1 00:00:02,800 --> 00:00:05,200 Speaker 1: Cura and welcome to the Business of Tech powered by 2 00:00:05,240 --> 00:00:08,799 Speaker 1: Two Degrees Business. I'm Peter Griffin and on this week's episode, 3 00:00:09,160 --> 00:00:12,600 Speaker 1: once again we're looking at artificial intelligence, but this week 4 00:00:12,800 --> 00:00:16,639 Speaker 1: we drill into its impact on our workforce here in 5 00:00:16,680 --> 00:00:20,080 Speaker 1: New Zealand. It seems like anyone who really understands AI 6 00:00:20,280 --> 00:00:23,040 Speaker 1: is telling us that the change is coming to how 7 00:00:23,079 --> 00:00:26,040 Speaker 1: we work as a result of the adoption of AI 8 00:00:26,520 --> 00:00:29,760 Speaker 1: are monumental and we aren't doing enough as a nation 9 00:00:30,040 --> 00:00:32,559 Speaker 1: to prepare for them. But as you sit in your 10 00:00:32,600 --> 00:00:34,760 Speaker 1: car on the commute to the office, or if you're 11 00:00:35,240 --> 00:00:38,120 Speaker 1: working at it to gym, you may also be thinking, 12 00:00:38,240 --> 00:00:41,840 Speaker 1: right now, it ain't making a dent at my workplace, 13 00:00:42,080 --> 00:00:46,440 Speaker 1: and given how clunky and unreliable chat, GPT and co pilot, 14 00:00:46,479 --> 00:00:50,720 Speaker 1: are my job safe for the foreseeable future. Well, this 15 00:00:50,840 --> 00:00:54,680 Speaker 1: week's guest argues that yes, actually AI is coming for jobs, 16 00:00:54,720 --> 00:00:58,800 Speaker 1: particularly in the services industry. White collar rolls that AI 17 00:00:58,920 --> 00:01:02,000 Speaker 1: is quickly getting a lot better at not only augmenting 18 00:01:02,400 --> 00:01:08,040 Speaker 1: but replacing entirely. But doctor Kenny Ching, an organizational behavioral 19 00:01:08,120 --> 00:01:12,200 Speaker 1: expert and economists at the University of Auckland, argues that 20 00:01:12,280 --> 00:01:15,360 Speaker 1: New Zealand is going to be better placed than most 21 00:01:15,520 --> 00:01:19,520 Speaker 1: other Western nations to absorb the labor impacts of AI. 22 00:01:20,160 --> 00:01:24,880 Speaker 1: That's because agriculture is the base of our economy. Primary 23 00:01:24,920 --> 00:01:28,800 Speaker 1: sector only employees five point two percent of New Zealanders, 24 00:01:28,800 --> 00:01:32,680 Speaker 1: but it's responsible for nearly seventy percent of our export revenue. 25 00:01:33,680 --> 00:01:38,120 Speaker 1: Automation is happening in agriculture, but won't take hold nearly 26 00:01:38,120 --> 00:01:41,960 Speaker 1: as quickly as it is in the office. A primary 27 00:01:41,959 --> 00:01:47,119 Speaker 1: sector anchored economy, Smaller and flatter firms and more tangible, 28 00:01:47,560 --> 00:01:53,600 Speaker 1: less codified work mean automation diffuses over longer investment cycles 29 00:01:53,600 --> 00:01:58,600 Speaker 1: in orchards, shared construction sites, and supply chains, buying a 30 00:01:58,680 --> 00:02:03,280 Speaker 1: window to build capability before the curve steepens. For the 31 00:02:03,360 --> 00:02:08,480 Speaker 1: services industry, think marketing, banking, and insurance finance type jobs, 32 00:02:08,760 --> 00:02:11,880 Speaker 1: all the corporate admin that goes on across our businesses 33 00:02:11,919 --> 00:02:15,440 Speaker 1: all over the country, the threat to jobs is very real. 34 00:02:16,280 --> 00:02:20,600 Speaker 1: The latest biannual survey of businesses conducted by Victoria University 35 00:02:20,680 --> 00:02:24,520 Speaker 1: researchers in conjunction with the AI Forum, finds that fourteen 36 00:02:24,560 --> 00:02:28,799 Speaker 1: percent of organizations are now attributing job losses to AI. 37 00:02:29,240 --> 00:02:33,239 Speaker 1: This has doubled in the last six months, so AI 38 00:02:33,560 --> 00:02:36,920 Speaker 1: is starting to eat jobs six months ago, they're basically 39 00:02:36,960 --> 00:02:38,720 Speaker 1: saying we're not hiring. 40 00:02:38,440 --> 00:02:39,440 Speaker 2: As much as we used to. 41 00:02:39,560 --> 00:02:43,120 Speaker 1: Now they're actually attributing job losses to AI. That's a 42 00:02:43,160 --> 00:02:46,079 Speaker 1: big shift and it's only going to accelerate. What's going 43 00:02:46,120 --> 00:02:49,200 Speaker 1: on is really well summed up by Moe Gorditt, the 44 00:02:49,600 --> 00:02:53,960 Speaker 1: author and former chief business officer of Google x that's 45 00:02:54,000 --> 00:02:58,239 Speaker 1: the search giant's secretive research and development arm. Gord It 46 00:02:58,360 --> 00:03:03,280 Speaker 1: recently appeared on the Diary of a CEO podcast. Here's 47 00:03:03,320 --> 00:03:06,640 Speaker 1: what you had to say about AI's disruptive potential in 48 00:03:06,680 --> 00:03:09,639 Speaker 1: the services in Illustrie. There's why collar jobs. I've been 49 00:03:09,639 --> 00:03:11,240 Speaker 1: talking about. 50 00:03:10,840 --> 00:03:13,360 Speaker 3: AI is going to replace the grain of a human 51 00:03:13,520 --> 00:03:18,800 Speaker 3: and when the West, and it's interesting virtual colonies that 52 00:03:18,919 --> 00:03:22,880 Speaker 3: I call it basically outsourced or labor to the to 53 00:03:22,919 --> 00:03:27,280 Speaker 3: the developing nations. What the West publicly said at the 54 00:03:27,320 --> 00:03:30,880 Speaker 3: time is we're going to be a services economy where 55 00:03:30,720 --> 00:03:33,880 Speaker 3: we're not interested in making things and stitching things, and 56 00:03:33,919 --> 00:03:37,480 Speaker 3: so let the Indians and Chinese and you know, Bengalis 57 00:03:37,520 --> 00:03:41,080 Speaker 3: and Vietnamese do that. We're going to do more defined jobs. 58 00:03:41,240 --> 00:03:44,920 Speaker 3: Knowledge workers. We're going to Knowledge workers are people who 59 00:03:44,960 --> 00:03:48,040 Speaker 3: work with information and click on a keyboard and move 60 00:03:48,080 --> 00:03:50,880 Speaker 3: the mouse, and you know, sitting meetings and all we 61 00:03:50,960 --> 00:03:55,080 Speaker 3: produce in the Western societies is what works right or 62 00:03:55,200 --> 00:03:59,840 Speaker 3: designs maybe sometimes, but everything we produce can be produced by. 63 00:03:59,760 --> 00:04:03,800 Speaker 1: Any So pivoting to the knowledge economy for decades was 64 00:04:03,840 --> 00:04:07,400 Speaker 1: considered to be the thing to do to achieve higher 65 00:04:07,440 --> 00:04:11,160 Speaker 1: paying salaries for workers. But as it turns out, we're 66 00:04:11,280 --> 00:04:15,000 Speaker 1: lucky to produce a lot of actual stuff, physical stuff 67 00:04:15,040 --> 00:04:17,960 Speaker 1: that the world wants to buy and as it happens, 68 00:04:18,080 --> 00:04:20,760 Speaker 1: pay more for than they used to. Milk and meat 69 00:04:20,760 --> 00:04:23,960 Speaker 1: prices are looking pretty healthy at the moment, but Kenny 70 00:04:24,040 --> 00:04:27,279 Speaker 1: Ching suggests that this is not a reason to ignore 71 00:04:27,440 --> 00:04:33,000 Speaker 1: AI's applications in agriculture or be complacent about it. In fact, 72 00:04:33,360 --> 00:04:37,440 Speaker 1: we should embrace AI and become the best at developing 73 00:04:37,480 --> 00:04:40,960 Speaker 1: it in our agrotech sector, because AI is coming for 74 00:04:41,080 --> 00:04:45,240 Speaker 1: that sector too, just at a slower pace. Given we've 75 00:04:45,279 --> 00:04:49,479 Speaker 1: got great expertise in growing wool, meat, milk, Kiwi fruit 76 00:04:49,920 --> 00:04:54,040 Speaker 1: apples were well placed to become world leaders in agritech 77 00:04:54,160 --> 00:04:58,280 Speaker 1: and the application of AI in those industries. So here's 78 00:04:58,320 --> 00:05:01,640 Speaker 1: the interview with University of All Kenny Ching, which I 79 00:05:01,720 --> 00:05:05,680 Speaker 1: recorded last week after you published really interesting essay on 80 00:05:05,800 --> 00:05:15,599 Speaker 1: AI's potential impact in a tea. I really enjoyed your 81 00:05:15,640 --> 00:05:18,919 Speaker 1: conversation piece from a couple of weeks ago. 82 00:05:19,800 --> 00:05:20,680 Speaker 2: Really interesting. 