1 00:00:00,090 --> 00:00:02,390 Speaker 1: You're listening to AC N A podcast. 2 00:00:03,500 --> 00:00:06,940 Speaker 2: Hello, everyone. It's Christina here, Adrian is away. So it's 3 00:00:06,949 --> 00:00:10,319 Speaker 2: just me for the episode. Not a day goes by 4 00:00:10,329 --> 00:00:16,090 Speaker 2: without some news about generative artificial intelligence, the speed at 5 00:00:16,100 --> 00:00:18,860 Speaker 2: which it has rolled out the impact it has on 6 00:00:18,870 --> 00:00:22,799 Speaker 2: people's lives and jobs. Well, depending on who you talk 7 00:00:22,809 --> 00:00:27,360 Speaker 2: to can be either worrying or exciting on today's episode, 8 00:00:27,370 --> 00:00:30,239 Speaker 2: we want to break this down a bit more clearly. 9 00:00:30,520 --> 00:00:33,409 Speaker 2: What does it mean when we say A I is 10 00:00:33,418 --> 00:00:36,619 Speaker 2: coming for your job, who will be affected and how 11 00:00:36,630 --> 00:00:40,418 Speaker 2: should governments and companies be ready for this to help 12 00:00:40,430 --> 00:00:44,009 Speaker 2: me unpack and understand this a bit more. Is Nick Ayers. 13 00:00:44,069 --> 00:00:47,259 Speaker 2: He is the vice president of field engineering for Asia 14 00:00:47,270 --> 00:00:48,418 Speaker 2: Pacific and Japan 15 00:00:48,930 --> 00:00:53,168 Speaker 2: at a company called Data Bricks. Data Bricks is A 16 00:00:53,180 --> 00:00:56,729 Speaker 2: A I and data company. Welcome to our podcast, Nick. 17 00:00:56,740 --> 00:00:59,080 Speaker 2: Thank you, Christina. Thank you for having me. We usually 18 00:00:59,090 --> 00:01:02,669 Speaker 2: do this by way of background. Perhaps we can start 19 00:01:02,680 --> 00:01:06,080 Speaker 2: with your journey. I know you've come all the way 20 00:01:06,089 --> 00:01:09,940 Speaker 2: from South Africa. It's your origin story, so to speak. 21 00:01:10,099 --> 00:01:10,769 Speaker 2: How did that 22 00:01:10,855 --> 00:01:15,285 Speaker 2: happened? And of course, a little bit about data bricks. Yeah, absolutely. Hi, everyone. 23 00:01:15,294 --> 00:01:17,654 Speaker 2: Nick Airs. I am originally from South Africa. Born and 24 00:01:17,665 --> 00:01:20,054 Speaker 2: raised so a proud South African don't hold that against 25 00:01:20,065 --> 00:01:22,285 Speaker 2: me if you support the cricket or the rugby. But yeah, 26 00:01:22,294 --> 00:01:24,334 Speaker 2: born and raised in South Africa and cut my teeth 27 00:01:24,345 --> 00:01:28,235 Speaker 2: on technology pretty early on. So my career started building 28 00:01:28,245 --> 00:01:31,855 Speaker 2: management and information systems for financial services companies in Sub 29 00:01:31,925 --> 00:01:35,024 Speaker 2: Saharan Africa. And it's been an amazing career since then 30 00:01:35,035 --> 00:01:35,554 Speaker 2: because 31 00:01:35,860 --> 00:01:38,239 Speaker 2: I've seen the evolution of tech and how tech is 32 00:01:38,250 --> 00:01:41,940 Speaker 2: now helping transform business society and the environment by and 33 00:01:41,949 --> 00:01:44,160 Speaker 2: large through the use of data and A I over 34 00:01:44,169 --> 00:01:46,399 Speaker 2: the years and a couple of the big milestones there. 35 00:01:46,410 --> 00:01:48,400 Speaker 2: You know, early in my career, I was with stalwart 36 00:01:48,410 --> 00:01:51,389 Speaker 2: in the industry which is SAS statistical analysis software. I 37 00:01:51,400 --> 00:01:53,970 Speaker 2: was there for close on 10 years, direct, 14 years 38 00:01:53,980 --> 00:01:57,339 Speaker 2: indirect but saw the evolution of advanced analytics as a 39 00:01:57,349 --> 00:01:59,800 Speaker 2: board level agenda when I was at SAS and then 40 00:01:59,809 --> 00:02:02,599 Speaker 2: had the good fortune of working for another start up 41 00:02:02,610 --> 00:02:05,430 Speaker 2: called horse and works got a acquired by cloud era, 42 00:02:05,760 --> 00:02:07,559 Speaker 2: but they were kind of leading the way around the 43 00:02:07,569 --> 00:02:10,970 Speaker 2: open source revolution which drove all of this innovation that 44 00:02:10,979 --> 00:02:13,929 Speaker 2: we're seeing in the community around new algorithms and new 45 00:02:13,940 --> 00:02:17,758 Speaker 2: techniques and new collaborative ways of learning and leveraging technology 46 00:02:17,770 --> 00:02:20,139 Speaker 2: as a community, which was awesome. And now with data 47 00:02:20,149 --> 00:02:22,940 Speaker 2: bricks get the best of both worlds continuing to impact 48 00:02:22,949 --> 00:02:25,529 Speaker 2: advanced analytics at the sea level agenda and also with 49 00:02:25,538 --> 00:02:26,259 Speaker 2: citizens and 50 00:02:26,470 --> 00:02:30,369 Speaker 2: humanity by and large. But leveraging a very simple and 51 00:02:30,380 --> 00:02:34,550 Speaker 2: powerful software stack that has a phenomenal amount of scale 52 00:02:34,559 --> 00:02:36,360 Speaker 2: because it runs in the cloud and very simple and 53 00:02:36,369 --> 00:02:38,979 Speaker 2: easy to use. OK. So give me an example of 54 00:02:38,990 --> 00:02:42,649 Speaker 2: a typical client, right? Let's say if I am a 55 00:02:42,660 --> 00:02:50,679 Speaker 2: healthcare company, not really sure how to scale up using software. 56 00:02:51,210 --> 00:02:54,850 Speaker 2: So maybe just paint me this idea of what analytics 57 00:02:54,860 --> 00:02:58,750 Speaker 2: and data and all the software can do to really 58 00:02:59,389 --> 00:03:01,899 Speaker 2: make me more productive. I love the way you put 59 00:03:01,910 --> 00:03:03,899 Speaker 2: it because I'm a big proponent of data and A 60 00:03:03,910 --> 00:03:06,039 Speaker 2: I being used as an assistant to the human in 61 00:03:06,050 --> 00:03:08,589 Speaker 2: the loop in these processes. Health and life sciences is 62 00:03:08,600 --> 00:03:11,898 Speaker 2: a pretty good example actually. And in healthcare, probably one 63 00:03:11,910 --> 00:03:14,339 Speaker 2: of the most prolific examples of the use of A 64 00:03:14,350 --> 00:03:17,729 Speaker 2: I which is very much in use today at large 65 00:03:17,740 --> 00:03:21,240 Speaker 2: scale is all around radiology and medical images. 