1 00:00:04,320 --> 00:00:06,240 Speaker 1: This week in the Business of Tech powered by two 2 00:00:06,240 --> 00:00:09,520 Speaker 1: Degrees Business. We're talking India, a market of one point 3 00:00:09,560 --> 00:00:12,879 Speaker 1: four billion people, and one our Prime Minister has at 4 00:00:12,920 --> 00:00:15,280 Speaker 1: the top of his wish list of countries he wants 5 00:00:15,320 --> 00:00:17,280 Speaker 1: to seal a free trade deal with. 6 00:00:17,880 --> 00:00:21,079 Speaker 2: We've had an economic breakthrough in the relationship economic relationship 7 00:00:21,079 --> 00:00:24,840 Speaker 2: with India with the announcement of our comprehensive free trade agreement. 8 00:00:24,960 --> 00:00:29,440 Speaker 2: Negotiations able to commence, and so it's a really good win. 9 00:00:29,560 --> 00:00:32,159 Speaker 2: It's a great win for New Zealand. Obviously it forms 10 00:00:32,200 --> 00:00:34,960 Speaker 2: part of a desire to have a deeper and broader 11 00:00:35,000 --> 00:00:36,200 Speaker 2: relationship with India. 12 00:00:36,320 --> 00:00:39,480 Speaker 1: Hey, that's a great idea, and particularly at a time 13 00:00:39,479 --> 00:00:43,240 Speaker 1: when the US is wielding tariffs as a geopolitical weapon 14 00:00:43,280 --> 00:00:46,320 Speaker 1: around the world, doing more trade with countries that truly 15 00:00:46,600 --> 00:00:50,000 Speaker 1: value free trade is a win for small trading nations 16 00:00:50,080 --> 00:00:54,400 Speaker 1: like New Zealand. But agriculture and dairy in particular is 17 00:00:54,440 --> 00:00:56,160 Speaker 1: a bit of a stumbling block in the way for 18 00:00:56,320 --> 00:00:59,200 Speaker 1: free trade deal with India, where hundreds of thousands of 19 00:00:59,320 --> 00:01:03,680 Speaker 1: dairy farmers with small holdings are naturally terrified at the 20 00:01:03,720 --> 00:01:08,040 Speaker 1: prospect of Fonterra undercutting them with our more efficiently produced 21 00:01:08,200 --> 00:01:11,160 Speaker 1: dairy products. But my guest on the Business of Tech 22 00:01:11,319 --> 00:01:14,640 Speaker 1: this week argues that agriculture shouldn't be the focus of 23 00:01:14,720 --> 00:01:19,280 Speaker 1: our trade negotiations with India anyway, as Indian society and 24 00:01:19,319 --> 00:01:23,679 Speaker 1: the economy rapidly digitizes, the real opportunity lies in the 25 00:01:23,720 --> 00:01:28,200 Speaker 1: knowledge economy and selling software and digital services into India. 26 00:01:28,880 --> 00:01:33,640 Speaker 1: Carmen Visilich is doing exactly that with Velocity Global, her company, 27 00:01:33,840 --> 00:01:37,880 Speaker 1: which has transformed how property valuation from mortgage lending and 28 00:01:37,959 --> 00:01:41,160 Speaker 1: real estate deals is done here in New Zealand, in 29 00:01:41,240 --> 00:01:45,520 Speaker 1: Australia and also in India, where it provides its services 30 00:01:45,560 --> 00:01:49,240 Speaker 1: for twenty banks. Carmen was on the Prime Minister's trade 31 00:01:49,320 --> 00:01:52,800 Speaker 1: delegation to India last month. During that trip in New 32 00:01:52,880 --> 00:01:56,560 Speaker 1: Zealand signed a five year deal with Tata Consultancy Services, 33 00:01:56,960 --> 00:02:00,760 Speaker 1: the Indian IT services giant. It will advise the airline 34 00:02:00,800 --> 00:02:05,920 Speaker 1: on its use of cloud services, artificial intelligence, data analytics 35 00:02:05,960 --> 00:02:10,480 Speaker 1: and automation. Spark has since done a deal with Emphasis, 36 00:02:10,520 --> 00:02:15,040 Speaker 1: another major Indian player in IT services. Again, that's great 37 00:02:15,360 --> 00:02:19,400 Speaker 1: free trade, but what about tech related trade going the 38 00:02:19,520 --> 00:02:20,080 Speaker 1: other way? 39 00:02:20,440 --> 00:02:20,680 Speaker 3: Well. 40 00:02:21,400 --> 00:02:23,640 Speaker 1: Carmen, who is also the founder of a couple of 41 00:02:23,680 --> 00:02:29,000 Speaker 1: other interesting startups data insight and generate zero reckons. There's 42 00:02:29,200 --> 00:02:33,280 Speaker 1: huge potential for our startups to take advantage of India's 43 00:02:33,440 --> 00:02:37,720 Speaker 1: massive wave of digitization. It isn't an easy market, she 44 00:02:37,840 --> 00:02:40,239 Speaker 1: told me. Basically, if you can crack the Indian market, 45 00:02:40,280 --> 00:02:44,120 Speaker 1: you can succeed anywhere. But she's got some great advice 46 00:02:44,240 --> 00:02:48,360 Speaker 1: on how to approach this vast and promising market. So 47 00:02:48,480 --> 00:02:51,920 Speaker 1: without further ado, here's my interview with Velocity global founder 48 00:02:52,280 --> 00:03:02,040 Speaker 1: Carmen Vicilage. Carmen, welcome to the business of Tech. 49 00:03:02,080 --> 00:03:02,639 Speaker 3: How are you doing. 50 00:03:03,280 --> 00:03:05,840 Speaker 4: I'm super Peter. Great to have you, Thanks for having me. 51 00:03:05,919 --> 00:03:06,639 Speaker 4: Great to be here. 52 00:03:06,960 --> 00:03:09,760 Speaker 1: Yeah, great to have you on. This is a company, Velocity, 53 00:03:10,040 --> 00:03:13,720 Speaker 1: that I think is doing fantastic things from New Zealand, 54 00:03:13,720 --> 00:03:19,160 Speaker 1: but very much an international company now operations or using 55 00:03:19,200 --> 00:03:23,280 Speaker 1: your software and services and something like three five hundred cities, 56 00:03:23,320 --> 00:03:24,720 Speaker 1: so it's literally global. 57 00:03:25,400 --> 00:03:26,360 Speaker 3: We're going to get to that. 58 00:03:26,520 --> 00:03:29,880 Speaker 1: But you are an exporter, you are spending a lot 59 00:03:29,880 --> 00:03:32,480 Speaker 1: of time around the world. Really keen just to start 60 00:03:32,520 --> 00:03:35,840 Speaker 1: off with to get your perspective on what's going on 61 00:03:35,960 --> 00:03:38,800 Speaker 1: in the world of trade, particularly with all these tariffs 62 00:03:39,240 --> 00:03:45,000 Speaker 1: that the US has leveraged on other countries in the 63 00:03:45,040 --> 00:03:48,320 Speaker 1: way that they've responded. Does this directly affect any of 64 00:03:48,360 --> 00:03:51,600 Speaker 1: your businesses? You are essentially a digital exporter. They haven't 65 00:03:51,600 --> 00:03:52,800 Speaker 1: been hit by tariffs. 