Speaker 1: Welcome to Tech Stuff, a production from iHeartRadio. Today, we
Speaker 1: are witnessed to one of those rare moments in history,
Speaker 1: the rise of an innovative technology with the potential to
Speaker 1: radically transform business in society forever. That technology, of course,
Speaker 1: is artificial intelligence, and it's the central focus for this
Speaker 1: new season of Smart Talks with IBM. Join hosts from
Speaker 1: your favorite Pushkin podcasts as they talk with industry experts
Speaker 1: and leaders to explore how businesses can integrate AI into
Speaker 1: their workflows and help drive real change in this new
Speaker 1: era of AI, and of course, host Malcolm Gladwell will
Speaker 1: be there to guide you through the season and throw
Speaker 1: in his two cents as well. Look out for new
Speaker 1: episodes of Smart Talks with IBM every other week on
Speaker 1: the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts,
Speaker 1: and learn more at IBM dot com, slash smart Talks.
Speaker 2: Pushkin.
Speaker 3: Welcome, Welcome, Welcome to Smart Talks with IBM.
Speaker 4: Hello, Hello, Welcome to Smart Talks with IBM, a podcast
Speaker 4: from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gladwell. This season,
Speaker 4: we're diving back into the world of artificial intelligence, but
Speaker 4: with a focus on the powerful concept of open its possibilities, implications,
Speaker 4: and misconceptions. We'll look at openness from a variety of
Speaker 4: angles and explore how the concept is already reshaping industries,
Speaker 4: ways of doing business, and our very notion of what's possible.
Speaker 4: And for the first episode of this season, we're bringing
Speaker 4: you a special conversation.
Speaker 2: I recently said.
Speaker 4: That down with Rob Thomas. Rob is the senior vice
Speaker 4: president of Software and chief Commercial Officer of IBM. We
Speaker 4: spoke to him in front of a live audience as
Speaker 4: part of New York Tech Week. We discussed how businesses
Speaker 4: can harness the immense productivity benefits of AI while implementing
Speaker 4: it in a responsible and ethical manner. We also broke
Speaker 4: down a fascinating concept that Rob believes about AI, known
Speaker 4: as the productivity paradox. Okay, let's get to the conversation.
Speaker 2: How are we doing good, Rob?
Speaker 4: This is our second time. We did one of these
Speaker 4: in the middle of the pandemic. But now it's all
Speaker 4: such a blur now that us can figure out when
Speaker 4: 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, But.
Speaker 4: Well, it's good to see you, to meet you again.
Speaker 4: I wanted to start by going back. You've been an IBM.
Speaker 3: Two years right, twenty five in July, believe it or not.
Speaker 2: So you were you were a kid when you joined.
Speaker 3: I was four.
Speaker 4: So I want to contrast present day Rob and twenty
Speaker 4: five years ago Rob. When you arrive at IBM, what
Speaker 4: do you think your job is going to be?
Speaker 2: It, your career is going?
Speaker 4: Where do you think the kind of problems you're going
Speaker 4: to be addressing are?
Speaker 3: Well, it's kind of surreal because I joined IBM a
Speaker 3: consulting and I'm coming out of school, and you quickly realize, wait,
Speaker 3: the job of a consultant is to tell other companies
Speaker 3: what to do. And I was like, I literally know nothing,
Speaker 3: and so you're immediately trying to figure out, so how
Speaker 3: am I going to be relevant given that I know
Speaker 3: absolutely nothing to advise other companies on what they should
Speaker 3: be doing. And I remember it well, like we were
Speaker 3: sitting in a room. When you're a consultant, you're waiting
Speaker 3: for somebody else to find work for you. A bunch
Speaker 3: of us sitting in a room and somebody walks in
Speaker 3: and as we need somebody that knows visio. Does anybody
Speaker 3: know Visio? I'd never heard of visio. I don't know
Speaker 3: if anybody in the room has. So everybody's like sitting
Speaker 3: around looking at their shoes. So finally I was like,
Speaker 3: I know it. So I raised my hand. They're like, great,
Speaker 3: we got a project for you next week. So I
Speaker 3: was like, all right, I have like three days to
Speaker 3: figure out what visio is, and I hope I can
Speaker 3: actually figure out how to use it now. Luckily, it
Speaker 3: wasn't like a programming language. I mean, it's pretty much
Speaker 3: a drag and drop capability. And so I literally left
Speaker 3: the office, went to a bookstore bought the first three
Speaker 3: books on Visio I could find, spent the whole week
Speaker 3: in reading the books, and showed up and got their
Speaker 3: work on the project. And so it was a bit
Speaker 3: of a risky move, but I think that's kind of
Speaker 3: you this well, But if you don't take risk, you'll
Speaker 3: never you'll never achieve. And so does some extent. Everybody's
Speaker 3: making everything up all the time. It's like, can you
Speaker 3: learn faster than somebody else? Is what the difference is
Speaker 3: in almost every part of life. And so it was
Speaker 3: not planned, but it was an accident, but it kind
Speaker 3: of forced me to figure out that you're going to
Speaker 3: have to figure things out.
