1 00:00:06,440 --> 00:00:09,500 Speaker 1: Welcome to COVID Time, a podcast series on Markets and 2 00:00:09,510 --> 00:00:13,500 Speaker 1: Economies from Ds Group Research. I'm chief economist, welcoming you 3 00:00:13,510 --> 00:00:15,489 Speaker 1: to our 98th episode. 4 00:00:15,880 --> 00:00:19,180 Speaker 1: Today, we will put aside most of global macro. There 5 00:00:19,190 --> 00:00:21,040 Speaker 1: will be a little bit of it, but mostly it 6 00:00:21,049 --> 00:00:23,180 Speaker 1: will be not the usual global macro that we talk 7 00:00:23,190 --> 00:00:26,468 Speaker 1: about because we will delve into the world of tech startups. 8 00:00:26,969 --> 00:00:30,120 Speaker 1: PAA is co-founder of Grass, which was recently named by 9 00:00:30,129 --> 00:00:33,250 Speaker 1: C B Insights to its annual retail tech 100 list 10 00:00:33,259 --> 00:00:36,290 Speaker 1: showcasing the 100 most promising private retail tech companies in 11 00:00:36,299 --> 00:00:39,509 Speaker 1: the world. According to Grass's website, it is a growth 12 00:00:39,520 --> 00:00:43,049 Speaker 1: as a service technology solution provider using predictive A I 13 00:00:43,110 --> 00:00:45,669 Speaker 1: to help enhance the growth of e-commerce businesses. 14 00:00:45,959 --> 00:00:50,400 Speaker 1: Its proprietary platform integrates previously siloed e-commerce data, but more 15 00:00:50,409 --> 00:00:53,500 Speaker 1: about them about all that from Prem in a second. 16 00:00:53,790 --> 00:00:57,319 Speaker 1: But before Grass Prem founded several companies served at the 17 00:00:57,330 --> 00:01:00,020 Speaker 1: board of several more and has been deeply involved in 18 00:01:00,029 --> 00:01:02,840 Speaker 1: tech investment. Prem. Pat. Welcome to Kobe Time. 19 00:01:02,970 --> 00:01:07,529 Speaker 2: Thank you very much, big fan. I started listening back 20 00:01:07,540 --> 00:01:09,878 Speaker 2: in the early days of COVID and I haven't stopped. 21 00:01:09,889 --> 00:01:11,779 Speaker 2: So pleasure to be here. I've 22 00:01:11,790 --> 00:01:14,269 Speaker 1: spoken with your partner and some of your employees and 23 00:01:14,279 --> 00:01:16,139 Speaker 1: that has made me a fan of yours as well. 24 00:01:16,769 --> 00:01:20,260 Speaker 1: Um uh Prem uh we will talk a lot about 25 00:01:20,269 --> 00:01:23,419 Speaker 1: your journey as a tech entrepreneur, uh grass the startup 26 00:01:23,430 --> 00:01:25,470 Speaker 1: scene and so on. I really would love to hear 27 00:01:25,480 --> 00:01:27,739 Speaker 1: about your in and all of that. But first, um 28 00:01:27,750 --> 00:01:30,459 Speaker 1: we're discussing this in the middle of March. A few 29 00:01:30,470 --> 00:01:33,529 Speaker 1: things have happened in the form of Silicon Valley Bank 30 00:01:33,540 --> 00:01:35,839 Speaker 1: and a few other banks that have got under. So 31 00:01:35,849 --> 00:01:38,959 Speaker 1: talk to us about its impact on the tech startup scene. 32 00:01:39,970 --> 00:01:43,300 Speaker 2: Um Well, I mean, I think two lenses to it, 33 00:01:43,309 --> 00:01:48,220 Speaker 2: I think uh as a depositor, it's really quite worrying 34 00:01:48,230 --> 00:01:51,120 Speaker 2: that you can deposit money in, in uh in a 35 00:01:51,129 --> 00:01:54,169 Speaker 2: bank that you trust and then one day wake up 36 00:01:54,180 --> 00:01:58,769 Speaker 2: and not be able to withdraw. Right? Um That is 37 00:01:58,779 --> 00:02:02,830 Speaker 2: obviously extremely worrying. Um I think the other lens is 38 00:02:02,839 --> 00:02:06,980 Speaker 2: that um as a a technology startup, 39 00:02:07,250 --> 00:02:12,209 Speaker 2: uh if this were to continue, um along with high 40 00:02:12,220 --> 00:02:13,139 Speaker 2: interest rates, 41 00:02:13,770 --> 00:02:18,580 Speaker 2: I worry about the venture capital ecosystem and the ability 42 00:02:18,589 --> 00:02:22,250 Speaker 2: for other entrepreneurs and startups to, to raise money. Uh 43 00:02:22,270 --> 00:02:26,550 Speaker 2: things have fundamentally changed since from 12 months ago, since 44 00:02:26,559 --> 00:02:27,880 Speaker 2: we raised money at rest. 45 00:02:28,070 --> 00:02:30,698 Speaker 1: Right? And so let's stay with that a little longer. 46 00:02:30,710 --> 00:02:34,448 Speaker 1: So my understanding is two Fridays ago when the S 47 00:02:34,460 --> 00:02:37,360 Speaker 1: U V issue was front and center. A lot of 48 00:02:37,369 --> 00:02:37,559 Speaker 1: V C 49 00:02:37,809 --> 00:02:41,199 Speaker 1: lobbied Washington DC. Very hard. And their point was that 50 00:02:41,210 --> 00:02:44,440 Speaker 1: in S E V you have a lot of startups 51 00:02:44,449 --> 00:02:48,960 Speaker 1: and tech companies with much more than $250,000 deposits and 52 00:02:48,970 --> 00:02:50,919 Speaker 1: S E V going under without taking care of those 53 00:02:50,929 --> 00:02:54,220 Speaker 1: depositors would be very, very problematic. What were you hearing 54 00:02:54,229 --> 00:02:56,758 Speaker 1: from your peers? Or perhaps even you had some exposure 55 00:02:56,770 --> 00:02:58,279 Speaker 1: to S E V? I mean, what was the mindset 56 00:02:58,288 --> 00:02:59,020 Speaker 1: in those days? 57 00:02:59,288 --> 00:03:03,299 Speaker 2: Fortunately, no good. We are D BS born and bred. 58 00:03:03,949 --> 00:03:07,160 Speaker 2: Um No, I mean, I think, you know, everyone's worried about, 59 00:03:08,119 --> 00:03:08,729 Speaker 2: uh 60 00:03:09,600 --> 00:03:12,699 Speaker 2: what if they have made this mistake or what is 61 00:03:12,710 --> 00:03:16,649 Speaker 2: the mistake? The mistake is they manage risk badly, right. 62 00:03:17,059 --> 00:03:21,190 Speaker 2: Um I don't know where D Bs is my bank. 63 00:03:21,199 --> 00:03:24,899 Speaker 2: I don't know where D Bs invests, right? Um And 64 00:03:24,910 --> 00:03:28,330 Speaker 2: everyone is now worried it's fundamental, right? This is existential 65 00:03:28,339 --> 00:03:31,559 Speaker 2: almost uh that if you have to now double click 66 00:03:31,570 --> 00:03:35,589 Speaker 2: on where your bank invests and whether there's timing mismatch 67 00:03:35,960 --> 00:03:40,220 Speaker 2: do your deposits. Um Are they at risk? Right. And 68 00:03:40,229 --> 00:03:44,160 Speaker 2: that's really what is scaring everybody. Um I mean, I 69 00:03:44,169 --> 00:03:47,649 Speaker 2: think that, you know, at a more macro level, 70 00:03:48,380 --> 00:03:52,470 Speaker 2: we're sort of just seeing the after effects of the 71 00:03:52,479 --> 00:03:57,350 Speaker 2: COVID hangover. Uh And, you know, we saw a prolonged 72 00:03:57,360 --> 00:04:01,580 Speaker 2: period of zero interest rates. Uh We saw a lot 73 00:04:01,589 --> 00:04:06,130 Speaker 2: of liquidity in the system. Uh We saw some uh 74 00:04:06,559 --> 00:04:10,929 Speaker 2: fast and loose bets being made, uh during that, you know, 75 00:04:11,250 --> 00:04:15,210 Speaker 2: uh 0% interest rate regime. 76 00:04:15,759 --> 00:04:18,739 Speaker 2: Um And now you've got a fed which is basically 77 00:04:18,750 --> 00:04:23,989 Speaker 2: taking up interest prices and interest rates fast. Right. And, uh, 78 00:04:25,299 --> 00:04:28,209 Speaker 2: and, and, and you're going to see the after effects, 79 00:04:28,220 --> 00:04:32,350 Speaker 2: not just in banking, but in every single sector. Um 80 00:04:32,359 --> 00:04:35,690 Speaker 2: but I would have thought that, you know, banks would 81 00:04:36,019 --> 00:04:39,649 Speaker 2: have been able to manage risk better. Uh long story short. 82 00:04:39,959 --> 00:04:43,290 Speaker 2: Um And now I think you're faced with a sort 83 00:04:43,299 --> 00:04:48,250 Speaker 2: of a question on whether, you know, banks that invested 84 00:04:48,260 --> 00:04:50,959 Speaker 2: in crypto, for example, should they be back, stopped by 85 00:04:50,970 --> 00:04:54,489 Speaker 2: the government or their depositors be back stopped? And uh 86 00:04:55,040 --> 00:04:58,149 Speaker 2: Yeah, it's uh it's an interesting time. It's reminiscent of 87 00:04:58,160 --> 00:05:02,519 Speaker 2: 2008 in some respects, hopefully nowhere near as bad. Uh 88 00:05:02,529 --> 00:05:07,850 Speaker 2: where in 2008, a lot of it was banks and, 89 00:05:07,859 --> 00:05:13,428 Speaker 2: and housing, right? And now it's sort of banks and technology. 90 00:05:13,829 --> 00:05:15,989 Speaker 2: Um and uh 91 00:05:16,760 --> 00:05:20,170 Speaker 2: I think that, uh you know, a lot of people 92 00:05:20,178 --> 00:05:24,329 Speaker 2: have climbed all over venture capital. Um And I don't know, I, 93 00:05:24,339 --> 00:05:26,750 Speaker 2: I have a fairly contrarian opinion. 94 00:05:26,760 --> 00:05:28,269 Speaker 1: Right. Right. Now we're going to get into that in 95 00:05:28,279 --> 00:05:32,299 Speaker 1: greater detail. Um, talking to people in Silicon Valley, which 96 00:05:32,309 --> 00:05:34,170 Speaker 1: I'm sure you didn't during that time, 97 00:05:34,380 --> 00:05:36,899 Speaker 1: was your sense that there were a lot of companies 98 00:05:36,910 --> 00:05:39,510 Speaker 1: which only had business with S U V and therefore, 99 00:05:39,519 --> 00:05:41,700 Speaker 1: if S V V collapsed, they would not be able 100 00:05:41,709 --> 00:05:45,130 Speaker 1: to make payroll or was it exaggerated that actually companies 101 00:05:45,140 --> 00:05:46,200 Speaker 1: had multiple 102 00:05:46,209 --> 00:05:49,059 Speaker 2: banks. No, I think it was quite the opposite. I 103 00:05:49,070 --> 00:05:54,079 Speaker 2: think TB was, was home not just to funds, um 104 00:05:54,720 --> 00:05:57,589 Speaker 2: but also to their portfolio companies and 105 00:05:58,529 --> 00:06:02,299 Speaker 2: uh hindsight's a, a great thing. But look at the 106 00:06:02,309 --> 00:06:06,630 Speaker 2: end of the day startups, uh uh they've got a 107 00:06:06,640 --> 00:06:10,279 Speaker 2: lot to worry about. It's a bit like, um you know, 108 00:06:10,290 --> 00:06:14,339 Speaker 2: dancing between raindrops trying not to get wet and the 109 00:06:14,350 --> 00:06:16,950 Speaker 2: last thing you need to worry about is managing multiple 110 00:06:16,959 --> 00:06:18,040 Speaker 2: banking entities. 