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