Episode Transcript
[00:00:00] Good morning everyone.
[00:00:01] Today, after a two week tour through Asia, I want to spend some time on a topic that is a little bit contrary to what we've been saying about jobs and the super worker. And that is something I would title. AI is messing with the job market in ways we never anticipated. So as part of my trip through Asia, I think I met with 50 companies over there easily and did a bunch of speeches and I came to an interesting conclusion that I want to share with you. Bear with me for one minute. The last time we've had a technological revolution as big as the one we're now living through an AI was in 1981 when the IBM personal computer was introduced. And the reason I say that is prior to 1981 the only way you could touch a computer was either to go into the basement and get a badge reader and go into the mainframe computer room or maybe go to a museum. We didn't have access to computers at all. We had terminals. And then all of a sudden in 1981 we got PCs. And the original PCs were single function machines. They were mostly used for word processing originally and then later for spreadsheets and then thousands of other applications. And the impact on our jobs and our lives was kind of minimal at first because a lot of people resisted this. In fact, I was telling a lot of the folks in Asia that when, when I first got involved in this as a sales guy at IBM, a lot of executives refused to use PCs because they considered them to be administrative tools and they had their secretaries do their PC stuff for them. Of course, fast forward 40 years later, that's kind of a ridiculous thought. But that thing, that computer, that semiconductor based, operating system based, application based system created billions of new jobs. Billions. There was no package software industry before that. There was a limited, if any semiconductor industry before that. Retail distribution of computing and technology didn't exist before that, other than maybe buying CD ROMs or what were called M Music players. The database industry didn't exist before that. The corporate application industry kind of existed, but it was all based on mainframe. So it wasn't very big and there weren't that many companies in it. Just thousands and thousands of new things were created. Jobs roles, opportunities, technologies, innovations. Because of the pioneering technology of bringing a computer out of the basement and giving it to an individual. The Macintosh, for those of you that don't know, was all about anti PC. The whole reason that Apple exists is to make it easier to use a computer than IBM. That was Steve Jobs mission. And then he obviously did it in music, and then he did it with the phone. So that breakthrough created many, many jobs and many careers for us. If you're a software engineer now, for example, the beginnings of being a software engineer were things like Lotus 1, 2, 3 in 1981 and the job market and the digital transformation that then took place later. And the Internet and social media and all those, and even mobile apps all started with this idea of getting the computer away from the professional computing people and giving it to us. Well, in some ways, AI is exactly the same thing. All of these computing things we have used in our lives, whatever manifestation they may be, were programmed in advance. Until now. AI is not programmed in advance. It learns from us. So the paradigm of using a computer to do something that was programmed by somebody else, and then you play a game or you listen to music, or you analyze data, or you input transactions, or you schedule your shift or whatever it may be, is completely different now, because the computer, rather the software, is learning from you. You're not telling it what to do, it's telling you what to do. Or at least it's sharing with you what it has learned. And through the magic of what are called embeddings or vector processing, every word that it gives you or that it creates for you, it kind of knows what that word means in a sense, because it knows mathematically all the other words that are related to that word. So it looks like and feels like and behaves like it's very intelligent, quote, unquote. I mean, I have this daily ritual that I use a lot where I ask, oftentimes through Galileo, a bunch of questions about the news and the economy and different companies, and I ask for the history of companies, and I ask it to go find data for me. And it's responding to me as if it actually understands what I'm asking. It doesn't understand what I'm asking. It's doing mathematical calculations of every word in my inquiry and computing based on those mathematical calculations what the responding tokens or words should be. But it does it so fast and so exhaustively that it feels like it's listening to me. And it's getting smarter because as I use it, these embeddings are getting a little bit more tuned to either what I'm trying to ask or what other people are trying to ask and the way these words relate to each other. So if a new word entered the vocabulary that it never knew before, within a very short period of time, it would, quote, unquote, understand what that word is, because the embeddings would pick up the related usage of that word relative to other words or tokens. And it doesn't know what a word is, by the way. It's just a token. And by the way, this is the same thing. Video and graphics and other forms of AI work. So now we have this thing that is learning from us, learning from our world, learning from our language, learning from our visible world, and eventually learning from our physical world that we can use as easily as talking to it. We don't have to program it. We don't need an engineer or a software person to manipulate it. That, to me, is as big a paradigm change as the original personal computer. And we don't really know what's going to happen. Now, I see the symptoms of this, and I talk to many, many, many of you about it. Of course it improves productivity. Of course it makes things easier. Of course it writes for us and creates documents and images and graphics and movies and even eventually music, I assume. But now think about the impact on the job market. Now, our philosophy and thesis, which has bared out to be true so far, is that this creates superworkers in a sense, that we as humans, as genetic, you know, biological animals with biological intelligence, which is much more subtle and much more exhaustive and complete, in a sense, than machine intelligence. We use the machine intelligence to make ourselves smarter and to do better things and create more profitable and growing businesses. All kind of get that idea. But in order to get there from here, there's a massive amount of disruption taking place. And let me give you some examples. So. So in the United States, where there's about 165 million working people, there are essentially no jobs that have been created in 2025. I mean, you can debate what the Bureau of Labor Statistics data shows, but there was a new data set from ADP that came out this week. The federal one is now not coming out anymore, at least for a while, and it more or less shows zero growth. A new layoff report from Challenger, the company that manages and statistically validates layoffs, so said that there were a million job deletions or a million layoffs so far this year. This number is greater than all of the layoffs they've seen since 2020, I think. So that's 25 years ago. That's not good.