66 00:03:53,400 --> 00:03:56,680 Speaker 4: Yeah, that's right, and I guess yeah. We're very happy 67 00:03:56,800 --> 00:03:59,960 Speaker 4: with where we are as a digital exporter, particularly play 68 00:04:00,200 --> 00:04:02,680 Speaker 4: in the Mena region. We're in the UAE as well, 69 00:04:03,520 --> 00:04:05,960 Speaker 4: which is just so innovative and you know, they just 70 00:04:06,080 --> 00:04:11,040 Speaker 4: embrace technology and digital and very much India, which is unusual, 71 00:04:11,080 --> 00:04:13,160 Speaker 4: having just been on the Prime Minister's trip as one 72 00:04:13,200 --> 00:04:16,279 Speaker 4: of the rare companies exporting tech to India as opposed 73 00:04:16,320 --> 00:04:21,520 Speaker 4: to using tech resources to support you know, functions back 74 00:04:21,560 --> 00:04:25,000 Speaker 4: in New Zealand. And at the same time also having 75 00:04:25,520 --> 00:04:28,520 Speaker 4: Data Insight, the other company that I founded that's run 76 00:04:28,560 --> 00:04:31,719 Speaker 4: by an amazing team that also leverages data and AI. 77 00:04:31,800 --> 00:04:33,800 Speaker 4: And I think if we look at what's happening in 78 00:04:33,839 --> 00:04:37,039 Speaker 4: the world, you know, there's there's some really big themes 79 00:04:37,640 --> 00:04:41,120 Speaker 4: and we can't ignore tech and digital and the AI. 80 00:04:41,440 --> 00:04:42,920 Speaker 4: You know that we're going to look back at this 81 00:04:43,040 --> 00:04:46,520 Speaker 4: era as this amazing revolution of how we use data 82 00:04:46,560 --> 00:04:49,960 Speaker 4: and how we use digital, underpinned by the capability of AI. 83 00:04:50,080 --> 00:04:52,039 Speaker 4: And that's obviously the area that we're playing in with 84 00:04:52,240 --> 00:04:54,960 Speaker 4: both all three of my companies that I've founded. But 85 00:04:55,279 --> 00:04:57,200 Speaker 4: there is a trade war and it is back to 86 00:04:57,240 --> 00:05:02,000 Speaker 4: primary industries, and I think there there's there needs to 87 00:05:02,000 --> 00:05:06,080 Speaker 4: be the recognition that value creation isn't no longer just 88 00:05:06,120 --> 00:05:10,760 Speaker 4: about exporting things. Really, especially for a small nation. Value 89 00:05:10,760 --> 00:05:16,480 Speaker 4: creation is exporting exponentially and starting that same thing multiple 90 00:05:16,520 --> 00:05:19,640 Speaker 4: times for high margin And there's some great examples of 91 00:05:19,680 --> 00:05:22,400 Speaker 4: businesses doing exactly that. Obviously Zero is one of the 92 00:05:22,400 --> 00:05:26,640 Speaker 4: best examples. And the value creation that rock Juries created 93 00:05:26,720 --> 00:05:28,880 Speaker 4: from scratch from New Zealand. He didn't have to move 94 00:05:28,880 --> 00:05:31,200 Speaker 4: the company or the head office. You know, it's such 95 00:05:31,240 --> 00:05:32,840 Speaker 4: a great test case, and you know, they were a 96 00:05:32,880 --> 00:05:36,840 Speaker 4: global brand around the world, Rocket Labs. And so when 97 00:05:36,839 --> 00:05:38,600 Speaker 4: we think about that, it's actually, well, how do we 98 00:05:38,680 --> 00:05:41,919 Speaker 4: think things about things differently? And it's very much was 99 00:05:41,920 --> 00:05:45,160 Speaker 4: the conundrum when we were in India that India doesn't 100 00:05:45,160 --> 00:05:47,560 Speaker 4: need more cows, India doesn't need more milk, India doesn't 101 00:05:47,560 --> 00:05:50,240 Speaker 4: need more apples. They're really wanting to say, how do 102 00:05:50,320 --> 00:05:52,040 Speaker 4: we turn those things? You know, how do we turn 103 00:05:52,080 --> 00:05:54,680 Speaker 4: milk into yogurt and export that around the world and 104 00:05:54,800 --> 00:05:58,320 Speaker 4: partner around technology and capability and IP and it's a 105 00:05:58,400 --> 00:06:01,000 Speaker 4: different way of thinking. And when we see the trade wars, 106 00:06:01,040 --> 00:06:05,480 Speaker 4: we see that real protectionism, anti globalism that's happening overnight, 107 00:06:06,320 --> 00:06:08,559 Speaker 4: and we really have to recognize we need to adapt 108 00:06:08,640 --> 00:06:11,839 Speaker 4: our thinking to play to the macroeconomics that are happening 109 00:06:11,839 --> 00:06:12,360 Speaker 4: around us. 110 00:06:12,960 --> 00:06:17,359 Speaker 1: Yeah, digital trade is really at the moment is the 111 00:06:17,440 --> 00:06:20,040 Speaker 1: last bastian of free trade. I mean, you know, we 112 00:06:20,080 --> 00:06:23,240 Speaker 1: do charge gst on digital imports in the form of 113 00:06:23,360 --> 00:06:28,880 Speaker 1: Netflix subscriptions and Microsoft fees for using Azure and the like, 114 00:06:29,560 --> 00:06:32,640 Speaker 1: but essentially it's you know, the World Trade Organization has 115 00:06:32,640 --> 00:06:37,200 Speaker 1: a moratorium on charging tarifs on digital trade, which is fantastic, 116 00:06:37,760 --> 00:06:39,280 Speaker 1: as you say. You know, it's been good for us 117 00:06:39,279 --> 00:06:42,080 Speaker 1: with the likes of zero. So Paul Callahan said twenty 118 00:06:42,160 --> 00:06:44,520 Speaker 1: years ago, he said, get off the grass. We need 119 00:06:44,560 --> 00:06:47,480 Speaker 1: to embrace the knowledge economy, and we've sort of done that, 120 00:06:47,520 --> 00:06:50,919 Speaker 1: you know, tech, which includes a lot of hardware and telecommunications. 121 00:06:50,920 --> 00:06:54,560 Speaker 1: But it's now our second biggest export market behind dairy, 122 00:06:55,080 --> 00:06:59,320 Speaker 1: so it's sort of working. I guess the nightmare scenario is, 123 00:06:59,760 --> 00:07:03,320 Speaker 1: you know, if Trump decides to go after services as well. 124 00:07:03,760 --> 00:07:07,360 Speaker 1: Having said that they are a big exporter of services themselves, 125 00:07:07,440 --> 00:07:11,000 Speaker 1: you know, it's seven hundred billion dollars last year, about 126 00:07:11,040 --> 00:07:14,960 Speaker 1: about a quarter off US traders services. So they're not 127 00:07:15,000 --> 00:07:17,040 Speaker 1: going to shoot themselves in the foot like that, are they. 128 00:07:17,640 --> 00:07:22,000 Speaker 4: No, they're the largest exporter of services, and you know 129 00:07:22,200 --> 00:07:25,680 Speaker 4: that's where they've had also the most accreative growth over 130 00:07:25,680 --> 00:07:28,320 Speaker 4: the year as an overtaken you know, since nineteen forty 131 00:07:28,400 --> 00:07:31,560 Speaker 4: is overtaken Japan, and that is the value creation. The 132 00:07:31,560 --> 00:07:34,480 Speaker 4: most valuable businesses in the world are no longer of things. 133 00:07:34,480 --> 00:07:37,560 Speaker 4: It's no longer mobile Coca Cola and a brand. The 134 00:07:37,600 --> 00:07:41,720 Speaker 4: world has changed. The most valuable businesses are technology businesses 135 00:07:42,160 --> 00:07:44,600 Speaker 4: that leverage the power of data and connectivity and make 136 00:07:44,640 --> 00:07:47,880 Speaker 4: our lives easier and they've changed the way that we live. 137 00:07:47,960 --> 00:07:51,200 Speaker 4: And there's so many examples in that, and I think 138 00:07:51,240 --> 00:07:54,200 Speaker 4: it's really great to see the understanding of that. And 139 00:07:54,200 --> 00:07:57,480 Speaker 4: that was also a lot of the conversation in India 140 00:07:57,600 --> 00:08:01,440 Speaker 4: actually that we just had that minute. Todd had a 141 00:08:01,480 --> 00:08:05,120 Speaker 4: meeting with the Commerce commission Minister in India and chose 142 00:08:05,280 --> 00:08:07,560 Speaker 4: just a couple of companies to join them, and we 143 00:08:07,560 --> 00:08:10,960 Speaker 4: were very privileged to be one of those companies. And interestingly, 144 00:08:11,040 --> 00:08:13,960 Speaker 4: their minister also brought a couple of companies to join them, 145 00:08:14,360 --> 00:08:17,440 Speaker 4: and the companies he brought right away, we had opportunities 146 00:08:17,480 --> 00:08:19,960 Speaker 4: to partner and it was showing that we were thinking 147 00:08:19,960 --> 00:08:23,560 Speaker 4: outside of primary industries, and both ministers were very aligned 148 00:08:23,600 --> 00:08:27,880 Speaker 4: that that was the opportunities to create value for both countries. 