Speaker 4: You know, we're here to talk about AI, and I'm
Speaker 4: curious about the evolution of your understanding or IBM's understanding
Speaker 4: of my AI. At what point in the last twenty
Speaker 4: five years do you begin to think, oh, this is
Speaker 4: really going to be at the core of what we
Speaker 4: think about and work on at this company.
Speaker 3: The computer scientist John McCarthy, he was he's the person
Speaker 3: that's credited with coining the phrase artificial intelligence. It's like
Speaker 3: in the fifties, and he made an interesting comedy said
Speaker 3: he said, once it works, it's no longer called AI,
Speaker 3: and that then became it's called like the AI effect,
Speaker 3: which is it seems very difficult, very mysterious, but once
Speaker 3: it becomes commonplace, it's just no longer what it is.
Speaker 3: And so if you put that frame on it, I
Speaker 3: think we've always been doing AI at some level. And
Speaker 3: I even think back to when I joined IBM in
Speaker 3: ninety nine. At that point there was work on rules
Speaker 3: based engines, analytics, all of this was happening. So it
Speaker 3: all depends on how you really define that term. You
Speaker 3: could argue that you know, elements of statistics, probability. It's
Speaker 3: not exactly AI, but it certainly feeds into it, and
Speaker 3: so I feel like we've been working on this topic
Speaker 3: of how do we deliver better insights better automation since
Speaker 3: IBM was formed. If you read about what Thomas Watson
Speaker 3: Junior did, that was all about automating tasks, is that AI?
Speaker 3: Well probably certainly not by today's definition, but it's in
Speaker 3: the same zip code.
Speaker 4: So from your perspective, it feels a lot more like
Speaker 4: an evolution than a revolution.
Speaker 2: Is that a fair statement?
Speaker 3: Yes, yeah, which I think most great things in technology
Speaker 3: tend to happen that way. Yeah, many of the revolutions,
Speaker 3: if you will, tend to fizzle out.
Speaker 4: But even given that is there, I guess what I'm
Speaker 4: asking is, I'm curious about whether there was a moment
Speaker 4: in that evolution when you had to readjust your expectations
Speaker 4: about what AI was going to be capable of.
Speaker 2: I mean, was there, you know, was.
Speaker 4: There a particular innovation or a particular problem that was
Speaker 4: solved that made you think, oh, this is different than
Speaker 4: what I thought.
Speaker 3: I would say the moments that caught our attention certainly
Speaker 3: casper Off winning the chess tournament, nobody or Deep Blue
Speaker 3: beating casper Off. I should say nobody really thought that
Speaker 3: was possible before that, and then it was Watson winning Jeopardy.
Speaker 3: These were moments that said, maybe there's more here than
Speaker 3: we even thought was possible. And so I do think
Speaker 3: there's there's points in time where we realized maybe way
Speaker 3: more could be done than we had even imagined. But
Speaker 3: I do think it's consistent progress every month and every
Speaker 3: year versus some seminal moment. Now, certainly large language models
Speaker 3: as of recent have caught everybody's attention because it has
Speaker 3: a direct consumer application. But I would almost think of
Speaker 3: that as what Netscape was for the for the web browser. Yeah,
Speaker 3: it brought the Internet to everybody, but that didn't become
Speaker 3: the Internet per se. Yeah.
Speaker 4: I have a cousin who worked for I'd be up
Speaker 4: for forty one years. I saw him this weekend. He's
Speaker 4: in Toronto. By the way, I said, you work for
Speaker 4: Rob Thomas. He would like this, he goes.
Speaker 2: He said, I'm five layers down.
Speaker 4: But so I always whenever I see my cousin, I
Speaker 4: ask him, can you tell me again what you do?
Speaker 2: Because always changing? Right, I guess this is a function
Speaker 2: of working at IBM.
Speaker 4: So eventually he just gives up and says, you know,
Speaker 4: we're just solving problems, so what we're doing, which I
Speaker 4: sort of loved as a kind of frame, And I
Speaker 4: was curious, what's the coolest problem you ever worked on?
Speaker 4: Not biggest, not most important, but the coolest, the one
Speaker 4: that's like that sort of makes you smile when you
Speaker 4: think back on it.
Speaker 3: Probably when I was in microelectronics, because it was a
Speaker 3: world I had no exposure to. I hadn't studied computer science,
Speaker 3: and we were building a lot of high performance semiconductor technology,
Speaker 3: so just chips that do a really great job of
Speaker 3: processing something or other. And we figured out that there
Speaker 3: was a market in consumer gaming that was starting to happen,
Speaker 3: and we got to the point where we became the
Speaker 3: chip inside the Nintendo, We the Microsoft Xbox Sony PlayStation,
Speaker 3: so we basically had the entire gaming market running on
Speaker 3: ib AND chips.
Speaker 4: And to use every parent basically is pointing at you
Speaker 4: and saying you're the call.
Speaker 3: Probably well they would have found it from anybody, but
Speaker 3: it was the first time I could explain my job
Speaker 3: to my kids, who were quite young at that time,
Speaker 3: like what I did like it was more tangible for
Speaker 3: them than saying we solve problems or douce you know,
Speaker 3: build solutions like it became very tangible for them, and
Speaker 3: I think that's, you know, a rewarding part of the
Speaker 3: job is when you can help your family actually understand
Speaker 3: what you do. Most people can't do that. It's probably
Speaker 3: easier for you. They can, they can see the books,
Speaker 3: but for for some of us in the business, the
Speaker 3: business world, it's not always as obvious. So that was
Speaker 3: like one example where the dots really connected.
