Speaker 1: Hey everyone, it's Robert and Joe here. Today we've got
Speaker 1: something a little bit different to share with you. It
Speaker 1: is a new season of the Smart Talks with IBM
Speaker 1: podcast series.
Speaker 2: Today we are witnessed to one of those rare moments
Speaker 2: in history, the rise of an innovative technology with the
Speaker 2: potential to radically transform business and society forever. The technology,
Speaker 2: of course, is artificial intelligence, and it's the central focus
Speaker 2: for this new season of Smart Talks with IBM.
Speaker 1: Join hosts from your favorite Pushkin podcasts as they talk
Speaker 1: with industry experts and leaders to explore how businesses can
Speaker 1: integrate AI into their workflows and help drive real change
Speaker 1: in this new era of AI. And of course, host
Speaker 1: Malcolm Gladwell will be there to guide you through the
Speaker 1: season and throw in his two cents as well.
Speaker 2: Look out for new episodes of Smart Talks with IBM
Speaker 2: every other week on the iHeartRadio app, Apple Podcasts, or
Speaker 2: wherever you get your podcasts. And learn more at IBM
Speaker 2: dot com, slash smart Talks.
Speaker 3: Pushkin.
Speaker 4: Welcome, Welcome, Welcome to Smart Talks with IBM.
Speaker 3: Hello, Hello, Welcome to Smart Talks with IBM, a podcast
Speaker 3: from Pushkin Industries. iHeartRadio and IBM. I'm Malcolm Gladwell. This season,
Speaker 3: we're diving back into the world of artificial intelligence, but
Speaker 3: with a focus on the powerful concept of open its possibilities, implications,
Speaker 3: and misconceptions. We'll look at openness from a variety of
Speaker 3: angles and explore how the concept is already reshaping industries,
Speaker 3: ways of doing business, and our very notion of what's possible.
Speaker 3: And for the first episode of this season, we're bringing
Speaker 3: you a special conversation. I recently sat down with Rob Thomas.
Speaker 3: Rob is the senior vice president of Software and chief
Speaker 3: Commercial Officer of IBM. I spoke to him in front
Speaker 3: of a live audience as part of New York Tech Week.
Speaker 3: We discussed how businesses can harness the immense productivity benefits
Speaker 3: of AI while implementing it in a responsible and ethical manner.
Speaker 3: We also broke down a fascinating concept that Rob believes
Speaker 3: about AI, known as the productivity paradox. Okay, let's get
Speaker 3: to the conversation. How are we doing good? Rob? This
Speaker 3: is our our second time. We did one of these
Speaker 3: in the middle of the pandemic, but now it's all
Speaker 3: such a blur now that us can figure out when
Speaker 3: it was.
Speaker 4: I know it's hard to those are like a blurry years.
Speaker 5: You don't know what happened, right.
Speaker 3: But well, it's good to see you, to meet you again.
Speaker 3: I wanted to start by going back. You've been at
Speaker 3: IBM twenty years? Is that right?
Speaker 5: Twenty five in July, believe it or not.
Speaker 3: So you were a kid when you joined.
Speaker 5: I was four.
Speaker 3: W I want to contrast present day Rob and twenty
Speaker 3: five years ago Rob. When you arrive at IBM, what
Speaker 3: do you think your job is going to be? It,
Speaker 3: your career is going? Where do you think the kind
Speaker 3: of problems you're going to be addressing are?
Speaker 4: Well, it's kind of surreal because I I joined IBM
Speaker 4: Consulting and I'm coming out of school, and you quickly realize, wait,
Speaker 4: the job of a consultant is to tell other companies
Speaker 4: what to do. And I was like, I literally know nothing,
Speaker 4: and so you're immediately trying to figure out, so how
Speaker 4: am I going to be relevant given that I know
Speaker 4: absolutely nothing to advise other companies on what they should
Speaker 4: be doing. And I remember it well, like we were
Speaker 4: sitting in a room. When you're a consultant, you're waiting
Speaker 4: for somebody else to find work for you.
Speaker 5: A bunch of us.
Speaker 4: Sitting in a room and somebody walks in and says,
Speaker 4: we need somebody that knows visio.
Speaker 5: Does anybody know visio? I'd never heard of visio.
Speaker 4: I don't know if anybody in the room has. So
Speaker 4: everybody's likes sit around looking at their shoes. So finally
Speaker 4: I was like, I know it. So I raised my hand.
Speaker 4: They're like, great, we got a project for you next week.
Speaker 4: So I was like, all right, I have like three
Speaker 4: days to figure out what visio is, and I hope
Speaker 4: I can actually figure out how to use it now. Luckily,
Speaker 4: it wasn't like a programming language. I mean, it's pretty
Speaker 4: much a drag and drop capability. And so I literally
Speaker 4: left the office.
Speaker 5: Went to a bookstore, bought the first three books.
Speaker 4: On Visio I could find, spent the whole week in
Speaker 4: reading the books, and showed up and got their work
Speaker 4: on the project. And so it was a bit of
Speaker 4: a risky move, but I think that's kind of you
Speaker 4: doing this well. But if you don't take risk, you'll
Speaker 4: never you'll never achieve. And so does some extent. Everybody's
Speaker 4: making everything up all the time. It's like, can you
Speaker 4: learn faster than somebody else. Is what the difference is
Speaker 4: in almost every part of life. And so it was
Speaker 4: not planned, but it was an accident, but it kind
Speaker 4: of forced me to figure out that you're going to
Speaker 4: have to figure things out.