66 00:03:21,490 --> 00:03:23,929 Speaker 2: So you think about the radiologists and all of the 67 00:03:23,940 --> 00:03:27,000 Speaker 2: analysis that they need to do on the imagery around 68 00:03:27,008 --> 00:03:30,198 Speaker 2: an MRI scan or an X ray historically, that's the 69 00:03:30,210 --> 00:03:32,990 Speaker 2: radiologist waiting for that scan to come out and then 70 00:03:33,000 --> 00:03:40,160 Speaker 2: they manually correct. Now, it's an A I model that 71 00:03:40,169 --> 00:03:43,300 Speaker 2: has computer vision that can interpret that imagery 72 00:03:43,539 --> 00:03:47,949 Speaker 2: and assess and evaluate and recommend treatment and diagnosis to 73 00:03:47,960 --> 00:03:50,759 Speaker 2: the radiologist, not taking the radiologist out of the loop, 74 00:03:50,850 --> 00:03:54,289 Speaker 2: but making them so much more efficient and effective. And 75 00:03:54,300 --> 00:03:56,500 Speaker 2: I think the net result there is us being able 76 00:03:56,509 --> 00:03:58,880 Speaker 2: to impact more lives and to have a positive effect 77 00:03:59,009 --> 00:04:01,100 Speaker 2: on obviously the quality of life. So it not just 78 00:04:01,110 --> 00:04:04,130 Speaker 2: speeds up the process, let's say if I go for 79 00:04:04,139 --> 00:04:06,220 Speaker 2: a lung X ray, of course, I would still need 80 00:04:06,229 --> 00:04:06,960 Speaker 2: a radiologist 81 00:04:07,759 --> 00:04:10,839 Speaker 2: because frankly, half the time the radiologist is trying to 82 00:04:10,850 --> 00:04:14,300 Speaker 2: like calm me down. So you do need the human 83 00:04:14,309 --> 00:04:17,779 Speaker 2: person there. But what the computer does, it is so 84 00:04:17,790 --> 00:04:22,469 Speaker 2: precise with what it sees, it's so accurate, it tells 85 00:04:22,480 --> 00:04:25,219 Speaker 2: the radiologist. But in a way, the radiologist also has 86 00:04:25,230 --> 00:04:30,059 Speaker 2: to have all their experience reading scans over many people 87 00:04:30,070 --> 00:04:31,899 Speaker 2: to close the loop of that. 88 00:04:32,130 --> 00:04:34,149 Speaker 2: Yeah. And I think you touched on a very important point, 89 00:04:34,160 --> 00:04:37,149 Speaker 2: which is one of the areas that this technology does 90 00:04:37,160 --> 00:04:40,250 Speaker 2: not address incredibly well is the emotional intelligence side, right? 91 00:04:40,260 --> 00:04:42,160 Speaker 2: So a big part of this is we're still dealing 92 00:04:42,170 --> 00:04:44,670 Speaker 2: with humans, right? So there's the human in the loop 93 00:04:44,678 --> 00:04:47,269 Speaker 2: doing the diagnosis and the assessment of the treatment. But 94 00:04:47,279 --> 00:04:49,070 Speaker 2: understanding the human who we're serving 95 00:04:49,309 --> 00:04:51,589 Speaker 2: and how we connect with them at an emotional level 96 00:04:51,600 --> 00:04:53,600 Speaker 2: and give them the emotional support that they need to 97 00:04:53,609 --> 00:04:55,779 Speaker 2: go through that treatment plan to take on that next 98 00:04:55,790 --> 00:04:59,230 Speaker 2: step around getting help, whether it be cancer treatment or 99 00:04:59,238 --> 00:05:02,500 Speaker 2: mental health or whatever it is. Emotional intelligence is very 100 00:05:02,510 --> 00:05:06,420 Speaker 2: hard to reproduce through a model thank goodness for that. OK, 101 00:05:06,470 --> 00:05:08,570 Speaker 2: so let's get to the heart of this, right? To 102 00:05:08,579 --> 00:05:11,440 Speaker 2: me it feels negative like, oh A I is coming 103 00:05:11,450 --> 00:05:14,190 Speaker 2: after your job. I don't know why the narrative is 104 00:05:14,200 --> 00:05:15,959 Speaker 2: so negative to be honest. But 105 00:05:16,225 --> 00:05:19,355 Speaker 2: in any case, an IMF report says that 40% of 106 00:05:19,363 --> 00:05:21,815 Speaker 2: the global workforce will be affected by A I in 107 00:05:21,825 --> 00:05:25,244 Speaker 2: some shape or form at the very lowest level. Like 108 00:05:25,255 --> 00:05:29,725 Speaker 2: you said, it can replace routine repetitive tasks. And I 109 00:05:29,734 --> 00:05:33,265 Speaker 2: think nobody in their right mind would say that's a 110 00:05:33,274 --> 00:05:36,665 Speaker 2: bad idea. I would love it if I could do 111 00:05:36,674 --> 00:05:40,045 Speaker 2: half of the repetitive stuff I do. But at the 112 00:05:40,053 --> 00:05:43,005 Speaker 2: highest level, people are also worried 113 00:05:43,369 --> 00:05:47,309 Speaker 2: and I think we can't take this away about displacing 114 00:05:47,880 --> 00:05:51,409 Speaker 2: traditional work. And I can think of because I'm in 115 00:05:51,420 --> 00:05:56,829 Speaker 2: the media, photography and art, for instance, you know, I 116 00:05:56,839 --> 00:06:01,589 Speaker 2: see people online using A I to create 117 00:06:02,160 --> 00:06:05,529 Speaker 2: like a beautiful dragon. And I'm like, oh my God, 118 00:06:05,540 --> 00:06:09,459 Speaker 2: that could be used as artwork for any number of 119 00:06:09,470 --> 00:06:12,579 Speaker 2: things I could use A I to come up with 120 00:06:12,589 --> 00:06:16,250 Speaker 2: a podcast artwork. I don't need a person doing it anymore. 121 00:06:16,269 --> 00:06:19,410 Speaker 2: And so what happens to the person? I don't know 122 00:06:19,420 --> 00:06:21,238 Speaker 2: who's went to art school, 123 00:06:21,725 --> 00:06:24,325 Speaker 2: graphic design. What do you think? Like, how do you 124 00:06:24,334 --> 00:06:26,355 Speaker 2: see this? Yeah, I'll come to that point. But maybe 125 00:06:26,363 --> 00:06:29,505 Speaker 2: to start with a more fundamental point around, hey, this 126 00:06:29,515 --> 00:06:31,924 Speaker 2: sort of cyclical change isn't new, right? If we go 127 00:06:31,934 --> 00:06:34,285 Speaker 2: back in history and we look at all the sort 128 00:06:34,295 --> 00:06:38,964 Speaker 2: of big revolutions around industrial revolution. Agriculture, agriculture is quite 129 00:06:38,975 --> 00:06:41,295 Speaker 2: a good one. Right. You go back to the 18 hundreds. 