Speaker 1: Hey everyone, it's Robert and Joe here. Today we've got
Speaker 1: something a little different to share with you. It's a
Speaker 1: new season of the Smart Talks with IBM podcast series.
Speaker 2: This season, on smart Talks, Malcolm Gladwell and team are
Speaker 2: diving into the transformative world of artificial intelligence with a
Speaker 2: fresh perspective on the concept of open What does open
Speaker 2: really mean in the context of AI. It can mean
Speaker 2: open source code or open data, but it also encompasses
Speaker 2: fostering an ecosystem of ideas, ensuring diverse perspectives are heard,
Speaker 2: and enabling new levels of transparency.
Speaker 1: Join hosts from your favorite pushkin podcasts as they explore
Speaker 1: how opennes in AI is reshaping industries, driving innovation, and
Speaker 1: redefining what's possible. You'll hear from industry experts and leaders
Speaker 1: about the implication and possibilities of open AI, and of course,
Speaker 1: Malcolm Gladwell will be there to guide you through the
Speaker 1: season with his unique insights.
Speaker 2: Look out for new episodes of Smart Talks every other
Speaker 2: week on the iHeartRadio app, Apple Podcasts, or wherever you
Speaker 2: get your podcasts, and learn more at IBM dot com
Speaker 2: slash smart Talks.
Speaker 3: Hello, Hello, Welcome to smart Talks with IBM, a podcast
Speaker 3: from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Godwell. This season,
Speaker 3: we're diving back into the world of artificial intelligence, but
Speaker 3: with a focus on the powerful concept of open its possibilities, implications,
Speaker 3: and misconceptions. On today's episode, our season finale, I'm joined
Speaker 3: by Rick Lewis, the senior vice president of Infrastructure at IBM.
Speaker 3: Rick has had a remarkable career focused around product innovation.
Speaker 3: He was actually a few years into retirement when IBM
Speaker 3: came calling with an opportunity he just couldn't turn down. Thankfully,
Speaker 3: Rick came out of retirement and today he oversees a
Speaker 3: vast portfolio from storage and software to global customer support operations,
Speaker 3: and he's engaged in one of the key problems facing
Speaker 3: companies today, an explosion of data. In talking with Rick,
Speaker 3: I can see that this problem of having so much
Speaker 3: data is also an incredible opportunity because if you're able
Speaker 3: to leverage that data to get the most value out
Speaker 3: of it, then you can use it to help bring
Speaker 3: your business into the future. We talked about the serious
Speaker 3: computing power needed to scale AI, as well as the
Speaker 3: ways that infrastructure storage solutions can be essential to enabling
Speaker 3: this new world of possibilities. It's a really great conversation,
Speaker 3: so let's get to it. I'm here with Rick Lewis. Rick, Welcome,
Speaker 3: Thank Here. We are in the IBM's New York City
Speaker 3: headquarters at one Madison Avenue. I'm going to start with
Speaker 3: you're a hardware guy.
Speaker 4: I'm a hardware guy. I grew up doing hardware chip engineering.
Speaker 4: But like I tell a lot of people, a chip
Speaker 4: engineering project is actually a giant software project with a
Speaker 4: piece of hardware at the end of the project. I
Speaker 4: think if you have that analytical brain, you like to
Speaker 4: solve problems, you'd like to get things working. You can
Speaker 4: do that in soetwork.
Speaker 3: But as being someone coming from a hardware background mean
Speaker 3: that you think about problems in a different way.
Speaker 4: I think one thing that you do from a hardware background,
Speaker 4: and especially a chip background, is a chip spin and
Speaker 4: costs millions of dollars, So you're a lot more likely
Speaker 4: to make sure everything has a great chance of being
Speaker 4: perfect from the get go. Or if you start kind
Speaker 4: of from a software background, your general mindset is I
Speaker 4: don't know, try this, see if it works. I don't know,
Speaker 4: try that is if it work and you kind of
Speaker 4: it it iterate chip people are a little more uptight
Speaker 4: about okay, if this first round of the chip breaks
Speaker 4: costs us from building another new round of the chip.
Speaker 3: Yeah, so you're a little more You guys are spend
Speaker 3: more time.
Speaker 4: Planning and planning verifying, tons of time verifying.
Speaker 3: So you began your career as you look back, yes, correct,
Speaker 3: and you were there for how many years?
Speaker 4: I was there for thirty two years?
Speaker 3: Yes, And your last job there was I was leading.
Speaker 4: The software defining cloud business. I had grown up a
Speaker 4: hardware guy. I had done all kinds of hardware projects,
Speaker 4: big complicated Unix servers and things like that, and then came,
Speaker 4: you know, grew out of R and D and more
Speaker 4: into the business realm. And then I'm much an innovator
Speaker 4: at heart. I really like innovating new concepts things like that.
Speaker 4: And what I learned is I enjoyed innovating business models
Speaker 4: and software projects as much as I did hardware products
Speaker 4: and projects, and so getting teams inspired towards doing that
Speaker 4: was really a deep fascination for me. So I ended
Speaker 4: up doing a fantastic variety of experiences and had a
Speaker 4: successful run and honestly retired, intending to retire and do
Speaker 4: some of my outside activities and things like that.
Speaker 3: And then how long did you stay retired before IBM
Speaker 3: can close?
Speaker 4: Almost two years? And when I first got at ALL,
Speaker 4: I thought, no, I'm having too much fun. But I
Speaker 4: would say three things really got me thinking hard about it. One,
Speaker 4: the industry that we're in, the IT industry. I think
Speaker 4: it's the golden age. And what I mean by that
Speaker 4: is for twenty years of that career, it is kind
Speaker 4: of in the back office. Hey, make sure that stuff
Speaker 4: doesn't crash, and can you please reduce the cost as
Speaker 4: much as possible, because it's not that important to the
Speaker 4: main business. It's just a back office function. You can
Speaker 4: see it right now. It is at the forefront of
Speaker 4: all business revolution. It happened with the Internet. It happened
Speaker 4: again with cloud and how that changed every ounce of business,
Speaker 4: not just IT business, but all business. And I think
Speaker 4: it's happening again with AI. So to be in that
Speaker 4: career that long and to miss the kind of this
Speaker 4: age where it's like this is front and center. This
Speaker 4: changes everything about all businesses, not just technology businesses. I
Speaker 4: was kind of feeling like, gosh, you you trained to
Speaker 4: be in these really awesome environments. Why wouldn't you do
Speaker 4: that for a little while longer while you still can
Speaker 4: do it. That combined with IBM and IBM seeing the
Speaker 4: talent pool, the brilliant people at IBM, I worked with
Speaker 4: a ton of brilliant people before I saw a chance
Speaker 4: to work with even a larger staff of brilliant people.
Speaker 4: And then the assets that IBM had, which is, you know,
Speaker 4: they'd already been doing a lot of experimentation in AI,
Speaker 4: they're working in quantum, the deep, rich heritage of successful projects.
Speaker 4: I thought, who wouldn't want to kind of see if
Speaker 4: they could be part of that next great wave of IBM.
Speaker 4: And so I kind of decided, all right, I'm going
Speaker 4: to put the outside interest on hold for a while
Speaker 4: and get back in the game.
Speaker 3: Along between the phone call, the first phone call and
Speaker 3: you say, yes.
Speaker 4: It was a while, It was probably six months. Arvin's
Speaker 4: our CEO, teases me about that a lot. Yeah, he
Speaker 4: was like, I.