83 00:05:20,800 --> 00:05:24,840 Speaker 1: You know, you're essentially saying there that AI is definitely 84 00:05:24,880 --> 00:05:28,039 Speaker 1: coming for jobs, but it's going to hit you know, 85 00:05:28,080 --> 00:05:32,440 Speaker 1: the services industry particularly hard and earlier than other industries, 86 00:05:33,240 --> 00:05:36,200 Speaker 1: and you know, with our agricultural base, that may be 87 00:05:36,960 --> 00:05:39,800 Speaker 1: an advantage for us. So we'll get into that what 88 00:05:39,839 --> 00:05:43,240 Speaker 1: that actually means. But Kenny, in terms of how AI 89 00:05:43,520 --> 00:05:47,640 Speaker 1: is affecting the workforce, so far as you do a 90 00:05:47,720 --> 00:05:51,040 Speaker 1: horizon scan around the world, you know, what exactly are 91 00:05:51,080 --> 00:05:54,320 Speaker 1: you saying. I'm just reading a World Economic Forum report 92 00:05:54,480 --> 00:05:57,719 Speaker 1: that is basically saying, so far, it looks like software 93 00:05:57,760 --> 00:06:01,000 Speaker 1: development is getting hammered. That's how they put it. You've 94 00:06:01,040 --> 00:06:04,080 Speaker 1: got the likes of GitHub, which is a big repository 95 00:06:04,120 --> 00:06:08,160 Speaker 1: for software. It has four hundred and twenty million repositories 96 00:06:08,240 --> 00:06:10,520 Speaker 1: now and a lot of them are public, so there's 97 00:06:10,560 --> 00:06:13,159 Speaker 1: a lot of code there that people can use and 98 00:06:13,320 --> 00:06:17,360 Speaker 1: automate their workflows. So that's why we've seen big tech 99 00:06:17,400 --> 00:06:20,800 Speaker 1: companies laying off a relatively a lot of staff compared 100 00:06:20,839 --> 00:06:24,480 Speaker 1: to other industries. Customer support is another one they call 101 00:06:24,520 --> 00:06:28,960 Speaker 1: a setting duck AI automation in contact centers, for instance, 102 00:06:29,480 --> 00:06:35,560 Speaker 1: and finance that heavily employees. Machine learning and algorithmic trading 103 00:06:36,000 --> 00:06:37,880 Speaker 1: is on the front line of it as well. But 104 00:06:38,000 --> 00:06:39,800 Speaker 1: in terms of all the research you're seeing coming out 105 00:06:39,800 --> 00:06:42,520 Speaker 1: of various countries, what does it say about the real 106 00:06:42,560 --> 00:06:45,000 Speaker 1: impact on jobs when it comes to AI at the moment? 107 00:06:45,240 --> 00:06:47,680 Speaker 4: So, first of all, a lot of the data and 108 00:06:47,800 --> 00:06:50,720 Speaker 4: evidence that we have is pretty much within the rear. 109 00:06:50,720 --> 00:06:51,279 Speaker 2: At this moment. 110 00:06:51,560 --> 00:06:54,840 Speaker 4: We don't quite get no for sure, how it's all 111 00:06:54,880 --> 00:06:58,080 Speaker 4: going to pan out, but I think gradually we're getting 112 00:06:58,080 --> 00:07:01,480 Speaker 4: a good sense of what's going on. As I argue, 113 00:07:01,960 --> 00:07:04,880 Speaker 4: a lot of it's going to be centered around the 114 00:07:04,920 --> 00:07:08,240 Speaker 4: service industry, and that's where most of the disruption is 115 00:07:08,240 --> 00:07:08,960 Speaker 4: going to take place. 116 00:07:09,200 --> 00:07:09,640 Speaker 2: I think. 117 00:07:10,080 --> 00:07:13,120 Speaker 4: So some people have responded to my piece, for example, 118 00:07:13,160 --> 00:07:16,560 Speaker 4: saying that, Okay, so many of our New Zealand design 119 00:07:16,560 --> 00:07:19,400 Speaker 4: in the service sector, and most of the white college 120 00:07:19,600 --> 00:07:23,200 Speaker 4: in a white college jobs, so within that cosmes unemployment, 121 00:07:23,400 --> 00:07:24,440 Speaker 4: I don't quite think so. 122 00:07:24,600 --> 00:07:26,200 Speaker 2: I think there are a couple of reasons why. 123 00:07:26,360 --> 00:07:30,160 Speaker 4: One is that the best evidence that we have seems 124 00:07:30,160 --> 00:07:33,320 Speaker 4: to suggest that the way AI displacement's going to work 125 00:07:33,440 --> 00:07:36,280 Speaker 4: is that it's going to reconfigure jobs more than it 126 00:07:36,360 --> 00:07:40,360 Speaker 4: completely replaces them. So maybe about sixty to seventy percent 127 00:07:40,480 --> 00:07:44,480 Speaker 4: of what activities we're going to see some automation. But 128 00:07:44,600 --> 00:07:49,240 Speaker 4: it doesn't mean it disappears completely outright that overnight. 129 00:07:49,280 --> 00:07:50,680 Speaker 2: It just doesn't happen that way. 130 00:07:51,160 --> 00:07:53,720 Speaker 4: Maybe a third of them might might happen that way, 131 00:07:54,040 --> 00:07:56,720 Speaker 4: but it's not going to be all the jobs just disappear. 132 00:07:57,040 --> 00:07:59,400 Speaker 4: But I think there's another critical point, which is that 133 00:07:59,760 --> 00:08:02,960 Speaker 4: the way AI displacement is going to happen is that, 134 00:08:03,240 --> 00:08:05,440 Speaker 4: for example, if a company is typically is going to 135 00:08:05,520 --> 00:08:10,240 Speaker 4: hire five new hires for the hiring season, they will 136 00:08:10,240 --> 00:08:13,600 Speaker 4: probably hire three, right, and they expect that those three 137 00:08:14,160 --> 00:08:17,440 Speaker 4: will be augmented by AI tools. So and so the 138 00:08:17,520 --> 00:08:20,720 Speaker 4: effect of this placement shows up very gradually. It's not 139 00:08:20,840 --> 00:08:24,360 Speaker 4: gonna be a mass so disappearance of jobs. Rather, you're 140 00:08:24,360 --> 00:08:27,240 Speaker 4: going to see slower hiring and maybe hiring freezers. 141 00:08:27,240 --> 00:08:27,920 Speaker 2: That's all things. 142 00:08:28,160 --> 00:08:30,560 Speaker 4: But you're not going to see incredibly, like you know, 143 00:08:30,640 --> 00:08:34,040 Speaker 4: sixty percent of the of jobs just disappear overnight. 