1 00:00:07,320 --> 00:00:10,040 Speaker 1: Welcome to the Business of Tech powered by Two Degrees, 2 00:00:10,160 --> 00:00:14,040 Speaker 1: the podcast where we deep dive into the story, strategies 3 00:00:14,080 --> 00:00:17,840 Speaker 1: and innovations shaping the world of tech from our perspective. 4 00:00:18,120 --> 00:00:21,480 Speaker 1: In our tairoa, I'm your host, Peter Griffin, and today 5 00:00:21,840 --> 00:00:25,000 Speaker 1: I'm excited to introduce a guest who's no stranger to 6 00:00:25,079 --> 00:00:30,840 Speaker 1: building global tech success from New Zealand, John Daniel Trask. 7 00:00:31,440 --> 00:00:35,680 Speaker 1: Many will know JD as the founder of Raygun. The 8 00:00:35,760 --> 00:00:39,640 Speaker 1: software is a service platform that quietly powers error and 9 00:00:39,800 --> 00:00:45,760 Speaker 1: performance monitoring for everyone from startups to Fortune fifty giants. 10 00:00:46,240 --> 00:00:49,120 Speaker 1: If you've ever ordered a Domino's pizza or streamed Game 11 00:00:49,120 --> 00:00:53,199 Speaker 1: of Thrones, chances are Raygun was quietly working behind the 12 00:00:53,240 --> 00:00:56,960 Speaker 1: scenes to keep things running smoothly. But JD isn't here 13 00:00:56,960 --> 00:01:00,720 Speaker 1: to talk about past achievements. He's here to unveil his 14 00:01:00,920 --> 00:01:05,160 Speaker 1: bold new venture, Autohive. It was launched with a pretty 15 00:01:05,200 --> 00:01:09,479 Speaker 1: effervescent party on the Wellington Waterfront a couple of weeks ago. 16 00:01:10,040 --> 00:01:12,679 Speaker 1: Autohive is on a mission to make the power of 17 00:01:12,840 --> 00:01:16,520 Speaker 1: AI automation accessible to every business, not just those with 18 00:01:16,600 --> 00:01:20,440 Speaker 1: a crack team of engineers. Imagine a platform where you 19 00:01:20,480 --> 00:01:24,920 Speaker 1: can build your own AI agents to tackle repetitive, time 20 00:01:25,000 --> 00:01:28,880 Speaker 1: consuming tasks that bog down your week, from invoicing to 21 00:01:29,000 --> 00:01:33,679 Speaker 1: customer care, all through a simple, approachable interface. Now, there's 22 00:01:33,680 --> 00:01:37,160 Speaker 1: been a lot of hype around so called AI agents, 23 00:01:37,160 --> 00:01:40,200 Speaker 1: so we're going to unpack what exactly an AI agent 24 00:01:40,480 --> 00:01:44,479 Speaker 1: is and whether Autohive have anything more compelling to offer 25 00:01:44,959 --> 00:01:48,440 Speaker 1: than some of the sort of multinational versions of agents 26 00:01:48,480 --> 00:01:51,200 Speaker 1: that are coming onto the market. In this episode, you're 27 00:01:51,200 --> 00:01:54,720 Speaker 1: going to hear about JD's journey from Raygun to Autohive, 28 00:01:54,840 --> 00:01:59,240 Speaker 1: why he believes AI is an even bigger revolution than 29 00:01:59,440 --> 00:02:03,440 Speaker 1: the Internet, The light bulb moment that led to Autohive. 30 00:02:03,960 --> 00:02:07,520 Speaker 1: That was JD realizing that if AI is to transform business, 31 00:02:07,960 --> 00:02:11,160 Speaker 1: it must be easy for anyone, whether you're a florist 32 00:02:11,240 --> 00:02:14,600 Speaker 1: and accountant or a retailer, to build and use your 33 00:02:14,639 --> 00:02:18,760 Speaker 1: own agents. Talk a bit about how Autohive's marketplace will 34 00:02:18,800 --> 00:02:21,919 Speaker 1: let you share or monetize your custom agents, and why 35 00:02:22,000 --> 00:02:25,640 Speaker 1: JD is determined to keep humans in the loop, ensuring 36 00:02:25,720 --> 00:02:31,240 Speaker 1: automation remains approachable but under control. Plus practical advice for 37 00:02:31,280 --> 00:02:34,600 Speaker 1: getting hands on with AI, building digital skills, and overcoming 38 00:02:35,080 --> 00:02:38,920 Speaker 1: the hype and fear mongering that too often clouds the 39 00:02:39,040 --> 00:02:43,560 Speaker 1: AI Conversation. Yeah, lots of great stuff in this discussion 40 00:02:43,720 --> 00:02:46,520 Speaker 1: and some of JD's sorts about what we need to 41 00:02:46,520 --> 00:02:50,560 Speaker 1: do to stimulate the New Zealand economy as well. Whether 42 00:02:50,560 --> 00:02:53,680 Speaker 1: you're an AI skeptic, a business owner looking to boost 43 00:02:53,720 --> 00:02:56,840 Speaker 1: your productivity, or just curious about where this AI tech 44 00:02:56,960 --> 00:03:00,320 Speaker 1: is heading, this episode is packed with insights you won't 45 00:03:00,320 --> 00:03:02,640 Speaker 1: want to miss it. Stick around to the end where 46 00:03:02,840 --> 00:03:06,160 Speaker 1: JD shares how you can get started with auto Hive 47 00:03:06,240 --> 00:03:09,480 Speaker 1: for free and why now is the perfect time to experiment, 48 00:03:09,639 --> 00:03:12,400 Speaker 1: learn and help shape the future of AI in New 49 00:03:12,480 --> 00:03:26,119 Speaker 1: Zealand and beyond. JD, Welcome to the Business of Tech. 50 00:03:26,160 --> 00:03:26,720 Speaker 1: How are you doing. 51 00:03:27,040 --> 00:03:29,520 Speaker 2: I'm good, thank you, very very happy that the launchweek 52 00:03:29,600 --> 00:03:31,400 Speaker 2: is behind us. That was a lot of stress for 53 00:03:31,560 --> 00:03:33,880 Speaker 2: just getting it all sorted and get the product launched 54 00:03:33,880 --> 00:03:35,800 Speaker 2: and all of that. Yeah, that was a great event. 55 00:03:35,880 --> 00:03:40,720 Speaker 1: Autohive launched on the Wellington waterfront, great party and a 56 00:03:40,760 --> 00:03:43,000 Speaker 1: lot of energy which the city sort of has been 57 00:03:43,080 --> 00:03:45,120 Speaker 1: lacking in the tech sector. But it came off the 58 00:03:45,120 --> 00:03:48,440 Speaker 1: back of some big, great conferences, the Electrified conference that week, 59 00:03:48,440 --> 00:03:50,240 Speaker 1: a big clean tech conference. In the middle of it, 60 00:03:50,280 --> 00:03:53,480 Speaker 1: you had this new startup launching auto Hive, and we're 61 00:03:53,480 --> 00:03:56,920 Speaker 1: going to talk about Autohive and depth, but I just 62 00:03:56,960 --> 00:03:59,080 Speaker 1: want to dial back a little bit to talk about 63 00:03:59,440 --> 00:04:02,120 Speaker 1: the company. You've devoted the last fifteen at least years 64 00:04:02,160 --> 00:04:07,200 Speaker 1: to Raygun, a company that's gone global, very successful, probably 65 00:04:07,680 --> 00:04:10,839 Speaker 1: a bit below the radar for most business people. Tell 66 00:04:10,880 --> 00:04:13,400 Speaker 1: us about Raygun, what you set out all those years 67 00:04:13,400 --> 00:04:16,440 Speaker 1: ago to achieve and what you have achieved. 68 00:04:17,040 --> 00:04:20,359 Speaker 2: Raygun is not like a consumer brand, So for folks 69 00:04:20,360 --> 00:04:23,560 Speaker 2: who are thinking I've never heard of this before, absolutely 70 00:04:23,760 --> 00:04:26,839 Speaker 2: totally expected. You know. So what Raygun does is it 71 00:04:26,880 --> 00:04:31,839 Speaker 2: provides services through a SaaS platform to other technology companies 72 00:04:32,040 --> 00:04:34,680 Speaker 2: where they can track their faults and performance issues in 73 00:04:34,720 --> 00:04:37,880 Speaker 2: their software. And so, you know, we all sit there 74 00:04:37,880 --> 00:04:39,960 Speaker 2: and kind of cuss out our computers from time to 75 00:04:40,000 --> 00:04:43,000 Speaker 2: time when stuff just doesn't work. It's a fairly universal 76 00:04:43,040 --> 00:04:45,560 Speaker 2: problem and it's what Raygun helps with. So you can 77 00:04:45,600 --> 00:04:48,320 Speaker 2: think of Raygun as being like a black box flight recorder, 78 00:04:48,360 --> 00:04:50,919 Speaker 2: but the software code. So when something blows up, it 79 00:04:51,040 --> 00:04:53,280 Speaker 2: kind of gets the last message out to Sally, like, well, 80 00:04:53,320 --> 00:04:55,280 Speaker 2: this is what was going on at the time it 81 00:04:55,320 --> 00:04:59,760 Speaker 2: didn't work, and that is really helpful to the creators 82 00:04:59,800 --> 00:05:03,680 Speaker 2: of software, and so part to your question, you know, 83 00:05:03,680 --> 00:05:05,920 Speaker 2: why would kiwis not maybe know so much about us 84 00:05:05,920 --> 00:05:08,719 Speaker 2: as we're ninety three percent the export based, so most 85 00:05:08,720 --> 00:05:11,000 Speaker 2: of our money is we're pulling it in from overseas 86 00:05:11,000 --> 00:05:14,240 Speaker 2: and we're spending it here in Courtney Place, right, And 87 00:05:14,320 --> 00:05:17,800 Speaker 2: so we've got everything from small customers, new startups through 88 00:05:17,839 --> 00:05:22,440 Speaker 2: to Fortune fifty companies. So we've tracked for example, Domino's 89 00:05:22,480 --> 00:05:24,919 Speaker 2: Pizza Worldwide uses us, so if you're ever ordering a 90 00:05:24,920 --> 00:05:27,880 Speaker 2: Domino's pizza through the app whatever, and something goes wrong, 91 00:05:27,960 --> 00:05:30,279 Speaker 2: we collect the data, we helped them improve it. We 92 00:05:30,320 --> 00:05:34,480 Speaker 2: had Hbo originally as a customer and we tracked I 93 00:05:34,480 --> 00:05:37,520 Speaker 2: think it was eighty seven million concurrent viewers of the 94 00:05:37,520 --> 00:05:40,359 Speaker 2: finale A Game of Throne, So eighty seven million people 95 00:05:40,400 --> 00:05:42,840 Speaker 2: on Earth were sending data through our platform about how 96 00:05:42,839 --> 00:05:45,359 Speaker 2: long were they waiting for buffering? Was there any issues? 97 00:05:46,160 --> 00:05:49,279 Speaker 2: And so we operated a tremendous scale based just out 98 00:05:49,279 --> 00:05:51,880 Speaker 2: of here in Wellington. But that's our core business. That 99 00:05:52,000 --> 00:05:54,880 Speaker 2: was what we've been building for the last year ten years. 100 00:05:55,040 --> 00:05:57,040 Speaker 2: How did you get traction in the US? 101 00:05:57,320 --> 00:06:00,720 Speaker 1: Initially that led to the Dominoes and HBO signing up 102 00:06:00,720 --> 00:06:01,720 Speaker 1: what was the breakthrough. 103 00:06:02,279 --> 00:06:06,159 Speaker 2: Well, we've been selling products globally already under the brand 104 00:06:06,160 --> 00:06:08,719 Speaker 2: called Mindescape before that. That I started when I was 105 00:06:09,160 --> 00:06:11,520 Speaker 2: twenty three years old with a couple of other guys, 106 00:06:11,560 --> 00:06:15,360 Speaker 2: Jeremy Boyd and Andrew Peaters. They were the older, mature 107 00:06:15,440 --> 00:06:18,080 Speaker 2: people that I started the company with, and we sold 108 00:06:18,200 --> 00:06:21,400 Speaker 2: to developer tools there and then we built the raygun 109 00:06:21,520 --> 00:06:25,680 Speaker 2: product originally for Mindescape. It was our first subscription product. 110 00:06:25,760 --> 00:06:27,920 Speaker 2: I'd really admired what Rod had done with zero and 111 00:06:27,960 --> 00:06:29,800 Speaker 2: I thought, you know what, starting the month not at 112 00:06:29,880 --> 00:06:33,880 Speaker 2: zero is a good idea, and so we built a 113 00:06:33,880 --> 00:06:37,640 Speaker 2: subscription product around it and very quickly dominated all our 114 00:06:37,680 --> 00:06:42,440 Speaker 2: other products in terms of revenue. But simultaneously, because those 115 00:06:42,480 --> 00:06:45,360 Speaker 2: products are sold into software development teams, we knew from 116 00:06:46,160 --> 00:06:48,840 Speaker 2: literally day zero that we had to market this globally. 