[00:07:50] College grads are having a hard time finding work. There's a exhaustive article in the Wall Street Journal and also the New York Times and other places about this that upwards of 30% of college grads are unable to find A job and they're either moving back in with their parents or, or they're working as, you know, retail workers just to keep them going until they find something better. My nephew, who just got his CPA and is a newly minted accountant, told me that when he sends emails to clients, he doesn't even send the emails. He gets ChatGPT to craft the emails for him because it's easier for him to do that than to define what he needs. He's a little worried about his job even though he just started in his new career. And then there's a really interesting article that was, that had many, many examples and case studies in it of the entertainment industry in Southern California, which is mostly middle income creative workers running sound studios, creating graphics, doing scripts, doing writing, doing people's hair, doing people's makeup, doing all of the movie production and television production things that we love. That they're all worried about their jobs and that many of them are now deciding to work in food service and retail and maybe even healthcare because they can't find enough work. And it's not just that movie industry is moving around the world, it's that actually it is a lot easier to build movies than it ever was before. And I was then thinking as all this was going on about a funny experience that I had maybe two months ago when I was doing a video, a very detailed video for a company called Big Think Plus. It was, came out pretty well. But I went up to a house in Berkeley where they had outfitted a video studio for me and there must have been six people there, six, five or six cameras, all sorts of recording equipment, all sorts of sound deadening equipment. It took them an entire day to set up the room for me to come in for two or three hours and do the video. And I was looking around and thinking, wow, are you guys aware of what's going on in AI? And they kind of looked at me and said, yeah, yeah, we know, we know, we know. But this is what we do and this is what we love to do and this is what we know how to do. So every single one of us, including those of us in HR and recruiting and training, analytics and data management and leadership, whatever your job might be, sales, marketing, we're all facing this intelligent technology as transformational as the first computer we ever put our hands on, suddenly entering our lives through chat, like Galileo, through voice, on our phones, through our glasses, through meta, and probably through embedded devices that will either be woven into our clothes or just embedded into the environment. You know, when you go to China and Japan and some of these. More advanced is the word maybe that I would use. Countries where they have newer technology, there's cameras everywhere. It's interesting when you move around from country to country in Asia, your photo is all you need. All you need is your face. I mean, they do look at your passport, but they don't really use it anymore because your face is everywhere and easily used and understood by the AI. So these AI systems are radically changing the job market. And right now it is hard to find a job, it is hard to change jobs. The job market's frozen in the United States. People are not moving between companies very much at all right now because they're worried about this. And most big companies are trying to lay people off or downsize to meet the needs of Wall street because as I talked about in Asia, in the US alone, investors have already allocated a. A trillion dollars, excuse me, a trillion dollars to data centers energy build out of facilities for AI and chips that has gone into Nvidia or other companies. It's interesting how Oracle, which is essentially a software company, is now becoming a data center company because they're, you know, smart enough to see the writing on the wall here. And that trillion dol the US economy to the tune of about 3% of the economy.