130 00:06:41,540 --> 00:06:44,459 Speaker 2: I think it's something like 90% of the human workforce 131 00:06:44,470 --> 00:06:47,839 Speaker 2: across the world was in agriculture. Cast your mind forward 132 00:06:47,850 --> 00:06:50,118 Speaker 2: to kind of the 19 fifties. It dropped to 20%. 133 00:06:50,130 --> 00:06:52,799 Speaker 2: And you look at today it's 1 to 2% of 134 00:06:52,809 --> 00:06:55,019 Speaker 2: the total worldwide population that's in agriculture. 135 00:06:55,260 --> 00:06:58,690 Speaker 2: So these changes are not unique and we haven't seen 136 00:06:58,700 --> 00:07:01,669 Speaker 2: them before, but they're not instant, right? They don't happen 137 00:07:01,678 --> 00:07:04,049 Speaker 2: overnight as, as a reference in that example, it takes, 138 00:07:04,059 --> 00:07:05,850 Speaker 2: it takes a while for these changes to be fully 139 00:07:05,859 --> 00:07:08,700 Speaker 2: adopted and fully embraced. Um And agriculture is a good 140 00:07:08,709 --> 00:07:11,959 Speaker 2: example and agriculture is sort of a phenomenally strong industry today, 141 00:07:11,970 --> 00:07:15,679 Speaker 2: multibillion dollar industry transforming the way that they do farming 142 00:07:15,739 --> 00:07:17,890 Speaker 2: and again, largely driven by data and A I to 143 00:07:17,899 --> 00:07:22,359 Speaker 2: drive greater sustainability, greater yield and more efficient and effective 144 00:07:22,369 --> 00:07:24,519 Speaker 2: ways of harvesting and yielding crops. 145 00:07:24,940 --> 00:07:27,200 Speaker 2: So just as an example, it's just calling out that 146 00:07:27,209 --> 00:07:29,570 Speaker 2: this isn't unique. We've seen these sort of changes before 147 00:07:29,730 --> 00:07:33,320 Speaker 2: that being said, yeah, for sure, the fear, uncertainty and 148 00:07:33,329 --> 00:07:38,950 Speaker 2: darts is real. There's, there's definitely a lot of sensationalization 149 00:07:38,959 --> 00:07:41,230 Speaker 2: I think around it because it creates good headlines, good 150 00:07:41,239 --> 00:07:45,390 Speaker 2: taglines gets people interested and I'm not disputing the data, right. 151 00:07:45,399 --> 00:07:47,910 Speaker 2: So yes, there's various studies that kind of point to 152 00:07:48,019 --> 00:07:50,279 Speaker 2: job displacement, job disruption. 153 00:07:50,660 --> 00:07:52,200 Speaker 2: And I think it would be remiss of us to 154 00:07:52,209 --> 00:07:54,809 Speaker 2: ignore that. Right. But I think it's how we respond 155 00:07:54,820 --> 00:07:57,119 Speaker 2: to that. And what do we do proactively to be 156 00:07:57,130 --> 00:08:00,480 Speaker 2: prepared for that? And there's this big field around A 157 00:08:00,489 --> 00:08:02,809 Speaker 2: I preparedness that a lot of people are looking into. 158 00:08:02,820 --> 00:08:05,799 Speaker 2: So what can we do to respond as positively as 159 00:08:05,809 --> 00:08:08,420 Speaker 2: we can to these changes? Because they are coming, they 160 00:08:08,429 --> 00:08:11,220 Speaker 2: here they now and we need to respond appropriately. So, 161 00:08:11,230 --> 00:08:14,140 Speaker 2: to go to your creative example, actually, what I see 162 00:08:14,149 --> 00:08:14,519 Speaker 2: is 163 00:08:14,829 --> 00:08:17,489 Speaker 2: it again comes down to a lot about the psyche 164 00:08:17,500 --> 00:08:19,980 Speaker 2: of the individual and kind of the approach if they've 165 00:08:19,989 --> 00:08:22,950 Speaker 2: got a continuous learning mindset and they're looking to learn 166 00:08:22,959 --> 00:08:24,049 Speaker 2: and develop and grow 167 00:08:24,579 --> 00:08:27,720 Speaker 2: the creatives that embrace this technology, they're going to differentiate 168 00:08:27,730 --> 00:08:30,779 Speaker 2: themselves in the marketplace completely and they'll be able to 169 00:08:30,790 --> 00:08:33,218 Speaker 2: drive these models in a way that nobody else can 170 00:08:33,229 --> 00:08:36,700 Speaker 2: because their knowledge and experience is built on sound foundations 171 00:08:36,710 --> 00:08:40,099 Speaker 2: that they've got either through university or practical application work. 172 00:08:40,190 --> 00:08:44,400 Speaker 2: And they can feed these models with very precise inputs, 173 00:08:44,409 --> 00:08:48,390 Speaker 2: prompts to generate amazing content to your point. But without 174 00:08:48,400 --> 00:08:52,280 Speaker 2: that creative background and their creative foundations and those creative fundamentals, 175 00:08:52,580 --> 00:08:54,710 Speaker 2: the output you'll get out of the models, it will 176 00:08:54,719 --> 00:08:57,719 Speaker 2: be good may, may not be great. So I think 177 00:08:57,729 --> 00:09:00,169 Speaker 2: we still need to focus on education, educating folks on 178 00:09:00,179 --> 00:09:02,848 Speaker 2: the fundamentals, educating folks on how you can use these 179 00:09:02,859 --> 00:09:05,369 Speaker 2: tools and techniques work them into your workflow. As a 180 00:09:05,380 --> 00:09:09,159 Speaker 2: creative and as a creative, I think it unlocks your imagination. 181 00:09:09,169 --> 00:09:09,520 Speaker 2: So 182 00:09:09,869 --> 00:09:13,409 Speaker 2: instead of waiting on human resources to help you realize 183 00:09:13,419 --> 00:09:15,890 Speaker 2: what you've envisioned in your mind, in the way of 184 00:09:15,900 --> 00:09:19,280 Speaker 2: art or uh composing music, you can now feed it 185 00:09:19,289 --> 00:09:23,669 Speaker 2: into the model and the model will automatically realize your imagination. 