117 00:06:48,960 --> 00:06:52,239 Speaker 2: So you've got this thriving business. About twenty five people 118 00:06:52,279 --> 00:06:56,080 Speaker 2: involved in Reygun. Yeah, yeah, so have we. And this 119 00:06:56,200 --> 00:06:57,560 Speaker 2: is off a little bit at the back of the 120 00:06:57,560 --> 00:07:00,560 Speaker 2: AI story. We were actually pushing closer to sort of 121 00:07:00,680 --> 00:07:04,240 Speaker 2: sixty sixty five people a few years ago, and not 122 00:07:04,480 --> 00:07:07,080 Speaker 2: for you know, we haven't conducted rounds of layoffs or 123 00:07:07,120 --> 00:07:10,520 Speaker 2: anything like that, but when the AI stuff came along, 124 00:07:10,720 --> 00:07:13,760 Speaker 2: I kind of decided, well, if people were if people 125 00:07:13,800 --> 00:07:16,120 Speaker 2: were to leave, could we have a look at what 126 00:07:16,240 --> 00:07:19,200 Speaker 2: was necessary with technology to improve our output. I think 127 00:07:19,200 --> 00:07:21,880 Speaker 2: a lot of people kind of think that AI coming 128 00:07:21,880 --> 00:07:24,080 Speaker 2: along means they'll just do the AI version of their 129 00:07:24,120 --> 00:07:27,239 Speaker 2: current job. I'm increasingly not thinking that that is true. 130 00:07:27,440 --> 00:07:30,280 Speaker 2: I actually think it's an opportunity for people to really 131 00:07:30,320 --> 00:07:33,800 Speaker 2: think about what they like to do, and that suddenly 132 00:07:33,880 --> 00:07:37,840 Speaker 2: the how it's done is becoming a little less important 133 00:07:37,840 --> 00:07:39,200 Speaker 2: than it used to be. I'm not saying it's not 134 00:07:39,320 --> 00:07:41,720 Speaker 2: I'm not saying, you know that a person who, for example, 135 00:07:41,760 --> 00:07:44,800 Speaker 2: is never coded, is suddenly building operating systems, not at all. 136 00:07:45,440 --> 00:07:49,000 Speaker 2: But an example would be like these video creation tools 137 00:07:49,040 --> 00:07:51,239 Speaker 2: that have come out, and people are doing some amazing things. 138 00:07:51,240 --> 00:07:54,160 Speaker 2: And what I've noticed is it's not really the videographers 139 00:07:54,160 --> 00:07:55,880 Speaker 2: that are suddenly going, hey, I want to do this, 140 00:07:56,400 --> 00:07:59,560 Speaker 2: but it's it's seeing people that are genuinely quite caught 141 00:07:59,600 --> 00:08:02,560 Speaker 2: at their more creative that maybe it's like, maybe we 142 00:08:02,880 --> 00:08:06,080 Speaker 2: should have fifty times the number of directors now who 143 00:08:06,120 --> 00:08:09,559 Speaker 2: have creative talent that aren't blocked by needing an army 144 00:08:09,560 --> 00:08:12,840 Speaker 2: of people to deliver the output. Maybe there's more opportunities 145 00:08:12,880 --> 00:08:16,320 Speaker 2: for those types of roles to form. So anyway, backing 146 00:08:16,360 --> 00:08:19,000 Speaker 2: it up, so we let the head count generally sort 147 00:08:19,000 --> 00:08:21,880 Speaker 2: of drift a little down. We haven't seen any significant 148 00:08:21,880 --> 00:08:24,720 Speaker 2: slowdowns and productivity off the back of that. It wasn't 149 00:08:24,760 --> 00:08:28,200 Speaker 2: financially motivated, but it's becoming an attractor to see an 150 00:08:28,240 --> 00:08:31,480 Speaker 2: organization that kind of gets that this change is occurring, 151 00:08:32,160 --> 00:08:34,760 Speaker 2: and people's thinking, hey, well, at least go there. I'm 152 00:08:34,840 --> 00:08:36,840 Speaker 2: enabled to learn about how to move forward in this. 153 00:08:37,040 --> 00:08:39,760 Speaker 2: And I think that's one thing that is going to 154 00:08:39,800 --> 00:08:42,760 Speaker 2: catch a few organizations off guard if they kind of 155 00:08:42,800 --> 00:08:46,040 Speaker 2: continue to maybe play a wait and see approach, is 156 00:08:46,280 --> 00:08:50,400 Speaker 2: they're going to the people who don't really want to see. Yeah. 157 00:08:51,240 --> 00:08:54,080 Speaker 1: Look, this is a trend we're seeing, particularly in Silicon Valley. 158 00:08:54,120 --> 00:08:55,800 Speaker 1: There's a lot of talk about this, about the fact 159 00:08:55,880 --> 00:08:58,120 Speaker 1: that you might have a company that can do one 160 00:08:58,160 --> 00:09:01,000 Speaker 1: hundred million dollars in revenue, but it does need more 161 00:09:01,040 --> 00:09:04,440 Speaker 1: than twenty people. Anymore, the expectation was as you scale, 162 00:09:04,440 --> 00:09:07,560 Speaker 1: you got more VC money or whatever you added, your headcount. 163 00:09:07,080 --> 00:09:10,839 Speaker 2: Grew and grew and grew. It's not necessarily the case anymore. Yeah, well, 164 00:09:10,880 --> 00:09:13,520 Speaker 2: I think, and this is probably just me, but I 165 00:09:13,559 --> 00:09:16,400 Speaker 2: thought it was outrageous how much through Zerve all of 166 00:09:16,440 --> 00:09:19,480 Speaker 2: the product companies started to scale like they were services companies. 167 00:09:19,559 --> 00:09:22,319 Speaker 2: Like I totally understand if I'm building people out by 168 00:09:22,360 --> 00:09:24,200 Speaker 2: the hour, I need as many people as I can. 169 00:09:24,559 --> 00:09:26,840 Speaker 2: If I'm building a product that should scale out, I 170 00:09:26,880 --> 00:09:29,640 Speaker 2: should not need to keep scaling headcount as I scale 171 00:09:29,679 --> 00:09:32,920 Speaker 2: the product. The zero cost a duplication of software, that 172 00:09:33,080 --> 00:09:36,920 Speaker 2: is the benefit there. So I've always been a pretty 173 00:09:36,920 --> 00:09:40,920 Speaker 2: big proponent of smaller teams of highly capable people, very 174 00:09:40,960 --> 00:09:44,800 Speaker 2: empowered rather than giant factories. And in fact, you know, 175 00:09:44,920 --> 00:09:46,960 Speaker 2: probably a bit of a negative here, but I've been 176 00:09:47,000 --> 00:09:48,840 Speaker 2: saying this for about eighteen months now and I think 177 00:09:48,880 --> 00:09:50,920 Speaker 2: it's coming true, which is, if you want to avoid 178 00:09:50,960 --> 00:09:53,520 Speaker 2: the real risk of the downside of AI, do not 179 00:09:53,600 --> 00:09:56,040 Speaker 2: work in a company with tens of thousands of employees. 180 00:09:56,600 --> 00:10:00,200 Speaker 2: You know, you are by definition not wildly impactful, and 181 00:10:00,240 --> 00:10:03,040 Speaker 2: the ROI calculation is not going to be in your 182 00:10:03,480 --> 00:10:06,800 Speaker 2: favor when people are sitting there doing management by spreadsheets, 183 00:10:06,840 --> 00:10:09,640 Speaker 2: going oh, hang on a thousand people here, you know, 184 00:10:09,679 --> 00:10:12,320 Speaker 2: like that's some real money. I don't say that callously, 185 00:10:12,400 --> 00:10:14,360 Speaker 2: like I think it sucks, but I think that's what's 186 00:10:14,400 --> 00:10:16,640 Speaker 2: going to play out, and everybody should be sitting there 187 00:10:16,640 --> 00:10:18,000 Speaker 2: thinking about what's playing out. 188 00:10:18,400 --> 00:10:20,959 Speaker 1: Well, that's playing out in the big tech companies over 189 00:10:20,960 --> 00:10:23,880 Speaker 1: the last two to three years. You know, we're seeing 190 00:10:24,800 --> 00:10:27,920 Speaker 1: four thousand people laid off in one go because the 191 00:10:27,960 --> 00:10:32,280 Speaker 1: spreadsheet being countered looking at that and going this is ridiculous, 192 00:10:32,280 --> 00:10:34,120 Speaker 1: as too many people in the books, and this is. 193 00:10:34,080 --> 00:10:36,959 Speaker 2: Where you run the risk of zero sum game mentalities, right, 194 00:10:37,040 --> 00:10:38,880 Speaker 2: So there'll be a lot of people who freak out 195 00:10:38,960 --> 00:10:41,199 Speaker 2: kind of going, oh my god, the jobs are going away, 196 00:10:41,280 --> 00:10:43,800 Speaker 2: and it's like, well, no, the opportunity to create new 197 00:10:43,840 --> 00:10:47,760 Speaker 2: things are turning up, and you should be trying to 198 00:10:47,800 --> 00:10:50,520 Speaker 2: find what those are. So, you know, we were chatting 199 00:10:50,600 --> 00:10:54,720 Speaker 2: just beforehand about that Jeff Bezos quote where when you're 200 00:10:54,760 --> 00:10:57,080 Speaker 2: in a time of a lot of change, Jeff says 201 00:10:57,080 --> 00:10:58,959 Speaker 2: sort of the question isn't what's going to change, The 202 00:10:59,040 --> 00:11:01,800 Speaker 2: question is what's not going to change. And he gives 203 00:11:01,840 --> 00:11:04,120 Speaker 2: the example of Amazon where he's like, well, nobody's going 204 00:11:04,160 --> 00:11:06,280 Speaker 2: to turn around and tell me to ship things slower, 205 00:11:06,720 --> 00:11:09,600 Speaker 2: to increase the prices and all this. And that's where 206 00:11:09,679 --> 00:11:11,400 Speaker 2: I kind of look at this and I think, well, 207 00:11:11,440 --> 00:11:13,400 Speaker 2: at the end of the day, consumers are going to 208 00:11:13,440 --> 00:11:16,680 Speaker 2: continue to want to consume, you know, rightly or wrongly. 209 00:11:16,760 --> 00:11:18,880 Speaker 2: Human beings want more and more and more, you know. 210 00:11:19,760 --> 00:11:23,000 Speaker 2: And so you might sort of see an organization say 211 00:11:23,600 --> 00:11:28,120 Speaker 2: reducing head count but producing more. I don't think that 212 00:11:28,160 --> 00:11:31,440 Speaker 2: means that there isn't more organization starting up to also 213 00:11:31,600 --> 00:11:34,439 Speaker 2: produce things, you know. And so it really comes down 214 00:11:34,480 --> 00:11:36,600 Speaker 2: to that mindset. And I do worry a little bit 215 00:11:36,600 --> 00:11:39,640 Speaker 2: that the Kiwi mindset is too zero s. If somebody 216 00:11:39,640 --> 00:11:42,080 Speaker 2: else wins, there must be a loser. Therefore, you know, 217 00:11:42,200 --> 00:11:45,240 Speaker 2: we should penalize the winner. And it's like, no, you know, 218 00:11:45,280 --> 00:11:47,800 Speaker 2: we produce enough food to feed forty million people from 219 00:11:47,800 --> 00:11:50,840 Speaker 2: New Zealand. Maybe we could look at the next category 220 00:11:50,840 --> 00:11:53,080 Speaker 2: down and say, how did that actually served you know, 221 00:11:53,200 --> 00:11:55,880 Speaker 2: five x the number of people that will bring in 222 00:11:55,920 --> 00:11:58,200 Speaker 2: more money. That will be all that lifts the country 223 00:11:58,280 --> 00:12:00,320 Speaker 2: up is to bring more money into the country. And 224 00:12:00,360 --> 00:12:02,480 Speaker 2: you know what really bugs me about this, I've been 225 00:12:02,480 --> 00:12:04,240 Speaker 2: talking about this for years and now it sounds like 226 00:12:04,280 --> 00:12:06,920 Speaker 2: I'm talking Donald Trump's book, you know. I like, hey, 227 00:12:06,960 --> 00:12:08,680 Speaker 2: we shouldn't have a trade deficit. It's like when New 228 00:12:08,800 --> 00:12:11,240 Speaker 2: Zealand really shouldn't have a trade deficit, and it's had 229 00:12:11,280 --> 00:12:13,720 Speaker 2: one for nine out of the last ten years. Right, 230 00:12:13,880 --> 00:12:18,360 Speaker 2: we are progressively getting poorer and therefore we're asking a 231 00:12:18,360 --> 00:12:21,040 Speaker 2: lot of questions now, which are like, well, who can 232 00:12:21,080 --> 00:12:23,040 Speaker 2: we take the money off then if we can't figure 233 00:12:23,040 --> 00:12:25,560 Speaker 2: out how to earn it, And it's like no, no, no, no, guess, 234 00:12:25,800 --> 00:12:28,800 Speaker 2: let's work out how we can become hyper productive and 235 00:12:28,880 --> 00:12:31,520 Speaker 2: sell a whole lot more stuff to the world. That's 236 00:12:31,559 --> 00:12:33,960 Speaker 2: going to lift all the boats. You're going to increase 237 00:12:34,000 --> 00:12:36,920 Speaker 2: your tax take, you're going to increase people's quality of living. 