1 00:00:10,320 --> 00:00:11,560 Speaker 1: Welcome to the Business of Tech. 2 00:00:11,560 --> 00:00:13,920 Speaker 2: I'm Peter Griffin, and this week we're pulling back the 3 00:00:14,000 --> 00:00:16,920 Speaker 2: curtain on a story you're probably not hearing much about 4 00:00:17,000 --> 00:00:20,040 Speaker 2: in Silicon Valley's earnings Calls, which we're all about how 5 00:00:20,079 --> 00:00:24,120 Speaker 2: great US tech is. The global map of innovation has 6 00:00:24,160 --> 00:00:29,560 Speaker 2: been quietly but radically redrawn. Countries like China, once dismissed 7 00:00:29,600 --> 00:00:33,159 Speaker 2: as technical coffeecats, are now setting the pace, and a 8 00:00:33,200 --> 00:00:37,160 Speaker 2: new class of middle powers from the Nordics to Singapore 9 00:00:37,280 --> 00:00:40,680 Speaker 2: and the United Arab Emirates, is punching far above its 10 00:00:40,680 --> 00:00:45,400 Speaker 2: weight in AI, gaming, clean tech, and more. My guest 11 00:00:45,479 --> 00:00:49,480 Speaker 2: is innovation expert and author Mehran Gull, whose new book 12 00:00:49,600 --> 00:00:54,200 Speaker 2: The New Geography of Innovation, The Global Contest for Breakthrough Technologies, 13 00:00:54,760 --> 00:00:59,120 Speaker 2: asks a deceptively simple question, where are the most innovative 14 00:00:59,160 --> 00:01:02,960 Speaker 2: places in the world today and why? The answers he 15 00:01:03,040 --> 00:01:06,679 Speaker 2: comes up with challenge nearly every assumption we make about 16 00:01:06,720 --> 00:01:11,000 Speaker 2: tech unicorns, big tech talent, and what it really takes 17 00:01:11,000 --> 00:01:14,360 Speaker 2: for a small or distant country like ours to matter 18 00:01:14,560 --> 00:01:18,160 Speaker 2: in the next wave of technology. We talk about how 19 00:01:18,200 --> 00:01:21,959 Speaker 2: Canada's computer science labs help light the fuse for today's 20 00:01:22,040 --> 00:01:26,400 Speaker 2: AI boom, Why China's real edge is deployment at scale 21 00:01:26,480 --> 00:01:30,280 Speaker 2: billions of people rather than invention, and how countries like 22 00:01:30,360 --> 00:01:34,120 Speaker 2: Switzerland and Singapore have carved out powerful niches and everything 23 00:01:34,200 --> 00:01:39,440 Speaker 2: from digital trust to venture capital. Mahran also has some 24 00:01:39,560 --> 00:01:43,000 Speaker 2: uncomfortable truths and concrete ideas for New Zealand when it 25 00:01:43,040 --> 00:01:47,360 Speaker 2: comes to creating startups, fostering tech talents, and holding onto 26 00:01:47,360 --> 00:01:49,960 Speaker 2: our companies. So if you care about where the next 27 00:01:49,960 --> 00:01:52,440 Speaker 2: decade of tech power is really going to come from 28 00:01:52,480 --> 00:01:55,040 Speaker 2: and what it means for economies on the edge of 29 00:01:55,080 --> 00:01:58,480 Speaker 2: the traditional maps, this is a conversation you won't want 30 00:01:58,480 --> 00:02:11,880 Speaker 2: to miss. My interview with Around Gull after this, Maren, 31 00:02:11,960 --> 00:02:14,560 Speaker 2: Welcome to the Business of Tech all the way from Geneva. 32 00:02:14,639 --> 00:02:15,639 Speaker 3: Thanks so much, having Peter. 33 00:02:15,800 --> 00:02:17,400 Speaker 1: Were you at Davos this year? 34 00:02:17,560 --> 00:02:18,160 Speaker 3: No, I was not. 35 00:02:18,280 --> 00:02:21,200 Speaker 4: I was in Dubai for the Emirates Literature Festival, but 36 00:02:21,400 --> 00:02:23,760 Speaker 4: it was It was a very different Davos from usual 37 00:02:23,840 --> 00:02:24,480 Speaker 4: from what I've heard. 38 00:02:24,600 --> 00:02:30,160 Speaker 2: Yeah, from Trump turning up in Greenland over shadowing things. 39 00:02:30,200 --> 00:02:33,240 Speaker 2: But obviously you spent at least six years at the 40 00:02:33,280 --> 00:02:36,000 Speaker 2: World Economic Forum, so you've probably been to a few 41 00:02:36,000 --> 00:02:36,840 Speaker 2: of those conferences. 42 00:02:37,120 --> 00:02:39,280 Speaker 4: I went there every year while I was working for 43 00:02:39,320 --> 00:02:41,480 Speaker 4: the World Economic Forum, so I'm familiar with the event. 44 00:02:41,600 --> 00:02:44,080 Speaker 2: Yeah, and look, you were leading sort of digital transformation 45 00:02:44,120 --> 00:02:47,280 Speaker 2: of industries work at the w EF. They do some 46 00:02:47,320 --> 00:02:50,919 Speaker 2: great research and outputs. We read quite thoroughly down here 47 00:02:50,919 --> 00:02:53,920 Speaker 2: in New Zealand. Before that, you're you're at un IDA, 48 00:02:54,120 --> 00:02:58,160 Speaker 2: the UN agency responsible for Industrial Development, So that would 49 00:02:58,200 --> 00:03:01,360 Speaker 2: have been a fascinating insight and to innovation around the 50 00:03:01,360 --> 00:03:04,160 Speaker 2: world as well. Prior to that, studying and teaching at 51 00:03:04,200 --> 00:03:07,160 Speaker 2: Yale University. So it seems like, you know, the last 52 00:03:07,160 --> 00:03:09,200 Speaker 2: twenty years or so, if your career has really sort 53 00:03:09,240 --> 00:03:12,560 Speaker 2: of culminated in putting all of this knowledge into a 54 00:03:12,600 --> 00:03:16,080 Speaker 2: really great book, the New Geography of Innovation, at a 55 00:03:16,120 --> 00:03:19,119 Speaker 2: time when every country is thinking about where they fit 56 00:03:19,160 --> 00:03:21,760 Speaker 2: into this jigsaw puzzle in some ways. You know, in 57 00:03:21,800 --> 00:03:23,840 Speaker 2: the media it's presented as a two horse race these 58 00:03:23,919 --> 00:03:27,639 Speaker 2: days between the US and China for these breakthrough technologies, 59 00:03:27,680 --> 00:03:32,720 Speaker 2: things like artificial intelligence and quantum computing and space tech. 60 00:03:32,840 --> 00:03:35,480 Speaker 2: What I love, coming from a small country like New Zealand, 61 00:03:35,640 --> 00:03:37,480 Speaker 2: the message of your book is there's a lot of 62 00:03:37,520 --> 00:03:41,080 Speaker 2: great innovation going on in those smaller countries, these purple 63 00:03:41,200 --> 00:03:45,120 Speaker 2: patches around the world that you highlight where there's actually 64 00:03:45,120 --> 00:03:48,880 Speaker 2: incredible stuff going on. It's definitely not a two horse race. 65 00:03:48,840 --> 00:03:50,640 Speaker 4: Yes, of course, and that was the idea that I 66 00:03:50,680 --> 00:03:53,760 Speaker 4: wanted to push back on when I first started. You know, 67 00:03:53,800 --> 00:03:55,720 Speaker 4: the book just came out, but I started thinking about 68 00:03:55,720 --> 00:03:59,040 Speaker 4: this topic back in twenty seventeen, where even the question 69 00:03:59,120 --> 00:04:01,920 Speaker 4: of China and whether you know, it's doing real innovation 70 00:04:02,040 --> 00:04:05,200 Speaker 4: versus just copycat innovation, that was something that we heard 71 00:04:05,240 --> 00:04:08,080 Speaker 4: about all the time and within a relatively short amount 72 00:04:08,080 --> 00:04:11,840 Speaker 4: of time, that sounds almost a ridiculous statement to make 73 00:04:11,880 --> 00:04:14,160 Speaker 4: that you know, China's not making real innovation that so, 74 00:04:14,320 --> 00:04:16,799 Speaker 4: you know, that ought to give other countries a lot 75 00:04:16,880 --> 00:04:19,719 Speaker 4: more hope as well, that if one or two places 76 00:04:19,720 --> 00:04:23,640 Speaker 4: can change their relative importance in technology within such a 77 00:04:23,680 --> 00:04:26,040 Speaker 4: short time span, you know, five to seven years, and 78 00:04:26,080 --> 00:04:28,200 Speaker 4: it's not just China, you know Canada, for instance, the 79 00:04:28,240 --> 00:04:31,000 Speaker 4: outsize role that it has played in AI research. You know, 80 00:04:31,160 --> 00:04:33,719 Speaker 4: much of the AI boom that we see today comes 81 00:04:33,760 --> 00:04:38,239 Speaker 4: from just a couple of major advancements in AI that happened, 82 00:04:38,240 --> 00:04:41,320 Speaker 4: for instance, alex net, which was this seismic event in 83 00:04:41,400 --> 00:04:44,279 Speaker 4: neural networks. It can be traced back to a small 84 00:04:44,400 --> 00:04:47,279 Speaker 4: lab that Jeff Hinton ran at the University of Toronto. 85 00:04:47,400 --> 00:04:50,640 Speaker 4: Many of the biggest names in AI, like Elijah Satskiva, 86 00:04:50,760 --> 00:04:53,680 Speaker 4: who co founded open ai, came from that lab. So 87 00:04:54,040 --> 00:04:56,360 Speaker 4: the idea behind the book was to push back on 88 00:04:56,440 --> 00:04:58,920 Speaker 4: this narrative that it's just about one or two countries 89 00:04:59,120 --> 00:05:02,560 Speaker 4: and say that, look, is not just more happening around 90 00:05:02,560 --> 00:05:06,000 Speaker 4: the world, but that innovation is often taking different forms 91 00:05:06,000 --> 00:05:07,840 Speaker 4: in different places, and maybe we can get into that. 92 00:05:07,960 --> 00:05:09,880 Speaker 2: Yeah, and look, the first part of the book really 93 00:05:09,920 --> 00:05:13,400 Speaker 2: is reflecting back on the heart of innovation, Silicon Valley, 94 00:05:13,400 --> 00:05:16,360 Speaker 2: what's been achieved in the valley over the last fifty 95 00:05:17,120 --> 00:05:20,479 Speaker 2: sixty years, and really sort of asking people, is that 96 00:05:20,600 --> 00:05:23,280 Speaker 2: model still relevant? And I think the conclusion you come 97 00:05:23,320 --> 00:05:25,800 Speaker 2: to is definitely don't right off the valley. It still 98 00:05:26,040 --> 00:05:30,760 Speaker 2: has the greatest concentration of talent, intellectual property, and capital 99 00:05:31,400 --> 00:05:34,120 Speaker 2: in the world, but there is all this other great 100 00:05:34,120 --> 00:05:35,360 Speaker 2: stuff going on. 101 00:05:35,839 --> 00:05:36,880 Speaker 1: These sort of purple people. 102 00:05:37,000 --> 00:05:39,600 Speaker 2: You talk about the small set of countries that are 103 00:05:39,640 --> 00:05:45,039 Speaker 2: consistently producing breakthrough technologies. How do you define these places today? 104 00:05:45,080 --> 00:05:47,760 Speaker 2: And what sort of surprised you when you started going 105 00:05:47,800 --> 00:05:49,320 Speaker 2: through and mapping some of them out. 106 00:05:49,360 --> 00:05:51,960 Speaker 4: Just how hard It was to answer the simple question 107 00:05:52,160 --> 00:05:55,000 Speaker 4: what are the most connovative places in the world. You know, 108 00:05:55,080 --> 00:05:57,839 Speaker 4: once you get into it, you realize that it presents 109 00:05:57,920 --> 00:06:01,960 Speaker 4: all sorts of methodology and philosophical issues. Right. You know, 110 00:06:02,320 --> 00:06:04,760 Speaker 4: everyone kind of has an understanding that the US is 111 00:06:04,800 --> 00:06:07,159 Speaker 4: on top, everyone sort of has an understanding that China 112 00:06:07,200 --> 00:06:09,880 Speaker 4: sort of comes after. But very quickly after that it 113 00:06:09,920 --> 00:06:12,919 Speaker 4: becomes a very tight feel. You know, why have a 114 00:06:13,000 --> 00:06:16,039 Speaker 4: country like, for instance, India in there but not a 115 00:06:16,080 --> 00:06:18,920 Speaker 4: country like Brazil. You know, why have Switzerland in there 116 00:06:18,920 --> 00:06:22,039 Speaker 4: but not Sweden? You know, why talk about Germany but 117 00:06:22,160 --> 00:06:23,159 Speaker 4: not talk about France? 