WEBVTT - How Infrastructure is Powering the Age of AI

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<v Speaker 1>Hello, Hello, Welcome to Smart Talks with IBM, a podcast

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<v Speaker 1>from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Godwell. This season,

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<v Speaker 1>we're diving back into the world of artificial intelligence, but

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<v Speaker 1>with a focus on the powerful concept of open its possibilities, implications,

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<v Speaker 1>and misconceptions. On today's episode, our season finale, I'm joined

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<v Speaker 1>by Rick Lewis, the senior vice president of Infrastructure at IBM.

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<v Speaker 1>Rick has had a remarkable career focused around product innovation.

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<v Speaker 1>He was actually a few years into retirement when IBM

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<v Speaker 1>came calling with an opportunity he just couldn't turn down. Thankfully,

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<v Speaker 1>Rick came out of retirement and today he oversees a

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<v Speaker 1>vast portfolio from storage and software to global customer support operations,

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<v Speaker 1>and he's engaged in one of the key problems facing

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<v Speaker 1>companies today, an explosion of data. In talking with Rick,

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<v Speaker 1>I can see that this problem of having so much

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<v Speaker 1>data is also an incredible opportunity because if you're able

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<v Speaker 1>to leverage that data to get the most value out

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<v Speaker 1>of it, then you can use it to help bring

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<v Speaker 1>your business into the future. We talked about the serious

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<v Speaker 1>computing power needed to scale AI, as well as the

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<v Speaker 1>ways that infrastructure storage solutions can be essential to enabling

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<v Speaker 1>this new world of possibilities. It's a really great conversation,

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<v Speaker 1>so let's get to it. I'm here with Rick Lewis. Rick,

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<v Speaker 1>Welcome than here. We are in the IBM's New York

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<v Speaker 1>City headquarters at one Madison Avenue. I'm going to start

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<v Speaker 1>with you're a hardware guy.

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<v Speaker 2>I'm a hardware guy. I grew up doing hardware chip engineering.

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<v Speaker 2>But like I tell a lot of people, a chip

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<v Speaker 2>engineering project is actually a giant software project with a

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<v Speaker 2>piece of hardware at the end of the project. Wise,

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<v Speaker 2>I think if you have that analytical brain like to

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<v Speaker 2>solve problems, you'd like to get things working, you can

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<v Speaker 2>do that in.

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<v Speaker 1>Soet But as being someone coming from a hardware background

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<v Speaker 1>mean that you think about problems in a different way.

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<v Speaker 2>I think one thing that you do from a hardware background,

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<v Speaker 2>and especially a chip background, is a chip spin and

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<v Speaker 2>costs millions of dollars, So you're a lot more likely

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<v Speaker 2>to make sure everything has a great chance of being

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<v Speaker 2>perfect from the get go. Or if you start kind

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<v Speaker 2>of from a software background, your general mindset is I

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<v Speaker 2>don't know, try this, see if it works. I don't know,

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<v Speaker 2>try that if it work, and you kind of iterated,

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<v Speaker 2>iterate chip people are a little more uptight about Okay,

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<v Speaker 2>if this first round of the chip breaks, costs us

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<v Speaker 2>for building another new round of the chip.

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<v Speaker 1>Yeah, so you're a little more You guys are spend

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<v Speaker 1>more time planning and planning verifying, tons of time verifying.

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<v Speaker 1>So you began your career as you look backward, yes, correct,

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<v Speaker 1>and you were there for how many years?

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<v Speaker 2>I was there for thirty two years?

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<v Speaker 1>Yes, And your last job there was I was leading.

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<v Speaker 2>The software defining cloud business. I had grown up a

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<v Speaker 2>hardware guy. I had done all kinds of hardware projects,

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<v Speaker 2>big complicated Unix servers and things like that, and then

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<v Speaker 2>you know, grew out of R and D and more

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<v Speaker 2>into the business realm, and then I'm much an innovator

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<v Speaker 2>at heart. I really like innovating new concepts things like that.

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<v Speaker 2>And what I learned is I enjoyed innovating business models

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<v Speaker 2>and software projects as much as I did hardware products

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<v Speaker 2>and projects, and so getting teams inspired toward doing that

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<v Speaker 2>was really a deep fascination for me. So I ended

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<v Speaker 2>up doing a fantastic variety of experiences and had a

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<v Speaker 2>successful run and honestly retired, intending to retire and do

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<v Speaker 2>some of my outside activities and things like that.

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<v Speaker 1>And then how long did you stay retired before IBM

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<v Speaker 1>can close?

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<v Speaker 2>Almost two years? And when when I first got at ALL,

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<v Speaker 2>I thought, no, I'm having too much fun. But I

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<v Speaker 2>would say three things really got me thinking hard about it.

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<v Speaker 2>One the industry that we're in, the IT industry. I

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<v Speaker 2>think it's the golden age. And what I mean by

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<v Speaker 2>that is for twenty years of that career, it is

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<v Speaker 2>kind of in the back office, say make sure that

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<v Speaker 2>stuff doesn't crash, and can you please reduce the cost

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<v Speaker 2>as much as possible, because it's not that important to

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<v Speaker 2>the main business. It's just a back office function. You

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<v Speaker 2>can see it right now. It is at the forefront

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<v Speaker 2>of all business revolution. It happened with the Internet. It

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<v Speaker 2>happened again with cloud and how that changed every ounce

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<v Speaker 2>of business, not just IT business, but all business. And

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<v Speaker 2>I think it's happening again with AI. So to be

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<v Speaker 2>in that career that long and to miss the kind

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<v Speaker 2>of this age where it's like this is front and center.

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<v Speaker 2>This changes everything about all businesses, not just technology businesses.

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<v Speaker 2>I was kind of feeling like gosh, you you trained

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<v Speaker 2>to be in these really awesome environments. Why wouldn't you

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<v Speaker 2>do that for a little while longer while you still

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<v Speaker 2>can do it. That combined with IBM and IBM seeing

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<v Speaker 2>the talent pool, the brilliant people at IBM, I worked

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<v Speaker 2>with a ton of brilliant people before I saw a

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<v Speaker 2>chance to work with even a larger staff of brilliant people.

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<v Speaker 2>And then the assets that IBM had, which is, you know,

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<v Speaker 2>they'd already been doing a lot of experimentation in AI,

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<v Speaker 2>they're working in quantum, the deep, rich heritage of successful projects.

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<v Speaker 2>I thought, who wouldn't want to kind of see if

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<v Speaker 2>they could be part of that next great wave of IBM.

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<v Speaker 2>And so I kind of decided, all right, I'm going

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<v Speaker 2>to put the outside interest on hold for a while

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<v Speaker 2>and get back in the game.

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<v Speaker 1>Along between the phone call, the first phone call and

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<v Speaker 1>you say, yes.

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<v Speaker 2>It was a while, It was probably six months. Arvind's

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<v Speaker 2>our CEO, teases me about that a lot. Yeah, he

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<v Speaker 2>was like, I don't think six months is that long?

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<v Speaker 2>It took a while if you were in retirement. I know. Yeah,

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<v Speaker 2>It's one thing to compare I'm working here and doing

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<v Speaker 2>this stuff versus working there, it's really hard to compare.

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<v Speaker 2>I'm doing exactly as I want to do every single

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<v Speaker 2>day when I wake up, and now I'm not going

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<v Speaker 2>to get to do that again. It took a while

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<v Speaker 2>for me to get over and I thought, I can't

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<v Speaker 2>miss this wave, and I'm really really happy that I did,

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<v Speaker 2>because we're doing some amazing, fun things and I'm getting

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<v Speaker 2>challenged in ways that I never did, so it's really fun.

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<v Speaker 1>Talk a little bit about your job here at IBM.

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<v Speaker 1>You oversee a kind of massive portfolio.

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<v Speaker 2>It's a big group, so I run the Infrastructure organization.

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<v Speaker 2>There's three main groups of products at IBM. There's the

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<v Speaker 2>Infrastructure group, which I run, the software group, and the

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<v Speaker 2>consulting group. And infrastructure is built up of mainframes, which

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<v Speaker 2>is called our Z portfolio, our servers which is our

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<v Speaker 2>power portfolio storage. By the way, those businesses include the

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<v Speaker 2>supply chain to build all of that stuff, so that's

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<v Speaker 2>in the group. Then I have the worldwide Customer Support

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<v Speaker 2>Organization it's called TLS Technology life Cycle Services, which is

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<v Speaker 2>a network of about thirteen thousand people around the globe

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<v Speaker 2>that make sure that everything runs and works when you

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<v Speaker 2>buy IBM products. And then also our IBM Cloud, which

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<v Speaker 2>is how we host applications and deliver as a service

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<v Speaker 2>products for our client based. So there's a lot. I

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<v Speaker 2>think it's about forty five thousand people total.

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<v Speaker 1>Do those components of the Infrastructure group are they aligned

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<v Speaker 1>in their trajectory or are they on different paths? And

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<v Speaker 1>I'm just curious what so navigattle of both. It's interesting

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<v Speaker 1>you would ask that because I think of all of

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<v Speaker 1>the challenges coming to the new company, there were things

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<v Speaker 1>I expected, things that they didn't expect. But getting that

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<v Speaker 1>culture right in that group has been a big challenge.

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<v Speaker 2>IBM has a great culture toward quality products, toward emphasizing

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<v Speaker 2>passion for the client and making sure that the client

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<v Speaker 2>is happy, and for delivering innovation on a scale that

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<v Speaker 2>you know, for more than one hundred years has been

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<v Speaker 2>extremely powerful. But with success comes some challenges. And with

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<v Speaker 2>that success you can tend to get a little bit insular,

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<v Speaker 2>like you don't keep an eye on the competition as well,

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<v Speaker 2>you can get more siloed, where you know, this is

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<v Speaker 2>my business unit, this is my business unit, I compete

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<v Speaker 2>with the other business unit. That's not a good thing

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<v Speaker 2>when you're a company, and you can get really risk averse,

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<v Speaker 2>meaning you feel like, hey, this is a successful business.

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<v Speaker 2>I don't want to do anything to mess it up,

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<v Speaker 2>so I don't need to try new things. Well, that's

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<v Speaker 2>exactly the recipe to kind of be shrinking, and infrastructure

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<v Speaker 2>had been shrinking for a little while, and so a

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<v Speaker 2>lot of what the challenge was for me was to

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<v Speaker 2>invigorate that risk taking and get to a growth mindset

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<v Speaker 2>where you're trying new things and seeing what works and

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<v Speaker 2>what doesn't work. Changing some of the models, like investing

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<v Speaker 2>a little bit less in hardware for some software differentiation

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<v Speaker 2>that goes into the hardware. So it's been very successful

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<v Speaker 2>so far, and it's been a good journey. It's almost

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<v Speaker 2>four years now.

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<v Speaker 1>Give me an example of what was a really hard

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<v Speaker 1>problem that you've dealt with in those sway years.

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<v Speaker 2>So, boy, a really hard problem?

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<v Speaker 1>An interesting and are you interesting is a better word

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<v Speaker 1>than ours.

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<v Speaker 2>One of the first things that I kind of chewed

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<v Speaker 2>on a little bit is I talked about how we

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<v Speaker 2>have Z power and storage. The Z and Power product

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<v Speaker 2>lines are well known in the industry. Is is really

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<v Speaker 2>fit for purpose computing that have strengths that you know

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<v Speaker 2>Z runs, you know most of the world's economic backbone.

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<v Speaker 2>And if you use a credit card, ninety percent of

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<v Speaker 2>credit card transactions for the globe go through these Z

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<v Speaker 2>mainframes there in every bank there. You know, it's a

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<v Speaker 2>big business. It's well known in the industry. Same with power,

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<v Speaker 2>very tuned and optimized for smaller operations than our giants

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<v Speaker 2>Z mainframes, but really mission critical workloads for retail, for insurance,

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<v Speaker 2>for banking, for all of that. Our storage business not

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<v Speaker 2>so well known. In fact, when I came I thought,

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<v Speaker 2>did they have storage? Well, I have storage when I

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<v Speaker 2>come in too. I so I got online and I thought,

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<v Speaker 2>it's still hard for me to tell did they have

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<v Speaker 2>storage or not? Now I own a storage business. So

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<v Speaker 2>one of the things was not just to get the

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<v Speaker 2>market perception up, but to invest in that business. Because

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<v Speaker 2>if you look at infrastructure overall around the globe, it's

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<v Speaker 2>growing at five percent a year. The infrastructure business had

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<v Speaker 2>been kind of flat to declining, and so a challenge

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<v Speaker 2>was how do we grab onto the growth. Well, one

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<v Speaker 2>of the biggest growth areas due to the explosion of

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<v Speaker 2>data in the world is storage, So what do you

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<v Speaker 2>do to kind of get on that growth rate. So

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<v Speaker 2>we did a lot of reinvigoration of the innovation in

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<v Speaker 2>that a lot of software value, add a lot of

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<v Speaker 2>doubling down on the things that are working. Portfolio rationalization

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<v Speaker 2>where you segment the market and you say, okay, we're

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<v Speaker 2>going to do less of this and really go big

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<v Speaker 2>in these areas, and that's been probably the most dramatic

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<v Speaker 2>turnaround inside the group. Is our storage thing. When you

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<v Speaker 2>say it's a hard problem, it's not just oh, you know,

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<v Speaker 2>how do we do the math? No, it's it's cultural.