Speaker 3: Don't think six months is that long? It took a while.
Speaker 3: You're a retirement I know. Yeah.
Speaker 4: It's one thing to compare I'm working here and doing
Speaker 4: this stuff versus working there. It's really hard to compare.
Speaker 4: I'm doing exactly as I want to do every single
Speaker 4: day when I wake up, and now I'm not going
Speaker 4: to get to do that again. It took a while
Speaker 4: for me to get over and I thought, I can't
Speaker 4: miss this wave, and I'm really really happy that I did,
Speaker 4: because we're doing some amazing, fun things and I'm getting
Speaker 4: challenged in ways that I never did, so it's really fun.
Speaker 3: Talk a little bit about your job here at IBM.
Speaker 3: You oversee a kind of massive portfolio.
Speaker 4: It's a big group, so I run the Infrastructure organization.
Speaker 4: There's three main groups of products at IBM. There's the
Speaker 4: Infrastructure group, which I run, the Software group, and the
Speaker 4: Consulting group. And infrastructure is built up of mainframes, which
Speaker 4: is called our Z portfolio, our servers which is our
Speaker 4: power portfolio storage. By the way, those business include the
Speaker 4: supply chain to build all of that stuff, so that's
Speaker 4: in the group. Then I have the worldwide Customer Support
Speaker 4: Organization it's called TLS Technology life Cycle Services, which is
Speaker 4: a network of about thirteen thousand people around the globe
Speaker 4: that make sure that everything runs and works when you
Speaker 4: buy IBM products. And then also our IBM Cloud, which
Speaker 4: is how we host applications and deliver as a service
Speaker 4: products for our client base. So there's a lot. I
Speaker 4: think it's about forty five thousand people total. Do those.
Speaker 3: Components of the Infrastructure group are they aligned in their
Speaker 3: trajectory or are they on different paths?
Speaker 4: And I'm just curious what son little of both. It's
Speaker 4: interesting you would ask that because I think of all
Speaker 4: of the challenges coming to the new company, there were
Speaker 4: things I expected, things that they didn't expect. But getting
Speaker 4: that culture right in that group has been a big challenge.
Speaker 4: IBM has a great culture toward quality products, toward emphasizing
Speaker 4: passion for the client and making sure that the client
Speaker 4: is happy, and for delivering innovation on a scale that
Speaker 4: you know, for more than one hundred years has been
Speaker 4: extremely powerful. But with success comes some challenges. And with
Speaker 4: that success you can tend to get a little bit insular,
Speaker 4: like you don't keep an eye on the competition. As well,
Speaker 4: you can get more siloed, where you know, this is
Speaker 4: my business unit, this is my business unit, I compete
Speaker 4: with the other business unit. That's not a good thing
Speaker 4: when you're a company, and you can get really risk averse,
Speaker 4: meaning you feel like Hey, this is a successful business.
Speaker 4: I don't want to do anything to mess it up,
Speaker 4: so I don't need to try new things. Well, that's
Speaker 4: exactly the recipe to kind of be shrinking, and infrastructure
Speaker 4: had been shrinking for a little while, and so a
Speaker 4: lot of what the challenge was for me was to
Speaker 4: invigorate that risk taking and get to a growth mindset
Speaker 4: where you're trying new things and seeing what works and
Speaker 4: what doesn't work, and changing some of the models, like
Speaker 4: investing a little bit less in hardware for some software
Speaker 4: differentiation that goes into the hardware. So it's been very
Speaker 4: successful so far, and it's been a good journey. It's
Speaker 4: almost four years now.
Speaker 3: Give me an example of what was a really hard
Speaker 3: problem that you've dealt with in those four years.
Speaker 4: So, boy, a really hard problem.
Speaker 3: An interesting and are you interesting is a better word
Speaker 3: than art.
Speaker 4: One of the first things that I kind of chewed
Speaker 4: on a little bit is I talked about how we
Speaker 4: have Z power and storage. The Z and power product
Speaker 4: lines are well known in the industry. Is is really
Speaker 4: fit for purpose computing that have strengths that you know
Speaker 4: Z runs you know most of the world's economic backbone,
Speaker 4: and if you use a credit card. Ninety percent of
Speaker 4: credit card transactions for the globe go through these Z
Speaker 4: mainframes there in every bank there. You know, it's a
Speaker 4: big business. It's well known in the industry. Same with power,
Speaker 4: very tuned and optimized for smaller operations than our giant
Speaker 4: Z mainframes, but really mission critical workloads for retail, for insurance,
Speaker 4: for banking, for all of that. Our storage business not
Speaker 4: so well known. In fact, when I came I thought,
Speaker 4: did they have storage? Well, I have storage when I
Speaker 4: come in too. I So I got online and I thought,
Speaker 4: it's still hard for me to tell did they have
Speaker 4: storage or not? Now I own a storage business. So
Speaker 4: one of the things was not just to get the
Speaker 4: market perception up, but to invest in that business. Because
Speaker 4: if you look at infrastructure overall around the globe, it's
Speaker 4: growing at five percent a year. The infrastructure business had
Speaker 4: been kind of flat to declining, and so a challenge
Speaker 4: was how do we grab onto the growth. Well, one
Speaker 4: of the biggest growth areas due to the explosion of
Speaker 4: data in the world is storage, So what do you
Speaker 4: do to kind of get on that growth rate. So
Speaker 4: we did a lot of reinvigoration of the innovation in
Speaker 4: that a lot of software value, add a lot of
Speaker 4: doubling down on the things that are working. Portfolio rationalization
Speaker 4: where you segment the market and you say, okay, we're
Speaker 4: going to do less of this and really go big
Speaker 4: in these areas. And that's been probably the most dramatic
Speaker 4: turnaround inside the group. Is our storage thing. When you
Speaker 4: say it's a hard problem, it's not just oh, you know,
Speaker 4: how do we do the math? No, it's cultural. It's strategy,
Speaker 4: and how do you get the strategy set. It's segmentation,
Speaker 4: it's product strategy at a granular level across a bunch
Speaker 4: of dimensions, and then putting the investment behind it. It's
Speaker 4: a big challenge. It takes a long time, but it's working,
Speaker 4: so we're happy with Yeah.
Speaker 3: Tell me give me a little bit of perspective on
Speaker 3: you've been there four years. Imagine we're having this conversation
Speaker 3: four years ago.
Speaker 4: Yeah, what sorts.
Speaker 3: Of things have happened over the last four years that
Speaker 3: have surprised you that you didn't see come? At least
Speaker 3: we had exactly the same conversation four years ago.
Speaker 4: No, because I didn't know what was in I'll tell
Speaker 4: you some of the biggest surprises I thought from the outside,
Speaker 4: and you know, you hear from a lot of customers,
Speaker 4: especially ten years ago, we're all going to we're all
Speaker 4: so I thought, well, I wonder if the mainframe business
Speaker 4: is struggling. When I get inside of there, I found
Speaker 4: the opposite to be true. The mainframe business is actually
Speaker 4: flourishing because transaction demand across the globe has done nothing
Speaker 4: but grow. And even more surprising was the level of
Speaker 4: innovation that the team was already doing in mainframes before
Speaker 4: I got here was astounding. For example, we have AI.
