June 15, 2025 • 29 mins
"At IBM, we really work on two emerging technologies: hybrid cloud and AI for enterprise. These two are deeply connected. Hybrid cloud for us means that regardless of where the data sits whether the compute is on-premise, off-premise, or across multiple clouds. We believe the client should have the control and flexibility to choose where to run and place their data. If you look at the facts, a very high percentage of client data is still on-premise. It hasn't moved to the cloud for obvious reasons. So, how can you scale AI if you don’t have proper access to that data? AI is all about the data. That’s why we believe in a strategy that redefines and rethinks everything. We call it the Great Technology Reset." - Hans Dekkers
Fresh out of the studio, Hans Dekkers, General Manager of IBM Asia Pacific, joins us to explore how enterprise AI is reshaping business across the region. He shares his journey with IBM after business school, reflecting on the evolution of personal computers to AI today. Hans explains IBM's unique approach combining hybrid cloud infrastructure with AI for Enterprise, emphasizing how their granite models and data fabric enable businesses and governments to maintain control over their data while scaling AI capabilities. He highlights customer stories from Indonesian telecoms company to internal IBM transformations, showcasing how companies are re-engineering everything from HR to supply chains using domain-specific AI models. Addressing the challenges of AI implementation, he emphasizes the importance of foundational infrastructure and governance, while advocating for smaller, cost-effective models over GPU-heavy approaches. Closing the conversation, Hans shares his vision for IBM's growing presence in Asia as the key to enterprise AI success.
Episode Highlights:
[00:00] Quote of the Day by Hans Dekkers
[01:00] Introduction: Hans Dekkers from IBM
[05:00] Key career lesson from Hans Dekker
[06:51] IBM focuses on two emerging technologies: hybrid cloud and AI for Enterprise, deeply connected
[09:27] "Your data needs to remain your data" - IBM's fundamental AI principle for enterprise clients
[10:00] IBM's approach: Small, nimble, cost-effective AI models that can be owned and governed by clients
[13:59] "The cost of AI is still too high. It's about a hundred times too high" - IBM CEO's perspective on AI costs
[14:44] Small domain-specific models example: Banking AI trained for financial analysis, not Russian poetry
[18:00] IBM's internal transformation: HR, supply chain, and consulting completely re-engineered with AI
[21:18] Major partnership announcement: Indonesian telecom embracing IBM's watsonx platform
[22:23] AI agents demo: Multiple agents (HR, finance, legal) debating and constructing narratives
[25:00] "Everyone talks about AI equals GPU" - Hans wishes clients understood that inferencing is more important
[27:00] IBM's Asia Pacific vision: Reestablishing growing presence and differentiated technology approach
[28:00] Closing
Profile: Hans Dekkers, General Manager IBM Asia Pacific and China: https://www.linkedin.com/in/hans-a-t-dekkers/
Podcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format. Here are the links to watch or listen to our podcast.
Analyse Asia Main Site: https://analyse.asia
Analyse Asia Spotify: https://open.spotify.com/show/1kkRwzRZa4JCICr2vm0vGl
Analyse Asia Apple Podcasts: https://podcasts.apple.com/us/podcast/analyse-asia-with-bernard-leong/id914868245
Analyse Asia YouTube: https://www.youtube.com/@AnalyseAsia
Analyse Asia LinkedIn: https://www.linkedin.com/company/analyse-asia/
Analyse Asia X (formerly known as Twitter): https://twitter.com/analyseasia
Analyse Asia Threads: https://www.threads.net/@analyseasia
Sign Up for Our This Week in Asia Newsletter: https://www.analyse.asia/#/portal/signup
Subscribe Newsletter on LinkedIn https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7149559878934540288
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
In IBM, we really work on 2 emerging technologies, 1 is what
we call hybrid cloud and the other one is AI for enterprise.
That's right. And these two are deeply
connected because hybrid cloud for us is that regardless of
where the data sits, regardless of where the compute is on
premise, off premise, multiple clouds, we believe that the
client should have the control and flexibility on where to run
(00:23):
and place their data. Now if you just look at some
facts that a very high percentage of client data is
still on premise, That's right, It hasn't moved the cloud for
obvious reasons. Then how can you scale AI if you
don't have proper access to thatdata?
Because AI, it's all about. Data.
So we believe in a strategy first that redefines, rethinks.
(00:46):
We call it the great technology reset.
Welcome to Analyse Asia, the premier podcast dedicated to
dissecting the pulse of business, technology and media
in Asia. I'm Bernard Leong, and when we
talk about enterprise, AIIBM is definitely at the forefront.
So with me today, Hans Teckers, General Manager, IBM Asia
(01:08):
Pacific. And I want to understand what is
the impact of AI in the Asia Pacific?
What better place? And thanks to IBM, I actually
get to host this show with your video production team.
So welcome, Hans. Thank you.
Thank you. And it's so good to see you.
Yeah, you're becoming a superstar on on the Internet.
So it's the privilege is absolutely ours.
(01:28):
Yeah. Oh.
I'm sure my guess is actually the reason why I'm here.
So first of all, I'm actually, while doing research for this
interview, I was struck by your global journey by actually
steering the world's leading tech firms in the Asia Pacific.
And I wanted to solve some of your career journey because how
do you start your career journey?
And actually, what drew you to IBM immediately from your
Business School experience? I mean, first of all, I was one
(01:50):
of those kids that genuinely is deeply interested in technology.
So as a kid programmed the firstthe first games used a Commodore
64 if you. Maybe it's before your time.
You was there, the Commodore 64,the IBM PC?
IBM PC, the 286, the 386, the first Intel processors that
(02:11):
came. But I'm also very much into the
sampling cars, radios. I mean, we, we used to pull
everything apart and we're trying to put it back together.
Right, yeah, not like these dayswhere everything's integrated
together and don't know what's inside, right?
Try to fix a dice. Right.
Yes, that's. Right, not easy right.
So very much technology educatedin all that technology has from
(02:32):
network infrastructure design, chip design, programming, very
deep technical that my business studies have my own companies
and then really stopped in saying what's next?
Where do you as a young professional start your career?
And there are many places I was privileged to start at big
banks, at big trading firms, many technology companies.
(02:55):
But in the end, when I was young, still young, younger, I
said, who's the biggest technology company out there?
Who is the one that actually impacts the outcome of society?