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<v Speaker 2>It's strategy, and how do you get the strategy set.

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<v Speaker 2>It's segmentation, it's product strategy at a granular level across

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<v Speaker 2>a bunch of dimensions, and then putting the investment behind it.

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<v Speaker 2>It's a big challenge. It takes a long time, but

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<v Speaker 2>it's working, so we're happy with.

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<v Speaker 1>Yeah, tell me give me a little bit of perspective

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<v Speaker 1>on you've been there four years. Imagine we're having this

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<v Speaker 1>conversation four years ago. Yeah, what sorts of things have

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<v Speaker 1>happened over the last four years that have surprised you

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<v Speaker 1>that you didn't see come At least we had exactly

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<v Speaker 1>the same conversation four years ago.

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<v Speaker 2>No, because I didn't know what was in I'll tell

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<v Speaker 2>you some of the biggest surprises. I thought from the outside,

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<v Speaker 2>and you know, you hear from a lot of customers,

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<v Speaker 2>especially ten years ago, we're all going to cloud. We're

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<v Speaker 2>all doing it. So I thought, well, I wonder if

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<v Speaker 2>the main frame business is struggling. When I get inside

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<v Speaker 2>of there, I found the opposite to be true. The

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<v Speaker 2>mainframe business is actually flourishing because transaction demand across the

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<v Speaker 2>globe has done nothing but grow. And even more surprising

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<v Speaker 2>was the level of innovation that the team was already

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<v Speaker 2>doing in mainframes before I got here was astounding. For example,

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<v Speaker 2>we have AI. They were building AI technology into the

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<v Speaker 2>mainframe processors three years before chat GPT made everybody talk

0:12:29.760 --> 0:12:34.080
<v Speaker 2>about it in the industry, So that was really pleasantly surprising.

0:12:34.160 --> 0:12:38.960
<v Speaker 2>So that was wonderful. Other surprises. I knew about the

0:12:39.120 --> 0:12:42.079
<v Speaker 2>kind of the IP of IBM and the mystique in that,

0:12:42.440 --> 0:12:45.080
<v Speaker 2>and I used to joke with people, especially on the outside,

0:12:45.080 --> 0:12:46.520
<v Speaker 2>I said, I can't wait to get in there and

0:12:46.520 --> 0:12:49.360
<v Speaker 2>see what's in the big blue toolbox? Right, what are

0:12:49.400 --> 0:12:52.719
<v Speaker 2>all the things they have going on? I way underestimated

0:12:52.920 --> 0:12:55.280
<v Speaker 2>the size of the big blue toolbox and what was

0:12:55.320 --> 0:12:59.880
<v Speaker 2>in their meaning the amount of really hardcore research that

0:13:00.040 --> 0:13:03.480
<v Speaker 2>we're still doing into how to build chips and how

0:13:03.520 --> 0:13:06.600
<v Speaker 2>to get to things beyond two nanimeter and that kind

0:13:06.640 --> 0:13:12.600
<v Speaker 2>of capability packaging industry leading packaging technologies, and that's in

0:13:12.640 --> 0:13:15.439
<v Speaker 2>my hardware kind of patch quantum. The next thing that

0:13:15.480 --> 0:13:19.440
<v Speaker 2>will come after we're done talking about AI. You know,

0:13:20.080 --> 0:13:23.120
<v Speaker 2>all of those things were surprising, But it wasn't just that.

0:13:23.200 --> 0:13:25.760
<v Speaker 2>It was then the software innovations that are going on

0:13:25.920 --> 0:13:30.720
<v Speaker 2>heavy investment in AI technologies before it was really popular

0:13:30.760 --> 0:13:33.400
<v Speaker 2>to be talking about that. But as I saw that,

0:13:33.520 --> 0:13:36.120
<v Speaker 2>I thought this is going to be really fun because

0:13:36.160 --> 0:13:38.640
<v Speaker 2>I had a good feel for where the industry was going.

0:13:39.480 --> 0:13:42.120
<v Speaker 1>I just didn't and I knew, man, I know that.

0:13:42.120 --> 0:13:44.760
<v Speaker 2>Talent is really good and there's brilliant people there, but

0:13:44.800 --> 0:13:48.720
<v Speaker 2>I didn't know the level of IP frankly, that IBM

0:13:48.840 --> 0:13:51.760
<v Speaker 2>had at its disposal. And now you're seeing that in

0:13:51.800 --> 0:13:55.040
<v Speaker 2>things like Watson X and things like AI in mainframes,

0:13:55.040 --> 0:13:55.440
<v Speaker 2>et cetera.

0:13:55.840 --> 0:13:58.800
<v Speaker 1>Building on that, since you brought up AI, can you

0:13:58.840 --> 0:14:02.760
<v Speaker 1>walk me through what has to happen from your perspective,

0:14:02.760 --> 0:14:08.839
<v Speaker 1>from the infrastructure perspective to make the AI explosion work. Yeah,

0:14:08.880 --> 0:14:10.920
<v Speaker 1>so everyone wants to do more of this stuff. Yes,

0:14:11.080 --> 0:14:13.560
<v Speaker 1>clearly there has to be some underpinning of it.

0:14:14.000 --> 0:14:17.560
<v Speaker 2>Yeah, I would tell you, you know, I think that

0:14:17.720 --> 0:14:20.000
<v Speaker 2>people feel like where we're at right now in the

0:14:20.040 --> 0:14:23.120
<v Speaker 2>AI journey had to do with one specific piece of software.

0:14:23.480 --> 0:14:27.600
<v Speaker 2>I think the inflection point for that whole thing really

0:14:28.520 --> 0:14:32.240
<v Speaker 2>at its root was around hardware, meaning the algorithms needed

0:14:32.280 --> 0:14:34.880
<v Speaker 2>to do large language models. And all of that had

0:14:34.920 --> 0:14:37.560
<v Speaker 2>been around, they'd been talked about in the industry, but

0:14:37.600 --> 0:14:40.640
<v Speaker 2>at some point you hit a tipping point of hardware

0:14:40.720 --> 0:14:43.360
<v Speaker 2>capability where it's like, oh, now we can do this

0:14:43.440 --> 0:14:46.840
<v Speaker 2>in a broof force way, massive amounts of matrix math

0:14:46.920 --> 0:14:49.800
<v Speaker 2>to get weights correct so that you can do you know,

0:14:49.920 --> 0:14:53.240
<v Speaker 2>the right level of predictions that enable large language models.

0:14:53.600 --> 0:14:56.160
<v Speaker 2>And once we got to that horsepower, and that's why

0:14:56.200 --> 0:14:59.240
<v Speaker 2>you hear about giant GPUs that are driving this and

0:14:59.320 --> 0:15:01.600
<v Speaker 2>the sales of the et cetera. It's because we just

0:15:01.640 --> 0:15:03.560
<v Speaker 2>barely got over the hump where you can do these

0:15:03.600 --> 0:15:08.520
<v Speaker 2>big hard things in terms of hardware capability to do it.

0:15:09.000 --> 0:15:11.840
<v Speaker 1>Give me a layman, give me a sense of when

0:15:11.880 --> 0:15:14.040
<v Speaker 1>you say there was a kind of threshold where suddenly

0:15:14.040 --> 0:15:15.200
<v Speaker 1>these things became possible.

0:15:15.320 --> 0:15:18.120
<v Speaker 2>Yeah, I don't know if there's an exact number. But

0:15:19.280 --> 0:15:21.360
<v Speaker 2>and more basic question that I get from a lot

0:15:21.400 --> 0:15:24.080
<v Speaker 2>of people, you know, my friends and family outside, is

0:15:24.400 --> 0:15:29.120
<v Speaker 2>why GPUs. What does a GPU, it's graphics processor have

0:15:29.240 --> 0:15:33.640
<v Speaker 2>to do with AI. It's not, Well, graphics processors are

0:15:33.680 --> 0:15:37.440
<v Speaker 2>really good at this thing matrix math, because they're figuring

0:15:37.480 --> 0:15:40.720
<v Speaker 2>out how do I map a pixel? And and as

0:15:40.760 --> 0:15:44.840
<v Speaker 2>I move an object across the screen, it's essentially matrix

0:15:44.840 --> 0:15:47.000
<v Speaker 2>math to figure out, Okay, what does what does what

0:15:47.040 --> 0:15:49.640
<v Speaker 2>does this pixel on a screen look like? And what's

0:15:49.720 --> 0:15:52.600
<v Speaker 2>it's doing? And as you you know, we've gotten more

0:15:52.680 --> 0:15:56.400
<v Speaker 2>high resolution graphics, more high resolution monitors, et cetera. It's

0:15:56.440 --> 0:15:58.320
<v Speaker 2>a lot more pixels and a lot more math and

0:15:58.360 --> 0:16:00.760
<v Speaker 2>a lot more matrix math about how you compute that.

0:16:01.240 --> 0:16:03.560
<v Speaker 2>The first big thing that kind of started to look

0:16:03.680 --> 0:16:07.400
<v Speaker 2>like that, it turns out, was crypto and crypto mining,

0:16:07.440 --> 0:16:10.240
<v Speaker 2>and so you saw graphics companies starting to sell to crypto.

0:16:10.760 --> 0:16:12.840
<v Speaker 2>The technology got to a certain point and there were

0:16:12.920 --> 0:16:15.880
<v Speaker 2>use cases like bitcoin in that that kind of said, hey,

0:16:15.960 --> 0:16:17.840
<v Speaker 2>we need to do a lot of this matrix math

0:16:18.200 --> 0:16:20.960
<v Speaker 2>to be able to do that. So graphic chips were

0:16:20.960 --> 0:16:23.720
<v Speaker 2>a natural fit and that kind of sus same. But meanwhile,

0:16:23.760 --> 0:16:26.440
<v Speaker 2>behind the scenes, a lot of this ai AI is

0:16:26.480 --> 0:16:31.480
<v Speaker 2>about numeric calculations having to do with weights and matrices

0:16:31.480 --> 0:16:35.720
<v Speaker 2>that say, you know, giant consolidated things that predict what's

0:16:35.800 --> 0:16:38.200
<v Speaker 2>going to kind of happen based on what other things

0:16:38.200 --> 0:16:41.680
<v Speaker 2>have happened, just like predicting where pixel goes. But it's

0:16:41.760 --> 0:16:45.520
<v Speaker 2>really about being able to do enough data in jest

0:16:45.920 --> 0:16:48.320
<v Speaker 2>to be able to do and then the calculations to

0:16:48.360 --> 0:16:52.480
<v Speaker 2>be able to simplify things like entire sets of language

0:16:52.560 --> 0:16:55.920
<v Speaker 2>or giant chunks of the Internet, to get enough weightings

0:16:55.920 --> 0:16:58.120
<v Speaker 2>in there to be able to say, okay, we can

0:16:58.160 --> 0:17:01.760
<v Speaker 2>predict what you would say in this language based on

0:17:02.000 --> 0:17:04.560
<v Speaker 2>all of the volumes of stuff that we've seen that

0:17:04.600 --> 0:17:06.919
<v Speaker 2>when you start talking like this, the next word is

0:17:07.000 --> 0:17:08.600
<v Speaker 2>likely oh it's this yeah.

0:17:08.640 --> 0:17:10.919
<v Speaker 1>So, But my point is to get to that point.

0:17:11.000 --> 0:17:14.800
<v Speaker 1>That's threshold. We got there because was it because we

0:17:14.880 --> 0:17:17.719
<v Speaker 1>simply threw a lot more resources at the problem or

0:17:17.800 --> 0:17:22.040
<v Speaker 1>is it because the underlying technology got suddenly or gradually

0:17:22.200 --> 0:17:23.360
<v Speaker 1>so much more efficient.