Speaker 4: They were building AI technology into the mainframe processors three
Speaker 4: years before chat GPT made everybody talk about it in
Speaker 4: the industry, So that was really pleasantly surprising. So that
Speaker 4: was wonderful. Other surprises I knew about the kind of
Speaker 4: the IP of IBM and the mystique in that, and
Speaker 4: I used to joke with people, especially on the outside,
Speaker 4: I said, I can't wait to get in there and
Speaker 4: see what's in the big blue toolbox? Right, what are
Speaker 4: all the things they have going on? I way underestimated
Speaker 4: the size of the big blue toolbox and what was
Speaker 4: in their meaning amount of really hardcore research that we're
Speaker 4: still doing into how to build chips and how to
Speaker 4: get to things beyond two nanimeter and that kind of
Speaker 4: capability packaging industry leading packaging technologies, and that's in my
Speaker 4: hardware kind of patch quantum. The next thing that will
Speaker 4: come after we're done talking about AI. You know, all
Speaker 4: of those things were surprising, But it wasn't just that.
Speaker 4: It was then the software innovations that are going on
Speaker 4: heavy investment in AI technologies before it was really popular
Speaker 4: to be talking about that. But as I saw that,
Speaker 4: I thought this is going to get really fun. Because
Speaker 4: I had a good feel for where the industry was going.
Speaker 4: I just didn't and I knew, man, I know that
Speaker 4: talent is really good and there's brilliant people there, but
Speaker 4: I didn't know the level of IP frankly that IBM
Speaker 4: had at its disposal. And now you're seeing that in
Speaker 4: things like Watson X and things like AI in mainframes,
Speaker 4: et cetera.
Speaker 3: Building on that. Since you put AI, can you walk
Speaker 3: me through what has to happen from your perspective, from
Speaker 3: the infrastructure perspective to make the AI explosion work? Yeah,
Speaker 3: so everyone wants to do more of this stuff. Yes,
Speaker 3: clearly there has to be some underpinning of it.
Speaker 4: Yeah, I would tell you, you know, I think that
Speaker 4: people feel like where we're at right now in the
Speaker 4: AI journey had to do with one specific piece of software.
Speaker 4: I think the inflection point for that whole thing really
Speaker 4: at its root was around hardware, meaning the algorithms needed
Speaker 4: to do larger language models. And all of that had
Speaker 4: been around, they'd been talked about in the industry, but
Speaker 4: at some point you hit a tipping point of hardware
Speaker 4: capability where it's like, oh, now we can do this
Speaker 4: in a broof force way, massive amounts of matrix math
Speaker 4: to get weights correct so that you can do you know,
Speaker 4: the right level of predictions that enable large language models.
Speaker 4: And once we got to that horsepower, and that's why
Speaker 4: you hear about giant g pus that are driving this
Speaker 4: and the sales of those, et cetera. It's because we
Speaker 4: just barely got over the hump where you can do
Speaker 4: these big hard things in terms of hardware capability to
Speaker 4: do it.
Speaker 3: Give me a layman, give me a sense of when
Speaker 3: you say there was a kind of threshold where suddenly
Speaker 3: these things became possible.
Speaker 4: Yeah, I don't know if there's an exact number, but
Speaker 4: and more basic question that I get from a lot
Speaker 4: of people, you know, my friends and family outside. Is
Speaker 4: why GPUs. What does a GPU, a graphics processor have
Speaker 4: to do with AI. It's not, Well, graphics processors are
Speaker 4: really good at this thing matrix math, because they're figuring
Speaker 4: out how do I map a pixel? And as I
Speaker 4: move an object across the screen, it's essentially matrix math
Speaker 4: to figure out, Okay, what does what does this pixel
Speaker 4: on a screen look like? And what it's doing? And
Speaker 4: as you you know, we've gotten more high resolution graphics,
Speaker 4: more high resolution monitors, et cetera. It's a lot more
Speaker 4: pixels and a lot more math and a lot more
Speaker 4: matrix math about how you compute that. The first big
Speaker 4: thing that kind of started to look like that, it
Speaker 4: turns out, was crypto and crypto mining, and so you
Speaker 4: saw graphics companies starting to sell to crypto. The technology
Speaker 4: got to a certain point and there were use cases
Speaker 4: like bitcoin in that that kind of said, hey, we
Speaker 4: need to do a lot of this matrix math to
Speaker 4: be able to do that. So graphic chips were a
Speaker 4: natural fit and that kind of sustain But meanwhile, behind
Speaker 4: the scenes, a lot of this AI AI is about
Speaker 4: numeric calculations having to do with weights and matrices that say,
Speaker 4: you know, giant consolidated things that predict what's going to
Speaker 4: kind of happen based on what other things have happened,
Speaker 4: just like predicting where pixel goes. But it's really about
Speaker 4: being able to do enough data in jest to be
Speaker 4: able to do and then the calculations to be able
Speaker 4: to simplify things like entire sets of language or giant
Speaker 4: chunks of the Internet, to get enough weightings in there
Speaker 4: to be able to say, okay, we can predict what
Speaker 4: you would say in this language based on all of
Speaker 4: the volumes of stuff that we've seen that when you
Speaker 4: start talking like this, the next word is likely, oh
Speaker 4: it's this. Yeah.
Speaker 3: So, But my point is to get to that point,
Speaker 3: that's threshold. We got there because was it because we
Speaker 3: simply threw a lot more resources at the problem or
Speaker 3: is it because the underlying technology got suddenly or gradually
Speaker 3: so much more efficient.
Speaker 4: It's always yes and yes. But you know, the industry
Speaker 4: for a lot of years would talk about Moore's law.
Speaker 3: Well, quick, will you define for us More's law for
Speaker 3: those of those who's forgotten it.
Speaker 4: Yeah, So Gordon Moore at Intel coined this thing. It
Speaker 4: was basically that the horsepower I'm going to translate it
Speaker 4: roughly of technology will double every couple of years. We're
Speaker 4: still on Moore's law. Moore's law changed a little bit.
Speaker 4: For a while, it was always about frequency. Things would
Speaker 4: go faster, faster, faster. That kind of petered out. But
Speaker 4: what happened is, rather than faster, faster, faster, we did
Speaker 4: more and more and more. So rather than one operating
Speaker 4: unit going a lot faster on its throughput, you put
Speaker 4: ten operating units on a chip, now you put one
Speaker 4: hundred operating units on a chip, now a thousand. Some
Speaker 4: of these problems, the matrix math problems scale parallel extremely well.
Speaker 4: You don't have to do something really fast, you just
Speaker 4: have to do a lot of the similar things in
Speaker 4: parallel at the same time. So again that kind of
Speaker 4: that extension of Moore's law, more and more hardware on
Speaker 4: a chip to be able to do more and more
Speaker 4: of those calculations in parallel and come up with it.
Speaker 3: And we said, yeah, was that threshold predictable? In other words,
Speaker 3: see people in the industry, like you sit down X
Speaker 3: number of years ago and say, when we get here,
Speaker 3: AI is going to become much more of a It's funny.
Speaker 4: The horsepower that very predictable, the use cases not always
Speaker 4: so easy to kind of figure out. That's where the
Speaker 4: human spirit kind of gets involved. I think for some
Speaker 4: people that say, oh, I saw that coming, but people
Speaker 4: have been predicting kind of the rise of AI for
Speaker 4: twenty five years. Oh well, then when we get to
Speaker 4: this next gener oh when we get here, it kind
Speaker 4: of hadn't happened. There's always a magic point where you
Speaker 4: kind of get to where the technology and the use
Speaker 4: case and somebody does something to kind of make it
Speaker 4: catch on. And I think we're at one of those
Speaker 4: moments in AI for sure right now. And I don't
Speaker 4: think it's you know, people that have said, oh, this
Speaker 4: is just the latest wave of you know, I hear
Speaker 4: I've heard this about a lot of technologies, but AI
Speaker 4: is the technology the future, and it always will be.
