In short
The episode connects three big themes: massive AI investment and how CEOs decide capital spending; early real-world evidence on AI’s labor-market effects (wages vs. jobs); and two policy crises—U.S.-Canada trade talks under pressure and the Colorado River water shortage requiring rationing.
Guests and backgrounds
- Sam Palmisano, former IBM CEO; led capital investment in new technologies.
- Torsten Slock, chief economist at Apollo; co-authored an AI labor study using occupation-level data.
- Diane Gerson, senior advisor at Boston Consulting Group; previously IBM HR head.
- Chrystia Freeland, former Canadian deputy prime minister, foreign minister, and finance minister; special contributor on U.S.-Canada trade.
- Michael McKee (reporter), plus water-policy and climate voices: Jennifer Gimbel (former Bureau of Reclamation official), Dan McAvoy (Western Regional Climate Center climatologist), Brenda Berman (Central Arizona Project general manager), Andrew Leimgruber (Imperial Valley farmer), Dan Denham (San Diego County Water Authority general manager).
Key claims and notable examples
- AI investment cycles are long (7–10 years for R&D; 3–5 for operational capacity), and deals may be “memorandums” without teeth; downside planning (“plan B”) is essential. Historical pattern: infrastructure buildout then deployment/maturity (citing Carlotta Perez). China may use open-source/less GPU-dependent models (e.g., DeepSeek) to scale cheaply.
- Apollo study: among ~300 occupations, AI exposure correlates with weaker wage growth but no significant employment displacement yet; business formation is rising (highest ever).
- Gerson: automation hits repetitive, high-attrition roles first (customer service); companies aren’t cutting pay broadly, but spot wages for contractors may fall; radiology is cited as a “wages up” example due to more time with patients.
- Freeland: tentative U.S.-Canada deal likely prevents new Aug. 19 tariffs; Canada may accept lower but “permanent” tariffs on steel/aluminum/cars/parts—called a major Rubicon and potentially self-harming for U.S. manufacturers (including Detroit).
- Colorado River: worst conditions ever; lowest snowpack and early melt; Lake Mead/Powell declining; federal rationing plan proposes ~1.5 million acre-feet cuts annually for the lower basin (Arizona hardest). Examples include Imperial Valley irrigation impacts, and San Diego’s desalination-for-water exchange to stabilize rates.
Written by AI. May contain mistakes. Listen to the episode to check what was said.
Chapters
Tap a time to open that second in VOUS-Canada Trade Talks and AI Impact
0:30 to 0:50
Explore the implications of US-Canada trade talks and AI's impact on jobs.
“It's designed to help you move from a chaotic starting point to a reviewable first version.”
US-Canada Trade Talks and AI Impact
1:49 to 2:35
Explore the implications of US-Canada trade talks and AI's impact on jobs.
“I'm David Weston bringing you stories of capitalism.”
Investment in AI and Technology Decisions
2:35 to 4:14
Discuss how tech CEOs decide on investments in new technologies.
“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.”
Challenges in Projecting AI Returns
4:14 to 6:07
Understand the difficulties in projecting returns on AI investments.
“But right now, it seems like seven to 10 months in AI is a long time.”
Historical Patterns of Technological Revolutions
6:07 to 7:41
Examine historical patterns of technological revolutions and their implications.
“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.”
China's Approach to AI and Global Market Dynamics
7:41 to 11:28
Analyze China's strategy in AI and its potential impact on global markets.
“You get a hit, hot phone or whatever it happens to be.”
Future of AI and Its Workforce Impact
11:28 to 12:45
Look ahead to AI's future and its implications for the workforce.
“I would just go take the rest of the world, let the US have a sanction, say you can't come in, you can't sell on my market.”
Future of AI and Its Workforce Impact
14:03 to 14:32
Look ahead to AI's future and its implications for the workforce.
“stay with a project for hours if needed, and turn a goal into finished work.”
Impact of AI on Wages and Employment
15:06 to 19:16
Explore the effects of AI on wage growth and employment dynamics in various occupations.
“Until recently, most of the talk about the effect of AI on employees has been based on theory.”
Policymaking in an AI-Driven Economy
19:16 to 24:12
Understand the challenges policymakers face in an economy influenced by AI's growth and business creation.
“Based on what you've seen so far, including this study, what does it say for economic policy?”
Show all 26 chapters
The Future of Work with AI
24:12 to 27:23
Discuss the evolving job landscape and how companies can utilize AI to enhance workforce productivity.
“The projections right now, and you need them to justify the investment that's being made, is this is going to increase productivity substantially.”
The Future of Work with AI
28:01 to 28:15
Discuss the evolving job landscape and how companies can utilize AI to enhance workforce productivity.
“So all the source materials, briefs, and scattered information that you have to grind through to turn into something useful can just become something useful.”
US-Canada Trade Relations and Tariffs
29:00 to 33:44
Discussion on the tentative agreement regarding tariffs between the US and Canada.
“and Canada have been neighbors, allies and generally friends since Canada first became a country independent of the British Empire.”
Impact of Tariffs on the Auto Industry
33:44 to 35:44
Exploration of how tariffs affect the US-Canada auto industry collaboration.
“is a great historic example of losing by winning.”
Details of the USMCA Agreement
35:44 to 38:26
Insight into the USMCA and how the tentative agreement interacts with it.
“across the border seven or eight times before that car is completed.”
Canadian Perspective on Trade Agreement
38:26 to 40:13
Discussion on how the tentative agreement will be perceived in Canada.
“Seems like this is like so many of the deals that the Trump administration has been doing around the world.”
Colorado River Crisis Overview
40:13 to 40:37
Introduction to the crisis facing the Colorado River and its implications.
“But I think the unanimous national view is going to be we're being treated pretty shoddily.”
Current State of the Colorado River
40:37 to 42:01
Analysis of the ongoing water crisis affecting the American Southwest.
“www.chatchypt.com information that you have to grind through to turn into something useful can just become something useful.”
The Colorado River Crisis: An Overview
42:01 to 45:35
Learn about the ongoing crisis affecting the Colorado River and its impact on water supply.
“The American Southwest, as we know it, was built on cheap, reliable water from the Colorado River.”
State Negotiations and Water Allocations
45:36 to 53:19
Understand the complexities of state negotiations over Colorado River water allocations.
“And you have governor's representatives, and they're concerned about their constituents, they're concerned about the economy, they're concerned about food security, but there's only so much in the river.”