149 00:08:28,600 --> 00:08:31,880 Speaker 4: And their company was a drone company doing the surveying 150 00:08:32,679 --> 00:08:35,760 Speaker 4: for properties and for climate and you know when we 151 00:08:35,800 --> 00:08:37,959 Speaker 4: obviously work with banks and we're a platform for banks 152 00:08:37,960 --> 00:08:41,360 Speaker 4: for people making decisions. So it's a real natural partnership. 153 00:08:41,400 --> 00:08:44,040 Speaker 4: And they're so fast in India. Whatsappened me by the 154 00:08:44,120 --> 00:08:46,559 Speaker 4: end of the meeting to go, let's meet, let's partner, 155 00:08:47,200 --> 00:08:49,600 Speaker 4: and to see the alignment of the two governments on 156 00:08:49,679 --> 00:08:53,280 Speaker 4: that was really exciting, which I think probably feeds into yours. Well, 157 00:08:53,280 --> 00:08:55,240 Speaker 4: how do tech companies go to India and what do 158 00:08:55,240 --> 00:08:55,959 Speaker 4: you need to think about? 159 00:08:56,920 --> 00:08:59,760 Speaker 1: Yeah, there was one big deal announced out of that, 160 00:09:00,400 --> 00:09:03,960 Speaker 1: a five year agreement between Air New Zealand and Tata 161 00:09:04,120 --> 00:09:09,400 Speaker 1: Consultancy Services, a big IT consultancy. You know, that's really interesting. 162 00:09:09,880 --> 00:09:13,880 Speaker 1: Huge IT capability exists in India and Tata will be 163 00:09:13,920 --> 00:09:16,440 Speaker 1: helping Air New Zealand sort of upskill you know, the 164 00:09:16,520 --> 00:09:19,719 Speaker 1: engineering workforce and that and things like artificial intelligence use 165 00:09:19,760 --> 00:09:22,320 Speaker 1: of cloud tool So that's really cool. But what we 166 00:09:22,360 --> 00:09:25,920 Speaker 1: really want to see is digital experts going to India, 167 00:09:26,040 --> 00:09:29,280 Speaker 1: and it's been very little discussion of that because it 168 00:09:29,360 --> 00:09:32,440 Speaker 1: is sort of an immature area of trade. But you've 169 00:09:32,480 --> 00:09:37,120 Speaker 1: been India in India now with Velocity for several years. 170 00:09:37,679 --> 00:09:39,440 Speaker 1: First of all, tell us about the sort of the 171 00:09:39,480 --> 00:09:45,000 Speaker 1: common problem that Indian landowners and banks have that you've 172 00:09:45,040 --> 00:09:47,560 Speaker 1: been solving in New Zealand, which you've now taken to India. 173 00:09:48,480 --> 00:09:50,240 Speaker 4: Yeah. I think it's a really good question, and I 174 00:09:50,280 --> 00:09:52,960 Speaker 4: think you know the lessons is a common problem, but 175 00:09:53,000 --> 00:09:56,560 Speaker 4: it's also about localizing and telling the story differently so 176 00:09:56,640 --> 00:10:00,400 Speaker 4: that it's relevant to what they're China achieve. So every 177 00:10:00,440 --> 00:10:02,920 Speaker 4: bank in the world has to validate the value of 178 00:10:02,960 --> 00:10:05,520 Speaker 4: a property before they can lend money on it, and 179 00:10:05,559 --> 00:10:07,800 Speaker 4: in New Zealand and Australia we help banks do that 180 00:10:07,880 --> 00:10:10,360 Speaker 4: better and faster than ever, and we have really good 181 00:10:10,440 --> 00:10:13,560 Speaker 4: data here where we can use AI and automated valuation models, 182 00:10:13,960 --> 00:10:17,160 Speaker 4: or we can connect a bank to their valuers digitally 183 00:10:17,559 --> 00:10:19,480 Speaker 4: when they need to send a person to go and 184 00:10:19,520 --> 00:10:23,439 Speaker 4: validate that property. Well, in India, when we went there, 185 00:10:23,559 --> 00:10:26,720 Speaker 4: they had the same problem, but at massive scale. They have, 186 00:10:27,080 --> 00:10:31,160 Speaker 4: you know, urbanization. Modi said, there's one hundred million shortage 187 00:10:31,200 --> 00:10:35,240 Speaker 4: of housing. And you know, the overy bank is growing rapidly. 188 00:10:35,920 --> 00:10:38,480 Speaker 4: You know, there's demographics. Fifty percent of the population is 189 00:10:38,559 --> 00:10:40,880 Speaker 4: under twenty eight and people are buying their first home. 190 00:10:41,120 --> 00:10:43,600 Speaker 4: They don't have a credit score because it's very much 191 00:10:43,600 --> 00:10:46,240 Speaker 4: a cash society, so the bank really has to rely 192 00:10:46,400 --> 00:10:48,640 Speaker 4: on the value of the collateral of the property to 193 00:10:48,679 --> 00:10:51,960 Speaker 4: say yes. And what they were doing is they were going, 194 00:10:52,040 --> 00:10:53,640 Speaker 4: we were going, well, how do you do it today? 195 00:10:54,040 --> 00:10:56,800 Speaker 4: And the value was going into a branch, capturing, picking 196 00:10:56,840 --> 00:10:59,719 Speaker 4: up the documents, going to the property and bringing back 197 00:10:59,720 --> 00:11:03,559 Speaker 4: a whole copy, or in some instances they were emailing 198 00:11:03,640 --> 00:11:06,760 Speaker 4: through a PDF and sending back a PDF and then 199 00:11:06,840 --> 00:11:09,640 Speaker 4: keying that into the banking system. And it was taking weeks. 200 00:11:10,080 --> 00:11:12,280 Speaker 4: And the worst thing was there is no data. So 201 00:11:12,280 --> 00:11:15,040 Speaker 4: if you've been to India, there isn't an address start 202 00:11:15,120 --> 00:11:17,400 Speaker 4: when you go somewhere. It's on the corner of behind 203 00:11:17,440 --> 00:11:20,600 Speaker 4: this building. And when we first went, they kept going, 204 00:11:20,640 --> 00:11:22,320 Speaker 4: but what about the data? And I'm like, well, what 205 00:11:22,360 --> 00:11:25,160 Speaker 4: about the data? Is that a question? There is no data? 206 00:11:25,240 --> 00:11:27,240 Speaker 4: How do you do quality control. So how does a 207 00:11:27,320 --> 00:11:30,800 Speaker 4: bank that's scaling rapidly in this market the size of 208 00:11:30,880 --> 00:11:34,840 Speaker 4: Europe and as complexes Europe with different states, how do 209 00:11:34,880 --> 00:11:37,280 Speaker 4: you even know if it's right or wrong? And so 210 00:11:37,360 --> 00:11:40,760 Speaker 4: the regulator mandated over a certain value, you have to 211 00:11:40,760 --> 00:11:43,160 Speaker 4: get two valuations and you just and I'm like, well, 212 00:11:43,240 --> 00:11:44,760 Speaker 4: what do you do then? And they just compare the 213 00:11:44,800 --> 00:11:47,560 Speaker 4: two valuations and check the lower or the average. Sometimes 214 00:11:47,600 --> 00:11:49,640 Speaker 4: they get three. And I was sitting there in my 215 00:11:49,720 --> 00:11:52,720 Speaker 4: mind going click, click, click, And so we wasted a 216 00:11:52,720 --> 00:11:54,960 Speaker 4: lot of time trying to buy data and partner with 217 00:11:55,000 --> 00:11:56,880 Speaker 4: companies that said they had data, and we realized no 218 00:11:56,920 --> 00:11:58,840 Speaker 4: one had data, and we realized we had to change 219 00:11:58,840 --> 00:12:02,520 Speaker 4: our solution for India to really solve their problem. Well 220 00:12:02,559 --> 00:12:04,480 Speaker 4: is it worth us changing it? At the same time, 221 00:12:04,559 --> 00:12:07,320 Speaker 4: we also saw the macroeconomic things that Modi was building, 222 00:12:07,320 --> 00:12:12,000 Speaker 4: housing for all financial inclusion, massive tailwinds, fastest growing market 223 00:12:12,080 --> 00:12:15,120 Speaker 4: in the world, so very much like a tech any 224 00:12:15,160 --> 00:12:17,920 Speaker 4: tech company saying well, why will someone give me this information? 