1 00:00:00,000 --> 00:00:02,250 Speaker 1: This is a C N. A podcast. 2 00:00:05,170 --> 00:00:08,860 Speaker 2: Hello everyone and welcome to heart of the matter. If 3 00:00:08,860 --> 00:00:12,050 Speaker 2: you're a regular listener, you might wonder why is there 4 00:00:12,050 --> 00:00:15,730 Speaker 2: a female voice. Well, Stephen chair a regular host. If 5 00:00:15,730 --> 00:00:18,230 Speaker 2: he's aware an assignment and I will be with you 6 00:00:18,230 --> 00:00:20,560 Speaker 2: for the next couple of episodes. My name is kelly 7 00:00:20,560 --> 00:00:22,959 Speaker 2: Edwards and I present the news on C. N. A. 8 00:00:23,430 --> 00:00:26,160 Speaker 2: Today. We want to take a bit of a deep 9 00:00:26,160 --> 00:00:29,350 Speaker 2: dive in the tech space. Now I'm no tech whiz 10 00:00:29,350 --> 00:00:33,000 Speaker 2: but like many were listening, watching and reading the news. 11 00:00:33,000 --> 00:00:36,150 Speaker 2: I've seen the big headlines of major tech companies running 12 00:00:36,150 --> 00:00:39,800 Speaker 2: into some rough waters recently in the US big names 13 00:00:39,800 --> 00:00:43,860 Speaker 2: like Peloton, Netflix and Paypal. They're shedding staff and 14 00:00:43,923 --> 00:00:46,403 Speaker 2: closer to home and app most of us must be 15 00:00:46,403 --> 00:00:50,823 Speaker 2: familiar with. Shopping has also announced job cuts. Now Shopping's 16 00:00:50,823 --> 00:00:54,263 Speaker 2: parent companies see limited helmed by forrest, Lee has since 17 00:00:54,263 --> 00:00:57,633 Speaker 2: withdrawn from India and France and aborted its plans in 18 00:00:57,633 --> 00:01:00,863 Speaker 2: spain it's management including lee have said that they will 19 00:01:00,863 --> 00:01:04,399 Speaker 2: not take a salary until things have stabilized. See 20 00:01:04,416 --> 00:01:08,215 Speaker 2: maybe the most high profile, but it certainly isn't the 21 00:01:08,226 --> 00:01:11,676 Speaker 2: only one. Tesla stash away, crypto dot com are some 22 00:01:11,676 --> 00:01:13,965 Speaker 2: of the others who have cooled on job office and 23 00:01:13,965 --> 00:01:17,825 Speaker 2: are looking at bleak prospects. So what is going on 24 00:01:17,866 --> 00:01:20,825 Speaker 2: and how will this affect people who are in the industry? 25 00:01:20,836 --> 00:01:23,116 Speaker 2: Those who still want to get in and for the 26 00:01:23,116 --> 00:01:24,906 Speaker 2: overall tax seen in Singapore. 27 00:01:25,459 --> 00:01:29,050 Speaker 2: Well with me to discuss the issue today are the 28 00:01:29,050 --> 00:01:33,000 Speaker 2: chief operating officer at momentum works a firm that builds 29 00:01:33,000 --> 00:01:37,809 Speaker 2: and manages tech ventures and assistant professor at the N. U. S. 30 00:01:37,819 --> 00:01:42,819 Speaker 2: Business school. Well welcome both of you to our little studio. 31 00:01:42,830 --> 00:01:45,709 Speaker 2: Well let's kick things off with you first. So you 32 00:01:45,709 --> 00:01:48,180 Speaker 2: are the ceo of a fairly young company and you 33 00:01:48,180 --> 00:01:50,770 Speaker 2: helped build ventures as part of your work. You also 34 00:01:50,770 --> 00:01:53,880 Speaker 2: wrote a commentary about this very topic. So when you 35 00:01:53,880 --> 00:01:57,530 Speaker 2: first saw the headlines you know about c letting go staff, 36 00:01:57,540 --> 00:02:00,200 Speaker 2: what was your reaction when you go like oh big trouble? 37 00:02:00,470 --> 00:02:03,910 Speaker 2: I think it is expected because this happens quite often 38 00:02:03,910 --> 00:02:06,900 Speaker 2: in corporate and along the way I think she has 39 00:02:06,900 --> 00:02:09,040 Speaker 2: been growing really really fast the last few years they 40 00:02:09,040 --> 00:02:11,780 Speaker 2: were going for growth versus efficiency and as what we've 41 00:02:11,780 --> 00:02:14,320 Speaker 2: seen in chinese tech companies this is actually something that 42 00:02:14,320 --> 00:02:17,240 Speaker 2: happens every year and I think happening to see now 43 00:02:17,250 --> 00:02:18,560 Speaker 2: is actually a good thing 44 00:02:18,630 --> 00:02:21,130 Speaker 2: because if they had waited two or three more years 45 00:02:21,130 --> 00:02:24,090 Speaker 2: the restructuring would have been a lot more painful than now. 46 00:02:24,100 --> 00:02:27,420 Speaker 2: Is it something that you agree with the way you 47 00:02:27,419 --> 00:02:29,180 Speaker 2: were not shocked at all by Ci's troubles? 48 00:02:29,190 --> 00:02:32,810 Speaker 1: Yeah I agree. The overall macro economic environment has changed 49 00:02:32,810 --> 00:02:36,150 Speaker 1: drastically and I think I see has raised six billion 50 00:02:36,150 --> 00:02:39,320 Speaker 1: round to a sudden layoff and then there's a whole 51 00:02:39,320 --> 00:02:42,520 Speaker 1: host of factors that sort of proceeded these decisions like 52 00:02:42,520 --> 00:02:45,889 Speaker 1: the banning of Guerena in India 53 00:02:46,180 --> 00:02:48,820 Speaker 1: it's healthy right? It's something that you know adjustment has 54 00:02:48,820 --> 00:02:51,120 Speaker 1: been made like the tech companies are used to growth 55 00:02:51,120 --> 00:02:54,639 Speaker 1: at all costs kind of a mindset and the recent 56 00:02:54,650 --> 00:02:59,120 Speaker 1: situations with the war inflation interest rates may be to 57 00:02:59,120 --> 00:03:02,280 Speaker 1: add on. What is potentially more surprising is the way 58 00:03:02,280 --> 00:03:05,400 Speaker 1: they handled the layoffs. I think that part we can 59 00:03:05,400 --> 00:03:07,540 Speaker 1: have a conversation about that but that's it's a completely 60 00:03:07,540 --> 00:03:08,139 Speaker 1: different conversation 61 00:03:08,139 --> 00:03:10,790 Speaker 2: just very quickly in two sentences. What was the issue 62 00:03:10,790 --> 00:03:10,970 Speaker 2: with the 63 00:03:10,970 --> 00:03:15,630 Speaker 1: layoff? Two sentences I would say bad pr 64 00:03:15,850 --> 00:03:18,679 Speaker 1: is the first one and then maybe the second one 65 00:03:18,690 --> 00:03:24,150 Speaker 1: is whoa unprofessionally done. The first thing is that labor 66 00:03:24,150 --> 00:03:28,420 Speaker 1: mobility is a necessary part of the tech ecology. So 67 00:03:28,430 --> 00:03:31,799 Speaker 1: the flow of talent to and from tech companies and 68 00:03:31,810 --> 00:03:34,750 Speaker 1: institutions like universities or other think tanks. 