Speaker 1: Welcome, Welcome, Welcome to Smart Talks with IBM.
Speaker 2: Hello, Hello, Welcome to Smart Talks with IBM, a podcast
Speaker 2: from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gladwell. This season,
Speaker 2: we're diving back into the world of artificial intelligence, but
Speaker 2: with a focus on the powerful concept of open its possibilities, implications,
Speaker 2: and misconceptions. We'll look at openness from a variety of
Speaker 2: angles and explore how the concept is already reshaping industries,
Speaker 2: ways of doing business and our very notion of what's possible.
Speaker 2: And for the first episode of this season, we're bringing
Speaker 2: you a special conversation. I recently sat down with Rob Thomas.
Speaker 2: Rob is the senior vice president of Software and chief
Speaker 2: Commercial Officer of IBM. I spoke to him in front
Speaker 2: of a live audience as part of New York Tech Week.
Speaker 2: We discussed how business is can harness the immense productivity
Speaker 2: benefits of AI while implementing it in a responsible and
Speaker 2: ethical manner. We also broke down a fascinating concept that
Speaker 2: Rob believes about AI, known as the productivity paradox. Okay,
Speaker 2: let's get to the conversation. How are we doing good?
Speaker 3: Rob?
Speaker 2: This is our our second time. We did one of
Speaker 2: these in the middle of the pandemic. But now it's
Speaker 2: all such a blur now that us can figure out
Speaker 2: when it was.
Speaker 3: I know it's hard to those are like a blurry years.
Speaker 3: You don't know what happened, right.
Speaker 2: But well, it's good to see you, to meet you again.
Speaker 2: I wanted to start by going back. You've been at
Speaker 2: IBM twenty years.
Speaker 3: Is that right? Twenty five in July, believe it or not.
Speaker 2: So you were a kid when you joined.
Speaker 3: I was four.
Speaker 2: Yeah, So I want to contrast present day Rob and
Speaker 2: twenty five years ago. Rob. When you arrive at IBM,
Speaker 2: what do you think your job is going to be?
Speaker 3: It, your career is going.
Speaker 2: Where do you think the kind of problems you're going
Speaker 2: to be addressing are?
Speaker 1: Well, it's kind of surreal because I joined IBM Consulting
Speaker 1: and I'm coming out of school and you quickly realize
Speaker 1: what the job of a consultant is to tell other
Speaker 1: companies what to do. And I was like, I literally
Speaker 1: know nothing, and so you're immediately trying to figure out,
Speaker 1: so how am I going to be relevant given that
Speaker 1: I know absolutely nothing to advise other companies on what
Speaker 1: they should be doing. And I remember it well, like
Speaker 1: we were sitting in a room. When you're a consultant,
Speaker 1: you're waiting for somebody else to find work for you.
Speaker 1: A bunch of us sitting in a room, and somebody
Speaker 1: walks in and says, we need somebody that knows Visio.
Speaker 3: Does anybody know Visio? I'd never heard of Visio.
Speaker 1: I don't know if anybody in the room has. So
Speaker 1: everybody's like sitting around looking at their shoes. So finally
Speaker 1: I was like, I know it. So I raised my hand.
Speaker 1: They're like, great, we got a project for you next week.
Speaker 1: So I was like, all right, I have like three
Speaker 1: days to figure out what visio is, and I hope
Speaker 1: I can actually figure out how to use it now.
Speaker 3: Luckily, it wasn't like.
Speaker 1: A programming language. I mean, it's pretty much a drag
Speaker 1: and drop capability. And so I literally left the office,
Speaker 1: went to a bookstore, bought the first three books on
Speaker 1: Visio I could find, spent the whole week in reading
Speaker 1: the books, and showed up and got to work on
Speaker 1: the project.
Speaker 3: And so it was a bit of a risky move,
Speaker 3: but I think that's kind of you.
Speaker 1: This well, but if you don't take risk you'll never
Speaker 1: you'll never achieve, and so does some extent. Everybody's making
Speaker 1: everything up all the time. It's like, can you learn
Speaker 1: faster than somebody else? Is what the difference is in
Speaker 1: almost every part of life. And so it was not planned,
Speaker 1: but it was an accident, but it kind of forced
Speaker 1: me to figure out that you're gonna have to figure
Speaker 1: things out.
Speaker 2: You know, we're here to talk about AI. And I'm
Speaker 2: curious about the evolution of your understanding or IBM's understanding
Speaker 2: of my AI. At what point in the last twenty
Speaker 2: five years do you begin to think, oh, this is
Speaker 2: really going to be at the core of what we
Speaker 2: think about and work on at this company.
Speaker 1: The computer scientist John McCarthy, he was he's the person
Speaker 1: that's credited with coining the phrase artificial intelligence. It's like
Speaker 1: in the fifties, and he made an interesting comedy said
Speaker 1: he said, once it works, it's no longer called AI,
Speaker 1: and that then became it's called like the AI effect,
Speaker 1: which is it seems very difficult, very mysterious, but once
Speaker 1: it becomes commonplace, it's just no longer what it is.
Speaker 1: And so if you put that frame on it, I
Speaker 1: think we've always been doing AI at some level, and
Speaker 1: I even think back to when I joined IBM in
Speaker 1: ninety nine.