0:17:23.480 --> 0:17:26.720
<v Speaker 2>It's always yes and yes. But you know, the industry

0:17:26.800 --> 0:17:29.040
<v Speaker 2>for a lot of years would talk about Moore's.

0:17:28.760 --> 0:17:33.080
<v Speaker 1>Law, Well, quick, will you define for us Moore's law

0:17:33.119 --> 0:17:34.800
<v Speaker 1>for those of those who's forgotten it.

0:17:35.040 --> 0:17:38.200
<v Speaker 2>Yeah, So Gordon Moore at Intel coined this thing. It

0:17:38.240 --> 0:17:42.960
<v Speaker 2>was basically that the horsepower I'm going to translate it

0:17:43.080 --> 0:17:48.680
<v Speaker 2>roughly of technology will double every couple of years. We're

0:17:48.720 --> 0:17:51.400
<v Speaker 2>still on Moore's law. More's law changed a little bit.

0:17:51.800 --> 0:17:54.640
<v Speaker 2>For a while. It was always about frequency. Things would

0:17:54.640 --> 0:17:57.800
<v Speaker 2>go faster, faster, faster. That kind of petered out. But

0:17:57.920 --> 0:18:01.120
<v Speaker 2>what happened is, rather than faster, faster, faster, we did

0:18:01.160 --> 0:18:04.399
<v Speaker 2>more and more and more. So rather than one operating

0:18:04.520 --> 0:18:08.360
<v Speaker 2>unit going a lot faster on its throughput, you put

0:18:08.400 --> 0:18:10.840
<v Speaker 2>ten operating units on a chip, Now you put one

0:18:10.920 --> 0:18:13.960
<v Speaker 2>hundred operating units on a chip, now a thousand. Some

0:18:14.040 --> 0:18:18.680
<v Speaker 2>of these problems, the matrix math problems scale parallel extremely well.

0:18:18.680 --> 0:18:21.000
<v Speaker 2>You don't have to do something really fast, you just

0:18:21.080 --> 0:18:23.080
<v Speaker 2>have to do a lot of the similar things in

0:18:23.119 --> 0:18:26.080
<v Speaker 2>parallel at the same time. So again that kind of

0:18:26.080 --> 0:18:28.840
<v Speaker 2>that extension of Moore's law, more and more hardware on

0:18:28.880 --> 0:18:30.280
<v Speaker 2>a chip to be able to do more and more

0:18:30.280 --> 0:18:33.520
<v Speaker 2>of those calculations in parallel and come up with it.

0:18:33.640 --> 0:18:36.760
<v Speaker 1>And we said, yeah, was that threshold predictable? In other words,

0:18:36.760 --> 0:18:39.639
<v Speaker 1>see people in the industry, Like you sit down X

0:18:39.680 --> 0:18:41.720
<v Speaker 1>number of years ago and say, when we get here,

0:18:42.720 --> 0:18:44.359
<v Speaker 1>AI is going to become much more.

0:18:44.200 --> 0:18:50.800
<v Speaker 2>Of a It's funny. The horsepower that very predictable, the

0:18:51.040 --> 0:18:54.520
<v Speaker 2>use cases not always so easy to kind of figure out.

0:18:54.600 --> 0:18:58.199
<v Speaker 2>That's where the human spirit kind of gets involved. I

0:18:58.240 --> 0:19:00.439
<v Speaker 2>think for some people that say, oh, I saw that coming,

0:19:00.720 --> 0:19:03.840
<v Speaker 2>But people have been predicting kind of the rise of

0:19:03.920 --> 0:19:06.840
<v Speaker 2>AI for twenty five years. Oh well, then when we

0:19:06.920 --> 0:19:08.800
<v Speaker 2>get to this next gener oh, when we get here,

0:19:08.960 --> 0:19:11.919
<v Speaker 2>it kind of hadn't happened. There's always a magic point

0:19:12.840 --> 0:19:15.159
<v Speaker 2>where you kind of get to where the technology and

0:19:15.240 --> 0:19:17.639
<v Speaker 2>the use case and somebody does something to kind of

0:19:17.680 --> 0:19:20.359
<v Speaker 2>make it catch on. And I think we're at one

0:19:20.400 --> 0:19:22.320
<v Speaker 2>of those moments in AI for sure right now. And

0:19:22.359 --> 0:19:24.560
<v Speaker 2>I don't think it's you know, people have said, oh,

0:19:24.560 --> 0:19:27.480
<v Speaker 2>this is just the latest wave of you know, I

0:19:27.840 --> 0:19:30.080
<v Speaker 2>hear I've heard this about a lot of technologies, but

0:19:30.400 --> 0:19:33.280
<v Speaker 2>AI is the technology the future, and it always will be.

0:19:33.560 --> 0:19:36.040
<v Speaker 2>I used to hear that you're not airing that now,

0:19:36.240 --> 0:19:39.840
<v Speaker 2>right It's like, no, it's primetime. It will change everything,

0:19:40.040 --> 0:19:42.320
<v Speaker 2>just like some of these other things changed everything.

0:19:42.560 --> 0:19:46.960
<v Speaker 1>I noticed it if personally, when I speak somewhere or

0:19:47.040 --> 0:19:50.640
<v Speaker 1>I'm listening to an audience somewhere. Over the last let's

0:19:50.680 --> 0:19:55.720
<v Speaker 1>say twelve months, there's always a whole bunch of AI questions.

0:19:55.960 --> 0:19:56.160
<v Speaker 2>Yes.

0:19:56.320 --> 0:19:58.520
<v Speaker 1>And if I go back two years ago, there were

0:19:58.520 --> 0:19:59.439
<v Speaker 1>no AI questions.

0:19:59.480 --> 0:19:59.680
<v Speaker 2>Yes.

0:20:00.280 --> 0:20:02.600
<v Speaker 1>Now my question is, so there's been this explosion on

0:20:02.640 --> 0:20:07.040
<v Speaker 1>the in popular fascination with what's going on AI. It

0:20:07.119 --> 0:20:08.280
<v Speaker 1>seems like the last year.

0:20:08.400 --> 0:20:10.760
<v Speaker 2>I agree with you.

0:20:09.960 --> 0:20:16.000
<v Speaker 1>In your world, when did the explosion of conversation around

0:20:16.000 --> 0:20:16.480
<v Speaker 1>this start.

0:20:17.240 --> 0:20:26.520
<v Speaker 2>It's I love this question because IBM had a fairly

0:20:26.680 --> 0:20:31.760
<v Speaker 2>big effort and business called Watson before Watson X. And

0:20:31.800 --> 0:20:34.720
<v Speaker 2>this is going back kind of ten years. I'll give

0:20:34.720 --> 0:20:37.560
<v Speaker 2>you another kind of example. I knew about a lot

0:20:37.600 --> 0:20:41.040
<v Speaker 2>of tablet technology before there was an iPad, a lot.

0:20:41.240 --> 0:20:43.600
<v Speaker 2>For ten years, there were a lot, but it kind

0:20:43.600 --> 0:20:47.280
<v Speaker 2>of takes a magic combination of the technology, the user experienced,

0:20:47.359 --> 0:20:50.000
<v Speaker 2>the software, and the need and the market ready for

0:20:50.040 --> 0:20:52.240
<v Speaker 2>it to kind of go. Now it's the thing. Now

0:20:52.280 --> 0:20:54.359
<v Speaker 2>we all have either an iPad or we have the

0:20:54.680 --> 0:20:57.600
<v Speaker 2>Google equivalent Tom and so I think this is a

0:20:57.680 --> 0:21:00.600
<v Speaker 2>little like that, meaning IBM was on the right track

0:21:00.680 --> 0:21:03.600
<v Speaker 2>with Watson. Some of the hardware wasn't there, the use

0:21:03.640 --> 0:21:06.280
<v Speaker 2>cases weren't exactly figured out. Some of the early use

0:21:06.320 --> 0:21:09.439
<v Speaker 2>cases didn't pan out perfectly. But the good news about

0:21:09.440 --> 0:21:12.919
<v Speaker 2>that is it's back to that culture of risk taking.

0:21:13.000 --> 0:21:15.480
<v Speaker 2>You don't look back on that and say, oh, we

0:21:15.480 --> 0:21:17.119
<v Speaker 2>shouldn't have done that, that was a bad idea. I know,

0:21:17.200 --> 0:21:19.040
<v Speaker 2>you look back on that and say, what did we learn?

0:21:19.320 --> 0:21:21.520
<v Speaker 2>How should we try something new? How would we pivot

0:21:21.560 --> 0:21:23.800
<v Speaker 2>this time. That's what we've done with Watson X, and

0:21:25.000 --> 0:21:27.760
<v Speaker 2>now that's a growing, healthy piece of our business and

0:21:27.920 --> 0:21:29.560
<v Speaker 2>very important our strategic picture.

0:21:29.600 --> 0:21:32.520
<v Speaker 1>So we're all in I just I've always investigated by

0:21:32.600 --> 0:21:38.560
<v Speaker 1>the gap between insider sense of what is happening an

0:21:38.560 --> 0:21:42.040
<v Speaker 1>outsider sense, like it absolutely is that in this case,

0:21:42.119 --> 0:21:45.520
<v Speaker 1>we've all been talking about and thinking about AI and

0:21:46.200 --> 0:21:48.520
<v Speaker 1>is it time for that and what does this mean,

0:21:48.560 --> 0:21:51.280
<v Speaker 1>et cetera. And yet none of us really predicted that

0:21:51.359 --> 0:21:54.760
<v Speaker 1>actual moment, which is kind of you know, early twenty

0:21:54.840 --> 0:21:58.199
<v Speaker 1>twenty two where it was like, oh, now you have

0:21:58.359 --> 0:22:02.480
<v Speaker 1>a simple, huge in the interface of software innovation combined

0:22:02.520 --> 0:22:08.760
<v Speaker 1>with large language models. There's a moment there where you're like, oh, Unlike,

0:22:09.040 --> 0:22:10.560
<v Speaker 1>you know, I think all of us are frustrated if

0:22:10.600 --> 0:22:12.879
<v Speaker 1>we ask our phone, hey tell me about this and

0:22:12.920 --> 0:22:15.359
<v Speaker 1>it says I found this on the web page.

0:22:15.400 --> 0:22:17.080
<v Speaker 2>That does you no good. But you know, all of

0:22:17.080 --> 0:22:21.200
<v Speaker 2>a sudden, with Chad GPT and some of these other things,

0:22:21.200 --> 0:22:22.879
<v Speaker 2>you could ask a question, it would give you a

0:22:22.880 --> 0:22:25.600
<v Speaker 2>clear answer. Sometimes is wrong, but at least it was

0:22:25.680 --> 0:22:27.880
<v Speaker 2>like I'm getting an answer rather than hey, I don't

0:22:27.920 --> 0:22:30.399
<v Speaker 2>know if there's some references. Good luck to you. And

0:22:30.440 --> 0:22:31.760
<v Speaker 2>that's really changing.

0:22:32.320 --> 0:22:36.840
<v Speaker 1>Talk about the kind of macro trends that are going

0:22:36.880 --> 0:22:39.919
<v Speaker 1>to shape your infrastructure battle.

0:22:40.040 --> 0:22:42.960
<v Speaker 2>Yeah, We've talked about if you already, but I'm actually

0:22:42.960 --> 0:22:46.879
<v Speaker 2>going to go a little different direction. So macro trans first.

0:22:47.359 --> 0:22:51.600
<v Speaker 2>And this one has been before even even this AI conversation,

0:22:51.680 --> 0:22:56.840
<v Speaker 2>that we've had explosion of data. As humans, we don't

0:22:57.000 --> 0:23:02.480
<v Speaker 2>think exponentially very well. Really struggle with exponential thinking. We

0:23:02.560 --> 0:23:05.000
<v Speaker 2>think linearly, Oh, there'll be more, there'll be more, they'll

0:23:05.040 --> 0:23:07.199
<v Speaker 2>be more, but we don't think well when it's like no,

0:23:07.320 --> 0:23:09.040
<v Speaker 2>they'll be more, and they'll be ten times more, and

0:23:09.080 --> 0:23:11.360
<v Speaker 2>then there'll be ten times that more. That's what's going

0:23:11.400 --> 0:23:14.359
<v Speaker 2>on with data right now in our industry. It's one

0:23:14.359 --> 0:23:16.760
<v Speaker 2>of the reasons that that storage business is doing so

0:23:16.840 --> 0:23:19.119
<v Speaker 2>well is there's just more and more and more data.