69 00:03:35,580 --> 00:03:39,470 Speaker 1: It's crucial because you're innovating and growing. So any form 70 00:03:39,470 --> 00:03:42,640 Speaker 1: of letting go should be done in a way whereby 71 00:03:42,650 --> 00:03:44,650 Speaker 1: you don't create bad press. But 72 00:03:44,650 --> 00:03:46,550 Speaker 2: having said that, I mean forrest lee himself, he did 73 00:03:46,550 --> 00:03:48,070 Speaker 2: come out and say I'm not going to get paid 74 00:03:48,080 --> 00:03:50,750 Speaker 2: until the company turns around. I mean don't you think 75 00:03:50,750 --> 00:03:54,050 Speaker 2: that would have sort of appeased the staff or the 76 00:03:54,050 --> 00:03:58,360 Speaker 2: shareholders when they learn of something like that from my experience? 77 00:03:58,360 --> 00:03:59,040 Speaker 2: Because we do a 78 00:03:59,053 --> 00:04:01,883 Speaker 2: observe a lot of chinese companies and growth companies right? 79 00:04:01,893 --> 00:04:04,203 Speaker 2: We take a step back why this happened the way 80 00:04:04,203 --> 00:04:06,723 Speaker 2: it happened and it was badly done was that I 81 00:04:06,723 --> 00:04:09,283 Speaker 2: think c is still quite young company and if you 82 00:04:09,283 --> 00:04:11,063 Speaker 2: go back the last few years they have just been 83 00:04:11,063 --> 00:04:14,093 Speaker 2: growing and their focus has been growth and not really 84 00:04:14,103 --> 00:04:16,323 Speaker 2: pr if you notice they don't really come out to 85 00:04:16,323 --> 00:04:18,543 Speaker 2: talk to the press a lot and this happened a 86 00:04:18,543 --> 00:04:21,243 Speaker 2: lot faster than expected and hence they were not prepared 87 00:04:21,253 --> 00:04:22,392 Speaker 2: as compared to big, 88 00:04:22,706 --> 00:04:25,296 Speaker 2: where they are trained to how to handle lay off. 89 00:04:25,306 --> 00:04:28,516 Speaker 2: This is something that was happening in june in doing 90 00:04:28,516 --> 00:04:32,075 Speaker 2: the retrenchment of the office and in september, even though 91 00:04:32,076 --> 00:04:34,486 Speaker 2: it's not well done at least this time around, they 92 00:04:34,486 --> 00:04:37,136 Speaker 2: had the pr people coming out to say I'm working 93 00:04:37,136 --> 00:04:39,526 Speaker 2: the union etcetera etcetera. So you can see that they 94 00:04:39,526 --> 00:04:42,035 Speaker 2: are actually taking steps to address it. And even forest 95 00:04:42,036 --> 00:04:44,886 Speaker 2: is coming out. I want to talk about the capital 96 00:04:44,886 --> 00:04:45,986 Speaker 2: drawing up, 97 00:04:46,170 --> 00:04:50,200 Speaker 2: you know, at one point C was evaluated at 200 98 00:04:50,200 --> 00:04:52,680 Speaker 2: billion U. S. D. So how did the money just 99 00:04:52,680 --> 00:04:53,790 Speaker 2: evaporate like that? 100 00:04:54,940 --> 00:04:58,520 Speaker 1: So there's a whole thing about how tech companies behave 101 00:04:58,529 --> 00:05:01,750 Speaker 1: in a sense, most venture backed companies when they raise money, 102 00:05:01,760 --> 00:05:04,839 Speaker 1: let's say I'm a VC fund, I put in, you know, 103 00:05:04,839 --> 00:05:07,830 Speaker 1: 200 million into a tech company, I want you to spend, 104 00:05:07,839 --> 00:05:09,510 Speaker 1: you know, it's not a case whereby I give you 105 00:05:09,510 --> 00:05:13,169 Speaker 1: 200 million and I'm happy with, you know, 7% animal returns, 106 00:05:13,170 --> 00:05:15,969 Speaker 1: which as an average retail investor will be very happy 107 00:05:16,130 --> 00:05:19,219 Speaker 1: if I'm a big tech company and I receive venture money, 108 00:05:19,230 --> 00:05:22,930 Speaker 1: I need to spend for what and usually it's growth 109 00:05:22,940 --> 00:05:26,020 Speaker 1: and the easiest way to grow is to hire people 110 00:05:26,020 --> 00:05:28,540 Speaker 1: take up the most money of any form like just 111 00:05:28,540 --> 00:05:32,250 Speaker 1: salaries the biggest expenditure when they raise six billion. The 112 00:05:32,250 --> 00:05:33,969 Speaker 1: idea is that I'm going to spend spend, spend and 113 00:05:33,970 --> 00:05:36,740 Speaker 1: grow at all costs with the pandemic. That's the e 114 00:05:36,740 --> 00:05:37,820 Speaker 1: commerce boom. 115 00:05:37,960 --> 00:05:42,030 Speaker 1: There's like a huge intense demand for I. T. Like 116 00:05:42,029 --> 00:05:45,839 Speaker 1: zoom like zoom just went crazy. So there's a whole 117 00:05:45,850 --> 00:05:48,730 Speaker 1: tech boom that is sort of unique to the pandemic 118 00:05:48,740 --> 00:05:52,500 Speaker 1: and then immediately after the war happened and then interest rates. 119 00:05:52,510 --> 00:05:57,020 Speaker 1: So this huge shift, he went from raising $6 billion 120 00:05:57,020 --> 00:06:00,000 Speaker 1: like that too. Oh my God 121 00:06:00,300 --> 00:06:02,380 Speaker 1: they grow at all cost is not going to work 122 00:06:02,380 --> 00:06:03,880 Speaker 1: anymore in this new environment. Perhaps 123 00:06:03,880 --> 00:06:05,710 Speaker 2: they should have spent some of that money on that 124 00:06:05,720 --> 00:06:07,230 Speaker 2: improving their pr team as 125 00:06:07,430 --> 00:06:11,550 Speaker 1: a fraction of that. So all that led to all 126 00:06:11,550 --> 00:06:14,789 Speaker 1: this supposed value just dissipating 127 00:06:15,070 --> 00:06:17,140 Speaker 2: and when it comes to you know now interest rates 128 00:06:17,140 --> 00:06:20,710 Speaker 2: are up, inflation's up. And what we mentioned there's a 129 00:06:20,710 --> 00:06:23,680 Speaker 2: war going on pandemic was still kind of at the 130 00:06:23,680 --> 00:06:26,659 Speaker 2: tail end of things. Do you think all of that impacted, 131 00:06:26,670 --> 00:06:27,710 Speaker 2: sees performance. 132 00:06:27,726 --> 00:06:31,416 Speaker 2: It will definitely impact the performance, impacted every tech companies 133 00:06:31,416 --> 00:06:34,095 Speaker 2: performance per se in the industry. Right. This is a 134 00:06:34,096 --> 00:06:38,255 Speaker 2: time where companies are now rethinking the strategy and um 135 00:06:38,266 --> 00:06:40,906 Speaker 2: I believe that companies like grab and see they still 136 00:06:40,906 --> 00:06:42,976 Speaker 2: have actually quite a healthy run where a few years 137 00:06:42,976 --> 00:06:45,856 Speaker 2: more of cash burn to go. But I think now 138 00:06:45,856 --> 00:06:48,765 Speaker 2: they are actually becoming more prudent to say that what 139 00:06:48,766 --> 00:06:51,796 Speaker 2: happens if this war lasts longer than expected? What happens 140 00:06:51,796 --> 00:06:55,136 Speaker 2: if this inflation environment? It still remains when these fish 141 00:06:55,152 --> 00:06:58,301 Speaker 2: factors happen. It's not just the e commerce or inflation. 