Speaker 3: At that point there.
Speaker 1: Was work on rules based engines, analytics.
Speaker 3: All of this was happening.
Speaker 1: So it all depends on how you really define that term.
Speaker 1: You could argue that elements of statistics, probability, it's not
Speaker 1: exactly AI, but it certainly feeds into it. And so
Speaker 1: I feel like we've been working on this topic of
Speaker 1: how do we deliver better insights better automation since IBM
Speaker 1: was formed. If you read about what Thomas Watson Junior did,
Speaker 1: that was all about automating tasks that AI well, probably
Speaker 1: certainly not by today's definition, but it's in the same
Speaker 1: zip code.
Speaker 2: So from your perspective, it feels a lot more like
Speaker 2: an evolution than a revolution.
Speaker 1: Is that a fair statement, yes, which I think most
Speaker 1: great things in technology tend to happen that way. Many
Speaker 1: of the revolutions, if you will, tend to fizzle out.
Speaker 2: But even given that is there, I guess what I'm
Speaker 2: asking is, I'm curious about whether there was a a
Speaker 2: moment in that evolution when you had to readjust your
Speaker 2: expectations about what AI was going to be capable of.
Speaker 2: I mean, was there, you know, was there a particular
Speaker 2: innovation or a particular problem that was solved that made
Speaker 2: you think, oh, this is different than what I thought.
Speaker 1: I would say the moments that caught our attention certainly
Speaker 1: casper Off winning the chess tournament Nobody or Deep Blue
Speaker 1: beating casper Off. I should say, nobody really thought that
Speaker 1: was possible before that, and then it was Watson winning Jeopardy.
Speaker 1: These were moments that said, maybe there's more here than
Speaker 1: we even thought was possible. And so I do think
Speaker 1: there's points in time where we realized maybe way more could.
Speaker 3: Be done than we had even imagined.
Speaker 1: But I do think it's consistent progress every month and
Speaker 1: every year versus some seminal moment.
Speaker 3: Now.
Speaker 1: Certainly, large language models as of recent have caught everybody's
Speaker 1: attention because it has a direct consumer application. But I
Speaker 1: would almost think of that as what Netscape was for
Speaker 1: the for the web browser. Yeah, it brought the Internet
Speaker 1: to everybody, but that didn't become the Internet per se.
Speaker 3: Yeah.
Speaker 2: I have a cousin who worked for IBM for forty
Speaker 2: one years. I saw him this weekend. He's in Toronto.
Speaker 2: By the way, I said, do you work for Rob Thomas.
Speaker 3: He went like this.
Speaker 2: He goes, he said, I'm five layers down. But so
Speaker 2: I always whenever I see my cousin, I ask him,
Speaker 2: can you tell me again what you do? Because it's
Speaker 2: always changing, right, I guess this is a function of
Speaker 2: working at IBM. So eventually he just gives up and says,
Speaker 2: you know, we're just solving problems. So what we're doing,
Speaker 2: which I sort of loved as a kind of frame,
Speaker 2: And I was curious, What's what's the coolest problem you
Speaker 2: ever worked on? Not biggest, not most important, but the coolest,
Speaker 2: the one that's like that sort of makes you smile
Speaker 2: when you think back on it.
Speaker 1: Probably when I was in microelectronics, because it was a
Speaker 1: world I had no exposure to. I hadn't studied computer science,
Speaker 1: and we were building a lot of high performance semiconductor technology,
Speaker 1: so just chips that do a really great job of
Speaker 1: processing something or other. And we figured out that there
Speaker 1: was a market in consumer gaming that was starting to happen,
Speaker 1: and we got to the point where we became the
Speaker 1: chip inside the Nintendo. We the Microsoft Xbox Sony PlayStation,
Speaker 1: so we basically had the entire gaming market running on
Speaker 1: IBM chips and.
Speaker 2: To use every parent basically is pointing at you and saying.
Speaker 1: You're the Probably well, they would have found it from anybody.
Speaker 1: But it was the first time I could explain my
Speaker 1: job to my kids, who were quite young at that time,
Speaker 1: like what I did, Like it was more tangible for
Speaker 1: them than saying we solve problems or douce you know,
Speaker 1: build solutions like it became very tangible for them, and
Speaker 1: I think that's, you know, a rewarding part of the
Speaker 1: job is when you can help your family actually understand
Speaker 1: what you do. Most people can't do that. It's probably
Speaker 1: easier for you. They can, they can see the books,
Speaker 1: but for for some of us in the business the
Speaker 1: business world, it's not always as obvious. So that was
Speaker 1: like one example where the dots really connected.
Speaker 2: There were a couple there's a couple of stuck about
Speaker 2: a little bit of this in the context of of AI.
Speaker 2: I love because I love the frame of problem solving
Speaker 2: as a way of understanding what the function of the
Speaker 2: technology is. So I know that you guys did something,
Speaker 2: did some work with I never know how to pronounce
Speaker 2: it is it Sevilla Sevilla with the football club Severe
Speaker 2: in Spain. Tell me about Tell me a little bit
Speaker 2: about that. What problem were they trying to solve and
Speaker 2: why did they call you?
