Smart Talks with IBM: AI & the Productivity Paradox

Welcome to Tech Stuff, a production from iHeartRadio. Today, we are witnessed to one of those rare moments in history, the rise of an innovative technology with the potential to radically transform business in society forever. That technology, of course, is artificial intelligence, and it's the central focus for this

Pushkin.

Welcome, Welcome, Welcome to Smart Talks with IBM.

Hello, Hello, Welcome to Smart Talks with IBM, a podcast from Pushkin Industries, iHeartRadio and IBM. I'm Malcolm Gladwell. This season, we're diving back into the world of artificial intelligence, but with a focus on the powerful concept of open its possibilities, implications, and misconceptions. We'll look at openness from a variety of angles and explore how the concept is already reshaping industries, ways of doing business, and our very notion of what's possible.

I recently said.

That down with Rob Thomas. Rob is the senior vice president of Software and chief Commercial Officer of IBM. We spoke to him in front of a live audience as part of New York Tech Week. We discussed how businesses can harness the immense productivity benefits of AI while implementing it in a responsible and ethical manner. We also broke down a fascinating concept that Rob believes about AI, known as the productivity paradox. Okay, let's get to the conversation.

How are we doing good, Rob?

This is our second time. We did one of these in the middle of the pandemic. But now it's all such a blur now that us can figure out when it was.

I know, it's hard to those are like a blurry years. You don't know what happened, right, But.

Well, it's good to see you, to meet you again. I wanted to start by going back. You've been an IBM.

Two years right, twenty five in July, believe it or not.

So you were you were a kid when you joined.

I was four.

So I want to contrast present day Rob and twenty five years ago Rob. When you arrive at IBM, what do you think your job is going to be?

It, your career is going?

Where do you think the kind of problems you're going to be addressing are?

Well, it's kind of surreal because I joined IBM a consulting and I'm coming out of school, and you quickly realize, wait, the job of a consultant is to tell other companies what to do. And I was like, I literally know nothing, and so you're immediately trying to figure out, so how am I going to be relevant given that I know absolutely nothing to advise other companies on what they should be doing. And I remember it well, like we were sitting in a room. When you're a consultant, you're waiting

You know, we're here to talk about AI, and I'm curious about the evolution of your understanding or IBM's understanding of my AI. At what point in the last twenty five years do you begin to think, oh, this is really going to be at the core of what we think about and work on at this company.

The computer scientist John McCarthy, he was he's the person that's credited with coining the phrase artificial intelligence. It's like in the fifties, and he made an interesting comedy said he said, once it works, it's no longer called AI, and that then became it's called like the AI effect, which is it seems very difficult, very mysterious, but once it becomes commonplace, it's just no longer what it is. And so if you put that frame on it, I

So from your perspective, it feels a lot more like an evolution than a revolution.

Is that a fair statement?

Yes, yeah, which I think most great things in technology tend to happen that way. Yeah, many of the revolutions, if you will, tend to fizzle out.

But even given that is there, I guess what I'm asking is, I'm curious about whether there was a moment in that evolution when you had to readjust your expectations about what AI was going to be capable of.

I mean, was there, you know, was.

There a particular innovation or a particular problem that was solved that made you think, oh, this is different than what I thought.

I would say the moments that caught our attention certainly casper Off winning the chess tournament, nobody or Deep Blue beating casper Off. I should say nobody really thought that was possible before that, and then it was Watson winning Jeopardy. These were moments that said, maybe there's more here than we even thought was possible. And so I do think there's there's points in time where we realized maybe way

I have a cousin who worked for I'd be up for forty one years. I saw him this weekend. He's in Toronto. By the way, I said, you work for Rob Thomas. He would like this, he goes.

He said, I'm five layers down.

But so I always whenever I see my cousin, I ask him, can you tell me again what you do?

Because always changing? Right, I guess this is a function of working at IBM.

So eventually he just gives up and says, you know, we're just solving problems, so what we're doing, which I sort of loved as a kind of frame, And I was curious, what's the coolest problem you ever worked on? Not biggest, not most important, but the coolest, the one that's like that sort of makes you smile when you think back on it.

Probably when I was in microelectronics, because it was a world I had no exposure to. I hadn't studied computer science, and we were building a lot of high performance semiconductor technology, so just chips that do a really great job of

And to use every parent basically is pointing at you and saying you're the call.

