1 00:00:03,990 --> 00:00:06,390 Sean Aylmer: Welcome to the Fear and Greed Business Interview. I'm Sean 2 00:00:06,600 --> 00:00:10,590 Sean Aylmer: Aylmer. Artificial intelligence isn't just coming, it's here, and we're 3 00:00:10,590 --> 00:00:13,830 Sean Aylmer: starting to get a clearer picture of how it's going 4 00:00:13,830 --> 00:00:16,919 Sean Aylmer: to affect the workforce, the jobs it will make easier and 5 00:00:16,920 --> 00:00:21,360 Sean Aylmer: the ones it may replace altogether. GSFM fund manager partner 6 00:00:21,390 --> 00:00:24,960 Sean Aylmer: EPOC has released a white paper exploring the impact on 7 00:00:24,960 --> 00:00:28,139 Sean Aylmer: labor markets and as a result, the economic impact and 8 00:00:28,139 --> 00:00:31,559 Sean Aylmer: the implications for investors. Remember, this is general information only 9 00:00:31,559 --> 00:00:34,888 Sean Aylmer: and you should seek professional advice before making any investment 10 00:00:34,889 --> 00:00:39,720 Sean Aylmer: decisions. Dr. Kevin Hebner is global investment strategist at EPOC 11 00:00:39,780 --> 00:00:43,229 Sean Aylmer: Investment Partners. He joins me from New York. Kevin, welcome 12 00:00:43,229 --> 00:00:43,949 Sean Aylmer: to Fear and Greed. 13 00:00:44,490 --> 00:00:45,598 Kevin Hebner: Hey, Sean, how are you doing? 14 00:00:46,229 --> 00:00:49,439 Sean Aylmer: Well, thank you. Look, the whitepaper describes AI as the 15 00:00:49,440 --> 00:00:54,360 Sean Aylmer: fourth wave of digital technology after the PC, internet, and 16 00:00:54,360 --> 00:00:57,000 Sean Aylmer: mobile phones. Are we ready for it? 17 00:00:57,960 --> 00:01:01,590 Kevin Hebner: Well, I don't know if it matters if we're ready 18 00:01:01,590 --> 00:01:04,559 Kevin Hebner: for it. It's coming. As you said, we are living 19 00:01:04,559 --> 00:01:08,279 Kevin Hebner: in the age of AI, and to use an American 20 00:01:08,280 --> 00:01:11,938 Kevin Hebner: metaphor, we're in the first inning. This is just getting 21 00:01:11,940 --> 00:01:15,180 Kevin Hebner: started, but it's going to be the key investment theme, 22 00:01:15,540 --> 00:01:18,420 Kevin Hebner: certainly for this decade, maybe for the next couple of 23 00:01:18,420 --> 00:01:22,768 Kevin Hebner: decades. And it's not going to affect just the investment 24 00:01:22,770 --> 00:01:26,910 Kevin Hebner: landscape. But for example, we think 60% of jobs will 25 00:01:26,910 --> 00:01:29,849 Kevin Hebner: change pretty fundamentally over the next decade or two. 26 00:01:29,879 --> 00:01:33,330 Sean Aylmer: Okay, so let's talk about the jobs. What's interesting in 27 00:01:33,330 --> 00:01:38,399 Sean Aylmer: the report is technology forever seems to have been going 28 00:01:38,400 --> 00:01:42,029 Sean Aylmer: to change manual jobs. We're going to have robots doing 29 00:01:42,030 --> 00:01:44,969 Sean Aylmer: all that. And to some extent, assembly lines and things 30 00:01:44,969 --> 00:01:47,489 Sean Aylmer: like that have been mechanized. But when it comes to 31 00:01:47,490 --> 00:01:52,950 Sean Aylmer: AI, it's not necessarily the manual or the trades jobs 32 00:01:53,040 --> 00:01:55,650 Sean Aylmer: that are going to be hit hardest. It's kind of the other end 33 00:01:55,650 --> 00:01:57,510 Sean Aylmer: of the spectrum, the white collar jobs. Is that right? 