third solution, alsofrom the political field, was
built for an organization fromIndonesia.
It's called Bikara Udara andthey wanted to like in a
specific year.
Last year they had parliamentelections, so they wanted to
(15:18):
help citizens elect people whocare about their quality.
So they built a solution thatwas scraping internet looking
for any statements thatparliament candidates are making
about their quality, and theybuilt a tool that lets people
find the candidates that reallycare and choose the best
candidates of the candidatesthat are available in a specific
(15:40):
region you are voting.
So it's interesting, right?
Because we have one problem.
We have different kind oflayers of it, right from data
collection to, let's saydesigning the public policy and
making sure that peopleunderstand, to making sure that
the right people are elected tobe able to actually pass the
(16:01):
bill right, and it goes on andon.
In every other social problemarea.
We could use this methodologyfor health, for climate, for
education.
The possibilities are basicallylimitless.
But the difficult thing is howto make sure that this
technology is predictablyimpactful in a positive way and
(16:26):
how do we avoid potential harmsthat could come, for example,
from hallucinations.
Speaker 2 (16:31):
So you mentioned you work with AWS.
I understand you're also doingwork with Googleorg.
Speaker 3 (16:35):
Tech to the Rescue started as a bottom-up movement
of tech companies in Poland, butwe very quickly grew to the
global movement of anyone fromtwo people WordPress agency
working in India to the globalgiants working in the technology
field.
So Tech to the Rescue right nowis working very closely with
(16:56):
Googleorg and AWS.
They are our main supportersand recently, together, we
launched a whole new big programfor AI.
It's called AI for Changemakers, through which, together with
them, we are supporting 100awesome nonprofits with
implementing AI in a way whichis impactful and both which
(17:17):
reduces risks of doing harm.
Speaker 2 (17:21):
Importantly, you want to get the right people into
the right projects.
So how do you identify andselect the nonprofits and tech
companies that participate inyour projects?
Speaker 3 (17:30):
That's a difficult task and it's called matching,
and sometimes we tell peoplethat don't really understand how
Tech to the Rescue works.
We call ourselves a Tinder fortech companies and non-profits.
So, starting from non-profits,I think it's important to find a
nonprofit that is actuallycapable of using technology.
If you run a tech company andyou wanted to help someone right
(17:53):
away, your immediate instinctwould be to help orphanage which
is somewhere around, right,like in your neighborhood.
But does the orphanage reallyneed technology?
What do they need?
A new website?
A new CRM?
Like?
Most likely, they havedifferent needs, right?
So it's very important to beable to find a non-profit that
is in the stage of development,where the intervention is
scalable and they just need toimplement this solution to be
(18:16):
able to increase the impact.
Right?
So, every single day, our staffis scouting the world looking
for great organizations in allcontinents, organizations that
have proven social impact, thathave scalable interventions,
that could use the technologyand that has some level of
investment in technology to makesure that any technology built
(18:37):
by tech company will be thenmaintained and further developed
.
Right, because technology isnot only about building the
first iteration and vprprototype.
It's about constantly, it'sabout constant innovation.
So we try to selectorganizations that are ready,
that know what they want andthat are capable of working with
(19:00):
global tech companies.
From the tech perspective, it'sa little bit more difficult
because, first of all, we needto understand what the company
specializes in.
You will want to engage in adifferent project if you run a
React native company and adifferent project if you run a
(19:22):
generative AI company.
We need to understand what thecompany actually cares about,
because some people will bemotivated by helping people with
cancer and some other peoplewill be motivated by solving
climate change right.
So we ask company you know whatkind of social problems really
motivate them to work?
And also we need to understandwhat is the context of a tech
(19:45):
company.
You mentioned Bench.
Bench is one of them.
We actually mapped 12 differentuse cases for tech companies to
do pro bono work, and all thoseuse cases come with different
pros and cons.
For example, bench is greatbecause you have a fully
professional team available, butfor the limited time, right.
So you need to adjust the scopeof the project to the time
(20:07):
availability of the team, andthen we take all those factors
into one algorithm that isrecommending projects to tech
companies, which is alsosupported by the work of
beautiful human beings calledmatching managers, who recommend
nonprofits to tech companiesand who later facilitate the
(20:27):
conversation, because, guesswhat?
Tech companies are notperfectly prepared to work with
nonprofits, and vice versa.
Right, we need to translate thelanguage of technology to
nonprofits, language ofnonprofits to technologists, and
we need to make sure that thedeal is well scoped out and
everyone knows what they aregetting into.
Speaker 2 (20:46):
So, just coming back to AI for a moment, some of the
challenges that are there.
You mentioned Tinder as adating app.
I'm sure all the datingcompanies are very keen to
understand that they do somelevel of screening to make sure
that real people being matchedto genuine human beings.
So how do you ensure the AIsolutions that are developed as
part of these programs areethical and don't inadvertently
cause harm?
Speaker 3 (21:07):
It's the most important thing in our work, and
I mentioned the AI forTradersmakers program.
The program is divided intofive thematic cohorts and we are
working with a disastermanagement cohort right now.
So, in essence, we are workingwith humanitarian organizations
In the humanitarian world.
There is this very importantrule, which is an overarching
(21:27):
rule of the humanitarian work.
There is this very importantrule which is an overarching
rule of the humanitarian work,which is do no harm.
And if you visited us in any ofthe meetings that we have
within the program, you wouldquickly understand that there is
no better industry in the worldthat is managing risks than
humanitarian sector, becauseevery single bad decision could
(21:49):
lead to doing harm and they arereally conscious about it.
And so we ensure that thisconversation about the
responsible ai is present atevery stage of the project.
We talk about it when we talkabout what ai is right, just to
make sure that people understandits limitation.
It's not a magic wand that isdoing stuff.
(22:10):
It's technology, algorithmsthat have its own profile and
characteristics we need tounderstand.
Then, when we talk about theuse cases that organizations
have, we talk about you know, dowe need a human person in the
process to make sure that it'smaking good decisions.