Speaker 3: You know, we're here to talk about AI, and I'm
Speaker 3: curious about the evolution of your understanding or IBM's understanding
Speaker 3: of my AI. At what point in the last twenty
Speaker 3: five years do you begin to think, oh, this is
Speaker 3: really going to be at the core of what we
Speaker 3: think about and work on at this company.
Speaker 4: The computer scientist John McCarthy, he was he's the person
Speaker 4: that's credited with coining the phrase artificial intelligence. It's like
Speaker 4: in the fifties, and he made an interesting comedy said
Speaker 4: he said, once it works, it's no longer called AI,
Speaker 4: and that then became it's called like the AI effect,
Speaker 4: which is it seems very difficult, very mysterious, but once
Speaker 4: it becomes commonplace, it's just no longer what it is.
Speaker 4: And so if you put that frame on it, I
Speaker 4: think we've always been doing AI at some level. And
Speaker 4: I even think back to when I joined I in
Speaker 4: ninety nine. At that point there was work on rules
Speaker 4: based engines, analytics, all of this was happening, So it
Speaker 4: all depends on how you really define that term.
Speaker 5: You could argue that elements of.
Speaker 4: Statistics, probability, it's not exactly AI, but it certainly feeds
Speaker 4: into it.
Speaker 5: And so I feel like.
Speaker 4: We've been working on this topic of how do we
Speaker 4: deliver better insights better automation since IBM was formed. If
Speaker 4: you read about what Thomas Watson Junior did, that was
Speaker 4: all about automating tasks that AI will probably certainly not
Speaker 4: by today's definition, but it's in the same zip code.
Speaker 3: So from your perspective, it feels a lot more like
Speaker 3: an evolution than a revolution.
Speaker 4: Is that a fair statement, yes, which I think most
Speaker 4: great things in technology tend to happen that way. Many
Speaker 4: of the revolutions, if you will, tend to fizzle out.
Speaker 3: Even given that is there, I guess what I'm asking is,
Speaker 3: I'm curious about whether there was a moment in that
Speaker 3: evolution when you had to readjust your expectations about what
Speaker 3: AI was going to be capable of. I mean, was there,
Speaker 3: you know, was there a particular innovation or a particular
Speaker 3: problem that was solved that made you think, oh, this
Speaker 3: is different than what I thought.
Speaker 4: I would say the moments that caught our attention certainly
Speaker 4: casper Off winning the chess tournament Nobody or Deep Blue
Speaker 4: beating casper Off. I should say, nobody really thought that
Speaker 4: was possible before that, and then it was Watson winning Jeopardy.
Speaker 4: These were moments that said, maybe there's more here than
Speaker 4: we even thought was possible. And so I do think
Speaker 4: there's points in time where we realized maybe way more could.
Speaker 5: Be done than we had even imagined.
Speaker 4: But I do think it's consistent progress every month and
Speaker 4: every year versus some seminal moment. Now, certainly large language
Speaker 4: models as of recent have caught everybody's attention because it
Speaker 4: has a direct consumer application, but I would almost think
Speaker 4: of that as what Netscape was for the for the
Speaker 4: web browser. Yeah, it brought the Internet to everybody, but
Speaker 4: that didn't become the Internet per se.
Speaker 5: Yeah.
Speaker 3: I have a cousin who worked for IBM for forty
Speaker 3: one years. I saw him this weekend. He's in Toronto.
Speaker 3: By the way, I said, do you work for Rob Thomas?
Speaker 3: He went like this, he goes, He said, I'm five
Speaker 3: layers down. But so I always whenever I see my
Speaker 3: cousin I ask him, can you tell me again what
Speaker 3: you do? Because it's always changing, right, I guess this
Speaker 3: is a function of working at IBM. So eventually he
Speaker 3: just gives up and says, you know, we're just solving problems.
Speaker 3: So what we're doing, which I sort of as a
Speaker 3: kind of frame, And I was curious, what's the coolest
Speaker 3: problem you ever worked on? Not biggest, not most important,
Speaker 3: but the coolest, the one that's like that sort of
Speaker 3: makes you smile when you think back on it.
Speaker 4: Probably when I was in microelectronics, because it was a
Speaker 4: world I had no exposure to. I hadn't studied computer science,
Speaker 4: and we were building a lot of high performance semiconductor technology,
Speaker 4: so just chips that do a really great job of
Speaker 4: processing something or other. And we figured out that there
Speaker 4: was a market in consumer gaming that was starting to happen,
Speaker 4: and we got to the point where we became the
Speaker 4: chip inside the Nintendo, We the Microsoft Xbox Sony PlayStation,
Speaker 4: so we basically had the entire gaming market running on
Speaker 4: ib AND chips.
Speaker 3: And to use every parent basically is pointing at you
Speaker 3: and saying you're the call.
Speaker 5: Probably well they would have found it from anybody.
Speaker 4: But it was the first time I could explain my
Speaker 4: job to my kids, who were quite young at that time,
Speaker 4: like what I did, Like it was more tangible for
Speaker 4: them than saying we solve problems or douce you know,
Speaker 4: build solutions like it became very tangible for them, and
Speaker 4: I think that's, you know, a rewarding part of the
Speaker 4: job is when you can help your family actually understand
Speaker 4: what you do. Most people can't do that. It's probably
Speaker 4: easier for you. They can they can see the books,
Speaker 4: but for for some of us in the business, the
Speaker 4: business world, it's not always as obvious. So that was
Speaker 4: like one example where the dots really connected.