118 00:06:23,200 --> 00:06:24,120 Speaker 3: For example. 119 00:06:24,640 --> 00:06:26,440 Speaker 4: When I first wrote the proposal for the book, the 120 00:06:26,480 --> 00:06:29,279 Speaker 4: idea was to basically look at which countries are producing 121 00:06:29,279 --> 00:06:33,080 Speaker 4: the most number of unicorns, high growth, early stage companies. 122 00:06:33,160 --> 00:06:36,080 Speaker 4: But very quickly I ran into this issue that that 123 00:06:36,160 --> 00:06:40,320 Speaker 4: means that we're only defining innovation in terms of new companies. 124 00:06:40,360 --> 00:06:43,080 Speaker 4: But in a world where Invidia is now worth more 125 00:06:43,160 --> 00:06:45,920 Speaker 4: than the entire economy of Japan, it's worth more than 126 00:06:45,920 --> 00:06:48,919 Speaker 4: the entire economy of India, you can't really make the 127 00:06:49,040 --> 00:06:52,360 Speaker 4: argument that innovation only happens in new companies. One way 128 00:06:52,400 --> 00:06:55,159 Speaker 4: in which the new AI boom is different from the 129 00:06:55,200 --> 00:06:58,480 Speaker 4: Internet boom, for instance, is just the extent to which 130 00:06:58,680 --> 00:07:01,760 Speaker 4: the weight of this revolution is carried by older companies. 131 00:07:01,800 --> 00:07:04,120 Speaker 4: Your Meta at this point is a very mature company. 132 00:07:04,440 --> 00:07:08,479 Speaker 4: Google is a very mature company. Microsoft is at this 133 00:07:08,560 --> 00:07:11,560 Speaker 4: point an old company. And the outsized role that these 134 00:07:11,560 --> 00:07:14,960 Speaker 4: companies are playing in the AI revolution shows you that 135 00:07:15,080 --> 00:07:17,640 Speaker 4: you can't just look at new companies and you know, 136 00:07:17,800 --> 00:07:20,480 Speaker 4: how do old companies innovate? How do this stay relevant? 137 00:07:20,560 --> 00:07:22,640 Speaker 4: That is something that you need to talk about as well. 138 00:07:23,200 --> 00:07:26,119 Speaker 4: And then also that you can't just rely on valuations either. 139 00:07:26,760 --> 00:07:30,080 Speaker 4: There are smaller countries like Speeden, like Switzerland which rank 140 00:07:30,200 --> 00:07:33,920 Speaker 4: very highly on Innovation in disease, which don't just talk 141 00:07:33,960 --> 00:07:37,400 Speaker 4: about valuations and venture dollars. They also talk about things 142 00:07:37,440 --> 00:07:40,600 Speaker 4: like how many patterns are you filing, how many STEM 143 00:07:40,640 --> 00:07:43,840 Speaker 4: graduates are coming out of your universities. And I think 144 00:07:43,920 --> 00:07:46,760 Speaker 4: it was important to look at innovation in a much 145 00:07:46,800 --> 00:07:49,840 Speaker 4: more holistic way as well. Is your government innovating? Is 146 00:07:49,920 --> 00:07:52,640 Speaker 4: our public service is getting better? And so it became 147 00:07:52,720 --> 00:07:55,360 Speaker 4: a much more complex question to deal with than I 148 00:07:55,360 --> 00:07:58,440 Speaker 4: had initially imagined. And then in my introduction I basically 149 00:07:58,520 --> 00:08:01,240 Speaker 4: lay out three different approaches one is to look at 150 00:08:01,320 --> 00:08:03,560 Speaker 4: high growth companies, the second is to look at cumulative 151 00:08:03,600 --> 00:08:06,640 Speaker 4: market cap, and the third one is to look at 152 00:08:07,080 --> 00:08:10,520 Speaker 4: the White Boat's Global Innovation Index. And each the interesting 153 00:08:10,560 --> 00:08:14,600 Speaker 4: thing is that each measure gives you a very different 154 00:08:14,720 --> 00:08:16,560 Speaker 4: map of the world, and I thought that was a 155 00:08:16,600 --> 00:08:19,240 Speaker 4: really interesting starting point to look at one of the 156 00:08:19,280 --> 00:08:20,480 Speaker 4: most nog places. 157 00:08:20,200 --> 00:08:20,600 Speaker 3: In the world. 158 00:08:20,680 --> 00:08:23,200 Speaker 2: Look as a small country, we get a bit hung 159 00:08:23,280 --> 00:08:25,760 Speaker 2: up on some of this stuff where we actually fit 160 00:08:26,040 --> 00:08:28,559 Speaker 2: in the world, coming from the bottom of the world, 161 00:08:28,840 --> 00:08:33,720 Speaker 2: very isolated, geographically distant. We like to consider ourselves in 162 00:08:33,760 --> 00:08:38,439 Speaker 2: this group of small advanced economies, along with Singapore, with Ireland, 163 00:08:38,559 --> 00:08:42,200 Speaker 2: with Denmark and Finland and the like. But the figures 164 00:08:42,320 --> 00:08:44,679 Speaker 2: tell a different story. We ranked twenty sixth out of 165 00:08:44,720 --> 00:08:47,160 Speaker 2: one hundred and thirty nine countries on the Global Innovation 166 00:08:47,320 --> 00:08:50,080 Speaker 2: Index and haven't been looking at that flash really in 167 00:08:50,160 --> 00:08:52,800 Speaker 2: recent years as our R and D spend sort of 168 00:08:53,080 --> 00:08:56,880 Speaker 2: gets further and further behind OECD countries, and that I 169 00:08:57,000 --> 00:08:58,640 Speaker 2: sort of like that. You sort of say, don't get 170 00:08:58,679 --> 00:09:01,280 Speaker 2: hung up really on one particular measure because it doesn't 171 00:09:01,320 --> 00:09:03,640 Speaker 2: really mean anything. You have to look, you know, holistically, 172 00:09:03,640 --> 00:09:05,679 Speaker 2: at a number of things. We have a couple of 173 00:09:06,559 --> 00:09:09,960 Speaker 2: unicorns here, but compared to Israel, we've got hardly any 174 00:09:10,120 --> 00:09:12,520 Speaker 2: Our tech market cap is tiny here because most of 175 00:09:12,520 --> 00:09:15,640 Speaker 2: our companies established here and then they moved to the 176 00:09:15,760 --> 00:09:18,040 Speaker 2: US and list on the Nasdaq or something like that. 177 00:09:18,160 --> 00:09:20,360 Speaker 2: But you've obviously got a sense from looking at all 178 00:09:20,360 --> 00:09:22,800 Speaker 2: of these mid size and small countries where the real 179 00:09:23,440 --> 00:09:25,800 Speaker 2: momentum is going. And some of the bits I love 180 00:09:26,080 --> 00:09:28,720 Speaker 2: in the book the stories of companies like Supercell, you know, 181 00:09:28,760 --> 00:09:32,000 Speaker 2: the game development company out of Finland that is doing 182 00:09:32,040 --> 00:09:35,600 Speaker 2: incredibly well. It seems like you can lay the groundwork 183 00:09:35,640 --> 00:09:37,240 Speaker 2: as much as you want, but then you just get 184 00:09:37,240 --> 00:09:40,120 Speaker 2: these incredible successes that are really hard to predict. 185 00:09:40,240 --> 00:09:42,880 Speaker 4: Yeah, and it's not just sort of Supercell. The Nordic 186 00:09:42,920 --> 00:09:46,000 Speaker 4: region has been producing sort of one success story after another. 187 00:09:46,160 --> 00:09:48,560 Speaker 4: You know Lovable in Sweden, AI Company, you know, one 188 00:09:48,559 --> 00:09:50,240 Speaker 4: of the fastest growing companies ever. 189 00:09:50,280 --> 00:09:50,880 Speaker 3: For instance. 190 00:09:50,960 --> 00:09:53,600 Speaker 4: The gaming industry in general is big in the Nordics, 191 00:09:53,640 --> 00:09:57,520 Speaker 4: and you know, I do hear this point often that 192 00:09:57,640 --> 00:10:00,480 Speaker 4: you know countries that are small, you know how they 193 00:10:00,640 --> 00:10:04,280 Speaker 4: play an outsized role in the technology industry, given the 194 00:10:04,320 --> 00:10:08,480 Speaker 4: fact that at least now such heavy resource investments are 195 00:10:08,480 --> 00:10:11,240 Speaker 4: required to be relevant in for instance, the II industry, 196 00:10:11,280 --> 00:10:13,679 Speaker 4: with you know, companies having to invest hundreds of billions 197 00:10:13,679 --> 00:10:16,400 Speaker 4: of dollars in building out the A infrastructure. But I 198 00:10:16,440 --> 00:10:19,160 Speaker 4: also think that right now, you know, the word middle 199 00:10:19,200 --> 00:10:22,720 Speaker 4: Powers is coming up very frequently, and I at least 200 00:10:22,800 --> 00:10:25,400 Speaker 4: hear it sort of twice a week in some discussion 201 00:10:25,480 --> 00:10:28,040 Speaker 4: or the other. And there have been countries that, when 202 00:10:28,080 --> 00:10:31,079 Speaker 4: it comes to technology, have been able to have an 203 00:10:31,120 --> 00:10:34,839 Speaker 4: outsized influence. It's interesting how it's not. They're not all 204 00:10:34,920 --> 00:10:39,280 Speaker 4: clustered in one region. You mentioned Sweden, There's Switzerland, There's 205 00:10:39,360 --> 00:10:43,400 Speaker 4: South Korea, there's Singapore. The UE has been making very 206 00:10:43,600 --> 00:10:47,080 Speaker 4: massive investments in in AI data centers as well, in 207 00:10:47,320 --> 00:10:50,520 Speaker 4: making massive investments in American tech companies as well. So 208 00:10:50,600 --> 00:10:52,720 Speaker 4: I do think that even though this is a narrative, 209 00:10:52,760 --> 00:10:55,480 Speaker 4: that countries often tend to tell themselves that if only 210 00:10:55,520 --> 00:10:58,000 Speaker 4: we weren't very small, if only we weren't sort of, 211 00:10:58,080 --> 00:11:00,240 Speaker 4: you know, very distant from you know, the center of 212 00:11:00,240 --> 00:11:03,520 Speaker 4: where it's all happening. But for each of these examples, 213 00:11:03,720 --> 00:11:06,240 Speaker 4: I can come up with one counter example that, like 214 00:11:06,320 --> 00:11:08,920 Speaker 4: you know, Singapore is also at the periphery of the world. 215 00:11:08,960 --> 00:11:12,800 Speaker 4: It's also surrounded by countries that are much larger in comparison, 216 00:11:13,400 --> 00:11:16,640 Speaker 4: and South Korea, even though its population base is pretty big, 217 00:11:16,920 --> 00:11:19,040 Speaker 4: for much of its history it tended to think of 218 00:11:19,080 --> 00:11:22,080 Speaker 4: itself as a shrimp among whales. Where you know, you 219 00:11:22,160 --> 00:11:24,800 Speaker 4: have this massive country China right next door. You have 220 00:11:25,080 --> 00:11:27,600 Speaker 4: Japan next door as well, which had a very painful 221 00:11:27,640 --> 00:11:30,360 Speaker 4: colonial past with South Korea, and obviously, you know, coming 222 00:11:30,400 --> 00:11:32,120 Speaker 4: out of the Second World War is one of the 223 00:11:32,160 --> 00:11:34,520 Speaker 4: poorest countries in the world, and now it has so 224 00:11:34,640 --> 00:11:38,199 Speaker 4: many brands in technology that would be instantly recognizable anywhere 225 00:11:38,240 --> 00:11:42,160 Speaker 4: in the world. Samsung the largest manufacturer smartphones in the world. 