Speaker 1: In Every sports franchise is trying to get an advantage, right,
Speaker 1: Let's just be that clear. Everybody's how can I use data, analytics, insights,
Speaker 1: anything that will make us one percent better on the
Speaker 1: field at some point in the future. And Seville reached
Speaker 1: out to us because they had seen some of the
Speaker 1: We've done some work with the Toronto Raptors in the
Speaker 1: past and others, and their thought was maybe there's something
Speaker 1: we could do. They'd heard all about generative AI, they
Speaker 1: heard about large language models. And the problem, back to
Speaker 1: your point on solving problems, was we want to do
Speaker 1: a way better job of assessing talent, because really the
Speaker 1: lifeblood of a sports franchise is can you continue to
Speaker 1: cult a talent, Can you find talent that others don't find?
Speaker 1: Can you see something in somebody that they don't see
Speaker 1: in themselves or maybe no other.
Speaker 3: Team season them.
Speaker 1: And we ended up building somebody with them called Scout Advisor,
Speaker 1: which is built on Watson X, which basically just ingests
Speaker 1: tons and tons of data, and we like to think
Speaker 1: of it as finding you know, the needle in the
Speaker 1: haystack of you know, here's three players that aren't being considered.
Speaker 1: They're not on the top teams today, and I think
Speaker 1: working with them together we found some pretty good insights
Speaker 1: that's helped them out.
Speaker 2: How What was intriguing to me was we're not just
Speaker 2: talking about quantitative data. We're also talking about qualitative data.
Speaker 2: But that's the puzzle part of the thing that fastens me.
Speaker 2: How does one incorporate qualitative analysis into that sort of
Speaker 2: so you just feeding in scouting reports and things like that.
Speaker 1: I got to realize, think about how much I can
Speaker 1: act actually disclosed it. But if you think about so,
Speaker 1: quantitative is relatively easy. Every team collects that, you know,
Speaker 1: what's their forty yard dash? They use that term, certainly
Speaker 1: not in Spain. That's all quantitative. Qualitative is what's happening
Speaker 1: off the field. It could be diet, it could be habits,
Speaker 1: it could be behavior. You can imagine a range of
Speaker 1: things that would all feed into an athlete's performance and
Speaker 1: so relationships.
Speaker 3: There's many different aspects, and.
Speaker 1: So it's trying to figure out the right blend of
Speaker 1: quantitative and qualitative that gives you a unique insight.
Speaker 2: How transparent is that kind of system? I mean, is
Speaker 2: it telling you it's saying pick this guy not this guy,
Speaker 2: But is it telling you why it prefers this guy
Speaker 2: to this guy?
Speaker 3: Is that?
Speaker 1: I think for anything in the realm of AI, you
Speaker 1: have to answer the why question, otherwise you fall into
Speaker 1: the trap of the you know, the proverbial black box,
Speaker 1: and then wait, I made this decision, I'd never understood
Speaker 1: why it didn't work out.
Speaker 3: So you always have to answer why without a doubt?
Speaker 2: And how is why? Answered?
Speaker 1: Sources of data, the reasoning that went into it, and
Speaker 1: so it's basically just tracing back the chain of how
Speaker 1: you got to the answer. And in the case of
Speaker 1: what we do in Watson X is we have IBM models.
Speaker 1: We also use some other open source models, So it
Speaker 1: would be which model was used, what was the data
Speaker 1: set that was fed into that model, How is it
Speaker 1: making decisions?
Speaker 3: How is it performing? Is it robust?
Speaker 1: Meaning is it reliable in terms of if you feed
Speaker 1: it two of the same data set, do you get
Speaker 1: the same answer. These are all the you know, the
Speaker 1: technical aspects of understanding the why.
Speaker 2: How quickly do you expect all professional sports franchises to
Speaker 2: adopt some kind of are they already there? If I
Speaker 2: went out and pulled the general managers of the one
Speaker 2: hundred most valuable sports franchises in the world, how many
Speaker 2: of them would be using some kind of AI system
Speaker 2: to assist in their efforts.
Speaker 1: One hundred and twenty percent would, meaning that everybody's doing it,
Speaker 1: and some think they're doing way more than they probably
Speaker 1: actually are. So everybody's doing it. I think what's weird
Speaker 1: about sports is everybody's so convinced that what they're doing
Speaker 1: is unique that they generally speaking, don't want to work
Speaker 1: with a third party to do it because they're afraid
Speaker 1: that that would expose them. But in reality, I think
Speaker 1: most are doing eighty to ninety percent of the same things.
Speaker 3: So but without a doubt, everybody's doing it. Yeah.
Speaker 2: Yeah. The other I say that I loved was there
Speaker 2: was one but a shipping line tricon on the Mississippi River.
Speaker 2: Tell me a little bit about that project. What problem
Speaker 2: were they trying to solve?