0:23:20.400 --> 0:23:22.240
<v Speaker 2>You know, you'd say, well, how can there be more data?

0:23:22.280 --> 0:23:24.679
<v Speaker 2>It's just life and that thing. The things that we

0:23:24.760 --> 0:23:28.320
<v Speaker 2>care about, video capture, video images, you know, the the

0:23:29.200 --> 0:23:31.879
<v Speaker 2>you I don't know from my parents, you needed a

0:23:32.000 --> 0:23:35.000
<v Speaker 2>drawer with all your family photos. Now we need gigabytes

0:23:35.000 --> 0:23:35.560
<v Speaker 2>and gigabytes.

0:23:35.640 --> 0:23:37.959
<v Speaker 1>You knew how many pictures my wife has taken off

0:23:37.960 --> 0:23:42.520
<v Speaker 1>our children, you would exactly, exactly, So that's your case. Now.

0:23:42.600 --> 0:23:45.240
<v Speaker 2>Think of companies who used to just think about their

0:23:45.280 --> 0:23:49.280
<v Speaker 2>transaction data. What's the ledger say that now have video

0:23:49.320 --> 0:23:52.440
<v Speaker 2>assets of all of their campaigns and their marketing. They're

0:23:52.480 --> 0:23:55.200
<v Speaker 2>trying to figure out, you know, what campaigns are working

0:23:55.200 --> 0:23:57.919
<v Speaker 2>the best. So it's just an explosion of data and

0:23:57.960 --> 0:24:01.760
<v Speaker 2>that's not going to stop with that, and more importantly,

0:24:01.840 --> 0:24:07.320
<v Speaker 2>getting value from that data is a massive trend in

0:24:07.359 --> 0:24:11.520
<v Speaker 2>the industry. Second trend AI, and this is the AI.

0:24:11.720 --> 0:24:13.760
<v Speaker 2>Not like we were just talking about about how it

0:24:13.840 --> 0:24:16.040
<v Speaker 2>changes how I search for things or how I learn

0:24:16.119 --> 0:24:20.480
<v Speaker 2>about things. But I would argue, dealing with that data,

0:24:20.560 --> 0:24:23.080
<v Speaker 2>how do I figure out what's in all those video streams?

0:24:23.119 --> 0:24:25.840
<v Speaker 2>How do I figure out Okay, I want all of

0:24:25.880 --> 0:24:28.880
<v Speaker 2>the chunks of my corporate video that have to do

0:24:28.960 --> 0:24:33.000
<v Speaker 2>with client buying some specific product or something. That's a

0:24:33.640 --> 0:24:35.959
<v Speaker 2>different problem. It's not just okay, we'll look it up

0:24:35.960 --> 0:24:39.560
<v Speaker 2>in a spreadsheet and here's the math associated with that.

0:24:39.560 --> 0:24:41.920
<v Speaker 2>That is a huge trend in the industry. You're seeing

0:24:41.920 --> 0:24:44.159
<v Speaker 2>it play out in this regard. It's a little different

0:24:44.240 --> 0:24:48.240
<v Speaker 2>bent on AI. Fraud detection is the one that we

0:24:48.560 --> 0:24:51.439
<v Speaker 2>cite in our mainframes. It's a similar problem where it

0:24:51.600 --> 0:24:54.359
<v Speaker 2>was kind of a traditional AI problem. Look up a rule.

0:24:54.920 --> 0:24:58.399
<v Speaker 2>You know, if somebody does two small transactions, then a

0:24:58.440 --> 0:25:00.600
<v Speaker 2>massive one it might be fraud, right because they were

0:25:00.640 --> 0:25:04.280
<v Speaker 2>seeing whether it were now to detect fraud, you might

0:25:04.320 --> 0:25:08.680
<v Speaker 2>be saying, okay to transactions then a huge one. Plus

0:25:08.760 --> 0:25:12.159
<v Speaker 2>does this entity have a real address? Second, is there

0:25:12.200 --> 0:25:15.640
<v Speaker 2>any web traffic on you know, better business bureau kind

0:25:15.680 --> 0:25:17.399
<v Speaker 2>of things that says this is a bad business that

0:25:17.760 --> 0:25:19.720
<v Speaker 2>can help you with fraud. So it's a lot more

0:25:19.760 --> 0:25:22.840
<v Speaker 2>of a it's an expotent still problem. It's a holistic

0:25:22.920 --> 0:25:25.639
<v Speaker 2>problem that it takes a lot more than just you know,

0:25:25.880 --> 0:25:28.720
<v Speaker 2>little chunks of rules, et cetera. And then the third

0:25:28.720 --> 0:25:32.600
<v Speaker 2>one you know after AI is the nature of hybrid

0:25:32.600 --> 0:25:36.240
<v Speaker 2>it or hybrid computing. For a while ten years ago

0:25:36.320 --> 0:25:39.520
<v Speaker 2>when cloud was on the rise, I think the notion

0:25:39.600 --> 0:25:42.800
<v Speaker 2>of hybrid computing basically having to do with things in

0:25:42.840 --> 0:25:46.840
<v Speaker 2>the cloud versus things that people still have on the

0:25:46.880 --> 0:25:50.680
<v Speaker 2>premises inside of business, it was almost a religious argument.

0:25:51.240 --> 0:25:54.199
<v Speaker 2>Now it's no, it's the reality. And the reason is

0:25:54.240 --> 0:25:57.919
<v Speaker 2>because that data that I talked about is the lifeblood

0:25:57.960 --> 0:26:01.840
<v Speaker 2>of these companies, particularly IBM's come. Companies are clients that

0:26:02.040 --> 0:26:05.240
<v Speaker 2>usually that data has to be secure, they have to

0:26:05.240 --> 0:26:07.879
<v Speaker 2>be able to get value from it. It is the

0:26:07.920 --> 0:26:09.960
<v Speaker 2>lifeblood of the company. If you go to an ATM

0:26:10.000 --> 0:26:13.720
<v Speaker 2>and you can't get your money out to our financial transactions,

0:26:14.480 --> 0:26:16.800
<v Speaker 2>if that lasts a day, you're probably going to change

0:26:16.800 --> 0:26:19.840
<v Speaker 2>banks immediately. So it's like life or death for these companies.

0:26:21.720 --> 0:26:26.160
<v Speaker 2>So having that hybrid infrastructure so that they can still

0:26:26.200 --> 0:26:29.640
<v Speaker 2>hold their data, you still interact with clouds and still

0:26:29.640 --> 0:26:32.600
<v Speaker 2>get value from it from AI, that's kind of the

0:26:32.720 --> 0:26:37.320
<v Speaker 2>magic where we play, and it's a huge business opportunity.

0:26:37.600 --> 0:26:41.239
<v Speaker 2>It is a true inflection point for the industry. I'm

0:26:41.280 --> 0:26:42.280
<v Speaker 2>going to go back.

0:26:43.359 --> 0:26:45.200
<v Speaker 1>I interrupted you when you were in the middle of

0:26:45.240 --> 0:26:48.119
<v Speaker 1>a rellion. We were talking about what has to happen

0:26:48.240 --> 0:26:53.640
<v Speaker 1>for AI to scale from the infrastructure standpoint. You gave

0:26:53.680 --> 0:26:55.959
<v Speaker 1>one example that I got you off on a tangent.

0:26:56.320 --> 0:26:59.840
<v Speaker 1>Can you go back and talk very so practically. So,

0:27:00.119 --> 0:27:03.000
<v Speaker 1>I'm I'm a big company. I have all these dreams

0:27:03.080 --> 0:27:06.280
<v Speaker 1>of AI, of how I'm going to use this dratically.

0:27:06.560 --> 0:27:09.720
<v Speaker 1>So give me a very granular sense of the works

0:27:09.880 --> 0:27:12.800
<v Speaker 1>you have to do, yeah to make that dream possible.

0:27:13.240 --> 0:27:16.479
<v Speaker 2>So let me first say what the company has to do,

0:27:16.520 --> 0:27:18.480
<v Speaker 2>and then maybe I'll say, then how do I help them?

0:27:18.560 --> 0:27:20.720
<v Speaker 2>If that makes sense? So if I'm a company and

0:27:20.760 --> 0:27:22.719
<v Speaker 2>I want to do that, So it turns out I

0:27:22.760 --> 0:27:26.560
<v Speaker 2>am a company meaning I want to use AI in

0:27:26.600 --> 0:27:30.439
<v Speaker 2>my processes. I mentioned that I have a global network

0:27:30.480 --> 0:27:34.399
<v Speaker 2>of thirteen thousand employees that support our infrastructure around the world.

0:27:35.040 --> 0:27:40.440
<v Speaker 2>That challenge is a great challenge for AI. That means

0:27:40.480 --> 0:27:45.040
<v Speaker 2>I have data for every customer situation for thirteen thousand

0:27:45.080 --> 0:27:48.520
<v Speaker 2>employees globally around the world on what was their problem,

0:27:48.600 --> 0:27:52.040
<v Speaker 2>how did we fix it, what next steps did they

0:27:52.040 --> 0:27:54.480
<v Speaker 2>have to do, how did they remediate that? That data

0:27:54.560 --> 0:27:57.040
<v Speaker 2>is extremely valuable to me because if I can get

0:27:57.080 --> 0:27:59.960
<v Speaker 2>better at doing that than anybody else in the world,

0:28:00.080 --> 0:28:02.320
<v Speaker 2>that brings my cost down. I sell more products, I

0:28:02.359 --> 0:28:05.239
<v Speaker 2>sell more service, I sell more anything. So what I

0:28:05.359 --> 0:28:07.280
<v Speaker 2>have to do to get there is I have to

0:28:07.280 --> 0:28:10.760
<v Speaker 2>figure out, Okay, what's my objective. I have a couple objectives. One,

0:28:10.840 --> 0:28:13.359
<v Speaker 2>I want customers to be able to support themselves without

0:28:13.359 --> 0:28:17.239
<v Speaker 2>even calling me, first off, and I don't want when

0:28:17.280 --> 0:28:19.719
<v Speaker 2>they call for the first answer to come back to

0:28:19.720 --> 0:28:23.080
<v Speaker 2>be did you try rebooting? Because I think that irritates

0:28:23.119 --> 0:28:25.520
<v Speaker 2>every single one of them. Did you try? Of course

0:28:25.520 --> 0:28:28.280
<v Speaker 2>I tried rebooting. I've had a laptop my entire of course.

0:28:28.320 --> 0:28:32.480
<v Speaker 2>I well, okay, well then tell me, okay, what firmware version,

0:28:32.520 --> 0:28:34.600
<v Speaker 2>all that other stuff. Okay, we know this interaction. So

0:28:35.880 --> 0:28:37.800
<v Speaker 2>that's kind of the problem set. Do I want that

0:28:37.960 --> 0:28:41.280
<v Speaker 2>to be customers solving their own problems? Well, even for

0:28:41.400 --> 0:28:43.840
<v Speaker 2>my support agents, I want something in their pocket on

0:28:43.880 --> 0:28:46.800
<v Speaker 2>their phone where they say I'm seeing these symptoms. It says, oh,

0:28:47.040 --> 0:28:49.760
<v Speaker 2>this happening around the globe. Here's kind of specific me.

0:28:49.880 --> 0:28:53.480
<v Speaker 2>So there's my problems. What does it mean for infrastructure

0:28:53.520 --> 0:28:57.360
<v Speaker 2>on the back end? So first I got to get

0:28:57.360 --> 0:29:00.280
<v Speaker 2>all that data together, right, all of those customers, all

0:29:00.320 --> 0:29:03.600
<v Speaker 2>that customer support around the globe, et cetera. That needs

0:29:03.600 --> 0:29:06.160
<v Speaker 2>to be stored. That's a big set of data and

0:29:06.200 --> 0:29:09.520
<v Speaker 2>some of it's not just fix and that kind of thing.

0:29:09.600 --> 0:29:12.040
<v Speaker 2>Some of it is, Okay, you know what was the

0:29:12.080 --> 0:29:14.680
<v Speaker 2>firmware version, who was the tech because it can matter.

0:29:15.200 --> 0:29:17.280
<v Speaker 2>Is this their first time fixing this problem? Is it

0:29:17.320 --> 0:29:19.600
<v Speaker 2>there one hundred and fiftieth time? What's their level? It's

0:29:19.640 --> 0:29:25.080
<v Speaker 2>a very complicated problem. Ingesting all that data takes an architecture.