State Negotiations and Water Allocations
53:20 to 54:32
Understand the complexities of state negotiations over Colorado River water allocations.
“If there is a bad year next year on the river, the system will crash.”
AI Regulation and Economic Transformation
54:35 to 56:00
Explore the historical context of technology regulation and its relevance to AI.
“Ask radio to join the conversation right here on Bloomberg Radio.”
Government's Role in Tech Regulation
56:00 to 57:53
Explore the balance between government regulation and innovation in technology.
“I know they aren't exactly for your consumer audience, but that's where government comes into play.”
Expertise and Structure in AI Governance
57:53 to 1:01:02
Discuss the need for expertise within government agencies to handle AI regulation.
“I would argue that if the regulation, I take cyber security where I was on the commission for President Obama, you might recall.”
Addressing AI Safety Concerns
1:01:02 to 1:03:23
Understand the potential risks of AI technology and the importance of safety measures.
“I mean, everybody wanted to be participants in that because they saw money coming, whether it was OMB or it was people not.”
The Future of Technology Speed and Control
1:03:23 to 1:03:54
Discuss the implications of rapidly advancing technology and the need for control mechanisms.
“My generation would never have made that argument that you should put society at risk.”
Transcript
Automatic transcript. May contain errors.0:00In a startup landscape defined by the unicorns of the world, how do ventures rapidly scale? And are government initiatives growing access to capital and agile regulation changing what it means to be a founder? Find out more later in the podcast.
0:30Sam Palmisano:and turn a goal into finished work. It's designed to help you move from a chaotic starting point to a reviewable first version. So all the source materials, briefs, and scattered information that you have to grind through to turn into something useful can just become something useful. Put ChatGPT to work on your most ambitious ideas and projects. Get started at chatgpt.com by selecting Work Mode, available on Plus and Pro plans. If you like YouTube, you'll love YouTube Premium. Hi, I'm Sean Evans from Hot Ones, and I want to tell you about YouTube Premium. It has offline downloads, so you can watch without Wi-Fi.
1:08Sam Palmisano:Background play, so you can lock your phone, and it still plays, baby. Oh, and it is completely ad-free. Yes, I said it, ad-free. Try YouTube Premium for two months free at youtube.com slash premium. Trial eligibility varies, terms apply, cancel anytime.
1:29Sam Palmisano:Bloomberg Audio Studios. Podcasts, radio, news.
1:47This is Wall Street Week. I'm David Weston bringing you stories of capitalism. U.S.-Canada trade talks come to the brink. Former Canadian Foreign Minister Christian Freeland tells us what is at stake. And there's been a lot of speculation about what AI will mean for jobs. Apollo's Torsten Slock brings us the first study of its kind on 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.
2:34Sam 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 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?
3:17My particular case was AI, 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. And that could be, from the R &D cycle, it could 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.
3:53And 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. Seven to 10 years is a long time in any business. Yes. But right now, it seems like seven to 10 months in AI is a long time. Product cycle time, right? Exactly. How do you project out returns with AI that's changing so fast? Well, the software's changing fast. And the models, right?
4:32The learning models, the frontier models, that's what's changing really fast. data centers aren't changing fast. I mean, the estimates are three to five years before this stuff comes online. That's not unrealistic. Semiconductors, you get it online, probably seven, nuclear at least 10. So that's the energy requirement for a lot of these things. So these things are long cycle times before you actually get the capacity in place. Now, I mean, the challenge with this and the interim obviously is people, the large hyperscalers, the big guys out there, will probably have enough requirement that they'll in many ways control the market.
5:11I mean, because 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 even to participate in this market are going to, 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, they're memorandums of understanding. 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 adjusts say economically suppose for example just adoption slows it doesn't have to be an economic downturn just a simple thing your adoption curve will you assume to be seven to eight years, let's make it 10 to 12, that slows.
6:03You'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 bond holders, I mean, someone committed to put a shovel in the ground to build a data center. Someone agreed to add 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 investing capital.
6:45Yes. And we compare companies depending on their return. For example, I took a look and IBM is running about 10 % return on investing capital. Microsoft, you know, Google Alphabet is like 25%. Correct. 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 base of, say, IBM today,$60 or$70 billion. It was$100 billion when I was there,$105, whatever. Put on that base of some level. Our margins were a little higher than they are today. But put on the base that you already have a stream of several billion. you've got to add an additional several billion in an annual basis.
7:27And I used to say that at IBM, for us to grow at 7 % or 8 % just on the top line, we'd have to create a Fortune 50 company every 14 months. Now, in enterprise computing, that's tougher. Consumer and tech is probably an easier way to do that. You get a hit, hot phone or whatever it happens to be. But if you're selling to banks or telcos or governments, the odds of them taking up their expenditures at that rate say you could create a Fortune 50 company, it's pretty tough to do. If you look back at history, some of which you lived at IBM, but also going back to the Industrial Revolution, railways, things like that, are there patterns with these sort of technological revolutions that we see?
8:03Yeah, there are actually. There's a scholar at Cambridge, her name is Carlotta Perez, and she's done a lot of work going back to 1771 for the Industrial Revolution, then the steam engine, and then oil and gas, and then microelectronics and compute. and now she would probably run her sixth generation, but the pattern's the same over time. It starts with, 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. And then normally what has happened over this history here is that it has corrected for multiple reasons.
8:43It 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. In every situation, it adjusted. Now, her argument is that's good for capitalism because it resets the cost base. And then what happens, this build, she calls it the deployment cycle, then people build up, and then it goes to maturity with big societal impact. That pattern has repeated itself since the 18th century. Will it repeat itself again today? People argue it won't. I'd argue back in the dot-com bubble, they said it won't there as well.
9:20I mean, housing crisis, it was never going to go down. I mean, as you know, we've all lived these things. Unfortunately, circumstances do change. It's good for capitalism to reset the cost base. It's not necessarily good for investors who invested in that. That means somebody is taking a haircut along the way. As a CEO, how do you make sure that you don't get caught out in that? Well, basically, if I'm, now let's go back to my IBM story here. how we would be more conservative on the aggressive case. You have to admit, I think the cases are the optimistic, all thing goes well. I mean, we used to say all trees don't grow to heaven.