Speaker 1: Think about the problem that I would say every body
Speaker 1: noticed if you go back to twenty twenty was things
Speaker 1: are getting hold held up in ports. It was actually
Speaker 1: an article in the paper this morning kind of tracing
Speaker 1: the history of what happened twenty twenty twenty one and
Speaker 1: why ships were basically sitting at seas for months at
Speaker 1: a time. And at that stage we just we had
Speaker 1: a massive throughput issue. But moving even beyond the pandemic,
Speaker 1: you can see it now with ships getting through like
Speaker 1: Panama Canal, there's like a narrow window where you can
Speaker 1: get through, and if you don't have your paperwork done,
Speaker 1: you don't have the right approvals, you're not going through
Speaker 1: and it may cost you a day or two and
Speaker 1: that's a lot of money. In the shipping industry and
Speaker 1: the Tricon example, it's really just about when you're pulling
Speaker 1: into a port, if you have the right paperwork done,
Speaker 1: you can get goods off the ship very quickly. They
Speaker 1: ship a lot of food, which by definition, since it's
Speaker 1: not packaged food, it's fresh food, there is an expiration
Speaker 1: period and so if it takes them an extra two hours,
Speaker 1: certainly multiple hours or a day, they have a massive
Speaker 1: problem because then you're going to deal with spoilage and
Speaker 1: so it's going to set you back. And what we've
Speaker 1: worked with them on is using an assistant that we've
Speaker 1: built in Watson X called orchestrate, which basically is just
Speaker 1: AI doing digital labor, so we can replicate nearly any
Speaker 1: repetitive task and do that with software.
Speaker 3: Instead of humans.
Speaker 1: So, as you may imagine, shipping industry still has a
Speaker 1: lot of paperwork that goes on, and so being able
Speaker 1: to take forms that normally would be multiple hours of
Speaker 1: filling it out, Oh this isn't right, send it back.
Speaker 1: We've basically built that as a digital skill inside of
Speaker 1: watsonex orchestrate, and so now it's done in minutes.
Speaker 2: They did Did they realize that they could have that
Speaker 2: kind of efficiency by teaming up with you or is
Speaker 2: that something you came to them and said, guys, we
Speaker 2: can do this way better than you think.
Speaker 3: What's the.
Speaker 1: I'd say it's always, it's always both sides coming together
Speaker 1: at a moment that for some reason makes sense because
Speaker 1: you could say, why didn't this happen like five years ago,
Speaker 1: like seems so obvious. Well, technology wasn't quite ready then,
Speaker 1: I would say, But they knew they had a need
Speaker 1: because I forget what the precise number is, but you know,
Speaker 1: reduction of spoilage has massive impact on their bottom line,
Speaker 1: and so they knew they had a need, we.
Speaker 3: Thought we could solve it, and the two together.
Speaker 2: Who did you guys go to them thought? Or did
Speaker 2: they come to you?
Speaker 1: I recall that this one was an inbound meaning they
Speaker 1: had reached out to IBM and that we'd like to
Speaker 1: solve this problem. I think it went into one of
Speaker 1: our digital centers, if I if I recall so literary,
Speaker 1: I call yeah.
Speaker 2: But the other the reverse is more interesting to me
Speaker 2: because there seems to be a very very large universe
Speaker 2: of people who have problems that could be solved this
Speaker 2: way and they don't realize it.
Speaker 3: What's your.
Speaker 2: Is there a shining example of this of someone you
Speaker 2: just can't you just think could benefit so much and
Speaker 2: isn't benefiting right now?
Speaker 3: Maybe I'll answer it slightly differently.
Speaker 1: I'm I'm surprised by how many people can benefit that
Speaker 1: you wouldn't even logically think of.
Speaker 3: First, let me give you an example.
Speaker 1: There's a franchiser of hair salons, sport Clips is the name.
Speaker 1: My sons used to go there for haircuts because they
Speaker 1: have like TVs and you can watch sports, so they
Speaker 1: loved that they got entertained while they would get their haircut.
Speaker 1: I think the last place that you would think is
Speaker 1: using AI today would be a franchiser of hair salons. Yeah,
Speaker 1: but just follow it through. The biggest part of how
Speaker 1: they run their business is can I get people to
Speaker 1: cut hair? And this is the high turnover industry because
Speaker 1: there's a lot of different places you can work if
Speaker 1: you want to cut hair. People actually get injured cutting
Speaker 1: hair because you're on your feet all day, that type
Speaker 1: of thing. And they're using same technology orchestrate as part
Speaker 1: of their recruiting process. How can they automate a lot
Speaker 1: of people submitting resumes, who they speak to, how they qualify.
Speaker 3: Them for the position.
Speaker 1: And so the reason I give that example is the
Speaker 1: opportunity for AI, which is unlike other technologies, is truly unlimited.
Speaker 1: It will touch every single business. It's not the realm
Speaker 1: of the fortune five hundred or the fortune one thousand.
Speaker 1: This is the fortune any size. And I think that
Speaker 1: may be one thing that people underestimate about AI.
Speaker 2: Yeah, what about I mean I was thinking about education
Speaker 2: as as a kind of I mean, education is a
Speaker 2: perennial whipping boy for you guys that are living in
Speaker 2: the nineteenth century, right. I'm just curious about if a
Speaker 2: superintendent of a public school system or the president of
Speaker 2: the university sat down and had lunch with you and said,
Speaker 2: do the university first. My cost are out of control,
Speaker 2: my enrollment is down, my students hate me, and my
Speaker 2: board is revolting.
Speaker 3: Help.
Speaker 2: How would you think about helping someone in that situation.
Speaker 3: I spend some time with universities. I like to go
Speaker 3: back and there's.