Speaker 4: I used to hear that. You're not hearing that now, right,
Speaker 4: It's like, no, it's primetime. It will change everything, just
Speaker 4: like some of these other things changed everything.
Speaker 3: I noticed it if personally when I speak somewhere or
Speaker 3: I'm listening an audience somewhere. Over the last let's say
Speaker 3: twelve months, there's always a whole bunch of AI questions. Yes,
Speaker 3: And if I go back to years ago, there were
Speaker 3: no AI questions.
Speaker 4: Yes.
Speaker 3: Now my question is, so there's been this explosion on
Speaker 3: the in popular fascination with what's going on AI. It
Speaker 3: seems like the last year. I agree with you in
Speaker 3: your world, when did the explosion of conversation around this start?
Speaker 4: It's I love this question because IBM had a fairly
Speaker 4: big effort and business called Watson before Watson X. And
Speaker 4: this is going back kind of ten years. I'll give
Speaker 4: you another kind of example. I knew about a lot
Speaker 4: of tablet technology before there was an iPad, a lot.
Speaker 4: For ten years, there were a lot, but it kind
Speaker 4: of takes a magic combination of the technology, the user experienced,
Speaker 4: the software, and the need and the market ready for
Speaker 4: it to kind of go. Now it's the thing. Now
Speaker 4: we all have either an iPad or we have the
Speaker 4: Google equivalent Tom and so I think this is a
Speaker 4: little like that, meaning IBM was on the right track
Speaker 4: with Watson. Some of the hardware wasn't there, the use
Speaker 4: cases weren't exactly figured out. Some of the early use
Speaker 4: cases didn't pan out. Perfectly. But the good news about
Speaker 4: that is it's back to that culture of risk taking.
Speaker 4: You don't look back on that and say, oh, we
Speaker 4: shouldn't have done that, that was a bad idea. I
Speaker 4: know you look back on that and say, what did
Speaker 4: we learn? How should we try something new? How would
Speaker 4: we pivot this time? That's what we've done with Watson
Speaker 4: X and now that's a growing, healthy piece of our
Speaker 4: business and very important our strategic sure, so we're all in.
Speaker 3: I've always investigated by the gap between insider sense of
Speaker 3: what is happening in an outsider sense, like.
Speaker 4: It absolutely is that in this case, we've all been
Speaker 4: talking about and thinking about AI and is it time
Speaker 4: for that and what does this mean? Et cetera. And
Speaker 4: yet none of us really predicted that actual moment, which
Speaker 4: is kind of you know, early twenty twenty two where
Speaker 4: it was like, oh, now you have a simple human
Speaker 4: interface of software innovation combined with large language models. There's
Speaker 4: a moment there where you're like, oh, Unlike, you know,
Speaker 4: I think all of us are frustrated if we ask
Speaker 4: our phone, hey, tell me about this, and it says
Speaker 4: I found this on the web page. That does you
Speaker 4: no good? But you know, all of a sudden, with chat,
Speaker 4: GPT and some of these other things, you could ask
Speaker 4: a question, it would give you a clear answer. Sometimes
Speaker 4: is wrong, but at least it was like I'm getting
Speaker 4: an answer rather than hey, I don't know if there's
Speaker 4: 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.
Speaker 4: Yeah, we've talked about if you already, but I'm actually
Speaker 4: going to go a little different direction. So macro trans first,
Speaker 4: and this one has been before even even this AI conversation,
Speaker 4: that we've had explosion of data. As humans, we don't
Speaker 4: think exponentially very well. We really struggle with exponential thinking.
Speaker 4: We think linearly, Oh, there'll be more, there'll be more,
Speaker 4: they'll be more, but we don't think well when it's
Speaker 4: like no, they'll be more, and they'll be ten times more,
Speaker 4: and then there'll be ten times that more. That's what's
Speaker 4: going on with data right now in our industry. It's
Speaker 4: one of the reasons that that storage business is doing
Speaker 4: so well is they're just more and more and more data.
Speaker 4: You know, you'd say, well, how can there be more data?
Speaker 4: It's just life and that thing. The things that we
Speaker 4: care about, video capture, video images, you know, the the
Speaker 4: you I don't know from my parents, you needed a
Speaker 4: drawer with all your family photos. Now we need gigabytes
Speaker 4: and gigabytes.
Speaker 3: You knew how many pictures my wife has taken off
Speaker 3: our children, you would exactly exactly, So that's your case.
Speaker 4: Now. Think of companies who used to just think about
Speaker 4: their transaction data. What's the ledger say that now have
Speaker 4: video assets of all of their campaigns and their marketing.
Speaker 4: They're trying to figure out, you know, what campaigns are
Speaker 4: working the best. So it's just an explosion of data
Speaker 4: and that's not going to stop. Dealing with that, and
Speaker 4: more importantly, getting value from that data is a massive
Speaker 4: trend in the industry. Second trend AI, and this is
Speaker 4: the AI. Not like we were just talking about about
Speaker 4: how it changes how I search for things or how
Speaker 4: I learn about things. But I would argue, dealing with
Speaker 4: that data, how do I figure out what's in all
Speaker 4: those video streams? How do I figure out Okay, I
Speaker 4: want all of the chunks of my corporate video that
Speaker 4: have to do with client buying some specific product or something.
Speaker 4: That's a different problem. It's not just okay, we'll look
Speaker 4: it up in a spreadsheet and here's the math associated
Speaker 4: with that. That is a huge trend in the industry.
Speaker 4: You're seeing it play out in this regard, it's a
Speaker 4: little different bent on AI. Fraud detection is the one
Speaker 4: that we cite in our mainframes. It's a similar problem
Speaker 4: where it was kind of a traditional AI problem. Look
Speaker 4: up a rule. You know, if somebody does two small
Speaker 4: trend actions than a massive one, it might be fraud,
Speaker 4: right because they were seeing whether it were now to
Speaker 4: detect fraud, you might be saying, okay to transactions then
Speaker 4: a huge one. Plus does this entity have a real address? Second,
Speaker 4: is there any web traffic on you know, better Business
Speaker 4: Bureau kind of things that says this is a bad
Speaker 4: business that can help you with fraud. So it's a
Speaker 4: lot more of a it's an EXPERTI problem. It's a
Speaker 4: holistic problem that it takes a lot more than just
Speaker 4: you know, little chunks of rules, et cetera. And then
Speaker 4: the third one you know after AI, is the nature
Speaker 4: of hybrid it or hybrid computing. For a while ten
Speaker 4: years ago, when cloud was on the rise, I think
Speaker 4: the notion of hybrid computing basically having to do with
Speaker 4: things in the cloud versus things that people still have
Speaker 4: on the premises inside of business. It was almost a
Speaker 4: religious argument. Now it's no, it's the reality. And the
Speaker 4: reason is because that data that I talked about is
Speaker 4: the life blood of these companies, particularly IBM's companies are
Speaker 4: clients that usually that data has to be secure, they
Speaker 4: have to be able to get value from it. It
Speaker 4: is the lifeblood of the company. If you go to
Speaker 4: an ATM and you can't get your money out to
Speaker 4: our financial transactions, if that lasts a day, you're probably
Speaker 4: going to change banks immediately. So it's like life or
Speaker 4: death for these companies. So having that hybrid infrastructure so
Speaker 4: that they can still hold their data, you still interact
Speaker 4: with clouds and still get value from it from AI.