9:56So you have to have a downside case to go. We call it a plan B, quite honestly. So you'd have your plan A. You would drive to the plan A, which is the high optimistic case. But you had a plan B that if you had to correct quickly, you could adjust. And I would say probably six out of 10 times we were adjusting before that cycle was over. Because, again, it's very difficult to predict. Think about if you're dealing with just a flat-out demand statement. I mean, who can call it? They can't call supply chain demand for a year or two from now, much less 8 to 10, where you're making these assumptions.
10:31In the Perez work, she also talks about a financialization of these things, where the financial markets really kick in now. We're seeing that to some extent now. There are some reports about off-balance sheet financing. Do we have our arms around exactly how much these companies are investing? There's no transparency, as you probably know, in the off-balance sheet estimates. And the companies, at this point in time, given where the projects are, don't have a requirement to disclose. So you have a bunch of factors there where people are being estimated. But if you're a financial analyst in this space and you're trying to figure out the true liability or the true debt that you have relative to the company's debt capacity, it's very very difficult as people are projecting returns future returns which are speculative necessarily it's a new technology there's another factor that appears to be coming into play and that's China with a different approach their models and a less expensive one which could constrain those returns could it not yeah well they like there's an alternative today it's called open source I mean we did a program together on open source that happened to be with Linux in the operating system and then applications that were built on top of that well the chinese are arguing with deep seek and those sorts of things and the latest ones have just come out that there's no reason why they can't have this open source capability available and it doesn't require their models don't require the same capacity as the us models the proprietary models are the gpu capacity so they have an alternative that's less cost and that's what they're deploying my my point of view is that if i was china not the united states because the US would not like this.
12:08I was China. I would just go take the rest of the world, let the US have a sanction, say you can't come in, you can't sell on my market. Fine, but that today is probably less than 20, 25 % of the market. So go get the 75%. And if you look at what's going on in Asia, that's where they're going. And they're going to take their model, which the old industrial model, they'll build up a massive scale, low cost, high quality, which people say it couldn't be high quality. high quality, take it to market, and then we know what happened everywhere else. And they're running the same play here. Coming up, it's time to stop speculating and start looking at the data.
12:49Torsten Slock of Apollo takes us through his study of what the actual use of AI means for the workforce. Are unicorns old news? I'm Jacob Greaves, host of Economy of Scale, a series exploring how innovators and investors are transforming growth in key global sectors. And in this episode, we're diving into the world of SMEs and startups. Sandeep Sani, co-founder of Dubai-based Valio Health, has this to say on the evolution of unicorns. A billion dollar was a great deal, I think, up until two years ago. Now it's all about the trillion dollar startups, right? In this year alone, there are two trillion dollar IPOs that already happened and two more that probably will happen.
13:28I would say a billion today in the world post-2025 is an average. Roman Asensar, managing partner at early stage VC firm Antler, has this to say. I think this is for you as a founder, but also for the team to say, how can we shoot for the stars if we have a 50 % likelihood to happen?
13:46Sam Palmisano:But that creates that element of drive and intensity, which is extremely important. Listen to the full episode of Economy of Scale wherever you get your podcasts. Some people treat ChatGPT like some kind of smart search engine, and some use it to get work done. ChatGPT Work is a new way of working in ChatGPT that can take action across your apps and files, stay with a project for hours if needed, and turn a goal into finished work. It's designed to help you move from a chaotic starting point to a reviewable first version. So all the source materials, briefs, and scattered information that you have to grind through to turn into something useful can just become something useful.
14:27Sam Palmisano:Put ChatGPT to work on your most ambitious ideas and projects. Get started at ChatGPT.com by selecting Work Mode, available on Plus and Pro plans. AI is entering its most consequential phase where scale, safety, and sovereignty will determine who leads and who lags. Join Bloomberg Tech in London on November 2nd and 3rd as global leaders across business, finance, and policy examined the defining trade-offs shaping the future of AI. Thank you to our presenting sponsor, Salesforce, and supporting sponsors, IDA Ireland and Schneider Electric. Learn more at bloomberglive.com slash techlondon.
15:11This is a story of puts and takes. Until recently, most of the talk about the effect of AI on employees has been based on theory. But now we are starting to get some data. Torsten Slack is chief economist for Apollo and recently co-authored a paper analyzing those data for hundreds of occupations. And though it is early going, the initial results indicate that those most affected feel it in the size of their paycheck rather than whether they get to keep their job. It is now possible to look at what happened before chat GPT came along and what happened after chat GPT came along. And what we did was that we categorized the employment and wage numbers into occupations and asked what occupations were exposed a lot to AI and what occupations are exposed less to AI.
16:03And then we looked at as a natural experiment what happened before chat GPT came along and what happened after chat GPT came along. So when you look at the 300 different occupations that we looked at in terms of AI exposure. The conclusion is, at least where we sit right now, that those occupations that are highly exposed to AI, they are seeing weaker wage growth, but the employment effect on everyone is insignificant. What surprised you about the results that you might not have expected going in? Well, the debate around AI in the labor market for now some time has been about labor displacement.
16:34In other words, there's a lot of worries about that when AI comes along, then work processes are going to get automated, and therefore a lot of workers are going to lose their jobs. What we found was that that effect is not quite here yet. Instead, let's also not forget that when AI, of course, becomes more easily available as a tool, now it's also easier to open a new business. So if you look at the weekly data from the Centers for Business Formation, you are at the moment seeing business creation in the U.S. is at the highest level ever in U.S. history. So on the scale, you have on the one hand an automation and replacement effect that certainly is saying that the labor market should get worse.
17:09But on the other hand, so far, the dominating effect has been that there is also a much more dynamic economy where people can now invent ideas, use agents, use loops, graphs to come up with ideas. And as a result, create more businesses that ultimately likely will also create some more employment. And talk about the labor market getting worse. As I understand your study, it said it gets worse more on the wage growth than it does actually in people losing jobs. But it's not evenly distributed. Yeah, there are some occupations that are more negatively impacted. We chose, and this was somewhat randomly, to categorize the buckets of occupations into those occupations that are more than half exposed to AI and those that are less than half exposed to AI.
17:50In other words, try to cut the sample into different categories of who is it that has high exposure, who is it that has low exposure, again by this actual user's data. And that does show you that those who have higher exposure generally saw lower wage growth. So in other words, you can begin to worry about that maybe AI, because it is replacing knowledge workers, is going to create some downward pressure on waste growth. So far, not on employment, but it's going to create some downward pressure on waste growth. You say in your report that it's still early days, that the effects may grow over time.