238 00:12:37,040 --> 00:12:39,520 Speaker 2: All of these things come from business. They all come 239 00:12:39,559 --> 00:12:42,640 Speaker 2: from the private sector, and ideally in an exporting situation. 240 00:12:42,920 --> 00:12:45,800 Speaker 1: Yeah, and look, we've got a government going for growth, 241 00:12:45,960 --> 00:12:49,720 Speaker 1: so that the rhetoric of the political rhetoric is in 242 00:12:49,760 --> 00:12:52,319 Speaker 1: that camp. You know, there's more momentum now, but our 243 00:12:52,360 --> 00:12:55,959 Speaker 1: execution has not been great in our trek record of this. 244 00:12:56,120 --> 00:12:59,960 Speaker 1: We have great companies who scale and employ people. You know, 245 00:13:00,720 --> 00:13:03,640 Speaker 1: take Sect. One hundred thousand dollars plus salaries. It's just 246 00:13:03,640 --> 00:13:05,040 Speaker 1: how do we do more of it? I guess is 247 00:13:05,040 --> 00:13:05,480 Speaker 1: the question. 248 00:13:05,960 --> 00:13:08,360 Speaker 2: Yeah, and look there's lots of ways they could you know, 249 00:13:09,120 --> 00:13:11,839 Speaker 2: there's plenty of things we could do around improving our 250 00:13:11,960 --> 00:13:15,920 Speaker 2: R and D tax settings, for example. And the problem 251 00:13:16,000 --> 00:13:18,280 Speaker 2: is is that the tighter things become, you know, the 252 00:13:18,720 --> 00:13:22,080 Speaker 2: less options there are available. And I don't think many 253 00:13:22,120 --> 00:13:25,720 Speaker 2: Kiwis realize how tight they've already become. And that was why, 254 00:13:25,840 --> 00:13:28,839 Speaker 2: you know, part of the rhetoric at our auto Hive 255 00:13:29,000 --> 00:13:32,600 Speaker 2: launch is just sort of say, look, we can all 256 00:13:32,600 --> 00:13:36,480 Speaker 2: set around complaining and look, I've complained more than most 257 00:13:36,640 --> 00:13:39,400 Speaker 2: you know about about Winger's the government or the local 258 00:13:39,440 --> 00:13:41,960 Speaker 2: government going to step in and do something. But the 259 00:13:42,040 --> 00:13:45,400 Speaker 2: reality is there's actually society that does all of the work, 260 00:13:45,520 --> 00:13:48,200 Speaker 2: you know, and that includes the people in government. I'm 261 00:13:48,240 --> 00:13:51,800 Speaker 2: not sort of us them in that regard, but it's like, 262 00:13:52,200 --> 00:13:57,240 Speaker 2: why do I worry that too many people have since 263 00:13:57,440 --> 00:13:59,320 Speaker 2: at least since Az a kid has sort of shifted 264 00:13:59,320 --> 00:14:01,800 Speaker 2: their mentality here that they leave home and the government 265 00:14:01,840 --> 00:14:04,320 Speaker 2: becomes their parent, and it's like, what the hell are 266 00:14:04,320 --> 00:14:06,280 Speaker 2: you doing about? This is your shot to go and 267 00:14:05,960 --> 00:14:08,679 Speaker 2: make your impact in the world, you know, run out 268 00:14:08,720 --> 00:14:11,760 Speaker 2: there and do something amazing. Stop whinging about. I mean, 269 00:14:13,080 --> 00:14:14,920 Speaker 2: I have that issue when I see people saying there's 270 00:14:14,960 --> 00:14:17,240 Speaker 2: no jobs and I'm like, well, I've had exactly zero 271 00:14:17,320 --> 00:14:19,280 Speaker 2: people come and knock on my door saying, hey, can 272 00:14:19,320 --> 00:14:20,920 Speaker 2: I know your lawn's going to paint your shared Like, 273 00:14:21,080 --> 00:14:24,440 Speaker 2: I'm like, you know, where is the agency here? You know, 274 00:14:24,520 --> 00:14:26,840 Speaker 2: it's just no, no, the government should pay me more 275 00:14:26,840 --> 00:14:29,960 Speaker 2: money while I'm not working. Yeah, I'm not so sure 276 00:14:29,960 --> 00:14:31,880 Speaker 2: of that. I think we want and I think that 277 00:14:31,960 --> 00:14:36,360 Speaker 2: pioneer spirit is actually was the Kiwi spirit. I'm not 278 00:14:36,400 --> 00:14:39,160 Speaker 2: saying that to hammer people. I'm just sort okay, let's 279 00:14:39,160 --> 00:14:41,760 Speaker 2: bring that back. That was a much more fun attitude 280 00:14:41,800 --> 00:14:44,040 Speaker 2: to sort of look at the world through. Yeah. 281 00:14:44,120 --> 00:14:47,880 Speaker 1: Yeah, So the other night you related to quite an 282 00:14:47,880 --> 00:14:51,720 Speaker 1: interesting story about early twenty twenty three. I think it 283 00:14:51,840 --> 00:14:55,480 Speaker 1: was the rise of generative AI. Chat ChiPT had come out. 284 00:14:55,520 --> 00:14:59,000 Speaker 1: The previous novembers would have wowed everyone, and you're sort 285 00:14:59,040 --> 00:15:01,840 Speaker 1: of planning the future of Reaguan and sitting down and 286 00:15:01,880 --> 00:15:04,280 Speaker 1: this time around was a bit different. You're like, I 287 00:15:04,280 --> 00:15:06,600 Speaker 1: don't really know where this is going. We really need 288 00:15:06,640 --> 00:15:08,760 Speaker 1: to think about what the impact. You said, it was 289 00:15:08,760 --> 00:15:11,800 Speaker 1: an electrifying moment playing with all this new generative AI 290 00:15:11,880 --> 00:15:14,360 Speaker 1: stuff and then thinking about how do we apply this 291 00:15:14,400 --> 00:15:17,480 Speaker 1: to Reagan and then thinking is there a business here 292 00:15:17,880 --> 00:15:21,440 Speaker 1: helping small and medium sized businesses use AI. Tell us 293 00:15:21,440 --> 00:15:24,200 Speaker 1: about that sort of that moment, that light bulb moment, 294 00:15:24,240 --> 00:15:26,840 Speaker 1: and what it meant for you as as an entrepreneur 295 00:15:26,920 --> 00:15:27,720 Speaker 1: and a business. 296 00:15:28,360 --> 00:15:30,840 Speaker 2: To set the scene a little, I was in my 297 00:15:30,920 --> 00:15:34,040 Speaker 2: early teenage years when the Internet sort of came along, 298 00:15:34,720 --> 00:15:36,720 Speaker 2: and I was a huge nerd, so I loved playing 299 00:15:36,720 --> 00:15:41,960 Speaker 2: with that, you know, and I was cognizant at the 300 00:15:42,040 --> 00:15:44,960 Speaker 2: time that I was probably too young to sort of 301 00:15:45,000 --> 00:15:47,280 Speaker 2: make a go of building a business on the internet, 302 00:15:47,320 --> 00:15:49,240 Speaker 2: if you will, at that point. Now, admittedly I did 303 00:15:49,280 --> 00:15:51,120 Speaker 2: start building a business on the Internet when I was 304 00:15:51,120 --> 00:15:54,160 Speaker 2: twenty three, but in my teenage years, I didn't really 305 00:15:54,200 --> 00:15:57,320 Speaker 2: feel like I was in the position to really exploit 306 00:15:57,360 --> 00:15:59,760 Speaker 2: what the Internet was enabling. I spent a lot of 307 00:15:59,760 --> 00:16:02,400 Speaker 2: time reading about Microsoft and all the tech giants at 308 00:16:02,400 --> 00:16:05,000 Speaker 2: the time. You know, There's a great book called The 309 00:16:05,000 --> 00:16:10,040 Speaker 2: Microsoft Way that I enjoyed, and they talked a lot 310 00:16:10,080 --> 00:16:12,960 Speaker 2: about how those organizations were trying to grapple with this 311 00:16:13,080 --> 00:16:18,760 Speaker 2: shift in technology and so when this when the Internet, 312 00:16:19,120 --> 00:16:21,240 Speaker 2: so when the AI stuff sort of kicked off, this 313 00:16:21,400 --> 00:16:24,680 Speaker 2: to me, I sort of immediately recognized all the same 314 00:16:24,720 --> 00:16:28,359 Speaker 2: feelings and I think, you know, this probably applies to everybody, 315 00:16:28,400 --> 00:16:30,320 Speaker 2: but the older you get, you know, the more you 316 00:16:30,400 --> 00:16:34,640 Speaker 2: realize that your first inclinational gut feeling was absolutely correct, 317 00:16:35,000 --> 00:16:36,280 Speaker 2: you know. And that was why I was like, I 318 00:16:36,320 --> 00:16:39,880 Speaker 2: cannot put down thinking about this. This this is going 319 00:16:39,920 --> 00:16:42,360 Speaker 2: to be a bigger revolution than the Internet. It's going 320 00:16:42,400 --> 00:16:44,120 Speaker 2: to be bigger than computing. It's going to be the 321 00:16:44,120 --> 00:16:48,440 Speaker 2: biggest wave that mankind has ever seen. And if I 322 00:16:48,600 --> 00:16:50,520 Speaker 2: go back, I talked to the team. As you said, 323 00:16:50,520 --> 00:16:52,320 Speaker 2: I kind of went in and I didn't have a 324 00:16:52,320 --> 00:16:54,320 Speaker 2: clear vision for the year. I just knew that this 325 00:16:54,320 --> 00:16:56,840 Speaker 2: thing was colossal. I hadn't had enough time to play 326 00:16:56,840 --> 00:16:59,720 Speaker 2: with it. I hadn't formed strong enough opinions, And so 327 00:16:59,840 --> 00:17:02,880 Speaker 2: we spent the time talking about the nineties and what 328 00:17:02,920 --> 00:17:06,280 Speaker 2: did we see from the Bellgates Internet tied awave memo. 329 00:17:07,280 --> 00:17:11,240 Speaker 2: A few people realize this, but like Jeff Bezos was 330 00:17:11,280 --> 00:17:14,080 Speaker 2: working at a hedge fund before he started Amazon, and 331 00:17:14,119 --> 00:17:17,000 Speaker 2: the story goes that he's researching different industries and he 332 00:17:17,040 --> 00:17:19,720 Speaker 2: sees that the Internet is growing at twenty four hundred 333 00:17:19,760 --> 00:17:22,680 Speaker 2: percent per annum, and he just looks at this as 334 00:17:22,720 --> 00:17:26,080 Speaker 2: like nothing grows this fast. You know, something is going 335 00:17:26,119 --> 00:17:29,320 Speaker 2: on here, and we're seeing that, but potentially in order 336 00:17:29,320 --> 00:17:32,560 Speaker 2: of magnitude even bigger, you know, and adoption rates right now. 337 00:17:32,600 --> 00:17:34,479 Speaker 2: So I had this option where I was like, hey, 338 00:17:34,520 --> 00:17:37,200 Speaker 2: I've got this successful business. It's doing great stuff. That's 339 00:17:37,240 --> 00:17:41,119 Speaker 2: really cool. I see this giant thing happening. I definitely 340 00:17:41,119 --> 00:17:43,359 Speaker 2: need to apply some of that to what I'm doing today. 341 00:17:43,359 --> 00:17:47,720 Speaker 2: But also I didn't want to be completely handcuffed to 342 00:17:47,960 --> 00:17:51,119 Speaker 2: an old way of doing things. And so hence auto 343 00:17:51,200 --> 00:17:54,359 Speaker 2: Hive both as a separate brand with the intention later 344 00:17:54,400 --> 00:17:57,120 Speaker 2: of spinning it out as its own company. So it's 345 00:17:57,160 --> 00:18:00,639 Speaker 2: not pivoting Reagun Raguan just happens to be birthing this company. 346 00:18:02,119 --> 00:18:04,840 Speaker 2: And as you say, so we built. We sort of 347 00:18:04,880 --> 00:18:07,960 Speaker 2: had that time in early twenty twenty three. I had 348 00:18:08,000 --> 00:18:10,760 Speaker 2: the meeting with the whole team and was sort of saying, hey, 349 00:18:10,760 --> 00:18:13,280 Speaker 2: this is what's going on. We decided we then needed 350 00:18:13,280 --> 00:18:16,159 Speaker 2: to learn a lot more. So we had our AI 351 00:18:16,160 --> 00:18:18,720 Speaker 2: week in May twenty twenty three where we stopped all 352 00:18:18,800 --> 00:18:22,560 Speaker 2: work except for doing customer support. You know, I don't 353 00:18:22,560 --> 00:18:25,240 Speaker 2: want to leave a customer in the lurch, And in 354 00:18:25,280 --> 00:18:27,280 Speaker 2: the first four hours we got everybody to do our 355 00:18:27,359 --> 00:18:31,040 Speaker 2: engineering onboarding exam. So when we're hiring an engineer, you know, 356 00:18:31,119 --> 00:18:33,879 Speaker 2: we give them a test. And at the risk of 357 00:18:33,960 --> 00:18:37,880 Speaker 2: sounding a bit arrogant here, we have really really good 358 00:18:37,920 --> 00:18:42,400 Speaker 2: engineers at Reygun. Like I say, we scouted at eighty 359 00:18:42,400 --> 00:18:45,320 Speaker 2: seven million concurrent users for HBO. You know, I'm like, 360 00:18:46,480 --> 00:18:48,280 Speaker 2: look at trade meet, don't have to have everyone in 361 00:18:48,320 --> 00:18:50,560 Speaker 2: the country visit to just hit five point five million. 