225 00:12:18,320 --> 00:12:21,439 Speaker 4: We thought, well, every day, tens of thousands of valuers 226 00:12:21,480 --> 00:12:24,800 Speaker 4: are going to properties, So how do you digitize that 227 00:12:24,880 --> 00:12:28,640 Speaker 4: when they have no technology? They don't have big budgets 228 00:12:28,640 --> 00:12:31,840 Speaker 4: to build their own. So we built, especially for India, 229 00:12:31,880 --> 00:12:34,760 Speaker 4: an app that the valuer gets the job. In our app, 230 00:12:35,440 --> 00:12:38,320 Speaker 4: it pre populates the template for the banks, so we 231 00:12:38,400 --> 00:12:41,040 Speaker 4: digitize their template and when they go to the property, 232 00:12:41,040 --> 00:12:44,600 Speaker 4: it geotags the address, It timestamps the photographs. They can 233 00:12:44,720 --> 00:12:47,240 Speaker 4: drag and drop and they can finish the calculation. It 234 00:12:47,280 --> 00:12:50,880 Speaker 4: sends it straight back and so suddenly overnight, this process 235 00:12:50,920 --> 00:12:54,160 Speaker 4: takes hours and is instant with data instead of taking 236 00:12:54,200 --> 00:12:57,720 Speaker 4: weeks with PDFs and hard copies and WhatsApps and things. 237 00:12:58,000 --> 00:13:00,679 Speaker 4: So it was revolutionary. Obviously there was is to make 238 00:13:00,720 --> 00:13:03,000 Speaker 4: it use a friendly for valuers had to work online, 239 00:13:03,040 --> 00:13:06,120 Speaker 4: had to work offline. They started dragging and dropping four 240 00:13:06,200 --> 00:13:09,080 Speaker 4: hundred fields and doing valuations for plants and machinery for 241 00:13:09,160 --> 00:13:13,360 Speaker 4: hospitals and using our app for everything. And then obviously adoption, 242 00:13:13,480 --> 00:13:15,599 Speaker 4: well you know, what's this foreign owned company doing with 243 00:13:15,640 --> 00:13:18,160 Speaker 4: the data and all of that data privacy things. But 244 00:13:18,360 --> 00:13:20,240 Speaker 4: you know, the long story short is today we have 245 00:13:20,280 --> 00:13:23,000 Speaker 4: twenty banks. But it was very much We created a 246 00:13:23,080 --> 00:13:27,600 Speaker 4: localized solution for India that now means for emerging markets, 247 00:13:27,600 --> 00:13:31,520 Speaker 4: we can go anywhere and digitized property and addressing and 248 00:13:31,640 --> 00:13:35,480 Speaker 4: data capture at scale, which is really phenomenally exciting. 249 00:13:36,559 --> 00:13:40,960 Speaker 1: That's incredible. Were there any sort of regulations or legal 250 00:13:40,960 --> 00:13:43,640 Speaker 1: issues you had to overcome? Sort of taking India on 251 00:13:43,679 --> 00:13:48,120 Speaker 1: this transition from very manual property valuation processes into the 252 00:13:48,160 --> 00:13:49,679 Speaker 1: sort of app driven version of it. 253 00:13:50,440 --> 00:13:53,520 Speaker 4: Very much so. So some banks weren't using cloud det 254 00:13:53,600 --> 00:13:56,839 Speaker 4: so we had to help them do their cloud policy. Obviously, 255 00:13:56,920 --> 00:13:59,080 Speaker 4: everywhere we go in the world, it needs to be 256 00:14:00,040 --> 00:14:03,480 Speaker 4: cloud local cloud. The data can't leave the country, and 257 00:14:03,520 --> 00:14:07,240 Speaker 4: I think it's important for anyone in tech to recognize 258 00:14:07,679 --> 00:14:11,640 Speaker 4: the right to growth, the right to scale, and think 259 00:14:11,679 --> 00:14:14,319 Speaker 4: about that from day one. So you know, we were 260 00:14:14,320 --> 00:14:17,319 Speaker 4: building world class global from day one. Because New Zealand 261 00:14:17,400 --> 00:14:20,680 Speaker 4: is a small market. There's only so many million houses. 262 00:14:21,000 --> 00:14:23,680 Speaker 4: There's only four big banks and then a few small ones. 263 00:14:23,720 --> 00:14:26,480 Speaker 4: So we were building this is a global problem that 264 00:14:26,520 --> 00:14:28,400 Speaker 4: we're solving, and we were doing this from day one. 265 00:14:28,960 --> 00:14:31,120 Speaker 4: So when they want us to do those things, they're 266 00:14:31,160 --> 00:14:33,000 Speaker 4: kind of the right to do business. And I think 267 00:14:33,080 --> 00:14:35,880 Speaker 4: that's what we really need to emphasize, and that's what 268 00:14:36,040 --> 00:14:38,160 Speaker 4: when we were in India with the ministers and the 269 00:14:38,160 --> 00:14:42,880 Speaker 4: Prime Minister, certainly my message was most good New Zealand 270 00:14:42,960 --> 00:14:46,880 Speaker 4: tech companies are global from day one. We're building to scale, 271 00:14:47,080 --> 00:14:49,960 Speaker 4: we're building to export because we recognize New Zealand's a 272 00:14:50,000 --> 00:14:52,200 Speaker 4: small market and so that's where we're such a great 273 00:14:52,200 --> 00:14:54,920 Speaker 4: test case for the world because it works here and 274 00:14:54,960 --> 00:14:57,280 Speaker 4: then we can scale it and that's the opportunity to 275 00:14:57,320 --> 00:14:59,640 Speaker 4: partner with these large markets where something has proven. But 276 00:14:59,680 --> 00:15:02,000 Speaker 4: we're all so very agile and very fast. 277 00:15:03,360 --> 00:15:06,280 Speaker 1: And yeah, look, you talk to any software entrepreneur in 278 00:15:06,280 --> 00:15:09,200 Speaker 1: New Zealand and that mantra is imprinted in their in 279 00:15:09,240 --> 00:15:12,640 Speaker 1: their brain. Global from day one. Having said that, most 280 00:15:12,880 --> 00:15:17,560 Speaker 1: software entrepreneurs don't sort of go New Zealand, Australia, India. 281 00:15:17,640 --> 00:15:22,120 Speaker 1: They go to North America, maybe Singapore or Europe. But 282 00:15:22,560 --> 00:15:25,520 Speaker 1: I mean it must have been incredibly daunting for you, thinking, look, 283 00:15:25,560 --> 00:15:28,080 Speaker 1: there's an opportunity that's a fast growing market in India, 284 00:15:28,120 --> 00:15:30,160 Speaker 1: but how do you get a foothold there when it's 285 00:15:30,200 --> 00:15:30,760 Speaker 1: so different. 