34 00:01:58,170 --> 00:02:03,240 Kevin Hebner: Yeah, and if you go back centuries with technology, whether 35 00:02:03,240 --> 00:02:05,459 Kevin Hebner: it was the printing press or the steam engine, it 36 00:02:05,459 --> 00:02:10,138 Kevin Hebner: was primarily manual jobs that are affected. AI is different 37 00:02:10,530 --> 00:02:14,010 Kevin Hebner: and that a lot of manual jobs say trades like 38 00:02:14,010 --> 00:02:18,840 Kevin Hebner: electricians and carpenters aren't very exposed at all, whether different 39 00:02:18,840 --> 00:02:25,889 Kevin Hebner: types of knowledge economy jobs, doctors, nurses, teachers, lawyers are 40 00:02:25,889 --> 00:02:29,430 Kevin Hebner: the ones that most exposed. Exposed doesn't mean you're going 41 00:02:29,430 --> 00:02:31,290 Kevin Hebner: to lose a job. It means your job is going 42 00:02:31,290 --> 00:02:33,870 Kevin Hebner: to change and it's good to be in a job if 43 00:02:33,870 --> 00:02:37,770 Kevin Hebner: you're exposed and as a result, your productivity improves because 44 00:02:37,770 --> 00:02:40,500 Kevin Hebner: then you're going to be paid more. But obviously it's 45 00:02:40,500 --> 00:02:43,649 Kevin Hebner: bad if you're exposed and then AI replaces your job. 46 00:02:43,950 --> 00:02:47,610 Kevin Hebner: For example, if you're a translator or you're working at 47 00:02:47,610 --> 00:02:51,029 Kevin Hebner: a call center, those places look difficult. But yeah, this 48 00:02:51,030 --> 00:02:53,518 Kevin Hebner: is very different technology from previous waves. 49 00:02:54,090 --> 00:02:56,339 Sean Aylmer: Okay. Is it kind of any role that's got a 50 00:02:56,340 --> 00:02:59,549 Sean Aylmer: high level of cognitive functioning? Is that broadly what we're 51 00:02:59,549 --> 00:03:00,119 Sean Aylmer: talking about? 52 00:03:00,389 --> 00:03:03,600 Kevin Hebner: Well, those are exposed, but if you're a teacher, for 53 00:03:03,600 --> 00:03:07,350 Kevin Hebner: example, so you're highly exposed by, in all likelihood, the nature of 54 00:03:07,350 --> 00:03:10,109 Kevin Hebner: your job is going to change a lot. You have 55 00:03:10,230 --> 00:03:12,360 Kevin Hebner: some type of chat box to help you as a 56 00:03:12,360 --> 00:03:15,120 Kevin Hebner: teaching assistant. So the way you do your job is 57 00:03:15,120 --> 00:03:17,279 Kevin Hebner: going to change a lot. The way that students learn 58 00:03:17,279 --> 00:03:19,950 Kevin Hebner: will change a lot, but it should be that teachers 59 00:03:19,950 --> 00:03:23,430 Kevin Hebner: are more productive, they're more effective, and they're actually going 60 00:03:23,430 --> 00:03:25,110 Kevin Hebner: to be more in demand and I think will be 61 00:03:25,110 --> 00:03:29,669 Kevin Hebner: paid more. Similar with doctors and nurses, lawyers, I think 62 00:03:29,669 --> 00:03:33,150 Kevin Hebner: it's a complementary technology. Their job will change a lot. 63 00:03:33,150 --> 00:03:36,690 Kevin Hebner: They become more productive, and one consequence will be the 64 00:03:36,690 --> 00:03:38,520 Kevin Hebner: remuneration will increase. 65 00:03:39,000 --> 00:03:41,310 Sean Aylmer: Okay. When we hear about these things, people immediately get 66 00:03:41,310 --> 00:03:44,910 Sean Aylmer: scared. What though should they be doing right now? So 67 00:03:44,910 --> 00:03:47,460 Sean Aylmer: if I'm a teacher and I'm married into a family 68 00:03:47,460 --> 00:03:50,879 Sean Aylmer: of teachers, my partner Jackie, she isn't, but all her 69 00:03:50,880 --> 00:03:54,240 Sean Aylmer: family are teachers. What should they be doing now? Because they 70 00:03:54,240 --> 00:03:56,790 Sean Aylmer: can take advantage of what's going on, particularly if there's 71 00:03:56,790 --> 00:03:58,590 Sean Aylmer: high pay at the end of it, rather than being 72 00:03:58,890 --> 00:04:00,240 Sean Aylmer: scared of it. What should they be doing? 