186 00:09:25,580 --> 00:09:27,260 Speaker 2: Funny enough, I can tell 187 00:09:27,984 --> 00:09:32,125 Speaker 2: when something is gone through chat GP T for writing. 188 00:09:32,184 --> 00:09:35,325 Speaker 2: Maybe now because the version of Chat GP T is 189 00:09:35,335 --> 00:09:38,775 Speaker 2: still maybe not sophisticated. But I don't know because I'm 190 00:09:38,784 --> 00:09:42,405 Speaker 2: a writer, I could tell there are certain phrases, there 191 00:09:42,414 --> 00:09:45,484 Speaker 2: are certain words, the way the sentence is structured is 192 00:09:45,494 --> 00:09:49,655 Speaker 2: so completely different. But of course, the caveat is, it 193 00:09:49,664 --> 00:09:53,195 Speaker 2: can become very sophisticated, correct. But I think again to 194 00:09:53,205 --> 00:09:54,164 Speaker 2: the point, it's uh 195 00:09:54,450 --> 00:09:57,869 Speaker 2: not relying on it wholesale copy paste, right? But it's 196 00:09:57,880 --> 00:10:01,020 Speaker 2: using it as an enabler to make your workflow more 197 00:10:01,030 --> 00:10:03,819 Speaker 2: effective and efficient. So it may not necessarily to your 198 00:10:03,830 --> 00:10:06,789 Speaker 2: point in the creative writing side, it may not form 199 00:10:06,799 --> 00:10:09,750 Speaker 2: the basis for 100% of the article. But maybe it 200 00:10:09,760 --> 00:10:13,869 Speaker 2: allows you to explore and analyze topics where maybe your 201 00:10:13,880 --> 00:10:16,510 Speaker 2: knowledge isn't as deep and quickly get summaries that you 202 00:10:16,520 --> 00:10:18,760 Speaker 2: can insert into your article and you put the creative 203 00:10:18,770 --> 00:10:21,169 Speaker 2: twist on top. Yeah. So for parts, the trans 204 00:10:21,270 --> 00:10:24,809 Speaker 2: scripts are quite important and the software that we use A, 205 00:10:24,820 --> 00:10:28,890 Speaker 2: I actually does a summary And so, yeah, and the 206 00:10:28,900 --> 00:10:32,468 Speaker 2: summary was pretty good, I have to say because it 207 00:10:32,479 --> 00:10:36,609 Speaker 2: cuts short, me having to go through the entire transcript, 208 00:10:36,989 --> 00:10:39,960 Speaker 2: listen to and then pick out the key points. It 209 00:10:39,969 --> 00:10:43,409 Speaker 2: basically does that for me. And that's a great example 210 00:10:43,510 --> 00:10:46,919 Speaker 2: of how when A I is infused and embraced, it 211 00:10:46,929 --> 00:10:48,909 Speaker 2: can really transform the way that you work. You become 212 00:10:48,919 --> 00:10:51,319 Speaker 2: more productive. And really what you get to focus on 213 00:10:51,330 --> 00:10:54,098 Speaker 2: is the amazing creative sort of questioning and thinking around 214 00:10:54,109 --> 00:10:57,130 Speaker 2: how to structure the podcast. And the tough questions you 215 00:10:57,140 --> 00:10:57,809 Speaker 2: throw at me. 216 00:10:58,479 --> 00:11:01,789 Speaker 2: No, not tough at all. OK. The Singapore government as 217 00:11:01,799 --> 00:11:08,079 Speaker 2: you know, things decades ahead with all sorts of committees 218 00:11:08,090 --> 00:11:13,459 Speaker 2: and blueprints and strategies. And so A I is definitely 219 00:11:13,469 --> 00:11:16,599 Speaker 2: on their radar. In fact, they recently revised a national 220 00:11:16,609 --> 00:11:20,710 Speaker 2: strategy regarding A I, the aim is to have systems 221 00:11:20,719 --> 00:11:24,559 Speaker 2: in place to support companies obviously to bring people like 222 00:11:24,570 --> 00:11:27,939 Speaker 2: you into the ecosystem. But ultimately, I think 223 00:11:28,440 --> 00:11:31,530 Speaker 2: the worry is for the workers. Singapore is what we 224 00:11:31,539 --> 00:11:34,739 Speaker 2: call an advanced economy, right? So PM ETS are a 225 00:11:34,750 --> 00:11:37,700 Speaker 2: very sizable part of that economy. So it's a knowledge 226 00:11:37,710 --> 00:11:40,719 Speaker 2: kind of thing. How do you look at this? What 227 00:11:40,729 --> 00:11:44,739 Speaker 2: is the role of the government? Should it be less 228 00:11:44,750 --> 00:11:46,489 Speaker 2: frantic about planning 229 00:11:47,000 --> 00:11:51,010 Speaker 2: and leave it to the ecosystem to just develop or 230 00:11:51,169 --> 00:11:54,530 Speaker 2: it actually plays a very critical role. I think it 231 00:11:54,539 --> 00:11:57,349 Speaker 2: plays an incredibly critical role, but there is balance that's 232 00:11:57,359 --> 00:12:00,270 Speaker 2: required Right. And I think the balance is a fine 233 00:12:00,280 --> 00:12:02,270 Speaker 2: line to strike. And I must admit I'm aware of 234 00:12:02,280 --> 00:12:04,739 Speaker 2: the government's position. We've looked at that ourselves and we've 235 00:12:04,750 --> 00:12:05,409 Speaker 2: been very fortunate 236 00:12:05,460 --> 00:12:08,030 Speaker 2: to talk to some of the government agencies ourselves as well. 237 00:12:08,219 --> 00:12:10,700 Speaker 2: And I think the Singapore stance is very progressive and 238 00:12:10,710 --> 00:12:13,150 Speaker 2: they're very well prepared. I mentioned that preparedness framework that 239 00:12:13,159 --> 00:12:15,609 Speaker 2: a lot of governments and companies and individuals are starting 240 00:12:15,619 --> 00:12:18,119 Speaker 2: to look at. There's actually a recent study around A 241 00:12:18,130 --> 00:12:21,280 Speaker 2: I preparedness and Singapore is one of the top countries worldwide. 242 00:12:21,289 --> 00:12:23,390 Speaker 2: I think it's followed only by Denmark and I forget 243 00:12:23,400 --> 00:12:25,609 Speaker 2: the third, but Singapore is right up there as leading 244 00:12:25,619 --> 00:12:27,159 Speaker 2: player in A I preparedness. 245 00:12:27,479 --> 00:12:29,450 Speaker 2: And I think it comes down to a couple of things, right? 