Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Please to say that
Speaker 1: our next guest, he sits right at the intersection of
Speaker 1: macro policy, AI enterprise tech. Gary Cohen is the former
Speaker 1: president of Goldman Sachs, former director of the US National
Speaker 1: Economic Council, and he's been vice chair of computing giant
Speaker 1: IBM since twenty twenty one.
Speaker 2: He joins US right now. Very great to have you here,
Speaker 2: Thanks for having me.
Speaker 1: I don't even know where to start with it because
Speaker 1: you cover everything, but I actually do want to start
Speaker 1: first off, because we haven't talked a lot about technology.
Speaker 1: There are a lot of disruptive forces going on from
Speaker 1: what's going on in the Middle East, and obviously we
Speaker 1: talk a lot about private capital, but AI, I think,
Speaker 1: by anyone's measure, is going to be transformative, whether that's
Speaker 1: going to be good or bat. You're at IBM, one
Speaker 1: of the biggest computing companies in the world, and I
Speaker 1: do want to point out, aren't you having a conference
Speaker 1: right now in Boston?
Speaker 2: When you're here in La your IBM think conference, this
Speaker 2: is your biggest conference.
Speaker 3: Why are you here because you're here, No, it's like
Speaker 3: we do have our largest conference in the year going
Speaker 3: on right now. I think conference in Boston. As you said,
Speaker 3: thousands and thousands of our clients. They're rolling at a
Speaker 3: lot of our new technology showcasing our new technology. But look,
Speaker 3: we're a global company and a lot of our most
Speaker 3: important clients are here as well. So I made the
Speaker 3: decision to be here. The rest of the IBM senior
Speaker 3: leadership team is in Boston right now, and some of
Speaker 3: my team is actually going out tonight to get to
Speaker 3: Boston for tomorrow.
Speaker 2: Well, that's what I'm curious about too.
Speaker 1: I mean, obviously there was a very market decision to
Speaker 1: be here, and I do wonder about when we talk
Speaker 1: about this AI transformation. We talk a lot about it
Speaker 1: from technology, but this is becoming a finance story as well.
Speaker 1: I mean, you need capital to make good on the
Speaker 1: promise of what this is. Where does IBM actually fit
Speaker 1: right now into all of that.
Speaker 3: Well, it's an interesting question. I'll leave the capital piece
Speaker 3: of the side. But when you talk about the AI
Speaker 3: architecture of the world today, because we are really redefining
Speaker 3: what AII architecture is, it's hybrid cloud, multi cloud all
Speaker 3: over the world. It still has on premise computing, and
Speaker 3: it's data and data manner. You know, we talk about
Speaker 3: AI all day long, but really what is AI. It's
Speaker 3: a system that goes out and finds data and does
Speaker 3: the research and does the computation.
Speaker 4: On the data.
Speaker 3: So at the end of the day, being able to
Speaker 3: manage the data wherever your data is.
Speaker 4: And that's hard.
Speaker 3: Sometimes you have very organized data, sometimes you have very
Speaker 3: disorganized data, but being able to pull from that data
Speaker 3: real time to make sure you get the right answer.
Speaker 3: We're sitting in the middle of the data management business
Speaker 3: as well as the on premise computing as well as
Speaker 3: the hybrid cloud structure, and those are we think those
Speaker 3: are three very important components of where the world's going.
Speaker 5: Well, I think about your time at IBM, as Romain mentioned,
Speaker 5: I mean you joined in twenty twenty one, and you
Speaker 5: think back on the conversation around AI in twenty twenty one.
Speaker 5: I mean, chat, GPT wasn't really on the scene yet.
Speaker 5: It wasn't, you know, so ubiquitous in modern life the
Speaker 5: way it is right now. And I just wonder, you know,
Speaker 5: how the shape of those conversations have changed. When you
Speaker 5: think about the last five.
Speaker 4: Years, it's changed dramatically.
Speaker 3: So when I got the IBM, as you said, five
Speaker 3: and a half years ago, everyone in the world was
Speaker 3: convinced that the world was going to cloud. Everything was
Speaker 3: going to be in a cloud, and every model was
Speaker 3: going to be a SaaS model. You were going to
Speaker 3: basically pay for consumption. Well, guess what, it didn't really
Speaker 3: work out that way. A lot of things went to
Speaker 3: the cloud and people found out the cloud it has
Speaker 3: enormose amount advantages, but on premise computing, and the mainframe
Speaker 3: still is a vital, important.
Speaker 4: Piece of quittent.
Speaker 3: In the United States, over ninety percent of financial transactions
Speaker 3: in the United States go through an IBM mainframe every day,
Speaker 3: every minute of the day.
Speaker 4: So this whole move that.
Speaker 3: We're going to put everything in the cloud and the
Speaker 3: cloud is safe and the cloud is perfect.
Speaker 4: It's not worked out that way.