Speaker 4: There were a couple's a couple of stuck about a
Speaker 4: little bit of this into context of of AI. I
Speaker 4: love because I love the frame of problem solving as
Speaker 4: a way of understanding what the function of the technology is.
Speaker 4: So I know that you guys did something, did some
Speaker 4: work with I never know how to pronounce it. Is
Speaker 4: it Seville Sevia, Sevia with the football club Sevia in Spain?
Speaker 4: Tell me about Tell me a little bit about that.
Speaker 4: What problem were they trying to solve and why did
Speaker 4: they call you?
Speaker 3: In? Every sports franchise is trying to get an advantage, right,
Speaker 3: Let's just be that clear. Everybody's how can I use data? Analytics, insights,
Speaker 3: anything that will make us one percent better on the
Speaker 3: field at some point in the future. And Seville reached
Speaker 3: out to us because they had seen some of that.
Speaker 3: We've done some work with the Toronto Raptors in the
Speaker 3: past and others, and their thought was maybe there's something
Speaker 3: we could do. They'd heard all about generative AI, that
Speaker 3: heard about large language models, and the problem, back to
Speaker 3: your point on solving problems, was we want to do
Speaker 3: a way better job of assessing talent, because really the
Speaker 3: lifeblood of a sports franchise is can you continue to
Speaker 3: cultivate talent, can you find talent that others don't find?
Speaker 3: Can you see something in somebody that they don't see
Speaker 3: in themselves or maybe no other team season them. And
Speaker 3: we ended up building somebody with them called Scout Advisor,
Speaker 3: which is built on Watson X, which basically just ingests
Speaker 3: tons and tons of data and we like to think
Speaker 3: of it as finding the needle in the haystack of
Speaker 3: you know, here's three players that aren't being considered. They're
Speaker 3: not on the top teams today, and I think working
Speaker 3: with them together, we found some pretty good insights that's
Speaker 3: helped them out.
Speaker 2: How what was intriguing to.
Speaker 4: Me was we're not just talking about quantitative data. We're
Speaker 4: also talking about qualitative data. That's the puzzle part of
Speaker 4: the thing that fastens me. How does what incorporate qualitative
Speaker 4: analysis into that sort of so you just feeding in
Speaker 4: scouting reports and things like that.
Speaker 3: I got to realize think about how much I can
Speaker 3: actually disclose it. But if you think about so, quantitative
Speaker 3: is relatively easy. Every team collects that, you know, what's
Speaker 3: their forty yard dashable think they use that term, certainly
Speaker 3: not in Spain. That's all quantitative. Qualitative is what's happening
Speaker 3: off the field. It could be diet, it could be habits,
Speaker 3: it could be behavior. You can imagine a range of
Speaker 3: things that would all feed into an athlete's performance, and
Speaker 3: so relationships. There's many different aspects, and so it's trying
Speaker 3: to figure out the right blend of quantitative and qualitative
Speaker 3: that gives you a unique insight.
Speaker 4: How transparent is that kind of system telling you? It's
Speaker 4: saying pick this guy, not this guy, But is it
Speaker 4: telling you why it prefers this guy to this guy?
Speaker 2: Was it?
Speaker 3: I think for anything in the realm of AI, you
Speaker 3: have to answer the why question. Yeah, otherwise you've fallen
Speaker 3: into the trap of the you know, the proverbial black box,
Speaker 3: and then wait, I made this decision, I'd never understood
Speaker 3: why it didn't work out, So you always have to
Speaker 3: answer why without a.
Speaker 2: Doubt and how is why answered?
Speaker 3: Sources of data, the reasoning that went into it, and
Speaker 3: so it's basically just tracing back the chain of how
Speaker 3: you got to the answer. And in the case of
Speaker 3: what we do in Watson X is we have IBM models.
Speaker 3: We also use some other open source models, so it
Speaker 3: would be which model was used, what was the data
Speaker 3: set that was fed into that model, How is it
Speaker 3: making decisions? How is it performing? Is it robust? Meaning
Speaker 3: is it reliable in terms of if you feed it
Speaker 3: two of the same data set, do you get the
Speaker 3: same answer? These are all the you know, the technical
Speaker 3: aspects of understanding the why.
Speaker 4: How quickly do you expect all professional sports franchises to
Speaker 4: adopt some kind of are they already there? If I
Speaker 4: went out and pulled the general managers of the one
Speaker 4: hundred most valuable sports franchises in the world, how many
Speaker 4: of them would be using some kind of AI system
Speaker 4: to assist in their efforts?
Speaker 3: One hundred and twenty percent would meaning that everybody's doing it,
Speaker 3: and some think they're doing way more than they probably
Speaker 3: actually are. So everybody's doing it. I think what's weird
Speaker 3: about sports is everybody's so convinced that what they're doing
Speaker 3: is unique that they generally speaking don't want to work
Speaker 3: with a third party to do it because they're afraid
Speaker 3: that that would expose them. But in reality, I think
Speaker 3: most are doing eighty to ninety percent of the same things.
Speaker 3: So but without a doubt, everybody's doing it.