226 00:11:42,520 --> 00:11:46,160 Speaker 4: One in four smartphones is a Samsung. Hyundai is now 227 00:11:46,240 --> 00:11:49,800 Speaker 4: the second most popular TV brand in the US after Tesla, 228 00:11:50,440 --> 00:11:52,400 Speaker 4: which is the fact that many people don't tend to 229 00:11:52,440 --> 00:11:54,680 Speaker 4: sort of hear about that often. So I think there's 230 00:11:54,880 --> 00:11:57,520 Speaker 4: many countries. I love the word middle powers and the 231 00:11:57,559 --> 00:12:00,400 Speaker 4: fact that there are so many of them you can 232 00:12:00,440 --> 00:12:02,360 Speaker 4: find in virtually every region out that. 233 00:12:02,559 --> 00:12:05,760 Speaker 2: Yeah, well, as Mark Kinney laid out in Davos, very 234 00:12:06,160 --> 00:12:11,120 Speaker 2: well received speech around the power potential latent power off 235 00:12:11,120 --> 00:12:15,000 Speaker 2: those middle sized economies if they can come together. So 236 00:12:15,040 --> 00:12:17,880 Speaker 2: I think we were watching on with glee as he 237 00:12:18,000 --> 00:12:20,360 Speaker 2: was saying that from the bottom of the world. But 238 00:12:20,920 --> 00:12:23,920 Speaker 2: you argue that these high growth companies that become unicorns 239 00:12:23,960 --> 00:12:26,720 Speaker 2: are sort of like the seeds, and the soil is 240 00:12:26,760 --> 00:12:31,680 Speaker 2: the wider sort of entrepreneurial environment around them. And I guess, 241 00:12:31,679 --> 00:12:34,079 Speaker 2: you know, we've been trying to learn from Silicon Valley 242 00:12:34,120 --> 00:12:37,120 Speaker 2: to till that soil and get it fertile enough to 243 00:12:37,360 --> 00:12:39,400 Speaker 2: allow these seeds to grow. But what are some of 244 00:12:39,440 --> 00:12:40,719 Speaker 2: the maybe if you look at some of the real 245 00:12:40,800 --> 00:12:43,480 Speaker 2: obvious things as you looked around the world, at these 246 00:12:43,480 --> 00:12:46,920 Speaker 2: small advanced nations that contribute in places like you know, 247 00:12:47,000 --> 00:12:49,719 Speaker 2: the Nordic region to the success, But are there really 248 00:12:49,800 --> 00:12:51,760 Speaker 2: sort of non obvious things that we might not be 249 00:12:51,800 --> 00:12:52,760 Speaker 2: thinking about as well. 250 00:12:52,880 --> 00:12:55,480 Speaker 4: So I think the interesting thing for me really was that, 251 00:12:55,679 --> 00:12:59,880 Speaker 4: you know, I couldn't identify ingredients as it were that 252 00:13:00,200 --> 00:13:03,240 Speaker 4: consistent across different places. You know, if in a place 253 00:13:03,400 --> 00:13:06,200 Speaker 4: like the Valley, it would be you know, one small 254 00:13:06,240 --> 00:13:10,400 Speaker 4: anchor university, and then having things like, for instance, non 255 00:13:10,440 --> 00:13:13,040 Speaker 4: complete clauses that make it easier for talent to go 256 00:13:13,160 --> 00:13:17,120 Speaker 4: to circulate in the environment, having lots of venture capital, 257 00:13:17,760 --> 00:13:21,880 Speaker 4: having a deal engine where you have larger companies constantly 258 00:13:21,920 --> 00:13:24,800 Speaker 4: acquiring smaller companies. If you look at a place like China, 259 00:13:24,800 --> 00:13:27,000 Speaker 4: for instance, it did not happen like that over there 260 00:13:27,040 --> 00:13:29,360 Speaker 4: at all. You did not really have this technology stack 261 00:13:29,440 --> 00:13:31,280 Speaker 4: where first you have the universities, then you have the 262 00:13:31,280 --> 00:13:33,959 Speaker 4: research centers, then you have the graduate students, and then 263 00:13:34,000 --> 00:13:36,280 Speaker 4: you have the big companies. It happened the other way around, 264 00:13:36,320 --> 00:13:38,760 Speaker 4: where first you had the big companies, and then from 265 00:13:38,800 --> 00:13:42,000 Speaker 4: the companies they went backwards into having the research centers 266 00:13:42,040 --> 00:13:44,920 Speaker 4: and the new innovations which are now consistently coming out 267 00:13:44,920 --> 00:13:47,640 Speaker 4: of China. In my book, for instance, I talk about ResNet, 268 00:13:48,120 --> 00:13:51,120 Speaker 4: which is at this point the most cited paper in 269 00:13:51,280 --> 00:13:54,480 Speaker 4: science ever, which came out of a lab in Beijing. 270 00:13:54,920 --> 00:13:58,600 Speaker 4: So you know, the idea that deployment just being better 271 00:13:58,640 --> 00:14:02,040 Speaker 4: at others at the deploying technology at scale is in 272 00:14:02,080 --> 00:14:04,880 Speaker 4: and of itself an advantage. Today we see a lot 273 00:14:04,880 --> 00:14:08,240 Speaker 4: of countries often second guessing themselves because they can't show 274 00:14:08,320 --> 00:14:11,200 Speaker 4: up to this foundation models race. And you know, all 275 00:14:11,200 --> 00:14:15,439 Speaker 4: of AI has been reduced to primarily large language models 276 00:14:15,520 --> 00:14:18,440 Speaker 4: and primarily these foundation model races that we see with 277 00:14:18,800 --> 00:14:21,200 Speaker 4: deep seek or with OpenAI. But the fact of the 278 00:14:21,200 --> 00:14:23,480 Speaker 4: matter is that AI is about a lot more than 279 00:14:23,560 --> 00:14:26,880 Speaker 4: just lllm's and beyond that, also, it's not about who 280 00:14:26,960 --> 00:14:30,280 Speaker 4: crosses this theoretical finished line first. You know, who is 281 00:14:30,360 --> 00:14:33,920 Speaker 4: able to absorb all of these technologies into their economy 282 00:14:33,960 --> 00:14:38,560 Speaker 4: widely might determine who has an economic edge that derives 283 00:14:38,640 --> 00:14:41,560 Speaker 4: from technology in the future. In my book, for instance, 284 00:14:41,560 --> 00:14:44,320 Speaker 4: I talk about how, you know, many of these technologies 285 00:14:44,440 --> 00:14:49,400 Speaker 4: like five G, autonomous driving, robotics, solar none of these 286 00:14:49,440 --> 00:14:52,040 Speaker 4: were invented in China. Even today, I don't think there 287 00:14:52,040 --> 00:14:55,200 Speaker 4: are that many major inventions that come out of China. 288 00:14:55,280 --> 00:14:58,200 Speaker 4: Where they really have an edge is that they adopt 289 00:14:58,240 --> 00:15:01,160 Speaker 4: these technologies faster than anyone else. They deployed them at 290 00:15:01,160 --> 00:15:04,360 Speaker 4: scale faster than anyone else. They have government incentives for 291 00:15:04,480 --> 00:15:07,720 Speaker 4: companies to be rolling out these technologies, and there are 292 00:15:07,840 --> 00:15:10,200 Speaker 4: entire cities that are coming up with a base of 293 00:15:10,240 --> 00:15:13,560 Speaker 4: technology on which you can build out new technologies. For instance, 294 00:15:13,600 --> 00:15:17,920 Speaker 4: I talk about Schongan where the entire infrastructure is built 295 00:15:17,920 --> 00:15:21,240 Speaker 4: around autonomous driving. So, you know, I think we can 296 00:15:21,360 --> 00:15:24,880 Speaker 4: over index on things like you know, what are the ingredients, 297 00:15:24,960 --> 00:15:28,240 Speaker 4: But the fact is that they are very different across 298 00:15:28,280 --> 00:15:31,000 Speaker 4: different places. In a place like Switzerland, where I am 299 00:15:31,120 --> 00:15:35,880 Speaker 4: right now, you will not find that many major domestic 300 00:15:35,920 --> 00:15:38,680 Speaker 4: tech companies, but at the same time you do have 301 00:15:38,760 --> 00:15:43,040 Speaker 4: these other examples of headline tech, like, for instance, the 302 00:15:43,040 --> 00:15:46,200 Speaker 4: Swiss transport network, where the Swiss train system since the 303 00:15:46,320 --> 00:15:52,080 Speaker 4: nineteen sixties has been entirely electric and runs on renewable energy. 304 00:15:52,160 --> 00:15:54,320 Speaker 4: And sure they don't have Google, they don't have Facebook, 305 00:15:54,560 --> 00:15:56,960 Speaker 4: but they do have these other examples. You know, I'm 306 00:15:57,000 --> 00:16:00,160 Speaker 4: not very far from the large Hadron collider, which is 307 00:16:00,440 --> 00:16:03,160 Speaker 4: the biggest piece of machinery, the most complicated piece of 308 00:16:03,160 --> 00:16:05,720 Speaker 4: machinery ever made. So I think different countries are taking 309 00:16:05,720 --> 00:16:10,000 Speaker 4: different routes to often sort of different destinations where for instance, 310 00:16:10,040 --> 00:16:13,520 Speaker 4: again you probably can't name any major tech companies that 311 00:16:13,640 --> 00:16:17,080 Speaker 4: came out of Singapore. I certainly can't. There's one creative 312 00:16:17,080 --> 00:16:19,880 Speaker 4: technologies that used to make speakers in sound systems back 313 00:16:19,880 --> 00:16:21,920 Speaker 4: in the nineties. But at the same time it's a 314 00:16:21,920 --> 00:16:25,840 Speaker 4: country which plays such a big role in technology investing. Singapore, 315 00:16:25,960 --> 00:16:29,080 Speaker 4: which is the second smallest country in the Asian region, 316 00:16:29,560 --> 00:16:33,840 Speaker 4: has more venture capital assets under management than all of 317 00:16:33,880 --> 00:16:36,920 Speaker 4: the other Asian region, which is eleven countries combined. So 318 00:16:36,960 --> 00:16:41,160 Speaker 4: you can have these niches where you play an outsized role. 319 00:16:41,280 --> 00:16:44,520 Speaker 4: Even if you're not showing up to the Foundation models 320 00:16:44,600 --> 00:16:47,920 Speaker 4: race and the robotics race and the space race, there 321 00:16:47,960 --> 00:16:51,160 Speaker 4: are certain areas where you can leave it your size 322 00:16:51,200 --> 00:16:53,160 Speaker 4: you're positioning in order to play a big row. 323 00:16:53,280 --> 00:16:56,160 Speaker 2: The sections in the book about Singapore, I think are 324 00:16:56,200 --> 00:16:59,239 Speaker 2: really pertinent to us. We're doing a lot more with Singapore, 325 00:16:59,440 --> 00:17:03,560 Speaker 2: the leadership they've shown internationally. You talk about gov Tech, 326 00:17:03,600 --> 00:17:06,960 Speaker 2: the agency in Singapore that sets the strategic direction on 327 00:17:07,560 --> 00:17:11,680 Speaker 2: tech investment around the country and has done some fantastic stuff. 328 00:17:11,720 --> 00:17:15,360 Speaker 2: It is a smart city. They've invested heavily in their 329 00:17:15,359 --> 00:17:19,080 Speaker 2: digital infrastructure and it's really paid off. But yeah, you're 330 00:17:19,119 --> 00:17:21,080 Speaker 2: you're also talking there about as you look around the world. 