Speaker 3: Now we've got this hybrid architecture, so being in the
Speaker 3: cloud as part of it, being a hybrid cloud as
Speaker 3: part of it. Being in the mainframe is part of it,
Speaker 3: And that's the way the world's evolving here. Then you
Speaker 3: bring in these large language models, which are a whole
Speaker 3: new set of technology, and you're right. When I got
Speaker 3: the IBM, you know, the world of AI was it's
Speaker 3: kind of interesting, maybe we should look at it. You know,
Speaker 3: sometimes it hallucinates, sometimes it gives you a great answer.
Speaker 3: And I think we all sat up there and said,
Speaker 3: you'll probably get this worked out. Not not crazy, but
Speaker 3: I'll tell you I'll tell you something. Now we're at
Speaker 3: the exact same place with quantum computing today. We're in
Speaker 3: a world where quantum computing kind of works. Sometimes might
Speaker 3: not give you the right answer, but it's going to
Speaker 3: get better and better over time. So in my short
Speaker 3: period of IBM, I've seen this sort of ebb and
Speaker 3: flow of new technology come in and even go back
Speaker 3: earlier in my career. You know, when we brought in
Speaker 3: new technology. You tend to see these ebbs and flows
Speaker 3: and what you think at the beginning of a cycle,
Speaker 3: it's not what the end of the cycle is going.
Speaker 4: To look like.
Speaker 5: Well, so your point on quantum computing, I mean, we
Speaker 5: typically talk about it in the context of, you know,
Speaker 5: this is being presented as the next AI, that's how
Speaker 5: disruptive it's going to be. But do you think that
Speaker 5: you know corporate America, that you know, America at large
Speaker 5: is prepared for that Because you think about how disruptive
Speaker 5: AI has been, you could make the case that a
Speaker 5: lot of folks were not positioned for that.
Speaker 3: So the world is not prepared for quantum. But the
Speaker 3: world was not prepared for AI three years ago. That's
Speaker 3: why I think the analogy is interesting. Three years ago
Speaker 3: people said, like AI, I don't know if we'll ever
Speaker 3: affect me.
Speaker 4: I don't know if I'll ever use it.
Speaker 3: No one would have ever talked about how many llms
Speaker 3: are downloaded per day, per hour, per second.
Speaker 4: So quantum is in a very similar place.
Speaker 3: I think today people are saying, okay, interesting, not sure
Speaker 3: what it is, not sure what it does, not sure
Speaker 3: it will affect my life. But I said, we've seen
Speaker 3: this movie. Quantum is going to be real. It is
Speaker 3: going to affect people's lives. It may not affect people's
Speaker 3: lives in a real time consumptive pattern the way people are.
Speaker 4: Using large language models today, but.
Speaker 3: It will have dramatic effect in the healthcare system, will
Speaker 3: have dramatic effect in the risk management system and pricing
Speaker 3: for banks. It'll have portfolio optimization in some of the
Speaker 3: chemicals and science and physical science world. What can go
Speaker 3: on in the quantum model, It's just nothing short of extraordinary.
Speaker 3: But we're still getting there. I don't want anyone to
Speaker 3: think we're there today. We're evolving, we're going through error
Speaker 3: correction rate, and the machines get better better every day.
Speaker 3: We've got about eighty five systems out in the world
Speaker 3: today with over three hundred clients using our quantum system.
Speaker 3: And it's you know, it's it's people. You would think,
Speaker 3: it's the Boeing Airlines, it's the JP Mortganes, it's the
Speaker 3: Cleveland Clinic looking at human genomes. So all different parts
Speaker 3: of the ecosystem are in the quantum world, playing around
Speaker 3: with it, experimenting with it, getting really interesting data out
Speaker 3: of it.
Speaker 2: There's a lot of upside to this.
Speaker 1: But as you know, as somebody obviously steeped in economics,
Speaker 1: there's still a lot of concern about the potential for
Speaker 1: job losses, certainly with AI and the potential productivity improvements
Speaker 1: that come with that. How do you balance that out?
Speaker 1: I mean, if you put your nec hat back on here,
Speaker 1: I mean, how do you think about that and making
Speaker 1: sure that whatever benefits a crue to the society overall,
Speaker 1: that it is going to be a rising tide.
Speaker 4: Let's everyone, this is not a unique discussion.
Speaker 3: Every time we've gone through a seismic technological invention in
Speaker 3: the world or in the country. We have obsessed about
Speaker 3: job loss, and by the way, we will see job
Speaker 3: loss in jobs.
Speaker 4: That are employing people today.
Speaker 3: But what we have seen historically, and I don't think
Speaker 3: this time will be different, what we see is massive
Speaker 3: productivity gains. When you see massive productivity gains, the economy grows.
Speaker 3: Is the economy grows, we still need to put people
Speaker 3: to work in different types of jobs.
Speaker 4: So you can go.
Speaker 3: Back in history and look at all the technological advancements
Speaker 3: and you'll read stories that are horrifying, saying all these
Speaker 3: people are going to lose their jobs, Oh my god,
Speaker 3: what's going to happen, And miraculously the economy grows.
Speaker 4: People get jobs.
Speaker 3: Like today, we have a massive shorter of shortage of
Speaker 3: people in the trades. You know, if you're a person
Speaker 3: that can put down a cement floor and get a
Speaker 3: perfectly level for a data center, you're.