(22:31):
Is this specific use caselife-threatening or life-saving?
You know what are the risks, toidentify where the AI could be
applied for with a risk that ismanageable.
And then every project can counton the advice of the world's
greatest companies and theirresponsible AI teams.
(22:53):
So, for example, every projectthat we do is going through
Google's committee of AIprinciples team, which is rating
the project under everyprinciple, and they suggest what
are the specific risks weshould take care of specifically
.
But this is a theory.
And then we have practice.
We need to deploy the model andwe need to test it right, and
(23:15):
then I believe that the work isgetting more and more difficult.
So, in this specific case, it'svery important to make sure
that nonprofits have resourcesand capabilities to really
deploy models in a testingenvironment, and this is
something that we put a lot ofattention to, and our program is
(23:37):
not a program in which wemobilize everyone to make
success.
It's the program that weencourage everyone to experiment
, learn and share the learnings.
So every nonprofit-profit thatwill implement AI solution will
be encouraged to organizetesting environment, to test
with real people on a limitedscale and then publish the
results, improve and then onlylaunch on production to everyone
(24:01):
who wants to use this solution.
Speaker 2 (24:03):
So measuring success and the performance of the
projects is obviously very key.
So how do you do that and howare you able to maintain the
quality and standard across allthe projects?
Speaker 3 (24:13):
The measurement is tricky because the whole
measurement thing in non-profitsis way more difficult than in
business.
In business the performance ofa company usually boils down to
profit or the revenue in somecases.
In non-profits the success willbe defined differently for
(24:36):
every other non-profit workingin different area, in different
intervention and so on.
So we try to find the commondenominator for organizations
and in our case it's one of thethree aspects.
First one is how many people orhow many beneficiaries the
organization can serve, sobasically how widely they adopt
(24:59):
their services and how manypeople or living beings are
impacted by them.
Second thing is the unit costof making one unit of impact, so
basically how expensive it isto change people's lives.
And third, one is time to makean impact, so how quickly
organizations are able todeliver their services.
(25:22):
And we measure those threemetrics when organizations join
Tech2DeRescue and we measurethose three metrics after two
years from the implementation oftechnology to see what's the
impact.
We want to see the positivechange in at least one of those
three factors.
But obviously, if you ask meabout this in a storytelling way
(25:43):
, I would tell you that we areworking with a nonprofit from
Nicaragua which is providingsexual health education to women
and before working with Tech tothe Rescue, they were driving
the bus around the country andproviding direct education to
women in rural areas and theywere able to serve 20,000 people
in three years and after theybuilt an app with Tech to the
(26:06):
Rescue, they served 50,000 womenin one year.
So this is the impact that wewant to see.
Speaker 2 (26:12):
So I could ask this question to corporates or
nonprofits, but what are some ofthe most common misconceptions
about using AI in the nonprofitsector?
Speaker 3 (26:21):
I see less misconceptions about AI in
nonprofits than in business.
I believe that the reason isthat in business, people are
more oriented towards buildingprocesses and towards building
scalable systems.
In nonprofits, people are morefocused on working with real
human beings and helping them,and I think that those negative
(26:43):
stories about using AI in somespecific context, the stories
that went wrong, are moreadopted or more picked up in a
nonprofit space, where peopleare afraid of doing some mistake
.
So we don't see people beingcrazily optimistic about AI.
We see people being responsiblyoptimistic about AI and also
(27:10):
having many concerns about thelong-term viability of those
solutions, how expensive theywill be because they operate on
limited budgets, and the veryspecific challenges that we see
in many cases are making surethat the systems, especially the
generative AI systems, willmaintain quality, especially if
(27:30):
they use models that are builtby black box tech companies
without much description or abenchmark test that they can
trust.
Second thing is we see thatmany non-profits, especially
those bigger ones, they workwith people in different nations
(27:53):
or languages and the bigquestion mark is, for example,
how well different foundationalmodels cope with less popular
languages.
It's not something you caneasily test using some online
tool, so many organizations areconsidering fine-tuning models
(28:14):
to less popular languages.
And I think, last but not least, there's a big question about
the cost.
Some tech companies are verygenerous and they provide
credits for the solutions.
So, for example, aws invested 6million dollars of AI credits
for our AI4Chainmakers program.
But obviously credits areavailable for a specific time
(28:37):
and after 24 months you need tostart paying right.
So we need to be really awareof the cost of the solution,
about the unit cost ofmaintaining the solution for
many people, and so far,comparison of different Gen AI
models, especially when it comesto the costs, are tricky.
(28:58):
It's not easy, it's not veryintuitive.
You need to talk to the expertto really understand.
Actually, you need to test itin real life to be sure what the
real costs are.
So there are many questions,but I wouldn't be afraid of
people not asking difficultquestions when they implement
projects.
More I will be afraid of thetech market being more
(29:22):
transparent and moreuser-friendly when it comes to
helping people understand howexpensive it will be to use
their products.
Speaker 2 (29:30):
So final question before we go to the quickfire
round, let's look to the future.
What are the future plans forTech to the Rescue and how do
you see the organization growingin the next decade, and how can
we bridge the gap between bigtech and nonprofits?
Speaker 3 (29:42):
Yeah.
So first of all, I want to seeevery bench in the world being
utilized for a nonprofit project.
I don't care if it's a bench ofIBM, if it's a bench of IBM if
it's a bunch of small Polish orIndian software development
company.
I want to see that people thatwork in tech companies, when
they see unutilized resources,they have an idea of working
(30:05):
with a nonprofit.
And this will take time.
We believe that this could takeus like five to 10 years.
There are multiple dimensions ofthe work that we need to do.
We need to make sure that doingpro bono will be easy, that it
will be manageable for companiesand that companies will be able
to measure the performance ofthose processes.
(30:25):
But also we need to make surethat it will be present in the
mass media culture.
Why?
Because look at the legalmarket.
In the legal market, doing probono is a standard.
Everyone knows that it'ssomething that companies do, and
they know it because some ofthe very, very popular pieces of
culture promote this.