0:29:25.120 --> 0:29:28.000
<v Speaker 2>We have a product called Scale, which is one of

0:29:28.000 --> 0:29:31.480
<v Speaker 2>our storage projects that actually makes it easy to ingest

0:29:31.480 --> 0:29:34.840
<v Speaker 2>all that data, get it organized, et cetera, and then

0:29:36.280 --> 0:29:38.880
<v Speaker 2>have a model. It's a whole different process to kind

0:29:38.880 --> 0:29:40.680
<v Speaker 2>of say did we train our model? We can train

0:29:40.720 --> 0:29:43.000
<v Speaker 2>our own models. Inside of IBM, we have a granite

0:29:43.040 --> 0:29:46.360
<v Speaker 2>set of models. Those models we fine tune and then

0:29:46.400 --> 0:29:48.800
<v Speaker 2>we inference based on those models. So we can do

0:29:48.840 --> 0:29:51.680
<v Speaker 2>that inferencing in our cloud. I have a cloud set

0:29:51.680 --> 0:29:54.200
<v Speaker 2>of infrastructure, or in my power servers. We can do

0:29:54.240 --> 0:29:59.080
<v Speaker 2>inferencing with our capabilities and say, okay, based on what

0:29:59.200 --> 0:30:01.920
<v Speaker 2>I'm saying, there's what the remediation that you should do

0:30:02.000 --> 0:30:05.280
<v Speaker 2>for that customer. We already are doing that today. We've

0:30:05.360 --> 0:30:10.920
<v Speaker 2>seen over a third of our support calls have had

0:30:11.080 --> 0:30:14.200
<v Speaker 2>significant reduction in the amount of time that it takes

0:30:14.200 --> 0:30:17.560
<v Speaker 2>to resolve that support call. Just by what I said

0:30:17.680 --> 0:30:18.360
<v Speaker 2>right there.

0:30:18.480 --> 0:30:22.040
<v Speaker 1>That I've really been curious about this if I had

0:30:22.080 --> 0:30:26.280
<v Speaker 1>reduced something like AI into that equation as you just did. Yeah,

0:30:26.320 --> 0:30:29.320
<v Speaker 1>and you said we've already seen a thirty percent Say

0:30:29.360 --> 0:30:30.719
<v Speaker 1>did you say thirty percent reduction?

0:30:31.080 --> 0:30:35.600
<v Speaker 2>Thirty percent of our interactions have seen significant reduction in

0:30:36.000 --> 0:30:36.600
<v Speaker 2>those time?

0:30:36.720 --> 0:30:39.720
<v Speaker 1>Was that your primary goal to reduce the time of

0:30:39.760 --> 0:30:42.520
<v Speaker 1>the interaction? And it was if you if everything else

0:30:42.600 --> 0:30:44.680
<v Speaker 1>was the same, but all but what you were doing

0:30:44.840 --> 0:30:45.960
<v Speaker 1>was shrinking the amount of time.

0:30:46.000 --> 0:30:48.760
<v Speaker 2>That would you want one of the primary goals, so

0:30:49.880 --> 0:30:53.560
<v Speaker 2>to us, in that business net promoter score kind of

0:30:53.560 --> 0:30:56.760
<v Speaker 2>the satisfaction of a client is the supreme goal. What

0:30:56.920 --> 0:31:01.000
<v Speaker 2>makes them satisfied doesn't cost me a fortune, happens really quickly,

0:31:01.200 --> 0:31:03.120
<v Speaker 2>and if I can do it myself, I'd be thrilled.

0:31:03.840 --> 0:31:06.560
<v Speaker 2>It affects all of those right. It kind of says

0:31:06.680 --> 0:31:09.040
<v Speaker 2>it got resolved faster, it didn't cost me an arm

0:31:09.040 --> 0:31:11.120
<v Speaker 2>and the leg because the deck was barely here, because

0:31:11.120 --> 0:31:14.160
<v Speaker 2>it's a common problem, or I solved it myself without

0:31:14.160 --> 0:31:17.920
<v Speaker 2>even calling, So all of those objectives would kind of

0:31:18.000 --> 0:31:19.960
<v Speaker 2>hit across all so that now you see it so

0:31:20.000 --> 0:31:22.440
<v Speaker 2>that's a little microcosm. That's just me and my customer

0:31:22.480 --> 0:31:25.920
<v Speaker 2>support business. Now, think of how many problems for businesses

0:31:25.960 --> 0:31:29.120
<v Speaker 2>around the world there are like that. It's not a

0:31:29.200 --> 0:31:32.160
<v Speaker 2>it's not like a new AI application that changes the

0:31:32.320 --> 0:31:37.160
<v Speaker 2>entire user experience. That's those will come, But right now

0:31:37.400 --> 0:31:39.760
<v Speaker 2>it's kind of practical, which is, I just want to

0:31:39.800 --> 0:31:42.720
<v Speaker 2>do what I'm doing better and faster, and I can

0:31:42.760 --> 0:31:45.240
<v Speaker 2>get immediate economic return from those things.

0:31:45.240 --> 0:31:48.320
<v Speaker 1>How long How long did it take you to just

0:31:48.400 --> 0:31:51.760
<v Speaker 1>stick with that example of the customer reduction reducing thirty

0:31:51.760 --> 0:31:54.440
<v Speaker 1>percent of the time? How long from the very beginning

0:31:54.480 --> 0:31:57.800
<v Speaker 1>of that project? Yeah to that thirty percent reduction was?

0:31:57.800 --> 0:31:58.200
<v Speaker 1>How long?

0:31:58.840 --> 0:32:04.200
<v Speaker 2>Less than a year? And yeah, So one of the challenges,

0:32:04.520 --> 0:32:08.160
<v Speaker 2>and this is interesting with a very large organization, as

0:32:08.200 --> 0:32:10.640
<v Speaker 2>you can imagine, just like you're seeing in the industry,

0:32:11.520 --> 0:32:15.120
<v Speaker 2>we don't have a problem of generating ideas for how

0:32:15.160 --> 0:32:18.800
<v Speaker 2>AI could help us. We actually have a problem filtering

0:32:19.200 --> 0:32:23.040
<v Speaker 2>the thousands of ideas from our employees and from everywhere.

0:32:23.080 --> 0:32:25.400
<v Speaker 2>It's like, hey, we could use AI to and filtering

0:32:25.440 --> 0:32:27.600
<v Speaker 2>down and saying, okay, which of these will have a

0:32:27.680 --> 0:32:31.440
<v Speaker 2>return on investment quickly and at a level that sustains

0:32:31.480 --> 0:32:34.200
<v Speaker 2>that's worth kind of going and investing in the infrastructure

0:32:34.200 --> 0:32:37.720
<v Speaker 2>and the software and kind of making that happen.

0:32:37.800 --> 0:32:41.120
<v Speaker 1>Is that unusual If I talked to you twenty five

0:32:41.200 --> 0:32:43.480
<v Speaker 1>years ago and said, do you have a problem with

0:32:43.560 --> 0:32:44.960
<v Speaker 1>too many good ideas or too few?

0:32:44.960 --> 0:32:51.120
<v Speaker 2>What was you said in this specific area? Probably too few,

0:32:51.280 --> 0:32:54.680
<v Speaker 2>because at some point you reach diminishing returns. So, for example,

0:32:54.760 --> 0:32:59.000
<v Speaker 2>let's use this same example. Can those thirteen thousand technicians

0:32:59.080 --> 0:33:03.440
<v Speaker 2>go faster? Can they spend less time driving to the side.

0:33:03.440 --> 0:33:05.160
<v Speaker 2>I mean, there's only so much you can kind of

0:33:05.200 --> 0:33:07.560
<v Speaker 2>do on those things. But if you can get them

0:33:07.560 --> 0:33:09.840
<v Speaker 2>an answer to the problem and maybe even avoid them

0:33:09.880 --> 0:33:12.440
<v Speaker 2>having to visit at all because the client helped themselves,

0:33:12.840 --> 0:33:15.840
<v Speaker 2>that's a step function. So that's why people are kind

0:33:15.840 --> 0:33:20.240
<v Speaker 2>of talking about there's a business revolution coming with AI

0:33:20.360 --> 0:33:23.360
<v Speaker 2>where there are some step function changes that can be there.

0:33:23.400 --> 0:33:26.760
<v Speaker 2>And notice I didn't say I'm going to have less

0:33:26.760 --> 0:33:30.320
<v Speaker 2>of those agents. That's not my objective. My objective and

0:33:30.400 --> 0:33:32.760
<v Speaker 2>I think that's the fear in the industry about AI

0:33:32.840 --> 0:33:35.200
<v Speaker 2>is going to eliminate all the jobs. No, I just

0:33:35.280 --> 0:33:39.240
<v Speaker 2>created thirteen thousand superpowered agents that can do more right

0:33:39.320 --> 0:33:41.520
<v Speaker 2>and so I'm not just going to support IBM products.

0:33:41.760 --> 0:33:43.920
<v Speaker 2>I'm going to go out and support other people's products

0:33:43.960 --> 0:33:45.600
<v Speaker 2>because I know how to do that really well. And

0:33:45.640 --> 0:33:48.400
<v Speaker 2>once I have the data on how to fix their problems,

0:33:48.840 --> 0:33:52.480
<v Speaker 2>I may just have a customer support business that's independent

0:33:52.520 --> 0:33:55.240
<v Speaker 2>of my boxes. So you know, I think that's where

0:33:55.280 --> 0:33:57.840
<v Speaker 2>people sometimes get it wrong. And the AI thing is

0:33:58.400 --> 0:34:04.400
<v Speaker 2>it's like, you know, word processing eliminate the need for writers. No,

0:34:04.560 --> 0:34:09.120
<v Speaker 2>it enabled writing instead of mucking around with mimeographic machines

0:34:09.120 --> 0:34:10.560
<v Speaker 2>and click and click typewriters.

0:34:10.560 --> 0:34:12.959
<v Speaker 1>It may have enabled too much writing? Yeah, maybe maybe

0:34:13.680 --> 0:34:16.839
<v Speaker 1>can I give you a hypothetical? Uh? And I asked

0:34:16.880 --> 0:34:18.920
<v Speaker 1>this because I read I was at some convers and

0:34:18.920 --> 0:34:21.040
<v Speaker 1>I ran to some guy from the I R S

0:34:22.160 --> 0:34:25.640
<v Speaker 1>who was really, really, really really excited about AI. So

0:34:25.719 --> 0:34:29.919
<v Speaker 1>let's suppose they call you up and they say, you're

0:34:29.920 --> 0:34:33.560
<v Speaker 1>going to talk to ask the I the IRKY. I

0:34:33.640 --> 0:34:38.560
<v Speaker 1>call you up and I say, Rick, Uh, clearly there's

0:34:38.560 --> 0:34:42.120
<v Speaker 1>something that we could do for the I R S

0:34:42.160 --> 0:34:44.280
<v Speaker 1>if we work together. Yeah, what would your answering?

0:34:45.560 --> 0:34:49.399
<v Speaker 2>Of course? No, I think we sell to a lot

0:34:49.440 --> 0:34:52.920
<v Speaker 2>of government agencies. I can imagine in the business that

0:34:52.960 --> 0:34:57.640
<v Speaker 2>we're in, we enable a lot of social security transactions

0:34:57.640 --> 0:35:01.600
<v Speaker 2>and things like that through our mainframes. And I think,

0:35:01.960 --> 0:35:05.080
<v Speaker 2>you know, we're in the business of helping whatever client

0:35:05.200 --> 0:35:07.120
<v Speaker 2>get the most out of their data and be able

0:35:07.160 --> 0:35:10.239
<v Speaker 2>to secure it and be able to do analytics with

0:35:10.600 --> 0:35:13.040
<v Speaker 2>and IRS has a heck of a lot of data,

0:35:13.160 --> 0:35:15.520
<v Speaker 2>So yes, we would help them. Do you know how

0:35:15.520 --> 0:35:17.520
<v Speaker 2>the amount of data they have compares to some of

0:35:17.560 --> 0:35:20.440
<v Speaker 2>the corporate clients you I don't know specifically for the

0:35:20.520 --> 0:35:22.680
<v Speaker 2>IRS how much data they have, but I would assume

0:35:22.719 --> 0:35:26.480
<v Speaker 2>it's a whole lot. It's mountains. But but that's our business.

