November 3, 2025 • 14 mins
In this episode, Kevin sits down with Gabriel Demombynes, Manager of the World Bank’s Human Capital Project, to explore how AI could either level the global playing field or make inequality permanent. Which jobs are most exposed? Why are women and urban workers more likely to feel the first wave of automation? And what happens when AI meets energy poverty? This is a look at the most important — and overlooked — question of the decade: who wins, and who’s left behind?
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:09):
Everybody always talks to me about artificial intelligence and whether
it's going to widen the world's wealth gap, and that
kind of got my attention to the folks over at
the IMF, International Monetary Fund and the World Bank. So
they just had their big meetings in Washington, DC, and
a lot of people think of it as a global
(00:30):
development conference and also an economic tsunami of everyone descending
upon Washington, DC to talk about ways to grow the
global economy and GDP and ratios and all of this stuff,
But really it's quickly become a technology conference, and artificial
(00:52):
intelligence one of my big takeaways from it. I think
could actually close the world's wealth gap if emphasis because
I'm not naive, but I am an optimist if it
is applied correctly. So I'm thrilled to sit down with
Gabriel Demobeans. He is the manager of the World Bank's
(01:12):
Human Capital Project. He's a big economist here at the
World Bank where I'm broadcasting from, and I want to
talk to him about how artificial intelligence could either level
the global playing field and hopefully shrink the wealth gap. Hello, Future,
it's me Kevin. This is a dispatch from the Digital Frontier.
The year is twenty twenty five. The planet is still
(01:33):
Planet Earth. My name is Kevin. Surreally, do me a favor.
Subscribe to our podcast wherever you get them iHeartRadio app, Spotify, iTunes, Pandora.
Just give us a subscribe. Thank you so much for
doing that. And Gabriel, thank you so much for being here.
You wrote this article that I love and I found
it on the World Bank and it says will artificial
(01:54):
intelligence change the course for human development?
Speaker 2 (02:00):
You write could yield large gains for learning.
Speaker 1 (02:02):
My friends who are teachers and even my own French
tutor use AI to write lesson plans and quizz This
application is available now and does not require specialized knowledge
or tools. Then you also write about AI and jobs quote.
Millions of words have been written speculating on the impact
of AI on jobs. Like all technology, AI will boost
productivity for some tasks, generate new jobs which I like.
(02:26):
That's me, by the way, not him, and replace some
jobs altogether.
Speaker 2 (02:30):
That's the part. Maybe we're a little.
Speaker 1 (02:31):
Nervous about the unprecedented nature of AI makes it impossible
to forecast with confidence what lies ahead. Okay, so forecast
with confidence that lies ahead. But I like this idea
of creating which is really the thread of all your research,
of creating new opportunities, and AI can help us do that.
Speaker 2 (02:47):
So will it shrink the wealth gap?
Speaker 3 (02:50):
Yeah?
Speaker 4 (02:50):
So the first thing to say, I think, as I
said in that article, is that there's just tremendous uncertainty
about what AI is going to mean for jobs. And
we can catch the broad outlines, but the specifics that's
all TBD. We're just gonna have to see what the
future holds. We do know that, as has always been
(03:12):
the case with technology, some jobs are going to end
up being automated and we're going to see reduced employment
in particular areas. But we also know that for many,
many jobs that AI is going to augment what people
can do. It's going to make them more capable. And
there's going to be also new jobs created, many of
(03:34):
which we can't even imagine what those jobs will be.
So what is the ultimate balance going to be? You know,
we don't quite know. I did some other work looking
at this measure of what we call AI job exposure
across the world, and you.
Speaker 1 (03:48):
Note that exposure doesn't mean that your job is going
to be replaced, right.
Speaker 2 (03:52):
AI's common whether we like it or not.
Speaker 1 (03:54):
I mean it's like folks should think of it like
the Internet, which is AI is coming and you just
got to adapt to it. But just as it's coming
doesn't mean your job's on the line.
Speaker 4 (04:01):
Right, So exposure it means if you look at a
particular job, a job is composed of tasks. Right, You're
doing a bunch of different things. Uh As as a journalist,
you're writing, you're interviewing, you're tracking down information.
Speaker 3 (04:15):
So what of those tasks going.
Speaker 2 (04:17):
To build a media company?
Speaker 3 (04:19):
Trying to build a media company making friends?
Speaker 2 (04:22):
I don't want to make it all about me.