And when you really take that aperture, there are only very,
very few. And IBM was was the one for me
at least that came on top. So I started as a as an
(03:16):
internship as a trainee at IBM about 17 years ago, and that's
how the journey started. So through IBM you let different
geographies from Europe to Asia,right?
What led you to Singapore and can you talk about your current
role in for IBM Asia Pacific? So first, I, I'm, I'm one of
those global kids. So I, I lived a large part of my
life in Asia, a large part in Europe and some of it in, in, in
(03:40):
Northern America, always in IBM,different, different roles I've
been from systems to software tocoverage to strategy, always
with client. I love client, always at the
heart of where you can make the most impact.
And I always loved and most privileged to leading big teams,
(04:00):
really get the best of that IBM team in service of your client
or your partner to get to a realoutcome.
So many, many different roles, many, many different
geographies. Coming back to Singapore because
it's the second time we as a family live in Singapore.
About 10 years ago, we were hereas well.
I see. And it's just a beautiful
(04:21):
region. It's got the deepest cultures,
the longest history. It's got 60 plus percent of GDP
growth. It is the geography that is
always on the move, right? Chinese, South Korea, you've got
Australia, New Zealand, ASEAN, you got India.
Always something's happening in any dimension, so it's great to
(04:43):
be back, yeah. So looking back, given the 70
years with IBM, what are the pivotal lessons you can share
about your career journey to a younger audience out there?
I think we always say right, finding the solution actually is
not that difficult. It's finding the right problem.
Interesting. And in, in IBM, it is always, if
(05:07):
you find the right problem, right and you supplement it with
data and with science and you find the right problem, then you
can work together with your client in actually addressing
it. And as a technology leader, as a
technology company, we're privileged because in some
topics we can see the future, maybe our client cannot.
And then to help a client from point A to point B is incredibly
(05:30):
rewarding. So, so you find that whole
customer journey, the ROI that you achieved, that is the most
rewarding. Yes, to do something that the
client or the the the the government or whoever you're
working with seemed to be impossible, right?
To do something that is, that istruly meaningful for them and
for societies, it's really what keeps me going.
(05:53):
So we are going to come to the main subject of the day.
I want to talk about IBM in the Asia Pacific and achieving AI
ROI even when I was working for one of the cloud companies.
Actually I think a lot of peopledon't look at the underlying
numbers. Actually one of the highest
revenue drivers for AI is actually in IBM, right?
And so maybe I want to explore, you know, the intersection of AI
and enterprise growth and also with IBM strategy.
(06:15):
So given that the Asia Pacific region is now experiencing so
much rapid digital transformation, what is the
scale of the AI market opportunity here in this region
from your perspective? It's unparalleled and it's, it's
I, I don't think we can even quantify how big that impact and
that opportunity will be. I think we have to recognize and
maybe we can, we can discuss it a bit more.
(06:37):
A couple foundational elements to what is AI, what is it based
on? And in the end, how do you scale
it? Because it's all about scaling
that capability. If you keep it small, what's the
in the end outcome? In IBM, we really work on 2
emerging technologies. 1 is whatwe call hybrid cloud and the
(06:57):
other one is AI for enterprise. That's right.
And these two are deeply connected because hybrid cloud
for us is that regardless of where the data sits, regardless
of where the compute is on premise, off premise, multiple
clouds, we believe that the client should have the control
and flexibility on where to run and place their data.
(07:18):
Now if you just look at some facts that a very high
percentage of client data is still on premise, That's right.
It hasn't moved the cloud for obvious reasons.
Then how can you scale AI if youdon't have proper access to that
data? Because AI, it's all about.
The. Data all about the data.
So we believe in a strategy first that redefines, rethinks
(07:43):
and we call it the great technology reset rethinks that
foundational capability of hybrid can we access and tap
into secure, govern the full foundational landscape of
infrastructure and data number one.
OK. If you are able to start there,
you can start scaling AI use cases.
(08:06):
If you don't have that in place in in with the right proper
governance, you'll struggle as an enterprise.
The first step where we help clients is this.
We create a horizontal platform,data platform across all of
their estate. So it's like changing all the
different data layers for the client, whether it's on Prem or
on the cloud, but then it just make sure that they have the
(08:28):
correct security and governance so that the AI can access and do
work with whatever data they have.
Right. And we do it by not moving the
data. In many cases, we leave the data
where it is, but we create that horizontally connected platform
underneath. You could do the same with your
infrastructure because many of our clients started on a journey
to cloud with no end, right? So they're using the
(08:52):
hyperscalers, which is fantastic.
It's really easy to start with and if they grow bigger, maybe
becomes less cost effective or geopolitical situation changes
and you want to move it from a hyperscaler to on Prem.
If you designed your infrastructure architectures
correctly, you're able to move fairly easily on Prem, off Prem
(09:12):
from X86 to maybe mainframes or from mainframes to X86 and vice
versa. You're very flexible.
So both infrastructure layer anddata layer, we believe that it
needs to be horizontally connected.
Then we get to AI. Now when you look at AI, we
believe in two fundamental things.
First, your data needs to remainyour data.
(09:35):
It's no one else's data. It's your data, true, right,
Which is very important if you're an enterprise client or
the government of Singapore. Your data is your.
Data, yeah. There's nothing IBM or or any
other company for that matter should have to do with your
data. It's your data.
It's your intellectual property.Second one is, is that we
(09:56):
believe that the AI models that we built are small and nimble.
They need to be cost effective and precise and they should and
can be owned and governed by youas an end client, which is very
important. Then there's a third piece that
we believe it needs to be open. So the models we govern need to
be open, enterprise grade ready,domain specific so you can get
(10:20):
the most value from it. So my understanding given that
you talk about the two sides, first is the data
infrastructure, then there is a second part which is the AI.
Of all the AI stuff I know the most ground breaking is
definitely Watson, our first bidding in normal international
chess. This is long before the days of
gold, you know, building Jeopardy.
Can you tell me a little bit more about how does IBM think
(10:43):
about AI as a product or maybe service for the customers and
then we can get in depth a little bit?
Right. OK, I'll get to that.
You started at at chess, right? Which the chess computer in that
time, I think it was the first time that a computer took up to
the human brain. Kasparov.
Kasparov lost that match. Lost that match, right?
(11:05):
The Deep blue match. The deep blue match now chess
now at that time no. But now I would say is a very
simple game because it's a game that is provisioned on finite
set of moves. That's right.
Now we have chess computers thatcan easily calculate you did
this, therefore I can do this. At the time was groundbreaking,
(11:26):
so it it was really the startingpoint of a computer against.
