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