[00:11:57] Honestly, I think the stock market is way overpriced for what's really happening. But you know, I'm not certainly the one to get investment advice from. I think we're in a massive bubble here of build out which is eventually going to pay off, of course to some degree, but there's going to be a lot of winners and losers. And if you're looking for a job or you're hiring people, you're scratching your head and saying, what about me? What does this mean for me? And it is an uncertain and frightening experience. All of the Gallup data shows that employee engagement is way down. Fear is up for a variety of reasons, politics being another part of it. But a lot of this has to do with economic uncertainty. And you know, in the countries that have low birth rates, particularly in Japan, where I just was, this is either going to be good in the sense that we'll be able to do more economic growth with fewer people or it'll be make life even worse. Because if we can't transform people into these new jobs, those countries aren't going to have young workers who know AI entering their workforce. By the way, if you read about what's going on in the education sector, and most of you probably Know this. If you have kids, young people now coming out of high school and college are using AI for everything. I mean, everything. Research, editing, analysis, all their homework, et cetera, et cetera. And so they're coming into the workforce trained on PCs. In a sense, if you think back to the 80s already way ahead of those of us that are trying to figure out how to use this stuff. You know, the paradigm is so different. The paradigm of technology in the past in the business world was always buy the software or tech, teach people how to use it, roll it out, and then move on. This is not like that. This stuff changes all the time. It gets smarter all the time. It gets more intelligent based on the data you put into it. Once you feed it, it grows and gets smarter. So you've got to be careful what data you put into it. And as I told everybody I talked to all over Asia, you learn about AI by using AI. You don't learn about AI by taking a course on AI. It's great to take a course on the background and how it all works, but you're not going to really know how to use it until you actually do use it. And that's the way it was with the PC, by the way. If you didn't use it, you didn't really know how to use it. And I think Apple made a big step forward with their operating system. But even today, 30, 40 years later, people are still hacking around their PCs, sharing tips on how to do this and how to do that. And that's going to be true in the world of AI for many, many decades yet to come. So the job market is really under stress. Now let's talk about the job market and what the job market is. The job market is a market that is dynamically reinventing itself through the creation of jobs by companies or managers. So when you sit down in your marketing team or sales team or HR team and you say to yourself, I need, because I have this new technology or whatever it is that's changing around here, I need somebody to do X. And you try to create a job title for X and you create a set of responsibilities in a job description. You have just created a new job. You have just changed the job market. Because if you created something new, it didn't exist before. And all of a sudden people apply for this job and try to align their skills toward this job. So it's a very, very dynamic supply and demand system.
[00:15:19] So by looking at the job market and studying it, you can very easily see the microeconomics of what's going on in the world. I know because we look at this and we have most of the lightcast or a lot of the lightcast data in Galileo. You can ask Galileo all sorts of questions. What are the trending skills in chemical engineers? What are the trending skills in recruiters? What are the trending skills in business partners? And on and on and on so we can observe this transformation taking place. And I'll tell you, it's happening fast. It is happening fast. The data shows that job titles, skills that are required, experiences that are required are rapidly moving towards this AI centric world we live in now. The fear factor is really one of our issues in change. We spent a lot of time with a lot of chros talking about transformation. And what I found and discovered was everybody gets this, but they don't know how to do it. And it's not that they don't know how to do it technologically. That's challenging enough because there's 10,000 people selling stuff, but they don't know how to do it organizationally either. So they're going through this blooming of flowers approach where we let people play around with the copilot and other tools and then we see what they're doing with it. By the way, one of our clients over there is really big into Galileo and they have, this is a company in Japan and they have already discovered, you know, dozens and dozens of things that I didn't even think were possible, to be honest. And we will be sharing all this with you. And then they say to themselves, well, that's great, but we want to do these bigger projects. So how do we realign our jobs towards these bigger projects? And so there's new jobs being created inside companies in real time right now. One of the companies I talked with was a large bank, Standard Charter. I met with the chief transformation officer there who's quite, really good at this. She's an ex Deloitte person and actually worked with a lot of people that I worked with. And they've, you know, decided to build a new agent that's going to do onboarding around Standard Charter in a very strategic way, interacting with successfactors in many of their other systems. And she said, you know, it's really a challenge because the technology's immature. A lot of the agent to agent protocols don't exist yet. One's called MCP and the other one's called A2A. And you know, the vendors are working directly with us and we're, you know, kind of winging this on Our own. And there she's way advanced for many of you I know. So this is an interesting world of building new jobs and responsibilities and job titles to respond to technology that is changing every month. So even if you could define what an AI implementation person, whatever that means, does, six months from now it's going to be different. Maybe three months from now it's going to be different. So the number one capability building exercise you have here is trying and using and experiencing this stuff. Now, the reason I'm a little bit worried is that a lot of jobs are being destroyed and it takes time for people to reinvent themselves. Some people are good at it, some people are not. And as employers, as HR people, as business leaders, as managers, we have to facilitate and encourage this. When you read our new research on the super manager, the super manager is someone who's good at this, is someone who encourages people to experiment, supports people learning, shares information between others in the group and helps to prioritize where and how we should invest in these new technologies. If I go back to the 80s with the PC, I would say the number one use of the PC for the first year or two was word processing. Word processing was actually a big deal because you had to do it by paper. I mean, other than a word processor, dedicated word processor, which nobody could afford at the time, none of us had ever had something we could write into. And then of course, email came along and the breakthrough application was the spreadsheet, which then became the birth of Lotus 1, 2, 3, which then became the birth of Microsoft Office. And you know where that went.