331 00:17:21,080 --> 00:17:22,959 Speaker 2: And I noticed this when I go and visit some 332 00:17:23,000 --> 00:17:25,400 Speaker 2: of these sort of tech hubs around the world, whether 333 00:17:25,440 --> 00:17:28,600 Speaker 2: it's in Cambridge in the UK or in the Bay 334 00:17:28,640 --> 00:17:31,960 Speaker 2: Area in San Francisco, they're sort of ticking the same boxes. 335 00:17:32,040 --> 00:17:35,160 Speaker 2: The hoodie culture is there. They all have their own 336 00:17:35,160 --> 00:17:38,600 Speaker 2: sort of darper like agencies. The lingo is the same. 337 00:17:38,680 --> 00:17:40,760 Speaker 2: A lot of that has been sort of inherited from 338 00:17:41,320 --> 00:17:44,040 Speaker 2: Silicon Valley and you know, y Combinator. A lot of 339 00:17:44,040 --> 00:17:47,760 Speaker 2: our entrepreneurs have been lucky enough to go through that program. 340 00:17:47,920 --> 00:17:50,399 Speaker 2: Are there fundamentals that we've settled on maybe that was 341 00:17:50,440 --> 00:17:54,159 Speaker 2: built in this American mold that have just become the 342 00:17:54,240 --> 00:17:57,520 Speaker 2: norm and the table stakes really that are essential to 343 00:17:57,640 --> 00:18:01,240 Speaker 2: having a you know, a thriving innovation system in your country. 344 00:18:01,400 --> 00:18:04,679 Speaker 4: Yes, And that I talk about in the book more 345 00:18:04,760 --> 00:18:08,000 Speaker 4: as something that I was a bit disappointed to find, 346 00:18:08,040 --> 00:18:12,680 Speaker 4: which is in every country now tech development is looking 347 00:18:12,800 --> 00:18:15,400 Speaker 4: more and more similar rather than more and more different. 348 00:18:15,440 --> 00:18:18,920 Speaker 4: Where everywhere the language of technology is now you know, 349 00:18:19,080 --> 00:18:22,600 Speaker 4: venture backed companies and on the private side, and then 350 00:18:22,680 --> 00:18:24,639 Speaker 4: on the public side it is having some sort of 351 00:18:24,720 --> 00:18:28,280 Speaker 4: DARPA like agency. If you go to Germany, you know, 352 00:18:28,359 --> 00:18:30,879 Speaker 4: this would be Sprint for instance. You know, if you 353 00:18:31,000 --> 00:18:33,280 Speaker 4: go to the UK, they have their own sort of 354 00:18:33,359 --> 00:18:37,159 Speaker 4: darker type agency. But I haven't seen any major differences 355 00:18:37,200 --> 00:18:41,560 Speaker 4: in terms of how systematically countries are trying to create 356 00:18:41,680 --> 00:18:44,200 Speaker 4: different types of tech economy. What I mean by that 357 00:18:44,440 --> 00:18:47,960 Speaker 4: is there are many major pieces of tech that were 358 00:18:48,040 --> 00:18:51,800 Speaker 4: developed not as a company but more as you know, 359 00:18:51,840 --> 00:18:54,520 Speaker 4: something that was not supposed to be commercialized. The Internet 360 00:18:54,560 --> 00:18:57,919 Speaker 4: would be a very major example. There's no Facebook like 361 00:18:58,040 --> 00:19:02,080 Speaker 4: entity that controls the Internet. There is no single company 362 00:19:02,080 --> 00:19:06,720 Speaker 4: that controls something like email, for instance, or the protocols 363 00:19:06,440 --> 00:19:09,520 Speaker 4: on top of which the World Wide Web is built. 364 00:19:10,080 --> 00:19:14,000 Speaker 4: And over time that way of building out technology just 365 00:19:14,400 --> 00:19:17,120 Speaker 4: stopped being relevant, and now just every piece of tech 366 00:19:17,160 --> 00:19:19,919 Speaker 4: that comes out is commercialized by a single company. It 367 00:19:19,960 --> 00:19:22,240 Speaker 4: doesn't have to be that way. There's two ways to 368 00:19:22,240 --> 00:19:24,320 Speaker 4: look at any competition. One is to look at it 369 00:19:24,480 --> 00:19:27,880 Speaker 4: as you know, ethnic competition, this is China versus the US, 370 00:19:28,000 --> 00:19:31,640 Speaker 4: or this is you know, Europe versus Asia, which I find, 371 00:19:31,920 --> 00:19:34,240 Speaker 4: you know, an unsatisfying way to sort of look at 372 00:19:34,280 --> 00:19:36,040 Speaker 4: the world. But a different way of looking at it 373 00:19:36,040 --> 00:19:38,040 Speaker 4: would be to look at it as a competition between 374 00:19:38,080 --> 00:19:42,040 Speaker 4: systems and so whether that is capitalism versus communism versus 375 00:19:42,080 --> 00:19:45,119 Speaker 4: social democracy, we are not really seeing anything of that 376 00:19:45,240 --> 00:19:48,600 Speaker 4: sort in technology today, where it's basically a competition among 377 00:19:48,840 --> 00:19:51,639 Speaker 4: different variations of the same system. If you go to 378 00:19:51,640 --> 00:19:54,520 Speaker 4: Shenzen or if you go to Silicon Valley, they're trying 379 00:19:54,560 --> 00:19:57,480 Speaker 4: to build out technology in a very similar way, except 380 00:19:57,520 --> 00:19:59,959 Speaker 4: in Shenzen the volume is turned all the way up 381 00:20:00,200 --> 00:20:03,560 Speaker 4: on that same model. And so for me, you know, 382 00:20:04,280 --> 00:20:06,480 Speaker 4: there's two ways to compete. You know, one is to 383 00:20:06,560 --> 00:20:08,520 Speaker 4: do what the other person is doing and try and 384 00:20:08,560 --> 00:20:11,439 Speaker 4: do it faster, try and do it harder. And the 385 00:20:11,480 --> 00:20:13,840 Speaker 4: other way to compete is to try and do it differently. 386 00:20:14,200 --> 00:20:17,520 Speaker 4: And that was not something that I was seeing. And 387 00:20:17,560 --> 00:20:19,640 Speaker 4: the trend line seemed to be that more people were 388 00:20:19,640 --> 00:20:23,400 Speaker 4: converging on the same model. For me, you know, having 389 00:20:23,560 --> 00:20:27,080 Speaker 4: a systemic foil to whatever's going on right now seemed 390 00:20:27,119 --> 00:20:30,600 Speaker 4: to be something that's missing and something that would be 391 00:20:30,720 --> 00:20:33,960 Speaker 4: very much needed and a very interesting way for new 392 00:20:34,040 --> 00:20:36,800 Speaker 4: countries entering the race to think about how they can 393 00:20:36,840 --> 00:20:38,760 Speaker 4: compete with these much more established ribles. 394 00:20:45,800 --> 00:20:49,800 Speaker 2: It's interesting you're talking there about China is thriving when 395 00:20:49,840 --> 00:20:52,720 Speaker 2: it comes to innovation. The whole World Economic Forum premise 396 00:20:52,760 --> 00:20:56,280 Speaker 2: of that organization is freedom and prosperity leads to innovation, 397 00:20:56,600 --> 00:20:59,600 Speaker 2: which the Western will you loves that concept and believes 398 00:20:59,600 --> 00:21:01,720 Speaker 2: in it. As we've seen in China, you can have 399 00:21:01,760 --> 00:21:05,080 Speaker 2: an authoritarian country that sets these five and ten year 400 00:21:05,160 --> 00:21:09,080 Speaker 2: plans and is smashing those goals, putting the investment in 401 00:21:09,520 --> 00:21:11,760 Speaker 2: that central sort of planned way of doing things. So 402 00:21:11,920 --> 00:21:14,120 Speaker 2: the book sort of does highlight there are definitely other 403 00:21:14,160 --> 00:21:16,080 Speaker 2: ways of doing things, and then you've got the sort 404 00:21:16,080 --> 00:21:18,960 Speaker 2: of the Europeans who I think the narrative around Europe 405 00:21:19,000 --> 00:21:22,320 Speaker 2: and you really you break Europe up obviously into the 406 00:21:22,359 --> 00:21:26,800 Speaker 2: triangle the Oxford Cambridge, London is doing incredible things. That 407 00:21:26,880 --> 00:21:29,959 Speaker 2: really is the innovation ecosystem in the UK. You've got Paris, 408 00:21:30,000 --> 00:21:32,879 Speaker 2: You talked about some of the amazing innovation coming out 409 00:21:32,920 --> 00:21:35,520 Speaker 2: of places like that. But I think the Europeans and 410 00:21:35,800 --> 00:21:38,840 Speaker 2: you quote UK venture capitalists saw Klein in the book 411 00:21:38,840 --> 00:21:42,159 Speaker 2: who argues that Europe's opportunities to build a values driven 412 00:21:42,160 --> 00:21:45,399 Speaker 2: ecosystem like we can do it the right way over 413 00:21:46,080 --> 00:21:48,560 Speaker 2: the US or China. Do you buy that or is 414 00:21:48,560 --> 00:21:51,919 Speaker 2: that sort of a comforting rhetoric for a continent that 415 00:21:52,080 --> 00:21:54,399 Speaker 2: has sort of been behind hasn't really fired when it 416 00:21:54,400 --> 00:21:55,120 Speaker 2: comes to innovation. 417 00:21:55,280 --> 00:21:57,800 Speaker 4: What suld was talking about has resonated with a lot 418 00:21:57,800 --> 00:22:01,440 Speaker 4: of people because I think the idea that humanist values 419 00:22:01,640 --> 00:22:05,040 Speaker 4: tend to be much more in evidence in places like Europe. 420 00:22:05,040 --> 00:22:08,120 Speaker 4: I think generally speaking that is true, but another venture 421 00:22:08,160 --> 00:22:11,760 Speaker 4: capitalist was quick to point out that companies in Europe 422 00:22:11,760 --> 00:22:15,359 Speaker 4: tend to have that language in their business plans. Also, 423 00:22:15,440 --> 00:22:19,080 Speaker 4: because a lot of the money for venture capital in 424 00:22:19,160 --> 00:22:22,520 Speaker 4: Europe comes from government sources, it comes from things like 425 00:22:22,760 --> 00:22:27,760 Speaker 4: the European Investment Fund and government funds for new companies 426 00:22:27,760 --> 00:22:30,760 Speaker 4: and for venture capital funds. Investing in new companies always 427 00:22:30,800 --> 00:22:35,600 Speaker 4: comes with some sort of public minded strings attached, and 428 00:22:35,640 --> 00:22:38,960 Speaker 4: so companies have that incentive to have that language in there. 429 00:22:39,000 --> 00:22:40,960 Speaker 4: So the reason why I put Soult's perspective in there 430 00:22:41,200 --> 00:22:44,840 Speaker 4: was because I thought there was something there where. It 431 00:22:44,960 --> 00:22:48,240 Speaker 4: is true that in the US the tech industry has 432 00:22:48,440 --> 00:22:52,200 Speaker 4: lost a lot of its shine over the past twenty years. 