Probably well they would have found it from anybody, but it was the first time I could explain my job to my kids, who were quite young at that time, like what I did like it was more tangible for them than saying we solve problems or douce you know, build solutions like it became very tangible for them, and I think that's, you know, a rewarding part of the job is when you can help your family actually understand what you do. Most people can't do that. It's probably

There were a couple's a couple of stuck about a little bit of this into context of of AI. I love because I love the frame of problem solving as a way of understanding what the function of the technology is. So I know that you guys did something, did some work with I never know how to pronounce it. Is it Seville Sevia, Sevia with the football club Sevia in Spain? Tell me about Tell me a little bit about that. What problem were they trying to solve and why did they call you?

In? Every sports franchise is trying to get an advantage, right, Let's just be that clear. Everybody's how can I use data? Analytics, insights, anything that will make us one percent better on the field at some point in the future. And Seville reached out to us because they had seen some of that. We've done some work with the Toronto Raptors in the past and others, and their thought was maybe there's something

How what was intriguing to.

Me was we're not just talking about quantitative data. We're also talking about qualitative data. That's the puzzle part of the thing that fastens me. How does what incorporate qualitative analysis into that sort of so you just feeding in scouting reports and things like that.

I got to realize think about how much I can actually disclose it. But if you think about so, quantitative is relatively easy. Every team collects that, you know, what's their forty yard dashable think they use that term, certainly not in Spain. That's all quantitative. Qualitative is what's happening off the field. It could be diet, it could be habits, it could be behavior. You can imagine a range of things that would all feed into an athlete's performance, and

How transparent is that kind of system telling you? It's saying pick this guy, not this guy, But is it telling you why it prefers this guy to this guy?

Was it?

I think for anything in the realm of AI, you have to answer the why question. Yeah, otherwise you've fallen into the trap of the you know, the proverbial black box, and then wait, I made this decision, I'd never understood why it didn't work out, So you always have to answer why without a.

Doubt and how is why answered?

Sources of data, the reasoning that went into it, and so it's basically just tracing back the chain of how you got to the answer. And in the case of what we do in Watson X is we have IBM models. We also use some other open source models, so it would be which model was used, what was the data set that was fed into that model, How is it making decisions? How is it performing? Is it robust? Meaning is it reliable in terms of if you feed it two of the same data set, do you get the

How quickly do you expect all professional sports franchises to adopt some kind of are they already there? If I went out and pulled the general managers of the one hundred most valuable sports franchises in the world, how many of them would be using some kind of AI system to assist in their efforts?

One hundred and twenty percent would meaning that everybody's doing it, and some think they're doing way more than they probably actually are. So everybody's doing it. I think what's weird about sports is everybody's so convinced that what they're doing is unique that they generally speaking don't want to work with a third party to do it because they're afraid that that would expose them. But in reality, I think most are doing eighty to ninety percent of the same things.

Yeah. Yeah.

The other that love was there was one but a shipping line tri gun on the Mississippi River. Tell me a little bit about that project. What problem were they trying to solve?

Think about the problem that I would say everybody noticed if you go back to twenty twenty was things are getting hold held up in ports. It was actually an article in the paper this morning kind of tracing the history of what happened twenty twenty twenty one and why ships were basically sitting at seas for months at a time. And at that stage we just we had a massive throughput issue. But moving even beyond the pandemic, you can see it now with ships getting through like Panama Canal.

They did they realize that they could have that kind of efficiency by teaming up with you? Or is that something you came to them and said, guys, we can do this way better than you think.

What's the.

I'd say it's always it's always both sides coming together at a moment that for some reason makes sense because you could say, why didn't this happen like five years ago, like this seems so obvious. Well, technology wasn't quite ready then, I would say, But they knew they had a need because I forget what the precise number is, but you know, reduction of spoilage has massive impact on their bottom line, and so they knew they had a need. We thought we could solve it and the two together.

Who did you guys go to the Now? Did they come to you?

I recall that this one was an inbound meaning they had reached out to IBM and we'd like to solve this problem. I think it went into one of our digital centers if I recall it a literary phone.

Call, but the other the reverse is more interesting to me because there seems to be a very very large universe of people who have problems that could be solved this way and they don't realize it.

What's your.

Is there a shining example of this of someone you just can't you just think could benefit so much and isn't benefiting right now?