Speaker 4: That's kind of the magic where we play, and it's
Speaker 4: a huge business opportunity. It is a true inflection point
Speaker 4: for the industry. I'm going to go back.
Speaker 3: I interrupted you when you were in the middle of
Speaker 3: a rellion. We were talking about what has to happen
Speaker 3: for AI to scale from the infrastructure standpoint. You gave
Speaker 3: one example that I got you off on a tangent.
Speaker 3: Can you go back and talk very so practically, like, so,
Speaker 3: I'm you know, I'm a big company. I have all
Speaker 3: these dreams of AI, of how I'm going to use
Speaker 3: this dratically, So give me a very granular sense of
Speaker 3: the works you have to do, yeah, to make that
Speaker 3: dream possible.
Speaker 4: So let me first say what the company has to do,
Speaker 4: and then maybe I'll say, then how do I help them?
Speaker 4: If that makes sense? So if I'm a company and
Speaker 4: I want to do that, So it turns out I
Speaker 4: am a company meaning I want to use AI in
Speaker 4: my processes. I mentioned that I have a global network
Speaker 4: of thirteen thousand employees that support our infrastructure around the world.
Speaker 4: That challenge is a great challenge for AI. That means
Speaker 4: I have data for every customer situation for thirteen thousand
Speaker 4: employees globally around the world on what was their problem,
Speaker 4: how did we fix it, what next steps did they
Speaker 4: have to do, how did they remediate that? That data
Speaker 4: is extremely valuable to me because if I can get
Speaker 4: better at doing that than anybody else in the world,
Speaker 4: that brings my cost down. I sell more products, I
Speaker 4: sell more service, I sell more anything. So what I
Speaker 4: have to do to get there is I have to
Speaker 4: figure out. Okay, what's my objective? I have a couple objectives. One,
Speaker 4: I want customers to be able to support themselves without
Speaker 4: even calling me, first off, and I don't want when
Speaker 4: they call for the first answer to come back to
Speaker 4: be did you try rebooting? Because I think that irritates
Speaker 4: every single one of us. Did you try? Of course
Speaker 4: I tried rebooting. I've had a laptop, of course I well, okay,
Speaker 4: well then tell me, okay, what firmware version, all that
Speaker 4: other stuff. Okay, we know this interaction. So that's kind
Speaker 4: of the problem set. Do I want that to be
Speaker 4: customers solving their own problems? Well, even for my support agents,
Speaker 4: I want something in their pocket on their phone where
Speaker 4: they say I'm seeing these symptoms and says, oh, this
Speaker 4: happening around the globe. Here's kind of specific me. So
Speaker 4: there's my problems. What does it mean for infrastructure on
Speaker 4: the back end? So first I got to get all
Speaker 4: that data together, right, all of those customer law, all
Speaker 4: that customer support around the globe, et cetera. That needs
Speaker 4: to be stored. That's a big set of data. And
Speaker 4: some of it's not just fix and that kind of thing.
Speaker 4: Some of it is Okay, you know what was the
Speaker 4: firmware version? Who was the tech because it can matter.
Speaker 4: Is this their first time fixing this problem? Is it
Speaker 4: there one hundred and fiftieth time? What's their level? It's
Speaker 4: a very complicated problem. Ingesting all that data takes an architecture.
Speaker 4: We have a product called Scale, which is one of
Speaker 4: our storage projects that actually makes it easy to ingest
Speaker 4: all that data, get it organized, et cetera, and then
Speaker 4: have a model. It's a whole different process to kind
Speaker 4: of say did we train our model? We can train
Speaker 4: our own models inside of IBM. We have a granite
Speaker 4: set of models. Those models we fine tune, and then
Speaker 4: we inference based on those models. So we can do
Speaker 4: that inferencing in our cloud I have a cloud set
Speaker 4: of infrastructure, or in my power servers. We can do
Speaker 4: inferencing with our capabilities and say, okay, based on what
Speaker 4: I'm saying, here's what the remediation that you should do
Speaker 4: for that customer. We already are doing that today. We've
Speaker 4: seen over a third of our support calls have had
Speaker 4: significant reduction in the amount of time that it takes
Speaker 4: to resolve that support call just by what I said
Speaker 4: right there.
Speaker 3: That I've really been curious about this. If I had
Speaker 3: reduced something like AI into that equation as you just did. Yeah,
Speaker 3: and you said we've already seen a thirty percent Say
Speaker 3: did you say thirty percent reduction?
Speaker 4: Thirty percent of our interactions have seen significant reduction in
Speaker 4: those time?
Speaker 3: Was that your primary goal to reduce the time of
Speaker 3: the interaction? But it was you if everything else was
Speaker 3: the same all, but what you were doing was shrinking
Speaker 3: the amount of time?
Speaker 4: That would you want one of the primary goals, So
Speaker 4: to us in that business net promoter score kind of
Speaker 4: the satisfaction of a client is the supreme goal. What
Speaker 4: makes them sad fine, doesn't cost me a fortune, happens
Speaker 4: really quickly, and if I can do it myself, I'd
Speaker 4: be thrilled. It affects all of those right. It kind
Speaker 4: of says it got resolved faster, it didn't cost me
Speaker 4: an arm and the leg because the deck was barely here,
Speaker 4: because it's a common problem, or I solved it myself
Speaker 4: without even calling, So all of those objectives would kind
Speaker 4: of hit across all so that now you see it.
Speaker 4: So that's a little microcosm. That's just me and my
Speaker 4: customer support business. Now think of how many problems for
Speaker 4: businesses around the world there are like that. It's not
Speaker 4: a it's not like a new AI application that changes
Speaker 4: the entire user experience. That's those will come, But right
Speaker 4: now it's kind of practical, which is, I just want
Speaker 4: to do what I'm doing better and faster, and I
Speaker 4: can get immediate economic return from those things.
Speaker 3: How long How long did it take you to just
Speaker 3: stick with that example of the customer reaction reducing thirty
Speaker 3: percent of the time? How long from the very beginning
Speaker 3: of that project, Yeah to that thirty percent reduction it was.
Speaker 4: Hell long, less than a year. And yeah, So one
Speaker 4: of the challenges, and this is interesting with a very
Speaker 4: large organization, as you can imagine, just like you're seeing
Speaker 4: in the industry, we don't have a problem of generating
Speaker 4: ideas for how AI could help us. We actually have
Speaker 4: a problem filtering the thousands of ideas from our employees
Speaker 4: and from everywhere. It's like, hey, we could use AI
Speaker 4: to and filtering down and saying, okay, which of these
Speaker 4: will have a return on investment quickly and at a
Speaker 4: level that sustains that's worth kind of going and investing
Speaker 4: in the infrastructure and the software and kind of making
Speaker 4: that happen. Is that unusual.
Speaker 3: If I talked to you twenty five years ago and said,
Speaker 3: do you have a problem of too many good ideas
Speaker 3: or too few?
Speaker 4: What was you said in this specific area, Probably too few,
Speaker 4: because at some point you reach diminishing returns. So, for example,
Speaker 4: let's use this same example. Can those thirteen thousand technicians
Speaker 4: go faster? Can they spend less time driving to the side.