286 00:15:31,600 --> 00:15:34,360 Speaker 4: Yeah, you're right, and lots of people think you're crazy 287 00:15:34,440 --> 00:15:36,800 Speaker 4: that New Zealand's you know, taking New Zealand tech to 288 00:15:36,960 --> 00:15:41,920 Speaker 4: India is definitely not the usual trodden entrepreneurial path. And 289 00:15:42,000 --> 00:15:46,480 Speaker 4: I think it's really important to recognize that you know, firstly, 290 00:15:46,720 --> 00:15:48,440 Speaker 4: is the size of the market worth it well one 291 00:15:48,520 --> 00:15:50,640 Speaker 4: hundred percent? You know, go big or go home. There's 292 00:15:50,680 --> 00:15:54,360 Speaker 4: nowhere bigger than India. But you really have to recognize 293 00:15:54,680 --> 00:15:57,080 Speaker 4: you're not going to be an overnight success. You have 294 00:15:57,160 --> 00:15:59,680 Speaker 4: to get on the airplanes. You have to appoint the 295 00:15:59,680 --> 00:16:02,160 Speaker 4: local advisory board that will tell you what you don't know. 296 00:16:02,600 --> 00:16:04,840 Speaker 4: You have to keep making sure you find the right 297 00:16:04,880 --> 00:16:07,920 Speaker 4: team on the ground. Certainly, being locked up for three 298 00:16:08,000 --> 00:16:11,880 Speaker 4: years and not being able to travel internationally really slowed 299 00:16:11,880 --> 00:16:14,440 Speaker 4: down growth for so many tech companies where you can't 300 00:16:14,480 --> 00:16:17,480 Speaker 4: do multimillion dollar deals over zoom and teams. You just 301 00:16:17,520 --> 00:16:20,920 Speaker 4: cannot and you can't get the nuancewers of meetings in 302 00:16:20,960 --> 00:16:22,920 Speaker 4: the room for those deals of what you need to 303 00:16:22,960 --> 00:16:25,800 Speaker 4: do to make it happen from a solution perspective without 304 00:16:25,800 --> 00:16:29,040 Speaker 4: being on the ground. So that was really that really 305 00:16:29,040 --> 00:16:34,360 Speaker 4: set us back. But you know, and certainly the success 306 00:16:34,360 --> 00:16:38,000 Speaker 4: that we've had has been quite daunting and just absolutely 307 00:16:38,080 --> 00:16:40,120 Speaker 4: quite surreal. Probably I would say, I mean I'm an 308 00:16:40,120 --> 00:16:44,080 Speaker 4: optimistic entrepreneur, but we were we were nominated and as 309 00:16:44,880 --> 00:16:47,760 Speaker 4: startup of the air and I think there's some phenomenal 310 00:16:47,880 --> 00:16:49,760 Speaker 4: number of startups that start up every single day and 311 00:16:49,800 --> 00:16:52,800 Speaker 4: it was a global competition, you know, from Israel and 312 00:16:52,200 --> 00:16:56,080 Speaker 4: India as well as the US and Asia, and we 313 00:16:56,120 --> 00:16:58,200 Speaker 4: won Startup of the Year. We didn't even have any 314 00:16:58,200 --> 00:17:00,600 Speaker 4: people with us. We just didn't expect to win. And 315 00:17:00,640 --> 00:17:03,200 Speaker 4: then two years later we one scale Up of the 316 00:17:03,240 --> 00:17:05,919 Speaker 4: Year in India because no fintech company went nationwide across 317 00:17:05,960 --> 00:17:07,600 Speaker 4: India that you have to be a little bit crazy. 318 00:17:08,040 --> 00:17:10,959 Speaker 4: And so you know, the success has been phenomenal, and 319 00:17:11,040 --> 00:17:14,360 Speaker 4: now we really want to encourage other New Zealand tech 320 00:17:14,359 --> 00:17:17,480 Speaker 4: companies to see the opportunity in India that it is 321 00:17:17,680 --> 00:17:21,720 Speaker 4: just you know, in English speaking, democratic and incredibly innovative 322 00:17:21,760 --> 00:17:25,639 Speaker 4: market that really wants to digitize everything. And then from India, 323 00:17:25,680 --> 00:17:28,280 Speaker 4: once you've done India, you get this credibility across the 324 00:17:28,320 --> 00:17:31,040 Speaker 4: region that nothing will be as hard as India. If 325 00:17:31,040 --> 00:17:33,320 Speaker 4: you've done it in India, must be able to work here, 326 00:17:33,440 --> 00:17:36,600 Speaker 4: and that's where that opportunity for companies going into emerging 327 00:17:36,640 --> 00:17:38,000 Speaker 4: markets is really massive. 328 00:17:39,000 --> 00:17:42,960 Speaker 1: Yes, so you've clearly explained now how you adapting to 329 00:17:43,000 --> 00:17:47,239 Speaker 1: those local conditions is crucial, and you've successfully done that. 330 00:17:47,880 --> 00:17:49,199 Speaker 1: The other side of it, I guess, and that was 331 00:17:49,240 --> 00:17:52,640 Speaker 1: the reason for the trade mission was government support, as 332 00:17:52,800 --> 00:17:56,560 Speaker 1: Christopher Luxen put it, removing the binnacles off the boat. 333 00:17:56,760 --> 00:17:59,720 Speaker 1: So things like visa improvements to allow the flow of 334 00:17:59,720 --> 00:18:03,280 Speaker 1: skill people between our countries, even having more direct flights 335 00:18:03,280 --> 00:18:07,880 Speaker 1: between India and New Zealand is really going to facilitate trade. 336 00:18:09,240 --> 00:18:12,840 Speaker 1: What did you get a sense when talking to Indian 337 00:18:12,840 --> 00:18:16,639 Speaker 1: business people up there, what do they really need to 338 00:18:17,600 --> 00:18:20,679 Speaker 1: facilitate that business to do more and have a deeper 339 00:18:20,760 --> 00:18:23,320 Speaker 1: relationship when it comes to business between the two countries. 340 00:18:24,240 --> 00:18:28,560 Speaker 4: Well, I think you know, everything is about relationships in India, 341 00:18:28,840 --> 00:18:33,359 Speaker 4: and you know, we really underestimated the opportunities there. And 342 00:18:33,480 --> 00:18:36,399 Speaker 4: it was you know, the last time that I was 343 00:18:36,400 --> 00:18:41,200 Speaker 4: there five years almost to the day previously with Winston 344 00:18:41,280 --> 00:18:44,399 Speaker 4: Peters when he was Deputy Prime Minister, and at the 345 00:18:44,520 --> 00:18:47,760 Speaker 4: time five years ago, Trump was there, the Australian Prime 346 00:18:47,760 --> 00:18:50,000 Speaker 4: minister was there, the Canadian Prime minister had been there, 347 00:18:50,280 --> 00:18:53,560 Speaker 4: and everybody was wooing India. So it was quite disappointing 348 00:18:53,640 --> 00:18:57,240 Speaker 4: to have five years of no support of governments building 349 00:18:57,280 --> 00:19:00,240 Speaker 4: relationships closely getting on a plane and going there. And 350 00:19:00,320 --> 00:19:02,879 Speaker 4: I first met Christopher Luxen before he was Prime Minister 351 00:19:02,920 --> 00:19:04,439 Speaker 4: and told him what we were doing in India and 352 00:19:04,480 --> 00:19:06,920 Speaker 4: he said to me, if I become Prime Minister, you 353 00:19:07,000 --> 00:19:08,520 Speaker 4: and I are going to go and we're going to 354 00:19:08,640 --> 00:19:11,640 Speaker 4: build this relationship. And he shook my hand and when 355 00:19:11,880 --> 00:19:15,240 Speaker 4: the trade delegation was announced, it was really privilege to 356 00:19:15,280 --> 00:19:19,240 Speaker 4: be included with such an amazing group of you know, 357 00:19:19,280 --> 00:19:22,040 Speaker 4: who's who of New Zealand business to go. So I 358 00:19:22,080 --> 00:19:25,199 Speaker 4: think what was done was phenomenal in terms of the 359 00:19:25,280 --> 00:19:28,080 Speaker 4: amount of events that we did in one week, the 360 00:19:28,280 --> 00:19:31,920 Speaker 4: understanding of what needs to happen, and we didn't come 361 00:19:31,960 --> 00:19:36,040 Speaker 4: in with this arrogance or expectation of an agreement overnight. 