January 28, 2025 • 55 mins
In this episode of Smart Talks with IBM, Malcolm Gladwell speaks with Ric Lewis, IBM’s Senior Vice President of Infrastructure. They discuss how hardware capability has enabled the matrix math required to run large language models. Furthermore, they delve into some creative examples of how to put AI to work: from your bank to your local coffee shop. Ric underscores the importance of infrastructure in unlocking the potential of AI, helping businesses harness their data to drive transformative outcomes.
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Episode Transcript
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Speaker 1 (00:04):
Welcome to tech Stuff, a production from iHeartRadio. This season
on smart Talks with IBM, Malcolm Gladwell and team are
diving into the transformative world of artificial intelligence with a
fresh perspective on the concept of open What does open
(00:24):
really mean in the context of AI. It can mean
open source code or open data, but it also encompasses
fostering an ecosystem of ideas, ensuring diverse perspectives are heard,
and enabling new levels of transparency. Join hosts from your
favorite Pushkin podcasts as they explore how openness and AI
is reshaping industries, driving innovation, and redefining what's possible. You'll
(00:49):
hear from industry experts and leaders about the implications and
possibilities of open AI, and of course, Malcolm Gladwell will
be there to guide you through the season with his
unique insights. Look out for new episodes of Smart Talks
every other week on the iHeartRadio app, Apple Podcasts, or
wherever you get your podcasts, and learn more at IBM
(01:09):
dot com slash smart Talks.
Speaker 2 (01:13):
Hello, Hello, Welcome to Smart Talks with IBM, a podcast
from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Godwell, this season,
we're diving back into the world of artificial intelligence, but
with a focus on the powerful concept of open its possibilities, implications,
and misconceptions. On today's episode, our season finale, I'm joined
(01:36):
by Rick Lewis, the senior vice president of Infrastructure at IBM.
Rick has had a remarkable career focused around product innovation.
He was actually a few years into retirement when IBM
came calling with an opportunity he just couldn't turn down. Thankfully,
Rick came out of retirement and today he oversees a
(01:57):
vast portfolio from storage and software to global customer support operations,
and he's engaged in one of the key problems facing
companies today, an explosion of data. In talking with Rick,
I can see that this problem of having so much
data is also an incredible opportunity because if you're able
to leverage that data to get the most value out
(02:20):
of it, then you can use it to help bring
your business into the future. We talked about the serious
computing power needed to scale AI, as well as the
ways that infrastructure storage solutions can be essential to enabling
this new world of possibilities. It's a really great conversation
so let's get to it. I'm here with Rick Lewis.
Speaker 3 (02:44):
Rick.
Speaker 2 (02:45):
Welcome, Thank Here. We are in the IBM's New York
City headquarters at one Madison Avenue. I'm going to start
with you're a hardware guy.
Speaker 3 (02:55):
I'm a hardware guy. I grew up doing hardware chip engineering.
But like I tell a lot of people, a chip
engineering project is actually a giant software project with a
piece of hardware at the end of the project. I
think if you have that analytical brain, you like to
solve problems, you'd like to get things working. You can
do that in soet desertwork.
Speaker 2 (03:15):
But as being someone coming from a hardware background mean
that you think about problems in a different way.
Speaker 3 (03:22):
I think one thing that you do from a hardware background,
and especially a chip background, is a chip spin and
costs millions of dollars. So you're a lot more likely
to make sure everything has a great chance of being
perfect from the get go. Or if you start kind
of from a software background, your general mindset is I
don't know, try this, see if it works. I don't know.
(03:43):
Try that is, if it work, and you're kind of iterated,
or to iterate. Chip people are a little more uptight
about Okay, if this first round of the chip breaks
costs us from building another new round of the chip.
Speaker 2 (03:55):
Yeah, so you're a little more You guys are spend more.
Speaker 3 (03:58):
Time planning and planning verifying, tons of time verifying. Yeah.
Speaker 2 (04:05):
So you began your career as you look backward, yes, correct,
And you were there for how many years?
Speaker 3 (04:10):
I was there for thirty two years?
Speaker 2 (04:12):
Yes, And your last job there was I was leading.
Speaker 3 (04:15):
The software defining cloud business. I had grown up a
hardware guy. I had done all kinds of hardware projects,
big complicated Unix servers and things like that, and then came,
you know, grew out of R and D and more
into the business realm, and then I'm much an innovator
at heart. I really like innovating new concepts things like that.
(04:39):
And what I learned is I enjoyed innovating business models
and software projects as much as I did hardware products
and projects, and so getting teams inspired towards doing that
was really a deep fascination for me. So I ended
up doing a fantastic variety of experiences and had a
successful run and honestly retired in tending to retire and
(05:02):
do some of my outside activities and things like that.
Speaker 2 (05:04):
And then how long did you stay retired before IBM
can close?
Speaker 3 (05:08):
Almost two years? And when I first got at ALL,
I thought, no, I'm having too much fun. But I
would say three things really got me thinking hard about it.
One the industry that we're in, the IT industry. I
think it's the golden age. And what I mean by
that is for twenty years of that career, it is
(05:30):
kind of in the back office, say make sure that
stuff doesn't crash, and can you please reduce the cost
as much as possible, because it's not that important to
the main business. It's just a back office function. You
can see it right now. It is at the forefront
of all business revolution. It happened with the Internet. It
happened again with cloud and how that changed every ounce
(05:51):
of business, not just IT business, but all business. And
I think it's happening again with AI. So to be
in that career that long and to miss the kind
of this age where it's like this is front and center.
This changes everything about all businesses, not just technology businesses.
I was kind of feeling like, Gosh, you trained to
be in these really awesome environments, why wouldn't you do
(06:16):
that for a little while longer while you still can
do it. That combined with IBM and IBM seeing the
talent pool, the brilliant people at IBM, I worked with
a ton of brilliant people before I saw a chance
to work with even a larger staff of brilliant people.
And then the assets that IBM had, which is, you know,
they'd already been doing a lot of experimentation in AI,
(06:38):
they're working in quantum, the deep, rich heritage of successful projects.
I thought, who wouldn't want to kind of see if
they could be part of that next great wave of IBM.
And so I kind of decided, all right, I'm going
to put the outside interest on hold for a while
and get back in the game.
Speaker 2 (06:54):
Along between the phone call, the first phone call and
you say, yes.
Speaker 3 (06:58):
It was a while, was probably six months. Arvind's our CEO,
teases me about that a lot. Yeah, he was like,
I don't think six months. Is that long? It took
a while you're retirement, I know. Yeah, it's one thing
to compare. I'm working here and doing this stuff versus
working there. It's really hard to compare. I'm doing exactly
as I want to do every single day. When I
(07:19):
wake up and now I'm not going to get to
do that again. It took a while for me to
get over and I thought, I can't miss this wave,
and I'm really really happy that I did, because we're
doing some amazing fun things and I'm getting challenged in
ways that I never did, so it's really fun.
Speaker 2 (07:37):
Talk a little bit about your job here at IBM.
You oversee a kind of massive portfolio.
Speaker 3 (07:44):
It's a big group, so I run the Infrastructure organization.
There's three main groups of products at IBM. There's the
Infrastructure group, which I run, the Software group, and the
Consulting group. And infrastructure is built up of mainframes, which
is called our Z portfolio, our servers which is our
power portfolio storage. By the way, those businesses include the
(08:08):
supply chain to build all of that stuff, so that's
in the group. Then I have the worldwide Customer Support Organization.
It's called TLS Technology life Cycle Services, which is a
network of about thirteen thousand people around the globe that
make sure that everything runs and works when you buy
IBM products. And then also our IBM Cloud, which is
how we host applications and deliver as a service products
(08:33):
for our client base, so there's a lot. I think
it's about forty five thousand people total.
Speaker 2 (08:38):
Do those components of the infrastructure group are they aligned
in their trajectory or do they are they on different paths?
And I'm just curious what so navigattle of both.
Speaker 3 (08:48):
It's interesting you would ask that because I think of
all of the challenges coming to the new company, there
were things I expected, things that they didn't expect. But
getting that culture right in that group has been a
big challenge. IBM has a great culture toward quality products,
(09:10):
toward emphasizing passion for the client and making sure that
the client is happy, and for delivering innovation on a
scale that you know, for more than one hundred years
has been extremely powerful. But with success comes some challenges.
And with that success you can tend to get a
little bit insular, like you don't keep an eye on
(09:32):
the competition as well, you can get more siloed, where
you know, this is my business unit, this is my
business unit, I compete with the other business unit. That's
not a good thing when when you're a company and
you can get really risk averse, meaning you feel like, hey,
this is a successful business I don't want to do
anything to mess it up, so I don't need to
try new things. Well, that's exactly the recipe to kind
(09:55):
of be shrinking, and infrastructure had been shrinking for a
little while, and so a lot of what the challenge
was for me was to invigorate that risk taking and
get to a growth mindset where you're trying new things
and seeing what works and what doesn't work, and changing
some of the models, like investing a little bit less
in hardware for some software differentiation that goes into the hardware.
(10:16):
So it's been very successful so far, and it's been
a good journey. It's almost four years now.
Speaker 2 (10:22):
Give me an example of what was a really hard
problem that you've dealt with in those four years.
Speaker 3 (10:27):
So, boy, a really hard problem?
Speaker 2 (10:30):
An interesting and are you interesting is a better word
than art.
Speaker 3 (10:33):
One of the first things that I kind of chewed
on a little bit is I talked about how we
have Z power and storage. The Z and power product
lines are well known in the industry. Is is really
fit for purpose computing that have strengths that you know
Z runs you know most of the world's economic backbone,
and if you use a credit card, ninety percent of
(10:55):
credit card transactions for the globe. Go through these Z
mainframes there in every bank there. You know, it's a
big business. It's well known in the industry. Same with power,
very tuned and optimized for smaller operations than our giant
Z mainframes, but really mission critical workloads for retail, for insurance,
(11:16):
for banking, for all of that. Our storage business not
so well known. In fact, when I came I thought,
did they have storage? Well, I have storage when I
come into I. So I got online and I thought,
it's still hard for me to tell did they have
storage or not? Now I own a storage business. So
one of the things was not just to get the
market perception up, but to invest in that business. Because
(11:37):
if you look at infrastructure overall around the globe, it's
growing at five percent a year. The infrastructure business had
been kind of flat to declining, and so a challenge
was how do we grab onto the growth. Well, one
of the biggest growth areas due to the explosion of
data in the world is storage. So what do you
do to kind of get on that growth rate. So
we did a lot of reinvigoration of the innovation in
(11:59):
that a lot of software value, add a lot of
doubling down on the things that are working. Portfolio rationalization,
where you segment the market and you say, okay, we're
going to do less of this and really go big
in these areas. And that's been probably the most dramatic
turnaround inside the group. Is our storage thing. When you
say it's a hard problem, it's not just oh, you know,
how do we do the math? No, it's cultural. It's strategy,
(12:23):
and how do you get the strategy set. It's segmentation,
it's product strategy at a granular level across a bunch
of dimensions, and then putting the investment behind it. It's
a big challenge. It takes a long time, but it's working.
So we're happy with Yeah.
Speaker 2 (12:36):
Tell me give me a little bit of perspective on
you've been there four years. Imagine we're having this conversation
four years ago. Yeah, what sorts of things have happened
over the last four years that have surprised you that
you didn't see come as we had exactly the same
conversation four years ago.