Speaker 3 (04:25):
You're doing a bunch of different things.
Speaker 4 (04:26):
And then each of those things, to some degree could
potentially be done with or by AI.
Speaker 2 (04:33):
I love it personally, I love it with or by right.
Speaker 4 (04:36):
And so then so a job which has a whole
bunch of tasks which could be done with or by AI,
we say.
Speaker 3 (04:42):
That's a highly exposed job.
Speaker 4 (04:44):
Right overall, if you look at what people are doing
in lower in middle income countries, on average, people have
much lower exposure to AI. That's because you know, if
you are a farmer, you maybe have some exposure to AI.
There's something you do that could be done potentially with AI,
but a lot of that physical labor that's not going
(05:07):
to be done by or within.
Speaker 2 (05:08):
Say I love this, and.
Speaker 1 (05:10):
I'm going to interrupt you, because I think it's a
really big moment, especially for our American listeners, because so
much of the press in America is doom and gloom,
fear about artificial intelligence having arrived. There are parts of
this world where they would do anything for that opportunity.
(05:32):
Exposure to the future, exposure to technology for the future,
can mean opportunity, and I think so often we forget.
And by the way, it's not the audience's fault, it's
the mainstream media's fault. Now, I know you don't want
to say that's All'll say it for you. But when
you're miseducating the public folks about what exposure to the
future means, it's not all doom and gloom. I would
(05:53):
much rather live in a country and a society that
embraces innovation and is at the forefront of innovation so
that we can create jobs of the future than to
be left behind. But it sounds like Gabriel to take
it back to what you're saying, is that in order
I believe that a rising tide lifts all ships in
order to make sure that other parts of the world
(06:14):
don't get left behind, which by the way, we should
all care about. I believe we need to make sure
that they have access and exposure to the future. And
in this specific technology sector artificial intelligence.
Speaker 4 (06:25):
Correct, Yeah, in our work, and we really emphasize the basics.
So if you think about what does somebody need to
be able to access AI tools, even to use just
any of the major AI platforms, you need electricity, you
need internet access, you need a device.
Speaker 3 (06:45):
You need not just.
Speaker 4 (06:46):
Internet access, but affordable internet access. And if you look
around the world, there's lots of places where people, yeah,
they can get a signal, but it's extremely expensive relative
to people's income to actually use the internet. So that's
why in the world has been very focused on just
getting those basics right. In particular, we're making a huge
push on electricity access as well as expanding affordable internet access.
(07:11):
And that's even before you get all to all of
the extremely exciting, really sexy AI applications.
Speaker 3 (07:18):
You've got to have those basics in place.
Speaker 1 (07:20):
Well.
Speaker 2 (07:21):
You also have to have education.
Speaker 1 (07:22):
You write in this report quote when analyzing the relationship.
Speaker 2 (07:26):
Between income groups.
Speaker 1 (07:28):
Exposure to AI in the workplace and education, two patterns
emerge in all countries. The more educated workers are, the
more likely they are to be exposed to artificial intelligence
in their daily work, and exposure increases slightly.
Speaker 2 (07:44):
With income level.
Speaker 1 (07:45):
In low income countries, around eighty percent of workers with
a post secondary degree are exposed to AI and their jobs,
rising to eighty two percent of lower middle income countries,
ninety two percent in upper middle income countries, and eighty
seven percent in higher income This is consistent with all
the other research. So if you don't have education, you're
more likely to get left behind.
Speaker 3 (08:06):
That's right, that's right.
Speaker 1 (08:07):
Yeah, and there's a lesson that we can learn there.
How is that applicable to some of the lessons that
folks can take away with education as a force for
being into the future, we're not necessarily reinventing the wheel.
Speaker 3 (08:19):
Yeah, well, there is.
Speaker 4 (08:20):
There's a minimal level of basic literacy, basic digital skills
that you need to be able to access these digital tools,
and that's something that the education system needs to provide
one way or the other.
Speaker 1 (08:34):
What surprised you most about the research that you conducted
in terms of the impact of exposure to artificial intelligence
on being able to create new opportunities or being a
force that leaves people behind.
Speaker 3 (08:47):
Yeah, well, I think you know the juries.