(30:47):
So there's this famous tv showabout lawyers the Suits, and in
the Suits one of the, mike, isdoing pro bono case in every
season of the series, and thenhundreds of millions of people
are watching the series and theysee that this is what legal
companies do.
So one of our dreams andinformal goals that we have is
(31:07):
to revive the Silicon Valleyseries one day and to make the
whole season of the series aboutthe guys building technology
for a non-profit.
So this is the ambition.
There are metrics.
So we hope to facilitateservices tech services of a
value of $1 billion until 2030.
(31:28):
And we hope to collaborate withboth big tech companies and
large tech companies like IBM,aws and Googleorg.
But we also hope to work withthe whole market of tech
companies that employ more than10 people and are able to donate
some weeks or some months tobuild something interesting and
(31:50):
impactful for a nonprofit andimpactful for a nonprofit,
having run six startups.
Speaker 2 (31:55):
Watching the Silicon Valley show about startups and
Pied Piper going from workingaround the kitchen table to a
huge conglomerate wasfascinating.
Not sure you'd want the SiliconValley team helping nonprofits.
I think there might be somedysfunctional behavior there,
but that's for another time.
We're almost out of time.
We're now to my favorite partof the show, the quickfire round
, where we can learn more aboutour guests, so I'm going to fire
some questions at you.
Window or aisle, aisle.
(32:16):
I'm tall.
I need space for my legs.
Iphone or Android?
Speaker 3 (32:20):
Android, your biggest hope for this year and next
We'll be able to ship at leastfive amazing AI projects for
nonprofits in the community.
I wish that AI could do all ofmy B boring communication.
The app you use most on yourphone, it's Whoop.
It's the fitness app that helpsme sleep better, train better
and become healthy.
Speaker 2 (32:40):
The best advice you've ever received.
Speaker 3 (32:41):
It's actually a piece of entrepreneurial advice.
So I was a participant of thementoring program with one very
esteemed manager and I asked himyou know what, in your opinion,
is a key characteristic of avery good leader?
And he told me there are twothings.
First of all, 95% of your workis an excellent communication.
(33:02):
Second thing 5% is an abilityto spot the opportunities and to
use them.
Speaker 2 (33:08):
What are you reading at the moment?
Speaker 3 (33:10):
I'm actually reading a book on startup boards because
at nonprofits one of the mostimportant aspects of running the
organization is having anexcellent board that helps
organization grow.
Speaker 2 (33:20):
One thing I've been pushing for a long time about
boards is having someone that'sdigitally curious on the board,
or more than one.
They actually understand thetechnology, so that is a key
thing for both corporate andnot-for-profit boards.
Who should I invite next ontothe podcast?
Speaker 3 (33:34):
key thing for both corporate and not-for-profit
boards.
Who should I invite next ontothe podcast?
I would recommend inviting thefounder of an organization
called Humane Technology.
It's Tristan Harris.
He used to work at Google.
He's the person who is behindthe famous Netflix show, which
is the Social Dilemma, and Ibelieve that he could bring lots
of interesting insights intothis whole AI craze.
(33:55):
He's the person to bring moreawareness on how we should
deploy this technologyresponsibly.
Speaker 2 (34:03):
Great suggestion.
The social dilemma is a fewyears old now, but I remember
encouraging my friends to watchthat to see how social media has
really developed.
I think we could probablyupdate that for AI, so I will
reach out to him.
Final quick fire question howdo you want to be remembered?
Speaker 3 (34:16):
I want to be remembered as a person who spent
his life on unlocking potentialof a wonderful human being that
cares about other people.
Speaker 2 (34:25):
As the actionable futurist, I always ask my guests
for three actionable thingsthey can do.
So what three things can ouraudience do this week to better
understand how AI can be usedfor good?
Speaker 3 (34:36):
First of all, check Tech2TheRescue out.
Tech2therescueorg is ourwebsite and you can join the
community and volunteer yourcompany to work with non-profits
.
I believe that any company inthe world has those
opportunities, like Bench, toengage in non-profit projects,
and sometimes it's as easy asone person in the company
signing the company up, and thenour team will help you discover
(34:58):
how to crack the code of doingpro bono at your company.
Second thing, following animpactful person who is the
Amazon CTO, werner.
Speaker 2 (35:08):
Vogels, I know Werner .
I've been on stage with him inAbu Dhabi.
I know him and we're in contact.
Yeah, he's a great guy.
Speaker 3 (35:14):
He's a great guy and he spends lots of his time on
finding people who care and whobuild very impactful solutions.
Also, werner recently announcedthat he will join our AI for
Changemakers program and he willoffer his personal time to
spend with nonprofits advisingthem on how to become better
CTOs and how to push theinnovation agenda harder and
(35:37):
more effectively.
And third thing is, I think,following our social media tech
to the rescue, because werecently launched our own
podcast where we speak withsocial impact innovators.
We publish lots of blog postsand insights into how to work
with AI when you are anon-profit, so I think it's a
(35:58):
pretty good source ofinformation on understanding how
to build impactful AI solutions.
Speaker 2 (36:03):
Jacek, a fantastic discussion about tech for good,
doing some real good.
How can we find out more aboutyou and your work?
Speaker 3 (36:10):
Tech2TheRescueorg is our website.
I'm most active on LinkedIn.
Thank you so much for your timetoday.
Thank you, Andrew.
Speaker 1 (36:18):
Thank you for listening to Digitally Curious.
You can find all of ourprevious shows at
digitallycuriousai.
Andrew's new book, digitallyCurious your simple guide to
navigating the future of AI andbeyond, is available to
pre-order at digitallycuriousai.
You can find out more aboutAndrew and how he helps
(36:41):
corporates become more digitallycurious with keynote speeches
and C-suite workshops atdigitallycuriousai you.
Welcome to Digitally Curious, a podcast to help you
navigate the future of AI andbeyond.
Your host is world-renownedfuturist and author of Digitally
Curious, Andrew Grill.
Andrew's guests will help youbecome more curious about the
latest tech and what's justaround the corner.