Speaker 3: There were a couple's a couple of stuck about a
Speaker 3: little bit of this into context of of AI love
Speaker 3: because I love the frame of problem solving as a
Speaker 3: way of understanding what the function of the technology is.
Speaker 3: So I know that you guys did something, did some
Speaker 3: work with I never know how to announced it is
Speaker 3: it Sevia, Sevia, Sevia with the football club Sevia in Spain.
Speaker 3: Tell me about Tell me a little bit about that.
Speaker 3: What problem were they trying to solve and why did
Speaker 3: they call you?
Speaker 4: In every sports franchise is trying to get an advantage, right,
Speaker 4: Let's just be that clear. Everybody's how can I use data, analytics, insights,
Speaker 4: anything that will make us one percent better on the
Speaker 4: field at some point in the future. And Sevie reached
Speaker 4: out to us because they had.
Speaker 5: Seen some of that.
Speaker 4: We've done some work with the Toronto Raptors in the
Speaker 4: past and others, and their thought was maybe there's something
Speaker 4: we could do. They'd heard all about generative AI, that
Speaker 4: heard about large language models.
Speaker 5: And the problem, back to your point on.
Speaker 4: Solving problems, was we want to do a way better
Speaker 4: job of assessing talent, because really the lifeblood of a
Speaker 4: sports franchise is can you continue to cultivate talent? Can
Speaker 4: you find talent that others don't find? Can you see
Speaker 4: something in somebody that they don't see in themselves or
Speaker 4: maybe no other.
Speaker 5: Team season them.
Speaker 4: And we ended up building some of them called Scout Advisor,
Speaker 4: which is built on Watson X, which basically just ingests
Speaker 4: tons and tons of data, and we like to think
Speaker 4: of it as finding, you know, the needle in the
Speaker 4: haystack of you know, here's three players that aren't being considered.
Speaker 4: They're not on the top teams today, and I think
Speaker 4: working with them together, we found some pretty good insights
Speaker 4: that's helped them out how What was intriguing to.
Speaker 3: Me was we're not just talking about quantitative data. We're
Speaker 3: also talking about qualitative data. But that's the puzzle part
Speaker 3: of the thing that fastens me. How does what incorporate
Speaker 3: qualitative analysis into that sort of so you just feeding
Speaker 3: in scouting reports and things like that.
Speaker 4: I got to realize think about how much I can
Speaker 4: actually disclose it. But if you think about so, quantitative
Speaker 4: is relatively easy.
Speaker 5: Yeah, every team collects that.
Speaker 4: You know, what's the forty yard dashable think they use
Speaker 4: that term, certainly not in Spain. That's all quantitative. Qualitative
Speaker 4: is what's happening off the field. It could be diet,
Speaker 4: it could be habits, it could be behavior. You can
Speaker 4: imagine a range of things that would all feed into
Speaker 4: an athlete's performance and so relationships.
Speaker 5: There's many different aspects, and.
Speaker 4: So it's trying to figure out the right blend of
Speaker 4: quantitative and qualitative that gives you a unique insight.
Speaker 3: How transparent is that kind of system? I mean, is
Speaker 3: it telling you it's saying pick this guy, not this guy,
Speaker 3: But is it telling you why it prefers this guy
Speaker 3: to this guy.
Speaker 4: Is that I think for anything the realm of AI,
Speaker 4: you have to answer the why question. Otherwise you've fallen
Speaker 4: into the trap of the you know, the proverbial black box.
Speaker 4: And then wait, I made this decision, I'd never understood
Speaker 4: why it didn't work out, So you always have to
Speaker 4: answer why without a doubt?
Speaker 3: And how is why answered?
Speaker 4: Sources of data, the reasoning that went into it, and
Speaker 4: so it's basically just tracing back the chain of how
Speaker 4: you got to the answer. And in the case of
Speaker 4: what we do in Watson X is we have IBM models.
Speaker 4: We also use some other open source models, So it
Speaker 4: would be which model was used, what was the data
Speaker 4: set that was fed into that model, How is it
Speaker 4: making decisions?
Speaker 5: How is it performing? Is it robust?
Speaker 4: Meaning is it reliable in terms of if you feed
Speaker 4: it two of the same data set, do you get
Speaker 4: the same answer? These are all the you know, the
Speaker 4: technical aspects of understanding the why.
Speaker 3: How quickly do you expect all professional sports franchises to
Speaker 3: adopt some kind of are they already there? If I
Speaker 3: went out and pulled the general managers of the one
Speaker 3: hundred most valuable sports franchises in the world. How many
Speaker 3: of them would be using some kind of AI system
Speaker 3: to assist in their efforts.
Speaker 4: One hundred and twenty percent would meaning that everybody's doing it,
Speaker 4: and some think they're doing way more than they probably
Speaker 4: actually are. So everybody's doing it. I think what's weird
Speaker 4: about sports is everybody's so convinced that what they're doing
Speaker 4: is unique that they generally speaking, don't want to work
Speaker 4: with a third party to do it because they're afraid
Speaker 4: that that would expose them. But in reality, I think
Speaker 4: most are doing eighty to ninety percent of the same things.
Speaker 4: So but without a doubt, everybody's doing it.