Speaker 3 (12:55):
No, because I didn't know what was in I'll tell
you some of the biggest surprises I thought from the outside,
and you know, you hear from a lot of customers,
especially ten years ago. We're all going to cloud. We're
all so I thought, well, I wonder if the mainframe
business is struggling. When I get inside of there, I
found the opposite to be true. The mainframe business is
(13:16):
actually flourishing because transaction demand across the globe has done
nothing but grow. And even more surprising was the level
of innovation that the team was already doing in mainframes
before I got here was astounding. For example, we have AI.
They were building AI technology into the mainframe processors three
(13:37):
years before chat GPT made everybody talk about it in
the industry, So that was really pleasantly surprising. So that
was wonderful. Other surprises. I knew about the kind of
the IP of IBM and the mystique in that, and
I used to joke with people, especially on the outside,
I said, I can't wait to get in there and
see what's in the big blue toolbox? Right, what are
(13:59):
all the things they have going on? I way underestimated
the size of the big blue toolbox and what was
in there, meaning the amount of really hardcore research that
we're still doing into how to build chips and how
to get to things beyond two nanimeter and that kind
of capability packaging industry leading packaging technologies, and that's in
(14:23):
my hardware kind of patch quantum. The next thing that
will come after we're done talking about AI. You know,
all of those things were surprising, But it wasn't just that.
It was then the software innovations that are going on
heavy investment in AI technologies before it was really popular
to be talking about that. But as I saw that,
(14:43):
I thought this is going to be really fun. Because
I had a good feel for where the industry was going.
I just didn't and I knew, man, I know that
talent is really good and there's brilliant people there, but
I didn't know the level of IP frankly that IBM
had at its disposal, And now you're seeing that in
things like Watson X and things like AI in mainframes,
(15:05):
et cetera.
Speaker 2 (15:06):
Building on that, since you brought up AI, can you
walk me through what has to happen from your perspective,
from the infrastructure perspective to make the AI explosion work? Yeah,
so everyone wants to do more of this stuff. Yes,
clearly there has to be some underpinning of it.
Speaker 3 (15:24):
Yeah, I would tell you, you know, I think that people
feel like where we're at right now in the AI
journey had to do with one specific piece of software.
I think the inflection point for that whole thing really
at its root was around hardware, meaning the algorithms needed
to do larger language models. And all of that had
(15:45):
been around, they'd been talked about in the industry, but
at some point you hit a tipping point of hardware
capability where it's like, oh, now we can do this
in a broof force way, massive amounts of matrix math
to get weights correct so that you can do you know,
the right level of predictions that enable large language models.
And once we got to that horsepower. And that's why
(16:06):
you hear about giant GPUs that are driving this and
the sales of those, et cetera. It's because we just
barely got over the hump where you can do these
big hard things in terms of hardware capability to do it.
Speaker 2 (16:19):
Give me a layman, give me a sense of when
you say there was a kind of threshold where suddenly
these things became possible.
Speaker 3 (16:25):
Yeah, I don't know if there's an exact number, But
and more basic question that I get from a lot
of people. You know, my friends and family outside is
why GPUs. What does a GPU, a graphics processor have
to do with AI. It's not, Well, graphics processors are
really good at this thing matrix math, because they're figuring
(16:47):
out how do I map a pixel? And as I
move an object across the screen, it's essentially matrix math
to figure out, Okay, what does what does this pixel
on a screen look like? And what's it's doing? And
as you know, we've gotten more high resolution graphics, more
high resolution monitors, et cetera. It's a lot more pixels
and a lot more math and a lot more matrix
(17:09):
math about how you compute that. The first big thing
that kind of started to look like that, it turns out,
was crypto and crypto mining, and so you saw graphics
companies starting to sell to crypto. The technology got to
a certain point and there were use cases like bitcoin
and that that kind of said, hey, we need to
do a lot of this matrix math to be able
to do that. So graphic chips were a natural fit
(17:32):
and that kind of sustain But meanwhile, behind the scenes,
a lot of this AI AI is about numeric calculations
having to do with weights and matrices that say you know,
giant consolidated things that predict what's going to kind of
happen based on what other things have happened, just like
predicting where pixel goes. But it's really about being able
(17:54):
to do enough data in jest to be able to
do and then the calculations to be able to simple
five things like entire sets of language or giant chunks
of the Internet, to get enough waitings in there to
be able to say, Okay, we can predict what you
would say in this language based on all of the
volumes of stuff that we've seen that when you start
(18:15):
talking like this, the next word is likely, oh it's this. Yeah.
Speaker 2 (18:19):
So, but my point is to get to that point.
That's threshold. We got there because was it because we
simply threw a lot more resources at the problem or
is it because the underlying technology got suddenly or gradually
so much more efficient.
Speaker 3 (18:33):
It's always yes and yes. But you know, the industry
for a lot of years would talk about Moore's law.
Speaker 2 (18:39):
Well, quick, will you define for us More's law for
those of those who's forgotten it.
Speaker 3 (18:45):
Yeah, So Gordon Moore at Intel coined this thing. It
was basically that the horsepower I'm going to translate it
roughly of technology will double every couple of years. We're
still on Moore's law. More's law changed a little bit.
For a while, it was always about frequency. Things would
go faster, faster, faster. That kind of petered out. But
(19:08):
what happened is, rather than faster, faster, faster, we did
more and more and more. So rather than one operating
unit going a lot faster on its throughput, you put
ten operating units on a chip, now you put one
hundred operating units on a chip, now a thousand. Some
of these problems, the matrix math problems scaled parallel extremely well.
(19:29):
You don't have to do something really fast, you just
have to do a lot of the similar things in
parallel at the same time. So again that kind of
that extension of Moore's law, more and more hardware on
a chip to be able to do more and more
of those calculations in parallel and come up with it.
Speaker 2 (19:44):
And we said, yeah, was that threshold predictable? In other words,
see people in the industry, like you sit down X
number of years ago and say, when we get here,
AI is going to become much more of a.
Speaker 3 (19:55):
It's funny the horsepower that very pretty. The use cases
not always so easy to kind of figure out. That's
where the human spirit kind of gets involved. I think
for some people that say, oh, I saw that coming,
but people have been predicting kind of the rise of
AI for twenty five years. Oh well, then when we
(20:17):
get to this next gener oh when we get here,
it kind of hadn't happened. There's always a magic point
where you kind of get to where the technology and
the use case and somebody does something to kind of
make it catch on. And I think we're at one
of those moments in AI for sure right now. And
I don't think it's you know, people that have said, oh,
this is just the latest wave of you know, I've
(20:38):
heard this about a lot of technologies, but AI is
the technology the future, and it always will be. I
used to hear that. You're not hearing that now, right,
It's like, no, it's primetime. It will change everything, just
like some of these other things changed everything.
Speaker 2 (20:52):
I noticed it if personally when I speak somewhere or
I'm listening in an audience somewhere. Over the last let's
say twelve months, there's always a whole bunch of AI
questions yes, And if I go back two years ago,
there were no AI questions.
Speaker 3 (21:09):
Yes.
Speaker 2 (21:10):
Now my question is, so there's been this explosion on
the in popular fascination with what's going on AI. It
seems like the last year. I agree with you in
your world, when did the explosion of conversation around this start.
Speaker 3 (21:27):
It's I love this question because IBM had a fairly
big effort and business called Watson before Watson X. And
this is going back kind of ten years. I'll give
you another kind of example. I knew about a lot
(21:47):
of tablet technology before there was an iPad, a lot.
For ten years, there were a lot, but it kind
of takes a magic combination of the technology, the user experienced,
the software, and the need and the market for it
to kind of go. Now it's the thing. Now we
all have either an iPad or we have the Google
equivalent to And so I think this is a little
(22:08):
like that. Meaning IBM was on the right track with Watson.
Some of the hardware wasn't there, the use cases weren't
exactly figured out, some of the early use cases didn't
pan out perfectly. But the good news about that is
it's back to that culture of risk taking. You don't
look back on that and say, oh, we shouldn't have
done that, that was a bad idea. I know you
look back on that and say, what did we learn?
(22:29):
How should we try something new? How would we pivot
this time? That's what we've done with Watson X, and
now that's a growing, healthy piece of our business and
very important our strategic picture. So we're all in.
Speaker 2 (22:41):
I've always investigated by the gap between insider sense of
what is happening in an outsider sense, like.
Speaker 3 (22:50):
It absolutely is that in this case, we've all been
talking about and thinking about AI and is it time
for that and what does this mean? Et cetera. And
yet none of us really predicted that actual moment, which
is kind of you know, early twenty twenty two where
it was like, oh, now you have a simple human
(23:10):
interface of software innovation combined with large language models. There's
a moment there where you're like, oh, Unlike, you know,
I think all of us are frustrated if we ask
our phone, hey, tell me about this and it says
I found this on the web page. That does you
no good. But you know, all of a sudden, with
Chad GPT and some of these other things, you could
(23:31):
ask a question, it would give you a clear answer.
Sometimes is wrong, but at least it was like I'm
getting an answer rather than hey, I don't know if
there's some references. Good luck to you, and that's really changing.
Talk about the kind of macro trends that are going
to shape your infrastructure battle. Yeah, we've talked about if
(23:52):
you already, but I'm actually going to go a little
different direction. So macro trans first. And this one has
been before even even this AI conversation, that we've had
explosion of data. As humans, we don't think exponentially very well.
We really struggle with exponential thinking. We think linearly, Oh,
(24:14):
there'll be more, there'll be more, they'll be more, But
we don't think well when it's like no, there'll be more,
and they'll be ten times more, and then there'll be
ten times that more. That's what's going on with data
right now in our industry. It's one of the reasons
that that storage business is doing so well is they're
just more and more and more data. You know, you'd say, well,
how can there be more data? It's just life and
that thing. The things that we care about, video captured
(24:36):
video images, you know, the you I don't know from
my parents, you needed a drawer with all your family photos.
Now we need gigabytes and gigabytes.
Speaker 2 (24:46):
You knew how many pictures my wife has taken off
our children, you would exactly exactly, So that's your case. Now.
Speaker 3 (24:52):
Think of companies who used to just think about their
transaction data. What's the ledger say that now have video
assets of all of their campaigns and their marketing. They're
trying to figure out, you know, what campaigns are working
the best. So it's just an explosion of data and
that's not going to stop. Dealing with that, and more importantly,
getting value from that data is a massive trend in
(25:17):
the industry. Second trend AI, and this is the AI.
Not like we were just talking about about how it
changes how I search for things or how I learn
about things. But I would argue, dealing with that data,
how do I figure out what's in all those video streams?
How do I figure out, Okay, I want all of
the chunks of my corporate video that have to do
(25:39):
with client buying some specific product or something. That's a
different problem. It's not just okay, We'll look it up
in a spreadsheet and here's the math associated with that
that is a huge trend in the industry. You're seeing
it play out in this regard. It's a little different
bent on the AI fraud detection is the one that
we cite in our mainframe. It's a similar problem where
(26:01):
it was kind of a traditional AI problem. Look up
a rule. You know, if somebody does two small transactions,
then a massive one it might be fraud, right because
they were seeing whether it were now to detect fraud,
you might be saying, okay to transactions then a huge one.
Plus does this entity have a real address? Second, is
(26:22):
there any web traffic on you know, better Business bureau
kind of things that says this is a bad business
that can help you with fraud. So it's a lot
more of a it's an expotent problem. It's a holistic
problem that it takes a lot more than just you know,
little chunks of rules, et cetera. And then the third
one you know, after AI, is the nature of hybrid
(26:42):
it or hybrid computing. For a while ten years ago
when cloud was on the rise, I think the notion
of hybrid computing basically having to do with things in
the cloud versus things that people still have on the
premises inside of business. It was almost religious argument. Now
it's no, it's the reality. And the reason is because
(27:05):
that data that I talked about is the lifeblood of
these companies, particularly IBM's companies are clients that usually that
data has to be secure, they have to be able
to get value from it. It is the lifeblood of
the company. If you go to an ATM and you
can't get your money out, you know, to our financial transactions,
if that lasts a day, you're probably going to change
(27:27):
banks immediately. So it's like life or death for these companies.