Speaker 4: I mean, there's only so much you can kind of
Speaker 4: do on those things. But if you can get them
Speaker 4: an answer to the problem and maybe even avoid them
Speaker 4: having to visit at all because the client helped themselves,
Speaker 4: that's a step function. So that's why people are kind
Speaker 4: of talking about there's a business revolution coming with AI
Speaker 4: where there are some step function changes that can be there.
Speaker 4: And notice I didn't say I'm going to have less
Speaker 4: of those agents. That's not my objective. My objective and
Speaker 4: I think that's the fear in the industry about AI
Speaker 4: is going to eliminate all the jobs. No, I just
Speaker 4: created thirteen thousand superpowered agents that can do more right.
Speaker 4: And so I'm not just going to support IBM products.
Speaker 4: I'm going to go out and support other people's products
Speaker 4: because I know how to do that really well. And
Speaker 4: once I have the data on how to fix their problems,
Speaker 4: I may just have a customer support business that's independent
Speaker 4: of my boxes. So you know, I think that's where
Speaker 4: people sometimes get it wrong. And the AI thing is,
Speaker 4: it's like, you know, did word processing eliminate the need
Speaker 4: for writers? No? It enabled writing instead of mucking around
Speaker 4: with mimeographic machines and click and click typewriters. It may
Speaker 4: have enabled too much writing? Yeah, maybe maybe can I
Speaker 4: give you a hypothetical?
Speaker 2: Uh?
Speaker 3: And I asked this because I ran I was at
Speaker 3: some conmis and I ran into some guy from the
Speaker 3: I R S who was really, really, really really excited
Speaker 3: about AI. So let's suppose they call you up and
Speaker 3: they say you're going to talk to the I R SKY.
Speaker 3: I call you up and I say, Rick, Uh, clearly
Speaker 3: there's something that we could do for the I R
Speaker 3: S if we work together.
Speaker 4: Yeah, what would your answer? Of course?
Speaker 2: No.
Speaker 4: I think we sell to a lot of government agencies.
Speaker 4: I can imagine in the business that we're in, we
Speaker 4: enable a lot of social security transaction and things like
Speaker 4: that through our mainframes. And I think, you know, we're
Speaker 4: in the business of helping whatever client get the most
Speaker 4: out of their data and be able to secure it
Speaker 4: and be able to do analytics with this. And IRS
Speaker 4: has a heck of a lot of data, so yes,
Speaker 4: we would help them.
Speaker 3: Do you know how the amount of data they have
Speaker 3: compares to some of the corporate clients you've I.
Speaker 4: Don't know specifically for the IRS how much data they have,
Speaker 4: but I would assume it's a whole lot. It's mountains.
Speaker 4: But that's our business. I mean, it's interesting sometimes people
Speaker 4: of that what's the most you know, what is it
Speaker 4: that IBM has that's of great value? Is it a server?
Speaker 4: Is it a storage array? Is it you know, software
Speaker 4: and all that. What we have is the most important
Speaker 4: entities in the world have their data on our stuff.
Speaker 4: The most important data in the world. It's not you know,
Speaker 4: pictures of your grandkids and things like that. Generally for us,
Speaker 4: it's all of the financial transactions that have and globally,
Speaker 4: right it's all of the it's the world's economy is
Speaker 4: kind of running through our systems, and so we take
Speaker 4: that really seriously. You know, you would be distraught if
Speaker 4: you lost one photo on your laptop or whatever, but
Speaker 4: you know, if we lose a transaction, like somebody moves
Speaker 4: a big amount of money and it's like, well, don't
Speaker 4: know what happened there. It is a massive deal, right,
Speaker 4: so that doesn't happen.
Speaker 3: But I want to go back to my irs example
Speaker 3: for US, Yes, so one, is it reasonable to assume
Speaker 3: that you could that somebody IBM or somebody else could
Speaker 3: in a short period of time put together not just
Speaker 3: the AI capability to audit returns, but also this the
Speaker 3: infrastructure support for that in a reasonable amount of time
Speaker 3: for a reasonable amount of cost. Or is it over?
Speaker 3: Is it going to the moon? Or is it it?
Speaker 4: Definitely? I mean so we're already doing that kind of
Speaker 4: thing right across a network of banks and others. Yeah,
Speaker 4: essentially all credit card transactions for all of the world
Speaker 4: to go through our systems, So that in some ways
Speaker 4: is more volume than the datch returns of the US people.
Speaker 4: And they're W two's and all that stuff, and we
Speaker 4: do that stuff too. I try not to describe it
Speaker 4: too much in detail, but we definitely do a lot
Speaker 4: of that. In fact, I think most of if you think, okay,
Speaker 4: what is super critical data, who would be doing the
Speaker 4: business transaction processing it is most likely us in almost
Speaker 4: all cases, whether it's government things or private or banks
Speaker 4: or that kind of thing. That's what we do.
Speaker 3: Rick we're going to end with the where we always
Speaker 3: end with a couple of quick fire questions. Okay, here
Speaker 3: we go. What single piece of advice would you give
Speaker 3: to businesses trying to use AI in an effective way?
Speaker 4: The simple version is get started. By get started, I
Speaker 4: mean think of what is something that I want to improve.
Speaker 4: The things that we have traction on right now in
Speaker 4: the market are around business process, automation, digital labor, those
Speaker 4: kind of things. But my other little piece of advice
Speaker 4: there is keep it simple to begin with. You're going
Speaker 4: to learn a lot, but getting started means you'll start
Speaker 4: that learning curve. I even advise you My friends like, hey,
Speaker 4: should I be playing around with some of this AI stuff?
Speaker 4: And I say yeah, because I think it will help
Speaker 4: you start to be more comfortable and you may find
Speaker 4: a use case personally for that. I think the same
Speaker 4: is true for businesses. The first step in that journey
Speaker 4: is always with what data. Notice when I talked about
Speaker 4: our customer support people, I thought about, Okay, what's the data.
Speaker 4: The data is all of those logs of all of
Speaker 4: those service engagements around the world, and what could I
Speaker 4: do with that? Well, I could use that to get
Speaker 4: to a knowledge base that really helps and hopefully that
Speaker 4: I can do it in multiple languages because it's global
Speaker 4: and I can you know, all of those things. That
Speaker 4: was kind of my data sent That one's not super simple,
Speaker 4: but we've had a lot of experience in AI for
Speaker 4: other people that might just be how do I automate
Speaker 4: filling out travel expense reports for my company? We can
Speaker 4: help people that we have consulting, we have wats and
Speaker 4: X tools. We can do that like this, and we're
Speaker 4: doing it globally for people around the world. Pick that thing.
Speaker 4: What's the data you have? In that case, it's data
Speaker 4: of expense reports and it's like, okay, we can help
Speaker 4: you automate that for people where they could do it
Speaker 4: just by you know, a verbal interface. What did you spend,
Speaker 4: where did you go? Who you were you with? Okay,
Speaker 4: we filled out your travel expense report for you and
Speaker 4: you don't have to mess around with it.
Speaker 3: So we were playing with this idea where we would
Speaker 3: pick a business and go in there and do it
Speaker 3: would be AI makeover. Yeah, I love that what's okay,
Speaker 3: what is the ideal business to do? We only have
Speaker 3: a couple months. We don't want to spend a kajillion dollars.
Speaker 3: We want to be able to show tangibly and quickly
Speaker 3: what AI can do. What's an ideal business to do
Speaker 3: that in It can be a small business. We're not
Speaker 3: talking this grand corporate thing there.