144 00:08:34,280 --> 00:08:38,679 Speaker 1: Yeah, you talk in your conversation piece, you mentioned David 145 00:08:38,840 --> 00:08:43,800 Speaker 1: Grabner's concept of bullshit jobs. Yeah, absolutely, He called them 146 00:08:43,920 --> 00:08:49,760 Speaker 1: rolls that add little genuine value. And between twenty twenty eighteen, 147 00:08:50,200 --> 00:08:54,720 Speaker 1: most knit job growth came from these low productivity service 148 00:08:54,760 --> 00:09:01,240 Speaker 1: sector jobs. We're talking about marketing, consulting, corporate administration, and 149 00:09:01,280 --> 00:09:03,160 Speaker 1: it's happened in New Zealand as well. You know, we 150 00:09:03,559 --> 00:09:07,440 Speaker 1: saw an expansion in those sectors. So I guess we 151 00:09:07,559 --> 00:09:10,920 Speaker 1: have a lot of jobs in the services sector, and 152 00:09:10,960 --> 00:09:14,640 Speaker 1: that is a big sway of things that services technically 153 00:09:14,679 --> 00:09:18,360 Speaker 1: cover everything from healthcare to retail to those sorts of 154 00:09:18,360 --> 00:09:21,600 Speaker 1: marketing roles. But I guess you know, we've got unemployment 155 00:09:21,640 --> 00:09:24,840 Speaker 1: at five point two percent. What you're saying there, and 156 00:09:24,880 --> 00:09:27,200 Speaker 1: this is what I'm hearing from the tech vendors who 157 00:09:27,240 --> 00:09:31,040 Speaker 1: are selling artificial intelligence systems. They're saying it will augment 158 00:09:31,120 --> 00:09:33,720 Speaker 1: your job, it will change your job. It won't necessarily 159 00:09:34,320 --> 00:09:39,040 Speaker 1: eliminate sways of jobs. But as you said, you're not 160 00:09:39,080 --> 00:09:42,640 Speaker 1: necessarily going to be hiring as many people in future. 161 00:09:43,000 --> 00:09:44,840 Speaker 1: And if you look at you know, for instance, Spark 162 00:09:45,040 --> 00:09:48,320 Speaker 1: just laid off thirteen hundred people in the last year. 163 00:09:48,679 --> 00:09:52,839 Speaker 1: Other companies have had significant cuts as well. It seems 164 00:09:52,840 --> 00:09:55,760 Speaker 1: to me that the main impact of AI so far 165 00:09:56,520 --> 00:09:59,880 Speaker 1: is signaling to companies we don't need to hire as 166 00:09:59,880 --> 00:10:02,520 Speaker 1: a aggressively as we have in the past, which I 167 00:10:02,520 --> 00:10:04,960 Speaker 1: guess is a bit of a concern for our return 168 00:10:05,000 --> 00:10:09,559 Speaker 1: to growth that the government wants that unemployment isn't necessarily 169 00:10:09,600 --> 00:10:13,080 Speaker 1: going to reduce as quickly because the need to hire 170 00:10:13,080 --> 00:10:14,000 Speaker 1: people has listened. 171 00:10:14,280 --> 00:10:17,600 Speaker 4: I think that's absolutely the case. I don't think there's 172 00:10:17,640 --> 00:10:20,840 Speaker 4: any other country that is not facing these similar issues 173 00:10:21,400 --> 00:10:24,240 Speaker 4: as you've described. I think what my argument is that 174 00:10:24,400 --> 00:10:27,480 Speaker 4: New Zealand's probably is a little bit different from other 175 00:10:27,559 --> 00:10:30,800 Speaker 4: countries in the sense that we tend to have our 176 00:10:30,800 --> 00:10:33,200 Speaker 4: companies tend to be a little bit smaller compared to 177 00:10:34,000 --> 00:10:36,320 Speaker 4: say the UK or the US. So I got some 178 00:10:36,440 --> 00:10:39,559 Speaker 4: stats here, for example that our firms on average has 179 00:10:39,600 --> 00:10:44,400 Speaker 4: about five employees compared to about thirteen for across the OECD, 180 00:10:44,640 --> 00:10:48,760 Speaker 4: and that and sense suggests that our companies are a 181 00:10:48,760 --> 00:10:53,000 Speaker 4: little bit flatter, few duplicated roles, you are more people 182 00:10:53,040 --> 00:10:56,080 Speaker 4: wearing multiple hats, and I would argue again that the 183 00:10:56,160 --> 00:10:58,600 Speaker 4: jobs tend to be closer to the real economy right, 184 00:10:58,679 --> 00:11:03,760 Speaker 4: so I ignore absolutely that will be tremendous impact on 185 00:11:04,760 --> 00:11:08,320 Speaker 4: the AI displayson is going to have tremendous impowers, should say, 186 00:11:08,760 --> 00:11:11,800 Speaker 4: but my argument is that New Zealand probably experience it 187 00:11:11,800 --> 00:11:15,880 Speaker 4: a little bit less compared to many other economies around world. 188 00:11:15,640 --> 00:11:18,360 Speaker 1: And a lot of people will be happy to hear that. 189 00:11:18,440 --> 00:11:21,880 Speaker 1: You know, we've got about two point eight million jobs 190 00:11:22,240 --> 00:11:25,400 Speaker 1: in New Zealand according to Inframetrics, which is looking at 191 00:11:25,400 --> 00:11:29,400 Speaker 1: stats n Z labor data. Construction around ten percent is 192 00:11:29,400 --> 00:11:31,439 Speaker 1: actually one of the biggest that may have come down 193 00:11:31,480 --> 00:11:33,880 Speaker 1: a bit because construction is a bit soft at the moment, 194 00:11:33,920 --> 00:11:38,280 Speaker 1: followed by healthcare and social assistance that's around ten percent, 195 00:11:38,400 --> 00:11:43,520 Speaker 1: then professional, scientific and technical services, followed by manufacturing. So 196 00:11:43,640 --> 00:11:45,240 Speaker 1: when you get down to the ones that the World 197 00:11:45,320 --> 00:11:48,240 Speaker 1: Economic Forum is saying is really at the front line 198 00:11:48,240 --> 00:11:52,480 Speaker 1: of this, financial services in New Zealand two point seven 199 00:11:52,520 --> 00:11:57,160 Speaker 1: percent and information media telecommunications you know, one point five percent. 200 00:11:57,240 --> 00:12:00,640 Speaker 1: So these are relatively small sectors for New Zealand. You've 201 00:12:00,720 --> 00:12:05,720 Speaker 1: argued in your conversation piece that our strengthen agriculture, which 202 00:12:05,760 --> 00:12:09,200 Speaker 1: is the majority of our exports currently, is going to 203 00:12:09,200 --> 00:12:12,960 Speaker 1: see us well through this wave of automation because literally 204 00:12:13,040 --> 00:12:14,840 Speaker 1: a lot of that stuff can be automated. 205 00:12:14,960 --> 00:12:16,320 Speaker 2: Yeah, so you're exactly right. 206 00:12:16,760 --> 00:12:20,040 Speaker 4: My argument is that the way that New Zealan economy 207 00:12:20,120 --> 00:12:22,920 Speaker 4: is organized with some people have mocked it in the 208 00:12:22,960 --> 00:12:25,640 Speaker 4: past that it sounds agrariant, but I think it's actually 209 00:12:25,679 --> 00:12:28,640 Speaker 4: the strength, right, that there's so much of economy is 210 00:12:28,679 --> 00:12:29,360 Speaker 4: actually real. 211 00:12:29,679 --> 00:12:31,960 Speaker 2: In both the food sectors. 212 00:12:31,520 --> 00:12:34,480 Speaker 4: The agriculture sectors, as well as domestic services, they all 213 00:12:34,520 --> 00:12:39,400 Speaker 4: tend to be performing real, high productivity stuff that's not 214 00:12:39,920 --> 00:12:44,720 Speaker 4: related to bureaucracy. And that's why I argue comparatively, New 215 00:12:44,800 --> 00:12:47,880 Speaker 4: Zealand's going to emerge probably a little bit better than 216 00:12:48,480 --> 00:12:52,880 Speaker 4: many other economies that have swung almost overbearingly towards so 217 00:12:53,040 --> 00:12:57,600 Speaker 4: much of the low productivity, bureaucratic sort of sectors. 218 00:12:57,640 --> 00:13:01,800 Speaker 1: Having said that, according to this data, five point two 219 00:13:01,840 --> 00:13:05,640 Speaker 1: percent of those jobs are in agriculture. So over the 220 00:13:05,720 --> 00:13:08,560 Speaker 1: last few decades we've done a very good job at 221 00:13:08,880 --> 00:13:13,000 Speaker 1: increasing productivity on farms, reducing the number of people that 222 00:13:13,080 --> 00:13:16,480 Speaker 1: physically need to be on an orchard or on a 223 00:13:16,559 --> 00:13:21,880 Speaker 1: dairy farm, for instance. So the number of jobs that 224 00:13:21,960 --> 00:13:25,160 Speaker 1: actually generate a lot of that value is relatively low. 225 00:13:25,240 --> 00:13:26,280 Speaker 2: That's a good thing. 