18:21Do you have any sense where we are in that process and how those effects may grow? We don't quite know at this point because the market is trying to figure out the answers to that question literally every day, namely, how quick is the AI payoff going to come? At the moment, enterprises are investing a lot of money in AI. But when you look at the actual margins for the S &P 493, meaning not the magnificent seven, margins have not gone up yet. Most people, including me, expect that we should begin to see some improvements in margins over the next several quarters. But at this point, it is still very early days where businesses are trying like an S-curve to figure out, well, now we have a new technology.
19:00Now we're trying to figure out how to use that technology. And once we then have a way to use that technology, we will likely see another step higher in the S. And the implication, of course, is that those industries that will be able to implement and adopt AI will, of course, be the next winners as this technology continues to develop. Based on what you've seen so far, including this study, what does it say for economic policy? What should people in Washington be focused on right now? Well, the challenge in policymaking at the moment is that we just don't know which scenario we're looking at.
19:29Let's say the unemployment rate goes to like 10, 15 percent because people lose their jobs. Then, of course, economic policy should be focusing on that problem. If, on the other hand, that the dominating effect here becomes that business creation is creating so many more jobs and therefore the unemployment rate, which is the consensus expectation, would actually be going down. Well, in that case, policymaking does not need to worry about displacement of workers. Then they should instead maybe worry about the economy overheating and another set of economic tools from policymaking will be needed.
19:57So that's why the problem where we sit right now here in 2026 is we just don't know whether the scenario at this point is one where the labor market is going to get a lot weaker or it's going to get a lot hotter. And for that reason, therefore, the best thing for policymakers at this point is just literally like the market to wait and see. Are you as optimistic about AI at the end of this study as you were at the beginning? I'm very optimistic about AI because I do think that the effect of creating a more dynamic economy, many more opening up new businesses in consulting, in finance, in legal services, and those new businesses will compete with the incumbents.
20:33And that will continue to put more and more competitive pressure on the economy. And that's something that we should all be very interested in because that creates more jobs, that creates more businesses. And ultimately, as a result of that, I truly believe that AI is a miracle drug that will both create higher productivity and also create higher employment. So I'm very optimistic on what AI will bring to the U.S. and the global economy. Whether AI eventually spells riches or ruin for workers, the numbers so far tell only part of the story. Inside companies, executives are already deciding which jobs can be automated, which workers can be retrained, and how best to allocate labor costs.
21:11Diane Gerson is a senior advisor at Boston Consulting Group, And she confronted those choices when she was head of human resources at IBM.
21:19Sam Palmisano:For certain jobs where there's high attrition, those are the jobs that are being automated, the first, with AI. Those jobs are being replaced at a lower rate. The demand for those jobs is lower, and it's your classic demand supply. So people are taking jobs at lower rates in those job categories, customer service being an example, or business services. Those are the types of jobs where you need fewer of them because the work can be done by fewer people faster. So to the extent that they are being hired, yes, they may be hired at a lower rate. But I don't see companies cutting pay. The only area that I would see this happening is in areas where you have contractors.
22:00Sam Palmisano:So what you're seeing is there's a lot of data available. There are data brokers that can tell hospitals that are hiring traveling nurses which of the traveling nurses have low credit scores or credit problems. And so they can offer a lower wage and know they'll get it. Or ride hailing services, if there's a driver who previously took a ride at a lower rate, they'll keep being offered that lower rate. So I think those are spot wages as opposed to employees. And you're going to see that happening faster. When you talk about categories that have, for example, high attrition, things like customer service, in general, and I understand this is a rough correlation, does that tend to correlate with lower income, lower salary?
22:45Sam Palmisano:Yes. Oh, absolutely. Yeah. So it's plausible that AI would affect first the lower paid employees before it gets to the upper levels. You know, I mean, yes. I mean, let's take legal. So it's the paralegals that are going to go first, right? Because so much of the paralegal work can now be done by, you know, one of the AI firms, Harvey or whatever. And so to the extent that a law firm says, oh, but we want these paralegals to be overseeing the AI, you know, then their jobs will change somewhat. Or we'd like to train them to become, you know, some junior level of lawyer that never existed before.
23:24Sam Palmisano:Because that kind of work, you know, we never really could accomplish with our junior lawyers. So there's a variety of different things going on. But yes, it's starting at the bottom with the more repetitive tasks, the analytical tasks, the known models. And of course, the big question is, as it moves up the stack, and it's already moving up the stack, particularly in tech, how are you going to train the senior people? Because if it's gobbling up the jobs below, the pipeline is lost. And so I'm seeing a lot of really thoughtful work being given to that by companies because they want to preserve their judgment capability.
24:04Sam Palmisano:They want to preserve their leadership capability and their highest levels of expertise. And so you've got to develop them through some set of jobs. The projections right now, and you need them to justify the investment that's being made, is this is going to increase productivity substantially. eventually, if that in fact delivers on the promise, what does that mean for the workforce? Some companies are thinking broadly and saying, you know, productivity actually isn't going to increase our valuation that much. I mean, just doing things faster and cheaper doesn't mean you're going to have the highest valuations.
24:40Sam Palmisano:But if we can create new products, if we can create new opportunities, if we can do breakthrough innovation, then we'll have a higher valuation because we'll have growth, right? So I think those companies are saying, what can we now do with AI that's going to elevate our capability as opposed to just making us more productive and faster? A motivated workforce is essential and was part of your central responsibility at IBM. As you look at AI, assuming you had your old job back, how would you use this to motivate the workforce? Because it could cut either way. It could demoralize people, really change the culture in a negative way, or I guess conceivably could motivate people.
Read the full transcript
25:24How would you use it?
25:26Sam Palmisano:Well, I think some companies have done this really well. And I'll call out Walmart. You know, they spoke with all of their employees and they said, AI is for you. You are in charge of the AI. It's to make your job better. And you're going to be involved in every stage of this implementation of AI. And so instead of it being done to them, they were made to feel like they're empowered with AI, right? And I think that's where, when people feel like they're losing their agency and their control, and they're just going to be, you know, I'm thinking about sort of, I love Lucy when they're on that chocolate line with Ethel, and it's going really, really fast.