111 00:06:18,709 --> 00:06:20,678 Speaker 2: Um So, yeah, I think most of them 112 00:06:21,559 --> 00:06:25,789 Speaker 2: uh struggle with payroll. Um and uh are still worried 113 00:06:26,049 --> 00:06:28,969 Speaker 2: about when the money and how it's going to come back. 114 00:06:28,980 --> 00:06:31,600 Speaker 2: I don't think it's a straightforward process, 115 00:06:32,230 --> 00:06:34,540 Speaker 1: the guarantee is there, but that doesn't mean that everything 116 00:06:34,549 --> 00:06:34,820 Speaker 1: can be. 117 00:06:36,540 --> 00:06:40,809 Speaker 2: And I think right now uh for startup founders, uh 118 00:06:40,940 --> 00:06:41,488 Speaker 2: you know, 119 00:06:42,329 --> 00:06:47,170 Speaker 2: I think 2020 2021 was very much about growth at 120 00:06:47,178 --> 00:06:48,019 Speaker 2: all costs. 121 00:06:48,899 --> 00:06:57,670 Speaker 2: Uh 2022 2023 very much about survival runway cash flow margin. 122 00:06:57,940 --> 00:07:02,549 Speaker 2: Um And, and yeah, cash flow is cash flow. That's 123 00:07:02,570 --> 00:07:02,880 Speaker 2: a good thing. 124 00:07:04,369 --> 00:07:08,250 Speaker 2: I agree. I, I think at a fundraising level, uh 125 00:07:08,779 --> 00:07:12,829 Speaker 2: we've raised 50 million bucks so far. Um Let me 126 00:07:12,839 --> 00:07:16,200 Speaker 2: just put it this way. Uh 40 million, the 1st 127 00:07:16,209 --> 00:07:16,660 Speaker 2: 40 128 00:07:17,329 --> 00:07:23,500 Speaker 2: Uh much easier than the last 10. Um and um yeah, investors, 129 00:07:23,510 --> 00:07:29,339 Speaker 2: their LPS everyone is looking at the unit economics um 130 00:07:29,350 --> 00:07:33,500 Speaker 2: and profitability with a magnifying glass and that was not 131 00:07:33,510 --> 00:07:35,130 Speaker 2: the case two years ago. I 132 00:07:35,480 --> 00:07:37,380 Speaker 1: Mean, one wonders whether the 133 00:07:38,440 --> 00:07:41,079 Speaker 1: good thing also has some downside, which is that you 134 00:07:41,089 --> 00:07:45,279 Speaker 1: do want a lot of creative destruction and free uh, 135 00:07:45,290 --> 00:07:48,510 Speaker 1: germination of ideas and very eager money. If that were 136 00:07:48,519 --> 00:07:51,130 Speaker 1: to get constrained a little bit, maybe that 137 00:07:51,760 --> 00:07:54,209 Speaker 1: put some dark cloud for the outlook that, you know, 138 00:07:54,220 --> 00:07:56,690 Speaker 1: we won't have that many interesting things percolating through the 139 00:07:56,700 --> 00:08:00,410 Speaker 1: system because the fear of failure would constrain money going in. 140 00:08:01,029 --> 00:08:03,019 Speaker 2: Yes, I mean, look, uh 141 00:08:03,859 --> 00:08:07,328 Speaker 2: I think if, if, if you look, if you total 142 00:08:07,339 --> 00:08:10,510 Speaker 2: up all venture capital globally, right? It's 143 00:08:11,179 --> 00:08:18,019 Speaker 2: uh 2021 was about $700 billion right? Uh uh 144 00:08:19,029 --> 00:08:27,339 Speaker 2: But I think it's settling that, that $400 billion 400 145 00:08:27,350 --> 00:08:27,880 Speaker 2: billion 146 00:08:29,170 --> 00:08:32,619 Speaker 2: on a global GDP of 100 trillion, right? 147 00:08:33,570 --> 00:08:38,020 Speaker 2: That is your investment and innovation. And if that dries up, 148 00:08:38,729 --> 00:08:39,299 Speaker 2: um 149 00:08:40,229 --> 00:08:41,318 Speaker 2: I think that, 150 00:08:42,020 --> 00:08:46,159 Speaker 2: you know, innovation in its truest sense, whether it's climate change, 151 00:08:46,169 --> 00:08:49,710 Speaker 2: whether it's health, whether it's commerce, whether it's financial inclusion. 152 00:08:50,429 --> 00:08:53,799 Speaker 2: Um that was slowed out. Um So 153 00:08:54,539 --> 00:08:59,520 Speaker 2: yeah, there's some larger, much larger implication than, than um 154 00:08:59,789 --> 00:09:01,549 Speaker 2: short term blame. Right. 155 00:09:01,559 --> 00:09:03,478 Speaker 1: Right. Now, which is, I think why we have seen 156 00:09:03,489 --> 00:09:07,069 Speaker 1: regulators particularly in the us? Uh not necessarily go with 157 00:09:07,080 --> 00:09:09,900 Speaker 1: the playbook of just letting people who have more than 158 00:09:09,909 --> 00:09:12,549 Speaker 1: $250,000 under the bus because I think those are the 159 00:09:12,559 --> 00:09:17,609 Speaker 1: concerns echoing through them to some extent, right from your journey. 160 00:09:17,799 --> 00:09:19,909 Speaker 1: Are you, are we going to start talking about cricket 161 00:09:19,919 --> 00:09:22,770 Speaker 1: or are we going to talk about something even before that? 162 00:09:23,270 --> 00:09:26,520 Speaker 2: Um Yeah, I mean, we can start anywhere you like 163 00:09:26,659 --> 00:09:29,359 Speaker 2: um how, where would you like to start? 164 00:09:29,369 --> 00:09:32,409 Speaker 1: Well, tell us about your journey. What brought you to grass? 165 00:09:32,419 --> 00:09:34,400 Speaker 1: What are going back to say 20, years? 166 00:09:35,650 --> 00:09:38,520 Speaker 2: Oh man, loaded question. Uh 167 00:09:39,219 --> 00:09:42,659 Speaker 2: So I guess my journey to borrow from Hollywood has 168 00:09:42,669 --> 00:09:43,630 Speaker 2: been um 169 00:09:46,030 --> 00:09:48,840 Speaker 2: I went from Mad Men and Maguire 170 00:09:50,090 --> 00:09:55,059 Speaker 2: to today, Money Ball and Minority Report. I 171 00:09:55,070 --> 00:09:56,839 Speaker 1: hope everybody got that. I got 172 00:09:58,619 --> 00:10:02,770 Speaker 2: it right. So uh grew up in a um uh 173 00:10:02,979 --> 00:10:06,598 Speaker 2: very much in a marketing family that was one job 174 00:10:06,609 --> 00:10:10,580 Speaker 2: only all his life at Unilever mom worked at an agency, 175 00:10:10,700 --> 00:10:16,880 Speaker 2: started off in, in, in advertising, moved into what embarrassed 176 00:10:16,890 --> 00:10:19,960 Speaker 2: my father, which was something called sports marketing. 177 00:10:20,359 --> 00:10:25,890 Speaker 2: Um and uh managing celebrities like the likes of Tendulkar 178 00:10:25,900 --> 00:10:30,549 Speaker 2: and Ganguli. And um and he was embarrassed 179 00:10:31,179 --> 00:10:34,109 Speaker 2: Until the movie Jerry Maguire came out and then it 180 00:10:34,119 --> 00:10:38,099 Speaker 2: became the new sexy thing to do. Um and yeah, 181 00:10:38,109 --> 00:10:41,679 Speaker 2: I ran that. I was fortunate had an exit uh 182 00:10:41,710 --> 00:10:45,479 Speaker 2: early in my 20's. Um and then went on to 183 00:10:45,489 --> 00:10:46,510 Speaker 2: found uh 184 00:10:47,140 --> 00:10:51,260 Speaker 2: uh tech and digital marketing companies across India and Southeast 185 00:10:51,270 --> 00:10:56,760 Speaker 2: Asia and the Valley um are now very firmly sconc 186 00:10:56,770 --> 00:11:01,090 Speaker 2: in the world of data and artificial intelligence. Uh But 187 00:11:01,099 --> 00:11:05,750 Speaker 2: with a marketing twist. Uh So marketing has run through my, 188 00:11:05,909 --> 00:11:08,669 Speaker 2: I said the common thread, I guess that's run through 189 00:11:08,679 --> 00:11:09,770 Speaker 2: my existence. 190 00:11:10,130 --> 00:11:12,809 Speaker 1: Right? Listen, I want to talk about grass of course. 191 00:11:12,820 --> 00:11:16,189 Speaker 1: But before that, so staying with that issue of your 192 00:11:16,200 --> 00:11:18,309 Speaker 1: sort of journey over the last few decades, I see that, 193 00:11:18,320 --> 00:11:20,340 Speaker 1: you know, you also advise a lot of companies and 194 00:11:20,349 --> 00:11:22,609 Speaker 1: you also act as a direct investor in certain stuff. 195 00:11:22,780 --> 00:11:25,700 Speaker 1: So those things are also with the marketing lens or 196 00:11:25,710 --> 00:11:27,140 Speaker 1: you do some other stuff as well. 197 00:11:27,270 --> 00:11:31,219 Speaker 2: Yeah, I mean, myself and my co-founder, we've been advising 198 00:11:31,229 --> 00:11:35,020 Speaker 2: and investing in a multiple startups for a few years, 199 00:11:35,030 --> 00:11:35,348 Speaker 2: but 200 00:11:36,059 --> 00:11:39,260 Speaker 2: more often than not, it's startups that, you know, where 201 00:11:39,270 --> 00:11:43,289 Speaker 2: we can add value uh that typically seed stage investments 202 00:11:43,299 --> 00:11:49,359 Speaker 2: anywhere up to like half a million dollars. Um And yes, 203 00:11:49,369 --> 00:11:52,469 Speaker 2: I would say marketing is the common thread because we're 204 00:11:52,479 --> 00:11:59,369 Speaker 2: investing in companies that have some consumer facing business. Um 205 00:11:59,840 --> 00:12:02,260 Speaker 2: uh advertising included. 206 00:12:03,010 --> 00:12:03,229 Speaker 1: OK. 207 00:12:03,530 --> 00:12:06,460 Speaker 1: All right. Let's talk about Grass and let's see how 208 00:12:06,469 --> 00:12:08,659 Speaker 1: that sort of culminates everything that you've been doing the 209 00:12:08,669 --> 00:12:09,880 Speaker 1: last two decades. 210 00:12:11,049 --> 00:12:14,530 Speaker 2: Um Yeah. So Grass actually is a sort of an 211 00:12:14,539 --> 00:12:18,189 Speaker 2: acronym for growth as a service, which is a nice 212 00:12:18,200 --> 00:12:24,460 Speaker 2: play on. Um And uh they simply put where 213 00:12:25,140 --> 00:12:30,260 Speaker 2: uh we help companies grow online. Um We help them 214 00:12:30,270 --> 00:12:34,200 Speaker 2: grow topline and we help them manage margin. Uh And 215 00:12:34,210 --> 00:12:37,929 Speaker 2: we use artificial intelligence and automation to, to do it. 216 00:12:38,289 --> 00:12:39,549 Speaker 2: Uh So we take 217 00:12:40,190 --> 00:12:44,679 Speaker 2: gut instinct out of the equation and, and we automate 218 00:12:44,869 --> 00:12:47,880 Speaker 2: we automate their decision making via data. 219 00:12:48,690 --> 00:12:52,549 Speaker 2: Um We're not your typical startup. Um 220 00:12:53,400 --> 00:13:00,030 Speaker 2: You know, grass uh raised money in April 22. Uh 221 00:13:00,039 --> 00:13:07,770 Speaker 2: we acquired two software companies. Um our thesis was that 222 00:13:07,780 --> 00:13:07,799 Speaker 2: uh 223 00:13:08,609 --> 00:13:09,380 Speaker 2: um 224 00:13:12,070 --> 00:13:12,919 Speaker 2: retail 225 00:13:14,219 --> 00:13:18,179 Speaker 2: and e-commerce and advertising are gonna become one and the 226 00:13:18,190 --> 00:13:22,909 Speaker 2: same thing. Um And, and our thesis from two years ago, 227 00:13:24,219 --> 00:13:26,669 Speaker 2: I think it's come true. Uh And we go into 228 00:13:26,679 --> 00:13:29,750 Speaker 2: that in a second. Um And 229 00:13:30,409 --> 00:13:31,789 Speaker 2: yeah, I think we're also 230 00:13:32,640 --> 00:13:36,900 Speaker 2: geography is important unlike the US, which, you know, when 231 00:13:36,909 --> 00:13:37,390 Speaker 2: you look at, 232 00:13:38,500 --> 00:13:41,840 Speaker 2: you look at a country's GDP, uh you look at, 233 00:13:41,849 --> 00:13:44,789 Speaker 2: let's say, at a global level, let's say the, the 234 00:13:44,799 --> 00:13:50,140 Speaker 2: world does 100 trillion and, and GDP retail is probably 235 00:13:50,150 --> 00:13:56,349 Speaker 2: about 30 trillion. E-commerce is probably six on that 30 trillion, right? 