Speaker 2 (00:22):
Today's guest is Jacek Czajkowski, the co-founder
and managing director of Techto the Rescue, who's at the
forefront of leveragingtechnology for social good.
Jacek has dedicated his careerto using technology to address
some of the world's mostpressing social and
environmental challenges.
At Tech to the Rescue, Jacekleads a global movement that
connects tech companies withnon-profits to solve real-world
(00:45):
problems using advancedtechnologies, including AI.
Today, we'll delve into how AIis being harnessed to drive
social change, the challengesnon-profits face in adopting new
technologies, and the future ofAI in the social sector.
Welcome, Jacek.
It's a pleasure to be here.
For those who haven't heardabout Tech to the Rescue, could
you outline your mission andyour journey to date?
Speaker 3 (01:05):
Tech to the Rescue.
Could you outline your missionand your journey to date?
Tech to the Rescue was foundedbecause there's one big problem
in their world, which isnon-profits.
Globally, they lack technologyresources to invest in scalable
solutions that could make theirwork more accessible, more
adopted and more impactful.
So we work with non, withnonprofits, every single day.
(01:25):
We see that all around the worldthere are thousands of great
social entrepreneurs who havetested proven social impact
interventions.
Basically, they know how tosolve social problems.
They work with beneficiarieshand in hand.
Every single day they crack thecode on how to change people's
lives.
But their impact is limited toworking with the community or
(01:57):
with a small region, becausetechnology gives this
opportunity to copy pastesomething that works everywhere.
Right, and without access totechnology, they are limited in
impact to small communities andbecause of that, the whole
humanity and the civilizationmisses the chance to, you know,
improve social problems here andnow.
The problem is big, yeah,because you know it exists in
every single country and it'sdifficult to solve because
technology is expensive todayand it will be very, very
(02:21):
difficult to bring additionalbillions of dollars into the
non-profit market.
So the solution thatTech2TheRescue founded and
believes makes sense is acultural solution.
So basically, what we want todo is we want to make sure that
every single tech company in theworld will collaborate with
non-profits on a pro bono basis,and we mean that every single
(02:43):
company can afford sending atleast one qualified professional
team for a non-profit for two,three, maybe six months per year
to build a technical solutionthat would help this non-profit
scale up their work.
Speaker 2 (02:57):
So we're going to talk about AI, which is now
everywhere.
So what do you see as the roleof AI within nonprofits and
social impact organizations?
Speaker 3 (03:05):
Many nonprofits that we work with have some
bottlenecks that are solvablewith automation.
So you know, if you run anonprofit, your typical size is
less than 50 people, you don'thave hundreds of staff, you
don't have automated facilities,have hundreds of staff, you
(03:26):
don't have automated facilitiesand sometimes you are limited by
simple things like okay, wedon't have that many advisors or
consultants to help everysingle person with problems to
understand what to do next.
And when we work withnonprofits, we see that they
have limitations in theirinternal work, understood by how
well do they organize processes?
(03:47):
But also with external work,basically meaning that they are
limited with serving many peoplebecause they lack staff.
And AI, especially generativeAI, brings these new
capabilities to the market andwe believe that almost every
single nonprofit-profit that isbigger than you know, working
with the community, couldleverage those solutions to a
(04:08):
rise.
More funds, because we couldautomate relationships with
donors, and this is veryimportant because funds are
unlocking many different things.
It's the same thing as sales inbusinesses.
Second thing non-profits couldmake sure that their work is
more efficient, right, and it'svery important to offer the
(04:29):
world with a high-performingsolution which is cheap to
deliver impact, right.
So implementing AI-basedsolutions could reduce the cost
of serving one person or oneanimal or one social problem and
, of course, because we couldautomate the interface of
interacting with the directbeneficiary.
(04:50):
In the end we could make surethat one organization can serve
much more people.
So we believe a giganticpotential in almost every field
of work within nonprofit.
But there's one challenge,obviously, because AI is in a
very early stage right now, thatwe need to be sure that we
won't provide people with thewrong information or that
(05:11):
algorithm won't providemisguiding hint to a beneficiary
, because in some cases thatcould be very detrimental.
Speaker 2 (05:19):
So it sounds like you've got a really important
mission that you've got here.
So tell me about how you set itup, what was the journey to say
we need this, no one else isdoing it in the way that we can
do it, and how you actually thengot this off the ground.
Speaker 3 (05:32):
Tech to the Rescue.
It's a movement story.
So prior to launching Tech tothe Rescue, I used to own my own
digital company.
It was basically a digitalagency that was building
software solutions for othernon-profits with a very special
twist because my company wasfocused on building gamified
solutions.
So basically, we weretransforming boring life into
(05:54):
interesting games that helppeople change habits and improve
behaviors.
The work was very impactful.
Everyone who worked with usobserved improvements in how
people behave in their own lives.
But every single time we wereapproached by a client meaning a
non-profit we saw a potential.
But we also saw that they couldafford hiring us for one month
(06:16):
or two months, because biddingtechnology is very, very
expensive, and how do you scaleup such a company if your
clients don't have budgets toactually afford any margin?
Right, you cannot hire morepeople, you cannot invest.
So you know, being in this typeof company was very fulfilling
in a social impact sense.
It was very difficult in abusiness sense.
(06:38):
So I was observing thesituation.
I saw clearly that so manygreat nonprofits lack resources
and, on the other hand, I was aparticipant of the digital
solutions market.
I was hiring the same people asthe commercial companies are
hiring and so on.
So I had this insight that whenyou run a digital services
(06:58):
company, sometimes you have thesituation where at least one of
your teams is in betweenprojects, right?
So, for example, they finishsome multi-year project with one
big client and before the nextclient buys their services, they
wait for two, three, sometimesfour months until something new
comes in.
And these people are waiting.
(07:19):
You won't fire them becauseit's too expensive to hire new
staff.
You want to have them availablebecause sometimes clients need
them right away and you knowthey're essentially you know
getting bored, frustratedbecause they don't develop
themselves as engineers.