26:03Sam Palmisano:But at the very end, they have to put it in boxes. And of course, humans can't put it in boxes as fast as the machine was going. So the chocolates were all over. That's sort of the image that a lot of people have of AI because it is machine-paced and it's going to speed stuff up. Is that what we're going to do to professional work? And that is happening in some places. But I think some companies are being much more thoughtful and saying, no, actually, it's to make you a better professional. It's to augment what you do. and that's where we're going to get our productivity because you'll be able to spend time on different things.
26:37Sam Palmisano:A great example is the radiologists, which everyone thought was gonna go away. Actually, their wages have gone up faster, 42 % faster than software engineers since 2021, even though AI can read the images. Why? Because they have more time to talk to the patient, to learn more about what's happening, to interpret things from their knowledge and experience so people can operate at the top of their license instead of doing the more menial parts of their jobs. That's a win if indeed you can create more value. So you're seeing kind of bigger thinking going on in some companies, but some of them are just, as I said, pressing the easy button and going for the layoff.
27:22Up next, going to the brink with Canada over trade. Our special contributor, Christia Freeland, takes us through where we are and what's at stake for the two countries.
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28:54Sam Palmisano:You're listening to Bloomberg Wall Street Week with David Weston from Bloomberg Radio. This is a story about couples therapy. The U.S. and Canada have been neighbors, allies and generally friends since Canada first became a country independent of the British Empire. But President Trump has put the relationship in play, particularly when it comes to trade relations. Chrystia Freeland served as Canada's deputy prime minister, foreign minister and finance minister. She is now a special contributor to Wall Street Week. Having gone to the brink during this week in U.S.-Canada trade relations, we're told that there is a tentative agreement.
29:34What at this point do we know about that agreement? What we know so far is it seems as if the U.S. and Canada have reached an agreement that will prevent the tariffs that the U.S. announced, the brand new ones, the Smoot-Hawley ones, from coming into force against Canada. Those tariffs were meant to be applied on the 19th of August. And on the basis of this almost concluded deal, they haven't come into force. What does it do to the tariffs that have an existing, For example, you and I have talked before about aluminum and steel. What happens to those separate tariffs? So we don't know the full outlines of this agreement.
30:23But what people are saying, both publicly and people close to the negotiations, is that what Canada is agreeing to, or is on the brink of agreeing to, is that U.S. booze will go back on the shelves of Canadian liquor stores. Some Canadian provinces had also put in express either buy Canadian or don't buy American provisions into procurement, that those will be lifted as well. When the 232 tariffs against Canada, the ones on steel, aluminum, cars, when those first went into effect, Canada imposed retaliation. Most of that has already been dropped. And on the other side, the August 19 Smoot-Hawley tariffs seem likely not to go into effect.
31:26And then on the 232 tariffs, which are steel, aluminum, cars and car parts, it seems as if those will remain in place, but at lower levels. And that's really a very critical element of this agreement, because up until now, Canada's position, and this has been Canada's historic position, is we have a free trade deal with the United States. Permanent tariffs outside that deal are unjustified and illegal, and we will not accept them. In this deal, Canada apparently will have lower tariffs on steel, aluminum, cars, and car parts, but tariffs will be in place, and Canada will accept the legitimacy of those tariffs.
32:22That's a real Rubicon that it looks like will be crossed. That feels like a pretty major give from Canada, understanding the tariffs will be lower, but conceding there will be tariffs despite a free trade agreement. I mean, you've negotiated on Canada's behalf in prior trade agreements. Is that something that can ever be pulled back for Canada, or is that more or less a permanent give? David, I think that is the smartest and most important question about this entire agreement. The future obviously is unknowable, but the idea that tariffs on these sectors would be accepted by Canada, that is a really big deal.
33:11Something that I think is really important to point out here is, you know, I think that is a bad outcome for Canada to have permanent tariffs on our steel, aluminum cars and car parts. Really harmful for those sectors. But I think it's a bad outcome for the United States as well. It's really important for people to recognize that you can lose by winning if you've defined winning in a way which is self-mutilating. I would say Brexit, the British decision to leave the EU, is a great historic example of losing by winning. And in this case, I think it's really important for Americans to understand that putting permanent tariffs on Canadian steel and on Canadian aluminum hurts U.S.
34:02manufacturing. These are inputs into the U.S. manufacturing sector, and you are choosing to make your own manufacturers weaker. The tariffs that were imposed in 2018, there have been a lot of academic studies on the impact and the net impact was harming U.S. manufacturing. Aluminum tariffs, particularly self-harming because aluminum is basically electricity in solid form. So the U.S. is basically imposing a tax on electricity. David, you are from Michigan originally. And so you know that Canada and the United States build cars together. We've done it for a century, for more than a century. And imposing permanent tariffs on cars and car parts is really going to hurt Detroit.
34:53So, you know, I think Canadians are real patriots and Canadians want to support our prime minister in this really challenging time. But I think you're going to hear Canadian unions who represent workers in the car sector in steel and aluminum quite concerned. You mentioned the auto industry, which is central, particularly in U.S.-Canadian trade relations. And as you know so well, there are auto parts that go back and forth between Windsor and Detroit all the time, several times in the making of a single vehicle. As far as we know with this tentative agreement, is the effect to just increase the price because there are some tariffs, or could it actually impede some of the flow back and forth?
35:37Another great question, David, and you're right. We really do build cars together, and the parts in a finished car can go across the border seven or eight times before that car is completed. The tariffs on cars and car parts are currently in place, and it looks as if this agreement will lower them. So it will be a better situation compared to what we have before the agreement. It will be worse, though, than the USMCA, where there were no tariffs. And I think the concern for anyone in the car sector will be, are we moving to a situation of permanent tariffs on cars and car parts? Something to watch in the details of the agreement is whether car parts and car parts that are made in NAFTA are excluded from the tariffs in the NAFTA zone or whether only car parts made in the U.S.
36:49are excluded from the tariffs. That's a really important distinction. When we were negotiating the USMCA, there was a moment when Bob Lighthizer wanted to include a provision that would require that a certain percentage of a car be manufactured in the US. Canada and I personally were really, really opposed to that because then you don't have a free trade deal. You have managed trade, and that's an entirely different principle. Ultimately, it's an entirely different way of running your economy. But what Bob was worried about was protecting workers' wages, which was something I did support. So what we agreed and what is in the current USMCA is something called the Labor Value Content Provision, which requires that a certain percentage of a car be made by workers earning above$16 an hour.