Speaker 4: Ah boy, small business that we could do and hey,
Speaker 4: I make over. Customer support is one of my favorites
Speaker 4: because it's a it's it's I have it on the
Speaker 4: business side where I provide customer support. I have it
Speaker 4: on the consumer side, where it drives me nuts when
Speaker 4: I have to go through thirty layers of phone menus.
Speaker 4: Speak to an agent, speak to an agent, speak to
Speaker 4: an agent. That for any business, I think is just
Speaker 4: ripe to be able to kind of say why do
Speaker 4: I have to click through these manucent messages. I just
Speaker 4: need to tell you in human language, here's the issue,
Speaker 4: and I'll be really good about telling you details about
Speaker 4: You know, I tried to set up this thing for
Speaker 4: my bank and I do da da da da da.
Speaker 4: They can go through all the menus automate that process.
Speaker 4: I think it would change everything because all that frustration
Speaker 4: as a consumer would go down dramatically, and it's all,
Speaker 4: you know, why are you making me the beep booth
Speaker 4: press one? Exactly? Well, don't offload to me, offload to AI.
Speaker 4: We can help you with that.
Speaker 3: Here's my version of that drives me crazy. Every morning
Speaker 3: I go to the same coffee shop and I get
Speaker 3: a cup of tea and a croissant.
Speaker 4: And here's what happens.
Speaker 3: A person has their screen and they go, I go,
Speaker 3: cup of tea, croissant, sparkling water, like at least twenty keystrokes,
Speaker 3: and then like then the screen is turned around. Like
Speaker 3: at this point we're like forty five seconds in, I'm like,
Speaker 3: why is this? First of all, it's not for me,
Speaker 3: all those keystrokes, it's their internal right, right, So they're
Speaker 3: burdening me in order to service.
Speaker 4: To back it, you should be able to walk in,
Speaker 4: go up and they go, I'm olc them the same
Speaker 4: thing and you just go yes, and then.
Speaker 3: The boom, We're done. Can we do AI makeover of
Speaker 3: my coffee shop?
Speaker 4: You notice I quickly jumped more to banks than your
Speaker 4: coffee shop because I think I'm a business person, but
Speaker 4: I'm not trying to kind of do a deal on
Speaker 4: one coffee shop.
Speaker 3: But this is interesting because it takes me back to
Speaker 3: something you said that I thought was really important. When
Speaker 3: you were talking about when you were using AI and
Speaker 3: your customer service thing, it was clear that your goal
Speaker 3: you could have any number of goals, yes, going in.
Speaker 3: It could be to cut costs, it could be to
Speaker 3: dramatically improved profits.
Speaker 4: Your goal, quite.
Speaker 3: Specifically, was to improve the experience of your customer, right,
Speaker 3: So you were.
Speaker 4: Using it to that. All the other things come from
Speaker 4: that come from. That is actually one of the beautiful
Speaker 4: pillars of the IBM culture is delighting clients is actually
Speaker 4: where all of the good stuff comes from.
Speaker 3: So my coffee shop thing is the same principle. Right now,
Speaker 3: they're making my customer experience worse and they don't want to,
Speaker 3: but their eyes are glued to the special a moment
Speaker 3: when I walk in and I want to say, Hi,
Speaker 3: how are you doing? We could have a conversation. You're
Speaker 3: too busy, busy pooping, So like, this is the same thing.
Speaker 3: If they had it that, oh, this isn't if they
Speaker 3: understood they had an operation need to improve the experience
Speaker 3: of their customer experience.
Speaker 4: I would not be surprised if a chain comes along
Speaker 4: where that is their value proposition. I would not be
Speaker 4: surprised at all. Yeah, yeah, right, So I mean and
Speaker 4: and when those things kind of catch hold, it becomes
Speaker 4: a revolution.
Speaker 3: You know, when the guy comes to do like to
Speaker 3: redo your roof and they put a sign out front,
Speaker 3: like you know, Joe's roofing. You guys could do the
Speaker 3: same with my coffee shop. But like I'd be i'ure
Speaker 3: was here exactly exactly.
Speaker 4: In five years, the main frame will be dot dot
Speaker 4: dot going strong, the mainframe going strong and with new capabilities,
Speaker 4: continuous new capabilities. I think when we announced the last
Speaker 4: version Z sixteen, the latest version, I should say, and
Speaker 4: we said, hey, there's AI processing built into it. This
Speaker 4: was before everybody was talking about that. I think a
Speaker 4: lot of people thought, what's that for? And we did
Speaker 4: it specifically for traditional AI fraud detection, et cetera. This
Speaker 4: next version, not only do we have the traditional AI
Speaker 4: built in, but we have optional cards that you can
Speaker 4: plug into it to allow you to do large language
Speaker 4: models for the enhanced fraud detection cases that we talked about,
Speaker 4: where you know, it's more than just what transactions were happening.
Speaker 4: So if you take that and say, okay, the next generations,
Speaker 4: we have more transaction volume than we've ever had in mainframes. Today,
Speaker 4: the business is growing, it's strong, we keep innovating. In
Speaker 4: five years it'll be going strong.
Speaker 3: But we're people. You're saying this in the context of
Speaker 3: for years people were predicting, weren't they that the main
Speaker 3: brand was going to go away.
Speaker 4: There were pundits in the market that said everything will
Speaker 4: go away there, no one will ever have a box,
Speaker 4: It'll all be online. I think this is something I've
Speaker 4: learned big time in my long career. You know in
Speaker 4: the IT industry is don't believe everything you hear. So
Speaker 4: I went back for my master's degree at Stanford after
Speaker 4: I had worked a while in as a hardware designer,
Speaker 4: and everybody told me be sure to do your masters
Speaker 4: in software. Hardware is dead. I went on to work
Speaker 4: for thirty plus years in hardware and infrastructure. Now software
Speaker 4: became important, and I'm glad I had that extra training
Speaker 4: in software because it helped me in hardware. But hardware
Speaker 4: wasn't dead. Then I heard all infrastructure will go into
Speaker 4: the cloud. There won't be that hasn't happened, it's not happening.
Speaker 4: Then I heard there will only be one cloud because
Speaker 4: one of the players will dominate. There's not one cloud.
Speaker 4: So I think it's as humans we like to oversimplify
Speaker 4: and go, oh, it's all going to be this, And
Speaker 4: kind of what I've learned is fit for purpose matters
Speaker 4: in everything. It matters in size of infrastructure, it matters
Speaker 4: in the stack that goes along with solving a specific
Speaker 4: use case. If you're willing to design something that's the
Speaker 4: best at that use case, if you're willing to design
Speaker 4: the coffee shop that is the best at greeting me,
Speaker 4: there's a spot for you, and there may be a
Speaker 4: big business in doing that. So oversimplifying is really.
Speaker 3: When you heard all those predictions, did you believe them
Speaker 3: at the time.
Speaker 4: They looked like they were trending in that direction. I'll
Speaker 4: tell you some right now which might be useful. There
Speaker 4: will only be one GPU company and they're going to
Speaker 4: end up taking over the world. It's a pretty obvious answer.