So I had this insight that mostprobably it is possible to
encourage thousands of companiesworldwide to dedicate these
(07:42):
people waiting for the nextproject for a non-profit project
, because that could bewin-win-win for everyone and for
a company.
It's a way to utilize andmobilize staff for those people
who are waiting for the nextproject, an opportunity to learn
new skills, to do somethingvery fulfilling and very
purposeful and, for a non-profit, the only way sometimes to
actually invest in technologyand build something which is not
(08:04):
doable using only the grantmoney, which is very little.
So I had this idea in my drawerbecause I thought well, you
know, building a movement ofthousands of companies is not
easy.
Would I have to attendthousands of meetings to
actually build this criticalmass that needs to be there?
But then something happened,something that everyone felt at
(08:26):
one moment, and that thing wasCOVID.
When that big crisis hit, likeCOVID, like war in Ukraine,
everyone in the society ismobilized and people want to be
useful, and people from thetechnology sector also want to
be useful.
It was, I think, april 2020,when one of the tech
entrepreneurs in Poland had thisinsight and he wanted to do
(08:49):
something.
So he published a LinkedIn postthat, basically, he will donate
his team to a non-profit thatknows what to do to stop the
pandemic.
He didn't feel as an epidemicexpert, he didn't know what to
do, but if there is anynonprofit that has a good recipe
for stopping this or forhelping people, then take my
(09:10):
people, use them for nine monthsand do whatever is needed.
So he published this post.
It got viral very quickly, sohe saw that people are picking
up the idea, so he included avery simple Google spreadsheet
link in the comment and he wroteif you like the idea and he
included a very simple Googlespreadsheet link in the comment
and he wrote if you like theidea and if you want to donate
your tech company stuff, justsign up here.
(09:31):
After the weekend there waslike 35 companies signed up.
So he felt like, wow, I didn'texpect it, maybe there's
something we should invest in.
So he asked his team that hewanted to donate to the
non-profit to actually promotethe idea.
So they built a website, theylaunched a product hand campaign
, they did everything thatstartups do and after six weeks
(09:52):
there were like 150 companiesalready.
So I was observing this and Iwas like, okay, this could be
the movement I was hoping for.
So I basically picked up thephone.
I called the person.
I offered him this insight.
I told him, you know, like thisinitiative will die if we don't
transform this into somethingbigger than the pandemic.
Then we started workingtogether.
(10:12):
The first 10 sponsors fromCentral Europe joined us like in
two weeks and then we startedbuilding this.
Right now, the community isbigger than 1500 tech companies
from over 60 countries and wesee every single day that this
idea, it makes sense.
People in technology arecreative, they want to do
something impactful and in theregular business sometimes they
(10:35):
don't have this opportunity.
So if some wonderful nonprofitfrom other parts of the world is
coming, they see those people,their motivation, they see how,
every day, they are changingpeople's lives.
They feel it.
They just want to do something,they just want to join.
This way it's scaling quicklyand this way it brings impact
every single day.
Speaker 2 (10:55):
Yeah, just to sort of reflect on what you said, when
I was at IBM and I was running aconsulting practice, often
people in other teams had peopleon the bench.
People listening to this willbe aware of that.
They haven't got a project atthe moment, they're in between
projects.
They're still keeping the benchwarm.
What a great idea to havepeople on the bench doing
something for good.
(11:15):
Tech for good.
It's an amazing initiative.
Covid has come and gone.
We've now got this new amazingtool called AI.
I mean, it's been around for 74years, but I think now
everyone's talking about it.
So maybe you could talk aboutsome examples of how AI has been
used in projects facilitated byTech to the Rescue.
Speaker 3 (11:33):
Let me start with saying that Tech to the Rescue
supports nonprofits working inany field.
We are not limiting ourselvesto climate or to health.
We try to be helpful foreveryone because we know that
tech companies worldwide havedifferent preferences and we
just want to make sure that anycompany that comes to Tech to
the Rescue, they will find theproject they will love.
But sometimes we launchcampaigns, so bigger efforts to
(11:59):
support organizations in a givenfield, and one example of such
a campaign is the campaign thatwe launched last year with the
support of AWS, where we wantedto move the needle of the air
quality movement worldwide.
So first we decided toprioritize two regions in the
world, which is Southeast Asiaand Sub-Saharan Africa, because
(12:21):
the air quality problem isgetting worse and worse in those
areas and there are not manynon-profits that are working on
this.
And then we scouted excellentnon-profits in almost all of the
countries in those regions andthen we basically asked those
organizations what kind oftechnology do you need to make
your work more impactful?
And I will tell you about threeexamples of solutions that we
(12:46):
built, I think, in quiteinteresting cases.
So first organization is calledSensors Africa.
The organization's mission isto make sure that the data about
air pollution in whole Africais publicly available so that
people could raise theirawareness and they could design
better public policies, knowingwhat actually the problem is,
(13:08):
right.
So they are building the globalnetwork of air quality sensors,
but those sensors are usuallylow-cost sensors and they
sometimes break.
So what organization asked usto do is to create AI-based
solution that will simulate theair quality sensor in a
situation where it has adowntime right.
(13:30):
So when it breaks, it's brokenfor one or two weeks and we miss
the data.
So we built a machine learningalgorithm that is estimating
what would be the air qualitylevel in a specific area if the
sensor was working.
So this is one of the cases.
Second case, a very interestingone, comes from Thailand.
(13:53):
So there's an organizationcalled Clean Air Thailand and
they designed a new air qualitybill that they want to take into
the parliament, but to be ableto do it, they need to collect
100,000 signatures from thecitizens, and it's not easy
because the bill is quitecomplicated and people don't
(14:13):
understand what it means forthem, especially that air
quality problem is not verypopular in Thailand.
So what they did is they builta generative AI-based solution
that is translating the bill toany person.
That is speaking to the bot,right?
So let's assume you are afarmer, andrew, and you want to
understand, okay?
(14:34):
So, if the bill is passed, howthis impacts my life, is my work
more difficult?