37:47I thought that was a great compromise. Protects good paying jobs, but doesn't introduce that element of protectionism. It will be really important to look in the details of a final agreement between the U.S. and Canada to see if it's specifically U.S. made parts that are excluded from the tariffs or whether it is NAFTA compliant parts. You mentioned the USMCA, which you know well having negotiated it. As far as we can tell with this, as I say, tentative agreement, how does it fit with USMCA? Is it an amendment to it? Does it supersede it? Is it subservient to it? How does it fit? Seems like it's completely separate.
38:28Seems like this is like so many of the deals that the Trump administration has been doing around the world. This is a one-off. It is about, you know, the U.S. creating a wound, creating a problem for its partner, and then entering into a negotiation with its partner to have the problem limited. So it's not connected with the USMCA and with the negotiations that have now been, that have been triggered around the USMCA. And that is a challenge because there's going to be a big question about whether some of the key issues that have been agreed in this deal automatically carry over to the USMCA negotiation.
39:24You know so well from your Canadian background that the United States trade policy in recent years has, shall I say, bruised some feelings north of our border. Will this tentative agreement, if it goes forward, help deal with some of those bruises? I don't think so. I think that, you know, Canadians are going to feel that we were being unfairly targeted. We were being unfairly treated. And now we're going to be subject to a little bit less abuse in exchange for not complaining about it. And each person, each worker, each business will judge whether that's a good outcome from the Canadian perspective.
40:13But I think the unanimous national view is going to be we're being treated pretty shoddily. Coming up, saving the southwestern U.S. from drying up. We look at the rationing plan developed by the U.S. government to save the Colorado River, which is responsible for supporting$1.4 trillion of the U.S. economy.
41:04Sam Palmisano:www.chatchypt.com information that you have to grind through to turn into something useful can just become something useful. Put ChatGPT to work on your most ambitious ideas and projects. Get started at ChatGPT.com by selecting work mode, available on plus and pro plans. Here at Bloomberg, we spend all week talking about markets and the economy. And on Fridays, we help you make sense of what it all means for your money. Bloomberg Money is our weekly look at the forces shaping your financial life. We explore personal finance, investing, and retirement with leading economists, strategists, and wealth managers.
41:43Sam Palmisano:Join me, Scarlett Fu. And me, Tom Keen, for smart conversations that help you make better financial decisions. Subscribe to Bloomberg Money on Apple, Spotify, or wherever you listen. You're listening to Bloomberg Wall Street Week with David Weston from Bloomberg Radio. This is a story about triage. The American Southwest, as we know it, was built on cheap, reliable water from the Colorado River. But for decades, the river has been shrinking, and this year, the fight over what's still left has come to a head. It's forcing a showdown over who cuts, who pays, and what life in the West costs when water is no longer a given.
42:24Bloomberg's Michael McKee has the story. We know the situation on the river. It's the worst it's ever been. Without water, there's no Imperial Valley.
42:35Sam Palmisano:We are indeed getting to be in a crisis situation. This has been a very difficult year. This is the Colorado River. At over 1 ,400 miles long, it provides water to seven states across the southwest. Starting in the Rocky Mountains, the river begins as snow, which melts and provides water to the upper basin, made up of Wyoming, Colorado, Utah, and New Mexico, and the lower basin, which includes California, Nevada, and Arizona. The water makes its way to kitchen sinks and front lawns, but also crops and businesses and reservoirs, which generate power for the cities in the western U.S. Lake Powell is very close to what they call the power pool, and that means that once the reservoir is below that level, It can't push the water through the turbines to create power.
43:26Sam Palmisano:That's going to affect power supply. Jennifer Gimbel is a water policy scholar and former Bureau of Reclamation official. She says the river's legal framework was built in layers, beginning with a compact in 1922. But a lot has changed since then. We have no safety net now. When all those other agreements were signed, there was still a decent amount of waters in the reservoir. But no longer. This winter, the Rocky Mountains saw the lowest snowpack ever recorded, and less snow means less water downstream. Dan McAvoy is a climatologist at the Western Regional Climate Center, where he has been tracking snowpack levels across the West.
44:05Why is snowpack so important? Yeah, it's critical to pretty much all of the Western United States in terms of water supply, of course. So for many regions around the West, you know, anywhere from 50 to more than 70 percent of the surface water that's used for public consumption, agriculture, irrigation, all the surface water supply. Most of that comes from the snowpack that accumulates in the winter season and then historically has slowly melted through the spring and summer. But that is changing and we saw a big shift in how that occurred this year. This is a widespread event where we've seen some of the lowest snowpack on record going back 50 to 75 years or even longer.
44:50And so that's something that kind of developed throughout the course of the year. Then we had this really unusual March heat wave, and that triggered this really abrupt and really early snowmelt, leading to this really catastrophic situation in terms of snowpack as a whole across the western U.S. And conditions are probably the worst in the Colorado River Basin right now. That lack of snowpack is now showing up in the system's balance sheet. Lake Mead, which hit its lowest level since it was filled 90 years ago, and Lake Powell are the two biggest reservoirs in the country. Both of their water levels have been declining for decades.
45:25This year, some of the rules governing the river are expiring. And with the seven states unable to reach a long-term agreement, the federal government is stepping in to divide the waters.
45:36Sam Palmisano:So now we're down to the nitty-gritty. who gets it and who doesn't. And you have governor's representatives, and they're concerned about their constituents, they're concerned about the economy, they're concerned about food security, but there's only so much in the river. And as they just couldn't come to an agreement. In the lower basin, the reclamation is called the water master. They control the contracts. In the upper basin, you don't have a large federal reservoir. So we don't have a way to call for that water and have it delivered. It's just a natural process. So each state is in control of its natural resources.
46:25The Bureau of Reclamation recently released plan covers the next two years, suggesting a one and a half million acre foot water usage cut annually for the lower basin, with Arizona getting hit the hardest.
46:36Sam Palmisano:We think I think there are fundamental flaws with the final environmental impact statement, the FEIS. We believe it's not following the law. It has significant problems. What the United States has put on the table simply isn't workable for Arizona. As the general manager for the Central Arizona Project, Brenda Berman oversees allocation of the Colorado River water to nearly 80 % of the state's population. The lower basin states, so Arizona, California, Nevada, What we've done is we've put forward a plan, and we did that before that document came out. And that plan is to create two years of stability on the river.