226 00:13:26,360 --> 00:13:30,800 Speaker 1: That shows that we're actually highly productive producers and therefore 227 00:13:31,000 --> 00:13:35,160 Speaker 1: our products are very popular around the world in terms 228 00:13:35,240 --> 00:13:37,880 Speaker 1: of the future automation that can be done in the 229 00:13:37,920 --> 00:13:41,000 Speaker 1: primary sector. What's your take on how we're doing. We 230 00:13:41,040 --> 00:13:45,240 Speaker 1: hear about automated tractors, automated milking sheds and the like. 231 00:13:45,840 --> 00:13:49,120 Speaker 1: Are we actually innovating to a great degree in those areas? 232 00:13:49,240 --> 00:13:51,560 Speaker 4: You know, primary industries is always going to remain New 233 00:13:51,640 --> 00:13:54,640 Speaker 4: Zealand's backbone. I think, as you said, we've seen some 234 00:13:54,720 --> 00:13:58,600 Speaker 4: bright spot We've definitely seen a lot more animation. For example, 235 00:13:58,720 --> 00:14:04,120 Speaker 4: in the grading of fruits, supply chain optimization. We're seeing 236 00:14:04,679 --> 00:14:07,160 Speaker 4: very interesting startups. For example, I'm using some of them 237 00:14:07,559 --> 00:14:10,840 Speaker 4: in my own teaching. For example, the company Halter using 238 00:14:10,880 --> 00:14:14,800 Speaker 4: AI powered callers to help farmers virtually fence. 239 00:14:14,600 --> 00:14:16,880 Speaker 2: And manage hurts. I think that's super interesting. 240 00:14:16,960 --> 00:14:22,520 Speaker 4: But relative to many other countries, like my favorite probably 241 00:14:22,560 --> 00:14:25,840 Speaker 4: in Netherlands or Israel, in terms of their investment into 242 00:14:26,480 --> 00:14:30,680 Speaker 4: R and D, we are definitely not quite there. So 243 00:14:30,720 --> 00:14:33,640 Speaker 4: for example, just to put things on context, is really 244 00:14:33,680 --> 00:14:37,040 Speaker 4: invests about six percent of their GDP into R and D. 245 00:14:38,320 --> 00:14:41,560 Speaker 4: Netherlands is I think it's definitely more than two percent. 246 00:14:41,960 --> 00:14:44,440 Speaker 4: We're only investing about one and a half percent of 247 00:14:44,440 --> 00:14:46,640 Speaker 4: our GDP into R and d My argument is that 248 00:14:46,720 --> 00:14:50,200 Speaker 4: we should be investing a lot more in sectors like agritech. 249 00:14:50,560 --> 00:14:53,840 Speaker 4: And to your point earlier that even though the number 250 00:14:53,840 --> 00:14:56,840 Speaker 4: of people that's actually hired in agriculture it may not 251 00:14:56,880 --> 00:15:00,400 Speaker 4: seem that much on the surface, but I argue, if 252 00:15:00,600 --> 00:15:04,080 Speaker 4: more effort is being done to what's really investing in 253 00:15:04,120 --> 00:15:09,000 Speaker 4: the innovation mechanisms and the machinery behind agriculture, we're going 254 00:15:09,040 --> 00:15:11,120 Speaker 4: to see a lot more employment of being picked up there. Right, 255 00:15:11,200 --> 00:15:12,920 Speaker 4: So we could be chanted we could have having a 256 00:15:12,920 --> 00:15:20,800 Speaker 4: lot more technicians testing equipment, testing new innovations in agriculture. 257 00:15:21,520 --> 00:15:25,360 Speaker 4: We could have a lot more veterinarians, we could have 258 00:15:25,400 --> 00:15:29,440 Speaker 4: a lot more highly skilled people that want to work 259 00:15:29,640 --> 00:15:31,520 Speaker 4: in innovating for agriculture. 260 00:15:31,640 --> 00:15:33,600 Speaker 2: Yeah, yeah, that's what I argue. 261 00:15:33,800 --> 00:15:38,040 Speaker 1: And look, we're seeing a resurgence and commodity prices for 262 00:15:38,400 --> 00:15:40,760 Speaker 1: our farmers at the moment, so that's one of the 263 00:15:40,800 --> 00:15:42,920 Speaker 1: few bright spots in the economy. 264 00:15:43,120 --> 00:15:43,560 Speaker 2: Exactly. 265 00:15:43,640 --> 00:15:47,040 Speaker 1: It does employ a relatively small number of New Zealander's currently. 266 00:15:47,480 --> 00:15:50,760 Speaker 1: What you're saying there is there's room for expansion there exactly, 267 00:15:50,840 --> 00:15:54,600 Speaker 1: And even though it's an area that is harder to automate, 268 00:15:54,960 --> 00:15:59,960 Speaker 1: you're also saying we shouldn't neglect artificial intelligence and robotic 269 00:16:00,280 --> 00:16:03,120 Speaker 1: automation in that sector. Actually, if we want to maintain 270 00:16:03,160 --> 00:16:06,080 Speaker 1: our competitive advantage, we actually need to really embrace it. 271 00:16:06,280 --> 00:16:09,440 Speaker 4: I think we should absolutely embrace it. We shouldn't be 272 00:16:09,480 --> 00:16:11,880 Speaker 4: afraid of AI. I think that we know that AI 273 00:16:11,960 --> 00:16:14,080 Speaker 4: is going to be there. Instead, we should be embracing 274 00:16:14,120 --> 00:16:18,200 Speaker 4: the fact that, for example, in certain niches, in certain industries, 275 00:16:18,440 --> 00:16:20,280 Speaker 4: New Zealand has a lot of strength in it. We 276 00:16:20,360 --> 00:16:24,120 Speaker 4: could be the global leaders in developing AI solutions for 277 00:16:24,240 --> 00:16:28,240 Speaker 4: those sectors. And I'm saying this for maybe the tenth time, 278 00:16:28,280 --> 00:16:30,760 Speaker 4: but I think for example, agritech, right, we could absolutely 279 00:16:30,760 --> 00:16:31,320 Speaker 4: will lead. 280 00:16:31,240 --> 00:16:31,560 Speaker 1: Us for that. 281 00:16:32,240 --> 00:16:35,120 Speaker 4: And the risk is that if we don't embrace it, 282 00:16:35,520 --> 00:16:38,040 Speaker 4: we could end up being price takers. 283 00:16:38,640 --> 00:16:41,600 Speaker 2: That would be really, really tough. Yeah, right, If. 284 00:16:41,440 --> 00:16:44,560 Speaker 4: You are actually not, if you're actually going to be 285 00:16:44,640 --> 00:16:47,120 Speaker 4: using other countries AI tools in the future and they 286 00:16:47,160 --> 00:16:49,800 Speaker 4: will come, you're going to end up purely as a 287 00:16:49,920 --> 00:16:54,120 Speaker 4: price taker. That would be a massive problem going forward 288 00:16:54,120 --> 00:16:58,800 Speaker 4: because we have literally no route out of this transformation 289 00:16:58,880 --> 00:16:59,440 Speaker 4: in the economy. 290 00:16:59,560 --> 00:17:02,440 Speaker 1: The World Economic Forum and others are basically saying at 291 00:17:02,480 --> 00:17:05,640 Speaker 1: the moment, what we're seeing is the industries and sectors 292 00:17:05,680 --> 00:17:08,959 Speaker 1: that are being automated very rapidly are the ones that 293 00:17:09,000 --> 00:17:13,040 Speaker 1: have really good data. Software development has great data because 294 00:17:13,480 --> 00:17:17,200 Speaker 1: there's a lot of code repositories, the large language models 295 00:17:17,680 --> 00:17:20,359 Speaker 1: have crawled all of that code, so they're very good 296 00:17:20,640 --> 00:17:24,560 Speaker 1: at creating code automatically. Finance also has a lot of 297 00:17:24,640 --> 00:17:27,600 Speaker 1: data available, a lot of it's proprietary data, but it's there. 