Speaker 3: Yeah. Yeah. The other I say that I loved was
Speaker 3: there was one but a shipping line tricon on the
Speaker 3: Mississippi River. Tell me a little bit about that project.
Speaker 3: What problem were they trying to solve?
Speaker 4: Think about the problem that I would say everybody noticed
Speaker 4: if you go back to twenty twenty was things are
Speaker 4: getting hold held up in ports. It was actually an
Speaker 4: article in the paper this morning kind of tracing the
Speaker 4: history of what happened twenty twenty twenty one and why
Speaker 4: ships were basically sitting at seas for months at a
Speaker 4: time and at that stage, we just we had a
Speaker 4: massive throughput issue. But moving even beyond the pandemic, you
Speaker 4: can see it now with ships getting through like Panama Canal,
Speaker 4: there's like a narrow window where you can get through,
Speaker 4: and if you don't have your paperwork done, you don't
Speaker 4: have the right approvals, you're not going through and it
Speaker 4: may cost you a day or two and that's a
Speaker 4: lot of money. In the shipping industry and the tricon example,
Speaker 4: it's really just about when you're pulling into a port,
Speaker 4: if you have the right paperwork done, you can get
Speaker 4: goods off the ship very quickly. Ship a lot of food,
Speaker 4: which by definition, since it's not packaged food, it's fresh food.
Speaker 4: There is an expiration period and so if it takes
Speaker 4: them an extra two hours, certainly multiple hours or a day,
Speaker 4: they have a massive problem because then you're going to
Speaker 4: deal with spoilage and so it's going to set you back.
Speaker 4: And what we've worked with them on is using an
Speaker 4: assistant that we've built in Watson X called Orchestrate, which
Speaker 4: basically is just AI doing digital labor, so we can
Speaker 4: replicate nearly any repetitive task and do that with software.
Speaker 5: Instead of humans.
Speaker 4: So as you may imagine shipping industry still has a
Speaker 4: lot of paperwork that goes on and so being able
Speaker 4: to take forms that normally would be multiple hours of
Speaker 4: filling it out. Oh this isn't right, send it back.
Speaker 4: We've basically built that as a digital skill inside of
Speaker 4: WATSONEX orchestrate, and so now it's done in minutes.
Speaker 3: They did they realize that they could have that kind
Speaker 3: of efficiency by teaming up with you? Or is that
Speaker 3: something you came to them and said, guys, we can
Speaker 3: do this way better than you think. What's the.
Speaker 4: I'd say it's always, it's always both sides coming together
Speaker 4: at a moment that for some reason makes sense because
Speaker 4: you could say, why didn't this happen like five years ago,
Speaker 4: like this seems so obvious. Well, technology wasn't quite ready then,
Speaker 4: I would say, But they knew they had a need
Speaker 4: because I forget what the precise number is, but you know,
Speaker 4: reduction of spoilage has massive impact on their bottom line,
Speaker 4: and so they knew they had a need.
Speaker 5: We thought we could solve it and the two together.
Speaker 3: Who did you guys go to them?
Speaker 5: Though?
Speaker 3: Did they come to you?
Speaker 4: I recall that this one was an inbound meaning they
Speaker 4: had reached out to IBM and we'd like to solve
Speaker 4: this problem. I think it went into one of our
Speaker 4: digital centers if I recall it a literary phone.
Speaker 3: Call, but the other the reverse is more interesting to
Speaker 3: me because there seems to be a very, very large
Speaker 3: universe of people who have problems that could be solved
Speaker 3: this way and they don't realize it. What's your Is
Speaker 3: there a shining example of this of someone you just
Speaker 3: can't you just think could benefit so much and isn't
Speaker 3: benefiting right now?
Speaker 5: Maybe I'll answer it slightly differently.
Speaker 4: I'm I'm surprised by how many people can benefit that
Speaker 4: you wouldn't even logically think of. First, let me give
Speaker 4: you an example. There's a franchiser of hair salons, sport
Speaker 4: Clips is the name. My sons used to go there
Speaker 4: for haircuts because they have like TVs and you can
Speaker 4: watch sports, so they loved that they got entertained while
Speaker 4: they would get their haircut. I think the last place
Speaker 4: that you would think is using AI today would be
Speaker 4: a franchiser of hair salons.
Speaker 5: But just follow it through.
Speaker 4: The biggest part of how they run their business is
Speaker 4: can I get people to cut hair? And this is
Speaker 4: the high turnover industry because there's a lot of different
Speaker 4: places you can work if you want to cut hair.
Speaker 4: People actually get injured cutting hair because you're on your
Speaker 4: feet all day, that type of thing. And they're using
Speaker 4: same technology orchestrate as part of their recruiting process. How
Speaker 4: can they automate a lot of people submitting resumes, who
Speaker 4: they speak to, how they qualify them for the position.
Speaker 4: And so the reason I give that example is the
Speaker 4: opportunity for AI, which is unlike other technologies, is truly unlimited.
Speaker 4: It will touch every single business. It's not the realm
Speaker 4: of the fortune five hundred or the fortune one thousand.
Speaker 5: This is the.
Speaker 4: Fortune any size. And I think that may be one
Speaker 4: thing that people underestimate about.