47:14Sam Palmisano:These reductions that we're talking about, 760 ,000 acre feet for Arizona, is something that our communities have been preparing for. It's not something we think we can do every year, but it's something that we are prepared for in 27 and 28. You can't balance the entire smaller river on the shoulders of either CAP's customers or in Arizona, California, and Nevada. This is something that has to be shared by all seven states who share the river. How do you negotiate something like this? Everybody is in a corner because there just isn't enough river water. Farmers in California are probably feeling like, well, we got it better because we're what they call senior rights to Arizona.
47:57Sam Palmisano:But California is still going to have to take some cuts also. Looking at the upper basin, we've been taking cuts every year during this drought. That old legal order is not abstract. The Colorado River supports$1.4 trillion of economic activity every year and jobs for 16 million Americans. I sell this crop by the ton, by the pound. One of those Americans is Andrew Leimgruber, whose farm in California's Imperial Valley has been run by his family for generations. We are the big target on the river because we are such a large user of water, but we're also the highest in priority. The way it works in the West, the first person to use the water beneficially, first in line.
48:40We have a system of laws in place that would have dealt with this crisis before it got to this point. Unfortunately, you know, Arizona has only been pulling water off the Colorado River since the late 1980s. The CAP was only approved by Congress with Arizona agreeing that in times of shortage, they would be the first to be reduced and cut off. For the last two decades, Arizona has been taking their full entitlement even though they didn't have the need for that water. We have strong standing water rights that have gone all the way to the Supreme Court. They've been adjudicated. They've been passed and processed.
49:15we have the law to stand on. If they can come in and take that away, that means there's no such thing as a property right in the United States. The impact reaches far beyond the Southwest. In the winter, much of the food that feeds American families across the country comes from desert farming regions like the Imperial Valley. Most of the population thinks that the food that they buy at the grocery store shows up in the back. They don't know that it comes from a source, that it's produced. A lot of hands and labor and blood, sweat and tears goes into producing all the bounty that we share in. Are you able to use less water?
49:51There are areas where there is efficiency that can be gained. It just costs money. Utilizing systems like drip, overhead sprinkler systems, automated flood systems, tailwater return systems, all these different uses of technology, but they're very expensive and costly. Unfortunately, the least expensive way to get water off of farms has been following. And following, around here we call it the F word. It's extremely detrimental to our communities. Following puts people out of work. This was not a cheap installation, I would imagine. No. This half-mile system that irrigates 180 acres was a half-million-dollar capital expenditure.
50:32To be sustainable, we also have to be economical and profitable as a business. The benefit, though, is with this system, I can produce more crop. If the Imperial Valley shows the cost of cutting use, San Diego shows the cost of cutting new supply. With an abundance of water at its disposal from desalinization, the county's water authority plans to help ease pressure on Arizona and Nevada for a price. How does this agreement with the other states work? First, we have to get all of the lawyers in the room and we have to get them to agree. We're looking to do things that have never been done before.
51:07As the general manager of San Diego County's Water Authority, Dan Denham is overseeing the effort of turning water into a financial exchange. So you'll send water to the states in exchange for them using less water from the Colorado? We're going to exchange money for water. And that's the basic construct behind it. It's the construct behind what we've done with the farming community in the Imperial Valley. It's allowed the Imperial Valley to make investments on farm efficiency projects while maintaining their high priority rights to water. We receive the water that, as I suggested, would have otherwise been cut by 50%.
51:49So similarly, Arizona and Nevada get water. And for the Water Authority, we get inventory off of our books. Inventory off the books in the form of water means more flattening of rates out into the future.
52:05Sam Palmisano:We can dampen the rate increases that have been really, really tough for us over the past three years. Does it mean that those states will not have to cut back as much? Yes. Desalinization can create a new supply, but it can't put water back in the Colorado River. It shows what scarcity pricing looks like when the cheap supply is no longer enough. This water at the desal plant, the most expensive, costs$3 ,500 a unit. Some of the other sources that we have are$50 a unit. Some are$800 a unit. Some are$600 a unit. Altogether, it's$1 ,400 a unit. And that's what makes this more affordable if it's just the only option you have.
52:51Sam Palmisano:Desalination is expensive, but quite honestly, we have not paid what our water is worth to us. And so we need to make that adjustment in our minds. The next chapter of the Colorado River economy will be built around pricing scarcity, not abundance. It's clear that the basin region needs more than a temporary fix. States and the federal government need to work together to build a new operating system. It all comes down to dollars and cents in economics. Hydrology is not going to wait for us. If there is a bad year next year on the river, the system will crash. We have a responsibility to our national economy.
53:34Sam Palmisano:We have put massive amounts of water out there. It is time for everyone who benefits this river to step up.
53:44Next, Sam Palmisano on the need for AI regulation at the right time.
54:10Sam Palmisano:hours if needed, and turn a goal into finished work. It's designed to help you move from a chaotic starting point to a reviewable first version. So all the source materials, briefs, and scattered information that you have to grind through to turn into something useful can just become something useful. Put ChatGPT to work on your most ambitious ideas and projects. Get started at ChatGPT.com by selecting work mode, available on Plus and Pro plans.
55:04Sam Palmisano:Ask radio to join the conversation right here on Bloomberg Radio.
55:12Artificial intelligence is on its way to transforming the economy, but it isn't the first time in history when a new technology from railroads to electricity to telecom has changed the way we live and the way we do business. Sam Palmisano ran IBM during the digital revolution, and he's seen when government regulation works and when it doesn't. Well, my point of view is that fundamentally, the government, if it needs to facilitate the opportunities of the new technology, not try to control the opportunities of the new technology within guardrails, I would argue that fundamentally what worked really, really well, go back to the Internet, was the private sector given encouragement by the government, but also establishing standards that you take for granted, like connectivity, data sharing, data structure, all those things that are kind of gawpy.
56:02I know they aren't exactly for your consumer audience, but that's where government comes into play. They established those standards, right? They had parties that let the private sector invest, and where it was especially important, like say DARPA, but like national security and defense, yes, there'd be more involvement. But for the commercial world, and we were in all of it, right, they would let that market mature. And you saw that happen in the Internet, and it was incredibly successful. And then there were a lot of debates along the way, as you know, opt in, opt out, all those sorts of things occurred.
56:36But fundamentally, they let innovation go. Now, people argue today, well, it went too far because of the social media guys. It's got a huge dominance here in certain things called advertising, what have you. But fundamentally, antitrust as it exists today does not address that concern because, remember, it was free. This is the flawed and the logic of the antitrust cases because you're not damaging the consumer. The European models are different, as you might recall, but for the U.S. model, it's free, so there's no consumer damage. As we talk about standards or regulation for AI, and again, put aside national security, special case, what we are hearing from a good part of the private sector is, don't do anything because it'll stifle innovation.