Is it easier, what it means,what it means for my children,
for my family, for my farm.
So, basically, they built asimple solution that is
explaining the impact of thebill and helps collect new
signatures, which I believe is avery interesting way to explain
(14:55):
politics and explain the impactof different policies on
everyday lives.
And the third solution, alsofrom the political field, was
built for an organization fromIndonesia.
It's called Bikara Udara andthey wanted to like in a
specific year.
Last year they had parliamentelections, so they wanted to
(15:18):
help citizens elect people whocare about their quality.
So they built a solution thatwas scraping internet looking
for any statements thatparliament candidates are making
about their quality, and theybuilt a tool that lets people
find the candidates that reallycare and choose the best
candidates of the candidatesthat are available in a specific
(15:40):
region you are voting.
So it's interesting, right?
Because we have one problem.
We have different kind oflayers of it, right from data
collection to, let's saydesigning the public policy and
making sure that peopleunderstand, to making sure that
the right people are elected tobe able to actually pass the
(16:01):
bill right, and it goes on andon.
In every other social problemarea.
We could use this methodologyfor health, for climate, for
education.
The possibilities are basicallylimitless.
But the difficult thing is howto make sure that this
technology is predictablyimpactful in a positive way and
(16:26):
how do we avoid potential harmsthat could come, for example,
from hallucinations.
Speaker 2 (16:31):
So you mentioned you work with AWS.
I understand you're also doingwork with Googleorg.
Speaker 3 (16:35):
Tech to the Rescue started as a bottom-up movement
of tech companies in Poland, butwe very quickly grew to the
global movement of anyone fromtwo people WordPress agency
working in India to the globalgiants working in the technology
field.
So Tech to the Rescue right nowis working very closely with
(16:56):
Googleorg and AWS.
They are our main supportersand recently, together, we
launched a whole new big programfor AI.
It's called AI for Changemakers, through which, together with
them, we are supporting 100awesome nonprofits with
implementing AI in a way whichis impactful and both which
(17:17):
reduces risks of doing harm.
Speaker 2 (17:21):
Importantly, you want to get the right people into
the right projects.
So how do you identify andselect the nonprofits and tech
companies that participate inyour projects?
Speaker 3 (17:30):
That's a difficult task and it's called matching,
and sometimes we tell peoplethat don't really understand how
Tech to the Rescue works.
We call ourselves a Tinder fortech companies and non-profits.
So, starting from non-profits,I think it's important to find a
nonprofit that is actuallycapable of using technology.
If you run a tech company andyou wanted to help someone right
(17:53):
away, your immediate instinctwould be to help orphanage which
is somewhere around, right,like in your neighborhood.
But does the orphanage reallyneed technology?
What do they need?
A new website?
A new CRM?
Like?
Most likely, they havedifferent needs, right?
So it's very important to beable to find a non-profit that
is in the stage of development,where the intervention is
scalable and they just need toimplement this solution to be
(18:16):
able to increase the impact.
Right?
So, every single day, our staffis scouting the world looking
for great organizations in allcontinents, organizations that
have proven social impact, thathave scalable interventions,
that could use the technologyand that has some level of
investment in technology to makesure that any technology built
(18:37):
by tech company will be thenmaintained and further developed
.
Right, because technology isnot only about building the
first iteration and vprprototype.
It's about constantly, it'sabout constant innovation.
So we try to selectorganizations that are ready,
that know what they want andthat are capable of working with
(19:00):
global tech companies.
From the tech perspective, it'sa little bit more difficult
because, first of all, we needto understand what the company
specializes in.
You will want to engage in adifferent project if you run a
React native company and adifferent project if you run a
(19:22):
generative AI company.
We need to understand what thecompany actually cares about,
because some people will bemotivated by helping people with
cancer and some other peoplewill be motivated by solving
climate change right.
So we ask company you know whatkind of social problems really
motivate them to work?
And also we need to understandwhat is the context of a tech
(19:45):
company.
You mentioned Bench.
Bench is one of them.
We actually mapped 12 differentuse cases for tech companies to
do pro bono work, and all thoseuse cases come with different
pros and cons.
For example, bench is greatbecause you have a fully
professional team available, butfor the limited time, right.
So you need to adjust the scopeof the project to the time
(20:07):
availability of the team, andthen we take all those factors
into one algorithm that isrecommending projects to tech
companies, which is alsosupported by the work of
beautiful human beings calledmatching managers, who recommend
nonprofits to tech companiesand who later facilitate the
(20:27):
conversation, because, guesswhat?
Tech companies are notperfectly prepared to work with
nonprofits, and vice versa.
Right, we need to translate thelanguage of technology to
nonprofits, language ofnonprofits to technologists, and
we need to make sure that thedeal is well scoped out and
everyone knows what they aregetting into.
Speaker 2 (20:46):
So, just coming back to AI for a moment, some of the
challenges that are there.
You mentioned Tinder as adating app.
I'm sure all the datingcompanies are very keen to
understand that they do somelevel of screening to make sure
that real people being matchedto genuine human beings.
So how do you ensure the AIsolutions that are developed as
part of these programs areethical and don't inadvertently
cause harm?
Speaker 3 (21:07):
It's the most important thing in our work, and
I mentioned the AI forTradersmakers program.
The program is divided intofive thematic cohorts and we are
working with a disastermanagement cohort right now.
So, in essence, we are workingwith humanitarian organizations
In the humanitarian world.
There is this very importantrule, which is an overarching
(21:27):
rule of the humanitarian work.
There is this very importantrule which is an overarching
rule of the humanitarian work,which is do no harm.
And if you visited us in any ofthe meetings that we have
within the program, you wouldquickly understand that there is
no better industry in the worldthat is managing risks than
humanitarian sector, becauseevery single bad decision could
(21:49):
lead to doing harm and they arereally conscious about it.
And so we ensure that thisconversation about the
responsible ai is present atevery stage of the project.
We talk about it when we talkabout what ai is right, just to
make sure that people understandits limitation.
It's not a magic wand that isdoing stuff.