142 00:06:58,312 --> 00:07:03,002 Speaker 2: Other factors are impacting their costs, logistic cost is going up. 143 00:07:03,012 --> 00:07:05,932 Speaker 2: People's consumption, power is going down. And I think along 144 00:07:05,932 --> 00:07:08,452 Speaker 2: the way you are also seeing other people cost and 145 00:07:08,452 --> 00:07:11,192 Speaker 2: hence I think they are picking well, I have, let's 146 00:07:11,192 --> 00:07:13,832 Speaker 2: say one or two years of runway, What will happen then? 147 00:07:13,832 --> 00:07:15,892 Speaker 2: I don't know, let's do something now. Which is what 148 00:07:15,892 --> 00:07:17,812 Speaker 2: I was going to ask. Is there a sense now 149 00:07:17,812 --> 00:07:21,152 Speaker 2: that they need to prolong this cash runway before they 150 00:07:21,152 --> 00:07:22,572 Speaker 2: even try to raise funds again, 151 00:07:22,790 --> 00:07:27,300 Speaker 1: The fundraising environment has changed again. Everything about how tech 152 00:07:27,300 --> 00:07:31,230 Speaker 1: companies behave, I receive funds spent immediately till the next 153 00:07:31,230 --> 00:07:36,140 Speaker 1: milestone and the next milestone is typically the next fundraising point. 154 00:07:36,400 --> 00:07:41,260 Speaker 1: And if liquidity is easy and the venture capital industry 155 00:07:41,270 --> 00:07:44,130 Speaker 1: is flush, then it's fine. You know, if I run 156 00:07:44,130 --> 00:07:46,630 Speaker 1: out of money, I pretty much can go to my 157 00:07:46,630 --> 00:07:48,600 Speaker 1: investors saying I need to raise this amount of money. 158 00:07:48,600 --> 00:07:50,590 Speaker 1: Look at how much I've grown. Even though I'm not 159 00:07:50,590 --> 00:07:53,520 Speaker 1: making as much profit. The purse strings are loose, 160 00:07:53,535 --> 00:07:57,085 Speaker 1: so to speak. But with the inflation, with the rising 161 00:07:57,085 --> 00:08:01,715 Speaker 1: interest rates, other forms of investments are more attractive now 162 00:08:01,715 --> 00:08:05,625 Speaker 1: than sort of these growth instruments, which tech stocks usually 163 00:08:05,625 --> 00:08:07,785 Speaker 1: are you going to find it more difficult to raise 164 00:08:07,785 --> 00:08:10,665 Speaker 1: funds and this escalates, right? So 165 00:08:10,830 --> 00:08:15,560 Speaker 1: everyone then starts becoming more prudent. Even people like alphabet 166 00:08:15,560 --> 00:08:19,120 Speaker 1: and meta stopped hiring because they sort of anticipated this 167 00:08:19,130 --> 00:08:23,350 Speaker 1: more stringent fundraising environment. So it's a combination. When we 168 00:08:23,350 --> 00:08:28,400 Speaker 1: think about runway really it's also about fundraising. So if 169 00:08:28,400 --> 00:08:30,680 Speaker 1: the fundraising environment is bad, then we have to care 170 00:08:30,680 --> 00:08:32,189 Speaker 1: more about the runway 171 00:08:32,510 --> 00:08:33,429 Speaker 1: in that sense. 172 00:08:33,440 --> 00:08:35,780 Speaker 2: So if you were to look into your crystal ball 173 00:08:35,780 --> 00:08:41,200 Speaker 2: right in and say crystal ball, question, how long can 174 00:08:41,200 --> 00:08:44,140 Speaker 2: they keep doing this before they actually run into serious 175 00:08:44,140 --> 00:08:44,800 Speaker 2: problem 176 00:08:44,809 --> 00:08:47,290 Speaker 1: doing what the fundraising or trying 177 00:08:47,290 --> 00:08:49,959 Speaker 2: to balance both you're mentioning with the runway as well 178 00:08:49,960 --> 00:08:51,679 Speaker 2: as trying to raise funds. 179 00:08:51,820 --> 00:08:53,660 Speaker 2: Crystal ball aside one thing to look at is look 180 00:08:53,660 --> 00:08:56,290 Speaker 2: at china because I think the companies like Alibaba and 181 00:08:56,290 --> 00:08:59,640 Speaker 2: made Taiwan, they actually have been doing restructuring quite regularly actually. 182 00:08:59,640 --> 00:09:01,820 Speaker 2: I think they do it every year in order to 183 00:09:01,820 --> 00:09:05,650 Speaker 2: pivot the strategy towards what makes sense, because we've been observing, 184 00:09:05,660 --> 00:09:07,960 Speaker 2: see for many years and tech companies in general for 185 00:09:07,960 --> 00:09:11,640 Speaker 2: many years, there will be some companies that will go bust, definitely. 186 00:09:11,650 --> 00:09:15,199 Speaker 2: But I think c is taking the right step now 187 00:09:15,210 --> 00:09:18,770 Speaker 2: to prevent that situation from getting worse, what will happen 188 00:09:18,770 --> 00:09:20,600 Speaker 2: in the future? I don't know, but I think they 189 00:09:20,600 --> 00:09:21,150 Speaker 2: are taking steps 190 00:09:21,165 --> 00:09:23,795 Speaker 2: to make sure that they have enough buffer to defend 191 00:09:23,795 --> 00:09:27,184 Speaker 2: themselves against something that will happen and for example, you 192 00:09:27,184 --> 00:09:30,064 Speaker 2: know that seed is now going deep into Fintech and 193 00:09:30,065 --> 00:09:32,405 Speaker 2: I think they have now a few banks, especially Indonesia, 194 00:09:32,415 --> 00:09:34,695 Speaker 2: one reason is because the cost of capital is now 195 00:09:34,695 --> 00:09:36,865 Speaker 2: going up and they need to find ways to get 196 00:09:36,865 --> 00:09:39,525 Speaker 2: low cost of capital to support the e commerce business. 197 00:09:39,535 --> 00:09:42,325 Speaker 2: So Indonesia, they have at least one bank, they have 198 00:09:42,335 --> 00:09:46,015 Speaker 2: insurance license and now they're diversifying just from e commerce, 199 00:09:46,025 --> 00:09:48,714 Speaker 2: this tells me that they actually have a smart strategy 200 00:09:48,725 --> 00:09:50,505 Speaker 2: just not NPR but 201 00:09:51,020 --> 00:09:52,329 Speaker 2: I 202 00:09:52,340 --> 00:09:55,000 Speaker 1: guess you know, if you look at in general how 203 00:09:55,000 --> 00:09:58,860 Speaker 1: these tech companies grow, like there is a mindset in 204 00:09:58,860 --> 00:10:00,979 Speaker 1: the beginning which is growth at all costs, which 205 00:10:01,130 --> 00:10:03,780 Speaker 1: a lot like she is still in the grand scheme 206 00:10:03,780 --> 00:10:07,600 Speaker 1: of things, a rather young company that mindset eventually as 207 00:10:07,600 --> 00:10:10,110 Speaker 1: a shift to, you know, I'm now a large established 208 00:10:10,110 --> 00:10:12,270 Speaker 1: company instead of growing at all costs, I need to 209 00:10:12,270 --> 00:10:15,140 Speaker 1: focus on things like value creation and delivering the value 210 00:10:15,140 --> 00:10:18,620 Speaker 1: to my customers or diversification as we've mentioned with the Fintech, 211 00:10:18,620 --> 00:10:20,880 Speaker 1: move all companies go through that, you know, so you 212 00:10:20,880 --> 00:10:25,100 Speaker 1: see that with google going to alphabet from facebook going 213 00:10:25,100 --> 00:10:27,660 Speaker 1: to meta, you know, you see that from Uber, going 214 00:10:27,660 --> 00:10:30,270 Speaker 1: from Uber cab to Uber to whatever it is now, 215 00:10:30,280 --> 00:10:31,030 Speaker 1: you know, you see that 216 00:10:31,040 --> 00:10:34,569 Speaker 1: happening with go jek and grab and every company eventually 217 00:10:34,570 --> 00:10:38,420 Speaker 1: becomes this large focusing on value as opposed to growth 218 00:10:38,420 --> 00:10:41,920 Speaker 1: at all costs. So these layoffs in a way, it's 219 00:10:41,920 --> 00:10:44,840 Speaker 1: part of that Rocky Nous that comes with that growth. 