Maybe I'll answer it slightly differently. I'm I'm surprised by how many people can benefit that you wouldn't even logically think of. First, let me give you an example. There's a franchiser of hair salons. Sport Clips is the name. My sons used to go there for haircuts because they have like and you can watch sports, so they loved that. They got entertained while they would get their haircut. I think the last place that you would think is using AI today would be a franchiser of hair salons. But

What about I mean, I was thinking about education as a kind of I mean, education is a perennial whipping boy for you guys that are living in the nineteenth century, right. I'm just curious about if a superintendent of a public school system or the president of the university sat down and had lunch with you and said, do the university first. My cost are out of control, my enrollment is down, my students hate me, and my board is revolting.

Help.

How would you how would you think about helping someone in that situation.

I spend some time with universities. I like to go back and visit Alma Maters where I went to school, and so I do that every year. The challenge I have hall is Seeming university is there has to be a will. Yeah, and I'm not sure the incentives are quite right today because bringing in new technology, let's say we want to go after we can help you figure out student recruiting or how you automate more of your education, everybody suddenly feels threatened that university. Hold on, that's my job.

Where how does when you say there has to be a will? Is that the kind of Is that a kind of thing that that people at IBM think about, Like when in this conversation you hype type a conversation you might have with the university president, would you give advice on where the will comes from?

I don't do that as much in a university context. I do that every day in a business context because if you can find the right person in a business that wants to focus on growth or the bottom line or how do you create more productivity. Yes, it's going to create a lot of organizational resistance potentially, but you can find somebody that will figure out how to push that through. I think for universities, I think that's also possible.

Yeah, yeah, yeah, let's let's find some terms.

AI years. I told you'd like to use What does that mean?

We just started using this term literally in the last three months, and it was a It was what we observed internally, which is most technology you build, you say, all right, what's going to happen in year one, year two, year three, and it's you know, largely by by a calendar. AI years are the idea that what used to be a year is now like a week, and that is how fast the technology is moving. Do you give you

Serious, actually useless, completely useless.

Yeah, that is how fast this is changing. And so the minute, same use case, same data, you give them the model from late January instead of October, the results are off the charts.

Yeah.

Wait, so what exactly happened between October and January?

The model got way better?

Could dig into that? Like, what do you mean by the way.

We are constant We have built large compute infrastructure where we're doing model training. And to be clear, model training is the realm of probably in the world my guess is five to ten companies. And so you build a model, you're constantly training it, you're doing fine tuning you're doing more training, You're adding data every day, every hour it gets better, And so how does it do that. You're feeding it more data, you're feeding it more live examples.

Does that are we in a steep part of the model learning carve or do you expect this to continue along this at this pace?

I think that is the big question and don't have an answer yet. By definition, at some point you would think it would have to slow down a bit, but it's not obvious that that is on the horizon.

Still speeding up. Yes, how fast can it get?

We've debated, can you actually have better results in the afternoon than you did in the morning. Really it's nuts, Yeah, I know, But that's that's why we came up with this term, because I think you also have to think of like concepts that gets people's attention so.

You're basically turning into a bakery. You're like the bread from yesterday. You know you can have it for twenty five cents. But I mean you do proferential pricing. You could say, we'll judge you x for yesterday's model, two x for today's model.

I think that's dangerous as a merchandising strategy, but I guess your point.

Yeah, but that's crazy.

And this, by the way, so this model is the same true for almost you're talking specifically about a model that was created to help some aspect of a financial services So is that kind of model accelerating faster and running faster than other models for other kinds of problems?

So this domain was code. Yeah, so by definition, if you're feeling feeding in more data some more code, you get those kind of results depend on the model type. There's a lot of code in the world, and so we can find that we can create it. Like I said, there's other aspects where there's probably less inputs available, which means you probably won't get the same level of iteration. But for code, that's certainly the cycle times that we're seeing.

Yea, and how do you know that. Let's stick with this one example of this model you have, how do you know that your model is better than.

Big company B down the street?

Client asks you, why would I go with IBM as opposed to some the s firm in the valley that says, as they have a model on this, what's your how do you frame your advantage?

Well, we benchmark all of this, and I think the most important is metric is price performance, Not price, not performance, but the combination of the two. And we're super competitive there. Well, for what we just released, with what we've done in open source, we know that nobody's close to us right now on code now. To be clear, that will probably change, yeah, because it's like leap frog. People will jump ahead, then

Yeah.

I mean this goes back to the point I was making in the beginning, so about the difference between your twenty something self in ninety nine and yourself today.

But this time.

Compression has to be a crazy adjustment. So the concept of what you're working on and how you make decisions internally and things has to undergo this kind of revolution. If you're switching from I mean back in the day, a model might be useful for how.

Long years years?