236 00:13:56,679 --> 00:14:02,348 Speaker 2: Uh It'll vary by country region. Uh But we play 237 00:14:02,690 --> 00:14:06,169 Speaker 2: in from Mumbai to Manila where you're looking at 238 00:14:06,890 --> 00:14:10,728 Speaker 2: A combined GDP of about seven trillion. Um 239 00:14:11,659 --> 00:14:12,189 Speaker 2: um 240 00:14:13,489 --> 00:14:18,320 Speaker 2: 30% of that maybe is retail. Um but e-commerce is 241 00:14:18,330 --> 00:14:21,750 Speaker 2: only $200 billion. Uh and so there's a lot of 242 00:14:21,760 --> 00:14:24,760 Speaker 2: room for growth. Now, what's peculiar is that 243 00:14:25,630 --> 00:14:28,229 Speaker 2: in this part of the world, unlike the US or 244 00:14:28,239 --> 00:14:29,599 Speaker 2: China and the US 245 00:14:30,440 --> 00:14:33,210 Speaker 2: to, to do well, online, you sort of, you know, 246 00:14:33,219 --> 00:14:36,250 Speaker 2: you have Amazon, you have maybe Shopify and in China, 247 00:14:36,260 --> 00:14:38,609 Speaker 2: you have Baba and you have G D dot com. And, 248 00:14:39,440 --> 00:14:43,140 Speaker 2: but for some reason in India and Southeast Asia, you 249 00:14:43,150 --> 00:14:48,270 Speaker 2: have a very peculiar um, scenario where you have, uh, 250 00:14:48,280 --> 00:14:51,260 Speaker 2: some of the world's largest companies going head to head 251 00:14:51,559 --> 00:14:57,289 Speaker 2: for two things. Uh, one is e-commerce wallet spend, the 252 00:14:57,299 --> 00:14:58,989 Speaker 2: other is advertising dollars. 253 00:14:59,419 --> 00:15:04,210 Speaker 2: And so you have, uh, Amazon that you have reliance, 254 00:15:04,219 --> 00:15:07,770 Speaker 2: you have 10cent, you have Alibaba, you have the C group, 255 00:15:07,780 --> 00:15:11,659 Speaker 2: you have Tiktok, you have Google, you have Facebook, you 256 00:15:11,669 --> 00:15:16,229 Speaker 2: have all these juggernauts um playing in this arena. 257 00:15:17,409 --> 00:15:20,299 Speaker 2: And so when players of that 258 00:15:20,979 --> 00:15:28,190 Speaker 2: intelligence capitalization and sophistication play in the same arena, um 259 00:15:28,929 --> 00:15:31,479 Speaker 2: the only people who lose out actually are the people 260 00:15:31,739 --> 00:15:35,599 Speaker 2: selling online, the brands, the companies, the, the small and 261 00:15:35,609 --> 00:15:37,000 Speaker 2: medium sized businesses. 262 00:15:37,619 --> 00:15:39,559 Speaker 2: And so we're here to try and level the playing 263 00:15:39,570 --> 00:15:41,510 Speaker 2: field uh via A I 264 00:15:42,390 --> 00:15:44,400 Speaker 1: OK. I'm going to give you a hypothetical. It's not 265 00:15:44,409 --> 00:15:47,549 Speaker 1: entirely hypothetical because I know the company. So this friend 266 00:15:47,559 --> 00:15:49,960 Speaker 1: of mine, he's trying to set up as a fledgling company. 267 00:15:49,969 --> 00:15:53,679 Speaker 1: Uh It's about uh sports content based out of Singapore, 268 00:15:53,690 --> 00:15:57,349 Speaker 1: broadcast sports content, but he has to face ESPN S 269 00:15:57,359 --> 00:16:00,179 Speaker 1: and the, you know, big TV networks of the world. 270 00:16:00,369 --> 00:16:02,289 Speaker 1: And so if he were to come to you and 271 00:16:02,299 --> 00:16:05,700 Speaker 1: say that, you know, I have, you know, some approvals 272 00:16:05,710 --> 00:16:09,539 Speaker 1: from the regulators to capture local sports content, 273 00:16:10,020 --> 00:16:11,789 Speaker 1: but I have no idea how to monetize this. You know, 274 00:16:11,799 --> 00:16:14,590 Speaker 1: who in Singapore wants to watch like school sports or 275 00:16:14,599 --> 00:16:17,530 Speaker 1: premier League of Singapore for football or something like that. 276 00:16:17,780 --> 00:16:20,250 Speaker 1: Um Again, we have not prepared for this at all. 277 00:16:20,260 --> 00:16:23,159 Speaker 1: I'm just completely extempore. What kind of, you know, sort 278 00:16:23,169 --> 00:16:25,280 Speaker 1: of strategy would you help a company like that do? 279 00:16:25,289 --> 00:16:26,609 Speaker 1: Because you're talking about smallish 280 00:16:26,619 --> 00:16:29,669 Speaker 2: companies. So he's not doing e-commerce, he's doing content, 281 00:16:29,679 --> 00:16:31,520 Speaker 1: right. But he wants to, hopefully, you know, get a 282 00:16:31,530 --> 00:16:34,169 Speaker 1: subscription service and sell it to people and that sort 283 00:16:34,179 --> 00:16:37,049 Speaker 1: of stuff or sell it to other large content managers 284 00:16:37,059 --> 00:16:37,690 Speaker 1: in the world 285 00:16:37,700 --> 00:16:40,190 Speaker 2: that so that I think is more sort of under 286 00:16:40,200 --> 00:16:44,260 Speaker 2: or subscription services. And, and uh 287 00:16:45,309 --> 00:16:48,859 Speaker 2: yeah, I mean, it's, it's, it's, it's not easy, it's 288 00:16:48,979 --> 00:16:49,010 Speaker 2: not 289 00:16:49,609 --> 00:16:50,409 Speaker 1: notice that already 290 00:16:51,010 --> 00:16:56,900 Speaker 2: you're seeing. So you've got basically two, essentially two monetization models. 291 00:16:56,909 --> 00:16:57,710 Speaker 2: You have 292 00:16:58,309 --> 00:17:02,830 Speaker 2: uh subscription, uh and you have advertising and sometimes you 293 00:17:02,840 --> 00:17:08,719 Speaker 2: have a hybrid. Um and advertising is obviously based on, um, 294 00:17:09,119 --> 00:17:11,589 Speaker 2: it used to be based on eyeballs and how many 295 00:17:11,599 --> 00:17:16,890 Speaker 2: people watch today. It's based on how many people buy. Correct. Right. 296 00:17:17,369 --> 00:17:21,319 Speaker 2: Um So a little more difficult for a generic content 297 00:17:21,329 --> 00:17:26,839 Speaker 2: operator like a Netflix to, to make the, um, 298 00:17:27,130 --> 00:17:30,800 Speaker 2: to tie the, the, the loop between watching something and 299 00:17:30,810 --> 00:17:31,780 Speaker 2: buying something, right. 300 00:17:33,099 --> 00:17:36,310 Speaker 2: So that leaves the other business model which is subscription 301 00:17:36,319 --> 00:17:40,119 Speaker 2: as a service or SAS. Um, look, the reality is, 302 00:17:40,130 --> 00:17:44,589 Speaker 2: is that in India and Southeast Asia, um SARS has 303 00:17:44,599 --> 00:17:51,030 Speaker 2: just started, um, you literally have, you know, a handful 304 00:17:51,040 --> 00:17:56,430 Speaker 2: of companies that have uh exited uh IP O or 305 00:17:56,439 --> 00:17:57,689 Speaker 2: in terms of acquisition. 306 00:17:58,030 --> 00:18:01,310 Speaker 2: Uh But the important thing is that it has started, 307 00:18:01,319 --> 00:18:05,300 Speaker 2: um you know, and uh it doesn't, the region doesn't 308 00:18:05,310 --> 00:18:09,319 Speaker 2: have the depth of, say A US. Um 309 00:18:09,910 --> 00:18:11,319 Speaker 2: uh But where, 310 00:18:12,209 --> 00:18:16,260 Speaker 2: But it's, it only started 234 years ago, maybe five. 311 00:18:16,939 --> 00:18:20,770 Speaker 2: and we're seeing a lot of innovation in the space 312 00:18:20,780 --> 00:18:22,760 Speaker 2: uh in terms of our monetization model. 313 00:18:23,510 --> 00:18:28,919 Speaker 2: Uh So my advice to him would be um you know, 314 00:18:29,430 --> 00:18:30,040 Speaker 2: uh 315 00:18:31,890 --> 00:18:36,819 Speaker 2: it's difficult uh content is just a very, very difficult business, 316 00:18:36,829 --> 00:18:37,859 Speaker 1: very investment 317 00:18:37,869 --> 00:18:43,849 Speaker 2: intensive, investment intensive, long payback periods and so be patient, 318 00:18:43,859 --> 00:18:46,880 Speaker 2: find your niche, right? But you know how you say 319 00:18:46,890 --> 00:18:51,260 Speaker 2: this and then you and then you have um 320 00:18:52,000 --> 00:18:55,069 Speaker 2: what I thought was almost impossible where, you know, you 321 00:18:55,079 --> 00:18:56,930 Speaker 2: have um 322 00:18:57,900 --> 00:19:02,930 Speaker 2: influencers like a Mr beast who every time they put 323 00:19:02,939 --> 00:19:05,869 Speaker 2: out something is watched more times than the Super Bowl. 324 00:19:05,880 --> 00:19:09,198 Speaker 1: My 10 year old is an avid viewer, Kim 325 00:19:09,209 --> 00:19:12,500 Speaker 2: Kardashian, another one. Every time she puts out something it's 326 00:19:12,510 --> 00:19:15,949 Speaker 2: watched by more people than the Super Bowl or the 327 00:19:15,959 --> 00:19:17,030 Speaker 2: IP L. So, 328 00:19:17,530 --> 00:19:21,069 Speaker 2: uh you got to find that skate, right, right. Or 329 00:19:21,079 --> 00:19:23,300 Speaker 2: you've got to go deep into a niche, I guess. 330 00:19:23,449 --> 00:19:25,420 Speaker 1: OK. This might be a little closer to your heart. 331 00:19:25,430 --> 00:19:27,410 Speaker 1: So I recently was in Bangladesh and we met a 332 00:19:27,420 --> 00:19:30,300 Speaker 1: B2B company. Uh they are trying to become like the 333 00:19:30,310 --> 00:19:33,300 Speaker 1: original form of Alibaba, which is bring brands to their 334 00:19:33,310 --> 00:19:36,619 Speaker 1: marketplace and then connect these brands to retail stores in 335 00:19:36,630 --> 00:19:39,969 Speaker 1: which they have uh good data and information about, you know, 336 00:19:39,979 --> 00:19:42,140 Speaker 1: what is the demand side and they're trying to get 337 00:19:42,150 --> 00:19:45,319 Speaker 1: like say a Unilever to Bangladesh and then market their products. 