220 00:10:44,850 --> 00:10:47,250 Speaker 1: It's not that it's sustainable or not, but it's like 221 00:10:47,260 --> 00:10:49,740 Speaker 1: growing up we all age and lose our hair and 222 00:10:49,750 --> 00:10:51,209 Speaker 1: things sag. So it 223 00:10:51,210 --> 00:10:53,890 Speaker 2: is comforting I guess for listeners out there who actually have, 224 00:10:53,900 --> 00:10:56,679 Speaker 2: you know, stocks in and see that you know that 225 00:10:56,679 --> 00:10:59,480 Speaker 2: there are still not that bad, they're not going under 226 00:10:59,480 --> 00:11:00,950 Speaker 2: anytime soon and for sure 227 00:11:01,130 --> 00:11:03,640 Speaker 2: at this point at least, But you know, touching on 228 00:11:03,640 --> 00:11:06,880 Speaker 2: the fact that many people lost their jobs, the job 229 00:11:06,880 --> 00:11:10,150 Speaker 2: offers being rescinded. The part of it, as you've mentioned 230 00:11:10,160 --> 00:11:13,150 Speaker 2: away was the fact that the firms were hiring too rapidly. 231 00:11:13,160 --> 00:11:14,130 Speaker 1: Right? 232 00:11:14,140 --> 00:11:17,980 Speaker 2: So if that's the case, how do we know if 233 00:11:17,990 --> 00:11:20,810 Speaker 2: tech unicorns have reached the tipping point 234 00:11:21,160 --> 00:11:23,370 Speaker 1: tipping point in terms of the labor market as in 235 00:11:23,370 --> 00:11:25,689 Speaker 1: they can't take in any more employees or tipping point 236 00:11:25,690 --> 00:11:27,140 Speaker 1: in the sense that the bubble and 237 00:11:27,320 --> 00:11:31,220 Speaker 2: there is a sense that the bubble not quite burst yet, 238 00:11:31,220 --> 00:11:33,610 Speaker 2: but it's like hovering there. I mean letting go of 239 00:11:33,610 --> 00:11:36,109 Speaker 2: staff and so I'm wondering in terms of like the 240 00:11:36,110 --> 00:11:37,089 Speaker 2: job market, 241 00:11:37,350 --> 00:11:38,730 Speaker 2: so 242 00:11:38,730 --> 00:11:41,870 Speaker 1: this is interesting. So the job market situation in tech 243 00:11:41,870 --> 00:11:45,990 Speaker 1: has fluctuated again drastically for many reasons, I guess I'll 244 00:11:45,990 --> 00:11:48,170 Speaker 1: start with the idea of a bubble. The idea of 245 00:11:48,170 --> 00:11:51,140 Speaker 1: a bubble floated around since the early two tents where 246 00:11:51,140 --> 00:11:54,060 Speaker 1: you have big companies for big companies now to start 247 00:11:54,059 --> 00:11:56,359 Speaker 1: ups such as Airbnb. Uber is kind of crazy to 248 00:11:56,360 --> 00:11:59,590 Speaker 1: think that Airbnb and Uber started in 2000 tens. The 249 00:11:59,590 --> 00:12:02,559 Speaker 1: valuations are crazy and people are going like what's going on? 250 00:12:02,559 --> 00:12:04,990 Speaker 1: And then like again there's fluctuations and all that. So 251 00:12:04,990 --> 00:12:05,700 Speaker 1: that's one, 252 00:12:06,030 --> 00:12:08,810 Speaker 1: then there's the jobs and then there's the pandemic situation. 253 00:12:08,809 --> 00:12:11,989 Speaker 1: So pandemic was what they called a K shaped recovery. 254 00:12:12,000 --> 00:12:14,270 Speaker 1: So the fact that a whole bunch of industries but 255 00:12:14,270 --> 00:12:19,179 Speaker 1: not tech actually did fantastically well during the pandemic. Then 256 00:12:19,190 --> 00:12:22,689 Speaker 1: again the bubble idea does this last like what happens 257 00:12:22,690 --> 00:12:25,440 Speaker 1: when we are no longer, you know, telecommuting, what happens 258 00:12:25,440 --> 00:12:27,910 Speaker 1: when people start going to shopping malls and brick mortar 259 00:12:27,910 --> 00:12:32,130 Speaker 1: comes back like e commerce, who knows then there's web three. 260 00:12:33,000 --> 00:12:35,960 Speaker 1: So web three became very big sort of towards the 261 00:12:35,960 --> 00:12:37,870 Speaker 1: tail end of the pandemic but to use that as 262 00:12:37,870 --> 00:12:41,510 Speaker 1: a timeframe with coin base, you know going public and 263 00:12:41,510 --> 00:12:44,990 Speaker 1: stuff like that and a 16 z very prominent american 264 00:12:44,990 --> 00:12:47,180 Speaker 1: venture firm, you know, having a huge push in the 265 00:12:47,179 --> 00:12:51,670 Speaker 1: Blockchain and then today we're seeing about a collapse of 266 00:12:51,679 --> 00:12:57,050 Speaker 1: multiple of those things. So Web three is not what 267 00:12:57,059 --> 00:12:59,790 Speaker 1: I might be biased here, but it's not what it 268 00:12:59,800 --> 00:13:01,200 Speaker 1: promised to be. 269 00:13:01,390 --> 00:13:04,700 Speaker 1: Blockchain is a 13 year old technology still trying to 270 00:13:04,700 --> 00:13:08,110 Speaker 1: find its problem and then now what's supposed to be 271 00:13:08,120 --> 00:13:10,780 Speaker 1: unregulated is now facing regulation as we have seen in 272 00:13:10,780 --> 00:13:13,850 Speaker 1: Singapore and then you have all these scandals again as 273 00:13:13,850 --> 00:13:16,339 Speaker 1: we have seen in Singapore of all these web three companies, 274 00:13:16,340 --> 00:13:18,969 Speaker 1: you know, which are valued at hundreds of millions of 275 00:13:18,970 --> 00:13:21,089 Speaker 1: dollars going to nothing overnight. 276 00:13:21,809 --> 00:13:25,309 Speaker 1: So is it a problem now there is a situation 277 00:13:25,309 --> 00:13:27,770 Speaker 1: whereby the Web three jobs for instance, which is a 278 00:13:27,770 --> 00:13:32,280 Speaker 1: huge part of the tech boom recently are gone. That 279 00:13:32,290 --> 00:13:37,250 Speaker 1: I think is not coming back my opinion again because 280 00:13:37,260 --> 00:13:39,530 Speaker 1: I'm not a fan, I'm just going to put it 281 00:13:39,530 --> 00:13:44,130 Speaker 1: out there. I don't think Web three is a thing. 282 00:13:44,140 --> 00:13:46,559 Speaker 1: Blockchain is a promising technology maybe 283 00:13:46,800 --> 00:13:50,600 Speaker 1: for companies that actually create and deliver value elections, you 284 00:13:50,600 --> 00:13:54,470 Speaker 1: talk about like c in case of like grab go jack, 285 00:13:54,480 --> 00:13:58,910 Speaker 1: like certain business models might not be sustainable. But these layoffs, 286 00:13:58,910 --> 00:14:02,140 Speaker 1: these kind of things are corrective mechanisms that create that 287 00:14:02,150 --> 00:14:05,160 Speaker 1: now for job seekers, it's awful. The jobs that I've 288 00:14:05,160 --> 00:14:08,580 Speaker 1: ever their promise to you is not there anymore. But again, 289 00:14:08,590 --> 00:14:12,469 Speaker 1: the tax base has always been a high mobility space. 