73 00:04:00,840 --> 00:04:03,599 Kevin Hebner: Well, I think for a start, this is sort of 74 00:04:03,599 --> 00:04:08,969 Kevin Hebner: normal. For example, 60% of the jobs today did not 75 00:04:08,969 --> 00:04:12,450 Kevin Hebner: exist 80 years ago, so not that long. If you 76 00:04:12,450 --> 00:04:14,940 Kevin Hebner: try to explain what you do for a living to 77 00:04:14,940 --> 00:04:19,260 Kevin Hebner: your grandfather or grandmother, they're going to look at you and say, Sean, that's very 78 00:04:19,260 --> 00:04:22,320 Kevin Hebner: nice. People pay you to do that? Because it's really 79 00:04:22,320 --> 00:04:25,529 Kevin Hebner: out of the realm of their experience and how they 80 00:04:25,529 --> 00:04:28,498 Kevin Hebner: were brought up, and that's true of 60% of jobs 81 00:04:28,500 --> 00:04:32,400 Kevin Hebner: today, they didn't exist not that long ago, and the 82 00:04:32,400 --> 00:04:34,770 Kevin Hebner: pace is speeding up. And so we do have to 83 00:04:34,770 --> 00:04:37,560 Kevin Hebner: think about it. So it's sort of the way things 84 00:04:37,560 --> 00:04:40,230 Kevin Hebner: have always been. It's just the pace is going to 85 00:04:40,230 --> 00:04:43,709 Kevin Hebner: be quicker. And then the types of jobs. So for 86 00:04:43,710 --> 00:04:47,520 Kevin Hebner: people who are starting careers... And I think the notion 87 00:04:47,520 --> 00:04:49,650 Kevin Hebner: that you're going to have one career for the next 40 or 88 00:04:49,680 --> 00:04:53,430 Kevin Hebner: 50 years and it's not going to change, I don't think... 89 00:04:55,620 --> 00:04:58,230 Kevin Hebner: That's been a thing for decades now. It certainly won't 90 00:04:58,230 --> 00:05:00,630 Kevin Hebner: be going forward, but I think you want to make 91 00:05:00,630 --> 00:05:03,928 Kevin Hebner: sure that you have skills so you can interact with 92 00:05:03,928 --> 00:05:08,130 Kevin Hebner: people, good communication skills, good empathy, good creative skills, but 93 00:05:08,130 --> 00:05:10,920 Kevin Hebner: also you have to understand how the machine works so 94 00:05:10,920 --> 00:05:13,860 Kevin Hebner: you can work well with the machine. And that's certainly 95 00:05:13,860 --> 00:05:17,759 Kevin Hebner: true, whether you're going to be an educator, a doctor, 96 00:05:17,759 --> 00:05:21,239 Kevin Hebner: a nurse, a lawyer, any of these types of jobs 97 00:05:21,270 --> 00:05:26,700 Kevin Hebner: in the knowledge economy, good communication, interpersonal empathy skills, but 98 00:05:26,700 --> 00:05:30,150 Kevin Hebner: also you want to work with the machine. Ultimately, if 99 00:05:30,150 --> 00:05:32,760 Kevin Hebner: you don't do that, then someone who works with AI 100 00:05:32,820 --> 00:05:36,208 Kevin Hebner: will replace you. You don't have to be a programmer, 101 00:05:36,540 --> 00:05:39,240 Kevin Hebner: but you have to get used to and feel comfortable 102 00:05:39,240 --> 00:05:44,970 Kevin Hebner: with a computer. During my career, initially, you learned to 103 00:05:44,970 --> 00:05:47,940 Kevin Hebner: work with PCs and then the internet and so forth, 104 00:05:48,150 --> 00:05:50,940 Kevin Hebner: and so you have to be flexible and learn to 105 00:05:50,940 --> 00:05:54,359 Kevin Hebner: work with technology, use it as a tool that complements 106 00:05:54,360 --> 00:05:58,469 Kevin Hebner: you, makes you more productive and increases your renumeration as 107 00:05:58,469 --> 00:05:59,159 Kevin Hebner: a result. 