362 00:18:50,640 --> 00:18:52,720 Speaker 2: That was only one of our customers. So we know 363 00:18:53,040 --> 00:18:56,159 Speaker 2: how to scale, we know how to build international, and 364 00:18:56,240 --> 00:18:59,679 Speaker 2: so I wanted to so we built a range of 365 00:18:59,720 --> 00:19:02,160 Speaker 2: age to help us in our own business. These are 366 00:19:02,160 --> 00:19:06,639 Speaker 2: things like improving our AdWords, analyzing and bound trials, anything 367 00:19:06,680 --> 00:19:08,520 Speaker 2: we could kind of look at and think, what is 368 00:19:08,600 --> 00:19:11,120 Speaker 2: some toil that happens in our company that we could 369 00:19:11,160 --> 00:19:15,879 Speaker 2: take away, And so we built these agents. And the 370 00:19:16,280 --> 00:19:18,320 Speaker 2: key learning I sort of had was like, these are 371 00:19:18,359 --> 00:19:21,520 Speaker 2: amazing they have had they have improved our business, they 372 00:19:21,600 --> 00:19:25,040 Speaker 2: have allowed us to make better decisions. This is great, 373 00:19:25,720 --> 00:19:27,919 Speaker 2: but they're also really hard to build, and that you 374 00:19:27,960 --> 00:19:30,720 Speaker 2: needed this crack team of engineers to put this together. 375 00:19:31,040 --> 00:19:33,320 Speaker 2: And so that was exactly what you said earlier. Was 376 00:19:33,359 --> 00:19:35,639 Speaker 2: sort of the epiphany was like, hang on a minute, 377 00:19:35,640 --> 00:19:38,160 Speaker 2: if this technology is supposed to be running in every 378 00:19:38,160 --> 00:19:41,159 Speaker 2: single business out there and you need to crack squad 379 00:19:41,160 --> 00:19:43,800 Speaker 2: of software engineers to get any value out of it today, 380 00:19:44,080 --> 00:19:47,160 Speaker 2: that's not going to work. And so we set about saying, 381 00:19:47,280 --> 00:19:51,439 Speaker 2: how could we build a platform or product for the 382 00:19:51,560 --> 00:19:55,520 Speaker 2: everyman basically or every woman to say, you know, how 383 00:19:55,560 --> 00:19:58,760 Speaker 2: does the local accountant or the florist or the retailer 384 00:19:58,960 --> 00:20:02,560 Speaker 2: like take advantage technology. Now we're all kind of going 385 00:20:02,600 --> 00:20:05,119 Speaker 2: into chat GBT and asking questions and getting stuff, but 386 00:20:06,000 --> 00:20:09,280 Speaker 2: at the risk of over laboring the analogies. I think 387 00:20:09,359 --> 00:20:10,840 Speaker 2: of that back to the Internet is like when you 388 00:20:10,920 --> 00:20:13,560 Speaker 2: got email and you're like, you know what. Email was great. 389 00:20:13,600 --> 00:20:15,480 Speaker 2: You didn't have to go and spend twenty five cents 390 00:20:15,480 --> 00:20:17,480 Speaker 2: to send a message, and it didn't take three to 391 00:20:17,560 --> 00:20:20,280 Speaker 2: five days to get there, you know. So we suddenly 392 00:20:20,280 --> 00:20:22,760 Speaker 2: had free mail and that was kind of the hook 393 00:20:22,840 --> 00:20:25,439 Speaker 2: for the Internet, but it wasn't actually the major value 394 00:20:25,440 --> 00:20:28,440 Speaker 2: of the Internet. And I think with AI, what we've 395 00:20:28,440 --> 00:20:30,840 Speaker 2: seen is that the chat GPT is like the hook, 396 00:20:31,480 --> 00:20:34,360 Speaker 2: but the real value is coming in these agentic systems. 397 00:20:34,400 --> 00:20:37,440 Speaker 2: So appreciate the listeners who have probably gone this far 398 00:20:37,480 --> 00:20:39,800 Speaker 2: and maybe wondering what the hell and AI agent even 399 00:20:40,000 --> 00:20:42,840 Speaker 2: maybe we should explain it. Yeah, but yeah, let's talk 400 00:20:42,880 --> 00:20:46,000 Speaker 2: to that so and you know one of the things, 401 00:20:46,000 --> 00:20:47,960 Speaker 2: and like, if you're listening to this and it still 402 00:20:48,000 --> 00:20:50,080 Speaker 2: doesn't make sense, please send me an email at Janie 403 00:20:50,119 --> 00:20:53,560 Speaker 2: Trusk at autohive dot com and I'll try it again, 404 00:20:53,600 --> 00:20:56,720 Speaker 2: because I do value being able to take technical concepts 405 00:20:56,760 --> 00:20:59,919 Speaker 2: and try to make them approachable. But when you were 406 00:21:00,000 --> 00:21:02,760 Speaker 2: working with say chat GPT, and you ask it a question, 407 00:21:02,880 --> 00:21:04,760 Speaker 2: you might say, you know, what was the best test 408 00:21:04,760 --> 00:21:06,359 Speaker 2: score in the rugby last year, and it gives you 409 00:21:06,359 --> 00:21:09,200 Speaker 2: an answer. That's cool. You might ask it something that's 410 00:21:09,240 --> 00:21:11,560 Speaker 2: happened today and you'll see it go and do a 411 00:21:11,800 --> 00:21:15,400 Speaker 2: Google search for example. That is what's called tool use. 412 00:21:15,560 --> 00:21:19,960 Speaker 2: So imagine the AI has been given a hammer, but 413 00:21:20,000 --> 00:21:22,000 Speaker 2: the hammer is called Google search, and so it kind 414 00:21:22,000 --> 00:21:24,400 Speaker 2: of goes, well, I've got this in my tool belt, 415 00:21:24,440 --> 00:21:26,800 Speaker 2: should I need it, And you've asked me for something 416 00:21:26,800 --> 00:21:29,560 Speaker 2: that's you know, clearly happened only today, so I'm probably 417 00:21:29,560 --> 00:21:31,359 Speaker 2: going to need to go get some more information, so 418 00:21:31,640 --> 00:21:35,960 Speaker 2: it'll use that. Tool agents are really, in my opinion, 419 00:21:36,080 --> 00:21:39,800 Speaker 2: are just the tool using AIS. And so the question 420 00:21:39,880 --> 00:21:42,359 Speaker 2: then becomes how can I give it some extra tools 421 00:21:42,400 --> 00:21:45,160 Speaker 2: that would make sense to do more? So in our case, 422 00:21:45,240 --> 00:21:48,560 Speaker 2: for example, we use HubSpot for our help desk and marketing. Well, 423 00:21:48,640 --> 00:21:51,640 Speaker 2: let's give the LEM a tool that lets ask questions 424 00:21:51,680 --> 00:21:55,080 Speaker 2: of HubSpot and our help desk, and maybe it could 425 00:21:55,119 --> 00:21:57,800 Speaker 2: even send messages through it, you know, those sorts of things. 426 00:21:58,040 --> 00:22:00,560 Speaker 2: And so then we can have an agent that says, well, 427 00:22:00,600 --> 00:22:02,720 Speaker 2: I'm watching the help desk and I saw that our 428 00:22:02,760 --> 00:22:05,800 Speaker 2: new ticket came through and I read it, and you 429 00:22:05,840 --> 00:22:07,280 Speaker 2: know what, I actually have the tool to put the 430 00:22:07,359 --> 00:22:10,440 Speaker 2: draft reply on there, so that when you know, JD 431 00:22:10,600 --> 00:22:12,720 Speaker 2: or somebody on the team has turned up to work, 432 00:22:12,760 --> 00:22:14,920 Speaker 2: they can just review that it makes sense and clicked scent. 433 00:22:15,800 --> 00:22:19,280 Speaker 2: And so it's allowing these the AI to sort of 434 00:22:19,359 --> 00:22:21,800 Speaker 2: quote unquote break out of the box. How does it 435 00:22:21,800 --> 00:22:24,679 Speaker 2: start to interact with the world around us, even if 436 00:22:24,680 --> 00:22:28,160 Speaker 2: for now it's software based interaction. And so that's where 437 00:22:28,160 --> 00:22:31,160 Speaker 2: we have these examples that we you know, we had 438 00:22:31,160 --> 00:22:33,280 Speaker 2: a demonstration I think on the night playing in a 439 00:22:33,359 --> 00:22:36,199 Speaker 2: video where I made a simple agent didn't take more 440 00:22:36,280 --> 00:22:38,560 Speaker 2: than a handful of minutes, where I said, you're going 441 00:22:38,640 --> 00:22:42,399 Speaker 2: to help me find properties and I said, we're in 442 00:22:42,440 --> 00:22:45,480 Speaker 2: New Zealand, you know, consider local councils, all this sort 443 00:22:45,520 --> 00:22:48,240 Speaker 2: of stuff. You just put that text in the prompt right, 444 00:22:48,280 --> 00:22:51,520 Speaker 2: which is the main instruction. You write that in English. 445 00:22:51,680 --> 00:22:53,920 Speaker 2: And I gave it the ability to search the web 446 00:22:53,920 --> 00:22:56,439 Speaker 2: and read web pages. That was all I did. And 447 00:22:56,480 --> 00:22:58,680 Speaker 2: now when I say, hey, I'm looking for a property 448 00:22:58,800 --> 00:23:00,880 Speaker 2: I wanted to spend you know, so one point two 449 00:23:00,920 --> 00:23:03,480 Speaker 2: million dollars in Mount Verk it needs to be in 450 00:23:04,080 --> 00:23:06,919 Speaker 2: you know, the school range of this thing. And you 451 00:23:06,960 --> 00:23:09,280 Speaker 2: watch this agent go off and spend the next sort 452 00:23:09,280 --> 00:23:12,040 Speaker 2: of ten to fifteen minutes browsing real estate, dot code 453 00:23:12,040 --> 00:23:14,920 Speaker 2: and Z trade me cross. It builds up a list 454 00:23:14,960 --> 00:23:17,440 Speaker 2: of like properties that's going to suggest. Then it goes 455 00:23:17,480 --> 00:23:19,679 Speaker 2: and checks the council websites and it gives you the 456 00:23:19,760 --> 00:23:21,920 Speaker 2: questions you might want to ask the real estate agent. 457 00:23:22,920 --> 00:23:26,040 Speaker 2: And this is just one simple example you know that 458 00:23:26,119 --> 00:23:28,159 Speaker 2: you can put together and then you start looking at 459 00:23:28,200 --> 00:23:30,600 Speaker 2: all these for business. So like I say the question, 460 00:23:30,840 --> 00:23:33,840 Speaker 2: I always always ask people, and I encourage the listeners 461 00:23:34,320 --> 00:23:37,479 Speaker 2: to think about this is just what is a task 462 00:23:37,760 --> 00:23:40,879 Speaker 2: that takes you one hour or more every week or month? 463 00:23:41,640 --> 00:23:47,000 Speaker 2: You just fre again hate everybody has a few and 464 00:23:47,119 --> 00:23:50,919 Speaker 2: voicing exactly. You have these things where you kind of go, gosh, 465 00:23:51,200 --> 00:23:53,320 Speaker 2: if somebody took that away, you know, I'd go and 466 00:23:53,320 --> 00:23:55,960 Speaker 2: focus in these areas. And that's that's kind of a 467 00:23:56,000 --> 00:23:57,800 Speaker 2: little bit what I think about when it comes back 468 00:23:57,800 --> 00:24:00,280 Speaker 2: to that disruption thing. It's like you're really ina blling 469 00:24:00,280 --> 00:24:02,520 Speaker 2: people to focus in the areas they actually want to 470 00:24:02,520 --> 00:24:05,440 Speaker 2: put the effort into, you know, and play to those 471 00:24:05,480 --> 00:24:08,639 Speaker 2: strengths and take away the toil work that's not actually 472 00:24:08,760 --> 00:24:09,880 Speaker 2: much fun for anybody. 