338 00:19:46,160 --> 00:19:50,290 Speaker 1: They probably can benefit a lot from market intelligence and 339 00:19:50,300 --> 00:19:52,920 Speaker 1: user data and so on. So your A I thread, 340 00:19:52,930 --> 00:19:54,270 Speaker 1: how does that sort of feed in? 341 00:19:55,250 --> 00:19:55,780 Speaker 1: So 342 00:19:57,119 --> 00:20:00,448 Speaker 2: I'll, I'll tell you a story about how grass started and, 343 00:20:00,459 --> 00:20:03,500 Speaker 2: and the moment at which I thought this is now 344 00:20:03,510 --> 00:20:07,290 Speaker 2: something very real and worth pursuing. Um I was talking to, 345 00:20:07,300 --> 00:20:11,770 Speaker 2: I'm not gonna name names but uh a very senior 346 00:20:12,130 --> 00:20:14,810 Speaker 2: chief marketing officer of one of the 347 00:20:15,500 --> 00:20:19,469 Speaker 2: F MC G companies in Asia. Um but he said 348 00:20:19,479 --> 00:20:20,349 Speaker 2: to me, he said, 349 00:20:21,060 --> 00:20:26,050 Speaker 2: if you ask me how many chocolates I sold today 350 00:20:26,060 --> 00:20:26,949 Speaker 2: in Vietnam 351 00:20:27,630 --> 00:20:34,040 Speaker 2: on how many different channels uh website marketplace, uh or 352 00:20:34,050 --> 00:20:36,150 Speaker 2: how much chocolate was like in my warehouse 353 00:20:36,900 --> 00:20:41,390 Speaker 2: or how much Facebook or Google or tiktok contributed towards 354 00:20:41,400 --> 00:20:42,629 Speaker 2: the sale of that chocolate. 355 00:20:43,449 --> 00:20:45,030 Speaker 2: He said it would take me the best part of 356 00:20:45,040 --> 00:20:48,829 Speaker 2: two weeks to find the answer. And I was like, wow, 357 00:20:48,979 --> 00:20:53,560 Speaker 2: if a company of this size, uh has that problem, 358 00:20:53,569 --> 00:20:56,530 Speaker 2: I wonder what's happening with smaller companies. And so we 359 00:20:56,540 --> 00:21:00,659 Speaker 2: went and asked, you know, dozens of, of ecommerce heads 360 00:21:00,670 --> 00:21:05,040 Speaker 2: and CEO S and the response was identical, which is 361 00:21:05,050 --> 00:21:06,649 Speaker 2: that it is almost 362 00:21:07,069 --> 00:21:12,099 Speaker 2: are impossible for companies to put all the different data 363 00:21:12,109 --> 00:21:14,959 Speaker 2: sources together. They're very much in silos. 364 00:21:15,589 --> 00:21:16,159 Speaker 2: Um 365 00:21:16,869 --> 00:21:20,300 Speaker 2: So that's problem. Number one, collecting the data. Problem number 366 00:21:20,310 --> 00:21:22,099 Speaker 2: two is 367 00:21:23,160 --> 00:21:25,900 Speaker 2: what do you do with the data? Right. Uh And 368 00:21:25,910 --> 00:21:27,149 Speaker 2: that's really where 369 00:21:27,910 --> 00:21:34,040 Speaker 2: data science um data engineering uh and A I come in. 370 00:21:34,369 --> 00:21:36,839 Speaker 2: Um So I think India and Asia 371 00:21:37,810 --> 00:21:41,930 Speaker 2: or Southeast Asia where at the start of the journey 372 00:21:41,939 --> 00:21:47,020 Speaker 2: where companies are, are understanding the power of data um 373 00:21:47,030 --> 00:21:48,859 Speaker 2: and putting it all together. 374 00:21:49,569 --> 00:21:52,119 Speaker 2: Um But I would say if you had, 375 00:21:52,770 --> 00:21:55,890 Speaker 2: If you were to ask me, I'd say less than 10% 376 00:21:55,900 --> 00:22:01,530 Speaker 2: of companies uh in this region have understood, how important data, 377 00:22:01,540 --> 00:22:02,159 Speaker 2: is 378 00:22:02,739 --> 00:22:05,229 Speaker 2: how to put it together and what to do with it. 379 00:22:05,670 --> 00:22:08,910 Speaker 2: Uh So it's very much the start of the journey. 380 00:22:09,000 --> 00:22:09,599 Speaker 2: Um 381 00:22:09,989 --> 00:22:14,619 Speaker 1: So I am a prime example of this complication. I 382 00:22:14,630 --> 00:22:17,589 Speaker 1: recognize the value of data. But this podcast, for example, 383 00:22:17,599 --> 00:22:19,109 Speaker 1: will go out on a podcast platform, 384 00:22:19,579 --> 00:22:22,569 Speaker 1: Apple Google, Spotify, et cetera. It'll go out on youtube 385 00:22:22,819 --> 00:22:24,869 Speaker 1: and there's ad V S website in which there will 386 00:22:24,880 --> 00:22:27,159 Speaker 1: be some of my clients who will come and download 387 00:22:27,170 --> 00:22:30,839 Speaker 1: the podcast or what the PDF version or the audio version, 388 00:22:30,930 --> 00:22:33,750 Speaker 1: I can consolidate all this. I cannot. So for me, 389 00:22:33,760 --> 00:22:35,410 Speaker 1: if you were to ask me a week from now, 390 00:22:35,780 --> 00:22:37,849 Speaker 1: how did the podcast go? I'll be able to give 391 00:22:37,859 --> 00:22:40,699 Speaker 1: you some isolated answers about how much download was done 392 00:22:40,709 --> 00:22:43,879 Speaker 1: through the a specific class platform, but I will not 393 00:22:43,890 --> 00:22:46,419 Speaker 1: be able to consolidate. It'll take me also hours, but 394 00:22:46,430 --> 00:22:46,520 Speaker 1: you're 395 00:22:46,530 --> 00:22:48,930 Speaker 2: not alone, you're not alone. Uh I mean, uh 396 00:22:50,109 --> 00:22:52,040 Speaker 2: a lot of people won't admit it but they're in 397 00:22:52,050 --> 00:22:53,839 Speaker 2: exactly the same position. I mean, 398 00:22:54,650 --> 00:22:57,319 Speaker 2: I think that, you know, if you again, pull back 399 00:22:57,569 --> 00:23:00,869 Speaker 2: for a second and you look at what's happened over 400 00:23:00,880 --> 00:23:03,079 Speaker 2: the last 20 or 30 years, I think that, 401 00:23:04,459 --> 00:23:05,109 Speaker 2: um 402 00:23:06,209 --> 00:23:09,219 Speaker 2: You know, there have been sort of 3, 4 mass 403 00:23:09,229 --> 00:23:10,619 Speaker 2: mega trends, right? 404 00:23:11,369 --> 00:23:16,739 Speaker 2: Um I think thanks to uh Nokia and then Steve Jobs, 405 00:23:16,750 --> 00:23:21,448 Speaker 2: I think uh mobile and, and mobile internet to be 406 00:23:21,459 --> 00:23:23,619 Speaker 2: specific was the big theme 407 00:23:24,880 --> 00:23:27,869 Speaker 2: in the 2000s, right? Um I think that 408 00:23:28,859 --> 00:23:35,010 Speaker 2: somewhere in the the succeeding decade that mega trend became 409 00:23:35,020 --> 00:23:38,790 Speaker 2: the cloud, right? Um And then 410 00:23:39,979 --> 00:23:41,959 Speaker 2: I think the next trend is A I, I mean, 411 00:23:41,969 --> 00:23:45,500 Speaker 2: there was a, there was smaller, maybe trends like crypto 412 00:23:45,569 --> 00:23:48,540 Speaker 2: and web three and, and the metaverse and who knows 413 00:23:48,550 --> 00:23:51,219 Speaker 2: how that will play out. But I think that A 414 00:23:51,229 --> 00:23:54,550 Speaker 2: I is the next mega trend and you're seeing with 415 00:23:54,560 --> 00:23:58,629 Speaker 2: T GP T and all that. Um But I think 416 00:23:58,640 --> 00:24:02,520 Speaker 2: it all begins with putting all the data together and 417 00:24:02,530 --> 00:24:05,859 Speaker 2: uh that's really what grass does. We're helping companies put 418 00:24:05,869 --> 00:24:06,949 Speaker 2: their data together, 419 00:24:07,479 --> 00:24:11,930 Speaker 2: we're helping them make decisions based on that data in 420 00:24:11,939 --> 00:24:17,589 Speaker 2: real time. Um And uh we're taking gut instinct out 421 00:24:17,599 --> 00:24:21,510 Speaker 2: of the equation and automating a lot of those decisions. No. 422 00:24:21,520 --> 00:24:23,290 Speaker 1: So you are at the cutting edge because I think that's, 423 00:24:23,300 --> 00:24:26,130 Speaker 1: that's a big hold right now. But even in my 424 00:24:26,140 --> 00:24:30,130 Speaker 1: podcast example, technically speaking, it's all out there, but I 425 00:24:30,140 --> 00:24:32,400 Speaker 1: don't have a neat set of API S or a 426 00:24:32,410 --> 00:24:35,500 Speaker 1: consolidator out there to give me a full picture at 427 00:24:35,510 --> 00:24:37,189 Speaker 1: any real time, a given moment. 428 00:24:37,579 --> 00:24:40,489 Speaker 1: Um, OK, so that certainly tells us a bit about 429 00:24:40,500 --> 00:24:43,060 Speaker 1: grass and you talked about your fundraising round. So congratulations 430 00:24:43,069 --> 00:24:46,030 Speaker 1: on the 50 million fundraising. So given that 431 00:24:46,859 --> 00:24:50,520 Speaker 1: companies right now are going to be struggling either in 432 00:24:50,530 --> 00:24:54,119 Speaker 1: terms of higher cost for the availability itself. Give us 433 00:24:54,130 --> 00:24:57,739 Speaker 1: a sense of the startup scene. Um maybe not just 434 00:24:57,750 --> 00:24:59,659 Speaker 1: what's happening right now, but overall, you know, I mean, 435 00:24:59,670 --> 00:25:03,270 Speaker 1: are you excited by South Asia and Southeast Asia startup 436 00:25:03,280 --> 00:25:03,989 Speaker 1: scene right now? 437 00:25:04,000 --> 00:25:07,160 Speaker 2: Yeah, look, I meet about 100 maybe 50 to 100 438 00:25:07,170 --> 00:25:12,000 Speaker 2: founders every month uh across, you know, seven countries that 439 00:25:12,010 --> 00:25:15,849 Speaker 2: we operate in. And uh it is hot. 440 00:25:16,300 --> 00:25:17,630 Speaker 2: I mean, it is, 441 00:25:18,369 --> 00:25:18,978 Speaker 2: oh, 442 00:25:20,400 --> 00:25:25,130 Speaker 2: In the innovation that's happening is honestly, it's for me, 443 00:25:25,140 --> 00:25:29,300 Speaker 2: I'm 48 years old. I haven't seen this, the volume 444 00:25:29,310 --> 00:25:32,729 Speaker 2: of innovation happening at the startup level. Um 445 00:25:33,670 --> 00:25:35,920 Speaker 2: But I think that what's happened over the last couple 446 00:25:35,930 --> 00:25:40,938 Speaker 2: of weeks um is really worrying because if venture capital 447 00:25:40,949 --> 00:25:42,879 Speaker 2: dries up, um 448 00:25:44,239 --> 00:25:46,819 Speaker 2: what happens then um I mean, at the end of 449 00:25:46,829 --> 00:25:47,250 Speaker 2: the day, 450 00:25:48,020 --> 00:25:51,930 Speaker 2: I have a question for you, right? I mean, if, 451 00:25:51,939 --> 00:25:53,229 Speaker 2: if you were a billionaire 452 00:25:54,479 --> 00:25:59,188 Speaker 2: And maybe you are good one. But if you were 453 00:25:59,199 --> 00:26:02,669 Speaker 2: a billionaire, would you rather put five, uh, put, put 454 00:26:02,680 --> 00:26:05,430 Speaker 2: your money into a fixed deposit at five 455 00:26:06,310 --> 00:26:10,219 Speaker 2: Or give it to a venture capitalist in the hope 456 00:26:10,229 --> 00:26:14,989 Speaker 2: that he could return 20% IRR triple your money for 457 00:26:15,000 --> 00:26:17,839 Speaker 2: in five years. It's, it's a tough call right now. 458 00:26:17,849 --> 00:26:20,438 Speaker 2: And so my question to you is that 459 00:26:21,130 --> 00:26:24,770 Speaker 2: I don't think most founders or if any founders have 460 00:26:24,780 --> 00:26:25,699 Speaker 2: ever seen 461 00:26:26,319 --> 00:26:30,879 Speaker 2: Um a global situation in which interest rates may remain 462 00:26:30,890 --> 00:26:33,430 Speaker 2: between five and 10% for the next five years. 