298 00:17:28,160 --> 00:17:30,479 Speaker 1: Other sectors is not so much healthcare. There's a lot 299 00:17:30,520 --> 00:17:36,680 Speaker 1: of privacy issues here, a lot of fragmentation of data, repositories, construction, 300 00:17:36,960 --> 00:17:41,680 Speaker 1: which is a big employer in New Zealand, manufacturing, and others. 301 00:17:42,040 --> 00:17:43,880 Speaker 1: There's that whole truth for you that the ones that 302 00:17:44,080 --> 00:17:46,600 Speaker 1: have a lot of data and it's been made available 303 00:17:46,800 --> 00:17:49,359 Speaker 1: so companies have done a lot of work to build 304 00:17:49,480 --> 00:17:52,280 Speaker 1: data warehouses and tap into all of their data, those 305 00:17:52,280 --> 00:17:54,600 Speaker 1: are the ones that can take advantage of automation that 306 00:17:54,720 --> 00:17:55,280 Speaker 1: much quicker. 307 00:17:55,440 --> 00:18:00,399 Speaker 4: I'm a research economist by training, right, so you like 308 00:18:00,480 --> 00:18:03,919 Speaker 4: to look for data sets that's easy for us to 309 00:18:03,960 --> 00:18:08,440 Speaker 4: do the analysis on, and so anything that has more 310 00:18:09,520 --> 00:18:13,919 Speaker 4: qualified data that's just put into nice data sheets and 311 00:18:13,960 --> 00:18:17,160 Speaker 4: I could just run my regressions absolutely right. So those 312 00:18:17,160 --> 00:18:20,960 Speaker 4: who exactly it's the same dynamics here for AI. Anything 313 00:18:21,000 --> 00:18:24,560 Speaker 4: that's easily that the AI program can easily make use 314 00:18:24,640 --> 00:18:28,080 Speaker 4: of any data, those will be the sectors that AI 315 00:18:28,160 --> 00:18:30,520 Speaker 4: is more easily going to. 316 00:18:29,960 --> 00:18:30,600 Speaker 2: To take over. 317 00:18:31,080 --> 00:18:34,720 Speaker 4: And that's why any sectors that rely much more on 318 00:18:34,960 --> 00:18:40,080 Speaker 4: less qualified data, more on tangible hands on work that's 319 00:18:40,240 --> 00:18:43,440 Speaker 4: not so easy to code right away and once in zeros, 320 00:18:44,040 --> 00:18:46,400 Speaker 4: those are the sectors that AI would take a little 321 00:18:46,440 --> 00:18:49,600 Speaker 4: bit longer to displace. But at the same time, because 322 00:18:49,640 --> 00:18:52,560 Speaker 4: we have that knowledge, we know how to do it, 323 00:18:53,160 --> 00:18:56,800 Speaker 4: we could be the leaders in the indie AI solutions 324 00:18:56,840 --> 00:18:57,840 Speaker 4: for those sectors. 325 00:18:57,920 --> 00:19:00,360 Speaker 1: Yeah, and that very much goes to the egg tech 326 00:19:00,480 --> 00:19:03,399 Speaker 1: story and likes a wholtery a Halter is now a 327 00:19:03,400 --> 00:19:07,199 Speaker 1: world leader and managing herds of of dairy and beef 328 00:19:07,920 --> 00:19:12,399 Speaker 1: cows in the US and New Zealand and Australia because 329 00:19:12,440 --> 00:19:15,679 Speaker 1: it has so much data on every single cow. 330 00:19:15,680 --> 00:19:19,560 Speaker 4: And the fact that we have we have great experience, yeah, 331 00:19:19,640 --> 00:19:20,719 Speaker 4: trying to raise cattle. 332 00:19:20,840 --> 00:19:23,879 Speaker 2: That's why you're even able to attempt a solution like 333 00:19:23,920 --> 00:19:26,240 Speaker 2: this because you have the experience of doing that. 334 00:19:26,280 --> 00:19:29,440 Speaker 4: It's not quite the same as just doing AI systems 335 00:19:29,560 --> 00:19:31,720 Speaker 4: for trading on stock markets. 336 00:19:31,800 --> 00:19:35,760 Speaker 1: Yes, No, that's a good point. It's very different decades 337 00:19:35,800 --> 00:19:40,720 Speaker 1: of experience being world class at breeding sheep or milking 338 00:19:40,800 --> 00:19:45,760 Speaker 1: cows and creating dairy products with those AI tools, that's 339 00:19:45,960 --> 00:19:50,240 Speaker 1: our competitive advantage in terms of the service industry. A 340 00:19:50,240 --> 00:19:53,000 Speaker 1: lot of people listening to this podcast will be sitting 341 00:19:53,000 --> 00:19:59,240 Speaker 1: there in marketing roles and communications, office administration, business strategy, 342 00:19:59,440 --> 00:20:03,000 Speaker 1: and con eltancy and they're probably thinking, well, you just 343 00:20:03,000 --> 00:20:05,520 Speaker 1: tell me I have a bullshit job. So what should 344 00:20:05,560 --> 00:20:07,879 Speaker 1: they be thinking? A lot of them will be in 345 00:20:07,920 --> 00:20:11,720 Speaker 1: their forties and fifties have been doing the same thing 346 00:20:11,760 --> 00:20:15,439 Speaker 1: for decades. Now are they about to be washed away 347 00:20:15,480 --> 00:20:16,879 Speaker 1: in the AI tsunami? 348 00:20:17,040 --> 00:20:19,920 Speaker 4: I think the first thing you acknowledge and this cuts 349 00:20:20,000 --> 00:20:23,440 Speaker 4: very close to my heart because as an educator, I'll 350 00:20:23,440 --> 00:20:28,080 Speaker 4: be very honest. I'm seeing firsthand and these are bright 351 00:20:28,160 --> 00:20:32,440 Speaker 4: young students, very motivated. They're really struggling to lend any 352 00:20:32,520 --> 00:20:34,960 Speaker 4: roles right now because basically firms are saying, hey, we 353 00:20:35,000 --> 00:20:38,160 Speaker 4: don't need five juniors, which does need three? Right? And 354 00:20:38,240 --> 00:20:39,879 Speaker 4: you know, we're just going to give that AI tools 355 00:20:39,880 --> 00:20:42,960 Speaker 4: to do the rest. And it's tough. I totally empathize 356 00:20:43,000 --> 00:20:45,760 Speaker 4: with that. Having said that, you know, and the other 357 00:20:45,800 --> 00:20:49,560 Speaker 4: thing as well is that I definitely empathize because I'm 358 00:20:49,600 --> 00:20:51,840 Speaker 4: really not good at the stuff that I think is 359 00:20:52,119 --> 00:20:54,159 Speaker 4: going to matter the most in this new world. I 360 00:20:54,200 --> 00:20:57,200 Speaker 4: actually really am bad at things like networking. I almost 361 00:20:57,240 --> 00:21:00,480 Speaker 4: really dislike it. So I understand how I'm the boy 362 00:21:00,600 --> 00:21:03,120 Speaker 4: is going to be because what I'm going to say 363 00:21:03,119 --> 00:21:06,280 Speaker 4: here is that this world is going to lean into 364 00:21:06,400 --> 00:21:09,960 Speaker 4: human connections back again and judgment, right, because if everything 365 00:21:10,000 --> 00:21:12,679 Speaker 4: can be automated, then what really makes the difference is 366 00:21:12,680 --> 00:21:16,000 Speaker 4: that you've got to really demonstrate that you are not AI, 367 00:21:16,359 --> 00:21:18,639 Speaker 4: that you are actually human. The things that's going to 368 00:21:18,680 --> 00:21:23,040 Speaker 4: matter again would be human connections and judgment. That's where 369 00:21:23,200 --> 00:21:28,200 Speaker 4: many of the folks who are afraid of AI displacement 370 00:21:28,560 --> 00:21:32,080 Speaker 4: in that sense, I think maybe they don't have that 371 00:21:32,240 --> 00:21:35,680 Speaker 4: much to fear because they're probably I think the people 372 00:21:35,680 --> 00:21:38,080 Speaker 4: who are a bit more scared would be those that 373 00:21:38,200 --> 00:21:40,680 Speaker 4: have been working for a good number of years. As 374 00:21:40,680 --> 00:21:44,199 Speaker 4: we're describing, they actually have a lot of advantages because 375 00:21:44,240 --> 00:21:46,880 Speaker 4: they have all those human connections, they have all those 376 00:21:46,920 --> 00:21:51,159 Speaker 4: human judgment, they have empathy that you know, it's not 377 00:21:51,280 --> 00:21:55,120 Speaker 4: easily a machine replaceable in that sense. I think their jobs, 378 00:21:55,440 --> 00:21:57,760 Speaker 4: you know, I wouldn't want to say it's secure, but 379 00:21:58,119 --> 00:22:00,480 Speaker 4: I would say they probably don't have to. 380 00:22:02,160 --> 00:22:03,000 Speaker 2: Be that worry. 