473 00:24:10,040 --> 00:24:15,359 Speaker 1: It's those time consuming tasks like invoicing or customer care 474 00:24:16,440 --> 00:24:19,960 Speaker 1: and automating that. But you've built a platform sitting behind 475 00:24:19,960 --> 00:24:22,600 Speaker 1: at is the large language models. We're using it in 476 00:24:22,640 --> 00:24:28,080 Speaker 1: a consumer sense, the open ayes models, presumably Claude and others. 477 00:24:28,080 --> 00:24:31,520 Speaker 1: So you're using the best large language model to apply 478 00:24:31,640 --> 00:24:35,040 Speaker 1: to a particular task, and you've built a beautiful interface 479 00:24:35,119 --> 00:24:37,400 Speaker 1: that makes it really simple to build your own agents. 480 00:24:38,119 --> 00:24:42,440 Speaker 2: Yeah, one hundred percent. So first off, it's I'm sure 481 00:24:42,440 --> 00:24:44,880 Speaker 2: the folks that are paying a bit of attention today, 482 00:24:44,960 --> 00:24:46,960 Speaker 2: I see this. You know this week Claude will be 483 00:24:46,960 --> 00:24:50,240 Speaker 2: the best. Next week CHATBT the week after that, you know, 484 00:24:50,400 --> 00:24:53,400 Speaker 2: Elon might finally ship something you know, all of these 485 00:24:53,440 --> 00:24:55,960 Speaker 2: things keep playing out, and so we want to sort 486 00:24:56,000 --> 00:24:59,600 Speaker 2: of say, hey, it's kind of it's somewhat irrelevant. You 487 00:24:59,640 --> 00:25:01,840 Speaker 2: should be to use whatever the best thing is the 488 00:25:01,920 --> 00:25:05,800 Speaker 2: time through our platform. You're right, The entire UI and 489 00:25:05,840 --> 00:25:08,040 Speaker 2: everything we're working on is to try and make something 490 00:25:08,080 --> 00:25:10,800 Speaker 2: that is not for nerds. We want this to be 491 00:25:11,119 --> 00:25:14,280 Speaker 2: for people who say I want to jump in and 492 00:25:14,359 --> 00:25:16,159 Speaker 2: as you say, maybe I want to automate a bit 493 00:25:16,200 --> 00:25:18,280 Speaker 2: of invoicing. I want to be able to like toggle 494 00:25:18,320 --> 00:25:21,720 Speaker 2: on the zero integration, you know, and say, hey, give 495 00:25:21,760 --> 00:25:24,080 Speaker 2: me a more readable version of my pen now, or 496 00:25:24,240 --> 00:25:26,879 Speaker 2: just describe what the biggest risks are or things like that. 497 00:25:26,960 --> 00:25:29,359 Speaker 2: You want to make people so they're empowered to do stuff. 498 00:25:30,000 --> 00:25:32,040 Speaker 2: One of the things we're trying really hard to do 499 00:25:32,080 --> 00:25:36,000 Speaker 2: as well is avoid all of the acronyms. Like you know, 500 00:25:36,040 --> 00:25:37,880 Speaker 2: I've been in the tech industry for a long enough 501 00:25:37,920 --> 00:25:41,800 Speaker 2: time that that's usually the first place we lose people. API. Yeah, 502 00:25:42,119 --> 00:25:44,960 Speaker 2: my API talking to the RAG system so that you know, 503 00:25:45,080 --> 00:25:47,280 Speaker 2: my AI can You're like, what the hell are you 504 00:25:47,359 --> 00:25:51,159 Speaker 2: talking about? You know, And so often the tech industry, 505 00:25:51,200 --> 00:25:54,040 Speaker 2: I think rightly gets a bit of an eye roll 506 00:25:54,080 --> 00:25:56,320 Speaker 2: because it almost feels like we set out to make 507 00:25:56,359 --> 00:25:58,760 Speaker 2: things more complicated than they should be. So yeah, we're 508 00:25:58,800 --> 00:26:01,200 Speaker 2: trying to make something where it's hey, leave the really 509 00:26:01,320 --> 00:26:03,520 Speaker 2: nerdy stuff on the back end to us. We'll figure 510 00:26:03,560 --> 00:26:05,560 Speaker 2: that out. I probably wouldn't talk about this on an 511 00:26:05,560 --> 00:26:09,200 Speaker 2: international podcast like mono Hive will be an international product, 512 00:26:09,320 --> 00:26:11,240 Speaker 2: but I want to have that impact on our local 513 00:26:11,280 --> 00:26:14,400 Speaker 2: businesses in New Zealand. We know we have a productivity issue. 514 00:26:14,680 --> 00:26:17,480 Speaker 2: We know this technology is coming, there is now something 515 00:26:17,520 --> 00:26:20,560 Speaker 2: home growing for it, and so we get a huge 516 00:26:20,600 --> 00:26:22,919 Speaker 2: amount of value from people just feeding back one are 517 00:26:22,960 --> 00:26:25,200 Speaker 2: the bits that suck. You know, it might sound really 518 00:26:25,200 --> 00:26:28,000 Speaker 2: weird to say, but it's like when you launch version one, 519 00:26:28,040 --> 00:26:29,840 Speaker 2: it's the worst state it's ever going to be in 520 00:26:29,920 --> 00:26:32,480 Speaker 2: for the consumer, right, so we want to learn as 521 00:26:32,480 --> 00:26:34,560 Speaker 2: fast as we can. You mentioned HubSpot. 522 00:26:34,640 --> 00:26:39,600 Speaker 1: You've got relationships with a number of box Gmail for instance, 523 00:26:39,680 --> 00:26:43,119 Speaker 1: So this is very much you know, it relies on 524 00:26:43,200 --> 00:26:46,840 Speaker 1: you getting that often sensitive but high quality company data 525 00:26:46,880 --> 00:26:50,360 Speaker 1: that's sitting in various data repositories. We've heard so much 526 00:26:50,400 --> 00:26:53,160 Speaker 1: from the big tech companies that have gone all in 527 00:26:53,240 --> 00:26:57,439 Speaker 1: on agents salesforce. In particular, Mike Benioff is betting the 528 00:26:57,480 --> 00:27:01,239 Speaker 1: company basically on agents, Microsoft, SA, all of them, and 529 00:27:01,280 --> 00:27:04,440 Speaker 1: their whole thing is bring your data to us, keep 530 00:27:04,480 --> 00:27:07,400 Speaker 1: it really clean on our platform, and it will allow 531 00:27:07,480 --> 00:27:09,880 Speaker 1: you to do all of this sort of stuff. How 532 00:27:09,880 --> 00:27:12,679 Speaker 1: do you approach that because you haven't got hold of 533 00:27:12,680 --> 00:27:15,000 Speaker 1: everyone's data in one place. Are we back to the 534 00:27:15,080 --> 00:27:18,959 Speaker 1: so called APIs to get that data into autohive? 535 00:27:19,520 --> 00:27:24,440 Speaker 2: Yeah, so let's break that question down. So firstly, all 536 00:27:24,480 --> 00:27:26,200 Speaker 2: the big guys want to be the hub in the wheel, 537 00:27:26,280 --> 00:27:28,320 Speaker 2: so they want all the data to bang on, you know. 538 00:27:28,400 --> 00:27:30,760 Speaker 2: And I always think if Mark Benioff really believed in 539 00:27:30,800 --> 00:27:32,639 Speaker 2: what he was doing, he wouldn't be selling three million 540 00:27:32,640 --> 00:27:36,120 Speaker 2: dollars with a stock every day, which he does. So 541 00:27:36,840 --> 00:27:40,720 Speaker 2: you know, we've been a Salesforce customer, and to be honest, 542 00:27:40,760 --> 00:27:42,560 Speaker 2: I think we're going to see some bad behavior off 543 00:27:42,600 --> 00:27:45,720 Speaker 2: the back of AI. For example, Salesforce have recently said 544 00:27:46,000 --> 00:27:48,639 Speaker 2: to some companies that they will prevent them from integrating 545 00:27:48,640 --> 00:27:51,440 Speaker 2: into Slack because they don't want AI companies to be 546 00:27:51,480 --> 00:27:54,920 Speaker 2: able to get that data. I think I own the 547 00:27:55,000 --> 00:27:57,920 Speaker 2: damn data, but what my team are talking to talking 548 00:27:57,960 --> 00:28:00,000 Speaker 2: about in Slack, and so I think we're going to 549 00:28:00,119 --> 00:28:02,679 Speaker 2: actually start to see some of these particularly big companies 550 00:28:02,760 --> 00:28:06,879 Speaker 2: start becoming quite bad actors about who actually owns company data. 551 00:28:06,920 --> 00:28:09,159 Speaker 2: And I don't think Salesforce is actually doing a very 552 00:28:09,200 --> 00:28:11,880 Speaker 2: good job of demonstrating good behavior. Now that's a very 553 00:28:11,880 --> 00:28:17,520 Speaker 2: Salesforce specific answer for a moment, but ultimately I think 554 00:28:17,600 --> 00:28:20,760 Speaker 2: that as much as anybody would love to sort of 555 00:28:20,840 --> 00:28:23,639 Speaker 2: own a business where every party puts one hundred percent 556 00:28:23,680 --> 00:28:26,080 Speaker 2: of their work on your system, it is it is 557 00:28:26,200 --> 00:28:29,320 Speaker 2: naive and foolish. I think the reality is we actually 558 00:28:29,359 --> 00:28:32,439 Speaker 2: work in a mixed world of various systems. You know, 559 00:28:32,600 --> 00:28:35,280 Speaker 2: I'm not going to suddenly move my accounting of zero 560 00:28:35,359 --> 00:28:38,080 Speaker 2: to something that Salesforce makes just to improve an AI. 561 00:28:38,280 --> 00:28:39,840 Speaker 2: I want the AI to be able to talk to 562 00:28:39,920 --> 00:28:42,840 Speaker 2: zero and do a great job. And then what we're 563 00:28:42,880 --> 00:28:45,640 Speaker 2: seeing out of a lot of the foundation or the 564 00:28:45,680 --> 00:28:48,360 Speaker 2: big call them the big guys if you want. And 565 00:28:48,400 --> 00:28:51,400 Speaker 2: this is a big differentiation from autohyper auto Hive should 566 00:28:51,400 --> 00:28:53,720 Speaker 2: be the glue between all of the systems that you've got. 567 00:28:53,760 --> 00:28:55,680 Speaker 2: It should be the interface that you go and use 568 00:28:55,760 --> 00:28:58,440 Speaker 2: to work with. Primarily, it should be nice and joyful 569 00:28:58,440 --> 00:29:02,200 Speaker 2: to use. Matter that you're pulling data out of Google Drive, 570 00:29:02,360 --> 00:29:04,680 Speaker 2: out of zero out of Gerro, out of HubSpot, and 571 00:29:04,720 --> 00:29:07,160 Speaker 2: we bring all of that together, we take care of 572 00:29:07,240 --> 00:29:10,200 Speaker 2: the messiness of how to do that behind the scenes 573 00:29:11,040 --> 00:29:13,880 Speaker 2: for people. And I think when you look at some 574 00:29:13,960 --> 00:29:19,360 Speaker 2: of these eye watering valuations on these AI companies, look, 575 00:29:19,920 --> 00:29:22,920 Speaker 2: maybe I'm just under and I already think I'm estimating 576 00:29:22,920 --> 00:29:25,160 Speaker 2: a pretty big size and scale of AI, but maybe 577 00:29:25,160 --> 00:29:28,880 Speaker 2: I'm underestimating. But some of those valuations seem to be 578 00:29:28,920 --> 00:29:33,160 Speaker 2: predicated on the idea that those AI companies completely own AI. 579 00:29:33,880 --> 00:29:36,880 Speaker 2: And I'd be remiss if I didn't sort of highlight this. 580 00:29:37,040 --> 00:29:40,400 Speaker 2: You know, we have some folks even in our business, 581 00:29:41,320 --> 00:29:43,880 Speaker 2: they definitely believe as an AI, but some of the 582 00:29:43,920 --> 00:29:47,360 Speaker 2: first call bs on the hype and I'm absolutely one 583 00:29:47,360 --> 00:29:49,840 Speaker 2: of those people too. We've talked a bit about the 584 00:29:49,960 --> 00:29:52,600 Speaker 2: potential around job losses and things like that, but the 585 00:29:52,640 --> 00:29:58,080 Speaker 2: reality is today it's about automating a handful of hours 586 00:29:58,120 --> 00:30:01,680 Speaker 2: every week in toil right. Technology very well may improve 587 00:30:01,760 --> 00:30:04,200 Speaker 2: over the coming years to make that more impactful and 588 00:30:04,240 --> 00:30:06,920 Speaker 2: more cavble. I have no doubt of that, But today 589 00:30:07,240 --> 00:30:10,320 Speaker 2: it's about save yourself a little bit of pain overall 590 00:30:10,360 --> 00:30:13,240 Speaker 2: in there. I don't think people want to be starting 591 00:30:13,240 --> 00:30:15,440 Speaker 2: that journey with I have to pile it all on 592 00:30:15,480 --> 00:30:16,280 Speaker 2: to one megatech. 