433 00:22:52,520 --> 00:22:55,719 Speaker 4: Where you know, I lived in the US back in 434 00:22:55,800 --> 00:23:00,000 Speaker 4: two thousand and seven. Back then, these entrepreneurs were unquestionably 435 00:23:00,160 --> 00:23:02,879 Speaker 4: seen as the heroes of the economy, you know, the 436 00:23:02,920 --> 00:23:06,240 Speaker 4: people in the hoodies and flip clops. Mark Zuckerberg was 437 00:23:06,240 --> 00:23:09,119 Speaker 4: a positive story, Elon Musk was a positive story. People 438 00:23:09,160 --> 00:23:12,480 Speaker 4: formed over these people. And over twenty years, you know, 439 00:23:12,520 --> 00:23:15,520 Speaker 4: the story has completely flipped and there's such a massive 440 00:23:15,600 --> 00:23:18,440 Speaker 4: backlash against tech billionaires that you know, before this call, 441 00:23:18,520 --> 00:23:20,879 Speaker 4: we were talking about all the billionaires lining up to 442 00:23:20,920 --> 00:23:23,639 Speaker 4: come to New Zealand, and you know, this popular backlash 443 00:23:23,640 --> 00:23:26,280 Speaker 4: has a lot to do with that. And similarly in 444 00:23:26,400 --> 00:23:30,440 Speaker 4: China as well, the tech industry. You know, in twenty 445 00:23:30,640 --> 00:23:34,720 Speaker 4: twenty twenty one went through this massive correction where companies 446 00:23:34,800 --> 00:23:38,200 Speaker 4: like Ali Baba find in the billions, DD find in 447 00:23:38,280 --> 00:23:41,240 Speaker 4: the billions. Many of the billionaires had to leave the country. 448 00:23:41,480 --> 00:23:41,639 Speaker 3: You know. 449 00:23:41,680 --> 00:23:44,280 Speaker 4: Singapore Washing became a huge thing where companies that were 450 00:23:44,280 --> 00:23:48,720 Speaker 4: previously Chinese, like Sheen for instance, suddenly rebranding themselves as 451 00:23:48,800 --> 00:23:52,800 Speaker 4: Singaporean companies. So there has been this massive upheaval that 452 00:23:52,840 --> 00:23:55,439 Speaker 4: has happened in the tech industries, both in China and 453 00:23:55,480 --> 00:23:58,800 Speaker 4: the US, and the idea that you can have a 454 00:23:58,840 --> 00:24:01,920 Speaker 4: tech industry and tech companies that are seen as being 455 00:24:02,000 --> 00:24:05,560 Speaker 4: more acceptable in the long run, and that is an 456 00:24:05,600 --> 00:24:09,000 Speaker 4: element of their competitiveness, the fact that you know, they 457 00:24:09,040 --> 00:24:12,960 Speaker 4: are not seen as being these alien entities in their environment. 458 00:24:13,280 --> 00:24:15,280 Speaker 4: I thought there was something to it, and you know, 459 00:24:15,320 --> 00:24:17,000 Speaker 4: the pin up company for me over there would be 460 00:24:17,000 --> 00:24:19,439 Speaker 4: deep mind where you know, it is not a company 461 00:24:19,440 --> 00:24:22,320 Speaker 4: that is seen negatively. It is a company that you know, 462 00:24:22,359 --> 00:24:25,000 Speaker 4: the UK tends to be very proud of and that 463 00:24:25,160 --> 00:24:28,720 Speaker 4: over time has has thought about reorganizing itself a long 464 00:24:28,880 --> 00:24:32,760 Speaker 4: more sort of public minded ways of organizing sort of 465 00:24:32,800 --> 00:24:35,480 Speaker 4: its corporate structure, which I talk about in the book. 466 00:24:35,600 --> 00:24:38,000 Speaker 4: So I think there's something that the idea that companies 467 00:24:38,040 --> 00:24:41,040 Speaker 4: can be more responsible and as a consequence that ensures 468 00:24:41,080 --> 00:24:46,199 Speaker 4: their longevity much more so than this almost predatory nature 469 00:24:46,480 --> 00:24:48,719 Speaker 4: of the tech industry that we see in the two 470 00:24:48,880 --> 00:24:49,800 Speaker 4: large tech economy. 471 00:24:49,840 --> 00:24:51,040 Speaker 2: This is a bit of a same in New Zealand. 472 00:24:51,080 --> 00:24:54,520 Speaker 2: When I ask people other than our natural advantage in 473 00:24:54,560 --> 00:24:58,000 Speaker 2: agriculture and sort of doing aggri take on top of that, 474 00:24:58,000 --> 00:25:01,400 Speaker 2: that is definitely a natural sw spot for us. When 475 00:25:01,440 --> 00:25:03,560 Speaker 2: I ask people in the software industry, you know, what 476 00:25:03,640 --> 00:25:07,120 Speaker 2: is our value add here? What's our real differentiating point? 477 00:25:07,200 --> 00:25:10,160 Speaker 2: A lot of them say, We're highly trusted and respected 478 00:25:10,320 --> 00:25:13,040 Speaker 2: and have low corruption here. So people like doing business 479 00:25:13,119 --> 00:25:15,879 Speaker 2: with us. They trust us, and we also have a 480 00:25:16,000 --> 00:25:20,080 Speaker 2: very strong indigenous population here, a focus on on language, 481 00:25:20,160 --> 00:25:22,919 Speaker 2: the Terreo Mari language here, and a lot of our 482 00:25:22,960 --> 00:25:25,840 Speaker 2: companies have done quite well in the AI world in 483 00:25:25,960 --> 00:25:29,679 Speaker 2: natural language and building high trust platforms for that. So 484 00:25:29,920 --> 00:25:33,080 Speaker 2: whether we can actually spin that into something that is 485 00:25:33,200 --> 00:25:35,600 Speaker 2: economically very valuable to the world, I think is yet 486 00:25:35,640 --> 00:25:38,800 Speaker 2: to be seen. But definitely that those humanistic values YAH 487 00:25:38,840 --> 00:25:41,720 Speaker 2: seems to be as a tech taker a consumer of 488 00:25:41,760 --> 00:25:44,000 Speaker 2: tech here from the US seems to be sort of 489 00:25:44,080 --> 00:25:46,480 Speaker 2: increasingly at odds with in the world of what AI 490 00:25:46,680 --> 00:25:49,520 Speaker 2: very exploitedive approach to technology, which we really saw with 491 00:25:49,600 --> 00:25:52,800 Speaker 2: the rise of Web two point zero social media platforms. 492 00:25:52,920 --> 00:25:55,760 Speaker 4: Yeah, and the idea that trust could be a differentiator. 493 00:25:55,800 --> 00:25:58,240 Speaker 4: It's something that you hear about in Switzerland very frequently 494 00:25:58,280 --> 00:26:00,880 Speaker 4: as well. I talk about, for instance, how now Switzerland 495 00:26:00,960 --> 00:26:03,760 Speaker 4: already plays a huge role in what are often called 496 00:26:03,800 --> 00:26:07,159 Speaker 4: the trust protocols of the global economy. Where you know, 497 00:26:07,200 --> 00:26:10,080 Speaker 4: a company like Sikpa, for instance, which already pays a 498 00:26:10,160 --> 00:26:13,639 Speaker 4: role in securing banknotes. For instance, when you talk about 499 00:26:13,880 --> 00:26:17,080 Speaker 4: the US dollar, the British pound, the Swiss franc, the 500 00:26:17,160 --> 00:26:20,280 Speaker 4: ink that's used in them is used by a company 501 00:26:20,800 --> 00:26:22,879 Speaker 4: from from Switzerland called SIPA. It could have been a 502 00:26:22,880 --> 00:26:26,000 Speaker 4: company from anywhere, but Swiss companies are seen as being 503 00:26:26,160 --> 00:26:29,520 Speaker 4: much more discrete, low profile, you know, not you know, 504 00:26:29,560 --> 00:26:32,280 Speaker 4: engaging in corruption all that much. And so you know, 505 00:26:32,359 --> 00:26:34,679 Speaker 4: that is something that we've seen carried over from the 506 00:26:34,720 --> 00:26:38,320 Speaker 4: analog economy into the digital economy as well. Where not 507 00:26:38,400 --> 00:26:40,240 Speaker 4: far from where I am right now, there's an entire 508 00:26:40,280 --> 00:26:44,320 Speaker 4: campus called the Unlimited Trust Campus, which is entirely devoted 509 00:26:44,400 --> 00:26:47,600 Speaker 4: to companies that operate in the space of digital trust. 510 00:26:48,400 --> 00:26:50,760 Speaker 4: So it doesn't necessarily have to be that. You know, 511 00:26:50,800 --> 00:26:54,359 Speaker 4: a country has cornered a specific technology like you know 512 00:26:54,440 --> 00:26:57,600 Speaker 4: quantum or AI. It can also corner you know, something 513 00:26:57,680 --> 00:26:59,920 Speaker 4: that is much more abstract, like you know it can 514 00:26:59,920 --> 00:27:02,760 Speaker 4: be trusted more or in the Nord experienstance, you know, 515 00:27:02,800 --> 00:27:06,520 Speaker 4: they haven't cornered one specific technology, except they've cornered the 516 00:27:06,600 --> 00:27:09,800 Speaker 4: general idea of design. You know, Spotify comes from there, 517 00:27:10,119 --> 00:27:13,159 Speaker 4: so many gaming companies come from there, so you know, 518 00:27:13,240 --> 00:27:15,800 Speaker 4: it's not just about are you inventing new technologies but 519 00:27:15,840 --> 00:27:18,440 Speaker 4: that are more abstract sort of corners of the new 520 00:27:18,480 --> 00:27:21,440 Speaker 4: digital economy that you can pain outsized role in as well. 521 00:27:21,520 --> 00:27:24,760 Speaker 2: Look New Zealand barely gets to mention in the book 522 00:27:25,320 --> 00:27:27,679 Speaker 2: just in passing really, which I think is quite telling 523 00:27:27,800 --> 00:27:30,320 Speaker 2: that we do consider ourselves in that camp of small 524 00:27:30,400 --> 00:27:34,320 Speaker 2: advanced economies, but we're not really punching at the same weight. 525 00:27:34,600 --> 00:27:37,439 Speaker 2: And this is recognized here that we are slipping behind 526 00:27:37,440 --> 00:27:40,400 Speaker 2: when it comes to innovation. I think you're pretty skeptical 527 00:27:40,480 --> 00:27:44,480 Speaker 2: really of just going through policy checklists, more steam education, 528 00:27:45,040 --> 00:27:49,840 Speaker 2: more R and D funding, more incubators and accelerators, borrowing 529 00:27:49,920 --> 00:27:52,719 Speaker 2: that why combinators sort of model. I think the message 530 00:27:52,720 --> 00:27:55,080 Speaker 2: from your book is if they don't translate into actual 531 00:27:55,160 --> 00:28:00,440 Speaker 2: globally significant companies that are generating export revenue for the country, 532 00:28:00,640 --> 00:28:04,760 Speaker 2: employing people in high value jobs, it's not really worth it. 533 00:28:04,840 --> 00:28:07,000 Speaker 2: So yeah, considering that, if you think about what you 534 00:28:07,040 --> 00:28:10,000 Speaker 2: know about New Zealand, if our policy advisors here in 535 00:28:10,040 --> 00:28:13,200 Speaker 2: Wellington rang you up tomorrow and ask for some really 536 00:28:13,280 --> 00:28:16,439 Speaker 2: honest priorities to make New Zealand matter more in the 537 00:28:16,480 --> 00:28:18,320 Speaker 2: next book that you're going to write, what would you 538 00:28:18,359 --> 00:28:18,800 Speaker 2: tell them? 539 00:28:18,960 --> 00:28:22,040 Speaker 4: I think a lot of places that were able to 540 00:28:22,160 --> 00:28:26,680 Speaker 4: produce repeat cycles and innovation had that one initial, big 541 00:28:26,720 --> 00:28:32,400 Speaker 4: success story that created this startup factory where every subsequent 542 00:28:32,480 --> 00:28:35,840 Speaker 4: wave was somehow connected to that first company. So what 543 00:28:35,880 --> 00:28:38,400 Speaker 4: I'm talking about over there is things like you know 544 00:28:38,800 --> 00:28:42,120 Speaker 4: Shopify in Canada, you know, massive company, you know, over 545 00:28:42,120 --> 00:28:45,680 Speaker 4: two hundred billion dollars and you know, unlike other companies 546 00:28:45,720 --> 00:28:48,280 Speaker 4: like Nortel was Canadian as well, but it was notorious 547 00:28:48,280 --> 00:28:51,760 Speaker 4: for keeping all innovation within its own walls. With a 548 00:28:51,800 --> 00:28:54,680 Speaker 4: company like Shopify, they actively encourage people