So having that hybrid infrastructure so that they can still
hold their data, you still interact with clouds and still
get value from it from AI. That's kind of the
magic where we play and it's a huge business opportunity.
(27:47):
It is a true inflection point for the industry.
Speaker 2 (27:51):
I'm going to go back. I interrupted you when you
were in the middle of a rellion. We were talking
about what has to happen for for AI to scale
from the infrastructure standpoint. You gave one example that I
got you off on a tangent. Can you go back
and talk very so practically, like so I'm you know,
I'm a big company. I have all these dreams of AI,
(28:15):
of how I'm going to use this dratically. So give
me a very granular sense of the works you have
to do, yeah to make that dream possible.
Speaker 3 (28:23):
So let me first say what the company has to do,
and then maybe I'll say, then how do I help them?
If that makes sense? So if I'm a company and
I want to do that, So it turns out I
am a company, meaning I want to use AI in
my processes. I mentioned that I have a global network
of thirteen thousand employees that support our infrastructure around the world.
(28:45):
That challenge is a great challenge for AI. That means
I have data for every customer situation for thirteen thousand
employees globally around the world on what was their problem,
how did we fix it, what next steps did they
have to do, how did they remediate that? That data
is extremely valuable to me because if I can get
(29:07):
better at doing that than anybody else in the world,
that brings my cost down. I sell more products, I
sell more service, I sell more anything. So what I
have to do to get there is I have to
figure out, Okay, what's my objective. I have a couple objectives.
One I want customers to be able to support themselves
without even calling me, first off, and I don't want
when they call for the first answer to come back
(29:29):
to be did you try rebooting? Because I think that
irritates every single one of us. Did you try? Of
course I tried rebooting. I've had a lap up, of course.
I well, okay, well then tell me, okay, what firmware version,
all that other stuff. Okay, we know this interaction. So
that's kind of the problem set. Do I want that
to be customers solving their own problems? Well, even for
(29:51):
my support agents, I want something in their pocket on
their phone where they say I'm seeing these symptoms. It says, oh,
this happening around the globe. Here's here's kind of specific.
So there's my problems. What does it mean for infrastructure
on the back end? So first I got to get
all that data together, right, all of those customer law,
all that customer support around the globe, et cetera. That
(30:13):
needs to be stored. That's a big set of data.
And some of it's not just fix and that kind
of thing. Some of it is, Okay, you know, what
was the firmware version? Who was the tech? Because it
can matter. Is this their first time fixing this problem?
Is that they're one hundred and fiftieth time. What's their level?
It's a very complicated problem. Ingesting all that data takes
(30:34):
an architecture. We have a product called Scale, which is
one of our storage projects that actually makes it easy
to ingest all that data, get it organized, et cetera,
and then have a model. It's a whole different process
to kind of say did we train our model? We
can train our own models inside of IBM. We have
a granite set of models. Those models we fine tune,
(30:56):
and then we inference based on those models. So we
can do that inferencing in our cloud I have a
cloud set of infrastructure or in my power servers, we
can do inferencing with our capabilities and say, okay, based
on what I'm saying, here's what the remediation you should
do for that customer. We already are doing that today.
We've seen over a third of our support calls have
(31:21):
had significant reduction in the amount of time that it
takes to resolve that support call. Just by what I
said right there.
Speaker 2 (31:28):
That I've really been curious about this. If I had
reduced something like AI into that equation as you just did. Yeah,
and you said we've already seen a thirty percent, Say
did you say thirty percent reduction?
Speaker 3 (31:41):
Thirty percent of our interactions have seen significant reduction in
those times?
Speaker 2 (31:47):
Was that your primary goal to reduce the time of
the interaction? But it was if you if everything else
was the same all, but what you were doing was
shrinking the amount of time That would you.
Speaker 3 (31:56):
Want one of the primary goals? So to us in
that business net promoter score kind of the satisfaction of
a client is the supreme goal. What makes them satisfied,
doesn't cost me a fortune, happens really quickly, and if
I can do it myself, I'd be thrilled. It affects
all of those right. It kind of says it got
(32:17):
resolved faster, it didn't cost me an arm and a leg,
because the deck was barely here, because it's a common problem,
or I solved it myself without even calling, So all
of those objectives would kind of hit across all so
that now you see it. So that's a little microcosm.
That's just me and my customer support business. Now think
of how many problems for businesses around the world there
(32:37):
are like that it's not a it's not like a
new AI application that changes the entire user experience. That's
those will come, But right now it's kind of practical,
which is, I just want to do what I'm doing
better and faster, and I can get immediate economic return
from those things.
Speaker 2 (32:55):
How long how long did it take you to just
stick with that example of the customer reaction reducing thirty
percent of the time? How long from the very beginning
of that project yeap to that thirty percent reduction was?
How long?
Speaker 3 (33:09):
Less than a year? And yeah, So one of the challenges,
and this is interesting with a very large organization, as
you can imagine, just like you're seeing in the industry,
we don't have a problem of generating ideas for how
AI could help us. We actually have a problem filtering
(33:29):
the thousands of ideas from our employees and from from everywhere.
It's like, hey, we could use AI to and filtering
down and saying, okay, which of these will have a
return on investment quickly and at a level that sustains
that's worth kind of going and investing in the infrastructure
and the software and kind of making that happen.
Speaker 2 (33:48):
Is that unusual? If I talked to you twenty five
years ago and said, do you have a problem of
too many good ideas or too few?
Speaker 3 (33:55):
What was you said in this specific carriot, Probably too few,
because at some point you reach diminishing returns. So, for example,
let's use this same example. Can those thirteen thousand technicians
go faster? Can they spend less time driving to the side.
I mean, there's only so much you can kind of
(34:15):
do on those things. But if you can get them
an answer to the problem and maybe even avoid them
having to visit at all because the client helped themselves,
that's a step function. So that's why people are kind
of talking about there's a business revolution coming with AI
where there are some step function changes that can be there.
And notice I didn't say I'm going to have less
(34:37):
of those agents. That's not my objective. My objective and
I think that's the fear in the industry about AI
is going to eliminate all the jobs. No, I just
created thirteen thousand superpowered agents that can do more right.
And so I'm not just going to support IBM products.
I'm going to go out and support other people's products
because I know how to do that really well. And
once I have the data on how to fix their problems.
(34:59):
I may just have a customer support business that's independent
of mind boxes. So you know, I think that's where
people sometimes get it wrong. And the AI thing is
it's like, you know, do word processing eliminate the need
for writers? No? It enabled writing instead of mucking around
with mimeographic machines and click and click typewriters.
Speaker 2 (35:20):
It may have enabled too much writing. Yeah, maybe maybe
can I give you a hypothetical? Uh? And I asked
this because I read I was at some convers and
I ran into some guy from the I R S
who was really, really, really really excited about AI. So
let's suppose they call you up and they say, you're
going to talk to the I R S. Okay, I
(35:44):
call you up and I say, Rick, Uh, clearly there's
something that we could do for the I R S
if we work together.
Speaker 3 (35:53):
Yeah.
Speaker 2 (35:53):
Who would your answer me?
Speaker 3 (35:55):
Of course?
Speaker 2 (35:57):
No.
Speaker 3 (35:57):
I think we sell to a lot of government agencies.
Can imagine in the business that we're in, we enable
a lot of social security transactions and things like that
through our mainframes. And I think, you know, we're in
the business of helping whatever client get the most out
of their data and be able to secure it and
(36:18):
and be able to do analytics with this. And IRS
has a heck of a lot of data, so yes,
we would help them.
Speaker 2 (36:25):
Do you know how the amount of data they have
compares to some of the corporate clients you've.
Speaker 3 (36:29):
I don't know specifically for the IRS how much data
they have, but I would assume it's a whole lot.
It's mountains. But but that's our business. I mean, it's
interesting sometimes people have that what's the most you know,
what what is it that that IBM has that's of
great value? Is it a server? Is it a storage array?
(36:49):
Is it you know, software and all that. What we
have is the most important entities in the world have
their data on our stuff. The most important data in
the world. It's not you know, pictures of your grandkids
and things like that. Generally for us, it's all of
the financial transactions that happen globally, right, It's all of
(37:09):
the it's the world's economy is kind of running through
our systems, and so we take that really seriously. You know,
you would be distraught if you lost one photo on
your laptop or whatever. But you know, if we lose
a transaction, like somebody moves a big amount of money
and it's like, well, don't know what happened there. It
is a massive deal, right, so that doesn't happen.
Speaker 2 (37:31):
But I want to go back to my irs example
for US, Yes, so one, is it reasonable to assume
that you could that somebody IBM or somebody else could
in a short period of time put together not just
the AI capability to audit returns, but also this the
infrastructure support for that in a reasonable amount of time
(37:53):
for a reasonable amount of cost. Or is it overall?
Is it going to the moon? Or is it it?
Speaker 3 (37:59):
Definitely? I mean, so we're already doing that kind of
thing right across a network of banks and others, essentially
all credit card transactions for all of the world to
go through our systems, so that in some ways is
more volume than the datch returns of the US people.
(38:19):
And they're W two's and all that stuff, and we
do that stuff too. I try not to describe it
too much in detail, but we definitely do a lot
of that. In fact, I think most of if you think, okay,
what is super critical data, who would be doing the
business transaction processing? It is most likely US in almost
(38:41):
all cases, whether it's government things or private or banks
or that kind of thing. That's what we do.
Speaker 2 (38:48):
Rick we're going to end with the where we always
end with a couple of quick fire questions.
Speaker 3 (38:52):
Okay, here we go.
Speaker 2 (38:54):
What single piece of advice would you give to businesses
trying to use AI in an effective way?
Speaker 3 (39:00):
The simple version is get started. By get started, I
mean think of what is something that I want to improve.
The things that we have traction on right now in
the market are around business process, automation, digital labor, those
kind of things. But my other little piece of advice
there is keep it simple to begin with. You're going
(39:21):
to learn a lot, but getting started means you'll start
that learning curve. I even advise my friends like, Hey,
should I be playing around with some of this AI stuff?
And I say yeah, because I think it will help
you start to be more comfortable and you may find
a use case personally for that. I think the same
is true for businesses. The first step in that journey
is always with what data. Notice when I talked about
(39:44):
our customer support people, I thought about, Okay, what's the data.
The data is all of those logs of all of
those service engagements around the world, and what could I
do with that? Well, I could use that to get
to a knowledge base that really helps hopefully that I
can do it in multiple languages because it's global and
I can you know, all of those things. That was
(40:06):
kind of my data sent That one's not super simple,
but we've had a lot of experience in AI for
other people that might just be how do I automate
filling out travel expense reports for my company? We can
help people that we have consulting, we have wats and
X tools. We can do that like this, and we're
doing it globally for people around the world. Pick that thing.
(40:26):
What's the data you have? In that case, it's data
of expense reports and it's like, okay, we can help
you automate that for people where they could do it
just by you know, a verbal interface. What did you spend,
where did you go, who you were you with? Okay,
we filled out your travel expense report for you and
you don't have to mess around with it.
Speaker 2 (40:43):
So we were playing with this idea where we would
pick a business and go in there and do it
would be AI makeover.
Speaker 3 (40:51):
Yeah, I love that.
Speaker 2 (40:52):
What's okay, what's the what is the ideal business to do.
We only have a couple months. We don't want to
spend a kajillion dollars. We want to be able to
show tangibly and quickly what AI can do. What's that
ideal business to do? That in it can be a
small business, but we're not talking. This isn't a grand corporate.