AI, it's the very first, actually, yeah.
It's the very first ingredient where we saw what could be
possible. Then Jeopardy came.
Yes, that was harder. Much harder because it was based
on natural language, but not in a way that our AI models work
today. So it was more based on machine
(11:48):
learning. It was still based on logic.
Today it's a much more math based prediction of what's the
next letter, what's the next word in a, in an AI model, Which
brings you brings us now to the conversation of how IBM looks at
AI. We fundamentally believe in
three big components of AI. One is the data and we call this
(12:13):
what's an X dot data. It's that data platform across
multiple infrastructures. It's your data link.
The starting point of this is what we call Watson X dot DI,
which is a studio where you can bring together multitude of
models. It's not one model, it's a
multitude of layering of models where you combine it into your
(12:35):
model as an enterprise OR as a government.
So you can create in our studio your model.
The third piece as important is a governance piece, Watson X dot
governance. Is the model biased?
Does it use the light language? Is it aligned to how you
communicate to your clients? Right.
(12:56):
So so you can the same way as you almost teach your children,
don't use this language, right? If you want to be on time, be on
time. So it's almost governing the
output of the model and you can tweak it so it's a controlling
function, which is very, very important.
Yeah. And also I remember because
while doing research, I also know that you all have a data
(13:16):
fabric layer that actually allows you to go even multi
cloud or multi architecture, correct.
And you're the only company thatdoes it at the moment.
That's correct, Correct. So I think we're the only
company that goes across a multitude of these clouds.
I was advising a retail client in Dubai on that and I was
working out that you guys are the only ones with the data
fabric, but maybe given the opportunity, right.
(13:38):
So you, you explained the data structure, you talk, talk about
the AI, right. How does IBM unit need position
to capture that opportunity in Asia Pacific?
So it's not only Asia Pacific, but I would say IBM wide.
We believe that and and our CEO talks about this a lot.
The cost of AI is still too high.
(13:59):
It's about 100 X times too high.It has to come down.
As with every technological innovation, if it's cheaper,
more people can use it. Once it becomes even cheaper, it
will become natural to the way we operate.
One of the main reasons to make it cheaper is to use smaller
domain specific models. Small models are very, very
(14:22):
accurate and we can train them really, really well.
We don't need massive amounts ofGPUs to train these smaller
models. You need less because they're
smaller. We believe in these smaller
models. There will be a place for big
models, about 11:50 of them. A dozen of them will exist in
the world, but the rest all willbe small.
(14:42):
Can you give me an example of a smaller model?
So imagine you're a bank and imagine you're on a trading
floor, right? You don't want your model to be
good and trained in Russian poetry.
Yeah, that's probably true. Right.
So you want it to be really, really good at running financial
analysis on maybe the market or in certain stocks based on your
(15:06):
history, based on all the data available in your enterprise.
We help our clients with those domain specific models.
So specifically, what are the top challenges for enterprise
when it's in this region, in theAsia Pacific region?
I'm sure you come across a lot of different kind of clients, I
guess. I guess maybe when you talk to
them, there must be some genericset of challenges they face.
(15:28):
What are the common ones? I think many of them are leaning
in very, very heavily to AI and have leaned in into, for
example, the hyperscalers a lot.I love Asia because they start,
they don't talk and talk and talk.
They start, they get going whilesaying that a lot of where we
are today needs a lot of foundation, foundational re
(15:49):
engineering or optimization. That's right.
Many enterprises haven't gone there yet.
So they want to jump onto AI. But if you're not in a, in a
hybrid by design environment, itbecomes very difficult to make
best use of AI capabilities thatare out there.
So first step I would say is to truly gauge where am I in my
(16:10):
technical health, where where amI and where do I need to improve
quickly to make best use of scaling AI use cases.
And this is where I think Asia Asian clients can be then go
very fast. In in the question then is how
do then the clients think about the ROI for AI then?
I think today that they're stillexperimenting.
(16:32):
They don't have a good idea yet.There are many numbers out there
of productivity improvements. We've got a lot of our own
experience. But in the end, when you project
a use case that we have done or with with our clients onto their
business, then I think if we really go through and help the
clients see it, I think that journey becomes very, very
meaningful. Are there like without
(16:54):
mentioning specific clients yet,because I'm going to come to
that later, but are there very specific kind of metrics like is
it really in terms of productivity savings or is it
something to do with say revenuegrowth that really interests the
clients more? I would say in the end, it's
about redefining how clients operate, how they process.
So it's business growth, right? There's a lot of productivity
(17:17):
gain, which is the low hanging fruit.
There's optimization on how you communicate with client, there's
optimization on HR, there's optimization in your supply
chains. These are all productivity
improvements which are great andwill make you better in serving
your end client. But I believe there's a huge
opportunity to re engineer the business model of the enterprise
themselves. So you definitely have done many
(17:38):
clients. Are there really like a actual
use case that you know, Yes, you're very proud of it and it's
a customer story that you can share.
I always like to start with whatwe have done as a company
because we are one big enterprise.
So if you look at how the HR teams re engineered, how we run
HR within IBM it, it's got, I mean if you look at the ROI and,
(18:02):
and we can, we can provide you those those details.
It's it's unbelievable our supply chain, how we re
engineered it. We create a lot of chips, we
create a lot of machines, a lot of hardware, completely re
engineered the supply chain. If you look at the way we do
forecasting and the way we do accurate reporting, all of this
has to be completely optimized with AI.
(18:23):
If you look at our consulting teams on how they optimize what
they do for end clients completely fueled with AI
capabilities, it is a great shift.
So what's the one thing that youknow about IB, Ms. focus on AI
that very few people do? That's an excellent question.
Yeah. So IBM creates its own models.
(18:43):
We call it IBM Granite, the family of granite models.
Which actually also aligns with what you mentioned earlier about
smaller models with the right expertise, right?
Because that is also the rationale of actually using the
company's IP. Correct 100%.
Yeah. So maybe now given that you
know, in Asia is such a culturally diverse and with so
(19:04):
many different regulatory, how do you see the landscape for
IBM's AI strategy to, to actually touch different
markets? I, I think guess Singapore, we
are lucky we have all the AI policies done on there.
But let's say we go into say a country like Indonesia where
still at a formulation stage, but maybe even Japan and Korea
will have a good AI policy, thenhow does IBM navigate?
(19:26):
I think in any maturity, depending on how you would
create that maturity, I think IBM can be a great player.