246 00:12:29,460 --> 00:12:33,200 Speaker 2: You want the government involved because in the absence of 247 00:12:33,210 --> 00:12:35,760 Speaker 2: government involvement, there's a couple of things that we should 248 00:12:35,770 --> 00:12:40,929 Speaker 2: be concerned about. One is obviously everything around ethics, responsibility, 249 00:12:41,500 --> 00:12:42,489 Speaker 2: making sure that 250 00:12:42,585 --> 00:12:46,125 Speaker 2: hey, when industry embraces this tech, that we do put 251 00:12:46,135 --> 00:12:47,705 Speaker 2: the citizen at the heart of it and we do 252 00:12:47,715 --> 00:12:49,354 Speaker 2: put the consumer at the heart of it. And we 253 00:12:49,364 --> 00:12:52,244 Speaker 2: think deeply and reflect deeply on the experience that we're 254 00:12:52,255 --> 00:12:55,184 Speaker 2: creating for citizens and consumers alike and that we're protecting 255 00:12:55,315 --> 00:12:58,575 Speaker 2: their personal confidential in that process. And that when, when 256 00:12:58,585 --> 00:13:01,444 Speaker 2: we make a decision to serve citizen and to serve consumer, 257 00:13:01,455 --> 00:13:04,825 Speaker 2: that decision has no bias in it, right? That it's 258 00:13:04,835 --> 00:13:08,265 Speaker 2: a very sound decision that's made on valid data, accurate 259 00:13:08,275 --> 00:13:09,684 Speaker 2: data and has no bias. 260 00:13:09,960 --> 00:13:12,699 Speaker 2: So I think it's really important that government plays an 261 00:13:12,710 --> 00:13:15,419 Speaker 2: active role and that comes down to regulation and compliance. 262 00:13:15,690 --> 00:13:16,960 Speaker 2: And a lot of people will look at that and go, 263 00:13:16,969 --> 00:13:19,780 Speaker 2: but Nick, that's going to slow things down. It's gonna 264 00:13:19,789 --> 00:13:22,719 Speaker 2: make it very convoluted and difficult. I would actually argue 265 00:13:22,729 --> 00:13:26,020 Speaker 2: the inverse. I think in the absence of regulation and compliance, 266 00:13:26,299 --> 00:13:29,900 Speaker 2: there's potential risk that people will distrust 267 00:13:30,229 --> 00:13:33,200 Speaker 2: everything that is A I driven A I infused there's 268 00:13:33,210 --> 00:13:36,979 Speaker 2: potentially uh companies that won't get behind the innovation agenda 269 00:13:36,989 --> 00:13:40,200 Speaker 2: of data and A I for fear and risk of 270 00:13:40,210 --> 00:13:43,929 Speaker 2: their IP being repurposed and, and taken by someone else. 271 00:13:44,030 --> 00:13:45,760 Speaker 2: So I think the government has to play a very 272 00:13:45,770 --> 00:13:50,049 Speaker 2: active role in governing it effectively without curbing the innovation, 273 00:13:50,929 --> 00:13:55,030 Speaker 2: too interfering. It's like parenting, right? Not to be too, 274 00:13:56,559 --> 00:13:59,760 Speaker 2: not too overbearing, but some guidance and some guard rails 275 00:13:59,770 --> 00:14:02,119 Speaker 2: are good. Yeah, I want to ask you when you 276 00:14:02,130 --> 00:14:05,090 Speaker 2: say that we are quite progressive and we want to 277 00:14:05,099 --> 00:14:09,728 Speaker 2: be prepared. What does being progressive really mean? What sets 278 00:14:09,739 --> 00:14:10,780 Speaker 2: us apart 279 00:14:11,200 --> 00:14:15,000 Speaker 2: uh from, let's say another country, maybe not so interested 280 00:14:15,010 --> 00:14:17,140 Speaker 2: in A I or not so focused on it. Yeah, 281 00:14:17,150 --> 00:14:20,460 Speaker 2: the focus on the workforce is something that is very clear, 282 00:14:20,650 --> 00:14:22,919 Speaker 2: there's a lot of effort and it's really well thought 283 00:14:22,929 --> 00:14:25,950 Speaker 2: out and well articulated around skills for the future and 284 00:14:25,960 --> 00:14:28,090 Speaker 2: making sure that the workforce of the future is something 285 00:14:28,099 --> 00:14:30,969 Speaker 2: that the country is focused on building and putting in 286 00:14:30,979 --> 00:14:36,640 Speaker 2: place proper education programs, upskilling reskilling initiatives to allow folks 287 00:14:36,650 --> 00:14:37,330 Speaker 2: to embrace 288 00:14:37,666 --> 00:14:42,306 Speaker 2: this data and A I wave of disruption, but embrace 289 00:14:42,315 --> 00:14:44,395 Speaker 2: that in a positive way. Right? So we have these 290 00:14:44,406 --> 00:14:46,726 Speaker 2: future skills for future roles that we don't even know 291 00:14:46,736 --> 00:14:49,096 Speaker 2: are potentially coming. I was reading a very interesting article 292 00:14:49,106 --> 00:14:52,926 Speaker 2: that 60% of the jobs today didn't exist 80 years ago. 293 00:14:53,075 --> 00:14:55,495 Speaker 2: So who knows what the jobs in the future are? 294 00:14:55,505 --> 00:14:58,406 Speaker 2: All we can do is be prepared by educating folks 295 00:14:58,416 --> 00:15:01,736 Speaker 2: on the fundamentals of this technology, how to embrace it, 296 00:15:01,745 --> 00:15:03,616 Speaker 2: how to use it. And I think the Singapore 297 00:15:03,731 --> 00:15:06,710 Speaker 2: government's got a great education strategy. I think the other 298 00:15:06,721 --> 00:15:11,601 Speaker 2: thing is specifically around the Consultative process and you reference yourself, right? 299 00:15:11,611 --> 00:15:13,191 Speaker 2: A lot of people look at it and there might 300 00:15:13,202 --> 00:15:15,052 Speaker 2: be a little bit look at it with a negative frame. 301 00:15:15,062 --> 00:15:17,492 Speaker 2: I think it's great that the government is consulting with 302 00:15:17,502 --> 00:15:21,072 Speaker 2: industry leaders, both the big mega corporations as well as 303 00:15:21,081 --> 00:15:25,072 Speaker 2: others to understand like, hey, how are you thinking about 304 00:15:25,081 --> 00:15:27,031 Speaker 2: using this? How are you thinking about embracing this and 305 00:15:27,041 --> 00:15:29,031 Speaker 2: how can we support you on the state and A 306 00:15:29,041 --> 00:15:29,872 Speaker 2: I agenda? 