362 00:19:36,160 --> 00:19:38,119 Speaker 4: We came in with where he had to build a relationship. 363 00:19:38,160 --> 00:19:41,600 Speaker 4: And that's really important because five years ago when they're like, 364 00:19:41,640 --> 00:19:44,240 Speaker 4: oh we didn't get trade agreement, you can't just have 365 00:19:44,440 --> 00:19:47,439 Speaker 4: one date and expect to get married. You have to 366 00:19:47,440 --> 00:19:49,800 Speaker 4: do a little bit of wooing in relationships, and that 367 00:19:49,920 --> 00:19:52,120 Speaker 4: is really important from not just a government level, from 368 00:19:52,119 --> 00:19:55,600 Speaker 4: a companies level. You have to build the relationships and 369 00:19:55,640 --> 00:19:58,080 Speaker 4: then the opportunities will come. And you have to also 370 00:19:58,160 --> 00:20:02,399 Speaker 4: understand what's the shared value exchange. So in our example, 371 00:20:02,600 --> 00:20:05,040 Speaker 4: is the same old these policies or housing for all 372 00:20:05,119 --> 00:20:08,879 Speaker 4: financial inclusion building one hundred million properties. We've continued to 373 00:20:08,920 --> 00:20:12,439 Speaker 4: innovate to deliver on their needs. So, for example, thirty 374 00:20:12,480 --> 00:20:15,560 Speaker 4: percent of properties in India are new builds under construction 375 00:20:16,160 --> 00:20:18,840 Speaker 4: and it takes three to five years and it's such 376 00:20:18,880 --> 00:20:22,680 Speaker 4: a manual process to release the funds as the building 377 00:20:22,760 --> 00:20:25,439 Speaker 4: progresses and everybody is having so many touch points. So 378 00:20:25,480 --> 00:20:30,000 Speaker 4: we created build iq specifically for India to deliver on 379 00:20:30,040 --> 00:20:31,840 Speaker 4: this need, which has now been on board with some 380 00:20:31,840 --> 00:20:35,600 Speaker 4: of the largest lenders to allow the lawyer, the builder, 381 00:20:35,680 --> 00:20:38,280 Speaker 4: the developer, the value and everybody to load the plans 382 00:20:38,320 --> 00:20:41,440 Speaker 4: and interact in velocity. And so I think you really 383 00:20:41,480 --> 00:20:44,560 Speaker 4: have to get to know the market and really build 384 00:20:44,560 --> 00:20:47,040 Speaker 4: the relationship so you understand the problem that you're solving, 385 00:20:47,400 --> 00:20:49,040 Speaker 4: and then you have to solve it better than ever 386 00:20:49,080 --> 00:20:53,720 Speaker 4: before and be innovative because there's no room for something 387 00:20:53,720 --> 00:20:57,720 Speaker 4: that's like MVP minimum viable product or something, because the 388 00:20:57,760 --> 00:21:00,520 Speaker 4: bar is really high and it's probably higher than New 389 00:21:00,600 --> 00:21:05,040 Speaker 4: Zealand and Australia. The compliance is really high and go 390 00:21:05,160 --> 00:21:07,480 Speaker 4: that's a given. We're working with banks, so you have 391 00:21:07,520 --> 00:21:09,959 Speaker 4: to set a really high bar. But the innovation and 392 00:21:10,000 --> 00:21:11,960 Speaker 4: the drive that you'll get from that market will make 393 00:21:12,000 --> 00:21:15,159 Speaker 4: you better globally, and I think it's important to recognize that. 394 00:21:16,200 --> 00:21:19,719 Speaker 1: Yeah, so we're hearing, you know, the primary exporters are 395 00:21:19,760 --> 00:21:22,600 Speaker 1: already starting to sort of band together to think strategically 396 00:21:22,640 --> 00:21:25,200 Speaker 1: about how do we how do we leverage this sort 397 00:21:25,200 --> 00:21:29,160 Speaker 1: of appetite for more trade with India. So that makes sense, 398 00:21:29,240 --> 00:21:31,840 Speaker 1: you know, whether it's you know, the Kiwi fruit growers 399 00:21:32,080 --> 00:21:35,399 Speaker 1: and our dairy exporters sort. 400 00:21:35,280 --> 00:21:36,520 Speaker 3: Of talking to each other. 401 00:21:36,720 --> 00:21:39,520 Speaker 1: Beef hopefully will will be in the mix at some point, 402 00:21:39,680 --> 00:21:43,400 Speaker 1: do you see any scope for our digital exporters sort 403 00:21:43,400 --> 00:21:48,320 Speaker 1: of or companies banding together to leverage their collective strengths. 404 00:21:48,359 --> 00:21:50,200 Speaker 1: I think there were a few others on the trip, 405 00:21:50,240 --> 00:21:53,400 Speaker 1: and Keta Dakar, for instance, capture the bug. I think 406 00:21:53,440 --> 00:21:59,119 Speaker 1: she's actually employing Indian Indian IT export experts over there 407 00:21:59,160 --> 00:22:05,280 Speaker 1: to work on identifying bugs and weaknesses and New Zealand 408 00:22:05,280 --> 00:22:08,840 Speaker 1: companies IT systems. So it's sort of the trade is 409 00:22:08,840 --> 00:22:10,960 Speaker 1: sort of going going the other way to some extent. 410 00:22:11,000 --> 00:22:13,240 Speaker 1: But were there other companies there that are in the 411 00:22:13,280 --> 00:22:15,240 Speaker 1: digital space where you thought, wow, maybe we could do 412 00:22:15,280 --> 00:22:15,879 Speaker 1: something together. 413 00:22:17,359 --> 00:22:20,719 Speaker 4: So Darren from CIRCA was also on the trip, and 414 00:22:20,920 --> 00:22:23,800 Speaker 4: you know, they're a little bit quite different in that 415 00:22:23,840 --> 00:22:27,440 Speaker 4: they're using tech resource there to build you know, where 416 00:22:27,480 --> 00:22:30,560 Speaker 4: they're going. And you know, just seeing how he's built 417 00:22:30,560 --> 00:22:33,359 Speaker 4: the culture and scale the culture across different countries and 418 00:22:33,359 --> 00:22:36,159 Speaker 4: done it so well is really great. So it's always 419 00:22:36,200 --> 00:22:39,000 Speaker 4: really great to learn from companies that are ahead of you, 420 00:22:39,800 --> 00:22:41,760 Speaker 4: that have been around for longer and you know some 421 00:22:41,800 --> 00:22:43,600 Speaker 4: of those lessons learned. So I think they are a 422 00:22:43,680 --> 00:22:47,200 Speaker 4: great example. And you know, and then it's really more 423 00:22:47,520 --> 00:22:51,040 Speaker 4: how do we remain relevant And it's I think the 424 00:22:51,520 --> 00:22:56,399 Speaker 4: technology the thinking has to change for us adaptable thinking 425 00:22:57,800 --> 00:23:01,760 Speaker 4: around It's not just about what ever, it's not about things, 426 00:23:01,840 --> 00:23:05,880 Speaker 4: it's about bringing together digital with those things. And so 427 00:23:06,200 --> 00:23:08,879 Speaker 4: I think that's where the thinking needs to expand and 428 00:23:08,920 --> 00:23:11,000 Speaker 4: we're seeing a lot of that here in New Zealand 429 00:23:11,640 --> 00:23:14,080 Speaker 4: with the work Data Insights doing around. You know, how 430 00:23:14,119 --> 00:23:18,119 Speaker 4: do companies become data driven and leverage AI and understand 431 00:23:18,160 --> 00:23:22,359 Speaker 4: digital because nothing that people you know, certainly you know 432 00:23:22,359 --> 00:23:24,880 Speaker 4: that have left school quite some time ago, nothing that 433 00:23:25,040 --> 00:23:27,800 Speaker 4: they learned is going to be relevant for the short 434 00:23:28,000 --> 00:23:31,080 Speaker 4: near term future. And we're seeing companies like shop Off 435 00:23:31,080 --> 00:23:34,000 Speaker 4: I say, everybody needs to use AI, even Circo who 436 00:23:34,040 --> 00:23:36,240 Speaker 4: was on the chip. You know, Darren and Bob have 437 00:23:36,320 --> 00:23:39,280 Speaker 4: said everybody in Circo needs to use AI, and you 438 00:23:39,320 --> 00:23:41,480 Speaker 4: know we're doing that across all three on my companies. 