Speaker 2: Yeah. Yeah.
Speaker 4: The other that love was there was one but a
Speaker 4: shipping line tri gun on the Mississippi River. Tell me
Speaker 4: a little bit about that project. What problem were they
Speaker 4: trying to solve?
Speaker 3: Think about the problem that I would say everybody noticed
Speaker 3: if you go back to twenty twenty was things are
Speaker 3: getting hold held up in ports. It was actually an
Speaker 3: article in the paper this morning kind of tracing the
Speaker 3: history of what happened twenty twenty twenty one and why
Speaker 3: ships were basically sitting at seas for months at a time.
Speaker 3: And at that stage we just we had a massive
Speaker 3: throughput issue. But moving even beyond the pandemic, you can
Speaker 3: see it now with ships getting through like Panama Canal.
Speaker 3: There's like a narrow window where you can get through,
Speaker 3: and if you don't have your paperwork done, you don't
Speaker 3: have the right approvals, you're not going through and it
Speaker 3: may cost you a day or two and that's a
Speaker 3: lot of money. In the shipping industry and the tricon example,
Speaker 3: it's really just about when you're pulled into a port,
Speaker 3: if you have the right paperwork done, you can get
Speaker 3: goods off the ship very quickly. They ship a lot
Speaker 3: of food which by definition, since it's not packaged food,
Speaker 3: it's fresh food, there is an expiration period and so
Speaker 3: if it takes them an extra two hours, certainly multiple
Speaker 3: hours or a day, they have a massive problem because
Speaker 3: then you're going to deal with spoilage and so it's
Speaker 3: going to set you back. And what we've worked with
Speaker 3: them on is using an assistant that we've built in
Speaker 3: Watson X called Orchestrate, which basically is just AI doing
Speaker 3: digital labor, so we can replicate nearly any repetitive task
Speaker 3: and do that with software instead of humans. So, as
Speaker 3: you may imagine, shipping industry still has a lot of
Speaker 3: paperwork that goes on and so being able to take
Speaker 3: forms that normally would be multiple hours of filling it out.
Speaker 3: Oh this isn't right, send it back. We've basically built
Speaker 3: that as a digital skill inside of Watson X orchestraate
Speaker 3: and so now it's done in minutes.
Speaker 4: They did they realize that they could have that kind
Speaker 4: of efficiency by teaming up with you? Or is that
Speaker 4: something you came to them and said, guys, we can
Speaker 4: do this way better than you think.
Speaker 2: What's the.
Speaker 3: I'd say it's always it's always both sides coming together
Speaker 3: at a moment that for some reason makes sense because
Speaker 3: you could say, why didn't this happen like five years ago,
Speaker 3: like this seems so obvious. Well, technology wasn't quite ready then,
Speaker 3: I would say, But they knew they had a need
Speaker 3: because I forget what the precise number is, but you know,
Speaker 3: reduction of spoilage has massive impact on their bottom line,
Speaker 3: and so they knew they had a need. We thought
Speaker 3: we could solve it and the two together.
Speaker 2: Who did you guys go to the Now? Did they
Speaker 2: come to you?
Speaker 3: I recall that this one was an inbound meaning they
Speaker 3: had reached out to IBM and we'd like to solve
Speaker 3: this problem. I think it went into one of our
Speaker 3: digital centers if I recall it a literary phone.
Speaker 4: Call, but the other the reverse is more interesting to
Speaker 4: me because there seems to be a very very large
Speaker 4: universe of people who have problems that could be solved
Speaker 4: this way and they don't realize it.
Speaker 2: What's your.
Speaker 4: Is there a shining example of this of someone you
Speaker 4: just can't you just think could benefit so much and
Speaker 4: isn't benefiting right now?
Speaker 3: Maybe I'll answer it slightly differently. I'm I'm surprised by
Speaker 3: how many people can benefit that you wouldn't even logically
Speaker 3: think of. First, let me give you an example. There's
Speaker 3: a franchiser of hair salons. Sport Clips is the name.
Speaker 3: My sons used to go there for haircuts because they
Speaker 3: have like and you can watch sports, so they loved that.
Speaker 3: They got entertained while they would get their haircut. I
Speaker 3: think the last place that you would think is using
Speaker 3: AI today would be a franchiser of hair salons. But
Speaker 3: just follow it through. The biggest part of how they
Speaker 3: run their business is can I get people to cut hair?
Speaker 3: And this is the high turnover industry because there's a
Speaker 3: lot of different places you can work if you want
Speaker 3: to cut hair. People actually get injured cutting hair because
Speaker 3: you're on your feet all day, that type of thing.
Speaker 3: And they're using same technology orchestrate as part of their
Speaker 3: recruiting process. How can they automate a lot of people
Speaker 3: submitting resumes, who they speak to, how they qualify them
Speaker 3: for the position. And so the reason I give that
Speaker 3: example is the opportunity for AI, which is unlike other technologies,
Speaker 3: is truly unlimited. It will touch every single business. It's
Speaker 3: not the realm of the fun five hundred or the
Speaker 3: fortune one thousand. This is the fortune any size. And
Speaker 3: I think that may be one thing that people underestimate
Speaker 3: about AI.