to go out 549 00:28:54,680 --> 00:28:56,640 Speaker 4: and start their own companies, act and they act as 550 00:28:56,640 --> 00:28:59,800 Speaker 4: angel investors in those new companies. And we've seen that 551 00:28:59,840 --> 00:29:02,480 Speaker 4: in so many different places, you know deep mind for instance, 552 00:29:02,520 --> 00:29:06,160 Speaker 4: seeing other AI companies in the UK, the role that 553 00:29:06,760 --> 00:29:10,240 Speaker 4: Sky played in producing more companies in the Nordics, or 554 00:29:10,280 --> 00:29:12,760 Speaker 4: shop or the role that Spotify places over there. So 555 00:29:12,800 --> 00:29:14,880 Speaker 4: I would say the hardest thing is to get go 556 00:29:15,000 --> 00:29:17,800 Speaker 4: from zero to one, to get that first company that 557 00:29:17,880 --> 00:29:22,040 Speaker 4: will then become the university for other entrepreneurs to learn 558 00:29:22,200 --> 00:29:25,560 Speaker 4: how how to make new companies. I was talking to 559 00:29:25,560 --> 00:29:28,520 Speaker 4: this venture capitalist in the UK and he was like, 560 00:29:28,880 --> 00:29:32,560 Speaker 4: you know, the advantage that a lot of these established 561 00:29:32,600 --> 00:29:36,520 Speaker 4: hubs have is not some kind of magical, mystical advantage 562 00:29:36,560 --> 00:29:39,080 Speaker 4: that we can't put in words. It's the simple fact 563 00:29:39,120 --> 00:29:42,200 Speaker 4: that people have seen the movie before. They've seen how 564 00:29:42,240 --> 00:29:44,840 Speaker 4: new companies are made, they've seen how they're scaled, they've 565 00:29:44,880 --> 00:29:48,360 Speaker 4: seen how customers are acquired. And I talked about the 566 00:29:48,400 --> 00:29:51,440 Speaker 4: Creative Destruction lab in the book as well, and the 567 00:29:51,880 --> 00:29:54,080 Speaker 4: person who leads it, a j Agarwall, gave me this 568 00:29:54,160 --> 00:29:58,200 Speaker 4: biological analogy that it is really like a flower seeding, 569 00:29:58,600 --> 00:30:01,000 Speaker 4: you know, more pollen around that you need to get 570 00:30:01,040 --> 00:30:05,040 Speaker 4: that first success, which can then produce you know, other 571 00:30:05,240 --> 00:30:09,000 Speaker 4: second and third order consequences in the economy. So my 572 00:30:09,280 --> 00:30:11,920 Speaker 4: first advice really would be to get behind that first 573 00:30:11,960 --> 00:30:15,840 Speaker 4: success that can get the flywheel going and then encourage 574 00:30:15,840 --> 00:30:18,000 Speaker 4: them to do you know, smaller things like For instance, 575 00:30:18,040 --> 00:30:20,880 Speaker 4: the big difference between companies in the UK and companies 576 00:30:20,880 --> 00:30:24,320 Speaker 4: in the US is that a much bigger proportion of 577 00:30:24,400 --> 00:30:27,680 Speaker 4: an early stage company is owned by its own employee 578 00:30:27,680 --> 00:30:29,360 Speaker 4: base in a place like the US. So if you're 579 00:30:29,360 --> 00:30:32,000 Speaker 4: a typical sort of seat stage company, you're own twenty 580 00:30:32,040 --> 00:30:34,120 Speaker 4: percent of your companies owned by your own employees in 581 00:30:34,160 --> 00:30:36,600 Speaker 4: the US. That figure drops down to ten percent in 582 00:30:36,640 --> 00:30:39,040 Speaker 4: the place like the UK. And the way in which 583 00:30:39,120 --> 00:30:42,400 Speaker 4: new companies are built is usually that one company does 584 00:30:42,400 --> 00:30:45,520 Speaker 4: an IPO. People who got rich off going public go 585 00:30:45,600 --> 00:30:48,280 Speaker 4: and start their own companies or invest in other tech companies, 586 00:30:48,560 --> 00:30:50,520 Speaker 4: and that's how you get the flywheel going. In the 587 00:30:50,520 --> 00:30:54,320 Speaker 4: tech economy, a typical tech investor or tech employee is 588 00:30:54,360 --> 00:30:57,480 Speaker 4: different from employees in other industries in that they don't 589 00:30:57,520 --> 00:30:59,320 Speaker 4: tend to buy a lot of real estate, they don't 590 00:30:59,360 --> 00:31:01,720 Speaker 4: tend to go and buy bonds and stock. They tend 591 00:31:01,760 --> 00:31:03,080 Speaker 4: to invest in other tech companies. 592 00:31:03,280 --> 00:31:04,240 Speaker 3: So I would say, you know. 593 00:31:04,240 --> 00:31:06,480 Speaker 4: Learn from the example of Europe why things did not 594 00:31:06,640 --> 00:31:09,800 Speaker 4: work out very well over there, which was primarily companies 595 00:31:09,800 --> 00:31:13,080 Speaker 4: were seen as these private assets that were to be 596 00:31:13,160 --> 00:31:16,080 Speaker 4: passed on from one generation to the next, and that 597 00:31:16,200 --> 00:31:18,880 Speaker 4: was encouraged in all sorts of ways on a policy 598 00:31:18,960 --> 00:31:21,520 Speaker 4: level as well, in that you know, they weren't very 599 00:31:21,560 --> 00:31:25,960 Speaker 4: high taxation, high capital taxes for passing companies on from 600 00:31:26,000 --> 00:31:28,520 Speaker 4: one generation to the next within a family. And so 601 00:31:28,560 --> 00:31:32,880 Speaker 4: systemically you are encouraging an economy where companies don't go 602 00:31:32,920 --> 00:31:37,040 Speaker 4: public and they stay private indefinitely. And that is one 603 00:31:37,040 --> 00:31:40,320 Speaker 4: of the big reasons why in Germany you used to 604 00:31:40,360 --> 00:31:43,760 Speaker 4: have these very competitive companies, but that's not the case anymore. 605 00:31:43,920 --> 00:31:47,320 Speaker 4: So get that first success out there, try and encourage 606 00:31:47,480 --> 00:31:49,880 Speaker 4: more of your company to be owned by your employee base, 607 00:31:50,120 --> 00:31:53,720 Speaker 4: try and discourage having purely private companies, and instead try 608 00:31:53,760 --> 00:31:55,800 Speaker 4: and get those companies listed on the stock market as 609 00:31:55,800 --> 00:31:58,000 Speaker 4: soon as possible. So I would say that would be 610 00:31:58,080 --> 00:32:01,520 Speaker 4: my sort of headline advice to anyone any on a 611 00:32:01,560 --> 00:32:03,320 Speaker 4: policy level trying to do this in their own countries. 612 00:32:03,480 --> 00:32:04,920 Speaker 2: Yeah, and I think from the point of view of 613 00:32:04,960 --> 00:32:07,720 Speaker 2: the public, there has to be recognition that, you know, 614 00:32:07,800 --> 00:32:11,600 Speaker 2: this is risky, high growth, high value companies. If taxpayer 615 00:32:11,600 --> 00:32:14,200 Speaker 2: dollars are going into them, there is risk there For instance, 616 00:32:14,240 --> 00:32:17,800 Speaker 2: we have a nuclear fusion startup here here in Wellington. 617 00:32:17,800 --> 00:32:20,760 Speaker 2: A couple of weeks ago they fired their first plasma 618 00:32:21,040 --> 00:32:23,920 Speaker 2: in a very small reactor there. They're trying to do 619 00:32:24,360 --> 00:32:28,480 Speaker 2: a very different way of creating a fusion reactor compared 620 00:32:28,520 --> 00:32:31,120 Speaker 2: to the Tokomac sort of reactors that the Americans and 621 00:32:31,160 --> 00:32:34,080 Speaker 2: Europeans and the Chinese are doing. The governments just put 622 00:32:34,120 --> 00:32:37,719 Speaker 2: thirty five million dollars into that, and you know that 623 00:32:37,840 --> 00:32:40,200 Speaker 2: is a moonshot, so that could go nowhere. So we 624 00:32:40,280 --> 00:32:43,040 Speaker 2: have to recognize it to get those first successes. Like 625 00:32:43,280 --> 00:32:46,000 Speaker 2: our other company, rocket Lab, which is a unicorn listed 626 00:32:46,000 --> 00:32:49,200 Speaker 2: on the Nasdaq, employees thousands of engineers in New Zealand, 627 00:32:49,280 --> 00:32:53,160 Speaker 2: has created a space industry around it, launching rockets from 628 00:32:53,520 --> 00:32:56,080 Speaker 2: the Mahia Peninsula here in New Zealand. Yeah, once you 629 00:32:56,160 --> 00:32:58,800 Speaker 2: get that established, you know the flywheel is going. But 630 00:32:58,920 --> 00:33:00,760 Speaker 2: we have to accept that not all of them are 631 00:33:00,760 --> 00:33:02,840 Speaker 2: going to succeed and failure is going to be part 632 00:33:02,880 --> 00:33:03,400 Speaker 2: of the journey. 633 00:33:03,480 --> 00:33:03,720 Speaker 3: Yeah. 634 00:33:03,720 --> 00:33:06,280 Speaker 4: And also the fact that you know, it's it's not 635 00:33:06,440 --> 00:33:08,560 Speaker 4: that the story in a lot of countries is not 636 00:33:08,680 --> 00:33:11,160 Speaker 4: that they're not producing companies. It's more that they're not 637 00:33:11,280 --> 00:33:14,120 Speaker 4: able to hold onto their companies. You mentioned rocket Lab 638 00:33:14,600 --> 00:33:17,600 Speaker 4: just now, you know, a major player in the space industry, 639 00:33:17,680 --> 00:33:20,680 Speaker 4: but in one way, New Zealand kind of lost that 640 00:33:20,800 --> 00:33:23,040 Speaker 4: company to the US. And we see that in so 641 00:33:23,040 --> 00:33:25,760 Speaker 4: many different cases. Very few people would know that Hugging 642 00:33:25,760 --> 00:33:28,600 Speaker 4: Face is a French company. It started at Station f 643 00:33:28,640 --> 00:33:31,680 Speaker 4: which I profile in the book as well. You know, UiPath, 644 00:33:31,760 --> 00:33:34,760 Speaker 4: which was the biggest success story to come out of Europe, 645 00:33:35,440 --> 00:33:39,160 Speaker 4: started in Bucharest, Romania. Arm started in the UK, even 646 00:33:39,200 --> 00:33:42,560 Speaker 4: if now it's listed in New York. So I think 647 00:33:42,680 --> 00:33:46,160 Speaker 4: the question, thankfully a lot in a lot of these countries, 648 00:33:46,280 --> 00:33:48,920 Speaker 4: is not that they're not able to produce new ideas 649 00:33:49,000 --> 00:33:51,440 Speaker 4: or new companies. It's more that they're not able to 650 00:33:51,440 --> 00:33:53,680 Speaker 4: hold onto them because you know, three years into it, 651 00:33:53,760 --> 00:33:56,160 Speaker 4: five years into it is just much more attractive for 652 00:33:56,280 --> 00:33:59,680 Speaker 4: these entrepreneurs to move their companies to the US. And 653 00:33:59,720 --> 00:34:02,920 Speaker 4: I think in still a lot of these places, it's 654 00:34:02,960 --> 00:34:06,320 Speaker 4: still about the mindset where I was talking to a 655 00:34:06,440 --> 00:34:10,600 Speaker 4: university president recently, and you know, they did not see 656 00:34:10,640 --> 00:34:14,520 Speaker 4: any major problem with their companies getting acquired by you know, 657 00:34:14,719 --> 00:34:18,719 Speaker 4: American parents, and I feel like that mentality needs to change, 658 00:34:18,719 --> 00:34:21,279 Speaker 4: the idea that you know, getting acquired by Google or 659 00:34:21,280 --> 00:34:25,120 Speaker 4: getting acquired by Apple is you know, the highest aspiration 660 00:34:25,320 --> 00:34:28,000 Speaker 4: that somebody who is starting a company in New Zealand 661 00:34:28,120 --> 00:34:30,200 Speaker 4: or in Singapore ought to have. And I think that 662 00:34:30,280 --> 00:34:33,840 Speaker 4: are enough cost details out there, like for instance, DeepMind, 663 00:34:33,880 --> 00:34:36,920 Speaker 4: where you know, when we talk about the frontier labs 664 00:34:36,960 --> 00:34:39,200 Speaker 4: that matter in the world, deep Mind is right up 665 00:34:39,239 --> 00:34:42,360 Speaker 4: there with open Ai, right up there with Anthropic, with Byedance. 