307 00:15:30,190 --> 00:15:33,080 Speaker 2: And then I think the third piece is continued investment 308 00:15:33,090 --> 00:15:35,849 Speaker 2: in innovation, which the Singapore government has set a fair 309 00:15:35,859 --> 00:15:39,010 Speaker 2: amount of money aside for and has some phenomenal programs underway. 310 00:15:39,020 --> 00:15:41,250 Speaker 2: I'm aware obviously of project Sea lion. I don't know, 311 00:15:41,260 --> 00:15:43,489 Speaker 2: if you saw that announcement, they're building a large language 312 00:15:43,500 --> 00:15:46,640 Speaker 2: model that caters for Southeast Asian languages. I think it's 313 00:15:46,650 --> 00:15:48,849 Speaker 2: a phenomenal initiative off the back of that. 314 00:15:49,280 --> 00:15:52,270 Speaker 2: You can imagine a world where products and services in 315 00:15:52,280 --> 00:15:55,650 Speaker 2: Southeast Asia, you'll now be able to engage and leverage 316 00:15:55,659 --> 00:15:57,710 Speaker 2: its products and services in your local language, in your 317 00:15:57,719 --> 00:16:01,049 Speaker 2: local country. Thanks to this amazing work. OK. They're doing 318 00:16:01,059 --> 00:16:04,190 Speaker 2: some good things that's good to know. OK, the listeners 319 00:16:04,200 --> 00:16:07,690 Speaker 2: of this podcast to be really honest care about their jobs, right? 320 00:16:08,450 --> 00:16:12,679 Speaker 2: And the government has said there's an estimated 15,000 jobs 321 00:16:12,690 --> 00:16:15,710 Speaker 2: related to A I that will be needed. Now, I 322 00:16:15,719 --> 00:16:18,799 Speaker 2: was reading this as well for all the new jobs 323 00:16:18,809 --> 00:16:21,090 Speaker 2: that will be created, there will be just as many, 324 00:16:21,099 --> 00:16:24,380 Speaker 2: in fact, more that will have to go, right? It's 325 00:16:24,390 --> 00:16:28,659 Speaker 2: a natural industrial revolution change. There is a group and 326 00:16:28,669 --> 00:16:30,960 Speaker 2: I would say not a small one 327 00:16:31,640 --> 00:16:35,369 Speaker 2: that feels pretty overwhelmed even if they wanted to, they 328 00:16:35,380 --> 00:16:39,599 Speaker 2: wouldn't know where to go and how to start. Whose 329 00:16:39,710 --> 00:16:44,690 Speaker 2: responsibility do you think this is? Is it the company 330 00:16:44,700 --> 00:16:47,409 Speaker 2: who says? Ok, you know what, I'm going to look 331 00:16:47,419 --> 00:16:49,570 Speaker 2: at this entire workforce that I have out of the 332 00:16:49,580 --> 00:16:53,270 Speaker 2: 100 people, these 20 guys are going to be hit 333 00:16:53,280 --> 00:16:56,380 Speaker 2: by automation. What am I going to do with this? 334 00:16:56,390 --> 00:16:59,929 Speaker 2: 20 guys and then actively look at that. Ok. Or 335 00:17:00,580 --> 00:17:05,099 Speaker 2: is it the 20 guys who are thinking, oh, shucks 336 00:17:05,729 --> 00:17:08,250 Speaker 2: the machine is coming for my job. I don't need 337 00:17:08,260 --> 00:17:12,589 Speaker 2: to subtitle physically anymore. A machine is going to subtitle 338 00:17:12,599 --> 00:17:17,020 Speaker 2: this perfectly. It's an, it's an end at the end 339 00:17:17,030 --> 00:17:19,959 Speaker 2: of the day, right? I think from the company's perspective, 340 00:17:19,969 --> 00:17:22,099 Speaker 2: they need to be very clear on what their data 341 00:17:22,109 --> 00:17:24,530 Speaker 2: and A I agenda and strategy is and they need 342 00:17:24,540 --> 00:17:25,479 Speaker 2: to articulate that. 343 00:17:25,750 --> 00:17:29,400 Speaker 2: And I would argue that any company organization that is 344 00:17:29,410 --> 00:17:31,879 Speaker 2: not thinking around having data and A I as a 345 00:17:31,890 --> 00:17:34,520 Speaker 2: core part of the strategic agenda, we could argue that 346 00:17:34,530 --> 00:17:36,739 Speaker 2: probably won't exist right in a couple of years time 347 00:17:36,750 --> 00:17:39,569 Speaker 2: because we have this old adage of software eating the 348 00:17:39,579 --> 00:17:41,949 Speaker 2: world today, it's data and A I that's eating software. 349 00:17:42,219 --> 00:17:44,849 Speaker 2: So if you're not thinking deeply and reflecting deeply around 350 00:17:44,859 --> 00:17:48,239 Speaker 2: how you embed DNA I into your overall business strategy, 351 00:17:48,310 --> 00:17:50,879 Speaker 2: one could argue that you probably won't be around because 352 00:17:50,890 --> 00:17:53,000 Speaker 2: everyone is using it to differentiate the products and the 353 00:17:53,010 --> 00:17:55,790 Speaker 2: services that they provide to citizen and consumer. So I 354 00:17:55,800 --> 00:17:57,439 Speaker 2: think the company needs to be very clear on the 355 00:17:57,449 --> 00:18:00,079 Speaker 2: agenda that they're setting the strategy that they're working towards 356 00:18:00,469 --> 00:18:03,390 Speaker 2: and then invite people within the organization to put up 357 00:18:03,400 --> 00:18:05,989 Speaker 2: their hands, to self select to nominate themselves, to put 358 00:18:06,000 --> 00:18:07,959 Speaker 2: themselves forward to go. Hey, I want to be a 359 00:18:07,969 --> 00:18:10,489 Speaker 2: part of this change. I want to contribute to this change. 360 00:18:10,660 --> 00:18:12,448 Speaker 2: And I would love to embrace this as a part 361 00:18:12,459 --> 00:18:15,659 Speaker 2: of my workflow. And I think that's the important part 362 00:18:15,670 --> 00:18:20,010 Speaker 2: is the sort of intellectual curiosity is what's required of 363 00:18:20,020 --> 00:18:20,869 Speaker 2: the individual 364 00:18:21,099 --> 00:18:24,780 Speaker 2: to be intellectually curious to embrace it, not resist it. 365 00:18:24,790 --> 00:18:26,929 Speaker 2: Put up your hand, get involved, figure out how you 366 00:18:26,939 --> 00:18:29,609 Speaker 2: can use these tools and techniques in your workflow and 367 00:18:29,619 --> 00:18:32,020 Speaker 2: give suggestions to the company around. Hey, we could do 368 00:18:32,030 --> 00:18:34,550 Speaker 2: this better as a result or with the time that 369 00:18:34,560 --> 00:18:36,478 Speaker 2: we freed up, hey, I can go on to do 370 00:18:36,489 --> 00:18:39,729 Speaker 2: these other strategic things, creative things that actually will have 371 00:18:39,739 --> 00:18:42,969 Speaker 2: a bigger impact on the company. So it's, it's an act. 372 00:18:43,089 --> 00:18:47,369 Speaker 2: OK. That's a, it's an end answer, but it makes sense. 