108 00:05:59,820 --> 00:06:01,320 Sean Aylmer: Stay with me, Kevin, and we'll be back in a 109 00:06:01,320 --> 00:06:10,709 Sean Aylmer: minute. My guest this morning is Dr. Kevin Hebner, global 110 00:06:10,710 --> 00:06:15,330 Sean Aylmer: investment strategist at EPOC Investment Partners. (inaudible) I do 111 00:06:15,330 --> 00:06:17,520 Sean Aylmer: a couple of things, this podcast, but I also have a 112 00:06:17,520 --> 00:06:19,950 Sean Aylmer: little writing agency. And what we have found, my partner 113 00:06:19,950 --> 00:06:22,500 Sean Aylmer: and I, initially, we looked at AI and thought everyone's 114 00:06:22,500 --> 00:06:25,260 Sean Aylmer: just going to use AI to write. In actual fact, 115 00:06:25,440 --> 00:06:27,240 Sean Aylmer: this has been a boon for us because we can 116 00:06:27,240 --> 00:06:30,808 Sean Aylmer: actually use AI and then overlay on top of the 117 00:06:30,900 --> 00:06:35,580 Sean Aylmer: basic writing function that it has our insight. And I 118 00:06:35,580 --> 00:06:38,190 Sean Aylmer: often think that maybe there are people like me and 119 00:06:38,190 --> 00:06:41,040 Sean Aylmer: others out there who actually AI is going to make life a 120 00:06:41,040 --> 00:06:41,610 Sean Aylmer: lot easier. 121 00:06:42,540 --> 00:06:45,270 Kevin Hebner: Yeah. And I think AI helps you. If you're creating 122 00:06:45,270 --> 00:06:48,989 Kevin Hebner: any type of content as a writer, as a coder, 123 00:06:48,990 --> 00:06:51,808 Kevin Hebner: as a composer, or as an artist, I think it 124 00:06:51,809 --> 00:06:54,419 Kevin Hebner: can help you get up to what we call a mid- 125 00:06:54,450 --> 00:06:58,740 Kevin Hebner: level sort of so- so level pretty quickly. But then 126 00:06:58,740 --> 00:07:02,760 Kevin Hebner: the value add and the real creativity is by going 127 00:07:02,760 --> 00:07:07,020 Kevin Hebner: from average to becoming excellent, and that's hard. So there 128 00:07:07,020 --> 00:07:10,620 Kevin Hebner: will be more excellent content of all sorts, which is 129 00:07:11,190 --> 00:07:15,090 Kevin Hebner: fantastic and wonderful. Unfortunately, there's also going to be a lot of 130 00:07:15,090 --> 00:07:18,870 Kevin Hebner: really mediocre content out there. We've already been flooded with 131 00:07:18,870 --> 00:07:22,110 Kevin Hebner: that over the last 10, 20 years. That's going to get worse. 132 00:07:22,320 --> 00:07:26,460 Kevin Hebner: And there are real concerns about deep fakes and privacy 133 00:07:26,460 --> 00:07:29,609 Kevin Hebner: and surveillance and all these things as well. But overall, 134 00:07:29,609 --> 00:07:32,880 Kevin Hebner: for content creators who want to excel and then people 135 00:07:32,880 --> 00:07:37,230 Kevin Hebner: who consume the content as readers or whatever, overall things 136 00:07:37,230 --> 00:07:37,980 Kevin Hebner: should get better. 137 00:07:38,970 --> 00:07:40,710 Sean Aylmer: So I want to come to investing in a moment, 138 00:07:40,710 --> 00:07:42,780 Sean Aylmer: but just before we do that, this must be good 139 00:07:42,780 --> 00:07:43,679 Sean Aylmer: for productivity. 140 00:07:44,849 --> 00:07:48,449 Kevin Hebner: Yeah. And overall, the estimate is that productivity over the 141 00:07:48,450 --> 00:07:53,460 Kevin Hebner: next say 15 years will increase, say by 15%. So 142 00:07:53,520 --> 00:07:56,130 Kevin Hebner: roughly one percentage point a year, once AI has started 143 00:07:56,130 --> 00:08:00,000 Kevin Hebner: to diffuse across the economy, which probably moves the needle 144 00:08:00,000 --> 00:08:03,749 Kevin Hebner: from say 2030, and that's in terms of a baseline 145 00:08:03,750 --> 00:08:08,250 Kevin Hebner: productivity growth of one to 1. 