Speaker 3 (41:08):
Thing there, ah boy, small business that we could do
and hey, I make over. Customer support is one of
my favorites because it's it's it's I have it on
the business side where I provide customer support. I have
it on the consumer side, where it drives me nuts
when I have to go through thirty layers of phone menus.
(41:31):
Speak to an agent, speak to an agent, speak to
an agent. That for any business, I think is just
ripe to be able to kind of say, why do
I have to click through these manuscent messages? I just
need to tell you in human language, here's the issue,
and I'll be really good about telling you details about
you know, I tried to set up this thing for
my bank and I do da da da da da.
(41:52):
They can go through all the menus automate that process.
I think it would change everything because all that frustration
is a consumer would go down dramatically and it's all,
you know, why are you making me the beep booth,
press one, offload press exactly, Well, don't offload to me,
offload to AI. We can help you with that.
Speaker 2 (42:13):
Here's my version of that drives me crazy. Every morning
I go to the same coffee shop and I get
a cup of tea and a croissant.
Speaker 3 (42:23):
And here's what happens.
Speaker 2 (42:24):
The person has their screen and they go I go,
cup of tea, croissant, sparkling water, like at least twenty keystrokes,
and then like then the screen is turned around. Like
at this point we're like forty five seconds in, I'm like,
why is this? First of all, it's not for me.
All those keystrokes, it's there in turn right right, So
(42:46):
they're burdening me in order to service.
Speaker 3 (42:48):
To back it, you should be able to walk in,
go up and they go, I'm olc them the same
thing and you just go yes, and then they boom,
We're done.
Speaker 2 (42:55):
Can we do AI makeover of my coffee shop?
Speaker 3 (42:59):
You notice I quickly jumped more to banks than your
coffee shop because I think I'm a business person, but
I'm not trying to kind of do a deal on
one coffee shop.
Speaker 2 (43:09):
No, But this is interesting because it takes me back
to something you said that I thought was really important.
When you were talking about when you were using AI
and your customer service thing, it was clear that your goal,
you could have any number of goals going in. It
could be to cut costs, it could be to dramatically
improved profits.
Speaker 3 (43:29):
Your goal, quite.
Speaker 2 (43:29):
Specifically, was to improve the experience of your customer, right,
So you were using it to that.
Speaker 3 (43:34):
All the other things come from that come from. That
is actually one of the beautiful pillars of the IBM
culture is delighting clients is actually where all of the
good stuff comes from.
Speaker 2 (43:45):
So my coffee shop thing is the same principle. Right now,
they're making my customer experience worse and they don't want to.
Speaker 3 (43:54):
Their eyes are glued to the special a.
Speaker 2 (43:56):
Moment when I walk in and I want to say, Hi,
how are you doing conversation? You're too busy, busy, So like,
this is the same thing. If they had it, oh,
we this is if they understood they had an opportunity
to improve the experience of their customer experience.
Speaker 3 (44:11):
I would not be surprised if a chain comes along
where that is their value proposition, I would not be
surprised at all. Yeah, right, So I mean and and
when those things kind of catch hold, it becomes a revolution.
Speaker 2 (44:26):
You know, when the guy comes to do like to
redo your roof and they put a sign out front,
like you know, Joe's roofing. You guys could do the
same with my coffee shop. But like I'd be was
here exactly exactly, in five years, the main frame will
be dot dot going strong.
Speaker 3 (44:49):
H the main frame going strong and with new capabilities,
continuous new capabilities. I think when we announced the last
versions six, the latest version, I should say, and we said, hey,
there's AI processing built into it. This was before everybody
was talking about that. I think a lot of people thought,
(45:10):
what's that for? And we did it specifically for traditional
AI fraud detection, et cetera. This next version, not only
do we have the traditional AI built in, but we
have optional cards that you can plug into it to
allow you to do large language models for the enhanced
fraud detection cases that we talked about, where you know,
(45:30):
it's more than just what transactions were happening. So if
you take that and say, okay, the next generations. We
have more transaction volume than we've ever had in mainframes. Today,
the business is growing, it's strong, we keep innovating. In
five years it'll be going strong.
Speaker 2 (45:47):
But we're people. You're saying this in the context of
for years people were predicting, weren't they that the main
brand was going to go away.
Speaker 3 (45:56):
There were pundits in the market that said everything will
go away there no one will ever have a box,
It'll all be online. I think this is something I've
learned big time in my long career. You know in
the IT industry is don't believe everything you hear. So
I went back for my master's degree at Stanford after
(46:16):
I had worked a while in as a hardware designer,
and everybody told me be sure to do your masters
in software. Hardware is dead. I went on to work
for thirty plus years in hardware and infrastructure. Now software
became important, and I'm glad I had that extra training
in software because it helped me in hardware. But hardware
wasn't dead. Then I heard all infrastructure will go into
(46:40):
the cloud. There won't be that hasn't happened. It's not happening.
Then I heard there will only be one cloud because
one of the players will dominate. There's not one cloud.
So I think it's as humans we like to oversimplify
and go, oh, it's all going to be this, and
kind of what I've learned is fit for purpose matters
in everything. It matters in size of infrastructure, it matters
(47:04):
in the stack that goes along with solving a specific
use case. If you're willing to design something that's the
best at that use case, If you're willing to design
the coffee shop that is the best at greeting me,
there's a spot for you, and there may be a
big business in doing that. So oversimplifying is really.
Speaker 2 (47:21):
When you heard all those predictions, did you believe them
at the time.
Speaker 3 (47:27):
They looked like they were trending in that direction. I'll
tell you some right now which might be useful. There
will only be one GPU company and they're going to
end up taking over the world. It's a pretty obvious answer.
Whose economic values risen dramatically. I don't think that's going
to be the case. In fact, I think that ninety
percent of processing for AI actually happen happens at inferencing,
(47:50):
and inferencing is not as GPU and hardware intensive as
the other things, and is a lot more amenable to
fit for purpose. So the model size will matter. The
tune matters a lot. As we're learning. We have a
product around instruct lab that's really focused on tuning. So
that was one thing is there'll be one GPU. The
other thing is that the biggest model will win. I
(48:12):
think is another thing that's kind of people are saying
right now. Don't believe that I believe they'll be fit
for purpose models. It takes a lot of money to
run to create a huge model, and then to run
a huge model, or to even infer off of a
huge model. I don't need a massive training GPU set
thing to solve my thirteen thousand people customer support issues.
(48:34):
So why would I feel like I got to go
farm that out for a big expensive thing. I can
do that on a small box. In some cases I
might even be able to do that on a laptop.
The other thing I'll say in this we are so
early innings in AI, A lot of things are going
to change. So anybody kind of saying it will all
be X, Y or Z, I just think you have
no idea how this is going to play out. And
(48:55):
it's up to us to go figure out how it
plays out.
Speaker 2 (48:58):
Yeah, yeah, all right, in five years, AI will be
dot dot dot.
Speaker 3 (49:03):
Still new. It will have moved a bunch in five years,
but the potential for the disruption in the world will
still will still be very early innings in that process.
And I think that's super important to realize. That's why
I say get started, start thinking about how that could change,
(49:24):
because it'll be some little things first, but it will
continue to snowball.
Speaker 2 (49:28):
This is a common observation that we the invention of
the capability uh massively predates the understanding of the capability, right,
Like I love that. Yeah, Like, yes, recorded recording shows
on television is invented in the sixties. Probably we don't
(49:52):
really understand what it's used for until the oughts was
what's really good for is being able to tell a
story sequentially, Yes, over time, because you know that the
person will all have see in the episode before, so
you got the Sopranos And yes, yes, Hollywood wanted to
ban the VCR in the beginning. Yeah, because they thought
it was good. They thought the point of it was
(50:14):
thought the din understand No, no, no, it's storytelling. It's actually
your business is getting better. Yes, Yes, took them twenty
years to figure that out, which is to your point,
why would we know what AI was four and five yearso?
Speaker 3 (50:24):
Well, that's why you hear people kind of say, oh
my gosh, AI, that's that will just eliminate jobs. No,
it'll make jobs better. That's how I view it.
Speaker 2 (50:31):
Yeah, what's the number one thing that people misunderstand about AI?
Speaker 3 (50:35):
Is that it that it'll I think that's that. That
would be the human kind of understanding part of it.
The technology part of it, I think would be what
I was talking about fit for purpose, meaning that it
isn't just going to be a GPU arms race all
of AI. I don't believe that at all. It will
change everything, but it's not just going to be a GPU.
Speaker 2 (50:56):
Armed Next question, what advice would you give yourself ten
years ago to better prepare you for today? I'm changing
this question, Okay. I want to say, let's imagine that
what was your what.
Speaker 3 (51:10):
College you to go to? I went to three of them.
My undergrad was Utah State University, my MBA was Santa
Clara University, and my master's in w was Stanford.
Speaker 2 (51:20):
Okay, any one of those three culture up and says
we want you to give the commencement address and imagine
that it's it's it's let's just say, for the sake
of argument, it's just to the stamp people.
Speaker 3 (51:34):
Those are the relevant parties here.
Speaker 2 (51:36):
What do you tell them?
Speaker 3 (51:38):
Boy? What do I tell them? Let's see. I think
I would start with life is a marathon, not a sprint.
It would be the first one. The second thing I
would say that in that spirit is be sure to
set yourself some big, hairy, audacious goals and don't be
(52:02):
overly disappointed if you don't hit them all. Going after
those big, hairy, audacious goals will get you on a
path where you will learn so much. You will achieve
more than you ever could imagine you would have achieved.
That's what the advice I give to my kids is,
set some big goals, get after it. You may or
(52:22):
may not achieve them, but you'll be better for the
whole process when you're done.
Speaker 2 (52:25):
By the way, as someone whose kids are younger than yours,
is it actually useful to give you give advice to
your kids the points exercise TVD.
Speaker 3 (52:34):
We're still on the journey, and I think we will
be for a long time. I don't know how.
Speaker 2 (52:39):
Are you already using AI in your day to day
life today?
Speaker 3 (52:44):
Personally, I would say it's replacing a good chunk of
my search. You know, I'm less likely to go blindly
stumbling through a bunch of web pages looking for stuff.
I'm more likely to ask a question from a few
AI engines kind of see get me in the right direction,
then I'll go bumble through a few things. At work,
I can tell you code development right now, we are
(53:08):
seeing massive improvements in code development and support. Products we
have like Watson Code Assistant that is really showing immediate
return for a code developers, and I think that will
again be a tool that increases productivity for code developers
immediately across the globe. Yeah.
Speaker 2 (53:28):
Last question, what's the one skill that every technology leader
needs that has nothing to do with technology.
Speaker 3 (53:34):
Being able to inspire a set of people toward a
common goal and collaborate to achieve it. That's at the
core of everything everything. That's a lovely way to end.
Thank you so much, Rick, Thank you.
Speaker 2 (53:50):
This conversation left me excited. I'm now imagining the potential
for new use cases for AI in all sorts of
different businesses. Rick didn't seem sold on my idea of
a coffee shop makeover. But it's clear there's lots of
opportunities here to increase speed and efficiency, to achieve your objectives,
and to dream beyond the current applications for this technology.