Speaker 4 (08:49):
There's this big question as to whether AI ultimately is
going to help those who are most skilled at the
top and then they'll end up pulling away from the others,
or if it's going to help those at the bottom
to move up towards the top. There was a bunch
of early research and I think it's probably still the
overall best take is that generally AI is likely to
(09:11):
help out people who are less skilled and help them
bring them up to the middle. So if you think
about using AI to improve writing, right, so, somebody who's
an extremely good writer, they're not going to get much
out of using AI to write something for them or
even to do their editing. But somebody who's a weaker writer,
maybe somebody who's writing in a language which is not
(09:32):
their first language, they can get a huge boost in
the quality of their writing from using an AI tool.
Speaker 3 (09:39):
So we hope that that will be the general finding.
Speaker 4 (09:44):
As I said, the jury is still out ultimately on
who is most going to benefit from using AI tools.
Speaker 1 (09:51):
So you've had a very global career, You've traveled all
over the world, You've lived in multiple parts of the
world throughout your career.
Speaker 2 (09:59):
I always, I always find it interesting.
Speaker 1 (10:01):
I don't think history necessarily repeats itself, but it sure
does rhyme when you look at other major technologies that
have launched over the course of your career, Internet and whatnot.
Are there similarities? Are their key differences and are similar
Are therese lessons being applied to the deployment of artificial intelligence?
Speaker 4 (10:19):
Yeah, I mean, you know, the general experience with technologies,
it takes a long time for technology to get adopted
and really used. Mean, that's been the lesson with electricity,
that's been the lesson with digital technology. Generally, it's a
really long road. If you think about technology adoption by
(10:40):
governments typically very slow. Think about how private companies, how
firms in developing countries pickup use of technology also.
Speaker 3 (10:48):
That can take decades. What you do see is that
individual use can happen much more rapidly.
Speaker 4 (10:55):
And you know, the big exception to the slow technology
adoption story has been phones. So cell phones were became
very widely used or a very short period of time,
and that was because there was so much individual demand
people wanted to be able to communicate with their family
and friends, and we're willing to pay for it, and
(11:16):
so I think, you know, with AI, my expectation, again,
it's impossible to predict the future, but my expectation is
when you look at developing countries, adoption by governments, adoption
by firms, it's going to take a while, but we're
already seeing very rapid adoption by individuals. And as I mentioned,
for example, teachers now in many middle income countries are
(11:37):
already using AI at extremely high rates. We don't really
have great data, but I think in many developing countries,
lots and lots of individuals are using AI themselves to
access health information, to look for jobs, for a whole
variety of different things. And that's all going to happen,
I you know, sort of with or without us. And
(11:59):
what we're what we're trying to do in our work
at the World Bank is trying to figure out the
best ways to use AI for health, for education, for
job search, to be able to advise and help people
use the technology effectively.
Speaker 2 (12:11):
How is artificial intelligence?
Speaker 1 (12:12):
The arrival of AI and the deployment of AI changed
the work of the Bank, not from a tactical internal standpoint,
but in terms of a product offering standpoint.
Speaker 3 (12:21):
Yeah, I think that's all in progress.
Speaker 4 (12:24):
So I, as you mentioned, I spent almost my entire
career at the World Bank, working in country offices I worked.
I was in Nairobi, Kenya, then in Hanoi, Vietnam, Manila
and the Philippines, and Bogota in Columbia.
Speaker 3 (12:38):
I came back to d.
Speaker 4 (12:39):
C just two years ago just as all the excitement
about generative AI was really taking off, and at that
point in time, there were a very few discussions happening
about what we should do with AI, and so I,
along with a few others at the Bank, started to
try to push this conversation to say, what can we
(13:00):
be doing to advise clients on how they can use
AI effectively? And I think we're you know, we're still
in a phase where we're trying to figure that out
and that. But that means figuring it out in collaboration
with clients, looking for pilot studies, for experiments we can
do in the course of our work so that a
(13:20):
little bit farther down the line, we'll be able to
identify the most effective solutions.
Speaker 2 (13:25):
I guess no one's thinking about quantum No.
Speaker 4 (13:27):
That's that's maybe the next annual.
Speaker 1 (13:30):
That'll be the next Yeah, that'll be the next year,
all right, Gabriel Demo beans the World Bank economists really
leading the charge at the Bank for artificial intelligence and
thinking about these big questions. You know, folks, when I
was a kid, it was all about it was just
a different conversation.
Speaker 2 (13:46):
This was pre AI.
Speaker 1 (13:47):
I always say gen Z the last generation that remembers
life before artificial intelligence. Think about that. Thanks so much
for saying hello to the future. And remember, folks, subscribe
to us. We'd love to have you subscribe. Check us out.
