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