290 00:14:12,480 --> 00:14:15,740 Speaker 1: The startup space more so so in my research I 291 00:14:15,740 --> 00:14:16,699 Speaker 1: find that for instance, 292 00:14:16,920 --> 00:14:20,130 Speaker 1: 90% of startups that hope to raise venture funding never 293 00:14:20,130 --> 00:14:23,479 Speaker 1: find venture funding and if you're working well and those startups, 294 00:14:23,480 --> 00:14:25,920 Speaker 1: what happens to you, you get fired. I mean like 295 00:14:25,920 --> 00:14:29,770 Speaker 1: it's just a thing you join another tech company and 296 00:14:29,770 --> 00:14:32,810 Speaker 1: that's in a way is how tech keeps becoming innovative 297 00:14:32,810 --> 00:14:37,530 Speaker 1: because you go run around and then companies 298 00:14:38,070 --> 00:14:44,610 Speaker 1: absorb ideas, people meet different people, such mobility is microscopically healthy. 299 00:14:44,620 --> 00:14:48,140 Speaker 2: So you you see staff laid off in, you know, 300 00:14:48,140 --> 00:14:51,200 Speaker 2: all the different unicorn coming to your company for instance 301 00:14:51,200 --> 00:14:53,740 Speaker 2: trying to get jobs, I see it, it's healthy for 302 00:14:53,740 --> 00:14:57,140 Speaker 2: the industry. What is happening is that companies see have 303 00:14:57,150 --> 00:14:59,760 Speaker 2: been actually training people really well and even Web three, 304 00:14:59,760 --> 00:15:01,680 Speaker 2: even though I don't know 305 00:15:02,300 --> 00:15:07,430 Speaker 2: Yeah, and I think it's kind of muddled up right now, 306 00:15:07,440 --> 00:15:09,540 Speaker 2: but I think people working in there are actually picking 307 00:15:09,540 --> 00:15:13,020 Speaker 2: up skills, knowledge experience, that even if the current company 308 00:15:13,020 --> 00:15:15,520 Speaker 2: doesn't work out, you as an individual, you have this 309 00:15:15,520 --> 00:15:18,190 Speaker 2: experience and when you go out to join another company, 310 00:15:18,200 --> 00:15:21,050 Speaker 2: you take along what you have learned, like what was 311 00:15:21,050 --> 00:15:23,940 Speaker 2: said then what you do is that you're actually going 312 00:15:23,940 --> 00:15:26,330 Speaker 2: to be enriching the ecosystem for the next to it 313 00:15:26,330 --> 00:15:27,050 Speaker 2: because ultimately 314 00:15:27,060 --> 00:15:31,520 Speaker 2: tech is an iterative industry where people learn fast fail 315 00:15:31,520 --> 00:15:34,090 Speaker 2: fast feedback learn again. And I think this for any 316 00:15:34,090 --> 00:15:36,750 Speaker 2: individual being laid off may not be a bad thing. 317 00:15:36,760 --> 00:15:39,040 Speaker 2: So you don't, you guys don't sound worried at all 318 00:15:39,040 --> 00:15:41,490 Speaker 2: the further, you know, the staff who have laid off 319 00:15:41,500 --> 00:15:43,910 Speaker 1: in a sense, I agree. So their soft skills, right? 320 00:15:43,910 --> 00:15:46,100 Speaker 1: So if you're working Web three, you have to gain 321 00:15:46,100 --> 00:15:49,890 Speaker 1: some soft skills and technological skills as an individual. You know, 322 00:15:49,890 --> 00:15:51,810 Speaker 1: you gain that kind of human capital 323 00:15:51,820 --> 00:15:56,410 Speaker 1: that is not firm specific or even industry specific. So 324 00:15:56,410 --> 00:16:00,000 Speaker 1: that's valuable and definitely you see that Web three will 325 00:16:00,000 --> 00:16:03,870 Speaker 1: have certain of these skills are required by other tech 326 00:16:03,870 --> 00:16:06,660 Speaker 1: companies or technologies that we might not even know about 327 00:16:06,660 --> 00:16:10,470 Speaker 1: right now. That's fair. The whole idea of what a 328 00:16:10,470 --> 00:16:12,530 Speaker 1: web story is and the metaverse and N. F. T. 329 00:16:12,530 --> 00:16:16,570 Speaker 1: S like that's not looking great specific to that sector. 330 00:16:21,330 --> 00:16:23,340 Speaker 2: Hi, my name is Sarah al Khaldi and I'm the 331 00:16:23,340 --> 00:16:26,620 Speaker 2: host of a new podcast called Money Talks. Yes, we 332 00:16:26,620 --> 00:16:29,220 Speaker 2: will be talking about money, but more than that, we'll 333 00:16:29,220 --> 00:16:33,360 Speaker 2: also be talking about life, personal choices, lucky breaks and 334 00:16:33,360 --> 00:16:36,550 Speaker 2: how money is the thread running through it all. So 335 00:16:36,550 --> 00:16:39,420 Speaker 2: look out for our episodes wherever you get your podcasts. 336 00:16:43,800 --> 00:16:46,400 Speaker 2: So is it possible then to ask you this question 337 00:16:46,400 --> 00:16:49,150 Speaker 2: that who then are the biggest losers? Well, it sounds 338 00:16:49,150 --> 00:16:50,940 Speaker 2: like the ones who have lost their job, which I 339 00:16:50,940 --> 00:16:54,030 Speaker 2: thought would be one of the losers. Sounds like you're 340 00:16:54,030 --> 00:16:56,790 Speaker 2: not so worried about them. So then who are the 341 00:16:56,790 --> 00:17:01,400 Speaker 2: biggest losers? Is it the banks, the partners investors? I 342 00:17:01,400 --> 00:17:03,740 Speaker 2: don't think we should be looking at winners or losers 343 00:17:03,740 --> 00:17:07,419 Speaker 2: per se. I really don't. Ultimately, this is a long 344 00:17:07,420 --> 00:17:09,720 Speaker 2: tail gain, right? And this is just one point in 345 00:17:09,720 --> 00:17:12,830 Speaker 2: the long journey losers would be people that stick to 346 00:17:12,840 --> 00:17:15,770 Speaker 2: where they are right now mope about. You know, I've 347 00:17:15,770 --> 00:17:18,360 Speaker 2: lost everything, I don't know what to do. Ultimately, if 348 00:17:18,359 --> 00:17:21,420 Speaker 2: you can take this situation. See what can I get 349 00:17:21,430 --> 00:17:23,980 Speaker 2: out of it, know that change is the only constant 350 00:17:23,980 --> 00:17:27,020 Speaker 2: that is alibaba core value and I can either change 351 00:17:27,020 --> 00:17:30,120 Speaker 2: myself or I can create change and then work towards 352 00:17:30,119 --> 00:17:30,419 Speaker 2: it 353 00:17:30,570 --> 00:17:32,679 Speaker 2: because there's no such thing as a good advice for 354 00:17:32,680 --> 00:17:35,470 Speaker 2: the job seekers out there, you know, wondering what should 355 00:17:35,470 --> 00:17:37,510 Speaker 2: I be doing next is really to try and find 356 00:17:37,510 --> 00:17:40,270 Speaker 2: what your expertise is and trying to link it to 357 00:17:40,270 --> 00:17:43,879 Speaker 2: the next sort of job that you're looking for or 358 00:17:43,880 --> 00:17:45,919 Speaker 2: or link it more to your personal dream. What do 359 00:17:45,920 --> 00:17:48,920 Speaker 2: you want to do ultimately you're in the situation where 360 00:17:48,930 --> 00:17:51,119 Speaker 2: you can either be taken for a ride or you 361 00:17:51,119 --> 00:17:53,610 Speaker 2: can actually get more control and now like what were 362 00:17:53,609 --> 00:17:55,520 Speaker 2: you saying that there's so many new things that 363 00:17:55,530 --> 00:17:58,150 Speaker 2: could either be the next big thing or it could 364 00:17:58,150 --> 00:18:00,270 Speaker 2: be a fraud, right? We don't know. And you wouldn't 365 00:18:00,270 --> 00:18:02,560 Speaker 2: know unless you work for the government or bank even 366 00:18:02,560 --> 00:18:04,770 Speaker 2: then you can get actually retrenched, you never know, right? 