Speaker 4: What about I mean, I was thinking about education as
Speaker 4: a kind of I mean, education is a perennial whipping
Speaker 4: boy for you guys that are living in the nineteenth century, right.
Speaker 4: I'm just curious about if a superintendent of a public
Speaker 4: school system or the president of the university sat down
Speaker 4: and had lunch with you and said, do the university first.
Speaker 4: My cost are out of control, my enrollment is down,
Speaker 4: my students hate me, and my board is revolting.
Speaker 2: Help.
Speaker 4: How would you how would you think about helping someone
Speaker 4: in that situation.
Speaker 3: I spend some time with universities. I like to go
Speaker 3: back and visit Alma Maters where I went to school,
Speaker 3: and so I do that every year. The challenge I
Speaker 3: have hall is Seeming university is there has to be
Speaker 3: a will. Yeah, and I'm not sure the incentives are
Speaker 3: quite right today because bringing in new technology, let's say
Speaker 3: we want to go after we can help you figure
Speaker 3: out student recruiting or how you automate more of your education,
Speaker 3: everybody suddenly feels threatened that university. Hold on, that's my job.
Speaker 3: I'm the one that decides that, or I'm the one
Speaker 3: that wants to dictate the course. So there has to
Speaker 3: be a will. So I think it's very possible, and
Speaker 3: I do think over the next decade you will see
Speaker 3: some universities that jump all over this and they will
Speaker 3: move ahead, and you see others that do not, because
Speaker 3: it's very possible.
Speaker 4: Where how does when you say there has to be
Speaker 4: a will? Is that the kind of Is that a
Speaker 4: kind of thing that that people at IBM think about,
Speaker 4: Like when in this conversation you hype type a conversation
Speaker 4: you might have with the university president, would you give
Speaker 4: advice on where the will comes from?
Speaker 3: I don't do that as much in a university context.
Speaker 3: I do that every day in a business context because
Speaker 3: if you can find the right person in a business
Speaker 3: that wants to focus on growth or the bottom line
Speaker 3: or how do you create more productivity. Yes, it's going
Speaker 3: to create a lot of organizational resistance potentially, but you
Speaker 3: can find somebody that will figure out how to push
Speaker 3: that through. I think for universities, I think that's also possible.
Speaker 3: I'm not sure there's there's a there's a return on
Speaker 3: investment for us to do that.
Speaker 4: Yeah, yeah, yeah, let's let's find some terms.
Speaker 2: AI years. I told you'd like to use What does
Speaker 2: that mean?
Speaker 3: We just started using this term literally in the last
Speaker 3: three months, and it was a It was what we
Speaker 3: observed internally, which is most technology you build, you say,
Speaker 3: all right, what's going to happen in year one, year two,
Speaker 3: year three, and it's you know, largely by by a calendar.
Speaker 3: AI years are the idea that what used to be
Speaker 3: a year is now like a week, and that is
Speaker 3: how fast the technology is moving. Do you give you
Speaker 3: an example. We had one client we're working with, They're
Speaker 3: using one of our granite models, and the results they
Speaker 3: were getting we're not very good. Accuracy was not there.
Speaker 3: Their performance was not there. So I was like scratching
Speaker 3: my head. I was like, what is going on? They
Speaker 3: were financial services, the bank, So I'm scratching my head,
Speaker 3: like what is going on? Everybody else is getting this
Speaker 3: and like these results are horrible. And I said to
Speaker 3: the team, which version of the model are you using?
Speaker 3: This was in February, Like we're using the one from October.
Speaker 3: I was like, all right, now we don't precisely the
Speaker 3: problem because the model from October is the effect useless
Speaker 3: now since we're here in February.
Speaker 2: Serious, actually useless, completely useless.
Speaker 3: Yeah, that is how fast this is changing. And so
Speaker 3: the minute, same use case, same data, you give them
Speaker 3: the model from late January instead of October, the results
Speaker 3: are off the charts.
Speaker 2: Yeah.
Speaker 4: 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 We have built large compute infrastructure where
Speaker 3: we're doing model training. And to be clear, model training
Speaker 3: is the realm of probably in the world my guess
Speaker 3: is five to ten companies. And so you build a model,
Speaker 3: you're constantly training it, you're doing fine tuning you're doing
Speaker 3: more training, You're adding data every day, every hour it
Speaker 3: gets better, And so how does it do that. You're
Speaker 3: feeding it more data, you're feeding it more live examples.
Speaker 3: We're using things like synthetic data at this point, which
Speaker 3: is we're basically creating data to do the training as well.
Speaker 3: All of this feeds into how useful the model is,
Speaker 3: and so using the October model, those were the results
Speaker 3: in October, just a fact, that's how good it was then.
Speaker 3: But back to the concept of AI years, two weeks
Speaker 3: is a long time.
Speaker 4: Does that are we in a steep part of the
Speaker 4: model learning carve or do you expect this to continue
Speaker 4: along this at this pace?
Speaker 3: I think that is the big question and don't have
Speaker 3: an answer yet. By definition, at some point you would
Speaker 3: think it would have to slow down a bit, but
Speaker 3: it's not obvious that that is on the horizon.
Speaker 2: Still speeding up. Yes, how fast can it get?