(54:13):
At the end of the day, the scaling of AI
will rely on the right infrastructure to support it. With
the right tools, you can solve problems that are unique
tier industry and improve the experience for your customers. Smart
(54:37):
Talks with IBM is produced by Matt Romano, Amy Gains
McQuaid and Jacob Goldstein. Were edited by Lydia gene Kott,
mastering by Jake Koorsky. Theme song by Gramoscope. Special thanks
to the eight Bar and IBM teams, as well as
the Pushkin marketing team. Smart Talks with IBM is a
(54:58):
production of Pushkin Industries and Ruby Studio at iHeartMedia. To
find more Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts,
or wherever you listen to podcasts. I'm Malcolm Gladwell. This
(55:19):
is a paid advertisement from IBM. The conversations on this
podcast don't necessarily represent IBM's positions, strategies or opinions.
Welcome to tech Stuff, a production from iHeartRadio. This season
on smart Talks with IBM, Malcolm Gladwell and team are
diving into the transformative world of artificial intelligence with a
fresh perspective on the concept of open What does open
(00:24):
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open source code or open data, but it also encompasses
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(00:49):
hear from industry experts and leaders about the implications and
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be there to guide you through the season with his
unique insights. Look out for new episodes of Smart Talks
every other week on the iHeartRadio app, Apple Podcasts, or
wherever you get your podcasts, and learn more at IBM
(01:09):
dot com slash smart Talks.
Speaker 2 (01:13):
Hello, Hello, Welcome to Smart Talks with IBM, a podcast
from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Godwell, this season,
we're diving back into the world of artificial intelligence, but
with a focus on the powerful concept of open its possibilities, implications,
and misconceptions. On today's episode, our season finale, I'm joined
(01:36):
by Rick Lewis, the senior vice president of Infrastructure at IBM.
Rick has had a remarkable career focused around product innovation.
He was actually a few years into retirement when IBM
came calling with an opportunity he just couldn't turn down. Thankfully,
Rick came out of retirement and today he oversees a
(01:57):
vast portfolio from storage and software to global customer support operations,
and he's engaged in one of the key problems facing
companies today, an explosion of data. In talking with Rick,
I can see that this problem of having so much
data is also an incredible opportunity because if you're able
to leverage that data to get the most value out
(02:20):
of it, then you can use it to help bring
your business into the future. We talked about the serious
computing power needed to scale AI, as well as the
ways that infrastructure storage solutions can be essential to enabling
this new world of possibilities. It's a really great conversation
so let's get to it. I'm here with Rick Lewis.
Speaker 3 (02:44):
Rick.
Speaker 2 (02:45):
Welcome, Thank Here. We are in the IBM's New York
City headquarters at one Madison Avenue. I'm going to start
with you're a hardware guy.
Speaker 3 (02:55):
I'm a hardware guy. I grew up doing hardware chip engineering.
But like I tell a lot of people, a chip
engineering project is actually a giant software project with a
piece of hardware at the end of the project. I
think if you have that analytical brain, you like to
solve problems, you'd like to get things working. You can
do that in soet desertwork.
Speaker 2 (03:15):
But as being someone coming from a hardware background mean
that you think about problems in a different way.
Speaker 3 (03:22):
I think one thing that you do from a hardware background,
and especially a chip background, is a chip spin and
costs millions of dollars. So you're a lot more likely
to make sure everything has a great chance of being
perfect from the get go. Or if you start kind
of from a software background, your general mindset is I
don't know, try this, see if it works. I don't know.
(03:43):
Try that is, if it work, and you're kind of iterated,
or to iterate. Chip people are a little more uptight
about Okay, if this first round of the chip breaks
costs us from building another new round of the chip.
Speaker 2 (03:55):
Yeah, so you're a little more You guys are spend more.
Speaker 3 (03:58):
Time planning and planning verifying, tons of time verifying. Yeah.
Speaker 2 (04:05):
So you began your career as you look backward, yes, correct,
And you were there for how many years?
Speaker 3 (04:10):
I was there for thirty two years?
Speaker 2 (04:12):
Yes, And your last job there was I was leading.
Speaker 3 (04:15):
The software defining cloud business. I had grown up a
hardware guy. I had done all kinds of hardware projects,
big complicated Unix servers and things like that, and then came,
you know, grew out of R and D and more
into the business realm, and then I'm much an innovator
at heart. I really like innovating new concepts things like that.
(04:39):
And what I learned is I enjoyed innovating business models
and software projects as much as I did hardware products
and projects, and so getting teams inspired towards doing that
was really a deep fascination for me. So I ended
up doing a fantastic variety of experiences and had a
successful run and honestly retired in tending to retire and
(05:02):
do some of my outside activities and things like that.
Speaker 2 (05:04):
And then how long did you stay retired before IBM
can close?
Speaker 3 (05:08):
Almost two years? And when I first got at ALL,
I thought, no, I'm having too much fun. But I
would say three things really got me thinking hard about it.
One the industry that we're in, the IT industry. I
think it's the golden age. And what I mean by
that is for twenty years of that career, it is
(05:30):
kind of in the back office, say make sure that
stuff doesn't crash, and can you please reduce the cost
as much as possible, because it's not that important to
the main business. It's just a back office function. You
can see it right now. It is at the forefront
of all business revolution. It happened with the Internet. It
happened again with cloud and how that changed every ounce
(05:51):
of business, not just IT business, but all business. And
I think it's happening again with AI. So to be
in that career that long and to miss the kind
of this age where it's like this is front and center.
This changes everything about all businesses, not just technology businesses.
I was kind of feeling like, Gosh, you trained to
be in these really awesome environments, why wouldn't you do
(06:16):
that for a little while longer while you still can
do it. That combined with IBM and IBM seeing the
talent pool, the brilliant people at IBM, I worked with
a ton of brilliant people before I saw a chance
to work with even a larger staff of brilliant people.
And then the assets that IBM had, which is, you know,
they'd already been doing a lot of experimentation in AI,
(06:38):
they're working in quantum, the deep, rich heritage of successful projects.
I thought, who wouldn't want to kind of see if
they could be part of that next great wave of IBM.
And so I kind of decided, all right, I'm going
to put the outside interest on hold for a while
and get back in the game.
Speaker 2 (06:54):
Along between the phone call, the first phone call and
you say, yes.
Speaker 3 (06:58):
It was a while, was probably six months. Arvind's our CEO,
teases me about that a lot. Yeah, he was like,
I don't think six months. Is that long? It took
a while you're retirement, I know. Yeah, it's one thing
to compare. I'm working here and doing this stuff versus
working there. It's really hard to compare. I'm doing exactly
as I want to do every single day. When I
(07:19):
wake up and now I'm not going to get to
do that again. It took a while for me to
get over and I thought, I can't miss this wave,
and I'm really really happy that I did, because we're
doing some amazing fun things and I'm getting challenged in
ways that I never did, so it's really fun.
Speaker 2 (07:37):
Talk a little bit about your job here at IBM.
You oversee a kind of massive portfolio.
Speaker 3 (07:44):
It's a big group, so I run the Infrastructure organization.
There's three main groups of products at IBM. There's the
Infrastructure group, which I run, the Software group, and the
Consulting group. And infrastructure is built up of mainframes, which
is called our Z portfolio, our servers which is our
power portfolio storage. By the way, those businesses include the
(08:08):
supply chain to build all of that stuff, so that's
in the group. Then I have the worldwide Customer Support Organization.
It's called TLS Technology life Cycle Services, which is a
network of about thirteen thousand people around the globe that
make sure that everything runs and works when you buy
IBM products. And then also our IBM Cloud, which is
how we host applications and deliver as a service products
(08:33):
for our client base, so there's a lot. I think
it's about forty five thousand people total.
Speaker 2 (08:38):
Do those components of the infrastructure group are they aligned
in their trajectory or do they are they on different paths?
And I'm just curious what so navigattle of both.
Speaker 3 (08:48):
It's interesting you would ask that because I think of
all of the challenges coming to the new company, there
were things I expected, things that they didn't expect. But
getting that culture right in that group has been a
big challenge. IBM has a great culture toward quality products,
(09:10):
toward emphasizing passion for the client and making sure that
the client is happy, and for delivering innovation on a
scale that you know, for more than one hundred years
has been extremely powerful. But with success comes some challenges.
And with that success you can tend to get a
little bit insular, like you don't keep an eye on
(09:32):
the competition as well, you can get more siloed, where
you know, this is my business unit, this is my
business unit, I compete with the other business unit. That's
not a good thing when when you're a company and
you can get really risk averse, meaning you feel like, hey,
this is a successful business I don't want to do
anything to mess it up, so I don't need to
try new things. Well, that's exactly the recipe to kind
(09:55):
of be shrinking, and infrastructure had been shrinking for a
little while, and so a lot of what the challenge
was for me was to invigorate that risk taking and
get to a growth mindset where you're trying new things
and seeing what works and what doesn't work, and changing
some of the models, like investing a little bit less
in hardware for some software differentiation that goes into the hardware.
(10:16):
So it's been very successful so far, and it's been
a good journey. It's almost four years now.
Speaker 2 (10:22):
Give me an example of what was a really hard
problem that you've dealt with in those four years.
Speaker 3 (10:27):
So, boy, a really hard problem?
Speaker 2 (10:30):
An interesting and are you interesting is a better word
than art.
Speaker 3 (10:33):
One of the first things that I kind of chewed
on a little bit is I talked about how we
have Z power and storage. The Z and power product
lines are well known in the industry. Is is really
fit for purpose computing that have strengths that you know
Z runs you know most of the world's economic backbone,
and if you use a credit card, ninety percent of
(10:55):
credit card transactions for the globe. Go through these Z
mainframes there in every bank there. You know, it's a
big business. It's well known in the industry. Same with power,
very tuned and optimized for smaller operations than our giant
Z mainframes, but really mission critical workloads for retail, for insurance,
(11:16):
for banking, for all of that. Our storage business not
so well known. In fact, when I came I thought,
did they have storage? Well, I have storage when I
come into I. So I got online and I thought,
it's still hard for me to tell did they have
storage or not? Now I own a storage business. So
one of the things was not just to get the
market perception up, but to invest in that business. Because
(11:37):
if you look at infrastructure overall around the globe, it's
growing at five percent a year. The infrastructure business had
been kind of flat to declining, and so a challenge
was how do we grab onto the growth. Well, one
of the biggest growth areas due to the explosion of
data in the world is storage. So what do you
do to kind of get on that growth rate. So
we did a lot of reinvigoration of the innovation in
(11:59):
that a lot of software value, add a lot of
doubling down on the things that are working. Portfolio rationalization,
where you segment the market and you say, okay, we're
going to do less of this and really go big
in these areas. And that's been probably the most dramatic
turnaround inside the group. Is our storage thing. When you
say it's a hard problem, it's not just oh, you know,
how do we do the math? No, it's cultural. It's strategy,
(12:23):
and how do you get the strategy set. It's segmentation,
it's product strategy at a granular level across a bunch
of dimensions, and then putting the investment behind it. It's
a big challenge. It takes a long time, but it's working.
So we're happy with Yeah.
Speaker 2 (12:36):
Tell me give me a little bit of perspective on
you've been there four years. Imagine we're having this conversation
four years ago. Yeah, what sorts of things have happened
over the last four years that have surprised you that
you didn't see come as we had exactly the same
conversation four years ago.
Speaker 3 (12:55):
No, because I didn't know what was in I'll tell
you some of the biggest surprises I thought from the outside,
and you know, you hear from a lot of customers,
especially ten years ago. We're all going to cloud. We're
all so I thought, well, I wonder if the mainframe
business is struggling. When I get inside of there, I
found the opposite to be true. The mainframe business is
(13:16):
actually flourishing because transaction demand across the globe has done
nothing but grow. And even more surprising was the level
of innovation that the team was already doing in mainframes
before I got here was astounding. For example, we have AI.