367 00:18:04,780 --> 00:18:06,879 Speaker 2: So then how can you actually take more control of 368 00:18:06,880 --> 00:18:09,960 Speaker 2: your own personal future? And there's so much information out there, 369 00:18:09,960 --> 00:18:11,900 Speaker 2: you don't actually even know what is real is not 370 00:18:11,910 --> 00:18:14,640 Speaker 2: and people are still figuring this out. So the best 371 00:18:14,640 --> 00:18:16,840 Speaker 2: thing is to just acknowledge and this is the Alibaba 372 00:18:16,840 --> 00:18:20,460 Speaker 2: core value I didn't come up with. So 373 00:18:20,880 --> 00:18:25,490 Speaker 2: disclaimers here today and he said that today is dark 374 00:18:25,500 --> 00:18:28,740 Speaker 2: the day after tomorrow is very beautiful. Most people die 375 00:18:28,740 --> 00:18:31,510 Speaker 2: tomorrow night, wow. So you just need to know how 376 00:18:31,510 --> 00:18:33,740 Speaker 2: to get through tomorrow night and the day after tomorrow 377 00:18:33,740 --> 00:18:36,340 Speaker 2: is beautiful. That's what alibaba is doing. I'm sure they're 378 00:18:36,340 --> 00:18:38,610 Speaker 2: not having the best of times right now. Right. But 379 00:18:38,609 --> 00:18:40,369 Speaker 2: I think they're still pushing through. 380 00:18:40,380 --> 00:18:44,590 Speaker 1: I completely agree. Overall, it's okay. It's, it's a feature, 381 00:18:44,590 --> 00:18:45,399 Speaker 1: not a bug, 382 00:18:45,440 --> 00:18:48,379 Speaker 1: but um, I think like if there is going to 383 00:18:48,380 --> 00:18:52,040 Speaker 1: be a loser is going to be discriminated groups. Um, 384 00:18:52,040 --> 00:18:54,190 Speaker 1: and by what I, what I say about that. So, um, 385 00:18:54,200 --> 00:18:58,290 Speaker 1: research has shown again and again, let the technology is ageist. 386 00:18:58,300 --> 00:19:02,110 Speaker 1: Older people do way worse in the technology. Whether is 387 00:19:02,109 --> 00:19:04,889 Speaker 1: it getting funding, whether is it, you know, finding jobs, 388 00:19:04,890 --> 00:19:07,500 Speaker 1: you know, I'm working on paper right now whereby if 389 00:19:07,500 --> 00:19:10,390 Speaker 1: you have inverted reporting permits meaning 390 00:19:10,560 --> 00:19:13,060 Speaker 1: your boss is younger than you, which 391 00:19:13,060 --> 00:19:16,530 Speaker 2: seems like it doesn't 392 00:19:16,530 --> 00:19:20,340 Speaker 1: work out for the employees, the employees tend to leave voluntarily. 393 00:19:20,340 --> 00:19:22,780 Speaker 1: Like it just doesn't work out. So in a sense 394 00:19:22,780 --> 00:19:26,800 Speaker 1: with constant turnover and layoffs like this. So the prime 395 00:19:26,800 --> 00:19:29,400 Speaker 1: for tech, it looks like at least in my research 396 00:19:29,410 --> 00:19:32,990 Speaker 1: is from about early thirties to sort of mid, late thirties. 397 00:19:33,000 --> 00:19:33,530 Speaker 1: And then 398 00:19:33,730 --> 00:19:37,859 Speaker 1: The early 40s and the mid-40s when things becomes precarious. 399 00:19:37,859 --> 00:19:39,590 Speaker 1: If you lose a job, unless 400 00:19:39,590 --> 00:19:40,240 Speaker 2: you're the boss, like 401 00:19:41,619 --> 00:19:44,230 Speaker 1: a boss, you know, if you're the boss, everything's fine. 402 00:19:44,240 --> 00:19:49,280 Speaker 1: But there's that situation that's one dimension, another dimension in 403 00:19:49,280 --> 00:19:52,440 Speaker 1: the technology, which we saw in the pandemic, which 404 00:19:52,450 --> 00:19:55,619 Speaker 1: is that any time, there's a crisis when liquidity is scarce, 405 00:19:55,640 --> 00:20:00,840 Speaker 1: when things are uncertain, People revert to the safest option. 406 00:20:00,850 --> 00:20:05,650 Speaker 1: And unfortunately the safest option often doesn't include like minorities. 407 00:20:05,650 --> 00:20:09,560 Speaker 1: So for instance, there's a finding by pitchbook which documents 408 00:20:09,560 --> 00:20:13,230 Speaker 1: venture capital financing, that they found that during the pandemic 409 00:20:13,230 --> 00:20:16,560 Speaker 1: when things are super uncertain, the investments are going to 410 00:20:16,560 --> 00:20:22,120 Speaker 1: safe options which were dominantly male, white or asian founders. 411 00:20:22,320 --> 00:20:25,310 Speaker 1: So if you're a female and if you don't fit 412 00:20:25,310 --> 00:20:28,310 Speaker 1: the average stereotype of what a tech founder looks like, 413 00:20:28,320 --> 00:20:31,390 Speaker 1: you get pushed out and in any form of labor 414 00:20:31,390 --> 00:20:34,179 Speaker 1: market restructuring, the same things can apply. So if jobs 415 00:20:34,180 --> 00:20:38,160 Speaker 1: are scarce, you'll find these sort of discriminatory practices or 416 00:20:38,170 --> 00:20:41,190 Speaker 1: evidence of such discrimination being amplified. Is 417 00:20:41,190 --> 00:20:42,669 Speaker 2: that for ASia as well? 418 00:20:42,680 --> 00:20:43,580 Speaker 1: Definitely. 419 00:20:43,910 --> 00:20:47,450 Speaker 1: Yes. And it's not an overt discrimination. It's not something 420 00:20:47,450 --> 00:20:51,199 Speaker 1: whereby people are doing it because I hate women and 421 00:20:51,200 --> 00:20:55,110 Speaker 1: minorities or old people or whatever. It's just, well I 422 00:20:55,109 --> 00:20:58,960 Speaker 1: have 20 candidates for two positions. I'm going to sort 423 00:20:58,960 --> 00:21:01,060 Speaker 1: of revert to that template 424 00:21:01,430 --> 00:21:06,659 Speaker 1: which unfortunately does not involve women, for instance, in any 425 00:21:06,660 --> 00:21:08,520 Speaker 1: situation of turmoil 426 00:21:08,530 --> 00:21:12,290 Speaker 2: that's interesting. That is something deserves a podcast on its own. 427 00:21:12,290 --> 00:21:14,180 Speaker 2: But I mean, New Orleans in your experience. And what 428 00:21:14,180 --> 00:21:16,659 Speaker 2: way he's saying is sort of based on research. And 429 00:21:16,670 --> 00:21:18,830 Speaker 2: is it something that you get a sense that it 430 00:21:18,830 --> 00:21:22,020 Speaker 2: happens here in ASIA as well when he's talking about male, 431 00:21:22,030 --> 00:21:24,940 Speaker 2: his white