439 00:23:42,160 --> 00:23:45,240 Speaker 4: So thinking has to change, and that's where you know, 440 00:23:45,240 --> 00:23:47,640 Speaker 4: we're even seeing Data Insights doing a lot of work 441 00:23:47,640 --> 00:23:49,920 Speaker 4: as an academy to say, well, how do you become 442 00:23:50,000 --> 00:23:51,680 Speaker 4: data driven and how do you change you think? Because 443 00:23:51,680 --> 00:23:54,080 Speaker 4: what does digital even mean for large media companies, for 444 00:23:54,160 --> 00:23:57,679 Speaker 4: large banks, for large talcos, large utilities that everyone of 445 00:23:57,720 --> 00:24:00,960 Speaker 4: the business doesn't normally use data, doesn't normally make data 446 00:24:01,000 --> 00:24:04,280 Speaker 4: different decisions? Normally digital was enough team and they did 447 00:24:04,320 --> 00:24:06,639 Speaker 4: that and suddenly you want everyone to know that. So 448 00:24:06,760 --> 00:24:11,080 Speaker 4: it's a massive organizational shift and cultural shift that needs 449 00:24:11,080 --> 00:24:14,200 Speaker 4: to happen in parallel with the technology shift. And that's 450 00:24:14,240 --> 00:24:17,239 Speaker 4: in every industry, but I think primary industries. There's some 451 00:24:17,280 --> 00:24:21,119 Speaker 4: great examples. Obviously Holter is one of those, you know, 452 00:24:21,280 --> 00:24:23,800 Speaker 4: some great examples that are digital first, but then there's 453 00:24:23,880 --> 00:24:26,440 Speaker 4: others that really need to embrace that to be able 454 00:24:26,440 --> 00:24:27,320 Speaker 4: to compete globally. 455 00:24:27,840 --> 00:24:30,199 Speaker 1: Yeah, I just wanted to finish off talking about one 456 00:24:30,240 --> 00:24:33,520 Speaker 1: of your other businesses you've mentioned, you know, Data Insights. 457 00:24:33,560 --> 00:24:39,960 Speaker 1: There that's obviously data analytics and business insights that's hugely popular. 458 00:24:41,000 --> 00:24:44,560 Speaker 1: Generate zero is another one of your businesses, and you know, 459 00:24:44,680 --> 00:24:48,200 Speaker 1: cluster of New Zealand companies again doing really innovative things, 460 00:24:48,200 --> 00:24:52,280 Speaker 1: in this case in sustainability, tracking your carbon footprints using 461 00:24:52,320 --> 00:24:56,040 Speaker 1: smart tools like AI. On the flip side, I guess 462 00:24:56,119 --> 00:24:58,920 Speaker 1: you know, we've just seen recently Trump sort of ramping 463 00:24:59,040 --> 00:25:03,040 Speaker 1: up mining and coal, going back to those dirty industries. 464 00:25:03,080 --> 00:25:05,520 Speaker 1: In the US, We've seen in the last couple of 465 00:25:05,600 --> 00:25:10,119 Speaker 1: years a little bit of a waning of the sustainability 466 00:25:10,440 --> 00:25:14,280 Speaker 1: mantra in the boardroom and that. So what's the sense 467 00:25:14,359 --> 00:25:18,480 Speaker 1: you have around the appetite for these sorts of services 468 00:25:18,560 --> 00:25:22,359 Speaker 1: that were all rage three years ago? You know, corporate responsibility, 469 00:25:22,400 --> 00:25:25,119 Speaker 1: we have to track this, we have to report on this. 470 00:25:25,600 --> 00:25:28,679 Speaker 1: Rules were introduced for our listed companies to report on 471 00:25:28,720 --> 00:25:33,520 Speaker 1: that in their financial record keeping. Is there still that 472 00:25:33,600 --> 00:25:36,080 Speaker 1: appetite for it or are they sort of paying lip 473 00:25:36,119 --> 00:25:36,840 Speaker 1: servis to it? 474 00:25:37,400 --> 00:25:40,040 Speaker 4: It's a really good question. One hundred percent is still 475 00:25:40,040 --> 00:25:43,560 Speaker 4: appetite for it? Not driving the appetite is the world 476 00:25:43,720 --> 00:25:47,159 Speaker 4: isn't run by boardrooms and charms only in for four years, 477 00:25:47,880 --> 00:25:50,960 Speaker 4: you know, it doesn't change, climate change. It doesn't change 478 00:25:51,000 --> 00:25:55,960 Speaker 4: the expectation of our children. And there's to say, actually, 479 00:25:56,320 --> 00:25:59,480 Speaker 4: the world is changing, there's an increase of climate impact. 480 00:25:59,680 --> 00:26:02,240 Speaker 4: And you know, when we look back and they say 481 00:26:02,240 --> 00:26:04,760 Speaker 4: to us, did we do all that we could? Did 482 00:26:04,800 --> 00:26:06,639 Speaker 4: you do everything that you could, we want to be 483 00:26:06,680 --> 00:26:09,560 Speaker 4: able to say yes. So Generate zero was driven over 484 00:26:09,600 --> 00:26:13,600 Speaker 4: the need to suddenly report and capture data on something 485 00:26:13,600 --> 00:26:15,719 Speaker 4: that was intangible and there was no way to do it. 486 00:26:15,720 --> 00:26:18,399 Speaker 4: We all know banks have data across different silos, and 487 00:26:18,480 --> 00:26:20,600 Speaker 4: yet suddenly they had to do this climate reporting and 488 00:26:20,640 --> 00:26:22,560 Speaker 4: we were hearing all about greenwashing and there was no 489 00:26:22,600 --> 00:26:24,760 Speaker 4: way to do it. It was very manual. So it 490 00:26:24,800 --> 00:26:27,400 Speaker 4: wasn't that I suddenly needed to start a third company. 491 00:26:27,440 --> 00:26:29,800 Speaker 4: It was like, well, this is a data problem, and 492 00:26:29,920 --> 00:26:33,960 Speaker 4: so Data Insights spun out Generate zero. That basically enables 493 00:26:34,000 --> 00:26:36,000 Speaker 4: AI to go in and capture all this data from 494 00:26:36,000 --> 00:26:39,200 Speaker 4: all the silos, pull it together in a platform. The 495 00:26:39,240 --> 00:26:43,000 Speaker 4: auditor can then look and see the reporting all automated. 