They were building AI technology into the mainframe processors three
(13:37):
years before chat GPT made everybody talk about it in
the industry, So that was really pleasantly surprising. So that
was wonderful. Other surprises. I knew about the kind of
the IP of IBM and the mystique in that, and
I used to joke with people, especially on the outside,
I said, I can't wait to get in there and
see what's in the big blue toolbox? Right, what are
(13:59):
all the things they have going on? I way underestimated
the size of the big blue toolbox and what was
in there, meaning the amount of really hardcore research that
we're still doing into how to build chips and how
to get to things beyond two nanimeter and that kind
of capability packaging industry leading packaging technologies, and that's in
(14:23):
my hardware kind of patch quantum. The next thing that
will come after we're done talking about AI. You know,
all of those things were surprising, But it wasn't just that.
It was then the software innovations that are going on
heavy investment in AI technologies before it was really popular
to be talking about that. But as I saw that,
(14:43):
I thought this is going to be really fun. Because
I had a good feel for where the industry was going.
I just didn't and I knew, man, I know that
talent is really good and there's brilliant people there, but
I didn't know the level of IP frankly that IBM
had at its disposal, And now you're seeing that in
things like Watson X and things like AI in mainframes,
(15:05):
et cetera.
Speaker 2 (15:06):
Building on that, since you brought up AI, can you
walk me through what has to happen from your perspective,
from the infrastructure perspective to make the AI explosion work? Yeah,
so everyone wants to do more of this stuff. Yes,
clearly there has to be some underpinning of it.
Speaker 3 (15:24):
Yeah, I would tell you, you know, I think that people
feel like where we're at right now in the AI
journey had to do with one specific piece of software.
I think the inflection point for that whole thing really
at its root was around hardware, meaning the algorithms needed
to do larger language models. And all of that had
(15:45):
been around, they'd been talked about in the industry, but
at some point you hit a tipping point of hardware
capability where it's like, oh, now we can do this
in a broof force way, massive amounts of matrix math
to get weights correct so that you can do you know,
the right level of predictions that enable large language models.
And once we got to that horsepower. And that's why
(16:06):
you hear about giant GPUs that are driving this and
the sales of those, et cetera. It's because we just
barely got over the hump where you can do these
big hard things in terms of hardware capability to do it.
Speaker 2 (16:19):
Give me a layman, give me a sense of when
you say there was a kind of threshold where suddenly
these things became possible.
Speaker 3 (16:25):
Yeah, I don't know if there's an exact number, But
and more basic question that I get from a lot
of people. You know, my friends and family outside is
why GPUs. What does a GPU, a graphics processor have
to do with AI. It's not, Well, graphics processors are
really good at this thing matrix math, because they're figuring
(16:47):
out how do I map a pixel? And as I
move an object across the screen, it's essentially matrix math
to figure out, Okay, what does what does this pixel
on a screen look like? And what's it's doing? And
as you know, we've gotten more high resolution graphics, more
high resolution monitors, et cetera. It's a lot more pixels
and a lot more math and a lot more matrix
(17:09):
math about how you compute that. The first big thing
that kind of started to look like that, it turns out,
was crypto and crypto mining, and so you saw graphics
companies starting to sell to crypto. The technology got to
a certain point and there were use cases like bitcoin
and that that kind of said, hey, we need to
do a lot of this matrix math to be able
to do that. So graphic chips were a natural fit
(17:32):
and that kind of sustain But meanwhile, behind the scenes,
a lot of this AI AI is about numeric calculations
having to do with weights and matrices that say you know,
giant consolidated things that predict what's going to kind of
happen based on what other things have happened, just like
predicting where pixel goes. But it's really about being able
(17:54):
to do enough data in jest to be able to
do and then the calculations to be able to simple
five things like entire sets of language or giant chunks
of the Internet, to get enough waitings in there to
be able to say, Okay, we can predict what you
would say in this language based on all of the
volumes of stuff that we've seen that when you start
(18:15):
talking like this, the next word is likely, oh it's this. Yeah.
Speaker 2 (18:19):
So, but my point is to get to that point.
That's threshold. We got there because was it because we
simply threw a lot more resources at the problem or
is it because the underlying technology got suddenly or gradually
so much more efficient.
Speaker 3 (18:33):
It's always yes and yes. But you know, the industry
for a lot of years would talk about Moore's law.
Speaker 2 (18:39):
Well, quick, will you define for us More's law for
those of those who's forgotten it.
Speaker 3 (18:45):
Yeah, So Gordon Moore at Intel coined this thing. It
was basically that the horsepower I'm going to translate it
roughly of technology will double every couple of years. We're
still on Moore's law. More's law changed a little bit.
For a while, it was always about frequency. Things would
go faster, faster, faster. That kind of petered out. But
(19:08):
what happened is, rather than faster, faster, faster, we did
more and more and more. So rather than one operating
unit going a lot faster on its throughput, you put
ten operating units on a chip, now you put one
hundred operating units on a chip, now a thousand. Some
of these problems, the matrix math problems scaled parallel extremely well.
(19:29):
You don't have to do something really fast, you just
have to do a lot of the similar things in
parallel at the same time. So again that kind of
that extension of Moore's law, more and more hardware on
a chip to be able to do more and more
of those calculations in parallel and come up with it.
Speaker 2 (19:44):
And we said, yeah, was that threshold predictable? In other words,
see people in the industry, like you sit down X
number of years ago and say, when we get here,
AI is going to become much more of a.
Speaker 3 (19:55):
It's funny the horsepower that very pretty. The use cases
not always so easy to kind of figure out. That's
where the human spirit kind of gets involved. I think
for some people that say, oh, I saw that coming,
but people have been predicting kind of the rise of
AI for twenty five years. Oh well, then when we
(20:17):
get to this next gener oh when we get here,
it kind of hadn't happened. There's always a magic point
where you kind of get to where the technology and
the use case and somebody does something to kind of
make it catch on. And I think we're at one
of those moments in AI for sure right now. And
I don't think it's you know, people that have said, oh,
this is just the latest wave of you know, I've
(20:38):
heard this about a lot of technologies, but AI is
the technology the future, and it always will be. I
used to hear that. You're not hearing that now, right,
It's like, no, it's primetime. It will change everything, just
like some of these other things changed everything.
Speaker 2 (20:52):
I noticed it if personally when I speak somewhere or
I'm listening in an audience somewhere. Over the last let's
say twelve months, there's always a whole bunch of AI
questions yes, And if I go back two years ago,
there were no AI questions.
Speaker 3 (21:09):
Yes.
Speaker 2 (21:10):
Now my question is, so there's been this explosion on
the in popular fascination with what's going on AI. It
seems like the last year. I agree with you in
your world, when did the explosion of conversation around this start.
Speaker 3 (21:27):
It's I love this question because IBM had a fairly
big effort and business called Watson before Watson X. And
this is going back kind of ten years. I'll give
you another kind of example. I knew about a lot
(21:47):
of tablet technology before there was an iPad, a lot.
For ten years, there were a lot, but it kind
of takes a magic combination of the technology, the user experienced,
the software, and the need and the market for it
to kind of go. Now it's the thing. Now we
all have either an iPad or we have the Google
equivalent to And so I think this is a little
(22:08):
like that. Meaning IBM was on the right track with Watson.
Some of the hardware wasn't there, the use cases weren't
exactly figured out, some of the early use cases didn't
pan out perfectly. But the good news about that is
it's back to that culture of risk taking. You don't
look back on that and say, oh, we shouldn't have
done that, that was a bad idea. I know you
look back on that and say, what did we learn?
(22:29):
How should we try something new? How would we pivot
this time? That's what we've done with Watson X, and
now that's a growing, healthy piece of our business and
very important our strategic picture. So we're all in.
Speaker 2 (22:41):
I've always investigated by the gap between insider sense of
what is happening in an outsider sense, like.
Speaker 3 (22:50):
It absolutely is that in this case, we've all been
talking about and thinking about AI and is it time
for that and what does this mean? Et cetera. And
yet none of us really predicted that actual moment, which
is kind of you know, early twenty twenty two where
it was like, oh, now you have a simple human
(23:10):
interface of software innovation combined with large language models. There's
a moment there where you're like, oh, Unlike, you know,
I think all of us are frustrated if we ask
our phone, hey, tell me about this and it says
I found this on the web page. That does you
no good. But you know, all of a sudden, with
Chad GPT and some of these other things, you could
(23:31):
ask a question, it would give you a clear answer.
Sometimes is wrong, but at least it was like I'm
getting an answer rather than hey, I don't know if
there's some references. Good luck to you, and that's really changing.
Talk about the kind of macro trends that are going
to shape your infrastructure battle. Yeah, we've talked about if
(23:52):
you already, but I'm actually going to go a little
different direction. So macro trans first. And this one has
been before even even this AI conversation, that we've had
explosion of data. As humans, we don't think exponentially very well.
We really struggle with exponential thinking. We think linearly, Oh,
(24:14):
there'll be more, there'll be more, they'll be more, But
we don't think well when it's like no, there'll be more,
and they'll be ten times more, and then there'll be
ten times that more. That's what's going on with data
right now in our industry. It's one of the reasons
that that storage business is doing so well is they're
just more and more and more data. You know, you'd say, well,
how can there be more data? It's just life and
that thing. The things that we care about, video captured
(24:36):
video images, you know, the you I don't know from
my parents, you needed a drawer with all your family photos.
Now we need gigabytes and gigabytes.
Speaker 2 (24:46):
You knew how many pictures my wife has taken off
our children, you would exactly exactly, So that's your case. Now.
Speaker 3 (24:52):
Think of companies who used to just think about their
transaction data. What's the ledger say that now have video
assets of all of their campaigns and their marketing. They're
trying to figure out, you know, what campaigns are working
the best. So it's just an explosion of data and
that's not going to stop. Dealing with that, and more importantly,
getting value from that data is a massive trend in
(25:17):
the industry. Second trend AI, and this is the AI.
Not like we were just talking about about how it
changes how I search for things or how I learn
about things. But I would argue, dealing with that data,
how do I figure out what's in all those video streams?
How do I figure out, Okay, I want all of
the chunks of my corporate video that have to do
(25:39):
with client buying some specific product or something. That's a
different problem. It's not just okay, We'll look it up
in a spreadsheet and here's the math associated with that
that is a huge trend in the industry. You're seeing
it play out in this regard. It's a little different
bent on the AI fraud detection is the one that
we cite in our mainframe. It's a similar problem where
(26:01):
it was kind of a traditional AI problem. Look up
a rule. You know, if somebody does two small transactions,
then a massive one it might be fraud, right because
they were seeing whether it were now to detect fraud,
you might be saying, okay to transactions then a huge one.
Plus does this entity have a real address? Second, is
(26:22):
there any web traffic on you know, better Business bureau
kind of things that says this is a bad business
that can help you with fraud. So it's a lot
more of a it's an expotent problem. It's a holistic
problem that it takes a lot more than just you know,
little chunks of rules, et cetera. And then the third
one you know, after AI, is the nature of hybrid
(26:42):
it or hybrid computing. For a while ten years ago
when cloud was on the rise, I think the notion
of hybrid computing basically having to do with things in
the cloud versus things that people still have on the
premises inside of business. It was almost religious argument. Now
it's no, it's the reality. And the reason is because
(27:05):
that data that I talked about is the lifeblood of
these companies, particularly IBM's companies are clients that usually that
data has to be secure, they have to be able
to get value from it. It is the lifeblood of
the company. If you go to an ATM and you
can't get your money out, you know, to our financial transactions,
if that lasts a day, you're probably going to change
(27:27):
banks immediately. So it's like life or death for these companies.
So having that hybrid infrastructure so that they can still
hold their data, you still interact with clouds and still
get value from it from AI. That's kind of the
magic where we play and it's a huge business opportunity.