[00:19:15] So, you know, some of those paradigms that we now consider to be very routine got invented in the first five to 10 years of that industry and all the jobs that were related around it. So what we have to do as professionals, as HR people, as policymakers, is we have to facilitate and monitor and move dynamically in the direction of these new jobs that are being created. And I'm not going to sit here and do one of these research reports like you read from LinkedIn, where they try to define where everything's going because it's just moving too fast. And you know, any report you read on the future of jobs and skills in October, you can sort of reread it in December, because it won't be that good for very long at the rate at which things are changing. Okay, My, I guess pessimism about this is that there are going to be people and companies that are going to be left behind. Now, that doesn't mean they're going to go out of business or that everybody's going to be unemployed because there's plenty of jobs still out there. 30% of the new jobs being created right now or more are in healthcare. And healthcare is a human delivery service, sales, marketing. I still believe that the human intelligence is so much more vast and creative than the machine intelligence that every job that becomes an AI centric job will have a higher level job around it for the humans. My nephew, who does accounting now, and he's relatively new to it, but he's been doing it for a while, is very smart about business and people and he can look at sort of the high level issue of what's going on. He does accounting for nonprofits. And so even though I know he's a little worried about AI taking over some of his tactical stuff, I think his judgmental and experience and perspectives on the financial world and accounting is going to be good and he's going to use it. So we're all going to become better business people, more systemic thinkers, more complex problem solvers and so forth. But it's going to be messy. And right now what keeps me up at night a little bit is the fact that we're maybe over built out and overinvested in AI and the rest of the economy is just about to feel the shock. And we could have a couple of quarters of a lot more layoffs and a lot more unemployment and it's up to us as business people to prevent that. I firmly believe that one of the biggest changes that's been taking place in business and HR is systemic hr. And in the systemic HR operating model, you wouldn't go through cycles of layoffs like we see. You wouldn't be hiring and hiring and then laying off and laying off and hiring and hiring and laying off and laying off because you would see the bigger system and you would be planning internal mobility and career growth at the same time. Your company is changing.
[00:21:56] So we as the leaders of the micro economy, the businesses, the companies that employ people, we are very much responsible for managing and making the best of this job transformation that's taking place, which is why we do so much of this in our company and why we spend so much time sharing with what you with what we learned. Okay, finally, let me talk about what's happening in the next week. So there's two really interesting podcasts coming out. One about Micron Technologies evolution to AI based skills based job design, and the other one from Seagate, Patricia Frost. One of the most fascinating and interesting chrs I've ever met talking about her career and what's going on at Seagate. I'm going to be at the SAP conference in Vegas this week and we're going to be introducing the Galileo solution for Joule for SAP customers. We're going to be demonstrating it there. So those of you that are SAP customers please take a look at what's happening and then reach out to us or SAP and we'll explain to you how you can bring the powers of Galileo to all of your employees, not just your HR department through SAP. It's early days on this stuff but it's coming and it's working. And we'll also be talking more about, you know, where AI is going and I'll give you a good feedback on what I learned about at SAP. SAP, interestingly enough, maybe a little bit different from Workday in terms of their strategy has been building AI infrastructure now for two or three years.
[00:23:23] So they have a lot of their own AI technology under the covers. I think Workday's technology is similar but they're a little more going through the acquisition strategy. But anyway, I'll share that with you. We also are getting ready to go to Europe to unleash where we'll be talking about the Super Manager and I'll be talking about that and the tech market in general at the conference. And I really want to encourage you to get Galileo because as an HR person or as a manager, it is a ready made solution to teach you about AI, give you hundreds and hundreds of pre developed prompts for HR for management, for leadership. You can flip between Galileo for HR and Galileo for managers with the click of a button. So if you're not an HR person, you can turn Galileo into a management coach and a management tool. And of course it's an open system so you can load your own company data in there and your own personal data in there and turn it into, you know, your own personal agent. We're also going to be doing an introduction on what we call and what is called the Digital Twin in October. We're working very closely with a very cool startup company that has built a digital Twin. We are using it in our company here so I can give you lots of details on how it works and what it does. And that'll be coming a little bit later in October, so stay tuned for that. So there's going to be a lot of cool stuff coming out from us in the next couple of weeks. But anyway, this job market thing is really somewhat dizzying and I hope this conversation gives you a little bit of perspective and reminds you that your job is to help people develop, transform, invent their careers, move into these new rules, learn about the technology, feel comfortable with technology, and this is really part of running a company. At this point, you have no choice but to do this yourself. You can't wait to for some vendor to come along and give you some end to end solution that's going to do all this for you. Okay, more to come. Have a great weekend everybody. Talk to you guys next week.