Speaker 3: We've debated, can you actually have better results in the
Speaker 3: afternoon than you did in the morning. Really it's nuts, Yeah,
Speaker 3: I know, But that's that's why we came up with
Speaker 3: this term, because I think you also have to think
Speaker 3: of like concepts that gets people's attention so.
Speaker 4: You're basically turning into a bakery. You're like the bread
Speaker 4: from yesterday. You know you can have it for twenty
Speaker 4: five cents. But I mean you do proferential pricing. You
Speaker 4: could say, we'll judge you x for yesterday's model, two
Speaker 4: x for today's model.
Speaker 3: I think that's dangerous as a merchandising strategy, but I
Speaker 3: guess your point.
Speaker 2: Yeah, but that's crazy.
Speaker 4: And this, by the way, so this model is the
Speaker 4: same true for almost you're talking specifically about a model
Speaker 4: that was created to help some aspect of a financial
Speaker 4: services So is that kind of model accelerating faster and
Speaker 4: running faster than other models for other kinds of problems?
Speaker 3: So this domain was code. Yeah, so by definition, if
Speaker 3: you're feeling feeding in more data some more code, you
Speaker 3: get those kind of results depend on the model type.
Speaker 3: There's a lot of code in the world, and so
Speaker 3: we can find that we can create it. Like I said,
Speaker 3: there's other aspects where there's probably less inputs available, which
Speaker 3: means you probably won't get the same level of iteration.
Speaker 3: But for code, that's certainly the cycle times that we're seeing.
Speaker 4: Yea, and how do you know that. Let's stick with
Speaker 4: this one example of this model you have, how do
Speaker 4: you know that your model is better than.
Speaker 2: Big company B down the street?
Speaker 4: Client asks you, why would I go with IBM as
Speaker 4: opposed to some the s firm in the valley that says,
Speaker 4: as they have a model on this, what's your how
Speaker 4: do you frame your advantage?
Speaker 3: Well, we benchmark all of this, and I think the
Speaker 3: most important is metric is price performance, Not price, not performance,
Speaker 3: but the combination of the two. And we're super competitive there. Well,
Speaker 3: for what we just released, with what we've done in
Speaker 3: open source, we know that nobody's close to us right
Speaker 3: now on code now. To be clear, that will probably change, yeah,
Speaker 3: because it's like leap frog. People will jump ahead, then
Speaker 3: we jump back ahead. But we're very confident that with
Speaker 3: everything we've done in the last few months, we've taken
Speaker 3: a huge leap forward here.
Speaker 2: Yeah.
Speaker 4: I mean this goes back to the point I was
Speaker 4: making in the beginning, so about the difference between your
Speaker 4: twenty something self in ninety nine and yourself today.
Speaker 2: But this time.
Speaker 4: Compression has to be a crazy adjustment. So the concept
Speaker 4: of what you're working on and how you make decisions
Speaker 4: internally and things has to undergo this kind of revolution.
Speaker 4: If you're switching from I mean back in the day,
Speaker 4: a model might be useful for how.
Speaker 2: Long years years?
Speaker 3: I think about you know, statistical models that set inside
Speaker 3: things like SPSS, which is a product that a lot
Speaker 3: of students use around the world. I mean, those have
Speaker 3: been the same models for twenty years and they're still
Speaker 3: very good at what they do. And so yes, it's
Speaker 3: a completely it's a completely different moment for how fast
Speaker 3: this is moving. And I think it just raises the
Speaker 3: bar for everybody, whether you're a technology provider like us,
Speaker 3: or you're a bank or an insurance company or a
Speaker 3: shipping company, to say, how do you really change your
Speaker 3: culture to be way more aggressive than you normally would be?
Speaker 4: Does this means it's a weird question, But does this
Speaker 4: mean a different set of kind of personality or character
Speaker 4: traits are necessary for a decision maker in tech now
Speaker 4: than twenty five years ago.
Speaker 3: There's a there's a book I saw recently, it's called
Speaker 3: The Geek Way, which talked about how technology companies have
Speaker 3: started to operate in different ways maybe than many you know,
Speaker 3: traditional companies, and more about being dated driven, more about delegation.
Speaker 3: Are you willing to have the smartest person in the
Speaker 3: room make decisions opposed to the highest paid person in
Speaker 3: the room. I think these are all different aspects that
Speaker 3: every company is going to face. Yeah.
Speaker 4: Yeah, next term, talk about open. When you use that
Speaker 4: word open, what do you mean.
Speaker 3: I think there's really only one definition of open, which
Speaker 3: is for technology is open source. An open source means
Speaker 3: the code is freely available. Anybody can see it, access it,
Speaker 3: contribute to it.
Speaker 4: And what is Tell me about why that's an important principle.
Speaker 3: When you take a topic like AI, I think it
Speaker 3: would be really bad for the world if this was
Speaker 3: in the hands of one or two companies, or three
Speaker 3: or four, doesn't matter the number, some small number. Think
Speaker 3: about like in history, sometime early nineteen hundreds, the Interstate
Speaker 3: Commerce Commission was created, and the whole idea was to
Speaker 3: protect farmers from railroads. Meaning they wanted to allow free trade,
Speaker 3: but they knew that, well, there's only so many railroad tracks,
Speaker 3: so we need to protect farmers from the shipping costs
Speaker 3: that railroads could impose. So good idea, but over time
Speaker 3: that got completely overtaken by the railroad lobby and then
Speaker 3: they use that to basically just increase prices and it
Speaker 3: made the lives of farmers way more difficult. I think
Speaker 3: you could play the same analogy through with AI. If
Speaker 3: you allow a handful of companies to have the technology,
Speaker 3: you regulate around the principles of those one or two companies,
Speaker 3: then you've trapped the entire world. That would be very bad.