381 00:22:03,760 --> 00:22:07,240 Speaker 4: My bigger fear, bigger worry I should say, is with 382 00:22:07,560 --> 00:22:12,240 Speaker 4: actually the freshies that's actually coming on the job market, 383 00:22:12,320 --> 00:22:15,640 Speaker 4: because they're getting into a job market is dramatically different, 384 00:22:16,680 --> 00:22:21,400 Speaker 4: and my prediction is not even that profound, is that 385 00:22:21,760 --> 00:22:25,119 Speaker 4: hiring is just going to keep slowing down. And my 386 00:22:25,200 --> 00:22:28,560 Speaker 4: bigger worry is that these students will be so demotivated 387 00:22:29,080 --> 00:22:32,119 Speaker 4: by what they're seeing and what they're experiencing, and we 388 00:22:32,200 --> 00:22:34,439 Speaker 4: have no easy solution for that. 389 00:22:34,880 --> 00:22:38,600 Speaker 1: Yeah, this is born out in my conversations, particularly with 390 00:22:38,920 --> 00:22:43,480 Speaker 1: tech companies. A lot of them have internships and they 391 00:22:43,520 --> 00:22:49,760 Speaker 1: take on board students fresh out of university or polytechnics 392 00:22:49,800 --> 00:22:52,600 Speaker 1: that have some sort of IT qualifications. It might be 393 00:22:52,640 --> 00:22:55,639 Speaker 1: a computer science degree, it might be a diploma or 394 00:22:55,640 --> 00:22:58,040 Speaker 1: something like that. So typically they would spend a year 395 00:22:58,119 --> 00:23:00,960 Speaker 1: or two doing the sort of grunt work helping out 396 00:23:01,280 --> 00:23:04,040 Speaker 1: the more senior code is maybe doing testing now with 397 00:23:04,280 --> 00:23:08,080 Speaker 1: AI agents able to do a lot of those roles 398 00:23:08,200 --> 00:23:11,240 Speaker 1: with human oversight. A lot of software development people are 399 00:23:11,240 --> 00:23:12,959 Speaker 1: saying to me, I don't really know what I'm going 400 00:23:13,000 --> 00:23:17,320 Speaker 1: to be doing with our fresh interns and new graduates 401 00:23:17,440 --> 00:23:22,040 Speaker 1: exactly soon they're basically having to say to them, you 402 00:23:22,119 --> 00:23:25,560 Speaker 1: need to become AI engineers, so you're going to oversee 403 00:23:25,600 --> 00:23:29,199 Speaker 1: the AI systems and interact with them. That is going 404 00:23:29,240 --> 00:23:32,080 Speaker 1: to be your future and software development. So I guess 405 00:23:32,200 --> 00:23:34,800 Speaker 1: if you're just coming out of a computer science degree, 406 00:23:34,840 --> 00:23:38,840 Speaker 1: that's a huge shift from probably what you were told 407 00:23:38,880 --> 00:23:40,080 Speaker 1: when you started that degree. 408 00:23:40,320 --> 00:23:40,520 Speaker 2: Yeah. 409 00:23:40,680 --> 00:23:44,680 Speaker 4: Absolutely, and I think not even just computer science, which 410 00:23:44,840 --> 00:23:48,320 Speaker 4: so for example, was just talking to a friend of 411 00:23:48,359 --> 00:23:52,560 Speaker 4: mine with a partner in a measure consulting company, and 412 00:23:52,640 --> 00:23:56,560 Speaker 4: he was describing to me that typically junior analysts, when 413 00:23:56,560 --> 00:23:59,439 Speaker 4: they hire them, they'll be doing sort of like almost 414 00:23:59,440 --> 00:24:04,359 Speaker 4: like runy taking minutes doing the market research and that 415 00:24:04,480 --> 00:24:08,560 Speaker 4: sort of thing. Those are definitely going to be replaced 416 00:24:08,560 --> 00:24:12,159 Speaker 4: by AI. The worries about that, you know, what's going 417 00:24:12,200 --> 00:24:15,320 Speaker 4: to happen to they're probably not going to hire junior 418 00:24:15,359 --> 00:24:16,960 Speaker 4: analysts at least not at the same. 419 00:24:16,800 --> 00:24:17,560 Speaker 2: Rate as before. 420 00:24:18,200 --> 00:24:21,520 Speaker 4: But I think he brought up a more subtle point, 421 00:24:21,680 --> 00:24:22,960 Speaker 4: which is something that I. 422 00:24:22,960 --> 00:24:25,639 Speaker 2: Worry a little bit more on a longer term basis. 423 00:24:25,320 --> 00:24:28,679 Speaker 4: Is that those grant work that they give to the 424 00:24:28,760 --> 00:24:32,280 Speaker 4: junior analysts it's partly also to allow them to get 425 00:24:32,320 --> 00:24:36,560 Speaker 4: to know the organizations, get them to form human connections, 426 00:24:36,640 --> 00:24:39,879 Speaker 4: get them to network, get them to know more people, 427 00:24:40,400 --> 00:24:43,720 Speaker 4: right because eventually that's going to be the basis of why, 428 00:24:44,640 --> 00:24:49,520 Speaker 4: and that's going to be the most important point and 429 00:24:50,119 --> 00:24:52,360 Speaker 4: source of their capabilities. 430 00:24:52,720 --> 00:24:53,400 Speaker 2: Right, It's that. 431 00:24:53,359 --> 00:24:55,840 Speaker 4: They're able to form connections, able to know people, They're 432 00:24:55,840 --> 00:24:59,400 Speaker 4: able to understand problems and network essentially. 433 00:24:59,560 --> 00:25:02,240 Speaker 2: So I worry that in a. 434 00:25:02,280 --> 00:25:06,440 Speaker 4: New era where so much of these junior work so 435 00:25:06,600 --> 00:25:10,680 Speaker 4: to speak, are displaced and being outsourced to AI systems, 436 00:25:11,160 --> 00:25:14,480 Speaker 4: we're not giving young people the chance to develop human 437 00:25:14,520 --> 00:25:20,560 Speaker 4: connections and learning opportunities just by doing these work. That 438 00:25:21,200 --> 00:25:23,560 Speaker 4: is something that don't really have easy solutions for. 439 00:25:23,880 --> 00:25:26,400 Speaker 1: Yeah, I really like the concept of getting out from 440 00:25:26,400 --> 00:25:29,200 Speaker 1: behind your desk so you're spending less time writing research 441 00:25:29,280 --> 00:25:34,359 Speaker 1: reports and minutes and summarizing meetings and inwardly focused all 442 00:25:34,400 --> 00:25:37,320 Speaker 1: the admin that goes with being in a modern organization 443 00:25:37,359 --> 00:25:42,439 Speaker 1: and actually getting out talking to customers, partners, networking that 444 00:25:42,560 --> 00:25:45,400 Speaker 1: face to face stuff, really understanding what your clients need. 445 00:25:45,480 --> 00:25:48,280 Speaker 1: That's actually I think can be a really empowering thing. 446 00:25:48,840 --> 00:25:51,679 Speaker 1: But it will be a transition for businesses I'm not 447 00:25:51,720 --> 00:25:54,080 Speaker 1: sure if they've got their head around it, and frankly, 448 00:25:54,119 --> 00:25:56,360 Speaker 1: as a country, I'm not sure if we have as well. 449 00:25:56,800 --> 00:26:00,960 Speaker 1: We now have an artificial intelligence strategy for the country. 