Speaker 3: Yeah, what about I mean I was thinking about education
Speaker 3: as a kind of I mean, education is a perennial
Speaker 3: whipping boy for you guys that are living in the
Speaker 3: nineteenth century, right. I'm just curious about if a superintendent
Speaker 3: of a public school system or the president of the
Speaker 3: university sat down and had lunch with you and said,
Speaker 3: do the university first. My cost are out of control,
Speaker 3: my enrollment is down, my students hate me, and my
Speaker 3: board is revolting help. How would you think about helping
Speaker 3: someone in that situation.
Speaker 5: I spend some time with universities.
Speaker 4: I like to go back and visit Alma Maters, where
Speaker 4: I went to school, and so.
Speaker 5: I do that every year.
Speaker 4: The challenge I have hall of Seeming university is there
Speaker 4: has to be a will. Yeah, and I'm not sure
Speaker 4: the incentives are quite right today because bringing in new technology,
Speaker 4: let's say we want to go after we can help
Speaker 4: you figure out student recruiting or how you automate more
Speaker 4: of your education, everybody suddenly feels threatened that university.
Speaker 5: Hold on, that's my job.
Speaker 4: I'm the one that decides that, or I'm the one
Speaker 4: that wants to dictate the course. So there has to
Speaker 4: be a will. So I think it's very possible, and
Speaker 4: I do think over the next decade you will see
Speaker 4: some universities that jump all over this and they will
Speaker 4: move ahead, and you see others that do not.
Speaker 5: Because it's very possible.
Speaker 3: Where how does when you say there has to be
Speaker 3: a will? Is that the kind of is that a
Speaker 3: kind of thing that that people that I beb to
Speaker 3: think about? Like when in this conversation you hypothetical conversation,
Speaker 3: you might have with the university president, would you give
Speaker 3: advice on on where the will comes from.
Speaker 4: I don't do that as much in a university context.
Speaker 4: I do that every day in a business context, because
Speaker 4: if you can find the right person in a business
Speaker 4: that wants to focus on growth or the bottom line
Speaker 4: or how do you create more productivity. Yes, it's going
Speaker 4: to create a lot of organizational resistance potentially, but you
Speaker 4: can find somebody that will figure out how to push
Speaker 4: that through. I think for universities, I think that's also possible.
Speaker 4: I'm not sure there's there's there's a return on investment
Speaker 4: for us to do that.
Speaker 3: Yeah, yeah, yeah, God, let's let's find some terms. AI years.
Speaker 3: I told you'd like to use. What does that mean?
Speaker 4: We just started using this term literally in the last
Speaker 4: three months, and.
Speaker 5: It was it was what we observed.
Speaker 4: Internally, which is most technology you build, you say, all right,
Speaker 4: what's going to happen in year one, year two, year three,
Speaker 4: and it's, you know, largely by a calendar.
Speaker 5: AI years are the idea that what.
Speaker 4: Used to be a year is now like a week,
Speaker 4: and that is how fast the technology is moving. Do
Speaker 4: you give you an example. We had one client we're
Speaker 4: working with. They're using one of our granite models, and
Speaker 4: the results they were getting we're not very good. Accuracy
Speaker 4: was not there, their performance was not there. So I
Speaker 4: was like scratching my head. I was like, what is
Speaker 4: going on? They were Financial services, the bank, So I'm
Speaker 4: scratching my head, like what is going on? Everybody else
Speaker 4: is getting this and like these results are horrible. And
Speaker 4: I said to the team, which version of the model
Speaker 4: are you using? This was in February, Like, we're using
Speaker 4: the one from October. I was like, all right, now
Speaker 4: we know precisely the problem because the model from October
Speaker 4: is effectively useless now since we're here in February.
Speaker 3: Serious, actually useless, completely useless.
Speaker 4: Yeah, that is how fast this has changed. And so
Speaker 4: the minute, same use case, same data, you give them
Speaker 4: the model from late January instead of October, the results
Speaker 4: are off the charts.
Speaker 3: Yeah. Wait, so what exactly happened between October and January?
Speaker 5: The model got way better?
Speaker 3: Could dig into that, Like, what do you mean by
Speaker 3: the way.
Speaker 5: We are constant?
Speaker 4: We have built large compute infrastructure where we're doing model training,
Speaker 4: and to be clear, model training is the realm of
Speaker 4: probably in the world. My guess is five to ten companies.
Speaker 5: And so.
Speaker 4: You build a model, you're constantly training it, You're doing
Speaker 4: fine tuning, you're doing more training, you're adding data every day,
Speaker 4: every hour it gets better. And so how does it
Speaker 4: do that. You're feeding it more data, you're feeding it
Speaker 4: more live examples. We're using things like synthetic data at
Speaker 4: this point, which is we're basically creating data to do
Speaker 4: the training as well. All of this feeds into how
Speaker 4: useful the model is.
Speaker 5: And so using the.
Speaker 4: October model, those were the results in October, just a fact,
Speaker 4: that's how good it was then. But back to the
Speaker 4: concept of AI years, two weeks is a long time.
Speaker 3: Does that are we in a steep part of the
Speaker 3: model learning carve or do you expect this to continue
Speaker 3: along this at this pace?
Speaker 5: I think that is the big question and don't have
Speaker 5: an answer yet.
Speaker 4: By definition, at some point you would think it would
Speaker 4: have to slow down a bit, but it's not obvious
Speaker 4: that that is on the horizon.
Speaker 3: Still speeding up. Yes, how fast can it get?
Speaker 4: We've debated can you actually have better results in the
Speaker 4: afternoon than you did in the morning. Really it's nuts, Yeah,
Speaker 4: I know, but that's why we came up with this
Speaker 4: term because I think you also have to think of
Speaker 4: like concepts that.