Everyone that wants to take thatnext step in in technology
actually can find a very, very good partner in in IBM,
including Singapore. Because the world outside US is
changing so fast. Geopolitical, it's changing.
(19:48):
If you look at the protectionismof data, for example, it's
changing very fast. You got data loss that are being
passed. All of this has a huge impact on
to how to run your technology estate and how do you get the
most benefit from it. You have vendors that increase
price dramatically if you're stuck with them.
That's a problem because it willconsume all of your budget.
(20:10):
If you're not flexible enough inmaking your own decisions,
something COVID really taught us, then you're in a deficit.
So it's the companies that builtthis agility of change and make
sure that their technology landscapes are ready for that
change that will reap the most benefits.
And in Asia, depending on how your great maturity, again, I
(20:32):
think every country, every enterprise can be on that
journey. Some are more mature than
others, but I believe any, anyone can benefit from someone
in Indonesia and Surabaya to somewhere in Pune in India.
It doesn't matter where you are physically.
I think, I think the benefits ofof what we can do together is is
unparalleled. So Hans, I want to specifically
(20:55):
go into customer use cases. Can you share like any
interesting customer stories where the IBMAI has actually
helped you know customers in this region and make an impact?
I mean, many, I would say the most recent one, right, recent,
recent is a collaboration, a true partnership we went into
with telecom in Indonesia, OK. And telecom is, is basically
(21:20):
it's got a huge infrastructure, a huge infrastructure provider
to government, to enterprises, small, medium businesses.
They're going to embrace IB, Ms.What's the next platform for all
use cases, which if you look at Indonesia as one of the bigger
countries makes total sense because your data as your data,
(21:41):
you can't control it. You can govern it.
We can apply it to a multitude of use cases.
So it's a beautiful example of where in this case a partner is
and us are completely collaborating to bring AI to a
country, country of Indonesia inthis.
Case nice. So like now we this year's a lot
of people say this is going to be the rise of AI agents, right?
(22:03):
What, what kind of opportunitiesdo you foresee for these in the
Asia Pacific region? I mean, agentic agents.
I think it's going to yet again transform how we experience AI.
We recently done a done a great POC where we had multitude of
agents, HR, finance, legal, business functions, basically
(22:28):
debate with each other and we were following the debate.
So we threw in the hypotheses. I want to create a presentation
on something and they were debating each other.
You could follow the flow by which you were debating and they
were constructing a narrative which gave us insights that we
would. Interesting.
Not come up with right. So this, this continuous
(22:50):
evolvement of, of how capable agents are, what they could do
is, is going to, is going to change how we how we operate in
business. So.
For I mean, given Asia Pacific such a dynamic growing region,
let's say for companies that arestill very early stage in their
AI journey, what would be your advice for them to think about
implementing AI successfully andsustainability sustainably?
(23:14):
So this is going to sound difficult.
It's not OK. Decide on infrastructure, Plumb
your data platform, make sure you have the right tool sets,
make sure you own the data, you own the model and then govern it
correctly, and then plug in the use cases that you want to run.
So what kind of principles that actually guides IBM in terms of
(23:37):
when they determine whether a new AI solution actually add
value to its clients at the enterprise scale?
I mean, we do it together with our clients.
So in many cases our client comes with a problem.
This is a problem I have or thisis a situation that I don't
really have a solution for. And then we work together, we've
(23:58):
got our client engineering teamsand we basically unpick that
problem using this technology. That's one way.
The other way is, is that we look at a company and we say
this is the way you've always operated.
What happens if we plug this technology onto how you always
operate it? Could it look very different?
And there it's really interesting.
(24:18):
As soon as the client allows us to have that conversation, a
whole new world of business models opens up and and this
will continue to evolve. So it's it's finding that
connect between IBM and the client on the right problem and
then the solutions will emerge. So what is the one insight you
wish more people would ask you about implementing AI that they
don't usually ask you? That they don't usually ask.
(24:41):
Yeah. They, they, you know that
sometimes you anticipate people ask you questions, right?
So there would be this one question you wish they would
have asked you to sort of help them to to understand IBMSAII.
Think the the the notion is is that today everyone talks about
AI equals GPU. The GPU is important, but what I
(25:04):
call inferencing is much more. Important SO.
Creating that initial model, that initial philosophy, fairly
simple. Then how do you train it?
If you have a three-year old kit, it's great, but a
three-year old kit cannot solve deep enterprise problems.
That is done through continuous learning of helping, in this
(25:24):
case a kit progress into that enterprise state.
This is where I hope clients basically work with us much more
deeply. They think AI needs GPUs, I need
AI need to pump in all my data and then I have an AI model I
can run a new business model. Right.
It's basically what I call the customer collaboration value,
(25:46):
right? To sort of get the customers to
work together to produce the solution that actually fits them
rather than thinking about just training the models and did I
get that correct? It is directionally absolutely
correct, right? How do we how do we re pivot to
what's the outcome a client wants and how do we get there
fastest? And often the the real work is
in the training and the tuning, it's not in the initial
(26:08):
creation. You think that inference is
going to be much more important in the next couple of years?
I think it will increase in importance.
I also know that you'll need a very different shaded set of
hardware to be good at inferencing.
The GPU is fantastic, but it's really good at what it does
today. It may not be optimized for
tuning. So you'll see that there are
(26:29):
what we call AP US AI processingas units.
They are much, much better at inferencing.
So my traditional closing question, what does great look
like for IBM in Asia Pacific in the next few years?
I think it's re establishing growing presence in doing
technology in the right way. And and the right way is a is a
(26:53):
perception. But I, I, I really together with
all IBM's across the region willwant to re establish IBM for
what it is today, not what it maybe was 20 years ago.
And we're such a growing technology force and we've got
such a differentiated view that I hope and I expect and I
believe that in in, in the yearsfrom now, we will be a much,
(27:17):
much bigger technology company in Asia.
So Hans, Many thanks for coming on the show and share with me
the insights on what IBM is doing specifically with AI and
data in the Asia Pacific region.I have two small quick closing
questions. First one, anything that have
inspired you recently that you want to recommend?
Like like what? A book or a book?
(27:38):
Movie, TV series, or even could be something inspiring, you
know? I I think depending on on on the
topic, so many inspirations. I I like reading books, but also
series. I don't have too much time, but
once I I I do. You got great series now and I
don't know if you watch the Lastof Us for.
