IBM前CEO解析AI资本开支周期与历史技术革命规律

This is Wall Street week. I'm David Weston bringing you stories of capitalism. US-Canada trade talks come to the brink. Former Canadian Foreign Minister Chrystia Freeland tells us what is at stake. And there's been a lot of speculation about what I will mean for jobs. Apollo's Torsten Slack brings us the first study of its kind and what's actually happened since ChatGPT came on the scene. Plus, it supports $1.4 trillion of U.S.

GDP and 16 million jobs. But the Colorado River is running out of water. We look at government plans to ration what's left, but we start with the hundreds of billions of dollars being raised to invest in artificial intelligence, including $500 billion in one deal done by Nvidia. Sam Palmisano served as CEO of IBM, where he was responsible for capital investment in new technologies and the need to make sure they made money for the company.

So, Sam, there is a lot of money being invested in AI, whether it's chips or whether it's data centers, including the Nvidia announced deal of $500 billion. Give us the perspective of a CEO of a big tech company. How do you decide how much to invest in a new technology? How we would have thought about it. As you know, I have one of these big tech companies we would have thought about what is. Where do we see the future state?

And you go back to Watson with the 360, because that was going to be from unit record to computation called a computer. Right. And my particular case was I Watson jeopardy. You know. Right. So how do you jump ahead of the next generation. Because the investment cycles in the R&D are going to be several, several years. So you have to start no different than quantum. That's another example of that. That's true for all tech companies.

I'd say it's true for many companies that are relying upon any form of technology. It could be energy and those sorts of things as well. So we would always look out in time. Uh, and that could be from the R&D. So like it'd be like a 7 to 10 years in the models, 3 to 5 for short term. We'd call that operational investment. But for the longer term things and then you and then basically you start out with goals and milestones and objectives because you don't have numbers.

And then as you get closer, you actually build your return equations as your business model dictates. So if your IRR is 15% or 14%, that would be the hurdle rate. The guys would have to come over before you launched. 7 to 10 years is a long time in any business. Yes. But right now it seems like 7 to 10 months. And I know it's a long time. Product cycle time. Right. Exactly. How do you how do you project out, uh, returns with AI that's changing so fast?

Well, the software is changing fast. And the models. Right. The learning models. The frontier models. That's what's changing really fast. Data centers aren't changing fast. I mean, well, the estimates are 3 to 5 years before this stuff comes online. That's not unrealistic. Semiconductors. You get it online. Probably seven. Nuclear at least ten. So that's the energy requirement for a lot of these things. So these things are long cycle times uh before you actually get the capacity in place.

Now I mean, the challenge with this in the interim obviously is is people, the large hyperscalers, the big guys out there will probably, uh, have enough, uh, requirement that they'll they'll in many ways control the market. I mean, because they'll take the make they'll make the big bets in the short term, they'll get the data centers, they'll get the energy. Other guys that are trying to compete or or even to participate in this market, I think, be squeezed out.

We have all these announcements. Is this money actually going to be invested? Is it going to happen? Well, right now, the memorandums of understanding, uh, people claim that there's there's commitments and there's teeth in these agreements. I have not read them. I have asked people who theoretically should know, and they tend not to comment on the actual specifics, so I'm just going to leave it at that. I, I can't imagine if this thing had just say economically.

Suppose, for example, just adoption slows. It doesn't have to be an economic downturn, just a simple thing. Your adoption curve, what you assumed would be 7 to 8 years. That's make it 10 to 12. That slows. You're not going to build out the capacity as quickly. That alone, you know, could cause I think a change in their plans. And then if there are firm commitments, it's one thing which I doubt as they become more variable commitments over time, someone again is going to have to deal with the shortages.

I mean, either the bondholders, I mean, someone committed to put a shovel in the ground to build a data center someone agreed to at energy capacity to support this aggressive case that we see today. At some point, if things do slow, that would have to adjust the way things are today. We tend to look at a company based on return on investment capital. Yes. And we compare companies depending on their return. For example, I took a look at IBM is running about 10% return with Microsoft.

You know, Google Alphabet is like 25% correct. Um, take the 10% number on $500 billion. That's a lot of incremental income that you have to generate every year, every year and put that on the basis of, I say, IBM, that I 60 or 70 billion I was 100 billion when I was there. I did five. Whatever puts on that basis, some level of our margins were a little higher than they are today. But put it on the basis that you already have a stream of several billion, you got to add an additional several billion in an annual basis.

And I used to say that at IBM, for us to grow at 7 or 8%, just on the top line, we'd have to be create a fortune 50 company every 14 months. Now, an enterprise computing that's tough for commercial consumer and attack is probably an easier way to do that. You get a hit hot phone or whatever it happens. But if you're selling the banks or telcos or governments, the odds of them taking up their expenditures at that rate say you could create a fortune 50 companies.

Pretty tough to do. If you look back in history, some of what you lived at IBM, but also going back to the Industrial Revolution, railways and things like that. Are there patterns with these sort of technological revolutions that we see? Yeah, there are actually there's a there's a scholar, a Cambridge, the name is Carlotta Perez, and she's done a lot of work, uh, going back to 1771 for the Industrial Revolution, then the steam engine and then, you know, oil and gas and then microelectronics and compute.

And now she would probably run her sixth generation. But the pattern is the same. Uh, over time it starts what she calls the installation cycle, we would call maybe the industrial I mean infrastructure build out today. That's the base that that gets put in place. And it takes several years to do that, obviously to get it to scale. Uh, and then normally what has happened over this history here is that it has corrected for multiple reasons.

It could be an economic reason. It could be policy. I mean, governments do things that sometimes don't always stimulate growth. So there could be multiple reasons why. And every situation it adjusted. Her argument is that's good for capitalism because it resets the cost base. And then what happens this bill. She calls it the deployment cycle. Then people build up and then it goes to maturity with big societal impact.

That's pattern has repeated itself since the 18th century. Will it repeat itself again today? People argue it won't. Um, I'd argue back in the.com bubble they said it won't there. And as well I mean housing prices it was never going to go down. I mean yes, you know we've all lived these things. Unfortunately, circumstances do change. It's good for capitalism to reset. The cost base is not necessarily good for investors who invested in that.

Yes, that means somebody is taking a haircut along the way. As a CEO. How do yo