Speaker 5: Gets people's attention.
Speaker 3: So you basically earning into a bakery, you're like the
Speaker 3: bread from yesterday. You know you can have it for
Speaker 3: twenty five cents. But I mean you do proferential pricing.
Speaker 3: You could say we'll judge you X for yesterday's model,
Speaker 3: two X for today's model.
Speaker 4: I think that's dangerous as a merchandising strategy, but I
Speaker 4: guess your point.
Speaker 3: Yeah, but that's crazy. And this, by the way, so
Speaker 3: this model is the same true for almost You're talking
Speaker 3: specifically about a model that was created to help some
Speaker 3: aspect of a financial services So is that kind of
Speaker 3: model accelerating faster and running faster than other models for
Speaker 3: other kinds of problems?
Speaker 4: So this domain was code. Yeah, so by definition, if
Speaker 4: you're feel feeding in more data some more code, you
Speaker 4: get those kind of results.
Speaker 5: It does depend on the model type.
Speaker 4: Yeah, there's a lot of code in the world, and
Speaker 4: so we can find that we can create it. Like
Speaker 4: I said, there's other aspect x where there's probably less
Speaker 4: inputs available, which means you probably won't get the same
Speaker 4: level of iteration. But for code that's certainly the cycle
Speaker 4: times that we're seeing.
Speaker 3: Yeah, and how do you know that Let's stick with
Speaker 3: this one example of this model you have. How do
Speaker 3: you know that your model is better than big company
Speaker 3: B down the street? The client asks you, why would
Speaker 3: I go with IBM as opposed to some the s
Speaker 3: firm in the valley that says, as they have a
Speaker 3: model on this, what's your how do you frame your advantage?
Speaker 4: Well, we benchmark all of this, and I think the
Speaker 4: most important is metric is price performance, not price, not performance,
Speaker 4: but the combination of the two. And we're super competitive there. Well,
Speaker 4: for what we just released, with what we've done in
Speaker 4: open source, we know that nobody's close to us right
Speaker 4: now on code now. To be clear, that will probably change, yeah,
Speaker 4: because it's like leapfrog. People will jump ahead, then we
Speaker 4: jump back ahead. But we're very confident that with everything
Speaker 4: we've done in the last few months, we've taken a
Speaker 4: huge leap forward here.
Speaker 3: Yeah, it's I mean, this goes back to the point
Speaker 3: I was making in the beginning, so about the difference
Speaker 3: between your twenty something self in ninety nine and yourself today.
Speaker 3: But this time compression has to be a crazy adjustment.
Speaker 3: So the concept of what you're working on and how
Speaker 3: you make decisions internally and things has to undergo this
Speaker 3: kind of revolution if you're switching from I mean back
Speaker 3: in the day, a model might be useful for how
Speaker 3: long years.
Speaker 4: Years I think about you know, statistical models that set
Speaker 4: inside things like SPSS, which is a product that a
Speaker 4: lot of students.
Speaker 5: Use around the world.
Speaker 4: I mean, those have been the same models for twenty
Speaker 4: years and they're still very good at what they do.
Speaker 4: And so yes, it's a completely it's a completely different
Speaker 4: moment for how fast this is moving. And I think
Speaker 4: it just raises the bar for everybody, whether you're a
Speaker 4: technology provider like us, or you're a bank or an
Speaker 4: insurance company or a shipping company, to say, how do
Speaker 4: you really change your culture to be way more aggressive
Speaker 4: than you normally would be.
Speaker 3: Does this means it's a weird question, but does this
Speaker 3: mean a different set of kind of personality or character
Speaker 3: traits are necessary for a decision maker in tech now
Speaker 3: than twenty five years ago.
Speaker 4: There's a book I saw recently, it's called The Geek Way,
Speaker 4: which talked about how technology companies have started to operate
Speaker 4: in different ways maybe than many you know, traditional companies,
Speaker 4: and more about being data driven, more about delegation. Are
Speaker 4: you willing to have the smartest person in the room
Speaker 4: make decisions opposed to the high paid.
Speaker 5: Person in the room.
Speaker 4: I think these are all different aspects that every company
Speaker 4: is going to face.
Speaker 3: Yeah, yeah, next term, talk about open. When you use
Speaker 3: that word open, what do you mean.
Speaker 4: I think there's really only one definition of open, which
Speaker 4: is for technology, is open source? An open source means
Speaker 4: the code is freely available. Anybody can see it, access it,
Speaker 4: contribute to it.
Speaker 3: And what is Tell me about why that's an important principle.
Speaker 4: When you take a topic like AI, I think it
Speaker 4: would be really bad for the world if this was
Speaker 4: in the hands of one or two companies, or three
Speaker 4: or four, doesn't matter the number some small number. Think
Speaker 4: about like in history sometimes early nineteen hundreds, the Interstate
Speaker 4: Commerce Commission was created, and the whole idea was to
Speaker 4: protect farmers from railroads. Meaning they wanted to allow free trade,
Speaker 4: but they knew that, well, there's only so many railroad tracks,
Speaker 4: so we need to protect farmers from the shipping costs
Speaker 4: that railroads could impose. So good idea, but over time
Speaker 4: that got completely overtaken by the railroad lobby, and then
Speaker 4: they use that to basically just increase prices, and it
Speaker 4: made the lives of farmers way more difficult. I think
Speaker 4: you could play the same analogy through with AI. If
Speaker 4: you allow a handful of companies to have the technology,
Speaker 4: you regulate around the principles of with those one or
Speaker 4: two companies, then.