(27:59):
Example The Last of Us. Yeah, it's pretty creepy, but
it's it's it's fun to watch friends and neighbors, funny,
but very US centric, I would say.
But I also, I also like to go into history.
So some of the history movies also on the region.
There recently was one on Korea,South Korea, OK, on the coupe
(28:20):
that happened. Yes.
So I like things that are connected to deep stories.
Yeah. On books I recently read a while
ago the geek way. I don't know if you know.
I haven't read about yet you. Should absolutely read it
because it talks about some of the business fundamentals that I
think are really important for future leaders and it talks
(28:40):
about radical candor, openness, science.
I think it's a great book to to have your mind framed correctly.
I should pick up that book all right, But my last question, how
do my audience find you? Find me in LinkedIn, find me by
coming to IBM, Find me online, very accessible.
So if you have questions, if youhave remarks, please do reach
(29:02):
out and we'll be in touch. You can definitely find us on
Spotify, YouTube, and all the other channels and then
subscribe to us. We just hit our 100K
subscribers. So Many thanks for the support
and Hans, it was a great conversation.
Many thanks for hosting me here and I look forward to speak to
you soon. Awesome.
Thank you. Thanks all.
In IBM, we really work on 2 emerging technologies, 1 is what
we call hybrid cloud and the other one is AI for enterprise.
That's right. And these two are deeply
connected because hybrid cloud for us is that regardless of
where the data sits, regardless of where the compute is on
premise, off premise, multiple clouds, we believe that the
client should have the control and flexibility on where to run
(00:23):
and place their data. Now if you just look at some
facts that a very high percentage of client data is
still on premise, That's right, It hasn't moved the cloud for
obvious reasons. Then how can you scale AI if you
don't have proper access to thatdata?
Because AI, it's all about. Data.
So we believe in a strategy first that redefines, rethinks.
(00:46):
We call it the great technology reset.
Welcome to Analyse Asia, the premier podcast dedicated to
dissecting the pulse of business, technology and media
in Asia. I'm Bernard Leong, and when we
talk about enterprise, AIIBM is definitely at the forefront.
So with me today, Hans Teckers, General Manager, IBM Asia
(01:08):
Pacific. And I want to understand what is
the impact of AI in the Asia Pacific?
What better place? And thanks to IBM, I actually
get to host this show with your video production team.
So welcome, Hans. Thank you.
Thank you. And it's so good to see you.
Yeah, you're becoming a superstar on on the Internet.
So it's the privilege is absolutely ours.
(01:28):
Yeah. Oh.
I'm sure my guess is actually the reason why I'm here.
So first of all, I'm actually, while doing research for this
interview, I was struck by your global journey by actually
steering the world's leading tech firms in the Asia Pacific.
And I wanted to solve some of your career journey because how
do you start your career journey?
And actually, what drew you to IBM immediately from your
Business School experience? I mean, first of all, I was one
(01:50):
of those kids that genuinely is deeply interested in technology.
So as a kid programmed the firstthe first games used a Commodore
64 if you. Maybe it's before your time.
You was there, the Commodore 64,the IBM PC?
IBM PC, the 286, the 386, the first Intel processors that
(02:11):
came. But I'm also very much into the
sampling cars, radios. I mean, we, we used to pull
everything apart and we're trying to put it back together.
Right, yeah, not like these dayswhere everything's integrated
together and don't know what's inside, right?
Try to fix a dice. Right.
Yes, that's. Right, not easy right.
So very much technology educatedin all that technology has from
(02:32):
network infrastructure design, chip design, programming, very
deep technical that my business studies have my own companies
and then really stopped in saying what's next?
Where do you as a young professional start your career?
And there are many places I was privileged to start at big
banks, at big trading firms, many technology companies.
(02:55):
But in the end, when I was young, still young, younger, I
said, who's the biggest technology company out there?
Who is the one that actually impacts the outcome of society?
And when you really take that aperture, there are only very,
very few. And IBM was was the one for me
at least that came on top. So I started as a as an
(03:16):
internship as a trainee at IBM about 17 years ago, and that's
how the journey started. So through IBM you let different
geographies from Europe to Asia,right?
What led you to Singapore and can you talk about your current
role in for IBM Asia Pacific? So first, I, I'm, I'm one of
those global kids. So I, I lived a large part of my
life in Asia, a large part in Europe and some of it in, in, in
(03:40):
Northern America, always in IBM,different, different roles I've
been from systems to software tocoverage to strategy, always
with client. I love client, always at the
heart of where you can make the most impact.
And I always loved and most privileged to leading big teams,
(04:00):
really get the best of that IBM team in service of your client
or your partner to get to a realoutcome.
So many, many different roles, many, many different
geographies. Coming back to Singapore because
it's the second time we as a family live in Singapore.
About 10 years ago, we were hereas well.
I see. And it's just a beautiful
(04:21):
region. It's got the deepest cultures,
the longest history. It's got 60 plus percent of GDP
growth. It is the geography that is
always on the move, right? Chinese, South Korea, you've got
Australia, New Zealand, ASEAN, you got India.
Always something's happening in any dimension, so it's great to
(04:43):
be back, yeah. So looking back, given the 70
years with IBM, what are the pivotal lessons you can share
about your career journey to a younger audience out there?
I think we always say right, finding the solution actually is
not that difficult. It's finding the right problem.
Interesting. And in, in IBM, it is always, if
(05:07):
you find the right problem, right and you supplement it with
data and with science and you find the right problem, then you
can work together with your client in actually addressing
it. And as a technology leader, as a
technology company, we're privileged because in some
topics we can see the future, maybe our client cannot.
And then to help a client from point A to point B is incredibly
(05:30):
rewarding. So, so you find that whole
customer journey, the ROI that you achieved, that is the most
rewarding. Yes, to do something that the
client or the the the the government or whoever you're
working with seemed to be impossible, right?
To do something that is, that istruly meaningful for them and
for societies, it's really what keeps me going.
(05:53):
So we are going to come to the main subject of the day.
I want to talk about IBM in the Asia Pacific and achieving AI
ROI even when I was working for one of the cloud companies.
Actually I think a lot of peopledon't look at the underlying
numbers. Actually one of the highest
revenue drivers for AI is actually in IBM, right?
And so maybe I want to explore, you know, the intersection of AI
and enterprise growth and also with IBM strategy.
(06:15):
So given that the Asia Pacific region is now experiencing so
much rapid digital transformation, what is the
scale of the AI market opportunity here in this region
from your perspective? It's unparalleled and it's, it's
I, I don't think we can even quantify how big that impact and
that opportunity will be. I think we have to recognize and
maybe we can, we can discuss it a bit more.