5%. So you can expect 146 00:08:08,250 --> 00:08:12,810 Kevin Hebner: productivity growth, say of 2%. And that's really important because ultimately 147 00:08:12,810 --> 00:08:16,859 Kevin Hebner: the reason why we're richer than our parents or grandparents 148 00:08:16,860 --> 00:08:19,650 Kevin Hebner: is because of productivity. And the reason there's been so 149 00:08:19,650 --> 00:08:24,689 Kevin Hebner: much improvement in productivity is technology. So the productivity side 150 00:08:24,690 --> 00:08:26,880 Kevin Hebner: of this story is really important. 151 00:08:27,540 --> 00:08:31,200 Sean Aylmer: So let's go to investing. Now, can AI solve some 152 00:08:31,200 --> 00:08:33,809 Sean Aylmer: of the great challenges of our generation and what's that 153 00:08:33,809 --> 00:08:36,270 Sean Aylmer: mean for investing? I suppose climate change is the big 154 00:08:36,270 --> 00:08:39,810 Sean Aylmer: one right now. Does AI have a role in that? 155 00:08:39,929 --> 00:08:41,730 Sean Aylmer: What's that mean in terms of investments? 156 00:08:42,660 --> 00:08:47,010 Kevin Hebner: Well, I'm not an expert on climate change, but there 157 00:08:47,010 --> 00:08:49,679 Kevin Hebner: are a lot of technologies that are involved with climate 158 00:08:49,679 --> 00:08:53,910 Kevin Hebner: change, and certainly AI will be helpful with that. But 159 00:08:53,910 --> 00:08:57,750 Kevin Hebner: overall, in terms of investing, there's lots of consequences of 160 00:08:58,109 --> 00:09:02,100 Kevin Hebner: AI. One feature is the winner takes most, and if 161 00:09:02,100 --> 00:09:05,310 Kevin Hebner: you've heard of the magnificent seven in the United States, 162 00:09:05,550 --> 00:09:08,309 Kevin Hebner: at least since 2015, we've had a very small number 163 00:09:08,309 --> 00:09:12,330 Kevin Hebner: of companies, seven to 10 companies representing 50% of the gain 164 00:09:12,330 --> 00:09:16,529 Kevin Hebner: in equity markets. So that will continue. And a lot 165 00:09:16,529 --> 00:09:19,800 Kevin Hebner: of growth is priced into some of these stocks, if 166 00:09:19,800 --> 00:09:23,550 Kevin Hebner: you think of NVIDIA or Tesla. And maybe that growth 167 00:09:23,550 --> 00:09:26,309 Kevin Hebner: will be realized, maybe it won't, but there's a lot 168 00:09:26,309 --> 00:09:30,900 Kevin Hebner: of companies, for example, Microsoft and Meta or Facebook, which 169 00:09:30,900 --> 00:09:33,750 Kevin Hebner: are already producing a lot of free cash flow, have 170 00:09:33,750 --> 00:09:37,530 Kevin Hebner: very high return on invested capital relative to their cost 171 00:09:37,530 --> 00:09:40,440 Kevin Hebner: of capital, and so they're producing the money here and 172 00:09:40,440 --> 00:09:43,590 Kevin Hebner: now, you're not just buying growth that may or may not happen. 173 00:09:43,890 --> 00:09:47,190 Kevin Hebner: And we think those type of companies look particularly interesting. 