Speaker 5: You've trapped the entire world. Think that would be very bad.
Speaker 5: So the danger of that happening for sure.
Speaker 4: I mean there's companies in Watson in Washington every week
Speaker 4: trying to achieve that outcome. And so the opposite of
Speaker 4: that is to say it's going to be an open
Speaker 4: source because nobody can dispute opens because it's right there,
Speaker 4: everybody can see it. And so I'm a strong believer
Speaker 4: that open source will win for AI.
Speaker 5: It has to win.
Speaker 4: It's not just important for business, but it's important for humans.
Speaker 3: On the I'm curious about on the list of things
Speaker 3: you worry about, actually, let me before I ask, let
Speaker 3: me ask this question very generally. What is the list
Speaker 3: of things you worry about? What's your top five business
Speaker 3: related worries right now?
Speaker 5: Tops from those are the first question. We could be
Speaker 5: here for hours for me to answer.
Speaker 3: I did say business related. We could leave you know,
Speaker 3: your kids haircuts got it out of.
Speaker 4: The Number one is always it's the thing that's probably
Speaker 4: always been true, which is just people. Do we have
Speaker 4: the right skills? Are we doing a good job of
Speaker 4: training our people? Are our people doing a good job
Speaker 4: of working with clients? Like that's number one? Number two
Speaker 4: is innovation? Are we pushing the envelope enough? Are we
Speaker 4: staying ahead? Number three is which kind of feeds into
Speaker 4: the innovation one is risk taking?
Speaker 5: Are we taking enough risk? Without risk, there is no growth?
Speaker 4: And I think the trap that every larger company inevitably
Speaker 4: falls into is conservatism. Things are good enough, and so
Speaker 4: it's are we pushing the envelope? Are we taking enough
Speaker 4: risk to really have an impact? I'd say those are
Speaker 4: probably the top three that I spend.
Speaker 3: Last turn to define productivity paradox something. I know you've
Speaker 3: thought a lot about what does that mean?
Speaker 4: So I started thinking hard about this because all I
Speaker 4: saw and read every day was fear about AI, and
Speaker 4: I studied economics, and so I kind of went back
Speaker 4: to like basic economics, and there's been like a macro
Speaker 4: invest formula. I guess I would say it's been around
Speaker 4: forever that says growth comes from productivity growth plus population
Speaker 4: growth plus debt growth. So if those three things are working, you'll.
Speaker 5: Get GDP growth.
Speaker 4: And so then you think about that and you say, well,
Speaker 4: debt growth, we're probably not going back to zero percent
Speaker 4: interest rates, so to some extent there's going to be
Speaker 4: a ceiling on that.
Speaker 5: And then you.
Speaker 4: Look at population growth. There are shockingly few countries or
Speaker 4: places in the world that will see population growth over
Speaker 4: the next thirty to fifty years. In fact, most places
Speaker 4: are not even at replacement rates. And so I'm like,
Speaker 4: all right, so population growth is not going to be there.
Speaker 4: So that would mean if you just take it to
Speaker 4: the extreme, the only chance of continued GDP growth is productivity.
Speaker 4: And the best way to solve productivity is AI That's
Speaker 4: why I say it's a paradox. On one hand, everybody's
Speaker 4: scared after death it's going to take over the world,
Speaker 4: take all of our jobs, ruin us. But in reality
Speaker 4: maybe it's the other way, which is it's the only
Speaker 4: thing that can save us.
Speaker 5: Yeah, and if you believe.
Speaker 4: That economic equation, which I think has proven quite true
Speaker 4: over hundreds of years, I do think it's probably the
Speaker 4: only thing that can save us.
Speaker 3: Actually looked at the numbers yesterday for totally random reason
Speaker 3: on population growth in Europe and receive this is a
Speaker 3: special bonus question. We'll see how smart you are. Which
Speaker 3: country in Europe? Condeently, Europe has the highest population growth?
Speaker 4: It's small continental Europe, probably one of the Nordics, I would.
Speaker 3: Guess, close. Luxembourg. Okay, something that's going on in Luxembourg.
Speaker 3: I feel like, well, all of us need to investigate.
Speaker 3: They're at one point four nine, which in the day,
Speaker 3: by the way, would be a relatively that's the best
Speaker 3: performing country. I mean in the day, you'd countries had
Speaker 3: routinely had two points something, you know, percent growth in
Speaker 3: a given year. Last question, you're writing a book. Now
Speaker 3: we were talking chatting about it backstage, and now I
Speaker 3: appreciate the paradox of this book, which is in a
Speaker 3: universe with a model, is better in the afternoon than
Speaker 3: it is in the morning. How do you write a
Speaker 3: book that's like printed on paper? I expected to reuseful.
Speaker 4: This is the challenge. And I am an incredible author
Speaker 4: of useless books. I mean, most of what I've spent
Speaker 4: time on in the last decade of stuff that's completely useless,
Speaker 4: like a year after it's written. And so when we
Speaker 4: were talking about it, I was like, I would like
Speaker 4: to do something around AI that's timeless.
Speaker 5: Yeah, that would be useful ten or twenty years.