Speaker 1: Alma maters where I went to school, and so I
Speaker 1: do that every year. The challenge I have hall of
Speaker 1: Seming University is there has to be a will. Yeah,
Speaker 1: and I'm not sure the incentives are quite right today
Speaker 1: because bringing in new technology, say we want to go
Speaker 1: after we can help you figure out student recruiting or
Speaker 1: how you automate more of your education, everybody suddenly feels
Speaker 1: threatened that university.
Speaker 3: Hold on, that's my job.
Speaker 1: I'm the one that decides that, or I'm the one
Speaker 1: that wants to dictate the course. So there has to
Speaker 1: be a will. So I think it's very possible, and
Speaker 1: I do think over the next decade you will see
Speaker 1: some universities that jump all over this and they will
Speaker 1: move ahead, and you see others that do not.
Speaker 3: Because it's very possible.
Speaker 2: Where how does when you say there has to be
Speaker 2: a will? Is that the kind is that a kind
Speaker 2: of thing that that people that IBM to think about,
Speaker 2: Like when in this conversation you hypothetical conversation you might
Speaker 2: have with the university president, would you give advice on
Speaker 2: where the will comes from?
Speaker 1: I don't do that as much in a university context.
Speaker 1: I do that every day in a business context, because
Speaker 1: if you can find the right person in a business
Speaker 1: that wants to focus on growth or the bottom line
Speaker 1: or how do you create more productivity. Yes, it's going
Speaker 1: to create a lot of organizational resistance potentially, but you
Speaker 1: can find somebody that will figure out how to push
Speaker 1: that through. I think for universities, I think that's also possible.
Speaker 1: I'm not sure there's there's there's a return on investment for.
Speaker 3: Us to do that.
Speaker 2: Yeah, yeah, yeah, God, let's let's find some terms. AI
Speaker 2: years I told you'd like to use What does that mean?
Speaker 1: We just started using this term literally in the last
Speaker 1: three months, and it was it was what we observed internally,
Speaker 1: which is most technology you build, you say, all right,
Speaker 1: what's going to happen in year one, year two, year three,
Speaker 1: and it's you know, largely by by a calendar. AI
Speaker 1: years are the idea that what used to be a
Speaker 1: year is now like a week. And that is how
Speaker 1: fast the technology is moving.
Speaker 3: And do you give you an example. We had one
Speaker 3: client we're working with.
Speaker 1: They're using one of our granite models, and the results
Speaker 1: they were getting were not very good. Accuracy was not there,
Speaker 1: their performance was not there. So I was like scratching
Speaker 1: my head. I was like, what is going on? They
Speaker 1: were financial services, the bank, So I'm scratching my head,
Speaker 1: like what is going on? Everybody else is getting this
Speaker 1: and like these results are horrible. And I said to
Speaker 1: the team, which version of the model are you using?
Speaker 1: This was in February, Like we're using the one from October.
Speaker 1: I was like, all right, now we know precisely the
Speaker 1: problem because the model from October is effectively useless now
Speaker 1: since we're here in February.
Speaker 2: Serious, actually useless, completely useless.
Speaker 1: Yeah, that is how fast this is changing. And so
Speaker 1: the minute, same use case, same day, you give them
Speaker 1: the model from late January instead of October, the results
Speaker 1: are off the charts.
Speaker 3: Yeah.
Speaker 2: Wait, so what exactly happened between October and January?
Speaker 3: The model got way better?
Speaker 2: Could dig into that, Like, what do you mean by
Speaker 2: the way.
Speaker 3: We are constant.
Speaker 1: We have built large compute infrastructure where we're doing model training.
Speaker 1: And to be clear, model training is the realm of
Speaker 1: probably in the world my guess is five to ten companies.
Speaker 3: And so.
Speaker 1: You build a model, you're constantly training it, you're doing
Speaker 1: fine tuning, you're doing more training, you're adding data every day,
Speaker 1: every hour it gets better. And so how does it
Speaker 1: do that. You're feeding it more data, you're feeding it
Speaker 1: more live examples. We're using things like synthetic data at
Speaker 1: this point, which is we're basically creating data to do
Speaker 1: the training as well. All of this feeds into how
Speaker 1: useful the model is. And so using the October model,
Speaker 1: those were the results in October, just a fact, that's
Speaker 1: how good it was then. But back to the concept
Speaker 1: of AI years, two weeks is a long time.
Speaker 2: Is that Are we in a steep part of the
Speaker 2: model learning carve or do you expect this to continue
Speaker 2: along this at this pace?
Speaker 3: I think that is the big question and don't have
Speaker 3: an answer yet.
Speaker 1: By definition, at some point you would think it would
Speaker 1: have to slow down a bit, but it's not obvious
Speaker 1: that that is on the horizon.
Speaker 2: Still speeding up. Yes, how fast. Can it get.
Speaker 1: We've debated, can you actually have better results in the
Speaker 1: afternoon than you did in the morning. Really it's nuts, Yeah,
Speaker 1: I know, but that's why we came up with this term,
Speaker 1: because I think you also have to think of like
Speaker 1: concepts that.
Speaker 3: Gets people's attention.
Speaker 2: So you're basically turning into a bakery. You're like the
Speaker 2: bread from yesterday. You know you can have it for
Speaker 2: twenty five cents. But I mean you do proferential pricing.