666 00:34:42,480 --> 00:34:44,640 Speaker 4: But you know, it would be hard to argue that 667 00:34:44,680 --> 00:34:47,200 Speaker 4: they did not make a massive mistake by selling themselves 668 00:34:47,239 --> 00:34:50,080 Speaker 4: for only about half a billion dollars ten years ago. 669 00:34:50,280 --> 00:34:50,440 Speaker 3: You know. 670 00:34:50,520 --> 00:34:52,960 Speaker 4: Compare that to the valuation of open Ai, which very 671 00:34:53,000 --> 00:34:55,440 Speaker 4: soon will be a trillion dollar company. Compare that to Entropic, 672 00:34:55,480 --> 00:34:57,440 Speaker 4: which is the two hundred and eighty billion dollar company. 673 00:34:57,880 --> 00:35:01,160 Speaker 4: So I think, you know, those stakes have been made, 674 00:35:01,400 --> 00:35:04,200 Speaker 4: and I think new entrepreneurs will look at that and 675 00:35:04,239 --> 00:35:09,320 Speaker 4: will reassess whether, you know, selling early to an American 676 00:35:09,400 --> 00:35:11,120 Speaker 4: tech company is the right thing to do. 677 00:35:11,320 --> 00:35:14,319 Speaker 2: Yeah, it's I've been writing about this for twenty years. Really, 678 00:35:14,320 --> 00:35:18,080 Speaker 2: it's the dilemma. They get to a certain point here. Unfortunately, 679 00:35:18,120 --> 00:35:20,400 Speaker 2: our stock exchange is quite weak. The New Zealand stock 680 00:35:20,440 --> 00:35:23,440 Speaker 2: exchange the nz X, you know, even zero, you know, 681 00:35:23,640 --> 00:35:27,320 Speaker 2: our biggest initial success, software as a service accounting, departed 682 00:35:27,360 --> 00:35:33,040 Speaker 2: for the ASX. They're recruiting here very aggressively, and it's just, 683 00:35:33,440 --> 00:35:35,840 Speaker 2: you know, a better proposition. So I think we've resigned 684 00:35:35,840 --> 00:35:39,160 Speaker 2: ourselves to the fact that our most promising companies are 685 00:35:39,200 --> 00:35:42,960 Speaker 2: going to be majority owned overseas at some point. But 686 00:35:43,080 --> 00:35:47,200 Speaker 2: you take rocket Lab NASDAQ listed, but there are forty 687 00:35:47,239 --> 00:35:50,759 Speaker 2: thousand New Zealand shareholders in that company, and when it 688 00:35:50,800 --> 00:35:55,160 Speaker 2: went public overnight, one hundred New Zealand engineers became millionaires. 689 00:35:55,160 --> 00:35:57,279 Speaker 2: So I think there is a balance here where we 690 00:35:57,560 --> 00:35:59,760 Speaker 2: get access to the capital markets and all the stuff 691 00:35:59,760 --> 00:36:02,000 Speaker 2: that go with that, and sometimes, as in the case 692 00:36:02,040 --> 00:36:04,280 Speaker 2: of rocket Lab, we literally had to become an American 693 00:36:04,320 --> 00:36:07,120 Speaker 2: company to do business with the US military. That's what 694 00:36:07,200 --> 00:36:10,399 Speaker 2: Peter Bick was up against. So I think there's some 695 00:36:10,800 --> 00:36:13,080 Speaker 2: middle ground there. But god, we would love to see 696 00:36:13,120 --> 00:36:16,839 Speaker 2: more New Zealand ownership of these great companies that we produce. 697 00:36:16,760 --> 00:36:20,000 Speaker 4: And very often the issue is not availability of capital. 698 00:36:20,080 --> 00:36:23,160 Speaker 4: It's more that it's getting competed away with too many 699 00:36:23,200 --> 00:36:26,000 Speaker 4: competing centers. In a place like Europe, for instance, they 700 00:36:26,000 --> 00:36:28,520 Speaker 4: have twenty seven different exchanges, where in the US that 701 00:36:28,560 --> 00:36:31,080 Speaker 4: are only three major exchanges. And you know that's why 702 00:36:31,120 --> 00:36:33,600 Speaker 4: in Europe now the idea that there needs to be 703 00:36:33,680 --> 00:36:37,080 Speaker 4: a regional stock market, you know, an equivalent of Naspak 704 00:36:37,160 --> 00:36:38,040 Speaker 4: that operates at. 705 00:36:37,920 --> 00:36:38,840 Speaker 3: The European level. 706 00:36:39,239 --> 00:36:42,279 Speaker 4: That is something that systemically can change the calculus for 707 00:36:42,360 --> 00:36:45,440 Speaker 4: where more of this innovation is happening. So you know, 708 00:36:45,520 --> 00:36:48,800 Speaker 4: it's not just that there's no capital, it's often also 709 00:36:49,120 --> 00:36:51,879 Speaker 4: you know, in for instance, the Asian region, you're seeing 710 00:36:51,920 --> 00:36:55,920 Speaker 4: increasingly Singapore play that role where companies would must rather 711 00:36:55,960 --> 00:36:58,040 Speaker 4: go and list there rather than list in Malaysia or 712 00:36:58,120 --> 00:37:00,840 Speaker 4: list in Indonesia for instance, and that I think is 713 00:37:00,840 --> 00:37:02,719 Speaker 4: to the benefit of the entire region. At least they're 714 00:37:02,719 --> 00:37:04,880 Speaker 4: not sort of listing in the US, which is the 715 00:37:04,960 --> 00:37:07,840 Speaker 4: case with GRAB for instance, which is the biggest stock 716 00:37:07,880 --> 00:37:11,400 Speaker 4: market SPAC debut that has ever happened, a Malaysian company 717 00:37:11,400 --> 00:37:14,279 Speaker 4: that then moved to Singapore, still deciding that the US 718 00:37:14,360 --> 00:37:16,439 Speaker 4: is the best place to the best place to list. 719 00:37:16,640 --> 00:37:20,279 Speaker 2: And look, just finally, Marin, you mentioned those billionaires who 720 00:37:20,320 --> 00:37:23,000 Speaker 2: want to come to New Zealand. Peter Thiel most famously, 721 00:37:23,000 --> 00:37:25,640 Speaker 2: who is now a New Zealand Citizen, and we have 722 00:37:26,000 --> 00:37:28,839 Speaker 2: just in the last year introduced these golden visas. Here, 723 00:37:28,880 --> 00:37:31,920 Speaker 2: you bring in five million dollars off investment, you have 724 00:37:31,960 --> 00:37:35,440 Speaker 2: to put it into a high growth sort of sector company, 725 00:37:36,120 --> 00:37:39,359 Speaker 2: so that apparently there's at least one hundred people have 726 00:37:39,360 --> 00:37:42,320 Speaker 2: taken that up in the first phase, so that is popular. 727 00:37:43,560 --> 00:37:46,359 Speaker 2: What's your take on how valuable this sort of thing 728 00:37:46,520 --> 00:37:51,320 Speaker 2: is for attracting not only capital into our tech sector, 729 00:37:51,400 --> 00:37:55,200 Speaker 2: but also the brains, the talents, the leadership and the 730 00:37:55,239 --> 00:37:57,640 Speaker 2: wisdom of people who've made it who might want to 731 00:37:57,640 --> 00:38:00,600 Speaker 2: come and live in New Zealand and can tribute to 732 00:38:00,719 --> 00:38:02,399 Speaker 2: our innovation ecosystem here. 733 00:38:02,520 --> 00:38:05,600 Speaker 4: I've seen, you know, these golden za, especially the ones 734 00:38:05,600 --> 00:38:07,839 Speaker 4: that have very high ticket prices like a million dollars, 735 00:38:07,920 --> 00:38:13,160 Speaker 4: five million dollars them working well for older sectors like 736 00:38:13,320 --> 00:38:15,960 Speaker 4: you know, real estate, you know, things like finance and 737 00:38:16,000 --> 00:38:18,520 Speaker 4: things like that. In the tech industry, I still feel like, 738 00:38:18,640 --> 00:38:22,080 Speaker 4: you know, who makes a high growth tech company, It's 739 00:38:22,160 --> 00:38:25,920 Speaker 4: usually not somebody who's very comfortable in life, you know, 740 00:38:26,000 --> 00:38:28,560 Speaker 4: it's these things are really hard, and you wouldn't do 741 00:38:28,640 --> 00:38:31,400 Speaker 4: it if there wasn't this hunger that was sort of 742 00:38:31,440 --> 00:38:33,799 Speaker 4: pushing you to start a company and go through all 743 00:38:33,880 --> 00:38:36,400 Speaker 4: the hard things that get thrown at you and trying 744 00:38:36,400 --> 00:38:37,840 Speaker 4: to sort of get to a billion dollars or a 745 00:38:37,840 --> 00:38:40,640 Speaker 4: ten billion dollar company. So I think the things that 746 00:38:40,680 --> 00:38:43,600 Speaker 4: I've seen in the US, where I don't think, you know, 747 00:38:43,680 --> 00:38:47,200 Speaker 4: the million dollar green card is what change changes their economy. 748 00:38:47,239 --> 00:38:50,400 Speaker 4: It's more if you're an alien of exceptional ability. You know, 749 00:38:50,440 --> 00:38:53,200 Speaker 4: they have these other categories, which is less about how 750 00:38:53,280 --> 00:38:56,440 Speaker 4: much money you're bringing into the economy and more about 751 00:38:56,520 --> 00:38:59,400 Speaker 4: the ideas and the skills that you're bringing. Germany just 752 00:38:59,440 --> 00:39:02,799 Speaker 4: started some thing like that as well. And incidentally, you know, 753 00:39:02,840 --> 00:39:06,320 Speaker 4: the economy that was often seen as being the most 754 00:39:06,360 --> 00:39:09,360 Speaker 4: inward looking, China, just came out with a CA visa 755 00:39:09,880 --> 00:39:15,120 Speaker 4: which has the most lax requirements for moving to China, 756 00:39:15,160 --> 00:39:16,680 Speaker 4: which is, you don't need a job off, you don't 757 00:39:16,719 --> 00:39:19,480 Speaker 4: need an offer from a university, you don't need any capital. 