Speaker 4: Earning hundreds of thousands of dollars.
Speaker 3: If you're a person that can wire that data center,
Speaker 3: you're a person that can bring in the heating, ventilation,
Speaker 3: air conditioning, all air conditioning system. Those jobs are massive
Speaker 3: payers today and there's a huge shortage of people. So
Speaker 3: some of the people that are doing tasks that they
Speaker 3: may not love to do. Because the quantum I'm sorry,
Speaker 3: the AI machine is better at media tasks. They can
Speaker 3: go from potentially doing an unsatisfactory job to a very
Speaker 3: satisfactory job in the trade.
Speaker 1: What do you think we start to see those productivity
Speaker 1: improvements actually show up in the actual economic data. I mean,
Speaker 1: I think back to Kevin Warsha's a confirmation hearings where
Speaker 1: he kind of made the case how some of those
Speaker 1: productivity improvements, at least in his view, could actually set
Speaker 1: the stage for actually cutting rates.
Speaker 2: Do you think we're at that inflection point?
Speaker 1: Because as you know, I mean for decades, I mean,
Speaker 1: productivity effectively stalled in this country.
Speaker 2: Do you see this as being a true accelerator of that?
Speaker 4: AI is a true accelerator.
Speaker 3: You don't see the productivity gains day by day. You're
Speaker 3: going to have to measure them over a quarter or
Speaker 3: month or year by year because companies are adapting. You know,
Speaker 3: we're sort of going from the interesting science experiment of
Speaker 3: AI to the economic value. We're in that transition from
Speaker 3: science to economic value. We at IBM just to be honest,
Speaker 3: we have used ourselves as client zero, meaning all the
Speaker 3: AI tools that we're out selling to our clients, we
Speaker 3: have implemented AM on ourselves.
Speaker 4: We have saved over four and a half.
Speaker 3: Billion dollars of expenses and our head count has not
Speaker 3: gone down. So we have been able to take people
Speaker 3: out of jobs they didn't like doing move them into
Speaker 3: jobs with much higher value added to them. So take
Speaker 3: someone in an HR department. You know historically you're buying
Speaker 3: a house, so you're renting an A department.
Speaker 4: You need a reference letter.
Speaker 3: You need to know how long you've worked at, what's
Speaker 3: your jobs, how much you've heard before AI. You would
Speaker 3: call up somebody, you would get them on the phone.
Speaker 3: You'd say, hey, this is so and so I need
Speaker 3: you to send a letter to so and so. They
Speaker 3: would have to go pull your data out of your
Speaker 3: file and send out the letter, and you'd hope and
Speaker 3: pray they do that quickly. Now you go to what's
Speaker 3: an HR bought. You put in your information, you tell
Speaker 3: them the sent to as many places you want, and
Speaker 3: after you hit sent, it's going back out the people
Speaker 3: doing that work that's not highly satisfying work. We've taken
Speaker 3: people that were doing that work in HR and now
Speaker 3: we have them out recruiting new talent to the firm,
Speaker 3: we have out mentoring to people to the firm, So
Speaker 3: we're getting much higher productivity out of that.
Speaker 5: Right, I do want to bring this into the context
Speaker 5: of the economy of what we're talking about with AI,
Speaker 5: because certainly that has been a big benefit to the
Speaker 5: US stock market, And there is the idea out there
Speaker 5: that you think about what's going on when it comes
Speaker 5: to energy prices right now really impacting lower income consumers,
Speaker 5: and then you have the wealth effect of AI really
Speaker 5: translating into higher income consumers who are invested heavily in assets.
Speaker 5: And the general idea is that we're going to see
Speaker 5: this K shape economy basically be exacerbated. Gary, and I
Speaker 5: wonder you know where you fall on that sort of logic.
Speaker 3: Well, look, we have a fairly massive wealth effect going on.
Speaker 3: You know, I look at the economy today is asset
Speaker 3: owners and non asset owners. You know, if you own
Speaker 3: assets today, your assets probably appreciate. Whether it's whether it's
Speaker 3: a house, it's a car, it's an investable asset. Most
Speaker 3: of these underlying assets are going up in value versus
Speaker 3: the other side of the k, which is a which
Speaker 3: is a large predominance of our country who is really
Speaker 3: suffering and really having a difficult time as their input
Speaker 3: car meaning their food cost, their energy costs, their insurance costs,
Speaker 3: their price of cars, price of used cars are all
Speaker 3: going up every day, and their wages are not keeping
Speaker 3: up with that. This is this is a problem. I mean,
Speaker 3: we can't sweep this under the rug. We have to
Speaker 3: understand that we've got a fundamental problem going on in
Speaker 3: the country, and look it's starting to resonate with more
Speaker 3: and more people. I think that AI is part of
Speaker 3: the solution. AI can train people. AI can move people
Speaker 3: in the higher paying jobs. Like I said, the jobs
Speaker 3: are out there. We just have to forget how to
Speaker 3: train people.
Speaker 5: All right, Gary, got to leave it there, so appreciate
Speaker 5: your time. I'm sure it's a busy conference for you,
Speaker 5: that is Gary Cohne. He is the vice chair of IBM.