(06:37):
A couple foundational elements to what is AI, what is it based
on? And in the end, how do you scale
it? Because it's all about scaling
that capability. If you keep it small, what's the
in the end outcome? In IBM, we really work on 2
emerging technologies. 1 is whatwe call hybrid cloud and the
(06:57):
other one is AI for enterprise. That's right.
And these two are deeply connected because hybrid cloud
for us is that regardless of where the data sits, regardless
of where the compute is on premise, off premise, multiple
clouds, we believe that the client should have the control
and flexibility on where to run and place their data.
(07:18):
Now if you just look at some facts that a very high
percentage of client data is still on premise, That's right.
It hasn't moved the cloud for obvious reasons.
Then how can you scale AI if youdon't have proper access to that
data? Because AI, it's all about.
The. Data all about the data.
So we believe in a strategy first that redefines, rethinks
(07:43):
and we call it the great technology reset rethinks that
foundational capability of hybrid can we access and tap
into secure, govern the full foundational landscape of
infrastructure and data number one.
OK. If you are able to start there,
you can start scaling AI use cases.
(08:06):
If you don't have that in place in in with the right proper
governance, you'll struggle as an enterprise.
The first step where we help clients is this.
We create a horizontal platform,data platform across all of
their estate. So it's like changing all the
different data layers for the client, whether it's on Prem or
on the cloud, but then it just make sure that they have the
(08:28):
correct security and governance so that the AI can access and do
work with whatever data they have.
Right. And we do it by not moving the
data. In many cases, we leave the data
where it is, but we create that horizontally connected platform
underneath. You could do the same with your
infrastructure because many of our clients started on a journey
to cloud with no end, right? So they're using the
(08:52):
hyperscalers, which is fantastic.
It's really easy to start with and if they grow bigger, maybe
becomes less cost effective or geopolitical situation changes
and you want to move it from a hyperscaler to on Prem.
If you designed your infrastructure architectures
correctly, you're able to move fairly easily on Prem, off Prem
(09:12):
from X86 to maybe mainframes or from mainframes to X86 and vice
versa. You're very flexible.
So both infrastructure layer anddata layer, we believe that it
needs to be horizontally connected.
Then we get to AI. Now when you look at AI, we
believe in two fundamental things.
First, your data needs to remainyour data.
(09:35):
It's no one else's data. It's your data, true, right,
Which is very important if you're an enterprise client or
the government of Singapore. Your data is your.
Data, yeah. There's nothing IBM or or any
other company for that matter should have to do with your
data. It's your data.
It's your intellectual property.Second one is, is that we
(09:56):
believe that the AI models that we built are small and nimble.
They need to be cost effective and precise and they should and
can be owned and governed by youas an end client, which is very
important. Then there's a third piece that
we believe it needs to be open. So the models we govern need to
be open, enterprise grade ready,domain specific so you can get
(10:20):
the most value from it. So my understanding given that
you talk about the two sides, first is the data
infrastructure, then there is a second part which is the AI.
Of all the AI stuff I know the most ground breaking is
definitely Watson, our first bidding in normal international
chess. This is long before the days of
gold, you know, building Jeopardy.
Can you tell me a little bit more about how does IBM think
(10:43):
about AI as a product or maybe service for the customers and
then we can get in depth a little bit?
Right. OK, I'll get to that.
You started at at chess, right? Which the chess computer in that
time, I think it was the first time that a computer took up to
the human brain. Kasparov.
Kasparov lost that match. Lost that match, right?
(11:05):
The Deep blue match. The deep blue match now chess
now at that time no. But now I would say is a very
simple game because it's a game that is provisioned on finite
set of moves. That's right.
Now we have chess computers thatcan easily calculate you did
this, therefore I can do this. At the time was groundbreaking,
(11:26):
so it it was really the startingpoint of a computer against.
AI, it's the very first, actually, yeah.
It's the very first ingredient where we saw what could be
possible. Then Jeopardy came.
Yes, that was harder. Much harder because it was based
on natural language, but not in a way that our AI models work
today. So it was more based on machine
(11:48):
learning. It was still based on logic.
Today it's a much more math based prediction of what's the
next letter, what's the next word in a, in an AI model, Which
brings you brings us now to the conversation of how IBM looks at
AI. We fundamentally believe in
three big components of AI. One is the data and we call this
(12:13):
what's an X dot data. It's that data platform across
multiple infrastructures. It's your data link.
The starting point of this is what we call Watson X dot DI,
which is a studio where you can bring together multitude of
models. It's not one model, it's a
multitude of layering of models where you combine it into your
(12:35):
model as an enterprise OR as a government.
So you can create in our studio your model.
The third piece as important is a governance piece, Watson X dot
governance. Is the model biased?
Does it use the light language? Is it aligned to how you
communicate to your clients? Right.
(12:56):
So so you can the same way as you almost teach your children,
don't use this language, right? If you want to be on time, be on
time. So it's almost governing the
output of the model and you can tweak it so it's a controlling
function, which is very, very important.
Yeah. And also I remember because
while doing research, I also know that you all have a data
(13:16):
fabric layer that actually allows you to go even multi
cloud or multi architecture, correct.
And you're the only company thatdoes it at the moment.
That's correct, Correct. So I think we're the only
company that goes across a multitude of these clouds.
I was advising a retail client in Dubai on that and I was
working out that you guys are the only ones with the data
fabric, but maybe given the opportunity, right.
(13:38):
So you, you explained the data structure, you talk, talk about
the AI, right. How does IBM unit need position
to capture that opportunity in Asia Pacific?
So it's not only Asia Pacific, but I would say IBM wide.
We believe that and and our CEO talks about this a lot.
The cost of AI is still too high.
(13:59):
It's about 100 X times too high.It has to come down.
As with every technological innovation, if it's cheaper,
more people can use it. Once it becomes even cheaper, it
will become natural to the way we operate.
One of the main reasons to make it cheaper is to use smaller
domain specific models. Small models are very, very
(14:22):
accurate and we can train them really, really well.
We don't need massive amounts ofGPUs to train these smaller
models. You need less because they're
smaller. We believe in these smaller
models. There will be a place for big
models, about 11:50 of them. A dozen of them will exist in
the world, but the rest all willbe small.
(14:42):
Can you give me an example of a smaller model?