Speaker 2 (14:01):
Hello Future.
Speaker 1 (14:01):
However you get your podcasts, appreciate it and have it
break tomorrow today
Everybody always talks to me about artificial intelligence and whether
it's going to widen the world's wealth gap, and that
kind of got my attention to the folks over at
the IMF, International Monetary Fund and the World Bank. So
they just had their big meetings in Washington, DC, and
a lot of people think of it as a global
(00:30):
development conference and also an economic tsunami of everyone descending
upon Washington, DC to talk about ways to grow the
global economy and GDP and ratios and all of this stuff,
But really it's quickly become a technology conference, and artificial
(00:52):
intelligence one of my big takeaways from it. I think
could actually close the world's wealth gap if emphasis because
I'm not naive, but I am an optimist if it
is applied correctly. So I'm thrilled to sit down with
Gabriel Demobeans. He is the manager of the World Bank's
(01:12):
Human Capital Project. He's a big economist here at the
World Bank where I'm broadcasting from, and I want to
talk to him about how artificial intelligence could either level
the global playing field and hopefully shrink the wealth gap. Hello, Future,
it's me Kevin. This is a dispatch from the Digital Frontier.
The year is twenty twenty five. The planet is still
(01:33):
Planet Earth. My name is Kevin. Surreally, do me a favor.
Subscribe to our podcast wherever you get them iHeartRadio app, Spotify, iTunes, Pandora.
Just give us a subscribe. Thank you so much for
doing that. And Gabriel, thank you so much for being here.
You wrote this article that I love and I found
it on the World Bank and it says will artificial
(01:54):
intelligence change the course for human development?
Speaker 2 (02:00):
You write could yield large gains for learning.
Speaker 1 (02:02):
My friends who are teachers and even my own French
tutor use AI to write lesson plans and quizz This
application is available now and does not require specialized knowledge
or tools. Then you also write about AI and jobs quote.
Millions of words have been written speculating on the impact
of AI on jobs. Like all technology, AI will boost
productivity for some tasks, generate new jobs which I like.
(02:26):
That's me, by the way, not him, and replace some
jobs altogether.
Speaker 2 (02:30):
That's the part. Maybe we're a little.
Speaker 1 (02:31):
Nervous about the unprecedented nature of AI makes it impossible
to forecast with confidence what lies ahead. Okay, so forecast
with confidence that lies ahead. But I like this idea
of creating which is really the thread of all your research,
of creating new opportunities, and AI can help us do that.
Speaker 2 (02:47):
So will it shrink the wealth gap?
Speaker 3 (02:50):
Yeah?
Speaker 4 (02:50):
So the first thing to say, I think, as I
said in that article, is that there's just tremendous uncertainty
about what AI is going to mean for jobs. And
we can catch the broad outlines, but the specifics that's
all TBD. We're just gonna have to see what the
future holds. We do know that, as has always been
(03:12):
the case with technology, some jobs are going to end
up being automated and we're going to see reduced employment
in particular areas. But we also know that for many,
many jobs that AI is going to augment what people
can do. It's going to make them more capable. And
there's going to be also new jobs created, many of
(03:34):
which we can't even imagine what those jobs will be.
So what is the ultimate balance going to be? You know,
we don't quite know. I did some other work looking
at this measure of what we call AI job exposure
across the world, and you.
Speaker 1 (03:48):
Note that exposure doesn't mean that your job is going
to be replaced, right.
Speaker 2 (03:52):
AI's common whether we like it or not.
Speaker 1 (03:54):
I mean it's like folks should think of it like
the Internet, which is AI is coming and you just
got to adapt to it. But just as it's coming
doesn't mean your job's on the line.
Speaker 4 (04:01):
Right, So exposure it means if you look at a
particular job, a job is composed of tasks. Right, You're
doing a bunch of different things. Uh As as a journalist,
you're writing, you're interviewing, you're tracking down information.
Speaker 3 (04:15):
So what of those tasks going.
Speaker 2 (04:17):
To build a media company?
Speaker 3 (04:19):
Trying to build a media company making friends?
Speaker 2 (04:22):
I don't want to make it all about me.
Speaker 3 (04:25):
You're doing a bunch of different things.
Speaker 4 (04:26):
And then each of those things, to some degree could
potentially be done with or by AI.
Speaker 2 (04:33):
I love it personally, I love it with or by right.