I think about you know, statistical models that set inside things like SPSS, which is a product that a lot of students use around the world. I mean, those have been the same models for twenty years and they're still very good at what they do. And so yes, it's a completely it's a completely different moment for how fast

Does this means it's a weird question, But does this mean a different set of kind of personality or character traits are necessary for a decision maker in tech now than twenty five years ago.

There's a there's a book I saw recently, it's called The Geek Way, which talked about how technology companies have started to operate in different ways maybe than many you know, traditional companies, and more about being dated driven, more about delegation. Are you willing to have the smartest person in the room make decisions opposed to the highest paid person in the room. I think these are all different aspects that every company is going to face. Yeah.

Yeah, next term, talk about open. When you use that word open, what do you mean.

I think there's really only one definition of open, which is for technology is open source. An open source means the code is freely available. Anybody can see it, access it, contribute to it.

And what is Tell me about why that's an important principle.

When you take a topic like AI, I think it would be really bad for the world if this was in the hands of one or two companies, or three or four, doesn't matter the number, some small number. Think about like in history, sometime early nineteen hundreds, the Interstate Commerce Commission was created, and the whole idea was to

On the I'm curious about on the list of things you worry about, actually, let me before I ask, let me ask this question very generally. What is the list of things you worry about? What's your top five business related worries right now?

Tops from those are the first question. We could be here for hours for me to answer.

I did say business related.

We could leave you know, your kid's haircuts got it out of.

The number one is always it's the thing that's probably always been true, which is just people. Do we have the rights skills? Are we doing a good job of training our people? Are our people doing a good job of working with clients? Like that's number one. Number two is innovation. Are we pushing the envelope enough? Are are we staying ahead? Number three is which kind of feeds into the innovation one is risk taking? Are we taking

Last turn to define productivity paradox something I know you've thought a lot about what does that mean?

So I started thinking hard about this because all I saw and read every day was was fear about AI. And I studied economics, and so I kind of went back to like basic economics. And there's been like a macro investing formula. I guess I would say it's been around forever that says growth comes from productivity growth plus population growth plus debt growth. So if those three things

Actually looked at the numbers yesterday for totally random reason on population growth in Europe and received. This is a special bonus question. We'll see how smart you are. Which country in Europe? Condellly Europe has the highest population growth?

It's small continental Europe, probably one of the Nordics.

I would yes, close Luxembourg.

Okay, something that's going on in Luxembourg. I feel like, well all of us need to investigate there. At one point four nine, which in the day, by the way, would be a relatively that's the best performing country. I mean in the day, you'd be countries had routinely had two points something, you know, percent growth in a given year.

Last question, you're writing a book. Now we were talking chatting about.

It backstage, and now I appreciate the paradox of this book, which is in a universe with a model, is better in the afternoon than it is in the morning. How do you write a book that's like printed on paper? I expected to reuseful.

This is the challenge. And I am an incredible author of useless books. I mean most of what I've spent time on in the last decade of stuff that's completely useless, like a year after it's written. And so when and we were talking about it as I would like to do something around AI that's timeless, that would be useful ten or twenty years from now. But then to your point, so, how is that even remotely possible if the model's better in the afternoon than in the morning. So that's the

How are you setting out to do that?

I think you have to to some extent level it up to bigger concepts, which is kind of why I go to things like macroeconomics, population geography as opposed to

Did you get any inkling that the pace that this AI year's phenomenon was going to that things with the pace of change was going to accelerate so much because you had More's law, right, you had a model in the technology world for this kind of exponential increase in so thinking about that kind of a similar kind of acceleration in the.

I think anybody had said they expected what we're seeing today is probably exaggerating. I think it's way faster than anybody expected. Yeah, but technologies, back to your point at More's law has always accelerated through the years. So I wouldn't say it's a shock, but it is surprising.

Yeah, You've had a kind of extraordinary privileged position to watch and participate in this revolution, right, I mean, how many other people have been in that have ridden this wave.

Like you have.

I do wonder is this really that much different or does it feel different just because we're here. I mean, I do think on one level. Yes, So in the time I've been an IBM, internet happened, mobile happened, social network happened, blockchain happened. AI. So a lot has happened.

I think the ending with the phrase I don't know it's a good idea though.

It's probably a great way to wrap this up.

Thank you so much, Thank you, Malcolm. In a field that is evolving as quickly as artificial intelligence, it was inspiring to see how adaptable Rob has been over his career. The takeaways from my conversation with Rob had been echoing in my head ever since. He emphasized how open source models allow AI technology to be by many players. Openness also allows for transparency. Rob told me about AI use