174 00:09:47,550 --> 00:09:50,370 Sean Aylmer: Okay. I mean, that's interesting because many people say that 175 00:09:50,730 --> 00:09:55,319 Sean Aylmer: the success of the US tech stocks, that period, because 176 00:09:55,320 --> 00:09:57,750 Sean Aylmer: interest rates are rising again, that might be coming to 177 00:09:57,750 --> 00:10:00,870 Sean Aylmer: an end, but you're saying partly because of AI, they've 178 00:10:00,870 --> 00:10:01,890 Sean Aylmer: still got a ways to go. 179 00:10:02,550 --> 00:10:06,300 Kevin Hebner: Yeah. Ultimately with digital technology, which we've been experiencing for 180 00:10:06,300 --> 00:10:09,000 Kevin Hebner: the last 20 years, and then AI, which is one 181 00:10:09,000 --> 00:10:13,708 Kevin Hebner: type of that, these companies have fantastically powerful business models, so 182 00:10:13,710 --> 00:10:16,620 Kevin Hebner: they're capital light, so they don't have much plant equipment, 183 00:10:16,920 --> 00:10:20,490 Kevin Hebner: relatively small labor forces, huge economies of scale, and these 184 00:10:20,490 --> 00:10:23,490 Kevin Hebner: fantastic network effects, if you think of Facebook and so 185 00:10:23,490 --> 00:10:27,570 Kevin Hebner: forth. So over the last say 20 years, you've seen 186 00:10:27,570 --> 00:10:32,429 Kevin Hebner: for the market X tech, basically margins and free cash flow haven't 187 00:10:32,429 --> 00:10:35,280 Kevin Hebner: done a lot, but it's tripled for the tech part 188 00:10:35,280 --> 00:10:38,968 Kevin Hebner: of the market. And I think AI continues that. And 189 00:10:38,970 --> 00:10:41,699 Kevin Hebner: so there are opportunities for investors outside of tech because 190 00:10:41,700 --> 00:10:46,289 Kevin Hebner: a lot of the companies using technology AI are not 191 00:10:46,289 --> 00:10:49,140 Kevin Hebner: tech companies, but a lot of the gains will go 192 00:10:49,140 --> 00:10:53,370 Kevin Hebner: to tech companies, the superstar companies, which get the bulk 193 00:10:53,370 --> 00:10:55,920 Kevin Hebner: of these. And you can think of the platform companies 194 00:10:56,220 --> 00:11:01,380 Kevin Hebner: like Microsoft, Google, Amazon, Facebook, and so forth are well 195 00:11:01,380 --> 00:11:03,839 Kevin Hebner: positioned, at least for the beginning of it. Ultimately, the 196 00:11:03,839 --> 00:11:06,840 Kevin Hebner: next stage is when we get hundreds and hundreds of 197 00:11:07,170 --> 00:11:10,860 Kevin Hebner: apps based on these platforms, different types of AI and 198 00:11:10,860 --> 00:11:14,098 Kevin Hebner: ChatGPT, and it's hard to know who those winners are 199 00:11:14,099 --> 00:11:16,290 Kevin Hebner: going to be, but that is going to be the 200 00:11:16,290 --> 00:11:19,170 Kevin Hebner: key theme certainly for the next decade. And I would 201 00:11:19,170 --> 00:11:19,920 Kevin Hebner: think beyond. 202 00:11:20,520 --> 00:11:22,080 Sean Aylmer: Kevin, we're going to have to keep in touch. This 203 00:11:22,080 --> 00:11:24,749 Sean Aylmer: is a great conversation. We haven't got any more time, 204 00:11:24,750 --> 00:11:27,990 Sean Aylmer: but there's certainly plenty of opportunity out there over the 205 00:11:27,990 --> 00:11:29,848 Sean Aylmer: next decade I'd say. Thank you very much for talking 206 00:11:29,850 --> 00:11:30,630 Sean Aylmer: to Fear and Greed. 207 00:11:31,170 --> 00:11:32,459 Kevin Hebner: Oh, thank you, Sean. It's great. 208 00:11:32,790 --> 00:11:36,240 Sean Aylmer: That was Dr. Kevin Hebner, global investment strategist at EPOC 209 00:11:36,240 --> 00:11:39,299 Sean Aylmer: Investment Partners. This is the Fear and Greed Business Interview. 210 00:11:39,299 --> 00:11:41,639 Sean Aylmer: Remember, this is general information only and you should seek 211 00:11:41,639 --> 00:11:45,420 Sean Aylmer: professional advice before making any investment decision. Join us every 212 00:11:45,420 --> 00:11:47,730 Sean Aylmer: morning for the full episode of Fear and Greed, Australia's 213 00:11:47,730 --> 00:11:50,790 Sean Aylmer: best business podcast. I'm Sean Aylmer. Enjoy your day.