So imagine you're a bank and imagine you're on a trading
floor, right? You don't want your model to be
good and trained in Russian poetry.
Yeah, that's probably true. Right.
So you want it to be really, really good at running financial
analysis on maybe the market or in certain stocks based on your
(15:06):
history, based on all the data available in your enterprise.
We help our clients with those domain specific models.
So specifically, what are the top challenges for enterprise
when it's in this region, in theAsia Pacific region?
I'm sure you come across a lot of different kind of clients, I
guess. I guess maybe when you talk to
them, there must be some genericset of challenges they face.
(15:28):
What are the common ones? I think many of them are leaning
in very, very heavily to AI and have leaned in into, for
example, the hyperscalers a lot.I love Asia because they start,
they don't talk and talk and talk.
They start, they get going whilesaying that a lot of where we
are today needs a lot of foundation, foundational re
(15:49):
engineering or optimization. That's right.
Many enterprises haven't gone there yet.
So they want to jump onto AI. But if you're not in a, in a
hybrid by design environment, itbecomes very difficult to make
best use of AI capabilities thatare out there.
So first step I would say is to truly gauge where am I in my
(16:10):
technical health, where where amI and where do I need to improve
quickly to make best use of scaling AI use cases.
And this is where I think Asia Asian clients can be then go
very fast. In in the question then is how
do then the clients think about the ROI for AI then?
I think today that they're stillexperimenting.
(16:32):
They don't have a good idea yet.There are many numbers out there
of productivity improvements. We've got a lot of our own
experience. But in the end, when you project
a use case that we have done or with with our clients onto their
business, then I think if we really go through and help the
clients see it, I think that journey becomes very, very
meaningful. Are there like without
(16:54):
mentioning specific clients yet,because I'm going to come to
that later, but are there very specific kind of metrics like is
it really in terms of productivity savings or is it
something to do with say revenuegrowth that really interests the
clients more? I would say in the end, it's
about redefining how clients operate, how they process.
So it's business growth, right? There's a lot of productivity
(17:17):
gain, which is the low hanging fruit.
There's optimization on how you communicate with client, there's
optimization on HR, there's optimization in your supply
chains. These are all productivity
improvements which are great andwill make you better in serving
your end client. But I believe there's a huge
opportunity to re engineer the business model of the enterprise
themselves. So you definitely have done many
(17:38):
clients. Are there really like a actual
use case that you know, Yes, you're very proud of it and it's
a customer story that you can share.
I always like to start with whatwe have done as a company
because we are one big enterprise.
So if you look at how the HR teams re engineered, how we run
HR within IBM it, it's got, I mean if you look at the ROI and,
(18:02):
and we can, we can provide you those those details.
It's it's unbelievable our supply chain, how we re
engineered it. We create a lot of chips, we
create a lot of machines, a lot of hardware, completely re
engineered the supply chain. If you look at the way we do
forecasting and the way we do accurate reporting, all of this
has to be completely optimized with AI.
(18:23):
If you look at our consulting teams on how they optimize what
they do for end clients completely fueled with AI
capabilities, it is a great shift.
So what's the one thing that youknow about IB, Ms. focus on AI
that very few people do? That's an excellent question.
Yeah. So IBM creates its own models.
(18:43):
We call it IBM Granite, the family of granite models.
Which actually also aligns with what you mentioned earlier about
smaller models with the right expertise, right?
Because that is also the rationale of actually using the
company's IP. Correct 100%.
Yeah. So maybe now given that you
know, in Asia is such a culturally diverse and with so
(19:04):
many different regulatory, how do you see the landscape for
IBM's AI strategy to, to actually touch different
markets? I, I think guess Singapore, we
are lucky we have all the AI policies done on there.
But let's say we go into say a country like Indonesia where
still at a formulation stage, but maybe even Japan and Korea
will have a good AI policy, thenhow does IBM navigate?
(19:26):
I think in any maturity, depending on how you would
create that maturity, I think IBM can be a great player.
Everyone that wants to take thatnext step in in technology
actually can find a very, very good partner in in IBM,
including Singapore. Because the world outside US is
changing so fast. Geopolitical, it's changing.
(19:48):
If you look at the protectionismof data, for example, it's
changing very fast. You got data loss that are being
passed. All of this has a huge impact on
to how to run your technology estate and how do you get the
most benefit from it. You have vendors that increase
price dramatically if you're stuck with them.
That's a problem because it willconsume all of your budget.
(20:10):
If you're not flexible enough inmaking your own decisions,
something COVID really taught us, then you're in a deficit.
So it's the companies that builtthis agility of change and make
sure that their technology landscapes are ready for that
change that will reap the most benefits.
And in Asia, depending on how your great maturity, again, I
(20:32):
think every country, every enterprise can be on that
journey. Some are more mature than
others, but I believe any, anyone can benefit from someone
in Indonesia and Surabaya to somewhere in Pune in India.
It doesn't matter where you are physically.
I think, I think the benefits ofof what we can do together is is
unparalleled. So Hans, I want to specifically
(20:55):
go into customer use cases. Can you share like any
interesting customer stories where the IBMAI has actually
helped you know customers in this region and make an impact?
I mean, many, I would say the most recent one, right, recent,
recent is a collaboration, a true partnership we went into
with telecom in Indonesia, OK. And telecom is, is basically
(21:20):
it's got a huge infrastructure, a huge infrastructure provider
to government, to enterprises, small, medium businesses.
They're going to embrace IB, Ms.What's the next platform for all
use cases, which if you look at Indonesia as one of the bigger
countries makes total sense because your data as your data,
(21:41):
you can't control it. You can govern it.
We can apply it to a multitude of use cases.
So it's a beautiful example of where in this case a partner is
and us are completely collaborating to bring AI to a
country, country of Indonesia inthis.
Case nice. So like now we this year's a lot
of people say this is going to be the rise of AI agents, right?
(22:03):
What, what kind of opportunitiesdo you foresee for these in the
Asia Pacific region? I mean, agentic agents.
I think it's going to yet again transform how we experience AI.
We recently done a done a great POC where we had multitude of
agents, HR, finance, legal, business functions, basically
(22:28):
debate with each other and we were following the debate.
So we threw in the hypotheses. I want to create a presentation
on something and they were debating each other.
You could follow the flow by which you were debating and they
were constructing a narrative which gave us insights that we
would. Interesting.
Not come up with right. So this, this continuous
(22:50):
evolvement of, of how capable agents are, what they could do
is, is going to, is going to change how we how we operate in
business. So.