0:35:26.600 --> 0:35:29.759
<v Speaker 2>I mean, it's interesting sometimes people of that what's the

0:35:29.880 --> 0:35:34.360
<v Speaker 2>most you know, what is it that that IBM has

0:35:34.480 --> 0:35:37.640
<v Speaker 2>that's of great value? Is it a server? Is it

0:35:38.000 --> 0:35:41.000
<v Speaker 2>a storage array? Is it you know, software and all that.

0:35:41.320 --> 0:35:45.520
<v Speaker 2>What we have is the most important entities in the

0:35:45.560 --> 0:35:49.120
<v Speaker 2>world have their data on our stuff. The most important

0:35:49.200 --> 0:35:52.680
<v Speaker 2>data in the world. It's not you know, pictures of

0:35:52.719 --> 0:35:55.399
<v Speaker 2>your grandkids and things like that. Generally for us, it's

0:35:55.440 --> 0:35:58.799
<v Speaker 2>all of the financial transactions that happen globally, right, It's

0:35:58.840 --> 0:36:01.520
<v Speaker 2>all of the it's the world's economy is kind of

0:36:01.560 --> 0:36:05.600
<v Speaker 2>running through our systems, and so we take that really seriously.

0:36:05.760 --> 0:36:08.600
<v Speaker 2>You know, you would be distraught if you lost one

0:36:08.600 --> 0:36:11.520
<v Speaker 2>photo on your laptop or whatever. But you know, if

0:36:11.520 --> 0:36:14.600
<v Speaker 2>we lose a transaction, like somebody moves a big amount

0:36:14.640 --> 0:36:17.440
<v Speaker 2>of money and it's like, well, don't know what happened there,

0:36:17.680 --> 0:36:21.160
<v Speaker 2>it is a massive deal, right, so that doesn't happen.

0:36:21.280 --> 0:36:22.840
<v Speaker 1>But I want to go back to my irs example

0:36:22.840 --> 0:36:26.720
<v Speaker 1>for us, Yes, so one, is it reasonable to assume

0:36:27.000 --> 0:36:32.040
<v Speaker 1>that you could that somebody IBM or somebody else could

0:36:32.080 --> 0:36:34.560
<v Speaker 1>in a short period of time put together not just

0:36:34.600 --> 0:36:40.160
<v Speaker 1>the AI capability to audit returns, but also this the

0:36:40.200 --> 0:36:43.439
<v Speaker 1>infrastructure support for that in a reasonable amount of time

0:36:43.440 --> 0:36:45.879
<v Speaker 1>for a reasonable amount of cost. Or is it over?

0:36:46.280 --> 0:36:49.120
<v Speaker 1>Is it going to the moon? Or is it it?

0:36:49.360 --> 0:36:53.200
<v Speaker 2>Definitely? I mean, so we're already doing that kind of

0:36:53.239 --> 0:36:58.520
<v Speaker 2>thing right across a network of banks and others, essentially

0:36:58.640 --> 0:37:02.560
<v Speaker 2>all credit card transactions for all of the world to

0:37:02.600 --> 0:37:05.839
<v Speaker 2>go through our systems, so that in some ways is

0:37:05.880 --> 0:37:09.400
<v Speaker 2>more volume than the datch returns of the US people.

0:37:09.480 --> 0:37:12.680
<v Speaker 2>And they're W two's and all that stuff, and we

0:37:12.760 --> 0:37:15.560
<v Speaker 2>do that stuff too. I try not to describe it

0:37:15.600 --> 0:37:17.799
<v Speaker 2>too much in detail, but we definitely do a lot

0:37:17.840 --> 0:37:22.799
<v Speaker 2>of that. In fact, I think most of what If

0:37:22.800 --> 0:37:26.200
<v Speaker 2>you think, okay, what is super critical data? Who would

0:37:26.200 --> 0:37:29.680
<v Speaker 2>be doing the business transaction processing? It is most likely

0:37:29.840 --> 0:37:33.440
<v Speaker 2>us in almost all cases, whether it's government things or

0:37:34.160 --> 0:37:37.239
<v Speaker 2>private or banks or that kind of thing. That's what

0:37:37.280 --> 0:37:37.600
<v Speaker 2>we do.

0:37:37.960 --> 0:37:39.920
<v Speaker 1>Rick We're going to end with the where we always

0:37:40.040 --> 0:37:41.880
<v Speaker 1>end with a couple of quick fire questions.

0:37:41.960 --> 0:37:42.960
<v Speaker 2>Okay, here we go.

0:37:43.840 --> 0:37:46.920
<v Speaker 1>What single piece of advice would you give to businesses

0:37:46.960 --> 0:37:50.280
<v Speaker 1>trying to use AI in an effective way? The simple

0:37:50.400 --> 0:37:54.200
<v Speaker 1>version is get started. By get started, I mean, think

0:37:54.239 --> 0:37:57.600
<v Speaker 1>of what is something that I want to improve. The

0:37:57.640 --> 0:37:59.759
<v Speaker 1>things that we have traction on right now in the

0:37:59.760 --> 0:38:05.680
<v Speaker 1>market market are around business process, automation, digital labor, those

0:38:05.760 --> 0:38:08.960
<v Speaker 1>kind of things. But my other little piece of advice

0:38:09.000 --> 0:38:11.000
<v Speaker 1>there is keep it simple to begin with. You're going

0:38:11.080 --> 0:38:13.760
<v Speaker 1>to learn a lot, but getting started means you'll start

0:38:13.800 --> 0:38:17.560
<v Speaker 1>that learning curve. I even advise you my friends like, hey,

0:38:17.640 --> 0:38:19.359
<v Speaker 1>should I be playing around with some.

0:38:19.320 --> 0:38:21.640
<v Speaker 2>Of this AI stuff? And I say yeah, because I

0:38:21.680 --> 0:38:24.279
<v Speaker 2>think it will help you start to be more comfortable

0:38:24.680 --> 0:38:26.759
<v Speaker 2>and you may find a use case personally for that.

0:38:26.800 --> 0:38:29.600
<v Speaker 2>I think the same is true for businesses. The first

0:38:29.640 --> 0:38:33.440
<v Speaker 2>step in that journey is always with what data. Notice

0:38:33.440 --> 0:38:37.760
<v Speaker 2>when I talked about our customer support people. I thought about, Okay,

0:38:37.840 --> 0:38:40.920
<v Speaker 2>what's the data. The data is all of those logs

0:38:40.960 --> 0:38:44.040
<v Speaker 2>of all of those service engagements around the world, and

0:38:44.080 --> 0:38:45.879
<v Speaker 2>what could I do with that? Well, I could use

0:38:45.880 --> 0:38:48.520
<v Speaker 2>that to get to a knowledge base that really helps

0:38:49.360 --> 0:38:51.920
<v Speaker 2>and hopefully that I can do it in multiple languages

0:38:52.000 --> 0:38:54.480
<v Speaker 2>because it's global and I can you know, all of

0:38:54.520 --> 0:38:56.799
<v Speaker 2>those things. That was kind of my data sent That

0:38:56.840 --> 0:38:59.279
<v Speaker 2>one's not super simple, but we've had a lot of

0:38:59.320 --> 0:39:02.520
<v Speaker 2>experience in AI for other people that might just be

0:39:03.040 --> 0:39:06.600
<v Speaker 2>how do I automate filling out travel expense reports for

0:39:06.800 --> 0:39:09.719
<v Speaker 2>my company? We can help people that we have consulting,

0:39:09.760 --> 0:39:11.920
<v Speaker 2>we have wats and X tools. We can do that

0:39:12.280 --> 0:39:14.880
<v Speaker 2>like this, and we're doing it globally for people around

0:39:14.920 --> 0:39:17.239
<v Speaker 2>the world. Pick that thing. What's the data you have?

0:39:17.360 --> 0:39:20.920
<v Speaker 2>In that case, it's data of expense reports and it's like, okay,

0:39:20.960 --> 0:39:23.319
<v Speaker 2>we can help you automate that for people where they

0:39:23.320 --> 0:39:26.440
<v Speaker 2>could do it just by you know, a verbal interface.

0:39:26.880 --> 0:39:28.799
<v Speaker 2>What did you spend, where did you go, who you

0:39:28.840 --> 0:39:31.240
<v Speaker 2>were you with? Okay, we filled out your travel expense

0:39:31.239 --> 0:39:33.160
<v Speaker 2>report for you and you don't have to mess around

0:39:33.239 --> 0:39:33.480
<v Speaker 2>with it.

0:39:33.600 --> 0:39:36.160
<v Speaker 1>So we were playing with this idea where we would

0:39:36.560 --> 0:39:39.920
<v Speaker 1>pick a business and go in there and do it

0:39:39.960 --> 0:39:43.160
<v Speaker 1>would be AI makeover. Yeah, I love that. What's okay?

0:39:43.200 --> 0:39:46.759
<v Speaker 1>What is the ideal business to do? We only have

0:39:46.800 --> 0:39:49.080
<v Speaker 1>a couple months. We don't want to spend a kajillion dollars.

0:39:49.320 --> 0:39:52.000
<v Speaker 1>We want to be able to show tangibly and quickly

0:39:52.040 --> 0:39:55.000
<v Speaker 1>what AI can do. What's the ideal business to do

0:39:55.040 --> 0:39:56.920
<v Speaker 1>that in it can be a small business. We're not talking.

0:39:57.239 --> 0:39:58.600
<v Speaker 1>This isn't a grand corporate thing.

0:39:58.680 --> 0:40:03.799
<v Speaker 2>There boy small business that we could do and hey,

0:40:03.880 --> 0:40:08.120
<v Speaker 2>I make over. Customer support is one of my favorites

0:40:08.200 --> 0:40:11.040
<v Speaker 2>because it's a it's it's I have it on the

0:40:11.040 --> 0:40:14.400
<v Speaker 2>business side where I provide customer support. I have it

0:40:14.440 --> 0:40:17.399
<v Speaker 2>on the consumer side, where it drives me nuts when

0:40:17.400 --> 0:40:20.320
<v Speaker 2>I have to go through thirty layers of phone menus.

0:40:20.840 --> 0:40:22.880
<v Speaker 2>Speak to an agent, Speak to an agent, speak to

0:40:22.920 --> 0:40:27.120
<v Speaker 2>an agent. That for any business, I think is just

0:40:27.360 --> 0:40:29.600
<v Speaker 2>ripe to be able to kind of say, why do

0:40:29.680 --> 0:40:31.960
<v Speaker 2>I have to click through these manucent messages? I just

0:40:32.000 --> 0:40:34.600
<v Speaker 2>need to tell you in human language, here's the issue,

0:40:34.600 --> 0:40:37.360
<v Speaker 2>and I'll be really good about telling you details about

0:40:37.880 --> 0:40:40.320
<v Speaker 2>you know, I tried to set up this thing for

0:40:40.480 --> 0:40:42.239
<v Speaker 2>my bank and I do da da da da da.

0:40:42.520 --> 0:40:46.200
<v Speaker 2>They can go through all the menus automate that process.

0:40:46.480 --> 0:40:48.800
<v Speaker 2>I think it would change everything because all that frustration

0:40:48.960 --> 0:40:52.359
<v Speaker 2>as a consumer would go down dramatically. And it's all,

0:40:52.880 --> 0:40:55.880
<v Speaker 2>you know, why are you making me the beep booth

0:40:56.080 --> 0:41:00.920
<v Speaker 2>press one load press exactly, Well, don't offload me. Offload

0:41:00.920 --> 0:41:02.879
<v Speaker 2>to AI. We can help you with that.

0:41:03.160 --> 0:41:06.440
<v Speaker 1>Here's my version of that drives me crazy. Every morning

0:41:06.600 --> 0:41:09.640
<v Speaker 1>I go to the same coffee shop and I get

0:41:10.239 --> 0:41:13.640
<v Speaker 1>a cup of tea and a croissant. And here's what happens.

0:41:13.640 --> 0:41:16.359
<v Speaker 1>The person has their screen and they go I go,

0:41:16.640 --> 0:41:24.880
<v Speaker 1>cup of tea, croissant, sparkling water, like at least twenty keystrokes,

0:41:25.640 --> 0:41:28.279
<v Speaker 1>and then like then the screen is turning around. Like

0:41:28.360 --> 0:41:30.719
<v Speaker 1>at this point we're like forty five seconds in. I'm like,

0:41:31.000 --> 0:41:32.920
<v Speaker 1>why is this? First of all, it's not for me,

0:41:33.000 --> 0:41:36.920
<v Speaker 1>all those keystrokes, it's their internal right, So they're burdening

0:41:37.000 --> 0:41:37.960
<v Speaker 1>me in order to service.