Speaker 3: So the danger of that happened for sure. I mean
Speaker 3: there's companies in Watson in Washington every week trying to
Speaker 3: achieve that outcome. And so the opposite of that is
Speaker 3: to say it's going to be an open source because
Speaker 3: nobody can dispute open source because it's right there, everybody
Speaker 3: can see it. And so I'm a strong believer that
Speaker 3: open source will win for AI. It has to win.
Speaker 3: It's not just important for business, but it's important for humans.
Speaker 4: On the I'm curious about on the list of things
Speaker 4: you worry about, actually, let me before I ask, let
Speaker 4: me ask this question very generally. What is the list
Speaker 4: of things you worry about? What's your top five business
Speaker 4: 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.
Speaker 4: We could leave you know, your kid's haircuts got it
Speaker 4: out of.
Speaker 3: The number one is always it's the thing that's probably
Speaker 3: always been true, which is just people. Do we have
Speaker 3: the rights skills? Are we doing a good job of
Speaker 3: training our people? Are our people doing a good job
Speaker 3: of working with clients? Like that's number one. Number two
Speaker 3: is innovation. Are we pushing the envelope enough? Are are
Speaker 3: we staying ahead? Number three is which kind of feeds
Speaker 3: into the innovation one is risk taking? Are we taking
Speaker 3: enough risk? Without risk? There is no growth? And I
Speaker 3: think the trap that every larger company inevitably falls into
Speaker 3: is conservatism. Things are good enough, and so it's are
Speaker 3: we pushing the envelope? Are we taking enough risk to
Speaker 3: really have an impact? I'd say those are probably the
Speaker 3: top three that I spend.
Speaker 4: Last turn to define productivity paradox something I know you've
Speaker 4: thought a lot about what does that mean?
Speaker 3: So I started thinking hard about this because all I
Speaker 3: saw and read every day was was fear about AI.
Speaker 3: And I studied economics, and so I kind of went
Speaker 3: back to like basic economics. And there's been like a
Speaker 3: macro investing formula. I guess I would say it's been
Speaker 3: around forever that says growth comes from productivity growth plus
Speaker 3: population growth plus debt growth. So if those three things
Speaker 3: are working, you'll get GDP growth. And so then you
Speaker 3: think about that and you say, well, debt growth, we're
Speaker 3: probably not going back to zero percent interest rates, so
Speaker 3: to some extent there's going to be a ceiling on that.
Speaker 3: And then you look at population growth. There are shockingly
Speaker 3: few countries or places in the world that will see
Speaker 3: population growth over the next thirty to fifty years. In fact,
Speaker 3: most places are not even at replacement rates. And so
Speaker 3: I'm like, all right, so population growth is not going
Speaker 3: to be there. So that would mean if you just
Speaker 3: take it to the extreme, the only chance of continued
Speaker 3: GDP growth is productivity. And the best way to solve
Speaker 3: productivity is AI That's why I say it's a paradox.
Speaker 3: On one hand, everybody's scared after death it's going to take
Speaker 3: over the world, take all of our jobs, ruin us.
Speaker 3: But in reality, maybe it's the other way, which is
Speaker 3: it's the only thing that can save us. Yeah, and
Speaker 3: if you believe that economic equation, which I think has
Speaker 3: proven quite true over hundreds of years, I do think
Speaker 3: it's probably the only thing that can save us.
Speaker 4: Actually looked at the numbers yesterday for totally random reason
Speaker 4: on population growth in Europe and received. This is a
Speaker 4: special bonus question. We'll see how smart you are. Which
Speaker 4: country in Europe? Condellly Europe has the highest population growth?
Speaker 3: It's small continental Europe, probably one of the Nordics.
Speaker 2: I would yes, close Luxembourg.
Speaker 4: Okay, something that's going on in Luxembourg. I feel like,
Speaker 4: well all of us need to investigate there. At one
Speaker 4: point four nine, which in the day, by the way,
Speaker 4: would be a relatively that's the best performing country. I
Speaker 4: mean in the day, you'd be countries had routinely had
Speaker 4: 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.
Speaker 4: It backstage, and now I appreciate the paradox of this book,
Speaker 4: which is in a universe with a model, is better
Speaker 4: in the afternoon than it is in the morning. How
Speaker 4: do you write a book that's like printed on paper?
Speaker 4: I expected to reuseful.
Speaker 3: This is the challenge. And I am an incredible author
Speaker 3: of useless books. I mean most of what I've spent
Speaker 3: time on in the last decade of stuff that's completely useless,
Speaker 3: like a year after it's written. And so when and
Speaker 3: we were talking about it as I would like to
Speaker 3: do something around AI that's timeless, that would be useful
Speaker 3: ten or twenty years from now. But then to your point, so,
Speaker 3: how is that even remotely possible if the model's better
Speaker 3: in the afternoon than in the morning. So that's the
Speaker 3: challenge in front of us. But the book is around
Speaker 3: AI value creation, so kind of links to this productivity paradox,
Speaker 3: and how do you actually get sustained value out of AI,
Speaker 3: out of automation, out of data science. And so the
Speaker 3: biggest challenge in front of us is can we make
Speaker 3: this relevant? That's the day that it's published.