463 00:26:34,829 --> 00:26:35,920 Speaker 2: What happens? 464 00:26:35,930 --> 00:26:36,280 Speaker 1: Right. 465 00:26:37,550 --> 00:26:40,389 Speaker 1: Prem I mean, I don't want to come across as 466 00:26:40,599 --> 00:26:43,780 Speaker 1: utterly pessimistic because, you know, I am with the founder 467 00:26:43,790 --> 00:26:45,560 Speaker 1: and I know founders tend to be optimistic. But in 468 00:26:45,569 --> 00:26:47,550 Speaker 1: this particular instance, I have no way of sugar quitting 469 00:26:47,560 --> 00:26:50,199 Speaker 1: the answer that the choice that you mentioned, I think 470 00:26:50,209 --> 00:26:52,719 Speaker 1: the answer is very clear. It's the 5% um 471 00:26:53,719 --> 00:26:56,849 Speaker 1: the zero rate environment enabled a lot of things that 472 00:26:56,859 --> 00:26:58,589 Speaker 1: you and I I think will agree that, you know, 473 00:26:58,750 --> 00:27:01,589 Speaker 1: whether there was access or not, it create an unleashing 474 00:27:01,599 --> 00:27:05,149 Speaker 1: of capital to all sorts of projects. And that was 475 00:27:05,859 --> 00:27:08,409 Speaker 1: even if there were excesses, it had an overall positive 476 00:27:08,420 --> 00:27:10,520 Speaker 1: impact to your point that, you know, the scene became 477 00:27:10,530 --> 00:27:13,390 Speaker 1: hot and there are all these, you know, dreamers and 478 00:27:13,400 --> 00:27:16,760 Speaker 1: thinkers and entrepreneurs, bringing their thoughts and ideas together. And 479 00:27:16,770 --> 00:27:19,698 Speaker 1: there was a capital of a flow of capital that 480 00:27:19,709 --> 00:27:21,910 Speaker 1: was available for them. It's going to be difficult. Uh 481 00:27:21,920 --> 00:27:25,750 Speaker 1: I spent earlier today talking about the impact of the 482 00:27:25,760 --> 00:27:29,229 Speaker 1: S E V uh collapse. And we can talk about 483 00:27:29,239 --> 00:27:31,050 Speaker 1: a wide range of impacts. But the bottom line is 484 00:27:31,060 --> 00:27:32,969 Speaker 1: it translates into higher cost of capital. 485 00:27:33,439 --> 00:27:36,219 Speaker 1: Uh and, and the higher cost of capital changes, a 486 00:27:36,229 --> 00:27:38,560 Speaker 1: wide range of calculations in terms of R O E, 487 00:27:38,569 --> 00:27:40,900 Speaker 1: in terms of margin and so on. But that takes 488 00:27:40,910 --> 00:27:43,129 Speaker 1: me back to my question for you that when you 489 00:27:43,140 --> 00:27:44,430 Speaker 1: look at entrepreneur, I 490 00:27:44,439 --> 00:27:47,369 Speaker 2: Had one question, right? So 491 00:27:48,130 --> 00:27:52,739 Speaker 2: the flip side to that is that valuations have halved, right? 492 00:27:53,069 --> 00:27:53,659 Speaker 2: Uh 493 00:27:54,839 --> 00:27:57,510 Speaker 2: It's a great time to be investing in, in, in 494 00:27:57,520 --> 00:28:04,630 Speaker 2: younger companies, right? And so 5% versus a curated list 495 00:28:04,640 --> 00:28:08,369 Speaker 2: of investments uh that could be the home run. 496 00:28:08,670 --> 00:28:10,639 Speaker 2: It's not that easy a call, right? OK. 497 00:28:10,650 --> 00:28:13,619 Speaker 1: So I I met an investor who's on that camp 498 00:28:13,630 --> 00:28:16,479 Speaker 1: yesterday and that investor was basically saying that, you know, 499 00:28:16,630 --> 00:28:19,339 Speaker 1: but it was not your typical, it was a family office. 500 00:28:19,349 --> 00:28:22,540 Speaker 1: They've been successful, they have a lot of cash and 501 00:28:22,550 --> 00:28:25,119 Speaker 1: they rather bluntly told me that they're waiting for the 502 00:28:25,130 --> 00:28:27,719 Speaker 1: crash because they want to deploy capital and they want 503 00:28:27,729 --> 00:28:29,530 Speaker 1: to buy a lot of things to your point because 504 00:28:29,540 --> 00:28:32,520 Speaker 1: they felt that the valuations were excessive in recent years 505 00:28:32,719 --> 00:28:35,000 Speaker 1: and they don't think the valuation adjustment is done, but 506 00:28:35,010 --> 00:28:36,760 Speaker 1: they have a lot of dry powder and they will 507 00:28:36,770 --> 00:28:37,619 Speaker 1: deploy it 508 00:28:38,079 --> 00:28:40,189 Speaker 1: my response to them was that if there are many 509 00:28:40,199 --> 00:28:42,550 Speaker 1: people like you, then maybe the valuation will not go 510 00:28:42,560 --> 00:28:44,890 Speaker 1: down much further because people are sniffing around and looking 511 00:28:44,900 --> 00:28:45,530 Speaker 1: for value. 512 00:28:47,310 --> 00:28:48,640 Speaker 2: Yeah. Well, I, I mean, I hope, 513 00:28:50,219 --> 00:28:51,829 Speaker 2: uh, one way or the other, I mean, I obviously 514 00:28:51,839 --> 00:28:56,560 Speaker 2: hope that interest rates do come down, um, which allows 515 00:28:56,790 --> 00:29:01,280 Speaker 2: LP S of investors and venture capital to, to, to 516 00:29:01,290 --> 00:29:04,400 Speaker 2: be a bit more bullish. But I do worry. As 517 00:29:04,410 --> 00:29:06,949 Speaker 2: I said earlier, I said, if venture capital as an 518 00:29:06,959 --> 00:29:09,900 Speaker 2: ecosystem dries up, um I think it will have a 519 00:29:09,910 --> 00:29:13,630 Speaker 2: fundamental effect on global innovation and 520 00:29:14,050 --> 00:29:17,020 Speaker 2: innovation that, you know, it could be in health care, 521 00:29:17,030 --> 00:29:21,060 Speaker 2: could be in climate change, it could be in science, commerce, 522 00:29:21,069 --> 00:29:25,329 Speaker 2: financial inclusion. Um And that really is the worry um 523 00:29:25,339 --> 00:29:30,219 Speaker 2: because if that dries up and that innovation evaporates or 524 00:29:30,229 --> 00:29:33,760 Speaker 2: slows down, that's not good for anyone, I think. 525 00:29:34,489 --> 00:29:36,430 Speaker 1: So, I just want to stay with that Southeast Asia, 526 00:29:36,439 --> 00:29:39,500 Speaker 1: South Asia startups a little longer. Um given that, you know, 527 00:29:39,510 --> 00:29:42,010 Speaker 1: you literally need hundreds of uh founders on a regular 528 00:29:42,020 --> 00:29:44,369 Speaker 1: basis and you look at their books um or the 529 00:29:44,380 --> 00:29:46,859 Speaker 1: playbooks rather um margin 530 00:29:47,459 --> 00:29:49,880 Speaker 1: are, are these e-commerce businesses in South and South East 531 00:29:49,890 --> 00:29:53,479 Speaker 1: Asia capable of showing the kind of margin that we 532 00:29:53,489 --> 00:29:55,910 Speaker 1: have seen in the US tech startup system. 533 00:29:56,430 --> 00:29:57,400 Speaker 2: Yeah, obviously, it's a 534 00:29:58,400 --> 00:30:01,900 Speaker 2: startups have and so a very wide spectrum. But let 535 00:30:01,910 --> 00:30:05,729 Speaker 2: me just sort of give you some examples um 536 00:30:06,439 --> 00:30:10,020 Speaker 2: of India, Southeast Asia in the sector that I'm familiar with, 537 00:30:10,030 --> 00:30:14,650 Speaker 2: which is, which is commerce, right? Um So it just 538 00:30:14,660 --> 00:30:15,890 Speaker 2: so happens that 539 00:30:16,829 --> 00:30:22,489 Speaker 2: the success stories, the local success stories are all have 540 00:30:22,500 --> 00:30:25,800 Speaker 2: a common flavor to it. So Southeast Asia, you would 541 00:30:25,810 --> 00:30:27,170 Speaker 2: probably say AC group 542 00:30:27,939 --> 00:30:30,630 Speaker 2: uh go to um 543 00:30:31,390 --> 00:30:37,229 Speaker 2: um grab um in India, um me uh maybe P 544 00:30:37,239 --> 00:30:38,900 Speaker 2: T M, right? For parts of it. 545 00:30:39,510 --> 00:30:40,199 Speaker 2: Um 546 00:30:42,089 --> 00:30:48,280 Speaker 2: We've seen C group demonstrate profitability last quarter Q four. 547 00:30:48,560 --> 00:30:51,900 Speaker 2: And that's great news. We need these local stars to, 548 00:30:52,890 --> 00:30:56,699 Speaker 2: to shine because if they don't shine, then, you know, 549 00:30:56,709 --> 00:31:00,560 Speaker 2: it makes things even more difficult for earlier stage startups. 550 00:31:02,329 --> 00:31:04,260 Speaker 2: The flip side to that is, is that, 551 00:31:05,739 --> 00:31:08,000 Speaker 2: and this again goes back to venture capital and the 552 00:31:08,010 --> 00:31:09,280 Speaker 2: ecosystem is that 553 00:31:10,420 --> 00:31:13,030 Speaker 2: you look at Southeast Asia and there is a hole 554 00:31:13,040 --> 00:31:13,469 Speaker 2: and 555 00:31:15,030 --> 00:31:16,540 Speaker 2: there are not that many exits, 556 00:31:17,729 --> 00:31:22,729 Speaker 2: right? Not yet, not yet, right? Um And so you 557 00:31:22,739 --> 00:31:25,069 Speaker 2: can't compare it to America because it's still early as 558 00:31:25,079 --> 00:31:28,770 Speaker 2: a region. Uh and I E commerce as a discipline. 559 00:31:29,229 --> 00:31:30,739 Speaker 2: Um But 560 00:31:33,949 --> 00:31:36,959 Speaker 2: we need the local heroes to shine, 561 00:31:37,890 --> 00:31:40,130 Speaker 2: right? Um And 562 00:31:41,489 --> 00:31:46,060 Speaker 2: if you break up commerce, let's say, was selling whatever 563 00:31:46,069 --> 00:31:49,060 Speaker 2: you want to sell ties, right? If you break up 564 00:31:49,069 --> 00:31:50,939 Speaker 2: your P N L at the highest level, 565 00:31:52,479 --> 00:31:56,410 Speaker 2: you'd find that the cost of goods would probably 566 00:31:57,140 --> 00:31:59,180 Speaker 2: Be about 30%. 567 00:32:00,239 --> 00:32:04,640 Speaker 2: Um You would find that logistics and warehousing and the 568 00:32:04,650 --> 00:32:07,030 Speaker 2: last mile delivery would probably be about 569 00:32:08,280 --> 00:32:09,290 Speaker 2: 20%. 570 00:32:10,349 --> 00:32:11,030 Speaker 2: And then 571 00:32:13,010 --> 00:32:17,699 Speaker 2: The last 50% would probably be advertising costs 572 00:32:18,510 --> 00:32:19,859 Speaker 2: 50%, 573 00:32:21,599 --> 00:32:23,729 Speaker 2: And that's where the game is being played now. 574 00:32:24,400 --> 00:32:28,810 Speaker 2: Uh And that's why you're seeing a blurring of boundaries 575 00:32:28,819 --> 00:32:35,880 Speaker 2: between Facebook, Google tiktok content companies, search engines, social media 576 00:32:35,890 --> 00:32:36,550 Speaker 2: networks 577 00:32:37,150 --> 00:32:43,880 Speaker 2: and commerce companies like Alibaba, Walmart Flip, Amazon, et cetera. 578 00:32:45,140 --> 00:32:48,050 Speaker 2: Amazon to give you an example and don't quote me 579 00:32:48,060 --> 00:32:51,469 Speaker 2: on this. But I, I'm, I'm maybe I'm off 5%. 