is of a certain age. 432 00:21:25,090 --> 00:21:27,110 Speaker 2: I think research speaks for itself. I mean I don't 433 00:21:27,109 --> 00:21:29,869 Speaker 2: agree from what I'm seeing in my company. I think 434 00:21:29,869 --> 00:21:35,460 Speaker 2: men are the minority. Very good. So I think it 435 00:21:35,460 --> 00:21:39,370 Speaker 2: depends on the industry, it depends on the individual that 436 00:21:39,380 --> 00:21:42,129 Speaker 2: you're looking for in a way and I think it 437 00:21:42,130 --> 00:21:45,500 Speaker 2: depends on the task you want for the company. If 438 00:21:45,500 --> 00:21:49,560 Speaker 2: I want to continue to have great talents join my company, 439 00:21:49,570 --> 00:21:52,310 Speaker 2: I also need to be flexible and if my company 440 00:21:52,320 --> 00:21:54,850 Speaker 2: did not fit like a flexible culture or 441 00:21:54,859 --> 00:21:57,909 Speaker 2: I just want certain people for the team, I think 442 00:21:57,920 --> 00:22:00,310 Speaker 2: my company couldn't survive because I'm just a startup, right? 443 00:22:00,310 --> 00:22:03,020 Speaker 2: I'm not a big company. Maybe what Willie's easings maybe 444 00:22:03,020 --> 00:22:05,830 Speaker 2: for the bigger companies are traditional. I wouldn't, I was 445 00:22:05,830 --> 00:22:07,820 Speaker 2: also thinking that during this time and all the stuff 446 00:22:07,820 --> 00:22:09,220 Speaker 2: are being let go that they're actually going to be 447 00:22:09,230 --> 00:22:12,390 Speaker 2: picked up by these bigger companies. You know that their 448 00:22:12,390 --> 00:22:15,750 Speaker 2: talent is also going to be absorbed by the big 449 00:22:15,750 --> 00:22:18,820 Speaker 2: tech firms. For instance, that's more established, well established, not 450 00:22:18,820 --> 00:22:21,419 Speaker 2: just the big tech firms and I'm seeing even traditional 451 00:22:21,420 --> 00:22:24,479 Speaker 2: companies trying to innovate poaching, not just picking up 452 00:22:24,710 --> 00:22:27,890 Speaker 2: that have been retrenched by poaching people to join them 453 00:22:27,890 --> 00:22:30,190 Speaker 2: to innovate. So you can see this is like what 454 00:22:30,190 --> 00:22:33,460 Speaker 2: you were saying initially this is actually a cycle that 455 00:22:33,470 --> 00:22:35,760 Speaker 2: the industry ultimately because better and better, 456 00:22:35,780 --> 00:22:38,920 Speaker 1: there's a trend whereby younger companies tend to be more 457 00:22:38,930 --> 00:22:41,720 Speaker 1: progressive to use that term. I'm an advisory board of 458 00:22:41,720 --> 00:22:44,680 Speaker 1: this incubator called entrepreneur first, which is also mostly women, 459 00:22:44,690 --> 00:22:47,800 Speaker 1: they hired a male recently called him their diversity hire, 460 00:22:47,800 --> 00:22:50,379 Speaker 1: which is like this is like the flip side of everything. 461 00:22:50,390 --> 00:22:53,820 Speaker 1: But the situation here is that there are multiple things 462 00:22:53,820 --> 00:22:54,390 Speaker 1: that play 463 00:22:54,800 --> 00:22:57,420 Speaker 1: when women are let go. Let's say if you're in 464 00:22:57,420 --> 00:23:00,960 Speaker 1: the intersection of being women and middle aged and with 465 00:23:00,960 --> 00:23:06,410 Speaker 1: kids like you become at that life course point whereby 466 00:23:06,410 --> 00:23:09,950 Speaker 1: you yourself are torn apart from the labor market. So 467 00:23:09,950 --> 00:23:12,370 Speaker 1: there are all these other factors involved. 468 00:23:12,380 --> 00:23:14,850 Speaker 2: I just want to pivot back again to this tech 469 00:23:14,850 --> 00:23:20,830 Speaker 2: sector and what you think this means for Singapore economy 470 00:23:20,840 --> 00:23:21,830 Speaker 2: because we have 471 00:23:21,840 --> 00:23:25,980 Speaker 2: have cast our lot with tech recently, the government announced 472 00:23:25,980 --> 00:23:28,719 Speaker 2: about this, oh, any past where you know, we're going 473 00:23:28,720 --> 00:23:32,050 Speaker 2: to attract the best talent, you know, in this area. 474 00:23:32,060 --> 00:23:35,850 Speaker 2: I'm wondering what sort of big picture lessons we can 475 00:23:35,850 --> 00:23:39,310 Speaker 2: learn from this and also how do you think this 476 00:23:39,310 --> 00:23:40,780 Speaker 2: is going to pan out? I mean, do you think 477 00:23:40,780 --> 00:23:42,980 Speaker 2: there's something deeper here that we need to look into 478 00:23:42,980 --> 00:23:45,310 Speaker 2: with this? You know, recent going on at sea and 479 00:23:45,310 --> 00:23:47,550 Speaker 2: all these other tech firms, they're letting people go or 480 00:23:47,560 --> 00:23:48,879 Speaker 2: is it like a warning sign. 481 00:23:49,550 --> 00:23:52,600 Speaker 1: So I think first and foremost is that tech is huge. 482 00:23:52,609 --> 00:23:55,909 Speaker 1: So what we're seeing of C and Web three and 483 00:23:55,910 --> 00:23:58,640 Speaker 1: you know, a collapse of a particular sector and hiring 484 00:23:58,640 --> 00:24:02,090 Speaker 1: freezes is just one segment of it and there's still 485 00:24:02,100 --> 00:24:05,530 Speaker 1: a lot going on. So the Fintech ecology in Southeast 486 00:24:05,530 --> 00:24:08,830 Speaker 1: Asia Healthtech, so there's a lot of technology that's going 487 00:24:08,830 --> 00:24:11,409 Speaker 1: to healthcare and view things that's very, very healthy And 488 00:24:11,410 --> 00:24:13,030 Speaker 1: the demand is always there. 489 00:24:13,200 --> 00:24:17,510 Speaker 1: If you view your skills as not being sector based 490 00:24:17,520 --> 00:24:19,700 Speaker 1: as if we view our labor market as not being 491 00:24:19,700 --> 00:24:22,869 Speaker 1: sector based, meaning we're not training and I hate that 492 00:24:22,869 --> 00:24:23,899 Speaker 1: we're training 493 00:24:24,270 --> 00:24:28,510 Speaker 1: but anyway, we're not training our people to participate in 494 00:24:28,510 --> 00:24:33,040 Speaker 1: a particular sector. Like we're not just offering Blockchain lessons 495 00:24:33,040 --> 00:24:37,510 Speaker 1: or e commerce platform lessons, but we are offering computer science, 496 00:24:37,510 --> 00:24:42,390 Speaker 1: we're offering statistics, you know, or machine learning like so broad, 497 00:24:42,400 --> 00:24:46,320 Speaker 1: more soft skills. Then as a labor market we are 498 00:24:46,320 --> 00:24:49,300 Speaker 1: fine from the point of singaporean labor market. If we 499 00:24:49,300 --> 00:24:50,030 Speaker 1: view 500 00:24:50,290 --> 00:24:54,710 Speaker 1: the process of our 1st 21 years of life as 501 00:24:54,720 --> 00:24:57,730 Speaker 1: training your sort of it's awful. So you're trained to 502 00:24:57,730 --> 00:25:00,240 Speaker 1: do one thing and one thing goes away, you know? 503 00:25:00,240 --> 00:25:03,470 Speaker 1: So if you view Singapore as we're only going into 504 00:25:03,480 --> 00:25:08,610 Speaker 1: each commerce, we're only going into web three, we're doomed. 