(27:47):
It is a true inflection point for the industry.
Speaker 2 (27:51):
I'm going to go back. I interrupted you when you
were in the middle of a rellion. We were talking
about what has to happen for for AI to scale
from the infrastructure standpoint. You gave one example that I
got you off on a tangent. Can you go back
and talk very so practically, like so I'm you know,
I'm a big company. I have all these dreams of AI,
(28:15):
of how I'm going to use this dratically. So give
me a very granular sense of the works you have
to do, yeah to make that dream possible.
Speaker 3 (28:23):
So let me first say what the company has to do,
and then maybe I'll say, then how do I help them?
If that makes sense? So if I'm a company and
I want to do that, So it turns out I
am a company, meaning I want to use AI in
my processes. I mentioned that I have a global network
of thirteen thousand employees that support our infrastructure around the world.
(28:45):
That challenge is a great challenge for AI. That means
I have data for every customer situation for thirteen thousand
employees globally around the world on what was their problem,
how did we fix it, what next steps did they
have to do, how did they remediate that? That data
is extremely valuable to me because if I can get
(29:07):
better at doing that than anybody else in the world,
that brings my cost down. I sell more products, I
sell more service, I sell more anything. So what I
have to do to get there is I have to
figure out, Okay, what's my objective. I have a couple objectives.
One I want customers to be able to support themselves
without even calling me, first off, and I don't want
when they call for the first answer to come back
(29:29):
to be did you try rebooting? Because I think that
irritates every single one of us. Did you try? Of
course I tried rebooting. I've had a lap up, of course.
I well, okay, well then tell me, okay, what firmware version,
all that other stuff. Okay, we know this interaction. So
that's kind of the problem set. Do I want that
to be customers solving their own problems? Well, even for
(29:51):
my support agents, I want something in their pocket on
their phone where they say I'm seeing these symptoms. It says, oh,
this happening around the globe. Here's here's kind of specific.
So there's my problems. What does it mean for infrastructure
on the back end? So first I got to get
all that data together, right, all of those customer law,
all that customer support around the globe, et cetera. That
(30:13):
needs to be stored. That's a big set of data.
And some of it's not just fix and that kind
of thing. Some of it is, Okay, you know, what
was the firmware version? Who was the tech? Because it
can matter. Is this their first time fixing this problem?
Is that they're one hundred and fiftieth time. What's their level?
It's a very complicated problem. Ingesting all that data takes
(30:34):
an architecture. We have a product called Scale, which is
one of our storage projects that actually makes it easy
to ingest all that data, get it organized, et cetera,
and then have a model. It's a whole different process
to kind of say did we train our model? We
can train our own models inside of IBM. We have
a granite set of models. Those models we fine tune,
(30:56):
and then we inference based on those models. So we
can do that inferencing in our cloud I have a
cloud set of infrastructure or in my power servers, we
can do inferencing with our capabilities and say, okay, based
on what I'm saying, here's what the remediation you should
do for that customer. We already are doing that today.
We've seen over a third of our support calls have
(31:21):
had significant reduction in the amount of time that it
takes to resolve that support call. Just by what I
said right there.
Speaker 2 (31:28):
That I've really been curious about this. If I had
reduced something like AI into that equation as you just did. Yeah,
and you said we've already seen a thirty percent, Say
did you say thirty percent reduction?
Speaker 3 (31:41):
Thirty percent of our interactions have seen significant reduction in
those times?
Speaker 2 (31:47):
Was that your primary goal to reduce the time of
the interaction? But it was if you if everything else
was the same all, but what you were doing was
shrinking the amount of time That would you.
Speaker 3 (31:56):
Want one of the primary goals? So to us in
that business net promoter score kind of the satisfaction of
a client is the supreme goal. What makes them satisfied,
doesn't cost me a fortune, happens really quickly, and if
I can do it myself, I'd be thrilled. It affects
all of those right. It kind of says it got
(32:17):
resolved faster, it didn't cost me an arm and a leg,
because the deck was barely here, because it's a common problem,
or I solved it myself without even calling, So all
of those objectives would kind of hit across all so
that now you see it. So that's a little microcosm.
That's just me and my customer support business. Now think
of how many problems for businesses around the world there
(32:37):
are like that it's not a it's not like a
new AI application that changes the entire user experience. That's
those will come, But right now it's kind of practical,
which is, I just want to do what I'm doing
better and faster, and I can get immediate economic return
from those things.
Speaker 2 (32:55):
How long how long did it take you to just
stick with that example of the customer reaction reducing thirty
percent of the time? How long from the very beginning
of that project yeap to that thirty percent reduction was?
How long?
Speaker 3 (33:09):
Less than a year? And yeah, So one of the challenges,
and this is interesting with a very large organization, as
you can imagine, just like you're seeing in the industry,
we don't have a problem of generating ideas for how
AI could help us. We actually have a problem filtering
(33:29):
the thousands of ideas from our employees and from from everywhere.
It's like, hey, we could use AI to and filtering
down and saying, okay, which of these will have a
return on investment quickly and at a level that sustains
that's worth kind of going and investing in the infrastructure
and the software and kind of making that happen.
Speaker 2 (33:48):
Is that unusual? If I talked to you twenty five
years ago and said, do you have a problem of
too many good ideas or too few?
Speaker 3 (33:55):
What was you said in this specific carriot, Probably too few,
because at some point you reach diminishing returns. So, for example,
let's use this same example. Can those thirteen thousand technicians
go faster? Can they spend less time driving to the side.
I mean, there's only so much you can kind of
(34:15):
do on those things. But if you can get them
an answer to the problem and maybe even avoid them
having to visit at all because the client helped themselves,
that's a step function. So that's why people are kind
of talking about there's a business revolution coming with AI
where there are some step function changes that can be there.
And notice I didn't say I'm going to have less
(34:37):
of those agents. That's not my objective. My objective and
I think that's the fear in the industry about AI
is going to eliminate all the jobs. No, I just
created thirteen thousand superpowered agents that can do more right.
And so I'm not just going to support IBM products.
I'm going to go out and support other people's products
because I know how to do that really well. And
once I have the data on how to fix their problems.
(34:59):
I may just have a customer support business that's independent
of mind boxes. So you know, I think that's where
people sometimes get it wrong. And the AI thing is
it's like, you know, do word processing eliminate the need
for writers? No? It enabled writing instead of mucking around
with mimeographic machines and click and click typewriters.
Speaker 2 (35:20):
It may have enabled too much writing. Yeah, maybe maybe
can I give you a hypothetical? Uh? And I asked
this because I read I was at some convers and
I ran into some guy from the I R S
who was really, really, really really excited about AI. So
let's suppose they call you up and they say, you're
going to talk to the I R S. Okay, I
(35:44):
call you up and I say, Rick, Uh, clearly there's
something that we could do for the I R S
if we work together.
Speaker 3 (35:53):
Yeah.
Speaker 2 (35:53):
Who would your answer me?
Speaker 3 (35:55):
Of course?
Speaker 2 (35:57):
No.
Speaker 3 (35:57):
I think we sell to a lot of government agencies.
Can imagine in the business that we're in, we enable
a lot of social security transactions and things like that
through our mainframes. And I think, you know, we're in
the business of helping whatever client get the most out
of their data and be able to secure it and
(36:18):
and be able to do analytics with this. And IRS
has a heck of a lot of data, so yes,
we would help them.
Speaker 2 (36:25):
Do you know how the amount of data they have
compares to some of the corporate clients you've.
Speaker 3 (36:29):
I don't know specifically for the IRS how much data
they have, but I would assume it's a whole lot.
It's mountains. But but that's our business. I mean, it's
interesting sometimes people have that what's the most you know,
what what is it that that IBM has that's of
great value? Is it a server? Is it a storage array?
(36:49):
Is it you know, software and all that. What we
have is the most important entities in the world have
their data on our stuff. The most important data in
the world. It's not you know, pictures of your grandkids
and things like that. Generally for us, it's all of
the financial transactions that happen globally, right, It's all of
(37:09):
the it's the world's economy is kind of running through
our systems, and so we take that really seriously. You know,
you would be distraught if you lost one photo on
your laptop or whatever. But you know, if we lose
a transaction, like somebody moves a big amount of money
and it's like, well, don't know what happened there. It
is a massive deal, right, so that doesn't happen.
Speaker 2 (37:31):
But I want to go back to my irs example
for US, Yes, so one, is it reasonable to assume
that you could that somebody IBM or somebody else could
in a short period of time put together not just
the AI capability to audit returns, but also this the
infrastructure support for that in a reasonable amount of time
(37:53):
for a reasonable amount of cost. Or is it overall?
Is it going to the moon? Or is it it?
Speaker 3 (37:59):
Definitely? I mean, so we're already doing that kind of
thing right across a network of banks and others, essentially
all credit card transactions for all of the world to
go through our systems, so that in some ways is
more volume than the datch returns of the US people.
(38:19):
And they're W two's and all that stuff, and we
do that stuff too. I try not to describe it
too much in detail, but we definitely do a lot
of that. In fact, I think most of if you think, okay,
what is super critical data, who would be doing the
business transaction processing? It is most likely US in almost
(38:41):
all cases, whether it's government things or private or banks
or that kind of thing. That's what we do.
Speaker 2 (38:48):
Rick we're going to end with the where we always
end with a couple of quick fire questions.
Speaker 3 (38:52):
Okay, here we go.
Speaker 2 (38:54):
What single piece of advice would you give to businesses
trying to use AI in an effective way?
Speaker 3 (39:00):
The simple version is get started. By get started, I
mean think of what is something that I want to improve.
The things that we have traction on right now in
the market are around business process, automation, digital labor, those
kind of things. But my other little piece of advice
there is keep it simple to begin with. You're going
(39:21):
to learn a lot, but getting started means you'll start
that learning curve. I even advise my friends like, Hey,
should I be playing around with some of this AI stuff?
And I say yeah, because I think it will help
you start to be more comfortable and you may find
a use case personally for that. I think the same
is true for businesses. The first step in that journey
is always with what data. Notice when I talked about
(39:44):
our customer support people, I thought about, Okay, what's the data.
The data is all of those logs of all of
those service engagements around the world, and what could I
do with that? Well, I could use that to get
to a knowledge base that really helps hopefully that I
can do it in multiple languages because it's global and
I can you know, all of those things. That was
(40:06):
kind of my data sent That one's not super simple,
but we've had a lot of experience in AI for
other people that might just be how do I automate
filling out travel expense reports for my company? We can
help people that we have consulting, we have wats and
X tools. We can do that like this, and we're
doing it globally for people around the world. Pick that thing.
(40:26):
What's the data you have? In that case, it's data
of expense reports and it's like, okay, we can help
you automate that for people where they could do it
just by you know, a verbal interface. What did you spend,
where did you go, who you were you with? Okay,
we filled out your travel expense report for you and
you don't have to mess around with it.
Speaker 2 (40:43):
So we were playing with this idea where we would
pick a business and go in there and do it
would be AI makeover.
Speaker 3 (40:51):
Yeah, I love that.
Speaker 2 (40:52):
What's okay, what's the what is the ideal business to do.
We only have a couple months. We don't want to
spend a kajillion dollars. We want to be able to
show tangibly and quickly what AI can do. What's that
ideal business to do? That in it can be a
small business, but we're not talking. This isn't a grand corporate.
Speaker 3 (41:08):
Thing there, ah boy, small business that we could do
and hey, I make over. Customer support is one of
my favorites because it's it's it's I have it on
the business side where I provide customer support. I have
it on the consumer side, where it drives me nuts
when I have to go through thirty layers of phone menus.