758 00:39:19,560 --> 00:39:22,680 Speaker 4: You just basically need to convince us that you're smart 759 00:39:22,800 --> 00:39:25,360 Speaker 4: enough to do something interesting once you land here. So 760 00:39:25,480 --> 00:39:29,000 Speaker 4: I think while there is a place for having, you know, 761 00:39:29,040 --> 00:39:32,560 Speaker 4: these very expensive ways to buy into your country, I 762 00:39:32,600 --> 00:39:35,080 Speaker 4: think that to me sounds a lot like Monoco, Like, 763 00:39:35,120 --> 00:39:38,160 Speaker 4: you know, that sounds like Luxembourg. That sounds like rich 764 00:39:38,200 --> 00:39:41,280 Speaker 4: people trying to find an alternative residence. In this case, 765 00:39:41,600 --> 00:39:45,000 Speaker 4: the pitch folks come for them, but not you know, 766 00:39:45,080 --> 00:39:48,440 Speaker 4: the twenty one year old in India or in Latin 767 00:39:48,440 --> 00:39:51,200 Speaker 4: America who wants to build the next one hundred billion 768 00:39:51,239 --> 00:39:54,560 Speaker 4: dollar company. So while it might be beneficial for New 769 00:39:54,680 --> 00:39:57,920 Speaker 4: Zealand to have these high networth individuals, I would lay 770 00:39:58,080 --> 00:40:01,640 Speaker 4: for the tech industry at least the relative emphasis on 771 00:40:02,120 --> 00:40:04,480 Speaker 4: how do we get smart people at the beginning of 772 00:40:04,520 --> 00:40:07,600 Speaker 4: their careers and not somebody who's already fifty sixty years 773 00:40:07,640 --> 00:40:09,799 Speaker 4: old and can sort of shell out those five million 774 00:40:09,880 --> 00:40:12,880 Speaker 4: dollars to buy a New Zealand residency, Like, how do 775 00:40:12,920 --> 00:40:14,799 Speaker 4: we get the guy who's nineteen year olds, who's twenty 776 00:40:14,800 --> 00:40:17,080 Speaker 4: one years old to come and change our economy? And 777 00:40:17,120 --> 00:40:19,920 Speaker 4: that's what really worked for Singapore, for instance, where I 778 00:40:20,000 --> 00:40:22,719 Speaker 4: talk about the C group in Singapore, Singapore gets its 779 00:40:22,719 --> 00:40:26,040 Speaker 4: fair share of very rich people. Eduardo Savrin, the founder 780 00:40:26,080 --> 00:40:29,719 Speaker 4: of Facebook, famously gave up his American citizenship for a 781 00:40:29,760 --> 00:40:33,440 Speaker 4: Singaporean one for purely tax reasons. But you also I 782 00:40:33,480 --> 00:40:37,600 Speaker 4: talk about Forest Lee in the book, somebody who became 783 00:40:38,160 --> 00:40:41,280 Speaker 4: the richest person in Singapore. Within ten years of landing 784 00:40:41,320 --> 00:40:46,320 Speaker 4: in the country, Chinese origin went to Stanford. Singaporean incentives 785 00:40:46,360 --> 00:40:49,799 Speaker 4: for talentry location do not depend on how rich you are. 786 00:40:49,880 --> 00:40:52,279 Speaker 4: They depend on how smart you are, how much you 787 00:40:52,280 --> 00:40:54,799 Speaker 4: can contribute to the economy. And maybe that is an 788 00:40:54,840 --> 00:40:57,239 Speaker 4: area in which New Zealand can lean into more. 789 00:40:57,360 --> 00:41:00,319 Speaker 2: And we haven't really talked that much about India. We've 790 00:41:00,320 --> 00:41:02,759 Speaker 2: just done a free trade deal with India which is 791 00:41:02,800 --> 00:41:06,080 Speaker 2: really going to open immigration. More Indians will be able 792 00:41:06,080 --> 00:41:08,960 Speaker 2: to come to New Zealand. If you look at how 793 00:41:08,960 --> 00:41:12,600 Speaker 2: that's worked in the US, the incredible talent inflows from 794 00:41:12,760 --> 00:41:15,279 Speaker 2: the into the US and the fact that we have 795 00:41:16,160 --> 00:41:19,279 Speaker 2: tech titans sat in Adell and people like that who 796 00:41:19,320 --> 00:41:22,560 Speaker 2: were in that wave of immigration that came to the 797 00:41:22,680 --> 00:41:26,880 Speaker 2: US young and hungry, often from a very poor background, 798 00:41:27,120 --> 00:41:30,520 Speaker 2: but very well educated, came to the US and rose 799 00:41:30,600 --> 00:41:34,439 Speaker 2: up through the ranks and a part of the firmament there. 800 00:41:34,480 --> 00:41:37,120 Speaker 1: Now. It's incredible. Maybe we can replicate some of that. 801 00:41:37,680 --> 00:41:40,760 Speaker 4: It's interesting you mentioned that, like I think New Zealand 802 00:41:41,040 --> 00:41:46,360 Speaker 4: is becoming in some whears the poster child for the opposite, 803 00:41:46,400 --> 00:41:51,560 Speaker 4: where I'm increasingly meeting more people who are not in 804 00:41:51,600 --> 00:41:53,880 Speaker 4: favor of that sort of immigration, you know in places 805 00:41:53,920 --> 00:41:56,000 Speaker 4: like Europe, like you know, people roughly in the right 806 00:41:56,000 --> 00:41:59,480 Speaker 4: wing who often hold up New Zealand as an example 807 00:41:59,560 --> 00:42:03,279 Speaker 4: where they say that the reason why New Zealand has 808 00:42:03,800 --> 00:42:07,440 Speaker 4: worked so well is precisely because it doesn't get the 809 00:42:07,520 --> 00:42:10,680 Speaker 4: kind of immigration that the US does or that Europe does, 810 00:42:10,719 --> 00:42:13,440 Speaker 4: simply because it's pudimot country for anyone to cross the 811 00:42:13,480 --> 00:42:17,040 Speaker 4: border on foot. And I guess it's an interesting debate, 812 00:42:17,080 --> 00:42:19,080 Speaker 4: like I never really thought about it that way, where 813 00:42:19,120 --> 00:42:22,680 Speaker 4: people see, you know, the high trust society that exists 814 00:42:22,680 --> 00:42:26,240 Speaker 4: in New Zealand, the social coherence that exists over there, 815 00:42:26,560 --> 00:42:28,560 Speaker 4: and they say that that is precisely because it gets 816 00:42:28,680 --> 00:42:31,520 Speaker 4: very little immigration off of that nature. It might get 817 00:42:31,560 --> 00:42:35,759 Speaker 4: you know, very high networth immigration, but it's it's a 818 00:42:35,840 --> 00:42:38,920 Speaker 4: relatively less diverse society and that's why it's a high 819 00:42:38,920 --> 00:42:41,560 Speaker 4: trust society. So, you know, not sure if I agree 820 00:42:41,560 --> 00:42:43,520 Speaker 4: with that or disagree with that, but just to say 821 00:42:43,520 --> 00:42:45,959 Speaker 4: that for some reason, New Zealand is becoming a poster 822 00:42:46,080 --> 00:42:50,960 Speaker 4: child for people who are against that Satianda della type immigration. 823 00:42:51,280 --> 00:42:52,279 Speaker 1: Yeah, look, it is. 824 00:42:52,920 --> 00:42:54,760 Speaker 2: There's a lot of debate around that at the moment 825 00:42:54,920 --> 00:42:58,520 Speaker 2: and because we've had so many Kiwis leaving the country 826 00:42:58,520 --> 00:43:01,719 Speaker 2: at the moment, part of that is leading to our 827 00:43:01,920 --> 00:43:05,360 Speaker 2: economic malays at the moment, we've written the booms of immigration. 828 00:43:05,440 --> 00:43:08,440 Speaker 2: When lots of people come into the country, demand for 829 00:43:08,880 --> 00:43:13,359 Speaker 2: housing increases, so everyone's property prices increase. So we've sort 830 00:43:13,360 --> 00:43:17,560 Speaker 2: of had that boom bust mentality. But yeah, as you know, 831 00:43:18,040 --> 00:43:23,439 Speaker 2: hardening of attitudes I think towards immigration, which goes into 832 00:43:23,520 --> 00:43:26,400 Speaker 2: dark territory, but that's a different podcast. But look, I 833 00:43:26,440 --> 00:43:28,680 Speaker 2: just want to thank you as a fascinating read. I 834 00:43:28,719 --> 00:43:31,239 Speaker 2: hope a lot of New Zealanders have read it or 835 00:43:31,320 --> 00:43:33,960 Speaker 2: will read it. If you're listening to this podcast, I 836 00:43:34,000 --> 00:43:36,240 Speaker 2: highly recommend it. Will put the links in the show notes. 837 00:43:36,960 --> 00:43:38,799 Speaker 2: Keep up the great work. I'm sure there'll be more 838 00:43:38,920 --> 00:43:41,560 Speaker 2: books coming from you in the next few years alone. 839 00:43:41,719 --> 00:43:43,759 Speaker 2: There's going to be so much go on, so that's 840 00:43:43,800 --> 00:43:46,759 Speaker 2: going to need unpacked. So looking forward to seeing what 841 00:43:46,800 --> 00:43:48,759 Speaker 2: you come up with next. And thanks so much for 842 00:43:48,800 --> 00:43:49,759 Speaker 2: being on the Business of Tech. 843 00:43:49,800 --> 00:43:51,360 Speaker 4: Thanks so much for having me. New Zealand is a 844 00:43:51,360 --> 00:43:53,359 Speaker 4: country that you know, I've been close to very often. 845 00:43:53,400 --> 00:43:56,080 Speaker 4: I go to Australia fairly frequently, but hopefully I can 846 00:43:56,120 --> 00:43:57,719 Speaker 4: be there myself. I have a lot of people reach 847 00:43:57,719 --> 00:43:59,719 Speaker 4: out to me from New Zealand who've come across the book, 848 00:44:00,120 --> 00:44:02,919 Speaker 4: so I really appreciate the opportunity to speak to sort 849 00:44:02,920 --> 00:44:04,960 Speaker 4: of a wider audience through your podcast. 850 00:44:04,960 --> 00:44:05,880 Speaker 3: So thanks so much for having me. 851 00:44:06,040 --> 00:44:09,160 Speaker 2: Well, we'd love to see in the capital and around 852 00:44:09,160 --> 00:44:12,720 Speaker 2: New Zealand sometimes soon, so look forward to showing around. 853 00:44:12,760 --> 00:44:23,840 Speaker 3: Thanks so much. I appreciate that. 854 00:44:23,840 --> 00:44:25,879 Speaker 2: That's it for this episode of the Business of Tech. 855 00:44:25,920 --> 00:44:29,640 Speaker 2: A few takeaways for me from Mehran. The real test 856 00:44:29,680 --> 00:44:33,000 Speaker 2: of an innovation ecosystem is how many companies you create 857 00:44:33,440 --> 00:44:36,440 Speaker 2: and whether they can go the distance. Everything you do, 858 00:44:36,560 --> 00:44:40,600 Speaker 2: research and development, policies and centers, interventions from the. 859 00:44:40,520 --> 00:44:42,880 Speaker 1: Government need to serve that purpose. 860 00:44:42,920 --> 00:44:45,920 Speaker 2: How many companies are you creating that go on to 861 00:44:46,040 --> 00:44:51,640 Speaker 2: generate export revenue, high value jobs, and ultimately go global 862 00:44:51,760 --> 00:44:56,120 Speaker 2: and form at presence overseas. Focus on the really promising 863 00:44:56,160 --> 00:44:59,759 Speaker 2: ones that can spawn and industry and get the flywheel going, 864 00:45:00,160 --> 00:45:04,600 Speaker 2: as rocket Lab and Zero did in space tech and fintech, respectively. 865 00:45:05,239 --> 00:45:09,320 Speaker 2: Incentivize your people by giving them equity, which until recently 866 00:45:09,360 --> 00:45:12,279 Speaker 2: was a novel concept in New Zealand, but has long 867 00:45:12,360 --> 00:45:16,600 Speaker 2: been standard in Silicon Valley. Forget about enticing rich tech 868 00:45:16,640 --> 00:45:20,239 Speaker 2: boomers here. We want young and hungry tech talent, the 869 00:45:20,280 --> 00:45:22,840 Speaker 2: best in the world to come here. We need to 870 00:45:22,840 --> 00:45:25,920 Speaker 2: do more to stop our most successful companies from exiting, 871 00:45:25,960 --> 00:45:29,799 Speaker 2: and admittedly that's a very challenging one for US given 872 00:45:29,840 --> 00:45:33,560 Speaker 2: our limited capital Marcus, and in a world where US 873 00:45:33,600 --> 00:45:38,160 Speaker 2: tech is less trusted, even viewed as predatory, our reputation 874 00:45:38,480 --> 00:45:40,000 Speaker 2: does give us an edge. 875 00:45:40,680 --> 00:45:42,279 Speaker 1: So thanks to Mehran for coming on. 876 00:45:42,600 --> 00:45:45,120 Speaker 2: Please follow the show and share the episode with someone 877 00:45:45,239 --> 00:45:48,920 Speaker 2: thinking about our country's tech future. You'll find more links 878 00:45:49,000 --> 00:45:51,960 Speaker 2: and context in the show notes, and as always, thanks 879 00:45:51,960 --> 00:45:54,160 Speaker 2: so much for listening. I'll catch you next week with 880 00:45:54,239 --> 00:45:56,359 Speaker 2: another episode of the Business of Tech.