Speaker 4 (04:36):
And so then so a job which has a whole
bunch of tasks which could be done with or by AI,
we say.
Speaker 3 (04:42):
That's a highly exposed job.
Speaker 4 (04:44):
Right overall, if you look at what people are doing
in lower in middle income countries, on average, people have
much lower exposure to AI. That's because you know, if
you are a farmer, you maybe have some exposure to AI.
There's something you do that could be done potentially with AI,
but a lot of that physical labor that's not going
(05:07):
to be done by or within.
Speaker 2 (05:08):
Say I love this, and.
Speaker 1 (05:10):
I'm going to interrupt you, because I think it's a
really big moment, especially for our American listeners, because so
much of the press in America is doom and gloom,
fear about artificial intelligence having arrived. There are parts of
this world where they would do anything for that opportunity.
(05:32):
Exposure to the future, exposure to technology for the future,
can mean opportunity, and I think so often we forget.
And by the way, it's not the audience's fault, it's
the mainstream media's fault. Now, I know you don't want
to say that's All'll say it for you. But when
you're miseducating the public folks about what exposure to the
future means, it's not all doom and gloom. I would
(05:53):
much rather live in a country and a society that
embraces innovation and is at the forefront of innovation so
that we can create jobs of the future than to
be left behind. But it sounds like Gabriel to take
it back to what you're saying, is that in order
I believe that a rising tide lifts all ships in
order to make sure that other parts of the world
(06:14):
don't get left behind, which by the way, we should
all care about. I believe we need to make sure
that they have access and exposure to the future. And
in this specific technology sector artificial intelligence.
Speaker 4 (06:25):
Correct, Yeah, in our work, and we really emphasize the basics.
So if you think about what does somebody need to
be able to access AI tools, even to use just
any of the major AI platforms, you need electricity, you
need internet access, you need a device.
Speaker 3 (06:45):
You need not just.
Speaker 4 (06:46):
Internet access, but affordable internet access. And if you look
around the world, there's lots of places where people, yeah,
they can get a signal, but it's extremely expensive relative
to people's income to actually use the internet. So that's
why in the world has been very focused on just
getting those basics right. In particular, we're making a huge
push on electricity access as well as expanding affordable internet access.
(07:11):
And that's even before you get all to all of
the extremely exciting, really sexy AI applications.
Speaker 3 (07:18):
You've got to have those basics in place.
Speaker 1 (07:20):
Well.
Speaker 2 (07:21):
You also have to have education.
Speaker 1 (07:22):
You write in this report quote when analyzing the relationship.
Speaker 2 (07:26):
Between income groups.
Speaker 1 (07:28):
Exposure to AI in the workplace and education, two patterns
emerge in all countries. The more educated workers are, the
more likely they are to be exposed to artificial intelligence
in their daily work, and exposure increases slightly.
Speaker 2 (07:44):
With income level.
Speaker 1 (07:45):
In low income countries, around eighty percent of workers with
a post secondary degree are exposed to AI and their jobs,
rising to eighty two percent of lower middle income countries,
ninety two percent in upper middle income countries, and eighty
seven percent in higher income This is consistent with all
the other research. So if you don't have education, you're
more likely to get left behind.
Speaker 3 (08:06):
That's right, that's right.
Speaker 1 (08:07):
Yeah, and there's a lesson that we can learn there.
How is that applicable to some of the lessons that
folks can take away with education as a force for
being into the future, we're not necessarily reinventing the wheel.
Speaker 3 (08:19):
Yeah, well, there is.
Speaker 4 (08:20):
There's a minimal level of basic literacy, basic digital skills
that you need to be able to access these digital tools,
and that's something that the education system needs to provide
one way or the other.
Speaker 1 (08:34):
What surprised you most about the research that you conducted
in terms of the impact of exposure to artificial intelligence
on being able to create new opportunities or being a
force that leaves people behind.
Speaker 3 (08:47):
Yeah, well, I think you know the juries.
Speaker 4 (08:49):
There's this big question as to whether AI ultimately is
going to help those who are most skilled at the
top and then they'll end up pulling away from the others,
or if it's going to help those at the bottom
to move up towards the top. There was a bunch
of early research and I think it's probably still the
overall best take is that generally AI is likely to
(09:11):
help out people who are less skilled and help them
bring them up to the middle. So if you think
about using AI to improve writing, right, so, somebody who's
an extremely good writer, they're not going to get much
out of using AI to write something for them or
even to do their editing. But somebody who's a weaker writer,
maybe somebody who's writing in a language which is not
(09:32):
their first language, they can get a huge boost in
the quality of their writing from using an AI tool.