(41:31):
Speak to an agent, speak to an agent, speak to
an agent. That for any business, I think is just
ripe to be able to kind of say, why do
I have to click through these manuscent messages? I just
need to tell you in human language, here's the issue,
and I'll be really good about telling you details about
you know, I tried to set up this thing for
my bank and I do da da da da da.
(41:52):
They can go through all the menus automate that process.
I think it would change everything because all that frustration
is a consumer would go down dramatically and it's all,
you know, why are you making me the beep booth,
press one, offload press exactly, Well, don't offload to me,
offload to AI. We can help you with that.
Speaker 2 (42:13):
Here's my version of that drives me crazy. Every morning
I go to the same coffee shop and I get
a cup of tea and a croissant.
Speaker 3 (42:23):
And here's what happens.
Speaker 2 (42:24):
The person has their screen and they go I go,
cup of tea, croissant, sparkling water, like at least twenty keystrokes,
and then like then the screen is turned around. Like
at this point we're like forty five seconds in, I'm like,
why is this? First of all, it's not for me.
All those keystrokes, it's there in turn right right, So
(42:46):
they're burdening me in order to service.
Speaker 3 (42:48):
To back it, you should be able to walk in,
go up and they go, I'm olc them the same
thing and you just go yes, and then they boom,
We're done.
Speaker 2 (42:55):
Can we do AI makeover of my coffee shop?
Speaker 3 (42:59):
You notice I quickly jumped more to banks than your
coffee shop because I think I'm a business person, but
I'm not trying to kind of do a deal on
one coffee shop.
Speaker 2 (43:09):
No, But this is interesting because it takes me back
to something you said that I thought was really important.
When you were talking about when you were using AI
and your customer service thing, it was clear that your goal,
you could have any number of goals going in. It
could be to cut costs, it could be to dramatically
improved profits.
Speaker 3 (43:29):
Your goal, quite.
Speaker 2 (43:29):
Specifically, was to improve the experience of your customer, right,
So you were using it to that.
Speaker 3 (43:34):
All the other things come from that come from. That
is actually one of the beautiful pillars of the IBM
culture is delighting clients is actually where all of the
good stuff comes from.
Speaker 2 (43:45):
So my coffee shop thing is the same principle. Right now,
they're making my customer experience worse and they don't want to.
Speaker 3 (43:54):
Their eyes are glued to the special a.
Speaker 2 (43:56):
Moment when I walk in and I want to say, Hi,
how are you doing conversation? You're too busy, busy, So like,
this is the same thing. If they had it, oh,
we this is if they understood they had an opportunity
to improve the experience of their customer experience.
Speaker 3 (44:11):
I would not be surprised if a chain comes along
where that is their value proposition, I would not be
surprised at all. Yeah, right, So I mean and and
when those things kind of catch hold, it becomes a revolution.
Speaker 2 (44:26):
You know, when the guy comes to do like to
redo your roof and they put a sign out front,
like you know, Joe's roofing. You guys could do the
same with my coffee shop. But like I'd be was
here exactly exactly, in five years, the main frame will
be dot dot going strong.
Speaker 3 (44:49):
H the main frame going strong and with new capabilities,
continuous new capabilities. I think when we announced the last
versions six, the latest version, I should say, and we said, hey,
there's AI processing built into it. This was before everybody
was talking about that. I think a lot of people thought,
(45:10):
what's that for? And we did it specifically for traditional
AI fraud detection, et cetera. This next version, not only
do we have the traditional AI built in, but we
have optional cards that you can plug into it to
allow you to do large language models for the enhanced
fraud detection cases that we talked about, where you know,
(45:30):
it's more than just what transactions were happening. So if
you take that and say, okay, the next generations. We
have more transaction volume than we've ever had in mainframes. Today,
the business is growing, it's strong, we keep innovating. In
five years it'll be going strong.
Speaker 2 (45:47):
But we're people. You're saying this in the context of
for years people were predicting, weren't they that the main
brand was going to go away.
Speaker 3 (45:56):
There were pundits in the market that said everything will
go away there no one will ever have a box,
It'll all be online. I think this is something I've
learned big time in my long career. You know in
the IT industry is don't believe everything you hear. So
I went back for my master's degree at Stanford after
(46:16):
I had worked a while in as a hardware designer,
and everybody told me be sure to do your masters
in software. Hardware is dead. I went on to work
for thirty plus years in hardware and infrastructure. Now software
became important, and I'm glad I had that extra training
in software because it helped me in hardware. But hardware
wasn't dead. Then I heard all infrastructure will go into
(46:40):
the cloud. There won't be that hasn't happened. It's not happening.
Then I heard there will only be one cloud because
one of the players will dominate. There's not one cloud.
So I think it's as humans we like to oversimplify
and go, oh, it's all going to be this, and
kind of what I've learned is fit for purpose matters
in everything. It matters in size of infrastructure, it matters
(47:04):
in the stack that goes along with solving a specific
use case. If you're willing to design something that's the
best at that use case, If you're willing to design
the coffee shop that is the best at greeting me,
there's a spot for you, and there may be a
big business in doing that. So oversimplifying is really.
Speaker 2 (47:21):
When you heard all those predictions, did you believe them
at the time.
Speaker 3 (47:27):
They looked like they were trending in that direction. I'll
tell you some right now which might be useful. There
will only be one GPU company and they're going to
end up taking over the world. It's a pretty obvious answer.
Whose economic values risen dramatically. I don't think that's going
to be the case. In fact, I think that ninety
percent of processing for AI actually happen happens at inferencing,
(47:50):
and inferencing is not as GPU and hardware intensive as
the other things, and is a lot more amenable to
fit for purpose. So the model size will matter. The
tune matters a lot. As we're learning. We have a
product around instruct lab that's really focused on tuning. So
that was one thing is there'll be one GPU. The
other thing is that the biggest model will win. I
(48:12):
think is another thing that's kind of people are saying
right now. Don't believe that I believe they'll be fit
for purpose models. It takes a lot of money to
run to create a huge model, and then to run
a huge model, or to even infer off of a
huge model. I don't need a massive training GPU set
thing to solve my thirteen thousand people customer support issues.
(48:34):
So why would I feel like I got to go
farm that out for a big expensive thing. I can
do that on a small box. In some cases I
might even be able to do that on a laptop.
The other thing I'll say in this we are so
early innings in AI, A lot of things are going
to change. So anybody kind of saying it will all
be X, Y or Z, I just think you have
no idea how this is going to play out. And
(48:55):
it's up to us to go figure out how it
plays out.
Speaker 2 (48:58):
Yeah, yeah, all right, in five years, AI will be
dot dot dot.
Speaker 3 (49:03):
Still new. It will have moved a bunch in five years,
but the potential for the disruption in the world will
still will still be very early innings in that process.
And I think that's super important to realize. That's why
I say get started, start thinking about how that could change,
(49:24):
because it'll be some little things first, but it will
continue to snowball.
Speaker 2 (49:28):
This is a common observation that we the invention of
the capability uh massively predates the understanding of the capability, right,
Like I love that. Yeah, Like, yes, recorded recording shows
on television is invented in the sixties. Probably we don't
(49:52):
really understand what it's used for until the oughts was
what's really good for is being able to tell a
story sequentially, Yes, over time, because you know that the
person will all have see in the episode before, so
you got the Sopranos And yes, yes, Hollywood wanted to
ban the VCR in the beginning. Yeah, because they thought
it was good. They thought the point of it was
(50:14):
thought the din understand No, no, no, it's storytelling. It's actually
your business is getting better. Yes, Yes, took them twenty
years to figure that out, which is to your point,
why would we know what AI was four and five yearso?
Speaker 3 (50:24):
Well, that's why you hear people kind of say, oh
my gosh, AI, that's that will just eliminate jobs. No,
it'll make jobs better. That's how I view it.
Speaker 2 (50:31):
Yeah, what's the number one thing that people misunderstand about AI?
Speaker 3 (50:35):
Is that it that it'll I think that's that. That
would be the human kind of understanding part of it.
The technology part of it, I think would be what
I was talking about fit for purpose, meaning that it
isn't just going to be a GPU arms race all
of AI. I don't believe that at all. It will
change everything, but it's not just going to be a GPU.
Speaker 2 (50:56):
Armed Next question, what advice would you give yourself ten
years ago to better prepare you for today? I'm changing
this question, Okay. I want to say, let's imagine that
what was your what.
Speaker 3 (51:10):
College you to go to? I went to three of them.
My undergrad was Utah State University, my MBA was Santa
Clara University, and my master's in w was Stanford.
Speaker 2 (51:20):
Okay, any one of those three culture up and says
we want you to give the commencement address and imagine
that it's it's it's let's just say, for the sake
of argument, it's just to the stamp people.
Speaker 3 (51:34):
Those are the relevant parties here.
Speaker 2 (51:36):
What do you tell them?
Speaker 3 (51:38):
Boy? What do I tell them? Let's see. I think
I would start with life is a marathon, not a sprint.
It would be the first one. The second thing I
would say that in that spirit is be sure to
set yourself some big, hairy, audacious goals and don't be
(52:02):
overly disappointed if you don't hit them all. Going after
those big, hairy, audacious goals will get you on a
path where you will learn so much. You will achieve
more than you ever could imagine you would have achieved.
That's what the advice I give to my kids is,
set some big goals, get after it. You may or
(52:22):
may not achieve them, but you'll be better for the
whole process when you're done.
Speaker 2 (52:25):
By the way, as someone whose kids are younger than yours,
is it actually useful to give you give advice to
your kids the points exercise TVD.
Speaker 3 (52:34):
We're still on the journey, and I think we will
be for a long time. I don't know how.
Speaker 2 (52:39):
Are you already using AI in your day to day
life today?
Speaker 3 (52:44):
Personally, I would say it's replacing a good chunk of
my search. You know, I'm less likely to go blindly
stumbling through a bunch of web pages looking for stuff.
I'm more likely to ask a question from a few
AI engines kind of see get me in the right direction,
then I'll go bumble through a few things. At work,
I can tell you code development right now, we are
(53:08):
seeing massive improvements in code development and support. Products we
have like Watson Code Assistant that is really showing immediate
return for a code developers, and I think that will
again be a tool that increases productivity for code developers
immediately across the globe. Yeah.
Speaker 2 (53:28):
Last question, what's the one skill that every technology leader
needs that has nothing to do with technology.
Speaker 3 (53:34):
Being able to inspire a set of people toward a
common goal and collaborate to achieve it. That's at the
core of everything everything. That's a lovely way to end.
Thank you so much, Rick, Thank you.
Speaker 2 (53:50):
This conversation left me excited. I'm now imagining the potential
for new use cases for AI in all sorts of
different businesses. Rick didn't seem sold on my idea of
a coffee shop makeover. But it's clear there's lots of
opportunities here to increase speed and efficiency, to achieve your objectives,
and to dream beyond the current applications for this technology.
(54:13):
At the end of the day, the scaling of AI
will rely on the right infrastructure to support it. With
the right tools, you can solve problems that are unique
tier industry and improve the experience for your customers. Smart
(54:37):
Talks with IBM is produced by Matt Romano, Amy Gains
McQuaid and Jacob Goldstein. Were edited by Lydia gene Kott,
mastering by Jake Koorsky. Theme song by Gramoscope. Special thanks
to the eight Bar and IBM teams, as well as
the Pushkin marketing team. Smart Talks with IBM is a
(54:58):
production of Pushkin Industries and Ruby Studio at iHeartMedia. To
find more Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts,
or wherever you listen to podcasts. I'm Malcolm Gladwell. This
(55:19):
is a paid advertisement from IBM. The conversations on this
podcast don't necessarily represent IBM's positions, strategies or opinions.
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