373 00:18:47,380 --> 00:18:50,659 Speaker 2: It perfectly makes sense because you can't just leave it 374 00:18:50,670 --> 00:18:52,938 Speaker 2: to the poor worker to figure it out. Right? I mean, 375 00:18:52,949 --> 00:18:56,020 Speaker 2: he doesn't have the resources, he doesn't have the agency, correct. 376 00:18:56,030 --> 00:18:57,989 Speaker 2: And they should be asking of the companies that they're in. 377 00:18:58,000 --> 00:19:00,180 Speaker 2: Where is our data and A I strategy? And if you, 378 00:19:00,189 --> 00:19:02,569 Speaker 2: if you're not getting a clear answer, that's a pretty 379 00:19:02,579 --> 00:19:03,689 Speaker 2: good indicator. Maybe you should. 380 00:19:04,979 --> 00:19:08,198 Speaker 2: Yeah. OK. But related to that, right? And this is 381 00:19:08,209 --> 00:19:10,550 Speaker 2: another thing that's come up in Singapore quite a bit. 382 00:19:10,560 --> 00:19:13,010 Speaker 2: I've heard this before that you need to be curious, 383 00:19:13,020 --> 00:19:15,719 Speaker 2: you need to be adaptable, you need to really want 384 00:19:15,729 --> 00:19:20,040 Speaker 2: to change. There are folks who won't, OK, let's just 385 00:19:20,050 --> 00:19:23,530 Speaker 2: face it. That's how life is, right? And so those 386 00:19:23,540 --> 00:19:30,079 Speaker 2: who will, will reap the benefits, I would say monetary benefits, everything, right? 387 00:19:30,089 --> 00:19:32,719 Speaker 2: All the spoils of this new world 388 00:19:33,329 --> 00:19:38,488 Speaker 2: and those who don't will be stuck and the stratification 389 00:19:38,500 --> 00:19:41,208 Speaker 2: becomes bigger. Is there something that worries people like you 390 00:19:41,219 --> 00:19:43,959 Speaker 2: guys in this space? Yeah. Look, I think it's fair 391 00:19:43,969 --> 00:19:49,109 Speaker 2: to say done incorrectly and executed incorrectly. There absolutely is 392 00:19:49,119 --> 00:19:54,419 Speaker 2: a risk that you create more divisive inequalities in the 393 00:19:54,430 --> 00:19:56,329 Speaker 2: haves and the have nots. And I think again, that's 394 00:19:56,339 --> 00:19:59,109 Speaker 2: where government needs to play a very prominent role, right in, 395 00:19:59,359 --> 00:20:02,000 Speaker 2: again, skills for the future, you know, upskilling and reskilling 396 00:20:02,010 --> 00:20:04,660 Speaker 2: the talent that we have to your point. There may 397 00:20:04,670 --> 00:20:07,040 Speaker 2: be individuals for whatever reason that just cannot go on 398 00:20:07,050 --> 00:20:09,339 Speaker 2: that journey that we've got to think around the social 399 00:20:09,349 --> 00:20:12,228 Speaker 2: and safety guards that we have in place, the safety 400 00:20:12,239 --> 00:20:13,879 Speaker 2: nets and the support that we have in place for 401 00:20:13,890 --> 00:20:15,260 Speaker 2: those individuals because 402 00:20:15,680 --> 00:20:20,079 Speaker 2: it shouldn't be that we're creating inequality that can't be addressed. 403 00:20:20,089 --> 00:20:22,520 Speaker 2: So that's something that needs to be considered 100%. I 404 00:20:22,530 --> 00:20:25,129 Speaker 2: think you're right, there will be individuals that will need 405 00:20:25,140 --> 00:20:28,379 Speaker 2: those social safety nets and that's where government needs to 406 00:20:28,390 --> 00:20:31,260 Speaker 2: step in and help, right? OK. Some are calling this 407 00:20:31,270 --> 00:20:36,020 Speaker 2: the new industrial revolution. What are your personal predictions? Of course, 408 00:20:36,060 --> 00:20:40,020 Speaker 2: you're biased as a data man. But now tell me, 409 00:20:40,030 --> 00:20:41,849 Speaker 2: do you think someday 410 00:20:42,640 --> 00:20:46,389 Speaker 2: even the radiologist will be replaced by a robot doing 411 00:20:46,400 --> 00:20:47,229 Speaker 2: the X ray? 412 00:20:47,900 --> 00:20:49,959 Speaker 2: I, I think we're a long way off from that. 413 00:20:49,969 --> 00:20:51,810 Speaker 2: I think we're a long way off from that maybe 414 00:20:51,829 --> 00:20:55,640 Speaker 2: that's 2.0 yeah, 2.01 point B. 415 00:20:57,869 --> 00:21:00,129 Speaker 2: Um But yeah, I think we're a long way off 416 00:21:00,140 --> 00:21:02,869 Speaker 2: from that. I envisage data and A I and be 417 00:21:02,880 --> 00:21:07,170 Speaker 2: embedded in every workflow for every industry and for every job, 418 00:21:07,180 --> 00:21:09,339 Speaker 2: what's your time frame within the next 3 to 5 419 00:21:09,349 --> 00:21:11,819 Speaker 2: years in some shape or form people will be adding 420 00:21:11,829 --> 00:21:13,119 Speaker 2: this into their workflow. 421 00:21:13,790 --> 00:21:16,890 Speaker 2: Does that mean that it's replacing the individual? I would 422 00:21:16,900 --> 00:21:20,550 Speaker 2: still argue? No, I think there's still a huge amount 423 00:21:20,560 --> 00:21:22,839 Speaker 2: of value having the human in the loop one to 424 00:21:22,849 --> 00:21:25,459 Speaker 2: make sure that again, we're being ethical and responsible and 425 00:21:25,469 --> 00:21:27,889 Speaker 2: in how we're using those two, I think to your 426 00:21:27,900 --> 00:21:30,949 Speaker 2: earlier points in the earlier discussion, the emotional intelligence, the 427 00:21:30,959 --> 00:21:32,819 Speaker 2: human intelligence that we need to add on top is 428 00:21:32,829 --> 00:21:35,709 Speaker 2: something that is going to be incredibly hard to, to 429 00:21:35,719 --> 00:21:38,099 Speaker 2: disrupt for quite a considered period of time. 430 00:21:38,359 --> 00:21:40,859 Speaker 2: And then three, I think folks are going to find 431 00:21:40,869 --> 00:21:44,449 Speaker 2: really creative ways to use that free time to go 432 00:21:44,459 --> 00:21:48,000 Speaker 2: and do other amazing things. So imagine if everybody embraces 433 00:21:48,010 --> 00:21:50,379 Speaker 2: their free time and puts that to work for good. 434 00:21:50,390 --> 00:21:54,260 Speaker 2: So think about the climate, think about the environment, think 435 00:21:54,270 --> 00:21:57,300 Speaker 2: about for good initiatives around mental health or well being, 436 00:21:57,520 --> 00:21:59,790 Speaker 2: if everyone could find some time to give back and 437 00:21:59,800 --> 00:22:01,780 Speaker 2: focus in on as an organization around the sort of 438 00:22:01,790 --> 00:22:05,359 Speaker 2: corporate social responsibility side, what an amazing place we'll be in. 439 00:22:05,369 --> 00:22:08,579 Speaker 2: I really like that. So recently I spent an hour 440 00:22:08,589 --> 00:22:12,599 Speaker 2: listening to Sam Altman. Of course, everybody knows who he is. So, 441 00:22:12,609 --> 00:22:15,520 Speaker 2: my teacher, friends I used to teach, tell me that 442 00:22:15,530 --> 00:22:16,660 Speaker 2: their kids 443 00:22:17,060 --> 00:22:21,800 Speaker 2: are using chat GP t to answer questions. But the 444 00:22:21,810 --> 00:22:27,160 Speaker 2: teachers too are using chat GP T to set questions. Yeah. 445 00:22:27,170 --> 00:22:31,150 Speaker 2: So he was talking to Trevor Noah, not a South African. 446 00:22:31,329 --> 00:22:33,829 Speaker 2: And Trevor asked him what I thought was a really 447 00:22:33,839 --> 00:22:35,000 Speaker 2: brilliant question. 448 00:22:35,290 --> 00:22:40,760 Speaker 2: The product that Sam Altman created will lead to people 449 00:22:40,770 --> 00:22:45,199 Speaker 2: losing jobs. I think that is certainly one small reality 450 00:22:45,209 --> 00:22:48,650 Speaker 2: in this whole business. Trevor's question to him was I 451 00:22:48,660 --> 00:22:52,709 Speaker 2: hope that you will know what it's like to lose 452 00:22:52,719 --> 00:22:56,149 Speaker 2: a job because you lost it for a hot minute, right? 453 00:22:56,160 --> 00:22:57,329 Speaker 2: And he said, yes, 454 00:22:57,760 --> 00:23:02,510 Speaker 2: it's sobered up to just how fast things can change. 455 00:23:03,150 --> 00:23:06,209 Speaker 2: You said 3 to 5 years, honestly, that would feel 456 00:23:06,219 --> 00:23:10,750 Speaker 2: like 24 hours, right? For businesses who are trying to compete. 457 00:23:10,989 --> 00:23:15,459 Speaker 2: Sam Altman basically said that he feels humans cannot be 458 00:23:15,469 --> 00:23:16,939 Speaker 2: defeated by machines. 459 00:23:18,489 --> 00:23:20,959 Speaker 2: You agree? I do agree. I don't see this as 460 00:23:20,969 --> 00:23:24,060 Speaker 2: the end of society by and large or humanity by 461 00:23:24,069 --> 00:23:27,319 Speaker 2: and large. I think again, those are quite sensationalist theories 462 00:23:27,329 --> 00:23:30,550 Speaker 2: and I know they're out there myself ever, the optimist. 463 00:23:30,560 --> 00:23:31,310 Speaker 2: I do think 464 00:23:31,699 --> 00:23:34,978 Speaker 2: what will set humanity apart is how we embrace and 465 00:23:34,989 --> 00:23:38,329 Speaker 2: leverage this for the greater good and to unlock the 466 00:23:38,500 --> 00:23:42,469 Speaker 2: limitless human potential that we have exactly our ability to 467 00:23:42,479 --> 00:23:48,189 Speaker 2: thrive to survive to succeed is unprecedented, right? We're still 468 00:23:48,199 --> 00:23:50,629 Speaker 2: at the apex for now. I don't know what will 469 00:23:50,640 --> 00:23:53,099 Speaker 2: happen in the future. But yeah, I think that there 470 00:23:53,109 --> 00:23:53,729 Speaker 2: is that 471 00:23:54,390 --> 00:23:56,839 Speaker 2: part which I think we need to embrace the fact 472 00:23:56,849 --> 00:24:00,859 Speaker 2: that we should always be the ones in control. 100%. 473 00:24:00,930 --> 00:24:02,869 Speaker 2: It goes back to that discussion around maybe one of 474 00:24:02,880 --> 00:24:04,829 Speaker 2: the other things I'll mention around what I see coming 475 00:24:04,839 --> 00:24:07,139 Speaker 2: is this concept of A I governance is going to 476 00:24:07,150 --> 00:24:09,819 Speaker 2: be so important, right? To your point. Like we need 477 00:24:09,829 --> 00:24:12,030 Speaker 2: to have control over this technology and we need to 478 00:24:12,040 --> 00:24:14,760 Speaker 2: have control over how these models are used and the 479 00:24:14,770 --> 00:24:18,040 Speaker 2: decisions that they're making. So this whole area around A 480 00:24:18,050 --> 00:24:20,540 Speaker 2: I governance again, ethics and responsibility. 481 00:24:20,829 --> 00:24:22,719 Speaker 2: In addition to everyone embracing the change is going to 482 00:24:22,729 --> 00:24:25,280 Speaker 2: be a big part of the success story here. Get 483 00:24:25,290 --> 00:24:27,619 Speaker 2: that wrong. You're gonna, we the pain, get it right. Yeah, 484 00:24:27,630 --> 00:24:30,280 Speaker 2: you'll be off to the races. Yeah. Yeah. But exciting 485 00:24:30,290 --> 00:24:33,209 Speaker 2: times I think. Thank you. Thank you Nick for joining 486 00:24:33,219 --> 00:24:36,458 Speaker 2: us on this wonderful conversation. I hope you enjoy 487 00:24:36,569 --> 00:24:39,149 Speaker 2: the conversation that I've had with Nick as well. Please 488 00:24:39,160 --> 00:24:41,369 Speaker 2: do us a big favor and go to Spotify and 489 00:24:41,380 --> 00:24:44,310 Speaker 2: Apple and follow us, leave us a review, comment or 490 00:24:44,319 --> 00:24:46,729 Speaker 2: question these things, help us a great deal in the 491 00:24:46,739 --> 00:24:49,229 Speaker 2: kind of content we serve up. Thank you to my 492 00:24:49,239 --> 00:24:52,260 Speaker 2: podcast team as well until we see you next week.