Speaker 2: How are you setting out to do that?
Speaker 3: I think you have to to some extent level it
Speaker 3: up to bigger concepts, which is kind of why I
Speaker 3: go to things like macroeconomics, population geography as opposed to
Speaker 3: going into the the weeds of the technology itself. If
Speaker 3: you're write about this is how you get better performance
Speaker 3: out of a model, we can agree that will be
Speaker 3: completely useless two years from now, maybe even two months
Speaker 3: from now, and so it will be less in the
Speaker 3: technical detail and more of what is sustained value creation
Speaker 3: for AI, which if you think on what is hopefully
Speaker 3: a ten or twenty year period. It's probably we're kind
Speaker 3: of substituting AI for technology now, I've realized, because I
Speaker 3: think this has always been true for technology. It's just
Speaker 3: now AI is the thing that everybody wants to talk about.
Speaker 3: But let's see if we can do it. Time will tell.
Speaker 4: Did you get any inkling that the pace that this
Speaker 4: AI year's phenomenon was going to that things with the
Speaker 4: pace of change was going to accelerate so much because
Speaker 4: you had More's law, right, you had a model in
Speaker 4: the technology world for this kind of exponential increase in
Speaker 4: so thinking about that kind of a similar kind of
Speaker 4: acceleration in the.
Speaker 3: I think anybody had said they expected what we're seeing
Speaker 3: today is probably exaggerating. I think it's way faster than
Speaker 3: anybody expected. Yeah, but technologies, back to your point at
Speaker 3: More's law has always accelerated through the years. So I
Speaker 3: wouldn't say it's a shock, but it is surprising.
Speaker 4: Yeah, You've had a kind of extraordinary privileged position to
Speaker 4: watch and participate in this revolution, right, I mean, how
Speaker 4: many other people have been in that have ridden this wave.
Speaker 2: Like you have.
Speaker 3: I do wonder is this really that much different or
Speaker 3: does it feel different just because we're here. I mean,
Speaker 3: I do think on one level. Yes, So in the
Speaker 3: time I've been an IBM, internet happened, mobile happened, social
Speaker 3: network happened, blockchain happened. AI. So a lot has happened.
Speaker 3: But then you go back and say, well, but if
Speaker 3: I'd been here between nineteen seventy and ninety five, there
Speaker 3: were a lot of things that are pretty fundamental then too,
Speaker 3: So I wondered, almost, do we always exaggerate the timeframe
Speaker 3: that we're in. I don't know. Yeah, but it's a
Speaker 3: good idea though.
Speaker 4: I think the ending with the phrase I don't know
Speaker 4: it's a good idea though.
Speaker 2: It's probably a great way to wrap this up.
Speaker 4: Thank you so much, Thank you, Malcolm. In a field
Speaker 4: that is evolving as quickly as artificial intelligence, it was
Speaker 4: inspiring to see how adaptable Rob has been over his career.
Speaker 4: The takeaways from my conversation with Rob had been echoing
Speaker 4: in my head ever since. He emphasized how open source
Speaker 4: models allow AI technology to be by many players. Openness
Speaker 4: also allows for transparency. Rob told me about AI use
Speaker 4: cases like IBM's collaboration with Sevilla's football club. That example
Speaker 4: really brought home for me how AI technology will touch
Speaker 4: every industry. Despite the potential benefits of AI, challenges exist
Speaker 4: in its widespread adoption. Rob discussed how resistance to change,
Speaker 4: concerns about job security and organizational inertia can slow down
Speaker 4: implementation of AI solutions. The paradox, though, according to Rob,
Speaker 4: is that rather than being afraid of a world with AI,
Speaker 4: people should actually be more afraid of a world without it. AI,
Speaker 4: he believes, has the potential to make the world a
Speaker 4: better place in a way that no other technology can.
Speaker 4: Rob painted an optimistic version of the future, one in
Speaker 4: which AI technology will continue to improve at an exponential rate.
Speaker 4: This will free up workers to dedicate their energy to
Speaker 4: more creative tasks. I for one am on board. Smart
Speaker 4: Talks with IBM is produced by Matt Romano, Joey Fishground
Speaker 4: and Jacob Goldstein. We're edited by Lydia Jane Kott. Our
Speaker 4: engineers are Sarah Bruguier and Ben Holiday theme song by
Speaker 4: Gramscow Special thanks to the eight Bar and ib M teams,
Speaker 4: as well as the Pushkin marketing team. Smart Talks with
Speaker 4: ib M is a production of Pushkin Industries and Ruby
Speaker 4: Studio at iHeartMedia. To find more Pushkin podcasts. Listen on
Speaker 4: the iHeartRadio app, Apple Podcasts, or wherever you listen to podcasts.
Speaker 4: I'm Malcolm Gladwell. This is a paid advertisement from IBM.
Speaker 4: The conversations on this podcast don't necessarily represent IBM's positions, strategies,
Speaker 4: or opinions.