Speaker 3 (09:39):
So we hope that that will be the general finding.
Speaker 4 (09:44):
As I said, the jury is still out ultimately on
who is most going to benefit from using AI tools.
Speaker 1 (09:51):
So you've had a very global career, You've traveled all
over the world, You've lived in multiple parts of the
world throughout your career.
Speaker 2 (09:59):
I always, I always find it interesting.
Speaker 1 (10:01):
I don't think history necessarily repeats itself, but it sure
does rhyme when you look at other major technologies that
have launched over the course of your career, Internet and whatnot.
Are there similarities? Are their key differences and are similar
Are therese lessons being applied to the deployment of artificial intelligence?
Speaker 4 (10:19):
Yeah, I mean, you know, the general experience with technologies,
it takes a long time for technology to get adopted
and really used. Mean, that's been the lesson with electricity,
that's been the lesson with digital technology. Generally, it's a
really long road. If you think about technology adoption by
(10:40):
governments typically very slow. Think about how private companies, how
firms in developing countries pickup use of technology also.
Speaker 3 (10:48):
That can take decades. What you do see is that
individual use can happen much more rapidly.
Speaker 4 (10:55):
And you know, the big exception to the slow technology
adoption story has been phones. So cell phones were became
very widely used or a very short period of time,
and that was because there was so much individual demand
people wanted to be able to communicate with their family
and friends, and we're willing to pay for it, and
(11:16):
so I think, you know, with AI, my expectation, again,
it's impossible to predict the future, but my expectation is
when you look at developing countries, adoption by governments, adoption
by firms, it's going to take a while, but we're
already seeing very rapid adoption by individuals. And as I mentioned,
for example, teachers now in many middle income countries are
(11:37):
already using AI at extremely high rates. We don't really
have great data, but I think in many developing countries,
lots and lots of individuals are using AI themselves to
access health information, to look for jobs, for a whole
variety of different things. And that's all going to happen,
I you know, sort of with or without us. And
(11:59):
what we're what we're trying to do in our work
at the World Bank is trying to figure out the
best ways to use AI for health, for education, for
job search, to be able to advise and help people
use the technology effectively.
Speaker 2 (12:11):
How is artificial intelligence?
Speaker 1 (12:12):
The arrival of AI and the deployment of AI changed
the work of the Bank, not from a tactical internal standpoint,
but in terms of a product offering standpoint.
Speaker 3 (12:21):
Yeah, I think that's all in progress.
Speaker 4 (12:24):
So I, as you mentioned, I spent almost my entire
career at the World Bank, working in country offices I worked.
I was in Nairobi, Kenya, then in Hanoi, Vietnam, Manila
and the Philippines, and Bogota in Columbia.
Speaker 3 (12:38):
I came back to d.
Speaker 4 (12:39):
C just two years ago just as all the excitement
about generative AI was really taking off, and at that
point in time, there were a very few discussions happening
about what we should do with AI, and so I,
along with a few others at the Bank, started to
try to push this conversation to say, what can we
(13:00):
be doing to advise clients on how they can use
AI effectively? And I think we're you know, we're still
in a phase where we're trying to figure that out
and that. But that means figuring it out in collaboration
with clients, looking for pilot studies, for experiments we can
do in the course of our work so that a
(13:20):
little bit farther down the line, we'll be able to
identify the most effective solutions.
Speaker 2 (13:25):
I guess no one's thinking about quantum No.
Speaker 4 (13:27):
That's that's maybe the next annual.
Speaker 1 (13:30):
That'll be the next Yeah, that'll be the next year,
all right, Gabriel Demo beans the World Bank economists really
leading the charge at the Bank for artificial intelligence and
thinking about these big questions. You know, folks, when I
was a kid, it was all about it was just
a different conversation.
Speaker 2 (13:46):
This was pre AI.
Speaker 1 (13:47):
I always say gen Z the last generation that remembers
life before artificial intelligence. Think about that. Thanks so much
for saying hello to the future. And remember, folks, subscribe
to us. We'd love to have you subscribe. Check us out.
Speaker 2 (14:01):
Hello Future.
Speaker 1 (14:01):
However you get your podcasts, appreciate it and have it
break tomorrow today
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