(22:10):
It's technology, algorithmsthat have its own profile and
characteristics we need tounderstand.
Then, when we talk about theuse cases that organizations
have, we talk about you know, dowe need a human person in the
process to make sure that it'smaking good decisions.
(22:31):
Is this specific use caselife-threatening or life-saving?
You know what are the risks, toidentify where the AI could be
applied for with a risk that ismanageable.
And then every project can counton the advice of the world's
greatest companies and theirresponsible AI teams.
(22:53):
So, for example, every projectthat we do is going through
Google's committee of AIprinciples team, which is rating
the project under everyprinciple, and they suggest what
are the specific risks weshould take care of specifically
.
But this is a theory.
And then we have practice.
We need to deploy the model andwe need to test it right, and
(23:15):
then I believe that the work isgetting more and more difficult.
So, in this specific case, it'svery important to make sure
that nonprofits have resourcesand capabilities to really
deploy models in a testingenvironment, and this is
something that we put a lot ofattention to, and our program is
(23:37):
not a program in which wemobilize everyone to make
success.
It's the program that weencourage everyone to experiment
, learn and share the learnings.
So every nonprofit-profit thatwill implement AI solution will
be encouraged to organizetesting environment, to test
with real people on a limitedscale and then publish the
results, improve and then onlylaunch on production to everyone
(24:01):
who wants to use this solution.
Speaker 2 (24:03):
So measuring success and the performance of the
projects is obviously very key.
So how do you do that and howare you able to maintain the
quality and standard across allthe projects?
Speaker 3 (24:13):
The measurement is tricky because the whole
measurement thing in non-profitsis way more difficult than in
business.
In business the performance ofa company usually boils down to
profit or the revenue in somecases.
In non-profits the success willbe defined differently for
(24:36):
every other non-profit workingin different area, in different
intervention and so on.
So we try to find the commondenominator for organizations
and in our case it's one of thethree aspects.
First one is how many people orhow many beneficiaries the
organization can serve, sobasically how widely they adopt
(24:59):
their services and how manypeople or living beings are
impacted by them.
Second thing is the unit costof making one unit of impact, so
basically how expensive it isto change people's lives.
And third, one is time to makean impact, so how quickly
organizations are able todeliver their services.
(25:22):
And we measure those threemetrics when organizations join
Tech2DeRescue and we measurethose three metrics after two
years from the implementation oftechnology to see what's the
impact.
We want to see the positivechange in at least one of those
three factors.
But obviously, if you ask meabout this in a storytelling way
(25:43):
, I would tell you that we areworking with a nonprofit from
Nicaragua which is providingsexual health education to women
and before working with Tech tothe Rescue, they were driving
the bus around the country andproviding direct education to
women in rural areas and theywere able to serve 20,000 people
in three years and after theybuilt an app with Tech to the
(26:06):
Rescue, they served 50,000 womenin one year.
So this is the impact that wewant to see.
Speaker 2 (26:12):
So I could ask this question to corporates or
nonprofits, but what are some ofthe most common misconceptions
about using AI in the nonprofitsector?
Speaker 3 (26:21):
I see less misconceptions about AI in
nonprofits than in business.
I believe that the reason isthat in business, people are
more oriented towards buildingprocesses and towards building
scalable systems.
In nonprofits, people are morefocused on working with real
human beings and helping them,and I think that those negative
(26:43):
stories about using AI in somespecific context, the stories
that went wrong, are moreadopted or more picked up in a
nonprofit space, where peopleare afraid of doing some mistake
.
So we don't see people beingcrazily optimistic about AI.
We see people being responsiblyoptimistic about AI and also
(27:10):
having many concerns about thelong-term viability of those
solutions, how expensive theywill be because they operate on
limited budgets, and the veryspecific challenges that we see
in many cases are making surethat the systems, especially the
generative AI systems, willmaintain quality, especially if
(27:30):
they use models that are builtby black box tech companies
without much description or abenchmark test that they can
trust.
Second thing is we see thatmany non-profits, especially
those bigger ones, they workwith people in different nations
(27:53):
or languages and the bigquestion mark is, for example,
how well different foundationalmodels cope with less popular
languages.
It's not something you caneasily test using some online
tool, so many organizations areconsidering fine-tuning models
(28:14):
to less popular languages.
And I think, last but not least, there's a big question about
the cost.
Some tech companies are verygenerous and they provide
credits for the solutions.
So, for example, aws invested 6million dollars of AI credits
for our AI4Chainmakers program.
But obviously credits areavailable for a specific time
(28:37):
and after 24 months you need tostart paying right.
So we need to be really awareof the cost of the solution,
about the unit cost ofmaintaining the solution for
many people, and so far,comparison of different Gen AI
models, especially when it comesto the costs, are tricky.
(28:58):
It's not easy, it's not veryintuitive.
You need to talk to the expertto really understand.
Actually, you need to test itin real life to be sure what the
real costs are.
So there are many questions,but I wouldn't be afraid of
people not asking difficultquestions when they implement
projects.
More I will be afraid of thetech market being more
(29:22):
transparent and moreuser-friendly when it comes to
helping people understand howexpensive it will be to use
their products.
Speaker 2 (29:30):
So final question before we go to the quickfire
round, let's look to the future.
What are the future plans forTech to the Rescue and how do
you see the organization growingin the next decade, and how can
we bridge the gap between bigtech and nonprofits?
Speaker 3 (29:42):
Yeah.
So first of all, I want to seeevery bench in the world being
utilized for a nonprofit project.
I don't care if it's a bench ofIBM, if it's a bench of IBM if
it's a bunch of small Polish orIndian software development
company.
I want to see that people thatwork in tech companies, when
they see unutilized resources,they have an idea of working
(30:05):
with a nonprofit.
And this will take time.
We believe that this could takeus like five to 10 years.
There are multiple dimensions ofthe work that we need to do.
We need to make sure that doingpro bono will be easy, that it
will be manageable for companiesand that companies will be able
to measure the performance ofthose processes.