Speaker 2: You could say, we'll judge you x for yesterday's model,
Speaker 2: two x for today's model.
Speaker 1: I think that's dangerous as a merchandising strategy, but I
Speaker 1: guess your point.
Speaker 2: Yeah, but that's crazy. And this, by the way, so
Speaker 2: this model is the same true for almost You're talking
Speaker 2: specifically about a model that was created to help some
Speaker 2: aspect of a financial services. So is that kind of
Speaker 2: model accelerating faster and learning faster than other models for
Speaker 2: other kinds of problems?
Speaker 3: So this domain was code.
Speaker 1: Yeah, and so by definition, if you're feeling feeding in
Speaker 1: more data some more code, you get those kind of results.
Speaker 1: It does depend on the model type. There's a lot
Speaker 1: of code in the world, and so we can find
Speaker 1: that we can create it. Like I said, there's other
Speaker 1: aspects where there's probably less inputs available, which means you
Speaker 1: probably won't get the same level of iteration. But for code,
Speaker 1: that's certainly the cycle times that we're seeing.
Speaker 2: Yeah, and how do you know that Let's stick with
Speaker 2: this one example of this model you have. How do
Speaker 2: you know that your model is better than big company
Speaker 2: B down the street? The client asks you, why would
Speaker 2: I go with IBM as opposed to some the some
Speaker 2: firm in the valley that says, let's they have a
Speaker 2: model on this, what's your how do you frame your advantage?
Speaker 1: Well, we benchmark all of this, and I think the
Speaker 1: most important is metric is price performance, not price, not performance,
Speaker 1: but the combination of the two.
Speaker 3: And we're super competitive there.
Speaker 1: Well for what we just released, with what we've done
Speaker 1: in open source, we know that nobody's close to us
Speaker 1: right now on code.
Speaker 3: Now.
Speaker 1: To be clear, that will probably change because it's like leapfrog.
Speaker 3: People will jump ahead, then we jump back ahead.
Speaker 1: But we're very confident that with everything we've done in
Speaker 1: the last few months taken a huge lead forward here.
Speaker 2: Yeah, it's I mean, this goes back to the point
Speaker 2: I was making in the beginning. So about the difference
Speaker 2: between your twenty something self in ninety nine and yourself today.
Speaker 2: But this time compression has to be a crazy adjustment.
Speaker 2: So the concept of what you're working on and how
Speaker 2: you make decisions internally and things has to undergo this
Speaker 2: kind of revolution. If you're switching from I mean back
Speaker 2: in the day, a model might be useful for how long.
Speaker 1: Years years I think about you know, statistical models that
Speaker 1: set inside things like SPSS, which is a product that
Speaker 1: a lot of.
Speaker 3: Students use around the world.
Speaker 1: I mean, those have been the same models for twenty
Speaker 1: years and they're still very good at what they do.
Speaker 1: And so yes, it's a completely it's a completely different
Speaker 1: moment for how fast this is moving.
Speaker 3: And I think it just.
Speaker 1: Raises the bar for everybody, whether you're a technology provider
Speaker 1: like us, or you're a bank or an insurance company
Speaker 1: or a shipping company, to say, how do you really
Speaker 1: change your culture to be way more aggressive than you
Speaker 1: normally would be?
Speaker 2: Does this mean it's a weird question, but does this
Speaker 2: mean a different set of kind of personality or character
Speaker 2: traits are necessary for a decision maker in tech now
Speaker 2: than twenty five years ago.
Speaker 1: There's a book I saw recently, it's called The Geek Way,
Speaker 1: which talked about how technology companies have started to operate
Speaker 1: in different ways maybe than many traditional companies, and more
Speaker 1: about being data driven, more about delegation. Are you willing
Speaker 1: to have the smartest person in the room make decisions
Speaker 1: opposed to the highest paid person in the room. I
Speaker 1: think these are all different aspects that ever company is
Speaker 1: going to face.
Speaker 2: Yeah, yeah, next term, talk about open. When you use
Speaker 2: that word open, what do you mean.
Speaker 1: I think there's really only one definition of open, which
Speaker 1: is for technology, is open source. An open source means
Speaker 1: the code is freely available. Anybody can see it, access it,
Speaker 1: contribute to it.
Speaker 2: And what is Tell me about why that's an important principle.
Speaker 1: When you take a topic like AI. I think it
Speaker 1: would be really bad for the world if this was
Speaker 1: in the hands of one or two companies, or three
Speaker 1: or four, doesn't matter the number, some small number. Think
Speaker 1: about like in history sometimes early nineteen hundreds, the Interstate
Speaker 1: Commerce Commission was created, and the whole idea was to
Speaker 1: protect farmers from railroads, meaning they wanted to allow free trade.
Speaker 1: But they knew that well, there's only so many railroad tracks,
Speaker 1: So we need to protect farmers from the shipping costs
Speaker 1: that railroads could impose. So good idea, but over time
Speaker 1: that got completely overtaken by the railroad lobby and then
Speaker 1: they use that to basically just increase prices, and it
Speaker 1: made the lives of farmers way more difficult. I think
Speaker 1: you could play the same analogy through with AI. If
Speaker 1: you allow a handful of companies to have the technology,
Speaker 1: you regulate around the principles of those one or two companies,
Speaker 1: then you've trapped the entire world.