0:41:38.000 --> 0:41:39.640
<v Speaker 2>To back it, you should be able to walk in,

0:41:39.960 --> 0:41:42.120
<v Speaker 2>go up and they go, I'm malcom the same thing,

0:41:42.760 --> 0:41:45.040
<v Speaker 2>and you just go yes, and then we're done.

0:41:45.120 --> 0:41:47.200
<v Speaker 1>Can we do AI makeover of my coffee shop?

0:41:49.120 --> 0:41:52.799
<v Speaker 2>You notice I quickly jumped more to banks than your

0:41:52.880 --> 0:41:55.759
<v Speaker 2>coffee shop because I think I'm a business person, but

0:41:56.160 --> 0:41:58.200
<v Speaker 2>I'm not trying to kind of do a deal on

0:41:58.320 --> 0:41:59.160
<v Speaker 2>one coffee shop.

0:41:59.480 --> 0:42:01.400
<v Speaker 1>But this isn't interesting because it takes me back to

0:42:01.480 --> 0:42:04.440
<v Speaker 1>something you said that I thought was really important. When

0:42:04.440 --> 0:42:07.200
<v Speaker 1>you were talking about when you were using AI and

0:42:07.280 --> 0:42:10.399
<v Speaker 1>your customer service thing, it was clear that your goal

0:42:10.600 --> 0:42:13.040
<v Speaker 1>you could have any number of goals, yes, going in.

0:42:13.320 --> 0:42:16.360
<v Speaker 1>It could be to cut costs, it could be to

0:42:16.480 --> 0:42:20.520
<v Speaker 1>dramatically improved profits. Your goal quite specifically, was to improve

0:42:20.600 --> 0:42:22.319
<v Speaker 1>the experience of your customer, right, so.

0:42:22.360 --> 0:42:24.680
<v Speaker 2>You were using it to that. All the other things

0:42:24.840 --> 0:42:27.600
<v Speaker 2>come from that come from. That is actually one of

0:42:27.680 --> 0:42:32.280
<v Speaker 2>the beautiful pillars of the IBM culture is delighting clients

0:42:32.480 --> 0:42:35.080
<v Speaker 2>is actually where all of the good stuff comes from.

0:42:35.239 --> 0:42:39.080
<v Speaker 1>So my coffee shop thing is the same principle. Right now,

0:42:39.520 --> 0:42:42.440
<v Speaker 1>they're making my customer experience worse and they don't want to,

0:42:43.040 --> 0:42:46.120
<v Speaker 1>but their eyes are glued to the space a moment

0:42:46.200 --> 0:42:47.960
<v Speaker 1>when I walk in and I want to say, Hi,

0:42:48.120 --> 0:42:51.359
<v Speaker 1>how are you doing? We could have a conversation. You're

0:42:51.360 --> 0:42:52.680
<v Speaker 1>too busy, busy baby pooping.

0:42:52.840 --> 0:42:54.480
<v Speaker 2>So like this is the same thing.

0:42:54.600 --> 0:42:56.360
<v Speaker 1>If they had it that oh this isn't if they

0:42:56.480 --> 0:42:59.080
<v Speaker 1>understood they had an opportunity to improve the experience of

0:42:59.160 --> 0:43:00.359
<v Speaker 1>their customer experience.

0:43:00.719 --> 0:43:04.560
<v Speaker 2>I would not be surprised if a chain comes along

0:43:05.040 --> 0:43:07.480
<v Speaker 2>where that is their value proposition. I would not be

0:43:07.560 --> 0:43:11.320
<v Speaker 2>surprised at all. Yeah, yeah, right, So I mean and

0:43:12.000 --> 0:43:15.240
<v Speaker 2>and when those things kind of catch hold, it becomes

0:43:15.280 --> 0:43:15.880
<v Speaker 2>a revolution.

0:43:16.160 --> 0:43:18.080
<v Speaker 1>You know, when the guy comes to do like to

0:43:18.160 --> 0:43:20.759
<v Speaker 1>redo your roof and they put a sign out front,

0:43:20.880 --> 0:43:23.440
<v Speaker 1>like you know, Joe's roofing. You guys could do the

0:43:23.480 --> 0:43:27.200
<v Speaker 1>same with my coffee shop. But like I'd be Ire

0:43:27.400 --> 0:43:27.960
<v Speaker 1>was here.

0:43:29.680 --> 0:43:30.800
<v Speaker 2>Exactly exactly.

0:43:32.760 --> 0:43:36.920
<v Speaker 1>In five years, the main frame will be dot dot

0:43:37.600 --> 0:43:42.799
<v Speaker 1>going strong, the mainframe going strong and.

0:43:43.200 --> 0:43:47.800
<v Speaker 2>With new capabilities, continuous new capabilities. I think when we

0:43:47.880 --> 0:43:51.800
<v Speaker 2>announced the last version, Z sixteen, the latest version, I

0:43:51.880 --> 0:43:55.719
<v Speaker 2>should say, and we said, hey, there's AI processing built

0:43:55.800 --> 0:43:58.279
<v Speaker 2>into it. This was before everybody was talking about that.

0:43:58.640 --> 0:44:00.759
<v Speaker 2>I think a lot of people thought, what's that for?

0:44:01.200 --> 0:44:04.080
<v Speaker 2>And we did it specifically for traditional AI fraud detection,

0:44:04.239 --> 0:44:07.360
<v Speaker 2>et cetera. This next version, not only do we have

0:44:07.440 --> 0:44:10.480
<v Speaker 2>the traditional AI built in, but we have optional cards

0:44:10.880 --> 0:44:12.960
<v Speaker 2>that you can plug into it to allow you to

0:44:13.040 --> 0:44:17.600
<v Speaker 2>do large language models for the enhanced fraud detection cases

0:44:17.640 --> 0:44:20.839
<v Speaker 2>that we talked about, where you know, it's more than

0:44:21.080 --> 0:44:24.680
<v Speaker 2>just what transactions were happening. So if you take that

0:44:24.840 --> 0:44:28.960
<v Speaker 2>and say, okay, the next generations, we have more transaction

0:44:29.120 --> 0:44:32.360
<v Speaker 2>volume than we've ever had in mainframes. Today, the business

0:44:32.520 --> 0:44:36.160
<v Speaker 2>is growing, it's strong, we keep innovating. In five years

0:44:36.200 --> 0:44:37.200
<v Speaker 2>it'll be going strong.

0:44:37.360 --> 0:44:39.920
<v Speaker 1>But we're people. You're saying this in the context of

0:44:40.840 --> 0:44:43.040
<v Speaker 1>for years people were predicting, weren't they that the main

0:44:43.080 --> 0:44:44.080
<v Speaker 1>brame was going to go away.

0:44:46.000 --> 0:44:48.399
<v Speaker 2>There were pundits in the market that said everything will

0:44:48.440 --> 0:44:50.400
<v Speaker 2>go away there, no one will ever have a box,

0:44:50.440 --> 0:44:53.080
<v Speaker 2>It'll all be online. I think this is something I've

0:44:53.160 --> 0:44:57.600
<v Speaker 2>learned big time in my long career. You know in

0:44:57.800 --> 0:45:01.560
<v Speaker 2>the IT industry is I don't believe everything you hear.

0:45:01.880 --> 0:45:05.920
<v Speaker 2>So I went back for my master's degree at Stanford

0:45:05.960 --> 0:45:10.760
<v Speaker 2>after I had worked a while in as a hardware designer,

0:45:11.080 --> 0:45:13.880
<v Speaker 2>and everybody told me be sure to do your masters

0:45:13.920 --> 0:45:16.919
<v Speaker 2>in software. Hardware is dead. I went on to work

0:45:17.200 --> 0:45:20.720
<v Speaker 2>for thirty plus years in hardware and infrastructure. Now software

0:45:20.760 --> 0:45:23.160
<v Speaker 2>became important, and I'm glad I had that extra training

0:45:23.239 --> 0:45:26.000
<v Speaker 2>in software because it helped me in hardware. But hardware

0:45:26.080 --> 0:45:29.640
<v Speaker 2>wasn't dead. Then I heard all infrastructure will go into

0:45:29.680 --> 0:45:32.640
<v Speaker 2>the cloud. There won't be that hasn't happened. It's not happening.

0:45:33.000 --> 0:45:35.400
<v Speaker 2>Then I heard there will only be one cloud because

0:45:35.480 --> 0:45:37.920
<v Speaker 2>one of the players will dominate. There's not one cloud.

0:45:38.000 --> 0:45:42.239
<v Speaker 2>So I think it's as humans we like to oversimplify

0:45:42.239 --> 0:45:44.440
<v Speaker 2>and go, oh, it's all going to be this, And

0:45:44.640 --> 0:45:48.200
<v Speaker 2>kind of what I've learned is fit for purpose matters

0:45:48.760 --> 0:45:54.440
<v Speaker 2>in everything. It matters in size of infrastructure, it matters

0:45:54.520 --> 0:45:57.160
<v Speaker 2>in the stack that goes along with solving a specific

0:45:57.400 --> 0:46:00.480
<v Speaker 2>use case. If you're willing to design something that's the

0:46:00.640 --> 0:46:02.960
<v Speaker 2>best at that use case, if you're willing to design

0:46:03.000 --> 0:46:05.279
<v Speaker 2>the coffee shop that is the best at greeting me,

0:46:05.680 --> 0:46:07.520
<v Speaker 2>there's a spot for you, and there may be a

0:46:07.600 --> 0:46:11.680
<v Speaker 2>big business in doing that. So oversimplifying is really when you.

0:46:11.760 --> 0:46:15.240
<v Speaker 1>Heard all those predictions, did you believe them at the time.

0:46:16.880 --> 0:46:19.600
<v Speaker 2>They looked like they were trending in that direction. I'll

0:46:19.640 --> 0:46:22.279
<v Speaker 2>tell you some right now which might be useful. There

0:46:22.320 --> 0:46:24.719
<v Speaker 2>will only be one GPU company and they're going to

0:46:25.440 --> 0:46:28.000
<v Speaker 2>end up taking over the world. It's a pretty obvious answer.

0:46:28.080 --> 0:46:31.440
<v Speaker 2>Whose economic values risen dramatically. I don't think that's going

0:46:31.520 --> 0:46:33.920
<v Speaker 2>to be the case. In fact, I think that ninety

0:46:34.000 --> 0:46:39.560
<v Speaker 2>percent of processing for AI actually happen happens at inferencing,

0:46:40.080 --> 0:46:43.560
<v Speaker 2>and inferencing is not as GPU and hardware intensive as

0:46:43.600 --> 0:46:46.000
<v Speaker 2>the other things and is a lot more amenable to

0:46:46.160 --> 0:46:49.200
<v Speaker 2>fit for purpose. So the model size will matter. The

0:46:49.360 --> 0:46:51.640
<v Speaker 2>tuning matters a lot. As we're learning. We have a

0:46:51.719 --> 0:46:55.759
<v Speaker 2>product around instruct lab that's really focused on tuning. So

0:46:56.200 --> 0:46:58.120
<v Speaker 2>that was one thing is there'll be one GPU. The

0:46:58.200 --> 0:47:02.080
<v Speaker 2>other thing is that the biggest model will win, I

0:47:02.160 --> 0:47:04.520
<v Speaker 2>think is another thing that's kind of people are saying

0:47:04.600 --> 0:47:06.640
<v Speaker 2>right now. Don't believe that I believe they will be

0:47:06.800 --> 0:47:10.040
<v Speaker 2>fit for purpose models. It takes a lot of money

0:47:10.080 --> 0:47:13.360
<v Speaker 2>to run to create a huge model, and then to

0:47:13.560 --> 0:47:16.279
<v Speaker 2>run a huge model, or to even infer off of

0:47:16.320 --> 0:47:19.560
<v Speaker 2>a huge model. I don't need a massive training GPU

0:47:19.840 --> 0:47:23.800
<v Speaker 2>set thing to solve my thirteen thousand people customer support issues.

0:47:23.840 --> 0:47:25.840
<v Speaker 2>So why would I feel like I got to go

0:47:26.080 --> 0:47:28.440
<v Speaker 2>farm that out for a big expensive thing. I can

0:47:28.520 --> 0:47:30.480
<v Speaker 2>do that on a small box. In some cases I

0:47:30.560 --> 0:47:32.240
<v Speaker 2>might even be able to do that on a laptop.