593 00:30:17,280 --> 00:30:20,040 Speaker 1: So it's been out there, you know, only launched it 594 00:30:20,160 --> 00:30:23,200 Speaker 1: maybe a couple of weeks ago. But what's been the feedback, 595 00:30:23,360 --> 00:30:25,120 Speaker 1: like in some of the use cases that are starting 596 00:30:25,120 --> 00:30:26,520 Speaker 1: to bubble up with auto high. 597 00:30:26,960 --> 00:30:31,480 Speaker 2: Yeah, so we actually launched it silently last Tuesday, and 598 00:30:31,480 --> 00:30:34,680 Speaker 2: then Wednesday was there, let's start making some noise on it. 599 00:30:34,760 --> 00:30:37,320 Speaker 2: So it's not actually been out a week yet, so 600 00:30:37,360 --> 00:30:41,880 Speaker 2: we're pushing four hundred odd folks who have now come 601 00:30:41,920 --> 00:30:44,680 Speaker 2: in and tried it out, which is really cool. We 602 00:30:44,760 --> 00:30:47,680 Speaker 2: can see folks have made a range of agents. I 603 00:30:47,720 --> 00:30:50,360 Speaker 2: will say we do full This sort of touches on 604 00:30:50,400 --> 00:30:54,760 Speaker 2: an earlier question, but there's full encryption across all of 605 00:30:54,760 --> 00:30:57,520 Speaker 2: the conversations. We can't actually read, you know, I can't 606 00:30:57,560 --> 00:30:59,400 Speaker 2: sort of sit there and go, oh, Peter, I asked 607 00:30:59,400 --> 00:31:02,800 Speaker 2: this question. That's all lockdown, and we're very mindful about 608 00:31:02,840 --> 00:31:05,760 Speaker 2: the trust and security side of things. But we can 609 00:31:05,840 --> 00:31:08,360 Speaker 2: see people making agents that they are sort of setting 610 00:31:08,480 --> 00:31:10,840 Speaker 2: up these different tools to run things. And I have 611 00:31:10,920 --> 00:31:13,360 Speaker 2: had a few people citing the examples like the property 612 00:31:13,360 --> 00:31:15,320 Speaker 2: one that I made, which is how I know exactly 613 00:31:15,400 --> 00:31:18,800 Speaker 2: what that was doing in there. The main piece of 614 00:31:19,560 --> 00:31:23,800 Speaker 2: addressable feedback that we'll be tackling over the next like 615 00:31:23,840 --> 00:31:26,000 Speaker 2: I said a couple of months, is that marketplace piece. 616 00:31:26,160 --> 00:31:29,800 Speaker 2: So it's great to come in and you can use 617 00:31:30,160 --> 00:31:33,080 Speaker 2: all of these l elems. You can use Chantypete, Claude, 618 00:31:33,160 --> 00:31:35,760 Speaker 2: Gemini all in here, and we actually have a you know, 619 00:31:35,880 --> 00:31:37,920 Speaker 2: it's free to start. You know, we have ten dollars 620 00:31:37,960 --> 00:31:40,560 Speaker 2: worth of credits on there, so you can just use 621 00:31:40,600 --> 00:31:44,280 Speaker 2: it for free to begin with. Then we have a 622 00:31:44,360 --> 00:31:46,320 Speaker 2: paid tire where we go to eighty dollars a month, 623 00:31:46,360 --> 00:31:48,680 Speaker 2: but this is not per user. So if you've got 624 00:31:48,680 --> 00:31:51,320 Speaker 2: a company of one hundred people, pay eighty bucks and 625 00:31:51,360 --> 00:31:54,280 Speaker 2: put them in there. Now, there is some usage pricing 626 00:31:54,320 --> 00:31:56,080 Speaker 2: that can go on top of that later on. So 627 00:31:56,160 --> 00:31:58,080 Speaker 2: if you do have a few people that are really 628 00:31:58,120 --> 00:32:01,440 Speaker 2: hardcore and they're using a lot, just be the price 629 00:32:01,440 --> 00:32:04,680 Speaker 2: of the tokens plus a small margin on there. Now, 630 00:32:04,720 --> 00:32:07,680 Speaker 2: why this is useful is because, and I can say 631 00:32:07,720 --> 00:32:10,040 Speaker 2: this even across the tech industry for those that are 632 00:32:10,080 --> 00:32:14,280 Speaker 2: listening that maybe aren't in tech companies, the adoption of 633 00:32:14,320 --> 00:32:19,800 Speaker 2: AI is very sort of in disparate groups right now. 634 00:32:19,880 --> 00:32:22,200 Speaker 2: So even in software companies, you're sort of seeing twenty 635 00:32:22,920 --> 00:32:25,600 Speaker 2: maybe twenty percent of people are really leaning in and 636 00:32:25,640 --> 00:32:29,400 Speaker 2: discovering what's possible. There's about fifty percent of people are 637 00:32:29,480 --> 00:32:32,320 Speaker 2: sort of, you know, maybe asking a handful of questions 638 00:32:32,360 --> 00:32:34,640 Speaker 2: each week, you know, and then twenty five percent of 639 00:32:34,680 --> 00:32:38,000 Speaker 2: people who are just not at all engaged. The problem 640 00:32:38,040 --> 00:32:40,960 Speaker 2: with that model is, obviously, if you're the CEO, you 641 00:32:41,000 --> 00:32:42,880 Speaker 2: want to get your team up to speed. You want 642 00:32:42,880 --> 00:32:45,200 Speaker 2: to roll out all the tools. If I was to 643 00:32:45,240 --> 00:32:48,320 Speaker 2: go and get Gemini, Claude and chatchby t for a 644 00:32:48,360 --> 00:32:50,600 Speaker 2: single team member, that would add up to sixty US 645 00:32:50,680 --> 00:32:53,760 Speaker 2: dollars for that one team member right now, if that 646 00:32:53,880 --> 00:32:56,120 Speaker 2: team member is in the group that isn't really using 647 00:32:56,120 --> 00:32:58,280 Speaker 2: it a lot, I'm going to start getting frustrated that 648 00:32:58,320 --> 00:33:00,840 Speaker 2: I'm paying for something that's not being you. And so 649 00:33:00,920 --> 00:33:03,640 Speaker 2: that's why we've tried to make the model one where 650 00:33:03,720 --> 00:33:06,120 Speaker 2: it's like, yes, there's the starting price, but that gives 651 00:33:06,120 --> 00:33:08,160 Speaker 2: you enough credits to sort of not hit the wall 652 00:33:08,200 --> 00:33:11,479 Speaker 2: too quickly. But then you're only going to scale it 653 00:33:11,840 --> 00:33:14,520 Speaker 2: based on if you're getting the utilization. And that's where 654 00:33:14,560 --> 00:33:16,560 Speaker 2: I think ultimately we want to be aligned with the 655 00:33:16,640 --> 00:33:19,480 Speaker 2: customers is to say it's in our interests and your 656 00:33:19,480 --> 00:33:22,920 Speaker 2: interests that we're delivering creating agents that are delivering real 657 00:33:22,960 --> 00:33:26,680 Speaker 2: business value. Because if I can get ten thousand dollars 658 00:33:26,760 --> 00:33:29,920 Speaker 2: worth of ROI out of two hundred dollars worth of spend, 659 00:33:29,960 --> 00:33:31,760 Speaker 2: I will do that deal every day of the week. 660 00:33:32,160 --> 00:33:36,000 Speaker 1: Yeah, and for a small so you've got a ten 661 00:33:36,040 --> 00:33:40,760 Speaker 1: person business or you know, twenty or thirty person business, 662 00:33:41,120 --> 00:33:44,000 Speaker 1: eighty bucks a months a month. If that's spread across 663 00:33:44,600 --> 00:33:47,880 Speaker 1: various agents, because you can set up multiple agents and 664 00:33:47,920 --> 00:33:49,840 Speaker 1: I see you know, they even have their own avatars. 665 00:33:49,920 --> 00:33:53,480 Speaker 1: So increasingly we're going to be working along these different agents. 666 00:33:53,480 --> 00:33:55,360 Speaker 1: You'll have the agent for invoicing, you'll have the one 667 00:33:55,360 --> 00:33:58,920 Speaker 1: for customer service, and in auto hive you'll see the 668 00:33:58,960 --> 00:34:02,640 Speaker 1: progress where what they're doing, what they're reporting, where they're at. 669 00:34:02,640 --> 00:34:05,880 Speaker 2: Yes, one hundred percent. So for those of you that 670 00:34:05,960 --> 00:34:08,560 Speaker 2: are listening, imagine a user interface that looks a little 671 00:34:08,560 --> 00:34:11,000 Speaker 2: bit like Slack or teams, but we say half of 672 00:34:11,040 --> 00:34:14,560 Speaker 2: the users are actually smart agents. So you might have 673 00:34:14,760 --> 00:34:18,360 Speaker 2: like a governance one that understands your shareholder agreement. You 674 00:34:18,440 --> 00:34:20,880 Speaker 2: might have I've made one that advises me on my 675 00:34:20,960 --> 00:34:23,960 Speaker 2: personal stock portfolio. You know, it can read that data 676 00:34:24,000 --> 00:34:25,840 Speaker 2: and it just sort of says, hey, you know, you 677 00:34:25,920 --> 00:34:28,640 Speaker 2: might want to rebalance this way, or here's the earnings 678 00:34:28,680 --> 00:34:31,359 Speaker 2: reports coming up, those sorts of things, and so what's 679 00:34:31,440 --> 00:34:34,320 Speaker 2: kind of fun about it is, don't think about bringing 680 00:34:34,320 --> 00:34:38,200 Speaker 2: autohib into your company, like, okay, cool, we need to 681 00:34:38,239 --> 00:34:41,080 Speaker 2: mentally spend you know, one hundred thousand dollars this big 682 00:34:41,120 --> 00:34:44,279 Speaker 2: bang deployment. We need all these agencies. He no, think 683 00:34:44,280 --> 00:34:46,000 Speaker 2: of it as like you could go and sign up 684 00:34:46,000 --> 00:34:48,600 Speaker 2: as the CE even you know, you should be able 685 00:34:48,600 --> 00:34:51,360 Speaker 2: to make a couple of agents to do something simple 686 00:34:51,760 --> 00:34:53,720 Speaker 2: and then you can invite your team in for free. 687 00:34:53,760 --> 00:34:57,640 Speaker 2: They don't cost anything more. It feels a bit more 688 00:34:57,680 --> 00:35:01,520 Speaker 2: like an infinite box of Lego, you know, where it's like, cool, 689 00:35:01,520 --> 00:35:04,160 Speaker 2: build something, just start to explore, you know, and then 690 00:35:04,239 --> 00:35:06,879 Speaker 2: you'll start realizing, oh, I could build even fancier things 691 00:35:06,880 --> 00:35:09,640 Speaker 2: and fancier things. And we found this even in building it. Right, 692 00:35:09,719 --> 00:35:13,719 Speaker 2: So we would have a discussion internally where somebody said, oh, 693 00:35:13,760 --> 00:35:15,680 Speaker 2: how could we build an agent that does X, Y Z, 694 00:35:16,000 --> 00:35:18,760 Speaker 2: And I was like, well, I'd probably do it. ABC 695 00:35:19,680 --> 00:35:22,000 Speaker 2: one of the younger team members was like, actually, we 696 00:35:22,040 --> 00:35:24,120 Speaker 2: could just have the agent talk to another agent and 697 00:35:24,160 --> 00:35:27,560 Speaker 2: do this, you know, And that's the power of the 698 00:35:27,600 --> 00:35:29,360 Speaker 2: creative thinking. Like a lot of us that are a 699 00:35:29,400 --> 00:35:31,719 Speaker 2: little bit older kind of have already got an idea 700 00:35:31,760 --> 00:35:33,840 Speaker 2: of how to work, and these things are quite a shift. 701 00:35:34,440 --> 00:35:37,480 Speaker 2: And so that's why even with an auto I've mentioned 702 00:35:37,840 --> 00:35:40,480 Speaker 2: the easy to use you I trying to make it finally, 703 00:35:40,560 --> 00:35:41,960 Speaker 2: sacronyms and all of that. 704 00:35:42,080 --> 00:35:44,000 Speaker 1: I guess the big promise from the Benioffs and that 705 00:35:44,280 --> 00:35:49,799 Speaker 1: is we've moved from this information generating ability with generative AI, 706 00:35:49,880 --> 00:35:53,360 Speaker 1: which is incredibly useful, through to doing stuff on your behalf. 707 00:35:53,719 --> 00:35:57,520 Speaker 1: And I remember twenty eighteen sitting at Google iOS conference 708 00:35:57,560 --> 00:36:01,480 Speaker 1: where they debut Google Duplex, which gone nowhere. But they 709 00:36:01,560 --> 00:36:04,239 Speaker 1: made a call or this AI agent made a call 710 00:36:04,360 --> 00:36:07,480 Speaker 1: to a salon and had a conversation. This was an 711 00:36:07,480 --> 00:36:11,400 Speaker 1: AI bot having a conversation with the salon owner to 712 00:36:11,480 --> 00:36:15,319 Speaker 1: book an appointment. So the point where we get to 713 00:36:15,560 --> 00:36:19,600 Speaker 1: aim making decisions on our behalf and adjusting if the 714 00:36:19,640 --> 00:36:21,640 Speaker 1: salon owner goes, we're booked out this week, you know 715 00:36:21,640 --> 00:36:24,320 Speaker 1: what about next week? Going and consulting your calendar to 716 00:36:24,360 --> 00:36:26,839 Speaker 1: see if you have a slot next week. How far 717 00:36:26,840 --> 00:36:29,720 Speaker 1: away are we from that sort of stuff, we're pretty 718 00:36:29,760 --> 00:36:30,520 Speaker 1: much there there. 