Speaker 3: I think that would be very bad. So the danger
Speaker 3: of that app for sure.
Speaker 1: I mean there's companies in Watson in Washington every week
Speaker 1: trying to achieve that outcome.
Speaker 3: And so the.
Speaker 1: Opposite of that is to say it's going to be
Speaker 1: an open source because nobody could dispute open source because
Speaker 1: it's right there, everybody can see it. So I'm a
Speaker 1: strong believer that open source will win for AI. It
Speaker 1: has to win. It's not just important for business, but
Speaker 1: it's important for humans.
Speaker 2: On the I'm curious about on the list of things
Speaker 2: you worry about, Actually, let me before I ask, let
Speaker 2: me ask this question very generally, what is the list
Speaker 2: of things you worry about. What's your top five business
Speaker 2: related worries right now?
Speaker 3: Tops from those are the first question. We could be
Speaker 3: here for hours for me to answer.
Speaker 2: I did say business related. We could leave. You know,
Speaker 2: your kids' haircuts got it out of.
Speaker 1: The Number one is always it's the thing that's probably
Speaker 1: always been true, which is just people. Do we have
Speaker 1: the right skills? Are we doing a good job of
Speaker 1: training our people? Are our people doing a good job
Speaker 1: of working with clients like that's number one? Number two
Speaker 1: is innovation? Are we pushing the envelope enough? Are are
Speaker 1: we staying ahead? Number three is which kind of feeds
Speaker 1: into the innovation one is risk taking? Are we taking
Speaker 1: enough risk? Without risk, there is no growth. And I
Speaker 1: think the trap that every larger company inevitably falls into
Speaker 1: is conservatism. Things are good enough, and so it's are
Speaker 1: we pushing the envelope? Are we taking enough risk to
Speaker 1: really have an impact? I'd say those are probably the
Speaker 1: top three that I spend talk about.
Speaker 2: The vast trend to define productivity paradox something I know
Speaker 2: you've thought a lot about what does that mean?
Speaker 1: So I started thinking hard about this because all I
Speaker 1: saw and read every day was fear about AI, and
Speaker 1: I studied economics, and so I kind of went back
Speaker 1: to like basic economics, and there's been like a macro
Speaker 1: investing formula I guess I would say it's been around
Speaker 1: forever that says growth comes from productivity growth plus population
Speaker 1: growth plus debt growth. So if those three things are working,
Speaker 1: you'll get GDP growth. And so then you think about
Speaker 1: that and you say, well, debt growth, we're probably not
Speaker 1: going back to zero percent interest rates, so to some
Speaker 1: extent there's going to be a ceiling on that. And
Speaker 1: then you look at population growth. There are shockingly few
Speaker 1: countries or places in the world that will see population
Speaker 1: growth over the next thirty to fifty years. In fact,
Speaker 1: most places are not even at replacement rates. And so
Speaker 1: I'm like, all right, so population growth is not going
Speaker 1: to be there.
Speaker 3: So that would mean if you just take.
Speaker 1: It to the extreme, the only chance of continued GDP
Speaker 1: growth is productivity. And the best way to solve productivity
Speaker 1: as AI.
Speaker 3: That's why I say it's a paradox.
Speaker 1: On one hand, everybody's scared after death it's going to
Speaker 1: take over the world, take all of our jobs, ruin us,
Speaker 1: But in reality, maybe it's the other way, which is
Speaker 1: it's the only thing that can save us.
Speaker 3: Yeah, and if you believe.
Speaker 1: That economic equation, which I think has proven quite true
Speaker 1: over hundreds of years, I do think it's probably the
Speaker 1: only thing that can save us.
Speaker 2: Actually looked at the numbers yesterday for total random reason
Speaker 2: on population growth in Europe and receive this is a
Speaker 2: special bonus question. See how smart you are? Which country
Speaker 2: in Europe continentally Europe has the highest population growth?
Speaker 1: It's small continental Europe, probably one of the Nordics, I
Speaker 1: would guess.
Speaker 2: Close Luxembourg. Okay, something that's going on in Luxembourg. I
Speaker 2: feel like, well, all of this need to investigate. There're
Speaker 2: at one point four nine, which in the day, by
Speaker 2: the way, would be a relatively that's the best performing country.
Speaker 2: I mean in the day, you'd countries had routinely had
Speaker 2: two points something, you know, percent growth in a given year.
Speaker 2: Last question, you're writing a book. Now, we were talking
Speaker 2: chatting about it backstage, and now I appreciate the paradox
Speaker 2: of this book, which is universe with a model is
Speaker 2: better in the afternoon than it is in the morning.
Speaker 2: How do you write a book that's like printed on paper?
Speaker 2: I expected to reuse Aul.
Speaker 1: This is the challenge. And I am an incredible author
Speaker 1: of useless books. I mean most of what I've spent
Speaker 1: time on in the last decade of stuff that's completely useless,
Speaker 1: like a year after it's written. And so when we
Speaker 1: were talking about it, I was like, I would like
Speaker 1: to do something around AI that's timeless. Yeah, that would
Speaker 1: be useful ten or twenty years from now. But then
Speaker 1: to your so, how is that even remotely possible if
Speaker 1: the model is better in the afternoon and in the morning.