Speaker 4: No, But then to your point, so, how is that
Speaker 4: even remotely possible if the model is better in the
Speaker 4: afternoon and in the morning. So that's the challenge in
Speaker 4: front of us. But the book is around AI value creation,
Speaker 4: so kind of links to this productivity paradox, and how
Speaker 4: do you actually get sustained value out of AI, out
Speaker 4: of automation, out of data science. And so the biggest
Speaker 4: challenge in front of us is can we make this
Speaker 4: relevant that's the day that it's published.
Speaker 3: How are you setting out to do that?
Speaker 4: I think you have to to some extent level it
Speaker 4: up to bigger concepts, which is kind of why I
Speaker 4: go to things like macroeconomics, population geography as opposed to
Speaker 4: going into the weeds of the technology itself. If you
Speaker 4: write about this is how you get better performance out
Speaker 4: of a model, we can agree that will be completely
Speaker 4: useful two years from now, maybe even two months from now,
Speaker 4: and so it will be less in the technical detail
Speaker 4: and more of what is sustained value creation for AI,
Speaker 4: which if you think on what is hopefully a ten
Speaker 4: or twenty year period, it's probably we're kind of substituting
Speaker 4: AI for technology. Now I've realized, because I think this
Speaker 4: has always been true for technology. It's just now AI
Speaker 4: is the thing that everybody wants to talk about. But
Speaker 4: let's see if we can do it. Time will tell.
Speaker 3: Did you get any inkling that the pace that this
Speaker 3: AI year's phenomenon was gonna that things with the pace
Speaker 3: of change was going to accelerate so much because you
Speaker 3: had More's law, right, you had a model in the
Speaker 3: technology world for this kind of exponential increase in so
Speaker 3: were you were you thinking about that kind of accelerate
Speaker 3: similar kind of acceleration in.
Speaker 4: The I think anybody had said they expect did what
Speaker 4: we're seeing today is probably exaggerating. I think it's way
Speaker 4: faster than anybody expected. Yeah, but technologies, back to your
Speaker 4: point at More's Law has always accelerated through the years,
Speaker 4: So I.
Speaker 5: Wouldn't say it's a shock, but it is surprising.
Speaker 3: Yeah, You've had a kind of extraordinary privileged position to
Speaker 3: watch and participate in this revolution, right, I mean, how
Speaker 3: many other people have been in that have ridden this wave.
Speaker 5: Like you have.
Speaker 4: I do wonder is this really that much different or
Speaker 4: does it feel different just because we're here?
Speaker 5: I mean, I do think on one level. Yes.
Speaker 4: So in the time I've been an IBM, internet happened,
Speaker 4: Mobile happened, social network happened, blockchain happened.
Speaker 5: AI. So a lot has happened.
Speaker 4: But then you go back and say, well, but if
Speaker 4: I'd been here between nineteen seventy and ninety five, there
Speaker 4: were a lot of things that are pretty fundamental. Then too, say,
Speaker 4: I wondered, almost do we do we always exaggerate the
Speaker 4: timeframe that we're in.
Speaker 5: I don't know.
Speaker 4: Yeah, but it's a good idea though.
Speaker 3: I think the ending with the phrase I don't know
Speaker 3: it's a good idea though. That's the great way to
Speaker 3: wrap this up. Thank you so much, Thank you, Malcolm.
Speaker 3: In a field that is evolving as quickly as artificial intelligence,
Speaker 3: it was inspiring to see how adaptable Rob has been
Speaker 3: over his career. The takeaways from my conversation with Rob
Speaker 3: had been echoing in my head ever since. He emphasized
Speaker 3: how open source models allow AI technology to be developed
Speaker 3: by many players. Openness also allows for transparency. Rob told
Speaker 3: me about AI use cases like IBM's collaborate with Sevilla's
Speaker 3: football club. That example really brought home for me how
Speaker 3: AI technology will touch every industry. Despite the potential benefits
Speaker 3: of AI, challenges exist in its widespread adoption. Rob discussed
Speaker 3: how resistance to change, concerns about job security and organizational
Speaker 3: inertia can slow down implementation of AI solutions. The paradox, though,
Speaker 3: according to Rob, is that rather than being afraid of
Speaker 3: a world with AI, people should actually be more afraid
Speaker 3: of a world without it. AI, he believes, has the
Speaker 3: potential to make the world a better place in a
Speaker 3: way that no other technology can. Rob painted an optimistic
Speaker 3: version of the future, one in which AI technology will
Speaker 3: continue to improve at an exponential rate. This will free
Speaker 3: up workers to dedicate their energy to more creative tasks.
Speaker 3: I for one am on board Smart Talks with IBM
Speaker 3: is produced by Matt Romano, Joey Fishground and Jacob Goldstein.
Speaker 3: We're edited by Lydia gene Kott. Our engineers are Sarah
Speaker 3: Bruguer and Ben Tolliday. Theme song by Gramscow. Special thanks
Speaker 3: to the eight Bar and IBM teams, as well as
Speaker 3: the Pushkin marketing team. Smart Talks with IBM is a
Speaker 3: production of Pushkin Industries and Ruby Studio at iHeartMedia. To
Speaker 3: find more Pushkin podcasts, listen on the iHeartRadio app, Apple Podcasts,
Speaker 3: or wherever you listen to podcasts. I'm Malcolm Gladwell. This
Speaker 3: is a paid advertisement from IBM. The conversations on this
Speaker 3: podcast don't necessarily represent IBM's positions, strategies, or opinions.