450 00:26:01,080 --> 00:26:04,000 Speaker 1: It was released a couple of months back by the government, 451 00:26:04,359 --> 00:26:07,639 Speaker 1: but it got a lot of criticism, particularly for you know, 452 00:26:07,640 --> 00:26:13,000 Speaker 1: it's lack of focus on skills development, reskilling, upskilling, preparing 453 00:26:13,040 --> 00:26:16,400 Speaker 1: our workforce for what's coming. Do you think that we're 454 00:26:16,480 --> 00:26:19,359 Speaker 1: sort of really light on that in a strategic sense. 455 00:26:20,400 --> 00:26:23,040 Speaker 4: I think yes, I know, I do think that New 456 00:26:23,119 --> 00:26:25,800 Speaker 4: Zealand's already doing a pretty decent job in the sense 457 00:26:25,800 --> 00:26:28,159 Speaker 4: that at least we're recognizing and I think here at 458 00:26:28,160 --> 00:26:32,200 Speaker 4: the university side, we're seeing a lot of momentum for sure, 459 00:26:32,440 --> 00:26:34,600 Speaker 4: that there's a lot of focused now thinking about AI. 460 00:26:35,280 --> 00:26:37,760 Speaker 4: How do we incorporate that into our teaching, how to 461 00:26:37,840 --> 00:26:40,480 Speaker 4: incorporate that in research, how do we propel students. 462 00:26:41,240 --> 00:26:43,240 Speaker 2: If you're comparing. 463 00:26:42,760 --> 00:26:45,800 Speaker 4: New Zealand to other countries, I'm sure we can find 464 00:26:45,840 --> 00:26:49,760 Speaker 4: better examples of even being more of countries being even 465 00:26:49,800 --> 00:26:54,960 Speaker 4: more proactive. I think Singapore, for example, extremely proactive in 466 00:26:55,080 --> 00:26:59,400 Speaker 4: thinking about AI. I think these as far back as 467 00:26:59,400 --> 00:27:02,040 Speaker 4: maybe five years ago, they've already been talking about this, 468 00:27:02,119 --> 00:27:08,480 Speaker 4: so even before the recent sort of attention due to 469 00:27:08,920 --> 00:27:12,120 Speaker 4: all these generative AI tools, the synophore is already well 470 00:27:12,160 --> 00:27:15,960 Speaker 4: preparing for this era of automation that's going to come. 471 00:27:16,680 --> 00:27:19,159 Speaker 4: I think one of the interesting things I think that 472 00:27:19,400 --> 00:27:22,639 Speaker 4: you might find of interest is that Singapore has this 473 00:27:23,280 --> 00:27:27,840 Speaker 4: pretty successful scheme called Skills Future, where basically they give 474 00:27:28,119 --> 00:27:31,520 Speaker 4: every adult credits basically for them to take courses so 475 00:27:31,560 --> 00:27:35,240 Speaker 4: they could reskill. And I think that many adults actually 476 00:27:35,240 --> 00:27:38,960 Speaker 4: taking this up right now because they fear what's going 477 00:27:39,000 --> 00:27:41,680 Speaker 4: to happen in the future or there or they may 478 00:27:41,720 --> 00:27:43,679 Speaker 4: be optimistic about what's going to happen in the future, 479 00:27:43,960 --> 00:27:47,760 Speaker 4: so they're picking up using these credits to learn things 480 00:27:47,760 --> 00:27:50,840 Speaker 4: like hey, how do I do marketing using gen AI tools? 481 00:27:51,040 --> 00:27:54,480 Speaker 4: And also even using these credits to learn things like hey, 482 00:27:54,840 --> 00:27:57,520 Speaker 4: how do I understand what are AI ethics? 483 00:27:57,920 --> 00:27:59,800 Speaker 2: Or how do I use AI tools in the more 484 00:28:00,160 --> 00:28:01,280 Speaker 2: go manner that sort of thing. 485 00:28:02,000 --> 00:28:04,439 Speaker 4: I think New Zealand should do something like this, and 486 00:28:04,480 --> 00:28:08,800 Speaker 4: I think it's definitely doable. We're not a huge population 487 00:28:09,119 --> 00:28:11,720 Speaker 4: and a five point nine million people, we definitely could 488 00:28:11,720 --> 00:28:16,240 Speaker 4: do something like this. So give every adult some subsidized 489 00:28:16,280 --> 00:28:21,800 Speaker 4: credit for them to undertake courses. It could be in universities, 490 00:28:21,800 --> 00:28:26,399 Speaker 4: it could be in vocational institutes. Just for them to 491 00:28:26,600 --> 00:28:29,879 Speaker 4: just get up to speed with all these AI tools 492 00:28:29,880 --> 00:28:33,040 Speaker 4: that's been coming on and all the issues that are 493 00:28:33,080 --> 00:28:33,439 Speaker 4: coming on. 494 00:28:33,480 --> 00:28:35,120 Speaker 2: I think those would be very, very helpful. 495 00:28:35,160 --> 00:28:37,720 Speaker 1: You took yeah there about Singapore. Actually that's really where 496 00:28:37,760 --> 00:28:40,280 Speaker 1: your career started, wasn't it, Kenny. You did a stint 497 00:28:40,400 --> 00:28:46,440 Speaker 1: at the Economic Development Board of Singapore. Incredible success in 498 00:28:46,960 --> 00:28:51,000 Speaker 1: tech and science, in the service industry and financial services 499 00:28:51,480 --> 00:28:53,200 Speaker 1: in Singapore. What was it like in that sort of 500 00:28:53,200 --> 00:28:56,680 Speaker 1: early days where they were getting some of these schemes together. 501 00:28:56,840 --> 00:29:00,640 Speaker 1: We saw the rise of a star, really really valuable 502 00:29:01,440 --> 00:29:04,480 Speaker 1: body up in Singapore as well. What was that like 503 00:29:04,520 --> 00:29:06,600 Speaker 1: and really what do you think we can learn from 504 00:29:06,640 --> 00:29:07,800 Speaker 1: the success of Singapore. 505 00:29:08,000 --> 00:29:10,880 Speaker 4: I came from Singapore originally. I'm born and bred actually 506 00:29:10,920 --> 00:29:14,920 Speaker 4: in Singapore. If I went overseas for my higher education 507 00:29:15,160 --> 00:29:17,920 Speaker 4: and then somehow found my way here. I started my career, 508 00:29:17,960 --> 00:29:21,600 Speaker 4: as you said, working in Singapore Economic development aboard Singapore. 509 00:29:22,440 --> 00:29:27,800 Speaker 4: And one of the things that Singapore is extremely good 510 00:29:27,800 --> 00:29:33,680 Speaker 4: at is this coordination. The country is super united in 511 00:29:34,000 --> 00:29:37,320 Speaker 4: the sense of that we have a very capable government 512 00:29:37,960 --> 00:29:43,200 Speaker 4: that sets in place targets that's with this way that 513 00:29:43,200 --> 00:29:47,560 Speaker 4: they want to achieve, and everything just spins into place 514 00:29:47,920 --> 00:29:50,440 Speaker 4: in terms of the coordination that we need in terms 515 00:29:50,480 --> 00:29:53,720 Speaker 4: of the structure and the resources and so on, your 516 00:29:53,800 --> 00:29:57,160 Speaker 4: message together, and we execute really well on that front. 517 00:29:57,600 --> 00:30:02,720 Speaker 4: It's probably not model that every other country could aspire to, 518 00:30:02,960 --> 00:30:06,560 Speaker 4: because there are natural advantages as Singapore has that most 519 00:30:06,600 --> 00:30:10,240 Speaker 4: other countries don't. The fact that it's a small country. 