Speaker 4: Whose economic values risen dramatically. I don't think that's going
Speaker 4: to be the case. In fact, I think that ninety
Speaker 4: percent of processing for AI actually happen happens at inferencing,
Speaker 4: and inferencing is not as GPU and hardware intensive as
Speaker 4: the other things and is a lot more amenable to
Speaker 4: fit for purpose. So the model size will matter. The
Speaker 4: tuning matters a lot. As we're learning. We have a
Speaker 4: product around instruct lab that's really focused on tuning. So
Speaker 4: that was one thing is there'll be one GPU. The
Speaker 4: other thing is that the biggest model will win. I
Speaker 4: think is another thing that's kind of people are saying
Speaker 4: right now. Don't believe that I believe they will be
Speaker 4: fit for purpose models. It takes a lot of money
Speaker 4: to run to create a huge model, and then to
Speaker 4: run a huge model, or to even infer off of
Speaker 4: a huge model. I don't need a massive training GPU
Speaker 4: set thing to solve my thirteen thousand people customer support issues.
Speaker 4: So why would I feel like I got to go
Speaker 4: farm that out for a big expensive thing. I can
Speaker 4: do that on a small box. In some cases I
Speaker 4: might even be able to do that on a laptop.
Speaker 4: The other thing I'll say in this we are so
Speaker 4: early innings in AI, A lot of things are going
Speaker 4: to change. So anybody kind of saying it will all
Speaker 4: be X, Y or Z, I just think you have
Speaker 4: no idea how this is going to play out, and
Speaker 4: it's up to us to go figure out how it
Speaker 4: plays out.
Speaker 3: Yeah, yeah, all right, in five years AI will be
Speaker 3: dot dot dot still new.
Speaker 4: It will have moved a bunch in five years, but
Speaker 4: the potential for the disruption in the world will still
Speaker 4: will still be very early innings in that process. And
Speaker 4: I think that's super important to realize. That's why I
Speaker 4: say get started, start thinking about how that could change,
Speaker 4: because it'll be some little things first, but it will
Speaker 4: continue to snowball.
Speaker 3: This is a common observation that we the invention of
Speaker 3: the capability massively predates the understanding.
Speaker 4: Of the capability, right, Like I love that.
Speaker 3: Yeah, Like yes, recorded recording shows on television is invented
Speaker 3: in the sixties. Probably we don't really understand what it's
Speaker 3: used for until the oughts was what's really good for
Speaker 3: is being able to tell a story sequentially, Yes, over time,
Speaker 3: because you know that the person will always see in
Speaker 3: the episode before, so you get the Sopranos. And yes, yes,
Speaker 3: Hollywood wanted to ban the VCR in the beginning, Yeah,
Speaker 3: because they thought it was good. They thought the point
Speaker 3: of it was thought then understand. No, No, No, it's storytelling.
Speaker 3: It's actually your business is getting better. Yes, took them
Speaker 3: twenty years to figure that out, which is to your point,
Speaker 3: why would we know what AI was four and five years.
Speaker 4: Well, that's why you hear people kind of say, oh
Speaker 4: my gosh, AI's that will just eliminate jobs. No, it'll
Speaker 4: make jobs better. That's how I view it.
Speaker 3: Yeah, what's the number one thing that people misunderstand about AI?
Speaker 4: Is that it that it'll I think that's that. That
Speaker 4: would be the human kind of understanding part of it.
Speaker 4: The technology part of it, I think would be what
Speaker 4: I was talking about fit for purpose, meaning that it
Speaker 4: isn't just going to be a GPU arms race all
Speaker 4: of AI. I don't believe that at all. It will
Speaker 4: change everything, but it's not just going to be a GPU.
Speaker 3: Armed Next question, what advice would you give yourself ten
Speaker 3: years ago to better prepare you for today?
Speaker 4: I'm changing this question, okay.
Speaker 3: I would say, let's imagine that at what was your
Speaker 3: what college.
Speaker 4: You to go to? I went to three of them.
Speaker 4: My undergrad was Utah State University, my NBA was Santa
Speaker 4: Clara University, and my master's in w was Stanford.
Speaker 3: Okay, any one of those three culture up and says
Speaker 3: we want you to give the commencement address and imagine
Speaker 3: it's it's it's let's just say, for the sake of argument,
Speaker 3: it's just to the stem people.
Speaker 4: Those are the relevant parties here. What do you tell them? Boy?
Speaker 4: What do I tell them? Let's see. I think I
Speaker 4: would start with life is a marathon, not a sprint.
Speaker 4: It would be the first one. The second thing I
Speaker 4: would say that in that spirit is be sure to
Speaker 4: set yourself some big, hairy, audacious goals and don't be
Speaker 4: overly disappointed if you don't hit them all. Going after
Speaker 4: those big, hairy, audacious goals will get you on a
Speaker 4: path where you will learn so much. You will achieve
Speaker 4: more than you ever could imagine you would have achieved.
Speaker 4: That's what the advice I give to my kids is, ye,
Speaker 4: set some big goals, get after it. You may or
Speaker 4: may not achieve them, but you'll be better for the
Speaker 4: whole process when you're done.
Speaker 3: By the way, as someone whose kids are younger than yours,
Speaker 3: is it actually useful to give your give advice to
Speaker 3: your kids?
Speaker 4: The pointless exercise TVD. We're still on the journey, and
Speaker 4: I think we will be for a long time. I
Speaker 4: don't know how are you already using AI in your
Speaker 4: day to day life today? Personally, I would say it's
Speaker 4: replacing a good chunk of my search. You know, I'm
Speaker 4: less likely to go blindly stumbling through a bunch of
Speaker 4: web pages looking for stuff. I'm more likely to ask
Speaker 4: a question from a few AI engines kind of see
Speaker 4: get me in the right direction. Then I'll go bumble
Speaker 4: through a few things. At work, I can tell you
Speaker 4: code development right now, we are seeing massive improvements in
Speaker 4: code development and support. Products we have like Watson Code
Speaker 4: Assistant that is really showing immediate return for a code developers,
Speaker 4: and I think that will again be a tool that
Speaker 4: increases productivity for code developers immediately across the globe. Yeah.
Speaker 3: Last question, what's the one skill that every technology leader
Speaker 3: needs that has nothing to do with technology.
Speaker 4: Being able to inspire a set of people toward a
Speaker 4: common goal and collaborate to achieve it. That's at the
Speaker 4: core of everything everything. That's a lovely way to end.
Speaker 4: Thank you so much, Rick, Thank you.
Speaker 3: This conversation left me excited. I'm now imagining the potential
Speaker 3: for new use cases for AI in all sorts of
Speaker 3: different businesses. Rick didn't seem soldom my idea of a
Speaker 3: coffee shop makeover, but it's clear there's lots of opportunities
Speaker 3: here to increase speed and efficiency, to achieve your objectives,
Speaker 3: and to dream beyond the current applications for this technology.
Speaker 3: At the end of the day, the scaling of AI
Speaker 3: will rely on the right infrastructure to support it. With
Speaker 3: the right tools, you can solve problems that are unique
Speaker 3: tier industry and improve the experience for your customers. Smart
Speaker 3: Talks with IBM is produced by Matt Romano, Amy Gains McQuaid,
Speaker 3: and Jacob Goldstein. Were edited by Lydia gene Kott, mastering
Speaker 3: by Jake Koorsky. Theme song by Gramoscope. Special thanks to
Speaker 3: the eight Bar and IBM teams, as well as the
Speaker 3: Pushkin marketing team. Smart Talks with IBM is a production
Speaker 3: of Pushkin Industries and Ruby Studio at iHeartMedia. To find
Speaker 3: more Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts,
Speaker 3: or wherever you listen to podcasts. I'm Malcolm Gladwell. This
Speaker 3: is a paid advertisement from IBM. The conversations on this
Speaker 3: podcast don't necessarily represent IBM's positions, strategies, or opinions,