496 00:26:43,359 --> 00:26:47,320 Speaker 4: But more importantly, the reduction module can also predict and say, well, 497 00:26:47,560 --> 00:26:49,480 Speaker 4: you know, use simulation to say how do you get 498 00:26:49,520 --> 00:26:53,120 Speaker 4: to your targets? And everybody in the company can see 499 00:26:53,119 --> 00:26:55,600 Speaker 4: your progress. And it's all data driven and it's been 500 00:26:55,640 --> 00:26:58,040 Speaker 4: a game change. And we have nine banks, five insurance, 501 00:26:58,080 --> 00:27:01,040 Speaker 4: whole of government, et cetera, and road Drewry from zero 502 00:27:01,080 --> 00:27:04,479 Speaker 4: as our investor in that business. And you know, so 503 00:27:04,880 --> 00:27:07,640 Speaker 4: those companies are not saying suddenly, we don't care about 504 00:27:07,640 --> 00:27:09,200 Speaker 4: this and we don't want to measure it. And it's 505 00:27:09,200 --> 00:27:12,440 Speaker 4: because their customers will judge them. So even if your 506 00:27:12,520 --> 00:27:15,840 Speaker 4: bored is not expecting it, your customers are expecting you 507 00:27:15,880 --> 00:27:18,640 Speaker 4: to do the right thing. And that is an enduring, 508 00:27:18,840 --> 00:27:21,320 Speaker 4: increasing thing. And we just recently had the Open Planet 509 00:27:21,480 --> 00:27:24,200 Speaker 4: Sir David Attenburgh film crew visiting here for a week 510 00:27:24,680 --> 00:27:28,440 Speaker 4: and they said the company countries that are most embracing 511 00:27:28,480 --> 00:27:33,440 Speaker 4: this well, those that are seeing the impact firsthand, India, Africa, 512 00:27:33,680 --> 00:27:36,679 Speaker 4: they are seeing the impact of climate change, you know 513 00:27:36,800 --> 00:27:39,800 Speaker 4: in the Amazon they see what happens and to article 514 00:27:39,800 --> 00:27:41,840 Speaker 4: where they see, you know, the number of dry months 515 00:27:42,359 --> 00:27:45,040 Speaker 4: where snow months, et cetera, and ice months, et cetera. 516 00:27:45,440 --> 00:27:48,720 Speaker 4: So the data is immutable and the waver is immutable. 517 00:27:48,760 --> 00:27:54,560 Speaker 4: One person's somewhat view, I'll try not be derogatory. Can't 518 00:27:54,680 --> 00:27:57,320 Speaker 4: change the world. And we're seeing the increase near the 519 00:27:57,320 --> 00:28:00,159 Speaker 4: rest of the world and particularly India where you know 520 00:28:00,160 --> 00:28:02,560 Speaker 4: they will be hosting a cop and they really are 521 00:28:02,560 --> 00:28:04,479 Speaker 4: wanting to embrace and lead the way we're seeing us 522 00:28:04,480 --> 00:28:07,840 Speaker 4: in the Mena region and the UAE, where you know 523 00:28:07,920 --> 00:28:12,200 Speaker 4: they are doing phenomenal things in Saudi and Niol where 524 00:28:12,200 --> 00:28:15,040 Speaker 4: they're boulding sustainable cities, And it's just because that's the 525 00:28:15,080 --> 00:28:17,280 Speaker 4: way that we can leap frog and do it, and 526 00:28:17,320 --> 00:28:19,000 Speaker 4: that's a better way to do the right thing. 527 00:28:19,520 --> 00:28:22,640 Speaker 1: Yeah, as you say, four years, one thousand days essentially, 528 00:28:23,240 --> 00:28:25,280 Speaker 1: and then it may be a completely different story. So 529 00:28:26,160 --> 00:28:30,399 Speaker 1: credit to you for pursuing that business. It's a great 530 00:28:31,320 --> 00:28:33,679 Speaker 1: business to be in, I think for New Zealand and 531 00:28:33,840 --> 00:28:35,840 Speaker 1: companies around the world to be able to make it 532 00:28:35,880 --> 00:28:40,360 Speaker 1: easy basically for them to do sustainability. Well, thanks so 533 00:28:40,440 --> 00:28:43,240 Speaker 1: much for coming on the business of tech. Good luck 534 00:28:43,280 --> 00:28:46,400 Speaker 1: for all your aspirations in India and beyond. You're in 535 00:28:46,800 --> 00:28:48,840 Speaker 1: UAE now as well. 536 00:28:48,840 --> 00:28:51,360 Speaker 4: Thanks. Yeah, we really love the UAE and they're leading 537 00:28:51,400 --> 00:28:54,320 Speaker 4: the world and AI and digital and they you know, 538 00:28:54,360 --> 00:28:58,760 Speaker 4: they are future thinkers. Everything that they do is pretty 539 00:28:58,840 --> 00:29:01,320 Speaker 4: much future thinking. So it's great to be driven by 540 00:29:01,360 --> 00:29:03,800 Speaker 4: innovative clients globally that make us better, and then we 541 00:29:03,840 --> 00:29:05,960 Speaker 4: bring that back to New Zealand and make New Zealand better. 542 00:29:06,000 --> 00:29:07,840 Speaker 4: So we're proud of the work that we're doing. Thanks 543 00:29:07,840 --> 00:29:08,640 Speaker 4: so much for having me. 544 00:29:09,000 --> 00:29:20,520 Speaker 3: Well then, thanks so much, Carmen. So there you go. 545 00:29:20,640 --> 00:29:23,720 Speaker 1: Velocity now a scale up company with a large business 546 00:29:23,760 --> 00:29:27,080 Speaker 1: in India. No reason why more of our startups and 547 00:29:27,160 --> 00:29:31,440 Speaker 1: more established tech businesses can't see India as a potential market, 548 00:29:31,520 --> 00:29:36,040 Speaker 1: particularly in those fast growing areas like fintech and health tech, 549 00:29:36,240 --> 00:29:38,760 Speaker 1: both niches of tech that we do very well in 550 00:29:39,120 --> 00:29:42,479 Speaker 1: here in New Zealand. So thanks to Carmen for coming on. 551 00:29:42,600 --> 00:29:45,480 Speaker 1: Thanks to two degrees for sponsoring the Business of Tech 552 00:29:45,760 --> 00:29:48,520 Speaker 1: enabling me to do these interviews and bring them to 553 00:29:48,560 --> 00:29:51,560 Speaker 1: you each week. You'll find the show notes, including my 554 00:29:51,720 --> 00:29:54,840 Speaker 1: top ten tech reads of the week, on the Business 555 00:29:54,840 --> 00:29:57,920 Speaker 1: Desk website. Just go to the podcast section at Business 556 00:29:57,960 --> 00:30:01,320 Speaker 1: Desk dot co dot enz. We're streaming each week on 557 00:30:01,480 --> 00:30:06,240 Speaker 1: iHeartRadio or wherever you get your podcasts. Please like the pod, 558 00:30:06,840 --> 00:30:09,200 Speaker 1: rate it and let other people know about it if 559 00:30:09,240 --> 00:30:11,640 Speaker 1: you find it useful. Get in touch with me with 560 00:30:11,760 --> 00:30:15,160 Speaker 1: ideas and feedback at Peter at Peter Griffin dot co 561 00:30:15,440 --> 00:30:18,360 Speaker 1: dot nz or look me up on LinkedIn. I'm on 562 00:30:18,440 --> 00:30:21,280 Speaker 1: there every day. Next week a return visit to the 563 00:30:21,320 --> 00:30:26,400 Speaker 1: podcast from Paris Marx, the Canadian author, journalist, and tech 564 00:30:26,440 --> 00:30:29,560 Speaker 1: commentator who is increasingly asking, you know, I think a 565 00:30:29,640 --> 00:30:33,520 Speaker 1: valid question in his own journalism, given what's going on 566 00:30:33,560 --> 00:30:36,000 Speaker 1: in the US at the moment, can we still trust 567 00:30:36,040 --> 00:30:39,200 Speaker 1: the American tech stack our government and most of our 568 00:30:39,240 --> 00:30:43,720 Speaker 1: businesses rely on. That's next week's episode. Paris is thought 569 00:30:43,800 --> 00:30:47,600 Speaker 1: provoking and eloquent as always, so tune in next Thursday 570 00:30:47,600 --> 00:30:49,800 Speaker 1: to catch my interview with him, Paris Marks. 571 00:30:50,280 --> 00:30:51,960 Speaker 3: Till then, have a great week.