0:47:32.760 --> 0:47:34.600
<v Speaker 2>The other thing I'll say in this we are so

0:47:34.880 --> 0:47:37.560
<v Speaker 2>early innings in AI. A lot of things are going

0:47:37.640 --> 0:47:40.000
<v Speaker 2>to change. So anybody kind of saying it will all

0:47:40.080 --> 0:47:42.440
<v Speaker 2>be X, Y or Z, I just think you have

0:47:42.640 --> 0:47:44.560
<v Speaker 2>no idea how this is going to play out, and

0:47:45.239 --> 0:47:46.880
<v Speaker 2>it's up to us to go figure out how it

0:47:46.920 --> 0:47:47.399
<v Speaker 2>plays out.

0:47:47.719 --> 0:47:51.600
<v Speaker 1>Yeah, yeah, all right, in five years, AI will be

0:47:51.960 --> 0:47:54.000
<v Speaker 1>dot dot dot still new.

0:47:56.600 --> 0:48:00.560
<v Speaker 2>It will have moved a bunch in five years, but

0:48:00.719 --> 0:48:05.040
<v Speaker 2>the potential for the disruption in the world will still

0:48:05.160 --> 0:48:08.120
<v Speaker 2>will still be very early innings in that process. And

0:48:08.200 --> 0:48:10.520
<v Speaker 2>I think that's super important to realize. That's why I

0:48:10.600 --> 0:48:14.000
<v Speaker 2>say get started, start thinking about how that could change,

0:48:14.000 --> 0:48:16.799
<v Speaker 2>because it'll be some little things first, but it will

0:48:16.880 --> 0:48:18.440
<v Speaker 2>continue to snowball. This is.

0:48:20.000 --> 0:48:24.520
<v Speaker 1>A common observation that we the invention of the capability

0:48:26.000 --> 0:48:28.400
<v Speaker 1>massively predates the understanding of.

0:48:28.440 --> 0:48:30.640
<v Speaker 2>The capability, right, Like I love that.

0:48:31.000 --> 0:48:36.799
<v Speaker 1>Yeah, Like yes, recorded recording shows on television is invented

0:48:36.920 --> 0:48:42.560
<v Speaker 1>in the sixties, probably the VCR. We don't really understand

0:48:42.719 --> 0:48:47.279
<v Speaker 1>what it's used for until the oughts. Was what was

0:48:47.320 --> 0:48:50.719
<v Speaker 1>really good for is being able to tell a story sequentially, Yes,

0:48:51.080 --> 0:48:53.280
<v Speaker 1>over time, because you know that the person will always

0:48:53.280 --> 0:48:55.400
<v Speaker 1>see in the episode before, so you got the Sopranos,

0:48:55.480 --> 0:48:59.279
<v Speaker 1>And yes, yes, Hollywood wanted to ban the VCR in

0:48:59.320 --> 0:49:01.799
<v Speaker 1>the beginning. Yeah, because they thought it was good. They

0:49:01.840 --> 0:49:04.920
<v Speaker 1>thought the point of it was thought THEYDN understand No, No, no,

0:49:05.080 --> 0:49:08.480
<v Speaker 1>it's storytelling. It's actually your business is getting better. Yes, yes,

0:49:08.760 --> 0:49:10.480
<v Speaker 1>took them twenty years to figure that out, which is

0:49:10.760 --> 0:49:13.080
<v Speaker 1>to your point, why would we know what AI was

0:49:13.120 --> 0:49:13.839
<v Speaker 1>four and five years.

0:49:14.080 --> 0:49:16.160
<v Speaker 2>Well, that's why you hear people kind of say, oh

0:49:16.239 --> 0:49:18.800
<v Speaker 2>my gosh, AI, that's that will just eliminate jobs. No,

0:49:19.000 --> 0:49:21.000
<v Speaker 2>it'll make jobs better. That's how I view it.

0:49:21.200 --> 0:49:24.640
<v Speaker 1>Yeah, what's the number one thing that people misunderstand about AI?

0:49:24.840 --> 0:49:27.960
<v Speaker 2>Is that it that it'll I think that's that. That

0:49:28.120 --> 0:49:30.960
<v Speaker 2>would be the human kind of understanding part of it.

0:49:31.120 --> 0:49:34.400
<v Speaker 2>The technology part of it, I think would be what

0:49:34.560 --> 0:49:38.719
<v Speaker 2>I was talking about, fit for purpose, meaning that it

0:49:38.840 --> 0:49:41.600
<v Speaker 2>isn't just going to be a GPU arms race all

0:49:41.680 --> 0:49:44.239
<v Speaker 2>of AI. I don't believe that at all. It will

0:49:44.320 --> 0:49:45.920
<v Speaker 2>change everything, but it's not just going to be a

0:49:46.000 --> 0:49:49.840
<v Speaker 2>GPU armed Next question, what advice would you give yourself

0:49:49.920 --> 0:49:52.000
<v Speaker 2>ten years ago to better prepare you for today?

0:49:52.480 --> 0:49:57.400
<v Speaker 1>I'm changing this question, okay. I would say, let's imagine

0:49:57.480 --> 0:50:00.800
<v Speaker 1>that what was your what what college you to go to?

0:50:01.680 --> 0:50:04.920
<v Speaker 2>I went to three of them. My undergrad was Utah

0:50:05.000 --> 0:50:08.320
<v Speaker 2>State University, my NBA was Santa Clara University, and my

0:50:08.640 --> 0:50:10.560
<v Speaker 2>master's in w was Stanford. OK.

0:50:11.920 --> 0:50:13.600
<v Speaker 1>Any one of those three called you up and says

0:50:14.000 --> 0:50:18.840
<v Speaker 1>we want you to give the commencement address, and imagine

0:50:18.880 --> 0:50:22.279
<v Speaker 1>it's let's just safe the sake of argument, it's just

0:50:22.360 --> 0:50:25.239
<v Speaker 1>to the stamp people, because those are the relevant parties here.

0:50:26.280 --> 0:50:29.160
<v Speaker 2>What do you tell them? Boy? What do I tell them?

0:50:33.040 --> 0:50:37.480
<v Speaker 2>Let's see, I think I would start with life is

0:50:37.520 --> 0:50:40.600
<v Speaker 2>a marathon, not a sprint. It would be the first one.

0:50:42.320 --> 0:50:44.800
<v Speaker 2>The second thing I would say that in that spirit

0:50:45.200 --> 0:50:48.960
<v Speaker 2>is be sure to set yourself some big, hairy, audacious

0:50:49.000 --> 0:50:54.040
<v Speaker 2>goals and don't be overly disappointed if you don't hit

0:50:54.160 --> 0:50:59.319
<v Speaker 2>them all. Going after those big hairy, audacious goals will

0:50:59.360 --> 0:51:02.399
<v Speaker 2>get you on a path where you will learn so much.

0:51:03.040 --> 0:51:06.160
<v Speaker 2>You will achieve more than you ever could imagine you

0:51:06.200 --> 0:51:08.600
<v Speaker 2>would have achieved. That's what the advice I give to

0:51:08.680 --> 0:51:11.400
<v Speaker 2>my kids is, Yeah, set some big goals, get after it.

0:51:12.120 --> 0:51:13.759
<v Speaker 2>You may or may not achieve them, but you'll be

0:51:13.840 --> 0:51:15.440
<v Speaker 2>better for the whole process when you're done.

0:51:15.440 --> 0:51:17.759
<v Speaker 1>By the way, as someone whose kids are younger than yours,

0:51:18.640 --> 0:51:20.880
<v Speaker 1>is it actually useful to give your give advice to

0:51:20.920 --> 0:51:23.680
<v Speaker 1>your kids, the pointments exercise TVD.

0:51:23.920 --> 0:51:25.799
<v Speaker 2>We're still on the journey and I think we will

0:51:25.880 --> 0:51:27.680
<v Speaker 2>be for a long time.

0:51:27.880 --> 0:51:30.640
<v Speaker 1>I don't know how are you already using AI in

0:51:30.760 --> 0:51:31.839
<v Speaker 1>your day to day life today?

0:51:33.960 --> 0:51:38.080
<v Speaker 2>Personally, I would say it's replacing a good chunk of

0:51:38.160 --> 0:51:41.120
<v Speaker 2>my search. You know, I'm less likely to go blindly

0:51:41.480 --> 0:51:44.240
<v Speaker 2>stumbling through a bunch of web pages looking for stuff.

0:51:44.680 --> 0:51:47.000
<v Speaker 2>I'm more likely to ask a question from a few

0:51:47.239 --> 0:51:49.960
<v Speaker 2>AI engines kind of see, get me in the right direction,

0:51:50.120 --> 0:51:52.680
<v Speaker 2>then I'll go bumble through a few things. At work,

0:51:53.200 --> 0:51:58.560
<v Speaker 2>I can tell you code development right now, we are

0:51:58.600 --> 0:52:02.600
<v Speaker 2>seeing massive improvements and code development and support products We

0:52:02.760 --> 0:52:07.520
<v Speaker 2>have like Watson Code Assistant that is really showing immediate

0:52:07.640 --> 0:52:10.680
<v Speaker 2>return for a code developers, and I think that will

0:52:10.840 --> 0:52:14.680
<v Speaker 2>again be a tool that increases productivity for code developers

0:52:15.120 --> 0:52:16.960
<v Speaker 2>immediately across the globe. Yeah.

0:52:17.800 --> 0:52:21.120
<v Speaker 1>Last question, what's the one skill that every technology leader

0:52:21.320 --> 0:52:23.480
<v Speaker 1>needs that has nothing to do with technology.

0:52:24.480 --> 0:52:28.560
<v Speaker 2>Being able to inspire a set of people toward a

0:52:28.640 --> 0:52:32.080
<v Speaker 2>common goal and collaborate to achieve it. That's at the

0:52:32.200 --> 0:52:35.960
<v Speaker 2>core of everything everything. That's a lovely way to end.

0:52:36.640 --> 0:52:37.920
<v Speaker 2>Thank you so much, Rick, Thank you.

0:52:40.480 --> 0:52:44.760
<v Speaker 1>This conversation left me excited. I'm now imagining the potential

0:52:44.840 --> 0:52:47.560
<v Speaker 1>for new use cases for AI in all sorts of

0:52:47.680 --> 0:52:51.400
<v Speaker 1>different businesses. Rick didn't seem soldom my idea of a

0:52:51.400 --> 0:52:54.880
<v Speaker 1>coffee shop makeover, but it's clear there's lots of opportunities

0:52:54.920 --> 0:52:58.759
<v Speaker 1>here to increase speed and efficiency, to achieve your objectives,

0:52:59.000 --> 0:53:02.480
<v Speaker 1>and to dream beyond the current applications for this technology.

0:53:03.480 --> 0:53:05.720
<v Speaker 1>At the end of the day, the scaling of AI

0:53:06.120 --> 0:53:09.440
<v Speaker 1>will rely on the right infrastructure to support it. With

0:53:09.560 --> 0:53:12.680
<v Speaker 1>the right tools, you can solve problems that are unique

0:53:12.719 --> 0:53:27.520
<v Speaker 1>tier industry and improve the experience for your customers. Smart

0:53:27.560 --> 0:53:30.840
<v Speaker 1>Talks with IBM is produced by Matt Romano, Amy Gains,

0:53:30.920 --> 0:53:35.160
<v Speaker 1>McQuaid and Jacob Goldstein. Were edited by Lydia gene Kott,

0:53:35.640 --> 0:53:41.200
<v Speaker 1>mastering by Jake Korsky. Theme song by Gramoscope. Special thanks

0:53:41.239 --> 0:53:43.759
<v Speaker 1>to the eight Bar and IBM teams, as well as

0:53:43.800 --> 0:53:47.680
<v Speaker 1>the Pushkin Marketing team. Smart Talks with IBM is a

0:53:47.719 --> 0:53:54.040
<v Speaker 1>production of Pushkin Industries and Ruby Studio at iHeartMedia. To

0:53:54.120 --> 0:53:59.200
<v Speaker 1>find more Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts,

0:53:59.600 --> 0:54:03.760
<v Speaker 1>or where ever you listen to podcasts. I'm Malcolm Gladwell.

0:54:08.800 --> 0:54:12.520
<v Speaker 1>This is a paid advertisement from IBM. The conversations on

0:54:12.600 --> 0:54:18.759
<v Speaker 1>this podcast don't necessarily represent IBM's positions, strategies, or opinions.