580 00:32:51,880 --> 00:33:00,469 Speaker 2: Um Amazon makes more profit from its ads business, which 581 00:33:00,479 --> 00:33:04,630 Speaker 2: is five years old at best than it does from 582 00:33:04,640 --> 00:33:08,060 Speaker 2: Amazon web services, which is a 20 year old business 583 00:33:08,239 --> 00:33:09,030 Speaker 1: supposed to be the crown 584 00:33:09,040 --> 00:33:11,849 Speaker 2: jewel, which is the crown jewel, but has a lot 585 00:33:11,859 --> 00:33:12,349 Speaker 2: of 586 00:33:13,119 --> 00:33:17,199 Speaker 2: uh Capex, correct. Advertising has no Capex. And so you'll 587 00:33:17,209 --> 00:33:17,890 Speaker 2: see 588 00:33:19,060 --> 00:33:22,689 Speaker 2: a lot of these guys look at Walmart's latest report. 589 00:33:22,699 --> 00:33:26,209 Speaker 2: Um They have never seen the margin like they have 590 00:33:26,219 --> 00:33:31,150 Speaker 2: in advertising, right? Uh If you look at Alibaba, if 591 00:33:31,160 --> 00:33:35,420 Speaker 2: you look at the, the C groups, advertising is making 592 00:33:35,430 --> 00:33:39,349 Speaker 2: up a huge component of profitability. Uh And why is that? 593 00:33:39,839 --> 00:33:42,079 Speaker 2: Why have they all been able to do it is 594 00:33:42,089 --> 00:33:42,930 Speaker 2: because 595 00:33:44,199 --> 00:33:48,209 Speaker 2: they sit on the data, they know that they are more, 596 00:33:49,290 --> 00:33:53,050 Speaker 2: has bought a B and C 21 days ago and 597 00:33:53,060 --> 00:33:57,250 Speaker 2: is likely to purchase again. They know that has a 598 00:33:57,260 --> 00:34:01,280 Speaker 2: family account and his wife buys D E N F. 599 00:34:01,619 --> 00:34:02,819 Speaker 2: Uh And 600 00:34:03,510 --> 00:34:06,520 Speaker 2: I would say that the power of balance over the 601 00:34:06,530 --> 00:34:11,560 Speaker 2: next decade is going to shift from Google Facebook um 602 00:34:12,418 --> 00:34:14,948 Speaker 2: To owners of 1st party data. 603 00:34:15,780 --> 00:34:16,860 Speaker 2: Uh It could be 604 00:34:17,520 --> 00:34:23,259 Speaker 2: retailers like Amazon Walmart could also be banks and credit 605 00:34:23,270 --> 00:34:25,419 Speaker 2: card companies. Um 606 00:34:26,129 --> 00:34:28,850 Speaker 2: I mean, the truth is, is that if you were 607 00:34:28,860 --> 00:34:30,100 Speaker 2: a shareholder in Apple 608 00:34:30,888 --> 00:34:32,698 Speaker 2: and you were sitting next to Tim Cook, 609 00:34:34,360 --> 00:34:38,649 Speaker 2: May you would ask him Tim, where's the next $50 610 00:34:38,659 --> 00:34:40,570 Speaker 2: billion dollars coming from? I know the answer. 611 00:34:41,860 --> 00:34:45,149 Speaker 2: I'd say there's a 5050 chance he'd say it's advertised. 612 00:34:45,739 --> 00:34:47,879 Speaker 1: I've been hearing in tech podcast for the last one 613 00:34:47,889 --> 00:34:50,570 Speaker 1: year that when is Apple going to get in the business? Right. Yeah. 614 00:34:50,669 --> 00:34:50,959 Speaker 1: And 615 00:34:50,969 --> 00:34:56,030 Speaker 2: So I think that we've reached 2023 is the inflection 616 00:34:56,040 --> 00:34:59,909 Speaker 2: point where commerce and advertising have become the same thing. 617 00:35:00,820 --> 00:35:05,250 Speaker 2: I might be wrong and, and correct me if I am. 618 00:35:05,260 --> 00:35:07,219 Speaker 2: But I think we've reached that stage now. 619 00:35:08,229 --> 00:35:10,719 Speaker 1: And of course, all of those things are interlinked with 620 00:35:10,729 --> 00:35:14,070 Speaker 1: the usage of data and getting intelligent insights and intelligent nudges. 621 00:35:14,229 --> 00:35:16,520 Speaker 2: If you have the data, if you sit on the data, 622 00:35:17,620 --> 00:35:20,948 Speaker 2: you can command advertising dollars. It's as simple as that 623 00:35:20,959 --> 00:35:23,590 Speaker 2: because you can prove purchase right 624 00:35:24,010 --> 00:35:25,939 Speaker 1: later in the podcast. I'm gonna ask you about this 625 00:35:25,949 --> 00:35:28,560 Speaker 1: A I driven search will that, you know, disrupt and 626 00:35:28,570 --> 00:35:31,449 Speaker 1: change the ad industry. But before I go there, um 627 00:35:31,459 --> 00:35:33,969 Speaker 1: I just want to talk about this issue of 628 00:35:34,659 --> 00:35:35,709 Speaker 1: the likely 629 00:35:36,350 --> 00:35:37,888 Speaker 1: sort of, you know, the run with that we have 630 00:35:37,899 --> 00:35:41,020 Speaker 1: ahead in terms of regulation, this banking system and problem 631 00:35:41,030 --> 00:35:43,929 Speaker 1: the need for regulators to step in or questions about 632 00:35:43,939 --> 00:35:47,270 Speaker 1: data privacy and the impact of social media interactions on 633 00:35:47,280 --> 00:35:50,129 Speaker 1: mental health. All these things are being discussed at length 634 00:35:50,139 --> 00:35:54,159 Speaker 1: in the political corridors of Washington DC and Europe elsewhere. 635 00:35:54,169 --> 00:35:56,780 Speaker 1: So on, even China sell the tech regulation 636 00:35:56,854 --> 00:35:59,425 Speaker 1: that Xi Jinping has undertaken has been in the guise 637 00:35:59,435 --> 00:36:03,834 Speaker 1: of that anti monopoly behavior, privacy and so on. So 638 00:36:03,844 --> 00:36:09,524 Speaker 1: how prepared is the tech startup scene to deal with 639 00:36:09,534 --> 00:36:14,225 Speaker 1: this likely tightening of the regulatory oversight on the sector? 640 00:36:14,830 --> 00:36:17,719 Speaker 2: So I think just to pull back again, I think 641 00:36:17,729 --> 00:36:21,560 Speaker 2: at a more fundamental level, ask me as an entrepreneur, 642 00:36:23,300 --> 00:36:26,270 Speaker 2: what would I choose between 643 00:36:27,500 --> 00:36:31,129 Speaker 2: interest which a bank provides me on my deposit 644 00:36:32,260 --> 00:36:33,189 Speaker 2: or safety 645 00:36:34,729 --> 00:36:35,600 Speaker 2: safety 646 00:36:37,110 --> 00:36:40,330 Speaker 2: every day of the week? Right. Right. And am I 647 00:36:40,340 --> 00:36:43,989 Speaker 2: prepared to pay for that? Yes. Yeah. Right. So I 648 00:36:44,000 --> 00:36:44,439 Speaker 2: think 649 00:36:45,050 --> 00:36:48,139 Speaker 2: you got to ask yourself whether banking as a business 650 00:36:48,149 --> 00:36:51,409 Speaker 2: model needs to change. Right. A little bit. And uh 651 00:36:52,080 --> 00:36:52,709 Speaker 2: um 652 00:36:54,209 --> 00:36:59,520 Speaker 2: Fundamentally the question is, do you want interest and return 653 00:36:59,530 --> 00:37:03,780 Speaker 2: on your deposit or do you want 100%? 654 00:37:05,790 --> 00:37:06,489 Speaker 1: Exactly. 655 00:37:06,570 --> 00:37:09,589 Speaker 2: So I, I tell you as a, as an individual, 656 00:37:10,459 --> 00:37:13,679 Speaker 2: I may gamble. I'm not gonna gamble with investor money. I'll, 657 00:37:13,689 --> 00:37:17,389 Speaker 2: I'll take safety seven days out of seven. Right. Simple. Right. 658 00:37:18,090 --> 00:37:22,379 Speaker 1: Um And so then if we want the Ecommerce system, 659 00:37:22,560 --> 00:37:25,340 Speaker 1: the data that e-commerce companies use that to be also 660 00:37:25,350 --> 00:37:28,929 Speaker 1: absolutely safe. And we start putting more and more safeguards 661 00:37:28,939 --> 00:37:32,239 Speaker 1: around that and we make tech companies more liable for 662 00:37:32,250 --> 00:37:34,138 Speaker 1: the data that they use. Um 663 00:37:35,030 --> 00:37:36,629 Speaker 1: I mean, do you see that coming? And if it 664 00:37:36,639 --> 00:37:39,760 Speaker 1: is coming, you know, how's that? I mean, are, are 665 00:37:39,770 --> 00:37:42,399 Speaker 1: the other entrepreneurs ready to deal with that? 666 00:37:43,949 --> 00:37:47,070 Speaker 2: Yeah. So privacy and data. Let's talk about that a 667 00:37:47,080 --> 00:37:47,600 Speaker 2: little bit. 668 00:37:48,760 --> 00:37:50,729 Speaker 2: What's your favorite restaurant in Singapore? 669 00:37:51,959 --> 00:37:55,089 Speaker 1: Well, right now, yes. Uh the revolvers, 670 00:37:55,100 --> 00:37:57,069 Speaker 2: but when you walk into a revolver, 671 00:37:59,310 --> 00:38:03,689 Speaker 2: you, you like it, when, when the says, hey, here's 672 00:38:03,699 --> 00:38:06,929 Speaker 2: your table, here's your, they say, oh, we might not 673 00:38:06,939 --> 00:38:09,040 Speaker 2: have the chicken but we have this. I think you'll 674 00:38:09,050 --> 00:38:10,419 Speaker 2: like it. You like that. Right. Right. Right. 675 00:38:11,689 --> 00:38:16,260 Speaker 2: That's one side of privacy, right? And that's sharing of data. 676 00:38:16,939 --> 00:38:19,810 Speaker 2: The other side is when it becomes creepy and, and 677 00:38:19,820 --> 00:38:24,639 Speaker 2: uh advertising follows you around and things like that. Um I, 678 00:38:24,649 --> 00:38:27,429 Speaker 2: I don't know what the answer is. Um I think that, 679 00:38:27,750 --> 00:38:29,449 Speaker 2: you know, you ask my kids, 680 00:38:30,050 --> 00:38:33,860 Speaker 2: our teenagers, they don't care about privacy like we do. 681 00:38:34,580 --> 00:38:36,060 Speaker 2: Um And so 682 00:38:37,239 --> 00:38:43,049 Speaker 2: the regulators unfortunately are more often than not people in 683 00:38:43,060 --> 00:38:48,060 Speaker 2: the latter part of their lives. Um And they may 684 00:38:48,070 --> 00:38:51,040 Speaker 2: not see it the same way as teenagers do. Uh 685 00:38:51,050 --> 00:38:54,409 Speaker 2: But I think privacy is, is um 686 00:38:55,350 --> 00:38:55,919 Speaker 2: um 687 00:38:56,600 --> 00:38:57,049 Speaker 2: but 688 00:39:01,270 --> 00:39:06,080 Speaker 2: it's, it's changing the definition of privacy. Um 689 00:39:06,699 --> 00:39:07,270 Speaker 2: And, 690 00:39:08,050 --> 00:39:12,469 Speaker 2: and time will tell, um we've seen what Apple has 691 00:39:12,479 --> 00:39:16,659 Speaker 2: done to Facebook in the guise of privacy um, 692 00:39:18,750 --> 00:39:22,659 Speaker 1: there's the opt in clause that they, after the year 693 00:39:22,770 --> 00:39:26,290 Speaker 2: two years ago. Yeah. Yeah. Now it's, uh, every new 694 00:39:26,300 --> 00:39:28,389 Speaker 2: app you open, you ask whether you want to opt 695 00:39:28,399 --> 00:39:31,899 Speaker 2: in chances are that you're gonna say no, uh, which 696 00:39:31,909 --> 00:39:34,270 Speaker 2: nine out of 10 people do and, and then that's 697 00:39:34,280 --> 00:39:41,469 Speaker 2: affected the companies like Facebook's ability to serve your advertising. Um, 698 00:39:41,479 --> 00:39:46,239 Speaker 2: and that's what's really caused the stock to implode. Um, 699 00:39:46,790 --> 00:39:50,250 Speaker 2: privacy is a pretty big debate. I don't know which 700 00:39:50,260 --> 00:39:51,359 Speaker 2: will play out. 701 00:39:52,139 --> 00:39:55,040 Speaker 1: What about this issue related to national security? So India 702 00:39:55,050 --> 00:39:58,239 Speaker 1: banned tiktok. The US is getting very tough on tiktok 703 00:39:58,310 --> 00:40:00,810 Speaker 1: is under the guise that you know, their data may 704 00:40:00,820 --> 00:40:03,399 Speaker 1: be used. American citizens data would be used by the 705 00:40:03,409 --> 00:40:08,520 Speaker 1: Chinese government. Um You, your take feel free to be controversial. 