520 00:30:10,720 --> 00:30:13,160 Speaker 4: You're very for We are very fortunate to have a 521 00:30:13,160 --> 00:30:19,080 Speaker 4: government that's extremely most skill knowledgeable and benign. It's very 522 00:30:19,120 --> 00:30:22,920 Speaker 4: difficult for every country to be able to replicate. But 523 00:30:23,680 --> 00:30:28,000 Speaker 4: things like being coordinated, things like thinking in terms of 524 00:30:28,040 --> 00:30:31,760 Speaker 4: long term planning, like thinking that there are going to 525 00:30:31,760 --> 00:30:33,280 Speaker 4: be challenges that's going to be coming. 526 00:30:33,400 --> 00:30:35,560 Speaker 2: We shouldn't run from it. That's just think what's the 527 00:30:35,600 --> 00:30:37,760 Speaker 2: best we could do about it. Those are things. 528 00:30:37,760 --> 00:30:40,640 Speaker 4: These are qualities that I'm sure every other country should 529 00:30:41,200 --> 00:30:42,080 Speaker 4: it really aspire to. 530 00:30:42,200 --> 00:30:45,320 Speaker 1: We are increasing our collaboration with Singapore. There's not much 531 00:30:45,360 --> 00:30:48,440 Speaker 1: new science funding coming through at the moment, but a 532 00:30:48,480 --> 00:30:51,000 Speaker 1: couple of the projects that have been topped up or 533 00:30:51,080 --> 00:30:54,640 Speaker 1: new projects funded recently are I think health tach collaborations 534 00:30:54,680 --> 00:30:59,400 Speaker 1: with Singapore, so it's just a natural partner to collaborate 535 00:30:59,520 --> 00:31:01,720 Speaker 1: with it. To our part of the world, we're really 536 00:31:01,720 --> 00:31:06,440 Speaker 1: focused on Asia Pacific. Singapore has particularly in the biotech space. 537 00:31:06,520 --> 00:31:09,160 Speaker 1: Singapore is doing some great stuff on the future of food, 538 00:31:09,320 --> 00:31:10,880 Speaker 1: so there's a lot of synergies there. 539 00:31:11,040 --> 00:31:15,360 Speaker 4: You know, Singapore basically they treat innovations as survival right, 540 00:31:15,400 --> 00:31:20,680 Speaker 4: so because Singapore doesn't have natural resources, basically make ourselves indispensable. 541 00:31:21,240 --> 00:31:24,320 Speaker 4: So in terms of like the amount of resources they're 542 00:31:24,320 --> 00:31:27,000 Speaker 4: poor into R and D is definitely much higher than 543 00:31:27,600 --> 00:31:28,160 Speaker 4: New Zealand. 544 00:31:28,160 --> 00:31:29,640 Speaker 2: So they do about two. 545 00:31:29,520 --> 00:31:31,960 Speaker 4: And a half percent in two point two too and 546 00:31:31,960 --> 00:31:35,840 Speaker 4: a half percent, so that's significantly more than what New 547 00:31:35,920 --> 00:31:36,520 Speaker 4: Zealand is doing. 548 00:31:36,560 --> 00:31:39,480 Speaker 2: So again there's something that New Zealand should aspire to. 549 00:31:39,840 --> 00:31:43,560 Speaker 4: I would also say that Singapore. There are certain things 550 00:31:43,560 --> 00:31:47,120 Speaker 4: about Singapore that I also have some reservations on. So 551 00:31:47,160 --> 00:31:50,200 Speaker 4: for example, I will get sing points. So much as 552 00:31:50,240 --> 00:31:52,880 Speaker 4: society as built a lot of expectations, right you you 553 00:31:53,080 --> 00:31:55,600 Speaker 4: sort of you. You want to do well in school, 554 00:31:55,680 --> 00:31:59,560 Speaker 4: get the right degree, join the right sector. You're almost 555 00:31:59,560 --> 00:32:02,360 Speaker 4: on the street half the success. So much of Singapore 556 00:32:02,360 --> 00:32:05,520 Speaker 4: has been built on that pop down self thinking. But 557 00:32:06,000 --> 00:32:08,440 Speaker 4: that works in the world where careers are very stable. 558 00:32:09,000 --> 00:32:10,080 Speaker 2: But now AI is. 559 00:32:10,120 --> 00:32:13,520 Speaker 4: Really reshaping the labor market in very unpredictable ways. I 560 00:32:13,800 --> 00:32:17,400 Speaker 4: worry that for a system that's been built so much 561 00:32:17,440 --> 00:32:22,080 Speaker 4: around certainty, around clear letters of success, that's going to 562 00:32:22,080 --> 00:32:25,120 Speaker 4: be huge cultural adjustments. I think in New Zealand we 563 00:32:25,200 --> 00:32:28,360 Speaker 4: don't have that quite the same level, you know, structure 564 00:32:28,440 --> 00:32:31,440 Speaker 4: or investment, but we're probably a little bit more used 565 00:32:31,520 --> 00:32:35,280 Speaker 4: to zigzagging a little bit more that you know. There's 566 00:32:35,320 --> 00:32:38,680 Speaker 4: not a straight line success, straight path to success or 567 00:32:38,720 --> 00:32:41,080 Speaker 4: of thinking, and I think that's why it, Luke gives 568 00:32:41,080 --> 00:32:43,960 Speaker 4: me a lot of hope that New Zealand will we'll 569 00:32:43,960 --> 00:32:47,080 Speaker 4: be able to navigate the next few years. 570 00:32:47,680 --> 00:32:49,479 Speaker 2: Well, well, that's good to hear, Kenny. 571 00:32:49,880 --> 00:32:51,880 Speaker 1: Thanks so much for coming on the business of take 572 00:32:51,960 --> 00:32:55,000 Speaker 1: great posting the conversation. We'll link to that in the 573 00:32:55,040 --> 00:32:57,560 Speaker 1: show notes, and you keep the insights coming. 574 00:32:57,640 --> 00:32:59,600 Speaker 2: Thank you very much. Appreciate the time. 575 00:33:04,680 --> 00:33:08,920 Speaker 1: That was. Kenny Cheng, economist and organizational behavior scholar, on 576 00:33:09,000 --> 00:33:12,840 Speaker 1: what AI is really doing to work for New Zealand. 577 00:33:12,960 --> 00:33:17,080 Speaker 1: He clearly sees opportunity lean into primary sector innovation while 578 00:33:17,120 --> 00:33:22,240 Speaker 1: scaling AI literacy across services, back agrotech, R and D 579 00:33:22,480 --> 00:33:27,320 Speaker 1: and data infrastructure think herd management, grading of fruit, logistics, 580 00:33:27,840 --> 00:33:31,800 Speaker 1: that sort of stuff. So local firms build proprietary data 581 00:33:31,840 --> 00:33:35,440 Speaker 1: sets and solutions instead of renting them from abroad. He 582 00:33:35,520 --> 00:33:39,320 Speaker 1: suggests to business leaders map roles to tasks and target 583 00:33:39,520 --> 00:33:45,640 Speaker 1: augmentation not blanket replacement. Protect entry level learning by redesigning 584 00:33:45,640 --> 00:33:49,600 Speaker 1: the on ramps. AI takes over the grunt work and 585 00:33:49,720 --> 00:33:54,240 Speaker 1: invest in upscaling, credits and partnerships that build capability ahead 586 00:33:54,280 --> 00:33:57,840 Speaker 1: of the next adoption wave. As he pointed out, Singapore's 587 00:33:57,960 --> 00:34:03,000 Speaker 1: Skills Future program gives every single Pourian upskilling opportunities. It's 588 00:34:03,000 --> 00:34:05,280 Speaker 1: a really good example of what we could do at 589 00:34:05,320 --> 00:34:09,640 Speaker 1: a national level to ready the workforce for AI. Anyway, 590 00:34:09,680 --> 00:34:11,920 Speaker 1: that's it for the Business of Tech this week. Follow 591 00:34:12,120 --> 00:34:15,760 Speaker 1: rate and share the podcast on iHeartRadio or your favorite 592 00:34:15,800 --> 00:34:19,479 Speaker 1: podcast app. Check the show notes out at Businessdesk, dot 593 00:34:19,480 --> 00:34:23,560 Speaker 1: co dot NZ. You'll find Kenny's conversation piece and examples 594 00:34:23,640 --> 00:34:26,920 Speaker 1: mentioned in the episode there too. Thanks so much for listening. 595 00:34:26,960 --> 00:34:29,799 Speaker 1: I'll catch you next week with another episode of the 596 00:34:29,800 --> 00:34:30,600 Speaker 1: Business of Tech.