719 00:36:30,719 --> 00:36:34,120 Speaker 2: Yeah. I mean the calendar example, just on the auto 720 00:36:34,160 --> 00:36:37,319 Speaker 2: hive side, I played with this. I just attached my 721 00:36:37,400 --> 00:36:40,000 Speaker 2: Google calendar and I started asking it questions like what, 722 00:36:40,440 --> 00:36:42,360 Speaker 2: you know, how would I optimize my week and things 723 00:36:42,400 --> 00:36:44,719 Speaker 2: like that. So the pieces are there, They're all on 724 00:36:44,760 --> 00:36:50,120 Speaker 2: the table. I often. That's why there is just fundamentally like, 725 00:36:50,480 --> 00:36:53,640 Speaker 2: the amount of opportunity in AI right now is just insane. 726 00:36:53,719 --> 00:36:56,680 Speaker 2: Any direction you look into is just a blue ocean 727 00:36:56,680 --> 00:37:01,040 Speaker 2: of opportunity right now. That I used to think when 728 00:37:01,080 --> 00:37:05,160 Speaker 2: I was building reagun, Gosh, wouldn't it be nice to 729 00:37:05,200 --> 00:37:07,480 Speaker 2: be in the blue ocean rather than like competitive field? 730 00:37:07,920 --> 00:37:09,799 Speaker 2: And now I kind of take the viewing like, boy, 731 00:37:10,160 --> 00:37:14,000 Speaker 2: it's quite disabling to have infinite opportunities in all directions 732 00:37:14,000 --> 00:37:16,440 Speaker 2: and how you kind of keep that focus, Which is 733 00:37:16,480 --> 00:37:20,440 Speaker 2: why we keep coming back to that SMBs, which by 734 00:37:20,480 --> 00:37:24,000 Speaker 2: New Zealand standards pretty much everybody you know, and ease 735 00:37:24,040 --> 00:37:26,279 Speaker 2: of use, you know, that's what we're going for. But 736 00:37:26,400 --> 00:37:29,080 Speaker 2: the ability to glue these things together to create these 737 00:37:29,120 --> 00:37:34,759 Speaker 2: complete experiences is there absolutely today I have to use 738 00:37:34,800 --> 00:37:37,880 Speaker 2: the iPod. It's a good example of that, which is 739 00:37:40,719 --> 00:37:43,480 Speaker 2: The original story of that was that one of the 740 00:37:44,160 --> 00:37:47,560 Speaker 2: hard drive makers I forget which one, had actually told 741 00:37:48,719 --> 00:37:51,359 Speaker 2: Apple that they were building this very very tiny hard 742 00:37:51,440 --> 00:37:55,080 Speaker 2: drive and they had no idea what to use it for. 743 00:37:55,600 --> 00:37:58,080 Speaker 2: And it was Steve Jobs and Johnny I've that kind 744 00:37:58,080 --> 00:37:59,640 Speaker 2: of sat down and we're like, well, hang on, if 745 00:37:59,640 --> 00:38:01,800 Speaker 2: we can put this much memory into its device of 746 00:38:01,880 --> 00:38:03,520 Speaker 2: this size, you know, we could do this, this and 747 00:38:03,800 --> 00:38:06,520 Speaker 2: two hundred songs. Yeah. But this is the thing I 748 00:38:06,560 --> 00:38:09,440 Speaker 2: always think is most interesting about a lot of innovation 749 00:38:09,800 --> 00:38:12,319 Speaker 2: is how frequently you kind of look back and you 750 00:38:12,360 --> 00:38:14,759 Speaker 2: realize all the pieces were on the table. It just 751 00:38:14,840 --> 00:38:17,000 Speaker 2: took a person working out the right way to put 752 00:38:17,000 --> 00:38:20,680 Speaker 2: them together that they make these phenomenal products. And I 753 00:38:20,719 --> 00:38:23,200 Speaker 2: feel right now like there is a tremendous amount of 754 00:38:23,239 --> 00:38:26,080 Speaker 2: pieces on the table, and we're going to spend years 755 00:38:26,120 --> 00:38:29,720 Speaker 2: building the ability to leverage it. Yeah. 756 00:38:29,760 --> 00:38:32,520 Speaker 1: So what's the plan for the you know, the remainder 757 00:38:32,520 --> 00:38:36,960 Speaker 1: of twenty twenty five. You're really building this with the 758 00:38:37,000 --> 00:38:40,880 Speaker 1: local focus first, but clearly trying to prove some use cases, 759 00:38:41,520 --> 00:38:44,120 Speaker 1: get some feedback from customers, very with a view to 760 00:38:44,200 --> 00:38:45,680 Speaker 1: very quickly taking it international. 761 00:38:46,200 --> 00:38:49,120 Speaker 2: Yeah. Absolutely, So there's nothing that prevents international people signing 762 00:38:49,200 --> 00:38:52,360 Speaker 2: up today. And because we do have the background in 763 00:38:52,440 --> 00:38:55,200 Speaker 2: Reagun like Reagun does actually have people outside in New 764 00:38:55,280 --> 00:38:58,640 Speaker 2: Zealand too, so we've already got you know, like you know, 765 00:38:58,680 --> 00:39:00,960 Speaker 2: we're already set up for account in the US and 766 00:39:01,000 --> 00:39:03,600 Speaker 2: we've already got the tax stuff and security and all 767 00:39:03,640 --> 00:39:06,160 Speaker 2: that sort of stuff is already in place, which is 768 00:39:06,200 --> 00:39:10,040 Speaker 2: good between now and the end of the year. The 769 00:39:10,120 --> 00:39:13,239 Speaker 2: main one is that marketplace capability. And I don't want 770 00:39:13,239 --> 00:39:15,120 Speaker 2: people to think of the marketplace is purely like I 771 00:39:15,160 --> 00:39:17,040 Speaker 2: have to go spend money. As you say, there could 772 00:39:17,080 --> 00:39:19,000 Speaker 2: be a lot of free agents on there, but it's 773 00:39:19,040 --> 00:39:21,320 Speaker 2: that ability for people to almost pick from a menu 774 00:39:21,440 --> 00:39:23,319 Speaker 2: rather than have to make something. You know, I really 775 00:39:23,360 --> 00:39:27,120 Speaker 2: want to dial that up to eleven. So those use cases. 776 00:39:27,280 --> 00:39:29,440 Speaker 2: We do have more than a thousand use cases listed 777 00:39:29,480 --> 00:39:31,680 Speaker 2: on the site, but just generally, how do we lower 778 00:39:31,719 --> 00:39:34,839 Speaker 2: that barrier to entry. So between now and Christmas you'll 779 00:39:34,880 --> 00:39:37,200 Speaker 2: get to a point where you might search for say 780 00:39:37,239 --> 00:39:40,680 Speaker 2: you might kind of go agent to maybe do zero invoicing. 781 00:39:41,160 --> 00:39:43,399 Speaker 2: You know, you want to have that come up and say, 782 00:39:43,480 --> 00:39:45,880 Speaker 2: well here's the page on the autohib site with you know, 783 00:39:45,920 --> 00:39:48,280 Speaker 2: there might be three of these agents. They should have reviews. 784 00:39:48,360 --> 00:39:50,520 Speaker 2: Click on that. Oh yeah, I like to look at 785 00:39:50,520 --> 00:39:52,920 Speaker 2: that one, but click get sign them with Google and 786 00:39:52,960 --> 00:39:55,239 Speaker 2: be dropped straight into talking to that agent you know, 787 00:39:55,400 --> 00:39:59,120 Speaker 2: able to assist you get people into the ecosystem. There. 788 00:40:00,840 --> 00:40:04,440 Speaker 2: One observation is just that we well have to put 789 00:40:04,440 --> 00:40:07,480 Speaker 2: a lot of work and this is a good thing actually, 790 00:40:07,880 --> 00:40:11,239 Speaker 2: and to really leveling up people's understanding of how to 791 00:40:11,680 --> 00:40:15,280 Speaker 2: even prompt these tools, there is definitely a real skill 792 00:40:15,360 --> 00:40:19,200 Speaker 2: in that. I think again, just as a tip maybe 793 00:40:19,239 --> 00:40:22,520 Speaker 2: for the people listening is I've often found that a 794 00:40:22,600 --> 00:40:26,640 Speaker 2: mistake people make is that they give too little context, 795 00:40:26,640 --> 00:40:29,120 Speaker 2: so they only maybe give one or two sentences, which 796 00:40:29,320 --> 00:40:31,560 Speaker 2: works really well if you say, to say, chat GBT, 797 00:40:31,719 --> 00:40:34,080 Speaker 2: you know who won the presidency in nineteen eighty four, 798 00:40:34,400 --> 00:40:36,760 Speaker 2: that's fine, But if you want to actually do something 799 00:40:36,800 --> 00:40:39,879 Speaker 2: somewhat complex, you've got to think of the prompt as 800 00:40:42,000 --> 00:40:45,560 Speaker 2: sort of setting the parameters in which the AI should 801 00:40:45,600 --> 00:40:51,120 Speaker 2: adhere to and then be open to accepting all possible outcomes. So, 802 00:40:51,239 --> 00:40:53,320 Speaker 2: you know, this is where we get the crazy stories 803 00:40:53,320 --> 00:40:55,840 Speaker 2: from the movies where it's like, you know, is it 804 00:40:55,960 --> 00:40:58,520 Speaker 2: two thousand and one Space Odyssey, where it kills everybody 805 00:40:58,520 --> 00:41:02,200 Speaker 2: on the ship. Now, you just said you needed the 806 00:41:02,200 --> 00:41:04,600 Speaker 2: people over here, and so they had to be alive, right, 807 00:41:05,080 --> 00:41:07,839 Speaker 2: And it's like that's the bit that trips people up 808 00:41:08,280 --> 00:41:12,280 Speaker 2: and it sounds like we're being insulting to AI. But actually, 809 00:41:12,320 --> 00:41:14,839 Speaker 2: if you think about it as just a general intelligence, 810 00:41:14,880 --> 00:41:19,040 Speaker 2: if you didn't say these things, you know, like, how 811 00:41:19,080 --> 00:41:22,600 Speaker 2: would it know? And so oftentimes when I see people struggling, 812 00:41:22,680 --> 00:41:26,000 Speaker 2: it's either putting in two little context or that're trying 813 00:41:26,000 --> 00:41:29,160 Speaker 2: to micromanager, like they literally want it to spit out, 814 00:41:29,239 --> 00:41:31,239 Speaker 2: like they know to the letter what they want in 815 00:41:31,280 --> 00:41:33,399 Speaker 2: the outcome to be. And it's like, don't do that either, 816 00:41:33,560 --> 00:41:37,160 Speaker 2: like you wouldn't you wouldn't expect that when you're managing, 817 00:41:37,239 --> 00:41:39,640 Speaker 2: say a junior team member either it's not going to 818 00:41:39,680 --> 00:41:40,879 Speaker 2: be a good way to work with them. 819 00:41:41,120 --> 00:41:44,920 Speaker 1: Yeah, And there's just been so many reports. Microsoft just 820 00:41:45,000 --> 00:41:49,440 Speaker 1: published one about the productivity potential of AI, you know, 821 00:41:49,480 --> 00:41:52,520 Speaker 1: so I've covered so many of these reports, but it 822 00:41:52,600 --> 00:41:55,920 Speaker 1: just seems to be a gap between what's possible in 823 00:41:55,960 --> 00:41:58,200 Speaker 1: the short term in the next five to ten years, 824 00:41:58,719 --> 00:42:02,600 Speaker 1: and are ability to exploit that. And it really comes 825 00:42:02,640 --> 00:42:05,080 Speaker 1: down to digital skills and people having the confidence to 826 00:42:05,120 --> 00:42:07,160 Speaker 1: actually get hands on with these tools. 827 00:42:07,160 --> 00:42:10,040 Speaker 2: We've got a lot of work to do there. Yeah, 828 00:42:10,200 --> 00:42:12,680 Speaker 2: at a fundamental level, totally agree with that. I do 829 00:42:12,760 --> 00:42:15,160 Speaker 2: think to your point, there's a lot of reports and 830 00:42:15,200 --> 00:42:17,520 Speaker 2: a lot of noise coming out of these big companies. 