(30:25):
But also we need to make surethat it will be present in the
mass media culture.
Why?
Because look at the legalmarket.
In the legal market, doing probono is a standard.
Everyone knows that it'ssomething that companies do, and
they know it because some ofthe very, very popular pieces of
culture promote this.
(30:47):
So there's this famous tv showabout lawyers the Suits, and in
the Suits one of the, mike, isdoing pro bono case in every
season of the series, and thenhundreds of millions of people
are watching the series and theysee that this is what legal
companies do.
So one of our dreams andinformal goals that we have is
(31:07):
to revive the Silicon Valleyseries one day and to make the
whole season of the series aboutthe guys building technology
for a non-profit.
So this is the ambition.
There are metrics.
So we hope to facilitateservices tech services of a
value of $1 billion until 2030.
(31:28):
And we hope to collaborate withboth big tech companies and
large tech companies like IBM,aws and Googleorg.
But we also hope to work withthe whole market of tech
companies that employ more than10 people and are able to donate
some weeks or some months tobuild something interesting and
(31:50):
impactful for a nonprofit andimpactful for a nonprofit,
having run six startups.
Speaker 2 (31:55):
Watching the Silicon Valley show about startups and
Pied Piper going from workingaround the kitchen table to a
huge conglomerate wasfascinating.
Not sure you'd want the SiliconValley team helping nonprofits.
I think there might be somedysfunctional behavior there,
but that's for another time.
We're almost out of time.
We're now to my favorite partof the show, the quickfire round
, where we can learn more aboutour guests, so I'm going to fire
some questions at you.
Window or aisle, aisle.
(32:16):
I'm tall.
I need space for my legs.
Iphone or Android?
Speaker 3 (32:20):
Android, your biggest hope for this year and next
We'll be able to ship at leastfive amazing AI projects for
nonprofits in the community.
I wish that AI could do all ofmy B boring communication.
The app you use most on yourphone, it's Whoop.
It's the fitness app that helpsme sleep better, train better
and become healthy.
Speaker 2 (32:40):
The best advice you've ever received.
Speaker 3 (32:41):
It's actually a piece of entrepreneurial advice.
So I was a participant of thementoring program with one very
esteemed manager and I asked himyou know what, in your opinion,
is a key characteristic of avery good leader?
And he told me there are twothings.
First of all, 95% of your workis an excellent communication.
(33:02):
Second thing 5% is an abilityto spot the opportunities and to
use them.
Speaker 2 (33:08):
What are you reading at the moment?
Speaker 3 (33:10):
I'm actually reading a book on startup boards because
at nonprofits one of the mostimportant aspects of running the
organization is having anexcellent board that helps
organization grow.
Speaker 2 (33:20):
One thing I've been pushing for a long time about
boards is having someone that'sdigitally curious on the board,
or more than one.
They actually understand thetechnology, so that is a key
thing for both corporate andnot-for-profit boards.
Who should I invite next ontothe podcast?
Speaker 3 (33:34):
key thing for both corporate and not-for-profit
boards.
Who should I invite next ontothe podcast?
I would recommend inviting thefounder of an organization
called Humane Technology.
It's Tristan Harris.
He used to work at Google.
He's the person who is behindthe famous Netflix show, which
is the Social Dilemma, and Ibelieve that he could bring lots
of interesting insights intothis whole AI craze.
(33:55):
He's the person to bring moreawareness on how we should
deploy this technologyresponsibly.
Speaker 2 (34:03):
Great suggestion.
The social dilemma is a fewyears old now, but I remember
encouraging my friends to watchthat to see how social media has
really developed.
I think we could probablyupdate that for AI, so I will
reach out to him.
Final quick fire question howdo you want to be remembered?
Speaker 3 (34:16):
I want to be remembered as a person who spent
his life on unlocking potentialof a wonderful human being that
cares about other people.
Speaker 2 (34:25):
As the actionable futurist, I always ask my guests
for three actionable thingsthey can do.
So what three things can ouraudience do this week to better
understand how AI can be usedfor good?
Speaker 3 (34:36):
First of all, check Tech2TheRescue out.
Tech2therescueorg is ourwebsite and you can join the
community and volunteer yourcompany to work with non-profits
.
I believe that any company inthe world has those
opportunities, like Bench, toengage in non-profit projects,
and sometimes it's as easy asone person in the company
signing the company up, and thenour team will help you discover
(34:58):
how to crack the code of doingpro bono at your company.
Second thing, following animpactful person who is the
Amazon CTO, werner.
Speaker 2 (35:08):
Vogels, I know Werner .
I've been on stage with him inAbu Dhabi.
I know him and we're in contact.
Yeah, he's a great guy.
Speaker 3 (35:14):
He's a great guy and he spends lots of his time on
finding people who care and whobuild very impactful solutions.
Also, werner recently announcedthat he will join our AI for
Changemakers program and he willoffer his personal time to
spend with nonprofits advisingthem on how to become better
CTOs and how to push theinnovation agenda harder and
(35:37):
more effectively.
And third thing is, I think,following our social media tech
to the rescue, because werecently launched our own
podcast where we speak withsocial impact innovators.
We publish lots of blog postsand insights into how to work
with AI when you are anon-profit, so I think it's a
(35:58):
pretty good source ofinformation on understanding how
to build impactful AI solutions.
Speaker 2 (36:03):
Jacek, a fantastic discussion about tech for good,
doing some real good.
How can we find out more aboutyou and your work?
Speaker 3 (36:10):
Tech2TheRescueorg is our website.
I'm most active on LinkedIn.
Thank you so much for your timetoday.
Thank you, Andrew.
Speaker 1 (36:18):
Thank you for listening to Digitally Curious.
You can find all of ourprevious shows at
digitallycuriousai.
Andrew's new book, digitallyCurious your simple guide to
navigating the future of AI andbeyond, is available to
pre-order at digitallycuriousai.
You can find out more aboutAndrew and how he helps
(36:41):
corporates become more digitallycurious with keynote speeches
and C-suite workshops atdigitallycuriousai you.
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