For I mean, given Asia Pacific such a dynamic growing region,
let's say for companies that arestill very early stage in their
AI journey, what would be your advice for them to think about
implementing AI successfully andsustainability sustainably?
(23:14):
So this is going to sound difficult.
It's not OK. Decide on infrastructure, Plumb
your data platform, make sure you have the right tool sets,
make sure you own the data, you own the model and then govern it
correctly, and then plug in the use cases that you want to run.
So what kind of principles that actually guides IBM in terms of
(23:37):
when they determine whether a new AI solution actually add
value to its clients at the enterprise scale?
I mean, we do it together with our clients.
So in many cases our client comes with a problem.
This is a problem I have or thisis a situation that I don't
really have a solution for. And then we work together, we've
(23:58):
got our client engineering teamsand we basically unpick that
problem using this technology. That's one way.
The other way is, is that we look at a company and we say
this is the way you've always operated.
What happens if we plug this technology onto how you always
operate it? Could it look very different?
And there it's really interesting.
(24:18):
As soon as the client allows us to have that conversation, a
whole new world of business models opens up and and this
will continue to evolve. So it's it's finding that
connect between IBM and the client on the right problem and
then the solutions will emerge. So what is the one insight you
wish more people would ask you about implementing AI that they
don't usually ask you? That they don't usually ask.
(24:41):
Yeah. They, they, you know that
sometimes you anticipate people ask you questions, right?
So there would be this one question you wish they would
have asked you to sort of help them to to understand IBMSAII.
Think the the the notion is is that today everyone talks about
AI equals GPU. The GPU is important, but what I
(25:04):
call inferencing is much more. Important SO.
Creating that initial model, that initial philosophy, fairly
simple. Then how do you train it?
If you have a three-year old kit, it's great, but a
three-year old kit cannot solve deep enterprise problems.
That is done through continuous learning of helping, in this
(25:24):
case a kit progress into that enterprise state.
This is where I hope clients basically work with us much more
deeply. They think AI needs GPUs, I need
AI need to pump in all my data and then I have an AI model I
can run a new business model. Right.
It's basically what I call the customer collaboration value,
(25:46):
right? To sort of get the customers to
work together to produce the solution that actually fits them
rather than thinking about just training the models and did I
get that correct? It is directionally absolutely
correct, right? How do we how do we re pivot to
what's the outcome a client wants and how do we get there
fastest? And often the the real work is
in the training and the tuning, it's not in the initial
(26:08):
creation. You think that inference is
going to be much more important in the next couple of years?
I think it will increase in importance.
I also know that you'll need a very different shaded set of
hardware to be good at inferencing.
The GPU is fantastic, but it's really good at what it does
today. It may not be optimized for
tuning. So you'll see that there are
(26:29):
what we call AP US AI processingas units.
They are much, much better at inferencing.
So my traditional closing question, what does great look
like for IBM in Asia Pacific in the next few years?
I think it's re establishing growing presence in doing
technology in the right way. And and the right way is a is a
(26:53):
perception. But I, I, I really together with
all IBM's across the region willwant to re establish IBM for
what it is today, not what it maybe was 20 years ago.
And we're such a growing technology force and we've got
such a differentiated view that I hope and I expect and I
believe that in in, in the yearsfrom now, we will be a much,
(27:17):
much bigger technology company in Asia.
So Hans, Many thanks for coming on the show and share with me
the insights on what IBM is doing specifically with AI and
data in the Asia Pacific region.I have two small quick closing
questions. First one, anything that have
inspired you recently that you want to recommend?
Like like what? A book or a book?
(27:38):
Movie, TV series, or even could be something inspiring, you
know? I I think depending on on on the
topic, so many inspirations. I I like reading books, but also
series. I don't have too much time, but
once I I I do. You got great series now and I
don't know if you watch the Lastof Us for.
(27:59):
Example The Last of Us. Yeah, it's pretty creepy, but
it's it's it's fun to watch friends and neighbors, funny,
but very US centric, I would say.
But I also, I also like to go into history.
So some of the history movies also on the region.
There recently was one on Korea,South Korea, OK, on the coupe
(28:20):
that happened. Yes.
So I like things that are connected to deep stories.
Yeah. On books I recently read a while
ago the geek way. I don't know if you know.
I haven't read about yet you. Should absolutely read it
because it talks about some of the business fundamentals that I
think are really important for future leaders and it talks
(28:40):
about radical candor, openness, science.
I think it's a great book to to have your mind framed correctly.
I should pick up that book all right, But my last question, how
do my audience find you? Find me in LinkedIn, find me by
coming to IBM, Find me online, very accessible.
So if you have questions, if youhave remarks, please do reach
(29:02):
out and we'll be in touch. You can definitely find us on
Spotify, YouTube, and all the other channels and then
subscribe to us. We just hit our 100K
subscribers. So Many thanks for the support
and Hans, it was a great conversation.
Many thanks for hosting me here and I look forward to speak to
you soon. Awesome.
Thank you. Thanks all.
Popular Podcasts
CrimeLess: Hillbilly Heist
It’s 1996 in rural North Carolina, and an oddball crew makes history when they pull off America’s third largest cash heist. But it’s all downhill from there. Join host Johnny Knoxville as he unspools a wild and woolly tale about a group of regular ‘ol folks who risked it all for a chance at a better life. CrimeLess: Hillbilly Heist answers the question: what would you do with 17.3 million dollars? The answer includes diamond rings, mansions, velvet Elvis paintings, plus a run for the border, murder-for-hire-plots, and FBI busts.
Crime Junkie
Does hearing about a true crime case always leave you scouring the internet for the truth behind the story? Dive into your next mystery with Crime Junkie. Every Monday, join your host Ashley Flowers as she unravels all the details of infamous and underreported true crime cases with her best friend Brit Prawat. From cold cases to missing persons and heroes in our community who seek justice, Crime Junkie is your destination for theories and stories you won’t hear anywhere else. Whether you're a seasoned true crime enthusiast or new to the genre, you'll find yourself on the edge of your seat awaiting a new episode every Monday. If you can never get enough true crime... Congratulations, you’ve found your people. Follow to join a community of Crime Junkies! Crime Junkie is presented by audiochuck Media Company.
Stuff You Should Know
If you've ever wanted to know about champagne, satanism, the Stonewall Uprising, chaos theory, LSD, El Nino, true crime and Rosa Parks, then look no further. Josh and Chuck have you covered.