505 00:25:08,619 --> 00:25:11,030 Speaker 1: But if you view it from that, okay, but not 506 00:25:11,040 --> 00:25:13,300 Speaker 1: training our people to go into those industries 507 00:25:13,480 --> 00:25:17,670 Speaker 1: or we're not attracting only those talents, we are educating 508 00:25:17,680 --> 00:25:20,290 Speaker 1: our people to be well versed in computer science and 509 00:25:20,290 --> 00:25:25,070 Speaker 1: statistics and technologies in general or have this idea of 510 00:25:25,070 --> 00:25:28,220 Speaker 1: a curiosity and innovativeness. 511 00:25:28,220 --> 00:25:30,810 Speaker 2: But do you think that the kids, you're in school 512 00:25:30,810 --> 00:25:33,700 Speaker 2: and studying computer science and all of that that we 513 00:25:33,700 --> 00:25:37,250 Speaker 2: are ahead of the curve or we somewhat still a 514 00:25:37,250 --> 00:25:38,410 Speaker 2: bit behind, 515 00:25:38,740 --> 00:25:41,030 Speaker 2: if I may say, I think one thing is that 516 00:25:41,030 --> 00:25:43,380 Speaker 2: what you study in school is not equivalent to what 517 00:25:43,380 --> 00:25:45,530 Speaker 2: you'll be doing for the rest of your life, right? 518 00:25:45,540 --> 00:25:47,300 Speaker 2: And I think, I think the key thing that we 519 00:25:47,310 --> 00:25:49,930 Speaker 2: have observed when I talk to young people, old people 520 00:25:49,930 --> 00:25:52,800 Speaker 2: is that especially in Singapore, there's two things. One is, 521 00:25:52,810 --> 00:25:56,680 Speaker 2: the mindset is really, really important. Um, some people are 522 00:25:56,680 --> 00:25:59,830 Speaker 2: not willing to push the boundaries, they are so reliant 523 00:25:59,830 --> 00:26:01,899 Speaker 2: on like just keeping within the box. Is there a 524 00:26:01,900 --> 00:26:02,380 Speaker 2: particular 525 00:26:02,390 --> 00:26:07,200 Speaker 2: age group you're referring to in general, in general, in Singapore? 526 00:26:07,210 --> 00:26:09,310 Speaker 2: And I think tech companies coming here to show them 527 00:26:09,310 --> 00:26:12,040 Speaker 2: that it's okay to make mistakes, it's okay to fail, 528 00:26:12,050 --> 00:26:14,370 Speaker 2: push the boundaries a bit more because there's so much 529 00:26:14,369 --> 00:26:16,080 Speaker 2: you can do and you can grow as a person. 530 00:26:16,090 --> 00:26:18,190 Speaker 2: I think this in the sense is also part of 531 00:26:18,200 --> 00:26:20,470 Speaker 2: what Singapore is in during the last 50 years, right? 532 00:26:20,470 --> 00:26:23,280 Speaker 2: Always pushing the boundaries to move up to the next level. 533 00:26:23,290 --> 00:26:25,830 Speaker 2: There's a reason why Singapore is now having a token 534 00:26:25,830 --> 00:26:26,030 Speaker 2: 20 535 00:26:26,040 --> 00:26:29,170 Speaker 2: 49 right now because government may not know where this 536 00:26:29,170 --> 00:26:32,250 Speaker 2: may be going, but this is equivalent to the 15 537 00:26:32,250 --> 00:26:35,660 Speaker 2: hundreds when the first explorers from Spain started venturing into 538 00:26:35,660 --> 00:26:38,730 Speaker 2: the new world, somebody was saying that who knew that 539 00:26:38,730 --> 00:26:42,320 Speaker 2: these 1500 you know, ships from Spain could lead to 540 00:26:42,330 --> 00:26:45,310 Speaker 2: cruise ship, could lead to like logistics, could lead to 541 00:26:45,310 --> 00:26:48,429 Speaker 2: feed forwarding that we have today. So we are pushing 542 00:26:48,430 --> 00:26:49,570 Speaker 2: the boundaries every day and I, 543 00:26:49,690 --> 00:26:52,409 Speaker 2: one lesson that everybody should know is that don't be 544 00:26:52,410 --> 00:26:54,900 Speaker 2: afraid to fail because you know your life is ahead 545 00:26:54,900 --> 00:26:57,330 Speaker 2: of you, but key thing number one push the boundaries 546 00:26:57,330 --> 00:27:00,460 Speaker 2: and number two having this kind of like logical thinking, 547 00:27:00,470 --> 00:27:03,119 Speaker 2: this is actually what we think we, we learn in school, 548 00:27:03,119 --> 00:27:06,150 Speaker 2: but until you try and you use it daily is 549 00:27:06,150 --> 00:27:08,680 Speaker 2: something that I see missing in lots of people here. 550 00:27:08,690 --> 00:27:10,899 Speaker 1: I agree, but at the same time I'm going to 551 00:27:10,900 --> 00:27:14,800 Speaker 1: give more credit to you know, gen, z especially singaporeans, 552 00:27:14,810 --> 00:27:16,750 Speaker 1: my observation in us at least 553 00:27:16,990 --> 00:27:23,160 Speaker 1: the increasing demand for you know, classes or even courses 554 00:27:23,160 --> 00:27:26,820 Speaker 1: or networking opportunities for entrepreneurship like meeting people or even 555 00:27:26,820 --> 00:27:30,660 Speaker 1: cross faculty collaborations, like it's increasing its, its on a 556 00:27:30,660 --> 00:27:33,740 Speaker 1: scale which we haven't seen before. So at the business 557 00:27:33,740 --> 00:27:35,679 Speaker 1: school I supervise the technology 558 00:27:35,700 --> 00:27:40,220 Speaker 1: innovation specialization and the demand for that has been going 559 00:27:40,220 --> 00:27:42,300 Speaker 1: up and up interestingly enough is not the business school 560 00:27:42,300 --> 00:27:45,470 Speaker 1: students who have the demand is the engineering students, it's 561 00:27:45,470 --> 00:27:48,570 Speaker 1: a science students who want to come to sort of 562 00:27:48,580 --> 00:27:52,780 Speaker 1: reach out beyond their you know, sort of hard skills, 563 00:27:52,790 --> 00:27:56,380 Speaker 1: quote unquote to double in something else. And I think 564 00:27:56,380 --> 00:27:57,950 Speaker 1: that kind of inquisitive Itty, 565 00:27:58,990 --> 00:27:59,300 Speaker 1: I see 566 00:27:59,300 --> 00:28:02,540 Speaker 2: that as well. Well, I am very hopeful after speaking 567 00:28:02,540 --> 00:28:05,290 Speaker 2: to the both of you that you know, we're not 568 00:28:05,290 --> 00:28:06,570 Speaker 2: in a bad shape at all. 569 00:28:09,090 --> 00:28:11,670 Speaker 2: Oh, big! Thank you to my guests for helping us 570 00:28:11,680 --> 00:28:15,460 Speaker 2: unpack this issue. And thanks to you, our listeners. Our 571 00:28:15,460 --> 00:28:18,480 Speaker 2: audio offerings are also available on the Sienna app, on 572 00:28:18,490 --> 00:28:22,700 Speaker 2: Apple and google podcasts and on Spotify. Now the team 573 00:28:22,700 --> 00:28:26,560 Speaker 2: behind this podcast is Jacqueline, chan, Joanne, channa, daniel, lee 574 00:28:26,560 --> 00:28:30,060 Speaker 2: and Christina robert and Kellie Edwards signing off.