831 00:42:18,200 --> 00:42:22,720 Speaker 2: I think they are over hyping things a bit. Simultaneously, 832 00:42:22,800 --> 00:42:24,959 Speaker 2: I think they are also fair mungering way too much. 833 00:42:25,040 --> 00:42:27,160 Speaker 2: They're creating them. I mean I saw a thing the 834 00:42:27,200 --> 00:42:28,920 Speaker 2: other day where one of them was like eighty five 835 00:42:28,960 --> 00:42:32,120 Speaker 2: percent of the time the anthropic model was blackmailing people. 836 00:42:32,120 --> 00:42:34,080 Speaker 2: I'm like, you would have had to go out of 837 00:42:34,120 --> 00:42:36,440 Speaker 2: your way to prompt that thing to do this, and 838 00:42:36,480 --> 00:42:39,319 Speaker 2: you're trying to scare the bejeebus out of people. You 839 00:42:39,360 --> 00:42:45,360 Speaker 2: know this, This is a bit crazy. So, as a 840 00:42:45,360 --> 00:42:47,239 Speaker 2: few people sort of highlight, a lot of those big 841 00:42:47,280 --> 00:42:50,160 Speaker 2: companies are really pushing to try and create a regulatory 842 00:42:50,239 --> 00:42:53,200 Speaker 2: capture environment where only a handful of them are allowed 843 00:42:53,239 --> 00:42:56,680 Speaker 2: to operate. Yes, and it's it's not cool. 844 00:42:56,840 --> 00:42:59,920 Speaker 1: Are you bootstrapping this out of your revenues from Reagan? 845 00:43:00,080 --> 00:43:03,640 Speaker 2: Yeah? So reagun is a fairly well established business. So 846 00:43:03,719 --> 00:43:09,880 Speaker 2: at the moment it's being funded through reagun. And once 847 00:43:10,120 --> 00:43:12,839 Speaker 2: we've driven up revenue. Like I say, the intention will 848 00:43:12,840 --> 00:43:15,160 Speaker 2: actually be to spin it out as a standalone company 849 00:43:15,239 --> 00:43:19,000 Speaker 2: rather than keep it as a business unit. Reagun itself, 850 00:43:19,080 --> 00:43:21,839 Speaker 2: like I said, is doing fine. It's still very valuable tool. 851 00:43:21,840 --> 00:43:24,040 Speaker 2: We're going to continue to add AI capability so that 852 00:43:24,080 --> 00:43:27,719 Speaker 2: we already have some in there overall. So yeah, that's 853 00:43:27,800 --> 00:43:31,000 Speaker 2: the that's the approach we're taking at the moment. 854 00:43:31,239 --> 00:43:34,520 Speaker 1: Yeah, so it's really about getting those use cases for 855 00:43:34,600 --> 00:43:37,440 Speaker 1: auto hive. People come back going Wow, this is saving 856 00:43:37,480 --> 00:43:41,120 Speaker 1: me a lot of time and energy, and then applying 857 00:43:41,160 --> 00:43:42,040 Speaker 1: that hopefully to others. 858 00:43:42,760 --> 00:43:45,160 Speaker 2: Yeah, this is soon one hundred percent. And your point 859 00:43:45,160 --> 00:43:48,240 Speaker 2: earlier about you know, the over hyping from the big guys. 860 00:43:48,600 --> 00:43:51,279 Speaker 2: That's why I say start rather than sort of having 861 00:43:51,320 --> 00:43:54,120 Speaker 2: this existential crisis of like if I bring this in, 862 00:43:54,200 --> 00:43:56,799 Speaker 2: am I implying that I don't need stuff, and like, no, 863 00:43:57,360 --> 00:44:01,239 Speaker 2: you're not. You're probably going to automate some of the 864 00:44:01,280 --> 00:44:04,680 Speaker 2: more boring things that happen in your business, and you're 865 00:44:04,719 --> 00:44:08,120 Speaker 2: probably going to find the wow factor is really just 866 00:44:08,160 --> 00:44:10,839 Speaker 2: to be able to say that some of that work 867 00:44:10,880 --> 00:44:14,120 Speaker 2: has been taken off your hands and there The other 868 00:44:14,120 --> 00:44:16,279 Speaker 2: thing I'd like to just address is I think we're 869 00:44:16,360 --> 00:44:18,960 Speaker 2: drawing to a close here, as you mentioned as well, 870 00:44:19,680 --> 00:44:22,400 Speaker 2: having these things do stuff on your behalf, and I 871 00:44:22,400 --> 00:44:24,279 Speaker 2: want to address that just because I'm sure there's some 872 00:44:24,360 --> 00:44:27,920 Speaker 2: people they're thinking, gosh, there is no way in heck 873 00:44:28,239 --> 00:44:30,520 Speaker 2: that I want this thing to do stuff on my behalf, 874 00:44:30,560 --> 00:44:34,520 Speaker 2: you know. So what we've done is we have what 875 00:44:34,680 --> 00:44:37,080 Speaker 2: is called human in the loop. And this is somewhat 876 00:44:37,120 --> 00:44:40,480 Speaker 2: common or becoming more common, but it's the idea that 877 00:44:40,520 --> 00:44:43,640 Speaker 2: you can choose the actions that need your explicit approval. 878 00:44:43,920 --> 00:44:46,880 Speaker 2: So the email example I have was I attach it 879 00:44:46,920 --> 00:44:49,440 Speaker 2: to my inbox and I could say what's my writing 880 00:44:49,480 --> 00:44:51,719 Speaker 2: style or give me some tips on improving of you know, 881 00:44:51,760 --> 00:44:56,040 Speaker 2: who's my most annoying person? Email? But I can choose 882 00:44:56,040 --> 00:44:58,919 Speaker 2: that to send an email will require that auto Hive 883 00:44:59,040 --> 00:45:02,880 Speaker 2: actually prompts me and says I'm going to send this yes, no? 884 00:45:03,440 --> 00:45:05,680 Speaker 2: Or do I want to interject and say maybe tweak 885 00:45:05,719 --> 00:45:07,840 Speaker 2: the message this way first and show it to me 886 00:45:08,200 --> 00:45:12,799 Speaker 2: so you have absolute control on what you're going to 887 00:45:12,840 --> 00:45:15,400 Speaker 2: allow it to do autonomously. That's how a lot of 888 00:45:15,400 --> 00:45:18,480 Speaker 2: the very cutting edge tools are operating. And my vision 889 00:45:18,520 --> 00:45:20,320 Speaker 2: for this and you asked about between now and the 890 00:45:20,400 --> 00:45:21,680 Speaker 2: end of the year, and I talked about the use 891 00:45:21,760 --> 00:45:24,720 Speaker 2: cases with the marketplace. We'll also have a mobile app 892 00:45:24,800 --> 00:45:26,960 Speaker 2: by the end of the year. My dream for this 893 00:45:27,400 --> 00:45:29,160 Speaker 2: is I want to wake up in the morning and 894 00:45:29,200 --> 00:45:33,040 Speaker 2: I want to have had these autohive agents have drafted 895 00:45:33,120 --> 00:45:35,359 Speaker 2: or my email replies. I want them to have read 896 00:45:35,400 --> 00:45:37,520 Speaker 2: my calendar and see that I have a board meeting 897 00:45:37,640 --> 00:45:39,279 Speaker 2: and I've got this meeting at this meeting, and I 898 00:45:39,320 --> 00:45:41,319 Speaker 2: want it to prepare all the work as far as 899 00:45:41,400 --> 00:45:44,000 Speaker 2: those agents can take it, but they won't necessarily have 900 00:45:44,080 --> 00:45:46,560 Speaker 2: completed it, so you know, it might have drafted, say 901 00:45:46,560 --> 00:45:48,799 Speaker 2: five email replies. I want to be able to get 902 00:45:48,800 --> 00:45:51,040 Speaker 2: out of bed. Maybe I'm still lying in bed, you know, 903 00:45:51,200 --> 00:45:54,600 Speaker 2: look at my phone. Yes, yes, no, no, yes, you know, 904 00:45:54,920 --> 00:45:56,960 Speaker 2: I'm like, you know what, before I've even got out 905 00:45:56,960 --> 00:45:59,319 Speaker 2: of bed, I've actually just kind of cleared a bunch 906 00:45:59,360 --> 00:46:03,840 Speaker 2: of this. Yeah, all's work. That's the goal, you know. Ultimately, 907 00:46:03,920 --> 00:46:07,440 Speaker 2: it's about trying to free people up to focus on 908 00:46:07,440 --> 00:46:09,719 Speaker 2: the stuff they actually enjoy doing. I don't. I don't 909 00:46:09,760 --> 00:46:12,120 Speaker 2: just like the people who send me emails, but running 910 00:46:12,160 --> 00:46:16,239 Speaker 2: emails is not my top most interesting task of the day, or. 911 00:46:16,360 --> 00:46:19,880 Speaker 1: Spending time on in New Zealand's website, flicking through looking 912 00:46:19,920 --> 00:46:22,120 Speaker 1: for flights and all that sort of thing, if I 913 00:46:22,120 --> 00:46:23,759 Speaker 1: can get the agent to just come back to me 914 00:46:23,760 --> 00:46:25,080 Speaker 1: and go, this is what I found. Do you want 915 00:46:25,080 --> 00:46:26,640 Speaker 1: me to go ahead and book that for you? I 916 00:46:26,640 --> 00:46:28,239 Speaker 1: think a lot of people be comfortable with that. 917 00:46:28,600 --> 00:46:30,600 Speaker 2: Yes, yeah, I was using that a fair bit when 918 00:46:30,640 --> 00:46:32,239 Speaker 2: I was traveling in the US, you know, like I 919 00:46:32,280 --> 00:46:33,719 Speaker 2: want to be in this area, I want to spend 920 00:46:33,760 --> 00:46:35,680 Speaker 2: this sort of money. Find me some options, you know, 921 00:46:35,840 --> 00:46:36,680 Speaker 2: just go and have a look. 922 00:46:37,120 --> 00:46:40,839 Speaker 1: Yeah, great, Well, thanks JD for the rundown and all 923 00:46:40,840 --> 00:46:43,160 Speaker 1: of that and on agents and where they're going. Good 924 00:46:43,239 --> 00:46:46,560 Speaker 1: luck with auto hive and as you've said, you're happy 925 00:46:46,600 --> 00:46:47,960 Speaker 1: to help anyone who wants to see it one up. 926 00:46:48,000 --> 00:46:50,360 Speaker 1: So that's a great opportunity for anyone listening. 927 00:46:50,440 --> 00:46:53,080 Speaker 2: Yeah, one hundred percent autohive dot com. It's free to 928 00:46:53,120 --> 00:46:55,520 Speaker 2: sign up. There's some free credits so on there as well, 929 00:46:55,600 --> 00:46:58,200 Speaker 2: and you can talk within our community and we're happy 930 00:46:58,239 --> 00:47:00,879 Speaker 2: to help. Ultimately, I just want to see you said, 931 00:47:00,880 --> 00:47:04,320 Speaker 2: I'm elevating on this and doing better with AI because 932 00:47:04,360 --> 00:47:06,360 Speaker 2: I think it can actually help us with some of 933 00:47:06,400 --> 00:47:13,440 Speaker 2: the challenges we face today. That's it. 934 00:47:13,480 --> 00:47:15,600 Speaker 1: For this episode if the Business of Tech. A huge 935 00:47:15,640 --> 00:47:19,960 Speaker 1: thanks to JD Trask for sharing his vision and some 936 00:47:20,120 --> 00:47:24,480 Speaker 1: practical wisdom on building with AI, not just for tech giants, 937 00:47:24,520 --> 00:47:27,239 Speaker 1: but for every business ready to take the leap. And 938 00:47:27,360 --> 00:47:29,359 Speaker 1: if you are ready to do that and see what 939 00:47:29,520 --> 00:47:33,399 Speaker 1: AI agents can do for you, check out autohive. Sign 940 00:47:33,480 --> 00:47:36,120 Speaker 1: Up is free at autohive dot com. The credits are 941 00:47:36,120 --> 00:47:39,040 Speaker 1: on the house the first ten dollars worth anyway, and 942 00:47:39,160 --> 00:47:42,320 Speaker 1: JD and his team are eager to help you get started. 943 00:47:42,440 --> 00:47:45,400 Speaker 1: At businesses dot co dot enz. In the podcast section 944 00:47:45,440 --> 00:47:47,919 Speaker 1: you'll find my show notes with so many other things 945 00:47:47,920 --> 00:47:51,480 Speaker 1: that JD mentioned, for instance, the Bill Gates famous Internet 946 00:47:51,480 --> 00:47:55,120 Speaker 1: title Wave Memo, which is worth a read. Find them 947 00:47:55,160 --> 00:47:58,359 Speaker 1: in the podcast section at businessdes dot co dot anz. 948 00:47:59,520 --> 00:48:02,680 Speaker 1: Don't let the future automate you out, that's the real message. 949 00:48:02,719 --> 00:48:07,279 Speaker 1: Get ahead and automate the future yourself. Next week, a 950 00:48:07,400 --> 00:48:11,080 Speaker 1: rising star of our space sector attempting to solve a 951 00:48:11,120 --> 00:48:14,640 Speaker 1: really big problem facing the companies that are launching ever 952 00:48:14,840 --> 00:48:19,320 Speaker 1: more satellites into orbit around the world. That's next Thursday. 953 00:48:19,680 --> 00:48:21,480 Speaker 1: I'm Peter Griffin. I'll catch you then,