706 00:40:08,629 --> 00:40:10,840 Speaker 2: So yeah, I mean, I don't know what the answer is. 707 00:40:10,850 --> 00:40:11,600 Speaker 2: Uh 708 00:40:12,459 --> 00:40:15,110 Speaker 2: um I think what I do know is this, 709 00:40:15,949 --> 00:40:19,530 Speaker 2: I think that India tiktok is uh 710 00:40:20,530 --> 00:40:23,969 Speaker 2: never going to allow them. And if the US and 711 00:40:23,979 --> 00:40:27,179 Speaker 2: Europe don't allow it, I think Tik Tok will double 712 00:40:27,189 --> 00:40:32,080 Speaker 2: down on Southeast Asia, this region of 700 million people 713 00:40:32,090 --> 00:40:35,939 Speaker 2: will become their primary play, play playground and it will 714 00:40:35,949 --> 00:40:40,120 Speaker 2: become a force to reckon with both on content and 715 00:40:40,129 --> 00:40:44,379 Speaker 2: ads and commerce. So watch out for Tik Tok and 716 00:40:44,389 --> 00:40:44,560 Speaker 2: it's 717 00:40:44,600 --> 00:40:47,949 Speaker 1: really been transformation, right? The way they deploy their technologies, 718 00:40:47,959 --> 00:40:48,760 Speaker 1: it's phenomenal. 719 00:40:49,110 --> 00:40:52,989 Speaker 2: It's a I at its very best like um Right. 720 00:40:53,000 --> 00:40:55,189 Speaker 1: OK. Since you mentioned A I I, I want to 721 00:40:55,199 --> 00:40:57,860 Speaker 1: talk a little bit about A I. Um Of course, 722 00:40:57,870 --> 00:40:59,830 Speaker 1: the last three months, the flavor of the last three 723 00:40:59,840 --> 00:41:02,500 Speaker 1: months has been chat GP T. Uh Even yesterday I 724 00:41:02,510 --> 00:41:06,279 Speaker 1: asked Chad G V T using just 21 2021 data 725 00:41:06,500 --> 00:41:09,469 Speaker 1: to give me a synopsis of Silicon Valley banks issues 726 00:41:09,479 --> 00:41:12,489 Speaker 1: and problems. It was amazing. It was absolutely amazing that 727 00:41:12,500 --> 00:41:13,319 Speaker 1: just using 728 00:41:13,600 --> 00:41:17,239 Speaker 1: 15 month old data, they flag the duration risk, they 729 00:41:17,250 --> 00:41:21,209 Speaker 1: flag the concentration risk and so on. Um so in 730 00:41:21,219 --> 00:41:25,280 Speaker 1: terms of using a I for search and using a 731 00:41:25,290 --> 00:41:27,149 Speaker 1: I for the kind of work that you are doing, 732 00:41:27,510 --> 00:41:29,669 Speaker 1: where do you see the most compelling use cases? 733 00:41:30,540 --> 00:41:32,669 Speaker 2: Again? Let me just step back and give you my 734 00:41:32,679 --> 00:41:35,259 Speaker 2: because I, I'm not a tech guy, I'm a business guy. 735 00:41:35,590 --> 00:41:37,189 Speaker 2: Here's my um 736 00:41:37,969 --> 00:41:42,699 Speaker 2: definition or my understanding of, of A I. Um So 737 00:41:43,629 --> 00:41:45,899 Speaker 2: imagine an Excel sheet 738 00:41:47,239 --> 00:41:51,129 Speaker 2: with a billion rows and a billion columns 739 00:41:51,959 --> 00:41:54,689 Speaker 2: and every cell moving in in real time. 740 00:41:56,050 --> 00:41:59,939 Speaker 2: And I imagine a billion of those excels, right? 741 00:42:00,939 --> 00:42:02,709 Speaker 2: The ability to 742 00:42:03,570 --> 00:42:06,259 Speaker 2: extract the beta and pattern match 743 00:42:07,199 --> 00:42:09,919 Speaker 2: is what we call machine learning, right? 744 00:42:11,290 --> 00:42:13,580 Speaker 2: The ability to predict what will happen 745 00:42:14,590 --> 00:42:15,320 Speaker 2: is A I, 746 00:42:15,989 --> 00:42:17,959 Speaker 2: right? Uh Now 747 00:42:20,110 --> 00:42:24,070 Speaker 2: GP T uh or open A I, the parent company. 748 00:42:24,080 --> 00:42:24,620 Speaker 2: Um 749 00:42:25,719 --> 00:42:29,570 Speaker 2: We've seen the, we've seen over the last few months. 750 00:42:30,419 --> 00:42:34,149 Speaker 2: I mean, it's amazing what it can do. Um But 751 00:42:34,159 --> 00:42:36,590 Speaker 2: it is, it's still text 752 00:42:37,340 --> 00:42:38,739 Speaker 2: I think the next version 753 00:42:39,659 --> 00:42:41,830 Speaker 2: uh probably would be a video 754 00:42:42,719 --> 00:42:47,770 Speaker 2: or photography and we text image video, right? And that's 755 00:42:48,590 --> 00:42:51,320 Speaker 2: uh I, I think that's gonna happen quite quickly. 756 00:42:51,479 --> 00:42:53,699 Speaker 1: So I will ask you to make me a movie 757 00:42:53,879 --> 00:42:57,989 Speaker 1: about such care hanging out with the party in 1997 758 00:42:58,000 --> 00:42:58,489 Speaker 1: and I'll do 759 00:42:58,500 --> 00:43:02,120 Speaker 2: it. Yeah. Or it can be uh give me um 760 00:43:03,909 --> 00:43:08,629 Speaker 2: 40 minute episode of Demo in podcasting and doing an 761 00:43:08,639 --> 00:43:09,719 Speaker 2: interview with the 762 00:43:10,729 --> 00:43:10,879 Speaker 1: Yeah. 763 00:43:11,080 --> 00:43:14,000 Speaker 2: Yeah. And it'll be then, you know, you have 30 seconds. 764 00:43:14,010 --> 00:43:19,509 Speaker 2: So what is, what, what you believe is real, is 765 00:43:19,520 --> 00:43:22,919 Speaker 2: going to change and what is actually real, what is certified? 766 00:43:22,929 --> 00:43:25,580 Speaker 2: And I think that has impacts on education. I, I 767 00:43:25,590 --> 00:43:27,080 Speaker 2: worry about, you know, 768 00:43:27,439 --> 00:43:30,489 Speaker 2: my Children asking me, can I use GP T for 769 00:43:30,500 --> 00:43:35,929 Speaker 2: this exam? How schools and universities will react? Um 770 00:43:36,739 --> 00:43:38,860 Speaker 2: So I think it's a, it's a game changer and I, 771 00:43:38,870 --> 00:43:42,439 Speaker 2: I also think that when a company like Microsoft puts 772 00:43:42,449 --> 00:43:45,089 Speaker 2: $10 billion in it, 773 00:43:46,560 --> 00:43:49,659 Speaker 2: it ain't looking for an extra term. It's looking at 774 00:43:49,669 --> 00:43:52,850 Speaker 2: something substantially more, right? And I think that 775 00:43:54,570 --> 00:43:58,069 Speaker 2: it's a very shrewd move because I think it's going 776 00:43:58,080 --> 00:44:03,770 Speaker 2: to enable them Microsoft to compete with Google or the browser, right? 777 00:44:04,080 --> 00:44:07,850 Speaker 2: Uh So it'll be Chrome versus whatever Google come up with. 778 00:44:08,250 --> 00:44:13,899 Speaker 2: Uh It'll help Microsoft compete with Amazon for the cloud. 779 00:44:14,659 --> 00:44:17,189 Speaker 2: Um And there's gonna be, 780 00:44:18,439 --> 00:44:20,649 Speaker 2: it's gonna be a wild ride the next two years. 781 00:44:20,659 --> 00:44:21,020 Speaker 2: Sounds 782 00:44:21,030 --> 00:44:23,010 Speaker 1: like you've already picked a favorite MS F T. 783 00:44:23,810 --> 00:44:27,250 Speaker 2: Yeah, I mean, you know, I think uh I've got, 784 00:44:28,969 --> 00:44:31,189 Speaker 2: I tell my Children, I've got two kids. I say 785 00:44:31,199 --> 00:44:31,860 Speaker 2: one of you 786 00:44:32,550 --> 00:44:35,540 Speaker 2: is your, your future is in the hands of Amazon 787 00:44:35,550 --> 00:44:38,639 Speaker 2: and the other, your future is in the hand of microphone. 788 00:44:40,040 --> 00:44:43,520 Speaker 1: Um um Agree or disagree that the advent of the 789 00:44:43,530 --> 00:44:48,360 Speaker 1: internet was subject to too light regulatory touch. As a result, 790 00:44:48,370 --> 00:44:51,199 Speaker 1: we saw some of the negative aspects of internet proliferate 791 00:44:51,209 --> 00:44:53,319 Speaker 1: in the last two or three decades and the issue 792 00:44:53,330 --> 00:44:55,580 Speaker 1: that we were talking about privacy and so on uh 793 00:44:55,919 --> 00:44:56,370 Speaker 1: and 794 00:44:57,080 --> 00:45:01,370 Speaker 1: chastened by that experience, regulators will be very, very tough 795 00:45:01,520 --> 00:45:04,399 Speaker 1: on A I companies that start rolling out these provocative 796 00:45:04,409 --> 00:45:05,040 Speaker 1: things that you're talking 797 00:45:05,050 --> 00:45:05,610 Speaker 2: about. Yeah. 798 00:45:06,750 --> 00:45:07,840 Speaker 2: How are they gonna stop 799 00:45:07,850 --> 00:45:07,979 Speaker 1: it? 800 00:45:08,989 --> 00:45:12,919 Speaker 1: They will make companies level if your voice is being 801 00:45:12,929 --> 00:45:16,229 Speaker 1: used in their platform to create a persona that is 802 00:45:16,239 --> 00:45:16,799 Speaker 1: not yours 803 00:45:18,590 --> 00:45:22,979 Speaker 2: and difficult one to police stack, right? Um It's uh 804 00:45:24,100 --> 00:45:25,060 Speaker 2: um I mean, 805 00:45:26,500 --> 00:45:26,949 Speaker 2: I'm not, 806 00:45:27,689 --> 00:45:32,419 Speaker 2: we saw the senate hearings on Facebook and privacy. 807 00:45:32,780 --> 00:45:35,219 Speaker 1: The senators did not wrap themselves in glory. 808 00:45:36,080 --> 00:45:40,590 Speaker 2: Uh A I is gonna be infinitely more complicated uh 809 00:45:40,600 --> 00:45:44,050 Speaker 2: because we're just starting on that journey. So how a 810 00:45:44,060 --> 00:45:47,659 Speaker 2: regulator or a senator or a politician is going to 811 00:45:47,669 --> 00:45:50,049 Speaker 2: be able to play out in their own minds? What 812 00:45:50,060 --> 00:45:50,919 Speaker 2: is coming ahead? 813 00:45:51,580 --> 00:45:55,070 Speaker 2: I, I, I don't, I'm not sure they're going to 814 00:45:55,080 --> 00:45:57,290 Speaker 2: be successful, 815 00:45:59,830 --> 00:46:02,120 Speaker 2: but yeah, I mean, as I said, earlier, I think 816 00:46:02,330 --> 00:46:06,500 Speaker 2: A I is not a pretender, like some people say 817 00:46:06,510 --> 00:46:12,189 Speaker 2: crypto and the metaverse were pretenders to the mega trend throne, right? 