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