57:21You'll slow us down, and we're in a race. How do you assess that if you're the government? At what point does it stifle innovation? What point is it necessary? That's not the user. People forget who it is. I mean, the people in all the meetings are the guys who don't want to be regulated themselves. It's not the people who are regulated, like a bank or health care or energy. It's not the other industries of our economy, which are much bigger than the tech industry, as we all know, right? But fundamentally, so that's the case that they make. I would argue that if the regulation, I take cyber security where I was on the commission for President Obama, you might recall.
58:01I mean, our point of view, when they say we're going to slow down innovation, I said, no, you only slow down innovation when you didn't design for it to be secure. If you designed up front for it to be secure, you don't slow it down. So why don't you accept the fact that you can design for a more secure internet, which we still don't have today, and then therefore you're not slowing down innovation because everybody's competing for those standards. You need guardrails. I say guardrails. I don't know that you need heavy regulation, but you need the appropriate, their associations, their standards, bodies, all these things already exist.
58:33You need their influence, I think, over how to do this properly and securely. It'll still be a huge market. This is the thing. It'll be a huge market. It's not going to all of a sudden, maybe it goes from 5 trillion to 4.2 trillion, whatever. It's not going to be a tremendous opportunity, But I think people should step back and have a perspective that what they should do is, in the long term, what's right for their customers, what's right for the society, and then you can argue it's right for the shareholder. Do we have the expertise that we need in the government to make these judgments? I mean, going back to the railroads, we had the Interstate Commerce Commission.
59:11Correct. In broadcasting, you had the FCC. Yes. You had people who really knew this stuff. Do we have people within our government who have that expertise? My observation at this point, at least in this field that we're talking about, I think the government is way behind the private sector. Not true in the past. There really were smart people when I was working in government. We might not agree, but that's a whole different point of view versus they weren't really smart. And I say that you have to bivocate government because if you get into where I spent a lot of time like national intelligence and defense they got some really smart people but you get the commercials out of government um i would not say they were honor roll students when they got out of school so if you had your way you could decide anything would you create a new agency specifically on the ai subject no what i would do at first i i there's too much bureaucracy already and just adding more bureaucracy and would you know then they'll fight about who has control who gets the money and that's solve that in cyber so we've lived that one recently i i think what you would really do i i would if possible i'd get some volunteers i think they call them something like subject governmental subject experts or something like that you know get some volunteers really smart people uh who've been around this thing who have industry knowledge and no technology who could come back with a strategy for the united states of america and obviously there'd be debate there'll be adoption, I got all that, right?
1:00:41But some very thoughtful group of people that are respected that can do that. I'd make the same recommendation for some of the agencies that exist within our government that need to have a different point of view than they have today. That would be, I believe, the most beneficial way to start. Then you can decide where it resides. I can go back to cyber as my analogy. I mean, everybody wanted to be participants in that because they saw money coming, whether it was OMB or it was people not. I mean, NSA, CIA, DOD had the expertise, but they didn't have any interest. Everybody else in the commercial side of government, right, always coming after the money.
1:01:23They saw the money coming. And I used to ask them this question. I said, can you show me the people you would use to staff the projects? It's just a resume. I can read a resume. I know technology. Can you show me that? And the response was, you know we don't have those people. And my response was, so I'm going to recommend to the president of the United States, where you don't have any expertise and talent, they fund you. And they go, yeah. I said, no. That's a true story, by the way. And the executive office is across the street from the White House. As we sit here today, how concerned are you about safety with AI?
1:02:00There are a lot of reports now about breaking out of sandboxes and AI sort of going off on its own and doing things it's been told not to do. Is that a big concern for you? If it's not addressed, yes, because it's only going to compound and get worse, right? Remember, as we say that, I mean, maybe it's accidental today when the agents get out of control or those sorts of things, and they're causing these issues as far as security and the concerns that we have today. but the point of it is that it's moving so fast and the technology is so fast if something's not done to address it it could be out of control in like minutes or seconds in those sorts of things so I think it needs to be addressed and it can be addressed it's back to this how you put these guardrails in place you know the red the rigor of the testing before you deploy and it's all sorts of things that can be put in place now of course guess what it takes time and it cost money you know right but the companies that are right now at least one of them has earnings and evaluation is off the charts right all the others have evaluations that are off the charts without money so there's a lot of money out there if I would argue if they would be encouraged to deploy it in a way that's sustainable for the long term I mean there's debates that they have to me with certain agencies of government where you're putting the country at risk are nonsense.
1:03:22Only people that would make that argument are immature and young. My generation would never have made that argument that you should put society at risk. But IBM was doing all that stuff. I mean, it would be totally irresponsible. We wouldn't even think about it or dream it. You know, now it's, you know, right? No, no, you guys don't get it. Let it rock and roll. I just think that in today's environment, the technology is such, I mean, it's only going to get faster. It's going to be light speed. Who can control? So human beings can't control light speed. And that's where it's going to go. That's where it's headed.
1:03:54And so therefore, at that level of speed, you're going to have to have those controls built into the systems to self-police them. That does it for us here at Wall Street Week. I'm David Weston. See you next week for more stories of capitalism.
1:04:21Sam Palmisano:The Bloomberg This Weekend Podcast. News, analysis, and the lighter side of Bloomberg, including our weekly news quiz. Which edgy American mall staple is being sold to the parent company of Spencer's and Spirit Halloween? I wrote Claire's. That's not edgy. What are you talking about? This is a place David Vera has never shopped in his life. Hot topic. Hot topic. The Bloomberg This Weekend Podcast. Subscribe today on Apple, Spotify, or wherever you listen.
From the publisher
This episode originally aired on August 21st, 2026.
On this fan favorite edition of the show for the long holiday weekend, former IBM CEO Sam Palmisano discusses why companies may benefit from having a “Plan B” for AI implementation. And, AI may not be causing mass layoffs yet, but early data suggest it is already slowing wage growth in exposed occupations. Plus, Canada may have dodged Trump’s newest tariffs, but the deal could leave lasting damage to USMCA, North American manufacturing and trust between two longtime allies. Later, who pays when the Colorado River can no longer meet the West’s demand?
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