818 00:46:12,800 --> 00:46:14,419 Speaker 2: I think A I is the real deal. 819 00:46:15,580 --> 00:46:18,429 Speaker 2: And I think that at a fundamental level 820 00:46:20,080 --> 00:46:24,399 Speaker 2: because it increases productivity. 821 00:46:28,290 --> 00:46:31,510 Speaker 2: I, I think it's just gonna be an amazing, great 822 00:46:31,520 --> 00:46:35,280 Speaker 2: thing for the global economy. OK? 823 00:46:35,290 --> 00:46:37,859 Speaker 1: You're convinced that A I raises productivity and I think 824 00:46:37,979 --> 00:46:41,529 Speaker 1: it's an easy case to make software services. How does 825 00:46:41,540 --> 00:46:44,959 Speaker 1: it increase productivity or does it not always increase productivity? 826 00:46:46,159 --> 00:46:48,149 Speaker 2: How does A I increase productivity? 827 00:46:48,260 --> 00:46:49,909 Speaker 1: Well, that I think we will agree that, you know, 828 00:46:49,919 --> 00:46:54,030 Speaker 1: potentially substantial. So just as software services or the subscription business, 829 00:46:54,679 --> 00:46:56,979 Speaker 1: I can see it's very lucrative from the seller of 830 00:46:56,989 --> 00:46:59,889 Speaker 1: that service. But is it always clear that the buyer 831 00:46:59,899 --> 00:47:02,029 Speaker 1: of the service is seeing significant productivity enhancement? 832 00:47:03,260 --> 00:47:06,709 Speaker 2: Yeah, I mean, I think so, I think at its 833 00:47:06,949 --> 00:47:09,389 Speaker 2: most atomic level is 834 00:47:10,020 --> 00:47:11,069 Speaker 2: you can, 835 00:47:14,209 --> 00:47:16,340 Speaker 2: you can pay when you want to, you can pay 836 00:47:16,350 --> 00:47:18,879 Speaker 2: on a daily basis, you can pay on a monthly basis, 837 00:47:18,889 --> 00:47:21,070 Speaker 2: you can pay on an annual basis. That's really what 838 00:47:21,080 --> 00:47:23,699 Speaker 2: soft you can eat as much as you can. You 839 00:47:23,709 --> 00:47:26,419 Speaker 2: remember the old days of software where, 840 00:47:27,429 --> 00:47:31,159 Speaker 2: you know, I B M and Microsoft and you were 841 00:47:31,169 --> 00:47:36,260 Speaker 2: like buying physical servers and, and C Ds C Ds. Yeah. 842 00:47:37,129 --> 00:47:40,370 Speaker 2: Um That's all changed because of a, now software is 843 00:47:40,379 --> 00:47:43,320 Speaker 2: being delivered over the internet and you can pay uh 844 00:47:43,330 --> 00:47:46,159 Speaker 2: when you want, you can switch it off when you want. 845 00:47:46,169 --> 00:47:49,239 Speaker 2: And so a is just a sort of a monetization. 846 00:47:49,669 --> 00:47:51,520 Speaker 2: I think um 847 00:47:53,340 --> 00:47:54,189 Speaker 2: I think 848 00:47:55,050 --> 00:47:55,739 Speaker 2: his 849 00:47:56,379 --> 00:47:57,110 Speaker 2: might, 850 00:47:58,250 --> 00:47:59,919 Speaker 2: that is that 851 00:48:02,939 --> 00:48:04,399 Speaker 2: I will 852 00:48:06,679 --> 00:48:10,560 Speaker 2: if it can become, if you can charge customers based 853 00:48:10,570 --> 00:48:11,330 Speaker 2: on outcome, 854 00:48:13,330 --> 00:48:14,280 Speaker 2: which is 855 00:48:15,010 --> 00:48:16,340 Speaker 2: you are selling something. 856 00:48:17,139 --> 00:48:21,010 Speaker 2: Um I offer you my software as a service but 857 00:48:21,020 --> 00:48:22,790 Speaker 2: I say, listen, don't pay me every month, 858 00:48:24,580 --> 00:48:26,580 Speaker 2: only pay me when it succeeds. 859 00:48:29,030 --> 00:48:31,879 Speaker 2: Um Tough thing to say no to, right. 860 00:48:32,649 --> 00:48:36,590 Speaker 2: Um I think that's what A I enables. Um I 861 00:48:36,610 --> 00:48:38,060 Speaker 2: think that just the 862 00:48:39,350 --> 00:48:41,149 Speaker 2: the ability to 863 00:48:41,969 --> 00:48:47,100 Speaker 2: increase productivity to automate decision making. 864 00:48:48,030 --> 00:48:51,879 Speaker 2: Um I think that's what A I delivers. Um 865 00:48:52,750 --> 00:48:57,399 Speaker 2: And it has a another implication on labor uh which 866 00:48:57,429 --> 00:49:01,479 Speaker 2: uh which no one wants to discuss. Um But the 867 00:49:01,489 --> 00:49:02,839 Speaker 2: reality is is that 868 00:49:03,479 --> 00:49:04,089 Speaker 2: um 869 00:49:04,679 --> 00:49:08,310 Speaker 2: you know, most companies, uh I read this amazing article 870 00:49:08,320 --> 00:49:09,719 Speaker 2: the other day about uh 871 00:49:11,540 --> 00:49:15,320 Speaker 2: you have leaders who give vision in a company and 872 00:49:15,330 --> 00:49:19,110 Speaker 2: then you have front line workers who execute on that vision. 873 00:49:20,209 --> 00:49:24,129 Speaker 2: Um And in the middle, you have what is called 874 00:49:24,139 --> 00:49:26,199 Speaker 2: process managers. I have read the article too. 875 00:49:26,639 --> 00:49:27,000 Speaker 1: Yes, it's 876 00:49:27,010 --> 00:49:30,520 Speaker 2: amazing. Yes. And that's going to and that is what 877 00:49:30,530 --> 00:49:36,340 Speaker 2: A I will kill because process management is overhead. That 878 00:49:36,350 --> 00:49:38,360 Speaker 2: isn't required because 879 00:49:41,659 --> 00:49:46,350 Speaker 2: you will tie data into workflows and decision making and 880 00:49:46,360 --> 00:49:50,129 Speaker 2: it will become instantaneous rather than hours and hours of 881 00:49:50,139 --> 00:49:54,000 Speaker 2: meetings and, and, and things like that. Um And I 882 00:49:54,010 --> 00:49:54,669 Speaker 2: think that 883 00:49:55,600 --> 00:49:58,989 Speaker 2: at least in the short term uh is what's worrying 884 00:49:59,000 --> 00:50:05,600 Speaker 2: people uh because it may fundamentally change uh employment and labor. 885 00:50:06,270 --> 00:50:09,479 Speaker 1: Well, then my final question is related to that in 886 00:50:09,489 --> 00:50:12,529 Speaker 1: your company right now. Is it fundamentally changing your notion 887 00:50:12,540 --> 00:50:15,189 Speaker 1: of labor? And also how globalized are you? I know 888 00:50:15,199 --> 00:50:17,169 Speaker 1: that your work is based in South and South Asia. 889 00:50:17,179 --> 00:50:19,959 Speaker 1: But do you have staff sitting in Silicon Valley and 890 00:50:19,969 --> 00:50:21,300 Speaker 1: Middle East elsewhere? 891 00:50:22,149 --> 00:50:22,839 Speaker 2: Um 892 00:50:24,090 --> 00:50:26,739 Speaker 2: How has it changed our company? Um 893 00:50:30,540 --> 00:50:32,780 Speaker 2: We try and run a lean ship. I mean, we 894 00:50:32,790 --> 00:50:36,629 Speaker 2: have 350 people across seven countries. Um 895 00:50:38,209 --> 00:50:41,590 Speaker 2: But I can tell you that it is already changing 896 00:50:41,600 --> 00:50:44,570 Speaker 2: our customers and, and how 897 00:50:45,350 --> 00:50:48,860 Speaker 2: they perceive it. The customers are turning around to us 898 00:50:48,870 --> 00:50:50,739 Speaker 2: and saying, um 899 00:50:52,090 --> 00:50:52,979 Speaker 2: I had 900 00:50:53,729 --> 00:50:57,899 Speaker 2: nine data analysts and with your software, I only need three. 901 00:50:58,560 --> 00:51:01,819 Speaker 2: Uh we have customers who are saying, um 902 00:51:02,590 --> 00:51:09,339 Speaker 2: I have staff who overlooks commerce on Shopify or Magento 903 00:51:09,350 --> 00:51:13,959 Speaker 2: or Lazada or shop or flip card or tiktok. Um 904 00:51:14,439 --> 00:51:17,209 Speaker 2: I don't need that many managers because with the also 905 00:51:17,219 --> 00:51:19,820 Speaker 2: fair solution because I, I, I open my laptop, I 906 00:51:19,830 --> 00:51:23,549 Speaker 2: look at your software at nine AM and by 9 15, 907 00:51:23,560 --> 00:51:26,580 Speaker 2: I know exactly what I need to do and, and 908 00:51:26,590 --> 00:51:27,979 Speaker 2: that's what the A I does, 909 00:51:29,600 --> 00:51:33,350 Speaker 1: Right? Um so the final bit. Do you have employees 910 00:51:33,360 --> 00:51:35,319 Speaker 1: around the world just beyond the seven core 911 00:51:35,330 --> 00:51:38,540 Speaker 2: markets? No, our employees are in the our seven core 912 00:51:38,550 --> 00:51:42,439 Speaker 2: markets and we have consultants and specialists and M L and, 913 00:51:42,449 --> 00:51:46,260 Speaker 2: and data science who sit elsewhere. But our employees uh 914 00:51:46,270 --> 00:51:50,419 Speaker 2: we're playing Mumbai to Manila until further notice, until 915 00:51:50,429 --> 00:51:51,899 Speaker 1: further notice. Um 916 00:51:52,389 --> 00:51:54,820 Speaker 1: Look, I mean, as I said, sometimes I take sort 917 00:51:54,830 --> 00:51:56,750 Speaker 1: of pessimist end of the argument, that's why I really 918 00:51:56,760 --> 00:52:00,120 Speaker 1: enjoy talking to our founders who have an optimistic bend. 919 00:52:00,280 --> 00:52:02,689 Speaker 1: So while I do worry about A I and privacy 920 00:52:02,699 --> 00:52:05,560 Speaker 1: and so on, I I see where you're coming from from. So, 921 00:52:05,570 --> 00:52:07,979 Speaker 1: uh thank you very much for your time and insights. 922 00:52:08,300 --> 00:52:11,370 Speaker 2: Thank you very much. Uh Good to meet you again. 923 00:52:11,379 --> 00:52:13,929 Speaker 2: Big fan of the show. And how many episodes have 924 00:52:13,939 --> 00:52:17,290 Speaker 2: you done? Now? We're at 98 98. Oh my God. 925 00:52:17,300 --> 00:52:19,290 Speaker 2: It's adding. Who is the guest on the 100? 926 00:52:19,300 --> 00:52:20,570 Speaker 1: It will be a surprise. 927 00:52:21,250 --> 00:52:23,790 Speaker 2: All right. Well, thanks. Thanks very much for inviting me 928 00:52:24,260 --> 00:52:24,979 Speaker 2: um 929 00:52:25,310 --> 00:52:27,270 Speaker 2: and a great chat. Thanks very 930 00:52:27,280 --> 00:52:29,080 Speaker 1: much. You know, I had a great time, learned a lot. 931 00:52:29,090 --> 00:52:32,050 Speaker 1: Uh Thank you to our listeners as well. Koby Time 932 00:52:32,060 --> 00:52:35,100 Speaker 1: was produced by Ken Delbridge, Violet, Lee and Daisy Sherma 933 00:52:35,110 --> 00:52:39,000 Speaker 1: provided additional assistance. All 98 episodes of Coby Time are 934 00:52:39,010 --> 00:52:42,280 Speaker 1: available on Apple Google, Spotify as well as on youtube. 935 00:52:42,590 --> 00:52:45,429 Speaker 1: It is for information only and does not constitute any 936 00:52:45,439 --> 00:52:49,219 Speaker 1: investment advice. As for all our research publications and webinars 937 00:52:49,229 --> 00:52:51,669 Speaker 1: are concerned you can find them all by Googling D 938 00:52:51,679 --> 00:52:53,860 Speaker 1: BS Research Library. Have a great day.