Speaker 3: So that's the challenge in front of us.
Speaker 1: But the book is around AI value creation, so kind
Speaker 1: of links to this productivity paradox, and how do you
Speaker 1: actually get sustained value out of AI, out of automation,
Speaker 1: out of data science. And so the biggest challenge in
Speaker 1: front of us is can we make this relevant?
Speaker 3: How's the day that it's published?
Speaker 2: How are you setting out to do that?
Speaker 1: I think you have to to some extent level it
Speaker 1: up to bigger concepts, which is kind of why I
Speaker 1: go to things like macroeconomics, population geography as opposed to
Speaker 1: going into the weeds of the technology itself. If you
Speaker 1: write about this is how you get better performance out
Speaker 1: of a model we can agree that will be completely
Speaker 1: useless two years from now, but maybe even two months
Speaker 1: from now, and so it will be less in the
Speaker 1: technical detail and more of what is sustained value creation
Speaker 1: for AI, which if you think on what is hopefully
Speaker 1: a ten or twenty year period, it's probably we're kind
Speaker 1: of substituting AI for technology. Now I've realized, because I
Speaker 1: think this has always been true for technology. It's just
Speaker 1: now AI is I think that everybody wants to talk about.
Speaker 1: But let's see if we can do it. Time will tell.
Speaker 2: Did you get any inkling that the pace that this
Speaker 2: AI year's phenomenon was gonna that things with the pace
Speaker 2: of change was going to accelerate so much? Because you
Speaker 2: had More's law, right, You had a model in the
Speaker 2: technology world for this kind of exponential increase in so
Speaker 2: we you were you thinking about that kind of accelerate
Speaker 2: similar kind of acceleration in the.
Speaker 1: I think anybody that said they expected what we're seeing
Speaker 1: today is probably exactly. I think it's way faster than
Speaker 1: anybody expected. Yeah, but technologies, back to your point at
Speaker 1: More's law has always accelerated through the years, so I
Speaker 1: wouldn't say it's a shock, but it is surprising.
Speaker 2: Yeah, you've had a kind of extraordinary privileged position to
Speaker 2: watch and participate in this revolution, right, I mean, how
Speaker 2: many other people have been in that have ridden this
Speaker 2: wave like you have?
Speaker 1: I do wonder is this really that much different or
Speaker 1: does it feel different just because we're here?
Speaker 3: I mean, I do think on one level.
Speaker 1: Yes, So in the time I've been an IBM, Internet happened,
Speaker 1: Mobile happened, social network happened, blockchain happened.
Speaker 3: AI, So a lot has happened.
Speaker 1: But then you go back and say, well, but if
Speaker 1: I'd been here between nineteen seventy and ninety five, there
Speaker 1: were a lot of things that are pretty fundamental then too, say,
Speaker 1: I wondered, almost do we always exaggerate the.
Speaker 3: Timeframe that we're in. I don't know. Yeah, but it's
Speaker 3: a good idea though.
Speaker 2: I think the ending with the phrase, I don't know
Speaker 2: it's a good idea though. Comd great way to wrap
Speaker 2: this up.
Speaker 3: Thank you so much, Thank you, Malcolm.
Speaker 2: In a field that is evolving as quickly as artificial intelligence,
Speaker 2: it was inspiring to see how adaptable Rob has been
Speaker 2: over his career. The takeaways from my conversation with Rob
Speaker 2: had been echoing in my head ever since. He emphasized
Speaker 2: how open source models allow AI technology to be developed
Speaker 2: by many players. Openness also allows for transparency. Rob told
Speaker 2: me about AI use cases like IBM's collaboration with Sevilla's
Speaker 2: football club. That exam really brought home for me how
Speaker 2: AI technology will touch every industry. Despite the potential benefits
Speaker 2: of AI, challenges exist in its widespread adoption. Rob discussed
Speaker 2: how resistance to change, concerns about job security and organizational
Speaker 2: inertia can slow down implementation of AI solutions. The paradox, though,
Speaker 2: according to Rob, is that rather than being afraid of
Speaker 2: a world with AI, people should actually be more afraid
Speaker 2: of a world without it. AI, he believes, has the
Speaker 2: potential to make the world a better place in a
Speaker 2: way that no other technology can. Rob painted an optimistic
Speaker 2: version of the future, one in which AI technology will
Speaker 2: continue to improve at an exponential rate. This will free
Speaker 2: up workers to dedicate their energy to more creative tasks.
Speaker 2: I for one am on board Smart Talks with IBM
Speaker 2: is produced by Matt Romano, Joey Fishground, and Jacob Goldstein.
Speaker 2: We're edited by Lydia gene kott Our engineers are Sarah
Speaker 2: Bruguer and Ben Tolliday. Theme song by Gramscow. Special thanks
Speaker 2: to the eight Bar and IBM teams, as well as
Speaker 2: the Pushkin marketing team. Smart Talks with IBM is a
Speaker 2: production of Pushkin Industries and Ruby Studio at iHeartMedia. To
Speaker 2: find more Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts,
Speaker 2: or wherever you listen to podcasts. I'm Malcolm Gladwell. This
Speaker 2: is a paid advertisement from IBM. The conversations on this
Speaker 2: podcast don't necessarily represent IBM's positions, strategies, or opinions.